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200 | 1a7e83fe9528b177246d6374ddaf2a76a0046e83 | <mask token>
def cos_dist(a, b):
if len(a) != len(b):
return None
part_up = 0.0
a_sq = 0.0
b_sq = 0.0
for a1, b1 in zip(a, b):
part_up += a1 * b1
a_sq += a1 ** 2
b_sq += b1 ** 2
part_down = math.sqrt(a_sq * b_sq)
if part_down == 0.0:
return None
else:
return part_up / part_down
<mask token>
| <mask token>
reload(sys)
sys.setdefaultencoding('utf-8')
<mask token>
def cos_dist(a, b):
if len(a) != len(b):
return None
part_up = 0.0
a_sq = 0.0
b_sq = 0.0
for a1, b1 in zip(a, b):
part_up += a1 * b1
a_sq += a1 ** 2
b_sq += b1 ** 2
part_down = math.sqrt(a_sq * b_sq)
if part_down == 0.0:
return None
else:
return part_up / part_down
<mask token>
for iii in range(len(vex1)):
sumdot += vex1[iii] * vex2[iii]
<mask token>
for index in range(len(vex1)):
sum12 += vex1[index] * vex2[index]
sum1 += vex1[index] ** 2
sum2 += vex2[index] ** 2
<mask token>
res.write('余弦: ' + str(Similarity_Cos) + '\n内积: ' + str(sumdot) +
'\nJaccard系数: ' + str(Similarity_Jaccard))
res.close()
print('余弦: ' + str(Similarity_Cos) + ' 内积: ' + str(sumdot) + ' Jaccard系数: ' +
str(Similarity_Jaccard))
| <mask token>
reload(sys)
sys.setdefaultencoding('utf-8')
<mask token>
sentence1 = sys.argv[1]
sentence2 = sys.argv[2]
Divlist1 = jieba.lcut(sentence1, cut_all=True)
Divlist2 = jieba.lcut(sentence2, cut_all=True)
Sen = [' '.join(Divlist1), ' '.join(Divlist2)]
vectorizer = CountVectorizer()
transformer = TfidfTransformer()
TFIDF_value = vectorizer.fit_transform(Sen)
word = vectorizer.get_feature_names()
matrix_value = TFIDF_value.toarray()
vex1 = list(matrix_value[0])
vex2 = list(matrix_value[1])
def cos_dist(a, b):
if len(a) != len(b):
return None
part_up = 0.0
a_sq = 0.0
b_sq = 0.0
for a1, b1 in zip(a, b):
part_up += a1 * b1
a_sq += a1 ** 2
b_sq += b1 ** 2
part_down = math.sqrt(a_sq * b_sq)
if part_down == 0.0:
return None
else:
return part_up / part_down
Similarity_Cos = cos_dist(vex1, vex2)
sumdot = 0.0
for iii in range(len(vex1)):
sumdot += vex1[iii] * vex2[iii]
Similarity_dot = sumdot
sum12 = 0.0
sum1 = 0.0
sum2 = 0.0
for index in range(len(vex1)):
sum12 += vex1[index] * vex2[index]
sum1 += vex1[index] ** 2
sum2 += vex2[index] ** 2
Similarity_Jaccard = sum12 / (sum1 + sum2 - sum12)
res = open('SIMresult.txt', 'w')
res.write('余弦: ' + str(Similarity_Cos) + '\n内积: ' + str(sumdot) +
'\nJaccard系数: ' + str(Similarity_Jaccard))
res.close()
print('余弦: ' + str(Similarity_Cos) + ' 内积: ' + str(sumdot) + ' Jaccard系数: ' +
str(Similarity_Jaccard))
| import jieba
import os
import sys
import math
reload(sys)
sys.setdefaultencoding('utf-8')
from sklearn import feature_extraction
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import CountVectorizer
sentence1 = sys.argv[1]
sentence2 = sys.argv[2]
Divlist1 = jieba.lcut(sentence1, cut_all=True)
Divlist2 = jieba.lcut(sentence2, cut_all=True)
Sen = [' '.join(Divlist1), ' '.join(Divlist2)]
vectorizer = CountVectorizer()
transformer = TfidfTransformer()
TFIDF_value = vectorizer.fit_transform(Sen)
word = vectorizer.get_feature_names()
matrix_value = TFIDF_value.toarray()
vex1 = list(matrix_value[0])
vex2 = list(matrix_value[1])
def cos_dist(a, b):
if len(a) != len(b):
return None
part_up = 0.0
a_sq = 0.0
b_sq = 0.0
for a1, b1 in zip(a, b):
part_up += a1 * b1
a_sq += a1 ** 2
b_sq += b1 ** 2
part_down = math.sqrt(a_sq * b_sq)
if part_down == 0.0:
return None
else:
return part_up / part_down
Similarity_Cos = cos_dist(vex1, vex2)
sumdot = 0.0
for iii in range(len(vex1)):
sumdot += vex1[iii] * vex2[iii]
Similarity_dot = sumdot
sum12 = 0.0
sum1 = 0.0
sum2 = 0.0
for index in range(len(vex1)):
sum12 += vex1[index] * vex2[index]
sum1 += vex1[index] ** 2
sum2 += vex2[index] ** 2
Similarity_Jaccard = sum12 / (sum1 + sum2 - sum12)
res = open('SIMresult.txt', 'w')
res.write('余弦: ' + str(Similarity_Cos) + '\n内积: ' + str(sumdot) +
'\nJaccard系数: ' + str(Similarity_Jaccard))
res.close()
print('余弦: ' + str(Similarity_Cos) + ' 内积: ' + str(sumdot) + ' Jaccard系数: ' +
str(Similarity_Jaccard))
| # coding:utf-8
import jieba
import os
import sys
import math
reload(sys)
sys.setdefaultencoding('utf-8')
from sklearn import feature_extraction
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import CountVectorizer
#import csv
#import pandas
#import numpy
sentence1 = sys.argv[1]
sentence2 = sys.argv[2]
#sentence1 = '他很喜欢玩游戏，也喜欢看小说'
#sentence2 = '他喜欢玩游戏，最喜欢看小说'
Divlist1 = jieba.lcut(sentence1, cut_all=True)
Divlist2 = jieba.lcut(sentence2, cut_all=True)
Sen = [" ".join(Divlist1), " ".join(Divlist2)]
vectorizer=CountVectorizer()#该类会将文本中的词语转换为词频矩阵
transformer=TfidfTransformer()#该类会统计每个词语的tf-idf权值
TFIDF_value=vectorizer.fit_transform(Sen)
word=vectorizer.get_feature_names()#获取词袋模型中的所有词语
matrix_value = TFIDF_value.toarray()
vex1 = list(matrix_value[0])
vex2 = list(matrix_value[1])
def cos_dist(a, b):
if len(a) != len(b):
return None
part_up = 0.0
a_sq = 0.0
b_sq = 0.0
for a1, b1 in zip(a,b):
part_up += a1*b1
a_sq += a1**2
b_sq += b1**2
part_down = math.sqrt(a_sq*b_sq)
if part_down == 0.0:
return None
else:
return part_up / part_down
Similarity_Cos = cos_dist(vex1, vex2) #余弦
sumdot = 0.0
for iii in range(len(vex1)):
sumdot += vex1[iii] * vex2[iii]
Similarity_dot = sumdot #内积
sum12 = 0.0
sum1 = 0.0
sum2 = 0.0
for index in range(len(vex1)):
sum12 += vex1[index] *vex2[index]
sum1 += vex1[index] ** 2
sum2 += vex2[index] ** 2
Similarity_Jaccard = sum12/(sum1 + sum2 - sum12) #jaccard相似度
res=open("SIMresult.txt", 'w')
res.write('余弦: '+str(Similarity_Cos)+'\n内积: '+str(sumdot)+'\nJaccard系数: '+str(Similarity_Jaccard))
res.close()
print('余弦: '+str(Similarity_Cos)+' 内积: '+str(sumdot)+' Jaccard系数: '+str(Similarity_Jaccard))
#print(' ')
#print(Similarity_dot)
#print(' ')
#print(Similarity_Jaccard)
| [
1,
2,
3,
4,
5
] |
201 | 7e7e96fb9377e4dc59a46a46951f5057ecae419a | <mask token>
| <mask token>
print(np.linalg.norm(mat))
| <mask token>
a = np.log(2) / 25
apdataX = np.random.random((5, 35))
quarter_way_arr = [False, False, False]
quarter_way_arr[0] = True
quarter_way_arr[1] = True
quarter_way_arr[2] = True
mat = np.eye(3)
print(np.linalg.norm(mat))
| import random
import gym
import numpy as np
from collections import deque
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from simulation_utils import box, simulation
from kinematics import pose3D
a = np.log(2) / 25
apdataX = np.random.random((5, 35))
quarter_way_arr = [False, False, False]
quarter_way_arr[0] = True
quarter_way_arr[1] = True
quarter_way_arr[2] = True
mat = np.eye(3)
print(np.linalg.norm(mat))
| # -*- coding: utf-8 -*-
import random
import gym
import numpy as np
from collections import deque
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from simulation_utils import box, simulation
from kinematics import pose3D
a = np.log(2)/25
apdataX = np.random.random((5, 35))
quarter_way_arr = [False, False, False]
quarter_way_arr[0] = True
quarter_way_arr[1] = True
quarter_way_arr[2] = True
mat = np.eye(3)
print(np.linalg.norm(mat))
| [
0,
1,
2,
3,
4
] |
202 | b6183daa943cc63fd2959e3e54fc1e6af5d761de | <mask token>
| <mask token>
print('valor de D: %.4f' % D)
print('valor de Rey: %.4f' % Rey)
print('valor de k: %.4f' % k)
| <mask token>
f = float(input('Digite o valor de f: '))
L = float(input('Digite o valor de L: '))
Q = float(input('Digite o valor de Q: '))
DeltaH = float(input('Digite o valor de DeltaH: '))
v = float(input('Digite o valor de v: '))
g = 9.81
e = 2e-06
D = (8 * f * L * Q * Q / (math.pi * math.pi * g * DeltaH)) ** 0.2
Rey = 4 * Q / (math.pi * D * v)
k = 0.25 / math.log10(e / (3.7 * D) + 5.74 / Rey ** 0.9) ** 2
print('valor de D: %.4f' % D)
print('valor de Rey: %.4f' % Rey)
print('valor de k: %.4f' % k)
| import math
f = float(input('Digite o valor de f: '))
L = float(input('Digite o valor de L: '))
Q = float(input('Digite o valor de Q: '))
DeltaH = float(input('Digite o valor de DeltaH: '))
v = float(input('Digite o valor de v: '))
g = 9.81
e = 2e-06
D = (8 * f * L * Q * Q / (math.pi * math.pi * g * DeltaH)) ** 0.2
Rey = 4 * Q / (math.pi * D * v)
k = 0.25 / math.log10(e / (3.7 * D) + 5.74 / Rey ** 0.9) ** 2
print('valor de D: %.4f' % D)
print('valor de Rey: %.4f' % Rey)
print('valor de k: %.4f' % k)
| # -*- coding: utf-8 -*-
import math
#COMECE SEU CÓDIGO AQUI
f = float(input('Digite o valor de f: '))
L = float(input('Digite o valor de L: '))
Q = float(input('Digite o valor de Q: '))
DeltaH = float(input('Digite o valor de DeltaH: '))
v = float(input('Digite o valor de v: '))
g = 9.81
e = 0.000002
#PROCESSAMENTO
D = ((8*f*L*Q*Q)/(math.pi*math.pi*g*DeltaH))**(0.2)
#CÁLCULO DO Rey
Rey = (4*Q)/(math.pi*D*v)
#CÁLCULO DO k
k = ((0.25)/((math.log10(((e)/(3.7*D)) + ((5.74)/(Rey**0.9))))**2))
#SAÍDA
print('valor de D: %.4f' %D)
print('valor de Rey: %.4f' %Rey)
print('valor de k: %.4f' %k)
| [
0,
1,
2,
3,
4
] |
203 | 1490fecd6e983c0e3093a45d77d6fb8afdb54718 | <mask token>
def graph_sketching(args):
with open('graphs/graphs_' + args.filename + '.json') as jsonfile:
graphs = json.load(jsonfile)
print('[ ', len(graphs), ' ] graphs read successfully')
sketch = Sketch()
sketch_file = sketch.shingle_sketch(graphs, args)
print('\n Done Batch Sketching...')
return sketch_file
<mask token>
| <mask token>
def graph_sketching(args):
with open('graphs/graphs_' + args.filename + '.json') as jsonfile:
graphs = json.load(jsonfile)
print('[ ', len(graphs), ' ] graphs read successfully')
sketch = Sketch()
sketch_file = sketch.shingle_sketch(graphs, args)
print('\n Done Batch Sketching...')
return sketch_file
<mask token>
def visualize_darpa():
if 1 == 1:
anomaly_detection = AnomalyDetection()
sketch_file = (
'sketches/darpa_10min/rdim_64_n_3_k_128p_0.2_l_50p10q5.csv')
sh_vector = anomaly_detection.read_sketch(sketch_file)
print('Sketch File: ', sketch_file)
true_anomalies = np.array(sh_vector['anomaly'])
sl_darpa = [0.0, 1.0000000000000004, 1.4200000000000008,
1.620000000000001, 1.8600000000000008, 2.4500000000000015,
2.660000000000001, 2.606666666666668, 2.163333333333334,
3.553333333333335, 4.0166666666666675, 3.8266666666666675,
2.5980000000000016, 2.075, 2.4666666666666677,
2.6790000000000003, 2.6266666666666674, 1.9041666666666677,
2.3625000000000007, 7.323666666666667, 2.3486666666666673,
3.3153333333333337, 3.063134920634921, 2.320262626262627,
5.969333333333335, 4.417166666666666, 6.199833333333332,
7.968666666666665, 3.1812142857142867, 6.162523809523808,
5.600134920634922, 8.386054112554113, 6.047714285714287,
4.551000000000001, 1.7697138972138982, 3.614246031746033,
2.480444444444445, 3.6538547008547004, 3.9801907308377915,
8.834181096681096, 5.261960317460318, 6.4440873015873015,
8.789714285714286, 4.714586219336219, 8.04961821511821,
2.5418132978132983, 7.096460317460318, 7.826078477078476,
2.5750670995671006, 7.68892007992008, 3.289951346039582,
13.420904761904762, 3.4483250670897743, 2.714681762681763,
4.418218118139173, 15.451951465201466, 6.739684550743376,
3.6353293650793663, 3.191541549953315, 4.221762570762571,
2.153847555952819, 3.109875457875458, 3.456950705727022,
2.0999702634677067, 1.7787056255054985, 1.764123928359223,
1.888398291251233, 2.1237179487179496, 2.3554988921579323,
27.04, 1.4998784545456214, 1.8871840829000228,
2.1192714078931822, 2.523507449178502, 2.239671578421579,
2.2529392409483693, 3.0643626177743837, 1.7397439836149518,
3.2541237553342834, 6.007199999999998, 4.979633699633699,
3.022961038961039, 5.0280000000000005, 3.695000000000001,
1.181985185185186, 5.990666666666666, 4.469416666666667,
5.625238095238096, 6.6290714285714305, 5.471904761904761,
8.187222222222223, 6.22639393939394, 5.630380952380951,
3.611518796992482, 3.755980392156863, 4.919714285714287,
5.687798346763989, 8.450095238095239, 13.118333333333334,
10.500526315789475, 5.1608359859095145, 6.445960317460319,
4.468441225441227, 94.62000000000003, 1.849484126984128,
6.652187590187591, 8.298173160173157, 5.727428571428573,
7.41424844720497, 6.326612612612613, 5.260150270781852,
4.67154761904762, 7.080579365079365, 3.9539688184156283,
5.400383394383394, 11.620560606060605, 6.3928064713064705,
7.091154061624651, 4.573704184704185, 6.098405483405483,
8.40033333333333, 8.324523809523805, 5.90677399738653,
9.351690476190479, 9.586746031746031, 4.338500000000001,
11.507795959595956, 6.9307226662226675, 10.670614870509608,
11.878396825396829, 7.195762597052069, 8.516702003878477,
6.877984126984128, 15.88485127629807, 52.858583105272764,
14.575487772812583, 8.832610667626147, 27.922016441891447,
24.288219378700596, 10.230674551488356, 9.96259378212087,
19.37658603966858, 20.71149731984562, 18.11692194476176,
11.720506869377614, 12.556118281296232, 14.40514704971671,
12.711814221023738, 13.611805977546764, 11.695705313453097,
9.0722865139116, 36.89595798259711, 13.599971055244287,
26.120275743359702, 15.813094768258562, 39.845679618163054,
17.78679301417537, 22.838877823016034, 15.62679340936206,
8.283368255660255, 27.842289826596406, 21.127534373749498,
15.221262595608625, 17.381740030622446, 9.146739426050868,
6.846115926499629, 13.071590108441631, 21.923272042996793,
10.317802546379866, 18.425980322620006, 30.234829644481206,
14.531590541059806, 9.573266545302635, 8.987315507648862,
16.427142411584526, 6.38453513760854, 26.95818628296048,
10.9001273295491, 21.382636009368646, 5.6808780355697746,
13.785458700084103, 6.208451253250925, 8.285535876579322,
13.75196263746655, 7.0574740066299375, 12.39048394540935,
24.12781351701881, 14.607803294602528, 5.907120080805687,
9.537022880394684, 28.923416540459883, 18.215603996608586,
26.229191444520392, 26.32272596263386, 26.439771672771684,
21.58314554772714, 11.215849289398824, 5.918494842282637,
8.92842238956368, 8.062072458727261, 27.439653670375037,
10.015162493688404, 29.779922410922403, 22.851694083694085,
9.900029127883176, 6.496280127897179, 31.12283203463203,
4.572369865562637, 5.786272283272283, 5.032613275613277,
17.240070485070483, 21.522490620490622, 25.278413882277693,
21.743482141473194, 14.61895238095238, 12.677347022860179,
8.837439198732305, 15.18930429864253, 7.245868686868688,
4.233264207433517, 7.6578774282402025, 6.6793896789485006,
5.597083333333333, 13.952092231619755, 8.786316017316016,
5.512380952380956, 6.562544733044734, 8.441238095238093,
5.790069675736741, 5.166466193373899, 8.4475087076666,
5.500767930152801, 5.12483543799952, 2.775991635543107,
7.107941169941168, 5.193493470655835, 174.24, 8.673855921855921,
6.023595571095573, 10.485618527671157, 9.90137228268807,
4.409333333333334, 7.986009984639015, 6.630210802269625,
9.920619047619047, 5.533016692801522, 4.587290043290044,
10.208107238392842, 21.54610844710845, 49.65733043769974,
34.89217267877051, 16.117369963369963, 5.2655962046904055,
6.129448051948052, 7.99017165148744, 6.420636392846921,
9.652349697404048, 34.35008129326216, 11.734443974383106,
15.562287938051423, 8.648869408369409, 7.01916688213747,
6.56917771701982, 8.584023549621826, 5.639651986575515,
6.725441919191919, 22.628654168516313, 34.157179059153165,
28.72741929323071, 33.914209571054805, 12.751380342289602,
59.56423990695492, 18.123795261927395, 44.88750797704535,
68.75638313538315, 68.84855960820367, 66.65561532058473,
66.4490010734879, 34.87412711580358, 19.30370496217802,
29.796296196875357, 25.745602983181634, 78.08579198910968,
37.06561628017687, 25.84962146907153, 37.46286858419658,
60.145880896174184, 28.8026926747768, 45.778784564969875,
47.00360624270333, 256.0000000000001, 45.765418059157476,
75.87339742141914, 217.4923333333333, 234.5466666666666,
51.611096143298596, 26.480645471444625, 42.8401704129204,
27.613920094485955, 22.4861896525969, 32.92889432390501,
24.508271045980088, 27.921504458934386, 41.08780572562321,
44.303331464358315, 21.904861023608355, 18.994658952034495,
90.9135948188927, 48.451170071758305, 19.610725088443647,
37.15938147749761, 36.828293539180876, 26.988203625818578,
50.42084675421529, 25.39270676488424, 18.69621855867736,
111.56150768551763, 160.23137468671678, 185.79399999999995,
14.259420460213292, 70.11899432584961, 49.45946264906402,
25.514827977984393, 72.91996915365337, 32.307471982403825,
51.804427351028025, 16.29518208087506, 17.316294907478554,
43.86812487507389, 12.880700724902727, 23.12664894119357,
58.12136223274934, 56.13361634199133, 34.4468547008547,
30.24839194012656, 10.030587700724277, 61.438566137566134,
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4.403300151711581, 4.759731180778725, 3.8647711922423826,
4.52486865057547, 4.735425311893816, 4.3766034558610825,
5.376900842334256, 4.598131294879514, 4.918490029782868,
6.8371626596129795, 4.872169305513437, 4.684251242900676,
3.900512214229233, 5.996606557125084, 8.125151359367539,
5.566181711213374, 6.706634458649089, 10.850682954591893,
6.714473010399906, 7.331497413578059, 11.67780166028143,
9.67575322696743, 6.061205995232639, 11.478172696462309,
2.8375823568536784, 7.2203224357780496, 5.827028671733267,
10.34326759433489, 4.613139081243259, 5.600291476154875,
128.48646356421358, 5.431000942003862, 5.252513633446825,
6.459847890055741, 7.054729151679627, 7.181252791962743,
7.020181744535614, 9.149252142468331, 7.202210062684409,
5.289929895431647, 4.978751045657459, 5.1388747450026955,
9.244788349370854, 5.821791588399578, 6.332825897812735,
8.065304498284418, 5.623134671978156, 5.606629774563818,
5.959237434979469, 11.578071332882597, 15.861222367167823,
6.35393810998111, 9.708331144289511, 6.086696291441945,
6.60053741394559, 54.515837398562795, 15.722434045737577,
10.008315374023312, 8.871021522689247, 21.624317628878106,
5.510329519067824, 32.98018634539461, 6.5999589321865955,
7.008401906117017, 4.1099600045486335, 14.444457634734126,
6.976358128572093, 6.592256761786417, 4.56624033723737,
5.016476183002307, 5.275973886626345, 5.35416504193835,
5.0154421733598085, 7.643538606180645, 6.426146969627452,
70.9972372994873, 4.944319241953642, 4.254748914038041,
4.017000491954552, 6.44844498867864, 7.206294774953469,
6.101646891772082, 7.653781086698148, 4.665751861402859,
6.58556643054661, 5.124590487527063, 6.566412057987424,
4.298708348059356, 3.9596742948064167, 3.6421459858208105,
7.20798796444913, 4.07718460824313, 6.46364410921741,
4.465138275249043, 5.879813150340306, 5.729058121734161,
4.879975242683147, 5.330568246724576, 4.727129096983263,
4.848036166386231, 5.181462852533911, 6.186087822947503,
4.85524473846445, 4.769768375894187, 3.8615481043443918,
3.0964197658115866, 5.617317189252712, 6.544208296224914,
4.72165616252538, 5.663674226215649, 5.151223525665638,
6.844958460518033, 4.6606074862637366, 4.836656592795673,
5.835568968476858, 41.70920706123569, 4.767507378784265,
7.818055897959608, 4.005250282176474, 3.763766359618647,
4.355001502474292, 4.8969936115093535, 5.332185065415433,
4.8338121738424675, 4.443237484385046, 4.178636084601197,
4.478867207700504, 5.230675826560404, 4.86893144603137,
5.014008311882265, 3.9752538379863585, 4.677860514738256,
4.433250031182235, 4.008829351110571, 4.792801804284789,
5.780030247779091, 4.086399741558739, 4.436430833005144,
3.871321992353297, 5.085489331037448, 4.005438109263738,
6.2592342729710335, 5.807582598757327, 4.713140096652398,
2.7267060487149677, 4.175702773840273, 2.9905042544548923,
6.167552621710821, 7.203140746969696, 5.013685769078192,
32.1875041825278, 6.940993693927512, 4.479328216611605,
6.402351834727751, 5.1451157789810695, 2.7965580965085053,
7.320230125431492, 5.033472190450266, 4.0344106435058835,
6.0311233765278764, 4.908887522951813, 4.122736115502366,
4.721776054478353, 5.110335422566141, 5.457618141563391,
5.75670224040943, 14.886885463878246, 10.221891437058687,
7.813015729211129, 8.2715536339262, 4.811977618422307,
4.869194728365406, 7.9011026794363595, 9.046350981968367,
4.404884700885976, 7.743818024916072, 4.134901942672659,
6.5317586107045855, 4.895086314701115, 6.247884759958221,
5.037790118606422, 5.0859762237638355, 4.664874368094808,
2.4733913530990366, 3.797824852441488, 7.2911169200458685,
3.1927109719170383, 4.329637119775794, 4.469469560493093,
151.80679166666664, 4.7499367714751255, 6.1767339798406935,
4.416326012580518, 5.2695722177955, 8.047562555431387,
4.431262565263395, 5.249558983157615, 4.577808670715448,
6.407353709939479, 4.972916468712027, 7.802963965783831,
3.8860603333858834, 3.928728780339955, 3.6769457069605833,
5.425274564468319, 3.9902681004521807, 8.594185420061525,
5.285789325433516, 4.827162128381098, 5.237624604302334,
4.215993039482868, 4.598739204843459, 5.402611484467485,
6.671780950016793, 5.030508424912956, 4.686833788599542,
4.162046282549573, 5.522113135966277, 3.629783194609431,
6.305956427880355, 5.409909120016655, 5.139258186361698,
5.827743123593672, 3.6918481176852085, 4.139091932815936,
5.388556533656358, 4.289286030062015, 3.4625849116503202,
3.3636027729364186, 3.437616390122559, 3.6943902811799836,
3.286999923280424, 3.907688532216298, 3.912678082613576,
3.7650547140830746, 3.7166117300415635, 4.241771708412309,
3.3134866392162055, 16.480731141101415, 3.7120814983911408,
3.502942580881363, 4.333483640989162, 4.348967293415747,
3.5224189300967175, 4.366389810529792, 4.005746168390999,
3.416781106488308, 3.1141564846127494, 2.925638543580092,
3.1571328648828407, 4.741723956800506, 3.311316758629223,
3.9040029512762966, 4.417526151247758, 3.9516035421129945,
2.9713646739563107, 4.2613893980050985, 3.0225042084584923,
3.852412736177483, 4.82234623475484, 4.6771850667963175,
7.2064289554480006, 8.451851693325798, 5.378651714349072,
10.378371153611406, 8.591846990337334, 8.628776768114083,
7.450583564783365, 7.951145130557014, 8.608923572731879,
16.38713260606374, 11.295415933138566, 10.131944488060856,
7.267458124548903, 9.966697921253065, 7.644225146452233,
6.824782137039566, 7.291170093238506, 7.345905891781853,
7.227252315568842, 6.341705869734229, 5.186770810346664,
8.177501166088557, 11.143009661358514, 7.045935039819131,
5.823058390619455, 8.230306748717004, 6.323413163783719,
13.992886626374753, 5.426350286828656, 5.89685412943534,
5.626669744701722, 6.455971261087143, 6.586258555782774,
7.073786775889847, 5.117954665304739, 7.197341362589045,
6.742441778672439, 7.218528026328704, 4.9720180457408345,
5.06067986044219, 12.372573547400428, 11.787884617450242,
9.471721328854983, 6.896140283671081, 9.837847335632839,
5.944317504981653, 6.771258342057394, 8.971725246958941,
7.955791815132692, 4.820432389754627, 11.967569401248786,
13.079975246263096, 6.5813124687873845, 6.726533531061057,
6.836431700029519, 6.920583377506266, 8.814982227333777,
8.897623800324572, 8.421348478544118, 13.567743988055893,
16.213814339915977, 7.5981070393279415, 20.559047937714894,
10.529720567433506, 7.89163459494797, 7.204729947803644,
8.353697321835694, 6.434465401552552, 6.539655086789874,
12.279861731272286, 5.1666505155780404, 5.295889304480655,
5.944718003322571, 4.145790071475841, 5.808263920013532,
3.719977460310492, 3.356935851491742, 4.641838317102404,
5.507657754439067, 3.408166930590262, 5.433240607016858,
6.072029820566021, 7.137200625428808, 7.475588840278538,
8.798189912805457, 7.263185565684637, 6.53674557682283,
7.838920815189002, 5.934159590354037, 7.962016591515269,
6.9974606552769485, 7.327765271677789, 9.565263116518993,
8.117502670179539, 6.550459363693736, 6.969116542775713,
6.330751978892521, 5.27681953546291, 6.878871714781645,
7.75957010760448, 5.212777184593145, 5.818471122868557,
29.17933267942936, 5.491198367001468, 6.145534714281291,
7.542096160444845, 9.475012348742567, 7.570856502946661,
7.728812344226875, 5.420917515548322, 6.5089045202296445,
7.942533341111784, 8.181560232395094, 8.309765634975992,
8.630290358394548, 7.327637146325995, 6.828941333544249,
5.879913322385453, 10.800569276473654, 9.378383133335527,
9.957710870677726, 6.921627806017991, 7.489791972628446,
5.8552076368492205, 6.8399297181239325, 6.3549898299788,
6.688408327153296, 12.059044615546984, 5.748941440951698,
6.884646229756494, 9.400261679936984, 6.052826108887397,
7.187975520358613, 4.940287376699257, 5.427202175834721,
4.661110017144526, 7.138958882096568, 5.730438432438063,
8.028055628960143, 20.909747695561542, 9.28433670777088,
8.641705775732527, 6.244071519947334, 20.568172934992482,
5.187876218499282, 6.348197552297058, 7.364689388288148,
10.912815053361573, 23.431347178464698, 7.826647777077516,
14.80460131114221, 6.060105235732271, 9.537434712428098,
5.474382703171666, 6.140056764849204, 4.179456189663782,
20.634122970460215, 12.774240214941466, 4.827698091198126,
6.93240828824991, 6.434585204167808, 5.939755052440748,
4.898306294793826, 6.162771302106341, 14.172903971460471,
168.87063393421892, 237.37000000000026, 199.07200000000014,
179.67333333333337, 184.6490000000001, 163.80233333333337,
130.4204372405373, 19.46801316117082, 15.192308756343374,
34.20477756957798, 7.372710324977523, 8.148285913326967,
6.15012854479911, 4.340034819359692, 4.689536482827476,
5.402717808388374, 5.872866609012086, 4.102834987651392,
8.541709311429534, 6.849109348320388, 5.090925582995589,
5.350790231382896, 4.9847414641041565, 6.353496366590833,
4.164712311981808, 4.993348809751632, 5.072246832953704,
5.227149205373926, 7.428399182988344, 4.995780169172565,
4.984792859662108, 4.680655846065624, 6.559046443108163,
6.024103546906881, 4.273367924699345, 4.944780843202242,
4.418063207027579, 4.758902528412509, 4.407852152144423,
4.307244378520254, 3.760777003213636, 5.143770190081977,
9.73445764297778, 6.9474394611917125, 7.4888280316313764,
8.233662165597716, 5.60401733406029, 12.550582378915623,
14.942956773566799, 23.174882832647224, 20.02162097824465,
23.66280503191029, 16.403792124542125, 17.15405504686043,
22.610207935394556, 17.010586910472426, 20.290111762970575,
20.03127490668623, 4.956097746282726, 4.918310550839017,
3.8824843663219792, 3.7425728822219786, 6.564283382486792,
3.9803420946978934, 4.5363764099171195, 7.514933982294955,
5.678323321412732, 4.735698087492399, 5.543298153827893,
4.302258542041373, 20.048797266903154, 3.6578781198583656,
4.562044214387566, 6.086009839396536, 6.949717664221121,
5.9569980821323405, 4.791959668225559, 5.683663481030626,
3.0383386401806747, 4.734706626750323, 5.3884728535232345,
4.267781092035992, 3.025192794714916, 3.0069229104806485,
3.4920541283128514, 2.803092424329961, 3.449125718833545,
4.047419820000714, 3.495211617900202, 3.137751951323503,
4.078090606199659, 4.077435505060176, 5.2160359937424,
4.8074115488243345, 4.604037685343379, 4.996908906650311,
4.539803828104704, 3.5338922775672184, 4.823305868933567,
4.228161377852135, 3.9129818364529103, 10.588495373877244,
5.272565738889914, 7.798209848286381, 5.436529674226188,
5.066943325612405, 7.787481774822503, 10.191956840917008,
35.07420451744297, 26.566789050023143, 20.074649774725525,
11.733322219449764, 6.772244452058734, 9.047012164329804,
7.598105724768269, 80.38359488212099, 5.790025754236513,
6.11193515748542, 7.911940128458676, 7.04417085404567,
5.415872510086622, 6.15011516603868, 5.907509201815567,
9.901025446954954, 7.014242574992935, 7.898658887767696,
19.625230630850997, 5.6190295890363515, 8.724152538964779,
13.79839626669689, 7.07189342182275, 14.908638453973465,
10.00707229609724, 8.628499334176121, 9.086245736995089,
13.02640797936521, 15.811316803455282, 14.36100254240862,
7.123728747433187, 6.8094696471805305, 6.80274296212756,
29.473624470345495, 15.56606551266405, 7.078460954121151,
6.286097749191182, 12.628836460727305, 10.972910341827081,
11.337576300187877, 7.422547695231272, 8.680527590503962,
6.889134126599722, 14.961641388964562, 5.150865483309711,
6.967325186233745, 9.387068315114968, 5.75737566739241,
6.781292729905152, 4.69866902216877, 7.959441085432782,
8.57698861669268, 7.41365315761497, 5.453339832300251,
6.829218190523225, 6.003049986883954, 5.787279372322578,
8.104651324118597, 7.035932273619949, 6.960746972199602,
6.190104177474828, 5.9656362159819185, 5.587974267291152,
5.3952485672350265, 5.197358799521997, 6.308841168814804,
5.770520133765092, 5.666563787417696, 5.093133769213094,
5.620794216200692, 9.48351513090556, 22.154731319580215,
8.720164931933521, 6.389975210089296, 6.489522864391742,
7.7234483024300875, 5.710480576425025, 6.896474243977068,
5.180801289778272, 7.52683689345378, 4.98790536383273,
8.279486809575708, 6.344842982575445, 6.346708427145821,
6.745636448046193, 4.367145081438637, 6.590269749096828,
4.597364100275982, 5.9114601906893425, 5.453606129566568,
6.310062388520356, 16.37593008385219, 6.8909111783528,
6.541159931285981, 7.579169159506998, 6.611496405315438,
5.696461729995997, 5.368917672172203, 6.402189145161245,
5.447100570432933, 6.681779511838211, 4.8794976690336656,
6.001353602118534, 6.335285287275466, 6.057808551901731,
6.145533480834151, 6.217694757019613, 7.650887840345928,
5.4401990060049075, 6.129073793095342, 5.1257031592254085,
6.292930805002428, 6.633253172305618, 5.363553301783041,
4.867259352477574, 4.433547601958522, 4.531206989778651,
6.425923090973049, 5.396312884886771, 4.468438094370893,
5.7340328755205165, 6.114518945165014, 6.47533299450849,
5.4559618597991975]
d1 = pd.Series([s for s in disp])
sorted_d1 = d1.sort_values(ascending=False)
N = 361
pred_rrcf = d1 > d1.quantile(0.9)
print('Displacement: ', d1.tolist())
for k in [100, 200, 300, 400]:
top_k_real = anomaly_detection.get_top_k_anomalies(sh_vector, k)
top_k_disp = anomaly_detection.get_top_k_displacement(sorted_d1, k)
anomaly_detection.get_top_k_performance(top_k_real, top_k_disp,
true_anomalies, pred_rrcf, N)
real = []
for index, row in sh_vector.iterrows():
if row['anom_count'] > 5000:
real.append(int(row['anom_count']) * 0.004)
else:
real.append(int(row['anom_count']) * 0.05)
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, sharex=True, sharey=True,
figsize=(10, 6))
ax1.plot(real, color='blue', linewidth=0.7)
ax1.plot(np.array(disp) * 0.5, color='red', linewidth=1)
ax1.set_title('SnapSketch', fontweight='bold', fontsize=8)
ax1.tick_params(axis='both', labelsize=6)
ax2.plot(real, color='blue', linewidth=0.7)
ax2.plot(np.array(sl_darpa) * 0.5, color='red', linewidth=1)
ax2.set_title('SpotLight', fontweight='bold', fontsize=8)
ax2.tick_params(axis='both', labelsize=6)
ax3.plot(real, color='blue', linewidth=0.7)
ax3.plot(np.array(ss_darpa) * 0.5, color='red', linewidth=1)
ax3.set_title('StreamSpot', fontweight='bold', fontsize=8)
ax3.tick_params(axis='both', labelsize=6)
plt.xlim([1200, 2600])
plt.ylim([0, 200])
plt.xlabel('Time', fontweight='bold', fontsize=6)
plt.ylabel('Anomaly Score', fontweight='bold', fontsize=6)
plt.tight_layout()
plt.savefig('results/darpaplot.pdf')
plt.show()
<mask token>
| <mask token>
def parse_args():
"""
Usual pythonic way of parsing command line arguments
:return: all command line arguments read
"""
args = argparse.ArgumentParser('GODIT')
args.add_argument('-d', '--sketch_size', default=256, type=int, help=
'Sketch Vector Size')
args.add_argument('-k', '--disc_shingle', default=32, type=int, help=
'# of Discriminative Shingle')
args.add_argument('-p', '--prob', default=0.2, type=int, help=
'Random Probability')
args.add_argument('--directed', dest='directed', action='store_true',
help='Graph is (un)directed. Default is undirected.')
args.add_argument('--undirected', dest='undirected', action='store_false')
args.add_argument('-l', '--walk_len', default=50, type=int, help=
'Length of random walk ')
args.add_argument('-n', '--n_shingle', default=3, type=int, help=
'Length of a shingle')
args.add_argument('-f', '--filename', default='vast', help=
'Dataset filename')
return args.parse_args()
def graph_sketching(args):
with open('graphs/graphs_' + args.filename + '.json') as jsonfile:
graphs = json.load(jsonfile)
print('[ ', len(graphs), ' ] graphs read successfully')
sketch = Sketch()
sketch_file = sketch.shingle_sketch(graphs, args)
print('\n Done Batch Sketching...')
return sketch_file
def main(args):
ad = AnomalyDetection()
files = ['graph2vec_mutag187_dim_128.csv']
for f in files:
print('\n\nFile: ', f)
sh_vector = ad.read_sketch_baseline('sketches/baseline/' + f)
ad.anomaly_detection(sh_vector, args)
<mask token>
def visualize_vast():
anomaly_detection = AnomalyDetection()
sketch_file = 'sketches/vast/dim_64_n_3_k_16p_0.2_l_50p10q5.csv'
sh_vector = anomaly_detection.read_sketch(sketch_file)
true_anomalies = np.array(sh_vector['anomaly'])
sl_vast_full = [0.0, 1.0000000000000007, 1.490000000000001,
1.7100000000000013, 1.7800000000000002, 1.455000000000001,
2.203333333333333, 2.5816666666666648, 3.0725000000000007,
2.468333333333332, 2.618333333333333, 3.1201666666666648,
4.885000000000001, 2.832666666666665, 2.8755952380952374,
2.147166666666667, 3.6497857142857155, 1.838214285714286,
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4.287068656591268, 33.50391164706565, 5.343432514636487,
3.2072694974904623, 9.46351477530971, 25.732486606692383,
6.6559933754946625, 5.326949710270922, 7.700448098163023,
9.623377196222025, 5.290016867006781, 10.318246630593064,
11.461276614000688, 4.899730136709521, 17.243929889510806,
5.7655490003853025, 7.7093964293703285, 6.71122268352426,
2.5904513597126537, 13.880125472166585, 11.93779872188574,
3.400180280000814, 16.709364926945955, 11.670224942230467,
4.184842382679839, 4.578218045197121, 4.582884649021197,
3.565759073710494, 14.524317514839522, 5.503138145637034,
6.238196683461711, 17.37220878781065, 20.434154485381526,
4.566787029804762, 13.975145735053681, 17.913545608559325,
6.3088131710986035, 12.210554128084466, 30.40746068494728,
4.827447692311119, 3.633284278162139, 4.1032110844421315,
3.31797008148174, 6.297678215654514, 3.2712620464394053,
3.709872068504734, 13.873046561745232, 7.323835385770356,
10.267732963177622, 5.524188548523757, 5.252260678752115,
4.010954034334723, 17.849252202690668, 2.717428598022562,
4.845309784015924, 3.588150991968713, 4.062358245225303,
6.5451953011362916, 4.594650998117036, 7.357212954984582,
3.1389851765506864, 8.155599887712313, 7.5305434677793395,
3.562559704028589, 3.8256866984521336, 4.161672991140334,
4.358439659411398, 8.175806749715456, 8.44925733109829,
2.5451409691372886, 124.2448057791355, 6.845275731725891,
10.342140409947344, 10.15561884780337, 46.86358071867744,
8.109758283078275, 4.728823941209188, 6.5954769007178395,
5.545256914810451, 9.85326934406742, 3.2157393937969823,
15.900778423566141, 3.1982417792285345, 4.289854078347183,
5.86527659162743, 11.544927404551617, 16.232418734444074,
3.057898031868858, 4.471957014796953, 5.206936491738273,
5.726270752686892, 5.8858024662886645, 8.990660740619703,
5.500586508383595, 5.264817521065193, 7.22545243886128,
4.265435581878583, 5.024075399314687, 4.382873514107971,
8.054286033925383, 8.365286052084514, 5.386371349005402,
4.84627035084442, 6.86109107089569, 3.9800194154174964,
120.42264703918288, 4.6910315210670435, 3.2779600617017404,
14.77103677143258, 76.04951159951159, 5.59717835438087,
7.208146610489702, 4.115273771682239, 4.921884396557897,
5.944808606025899, 4.195200296705362, 3.670358767437835,
5.145863145184387, 4.920670527026277, 6.279200579818427,
3.1012904123187117, 11.506139651028507, 3.1104296686970043,
6.245975221716039, 3.2316056706776695, 5.10035819455401,
7.53061693883638, 4.036234470283838, 14.563349377517,
5.267779411195879, 7.455462003840974, 4.430637090879873,
7.198560549183444, 5.248775198236952, 3.96895648524596,
3.6584862914483423, 5.650463593927018, 5.859260864850698,
9.108980264210008, 5.18051072642452, 17.305737697529732,
4.799317794891999, 3.1582815182487654, 2.7953608548807933,
7.240296823391461, 2.538957497910281, 6.651304218238559,
3.8882431343575825, 3.4505486491072968, 9.380542240066298,
3.369689205012699, 8.14420812869237, 3.8052677352809234,
3.0425601236589497, 5.702359888527926, 3.745680507246183,
7.0939399596565815, 4.84335022560825, 9.121713443449174,
6.793573370647204, 4.345075986194123, 4.524873022018129,
4.0978728158818996, 5.4709592216601335, 8.338998142586695,
5.264880013842203, 7.77800374494665, 4.711759300454508,
5.068280326085494, 5.293671002599666, 4.374937369959342,
4.283035196306944, 3.8280191538738966, 6.533628143028695,
4.324673648552489, 4.235564836752248, 3.5131775799173313,
9.465234097938572, 3.9624449844199057, 4.6570092578875855,
6.959980708774088, 5.199644453454408, 19.803562894236325,
4.375925742805079, 4.536184098255125, 4.586034209720847,
4.888118385738485, 5.23974726073579, 4.30720644872254,
7.320601213526189, 4.368802208014962, 7.7519187909365606,
4.762790099172769, 3.8753593657313172, 5.891853897183487,
4.722583725953089, 3.5466093412511914, 7.408643241285464,
6.82693110152808, 7.093303126162585, 4.680594262712849,
6.996386627101509, 7.327804297679037, 6.3415822151148316,
14.193108432314082, 4.030173909364596, 3.692230363050654,
7.8293511292817195, 5.784738608944195, 7.5889063293245655,
5.096278295534232, 3.746101786049061, 2.9919821734121177,
3.142188622251834, 7.358230595368869, 3.240346718412382,
9.11863385135738, 8.389137504029517, 3.4402517213808435,
4.702650682895258, 5.220843459233053, 4.517220453364481,
5.963155954281133, 28.550421333647698, 4.93714057863691,
4.670914274794189, 5.168806153082114, 7.16870761324864,
4.696063945402791, 6.584637115393755, 7.00269918912026,
5.130960764181455, 4.977492376617347, 3.251515420370515,
4.431062428217242, 7.682700802723183, 4.903494185488425,
3.528851521220606, 9.217290458047831, 4.18534470381575,
5.972318373472608, 5.2591364515332515, 3.8178467839019588,
7.679290012410782, 4.359930866965013, 6.209749921398044,
4.466098667334301, 4.35074835214217, 2.7613444616833083,
16.72524458456286, 4.402241542753284, 6.939970546380042,
8.297240870435441, 5.113116523281707, 4.740540751528764,
5.055747741686464, 4.022745917816791, 13.137256514943573,
3.6483731117599323, 3.4867979453521665, 10.222849232826183,
5.44526926404446, 7.77423201905466, 5.547698119559078,
4.52162109234846, 5.907769337511224, 5.722592273811731,
6.28274803071215, 20.23952127790497, 4.8575536643632775,
10.331131260798982, 5.279849215915478, 16.00043084572449,
6.899476437081788, 11.589425614503213, 4.242925522194997,
7.40819338515186, 3.5890713444554123, 4.9573216588424005,
3.7955871849022333, 6.141974232660044, 9.482789969172872,
5.143808107975955, 5.410408748598575, 11.67620107953851,
6.41779523791449, 3.4759992594037974, 4.635741323861855,
3.374276497853453, 3.076952914917783, 14.243249453802962,
8.550107804092484, 3.1644690646107003, 25.206608112607398,
8.514678531337816, 81.88144648208934, 4.03838253706934,
19.475949996762978, 8.69298425905638, 77.56302804140309,
16.99154529318515, 10.175701490182261, 12.235323949128562,
8.701438394142096, 7.053474190324598, 7.028903240835134,
98.16008881118881, 8.653968498412384, 6.140220718979678,
9.392051484642222, 78.7086210250804, 4.219458400907928,
8.090563413398275, 6.539354312376947, 6.993096866252306,
9.44147941190532, 9.376022306012919, 4.082741040407863,
4.496323206213832, 8.735737060263741, 4.855110825391274,
11.811151523313283, 10.798512248497625, 5.995242823780354,
9.667205604741838, 4.929436918620809, 8.012349600660778,
8.380470202977959, 4.895274133462526, 8.43796258605483,
4.6980071313934495, 5.605537416329118, 7.111768909317338,
4.837426710667193, 13.723565095581806, 4.237476278478476,
6.232069502267665, 6.95843319647167, 7.572192876082414,
3.8535901506152963, 8.945383848896428, 10.245353052702225,
5.773679264449326, 8.851156486611767, 5.18358587317758,
4.078695453583622, 5.926389033694539, 7.775306778288269,
14.701262168140024, 31.5559349203141, 6.3370981943614995,
6.084286502398295, 6.893686192349407, 5.85039978071514,
4.656068571549489, 3.9907279163162577, 5.803049172498916,
10.213612403392116, 8.802149038368533, 5.647589627061451,
5.682574509081085, 4.505895602651871, 4.7771723610001136,
4.954884489633116, 5.836035482551557, 8.48432459799846,
8.559520785689646, 5.080092088075714, 4.5143400922111,
3.2356411377139414, 4.35688397520755, 6.253646493330805,
3.2347681213577486, 4.1754490246288665, 4.246795169513299,
4.733331017365892, 5.399544811523383, 5.564378633087008,
3.451288489085621, 10.636772497444479, 14.134963848272129,
5.005722705038961, 4.15806604719205, 4.492440743943638,
5.057863665878178, 4.393856821746158, 5.811550332979167,
4.515961379221338, 2.7044037280266755, 3.586674866045954,
4.287671374082517, 4.519391614296786, 8.004253910390199,
4.383391110861225, 4.701812993778778, 25.22901575499826,
7.755497824386054, 4.959664943655632, 9.023873726302574,
4.227017026859467, 4.948121041718659, 5.353329291249265,
45.090020826509196, 3.566175953104494, 4.168202371194286,
3.0692349633064686, 7.960869020094999, 4.934509720541848,
6.410213167243775, 4.618053499871165, 9.478127760723714,
5.582944487523082, 5.704867829297357, 6.436000431445338,
6.841917236307939, 5.098072070748721, 6.3370368707862275,
4.553119367302885, 3.568697390609944, 28.031856254136947,
7.376789245554716, 5.038374893830336, 4.699700891705708,
16.019589659760044, 4.859629634401877, 4.41675014903738,
8.615531047423676, 33.254254357222635, 4.498705690337913,
4.209408988413425, 5.021513877849141, 6.154495252187022,
10.815950733178827, 4.898975986689111, 4.333347371161432,
3.3367879496291195, 11.119366957403923, 5.693470390689326,
10.093331611511582, 6.523024329671994, 5.313127910334534,
11.544399088207319, 4.640768235343204, 4.5906811013686735,
5.876647878104949, 10.032781710217716, 8.088181877012058,
5.007062806082465, 4.990841982428381, 10.381334674380781,
4.754486767388873, 5.145434397668821, 4.794402961816045,
4.5416664656796035, 5.497493323034263, 4.4240593880308,
10.072639246648427, 4.906330108661817, 3.9836192514115245,
5.573675306878233, 4.528693703895169, 7.15291368889014,
33.498251879291544, 6.056764760041608, 3.9349567796730116,
8.344128789379441, 4.233632936608567, 4.582922179948601,
4.363669173458646, 5.126811748713679, 8.053325017469811,
5.859751112097018, 3.6037688539479253, 5.018232307906261,
4.36824743891563, 4.074755624405861, 4.918462130933962,
6.8921597392245655, 5.631109247577801, 10.6405356300439,
5.74792698232398, 3.8744329683339616, 4.2451243679476285,
3.2206254165411092, 5.43161280107861, 8.272784850294613,
11.262044192385067, 13.34790269536158, 21.03973311226588,
6.637256912837035, 76.21368415356572, 4.170519658185719,
5.444735857901127, 3.511291366583334, 27.44086109313028,
17.2284553864111, 6.238288480830508, 93.42825235875235,
14.205268146312594, 9.370470424869858, 7.653869205092988,
84.90812245952397, 18.335954489820647, 3.9874612834410734,
24.94872355388109, 5.095030325030941, 6.2853069584719234,
2.8534773175150914, 4.067674711093241, 7.236127532729077,
12.702672836470757, 4.6294997878213815, 6.400526259198397,
7.002304109704079, 3.0079846147731684, 5.696283408470379,
9.285548003755759, 16.307335573979923, 4.79250981128249,
5.183219557314282, 4.271489314808509, 3.9649293523118487,
5.228027125352108, 7.931367896307529, 3.760562952298764,
4.9239291742590385, 21.847992503034092, 5.687764155336558,
6.7854367574116115, 4.053687054325117, 7.990017579965818,
13.541966545771945, 5.950335295074856, 4.898379498353713,
9.968785831023773, 7.319323979110153, 8.291096485429346,
6.019688618312564, 4.619755723667158, 6.037966108078858,
3.9786416038839345, 8.437999418080933, 5.066295655281356,
6.438209005367972, 3.614588178036152, 4.974102026431111,
7.513271924658028, 6.380559616320375, 6.214457905935818,
3.2273805194580456, 7.663188007219167, 7.38182560730083,
4.3737251770211305, 3.2269914229057757, 9.331663455275287,
4.815515498812364, 9.981319539439731, 3.003137153642821,
3.612970945213923, 2.9715983174974947, 4.978909250156718,
3.7546670177483916, 23.829626776856447, 4.80472166109154,
9.427197564202189, 6.893852204128162, 6.280011759944602,
3.672835019092789, 3.920436811705465, 4.904409218746314,
3.8965303558259428, 5.30437154108258, 4.519105958933951,
5.279126737501654, 10.447126806795405, 8.001379886800319,
5.479163464529284, 3.558483633961946, 20.173462483825126,
160.86820411935744, 141.5939125457876, 93.51120535836706,
68.51066008419684, 56.261066969213424, 25.035994011964995,
7.47022731347819, 89.10613414608267, 81.13326291417468,
3.959350977193689, 84.96572565811383, 77.96401073001466,
51.48801459792998]
disp = hash_d64_k16
d1 = pd.Series([s for s in disp])
sorted_d1 = d1.sort_values(ascending=False)
N = 140
pred_rrcf = d1 > d1.quantile(0.9)
print('Displacement: ', d1.tolist())
for k in [100, 200]:
top_k_real = anomaly_detection.get_top_k_anomalies(sh_vector, k)
top_k_disp = anomaly_detection.get_top_k_displacement(sorted_d1, k)
anomaly_detection.get_top_k_performance(top_k_real, top_k_disp,
true_anomalies, pred_rrcf, N)
real = []
for index, row in sh_vector.iterrows():
if row['anom_count'] > 100:
real.append(100)
else:
real.append(int(row['anom_count']))
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, sharex=True, sharey=True,
figsize=(8, 5))
ax1.plot(real, color='blue', linewidth=0.5)
ax1.plot(hash_d64_k16, color='red', linewidth=1)
ax1.set_title('SnapSketch', fontweight='bold', fontsize=8)
ax1.tick_params(axis='both', labelsize=6)
ax2.plot(real, color='blue', linewidth=0.5)
ax2.plot(sl_vast_full, color='red', linewidth=1)
ax2.set_title('SpotLight', fontweight='bold', fontsize=8)
ax2.tick_params(axis='both', labelsize=6)
ax3.plot(real, color='blue', linewidth=0.5)
ax3.plot(ss_vast_full, color='red', linewidth=1)
ax3.set_title('StreamSpot', fontweight='bold', fontsize=8)
ax3.tick_params(axis='both', labelsize=6)
plt.xlim([0, 1500])
plt.xlabel('Time', fontweight='bold', fontsize=6)
plt.ylabel('Anomaly Score', fontweight='bold', fontsize=6)
plt.tight_layout()
plt.savefig('results/vastplot.pdf')
plt.show()
def visualize_darpa():
if 1 == 1:
anomaly_detection = AnomalyDetection()
sketch_file = (
'sketches/darpa_10min/rdim_64_n_3_k_128p_0.2_l_50p10q5.csv')
sh_vector = anomaly_detection.read_sketch(sketch_file)
print('Sketch File: ', sketch_file)
true_anomalies = np.array(sh_vector['anomaly'])
sl_darpa = [0.0, 1.0000000000000004, 1.4200000000000008,
1.620000000000001, 1.8600000000000008, 2.4500000000000015,
2.660000000000001, 2.606666666666668, 2.163333333333334,
3.553333333333335, 4.0166666666666675, 3.8266666666666675,
2.5980000000000016, 2.075, 2.4666666666666677,
2.6790000000000003, 2.6266666666666674, 1.9041666666666677,
2.3625000000000007, 7.323666666666667, 2.3486666666666673,
3.3153333333333337, 3.063134920634921, 2.320262626262627,
5.969333333333335, 4.417166666666666, 6.199833333333332,
7.968666666666665, 3.1812142857142867, 6.162523809523808,
5.600134920634922, 8.386054112554113, 6.047714285714287,
4.551000000000001, 1.7697138972138982, 3.614246031746033,
2.480444444444445, 3.6538547008547004, 3.9801907308377915,
8.834181096681096, 5.261960317460318, 6.4440873015873015,
8.789714285714286, 4.714586219336219, 8.04961821511821,
2.5418132978132983, 7.096460317460318, 7.826078477078476,
2.5750670995671006, 7.68892007992008, 3.289951346039582,
13.420904761904762, 3.4483250670897743, 2.714681762681763,
4.418218118139173, 15.451951465201466, 6.739684550743376,
3.6353293650793663, 3.191541549953315, 4.221762570762571,
2.153847555952819, 3.109875457875458, 3.456950705727022,
2.0999702634677067, 1.7787056255054985, 1.764123928359223,
1.888398291251233, 2.1237179487179496, 2.3554988921579323,
27.04, 1.4998784545456214, 1.8871840829000228,
2.1192714078931822, 2.523507449178502, 2.239671578421579,
2.2529392409483693, 3.0643626177743837, 1.7397439836149518,
3.2541237553342834, 6.007199999999998, 4.979633699633699,
3.022961038961039, 5.0280000000000005, 3.695000000000001,
1.181985185185186, 5.990666666666666, 4.469416666666667,
5.625238095238096, 6.6290714285714305, 5.471904761904761,
8.187222222222223, 6.22639393939394, 5.630380952380951,
3.611518796992482, 3.755980392156863, 4.919714285714287,
5.687798346763989, 8.450095238095239, 13.118333333333334,
10.500526315789475, 5.1608359859095145, 6.445960317460319,
4.468441225441227, 94.62000000000003, 1.849484126984128,
6.652187590187591, 8.298173160173157, 5.727428571428573,
7.41424844720497, 6.326612612612613, 5.260150270781852,
4.67154761904762, 7.080579365079365, 3.9539688184156283,
5.400383394383394, 11.620560606060605, 6.3928064713064705,
7.091154061624651, 4.573704184704185, 6.098405483405483,
8.40033333333333, 8.324523809523805, 5.90677399738653,
9.351690476190479, 9.586746031746031, 4.338500000000001,
11.507795959595956, 6.9307226662226675, 10.670614870509608,
11.878396825396829, 7.195762597052069, 8.516702003878477,
6.877984126984128, 15.88485127629807, 52.858583105272764,
14.575487772812583, 8.832610667626147, 27.922016441891447,
24.288219378700596, 10.230674551488356, 9.96259378212087,
19.37658603966858, 20.71149731984562, 18.11692194476176,
11.720506869377614, 12.556118281296232, 14.40514704971671,
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8.720164931933521, 6.389975210089296, 6.489522864391742,
7.7234483024300875, 5.710480576425025, 6.896474243977068,
5.180801289778272, 7.52683689345378, 4.98790536383273,
8.279486809575708, 6.344842982575445, 6.346708427145821,
6.745636448046193, 4.367145081438637, 6.590269749096828,
4.597364100275982, 5.9114601906893425, 5.453606129566568,
6.310062388520356, 16.37593008385219, 6.8909111783528,
6.541159931285981, 7.579169159506998, 6.611496405315438,
5.696461729995997, 5.368917672172203, 6.402189145161245,
5.447100570432933, 6.681779511838211, 4.8794976690336656,
6.001353602118534, 6.335285287275466, 6.057808551901731,
6.145533480834151, 6.217694757019613, 7.650887840345928,
5.4401990060049075, 6.129073793095342, 5.1257031592254085,
6.292930805002428, 6.633253172305618, 5.363553301783041,
4.867259352477574, 4.433547601958522, 4.531206989778651,
6.425923090973049, 5.396312884886771, 4.468438094370893,
5.7340328755205165, 6.114518945165014, 6.47533299450849,
5.4559618597991975]
d1 = pd.Series([s for s in disp])
sorted_d1 = d1.sort_values(ascending=False)
N = 361
pred_rrcf = d1 > d1.quantile(0.9)
print('Displacement: ', d1.tolist())
for k in [100, 200, 300, 400]:
top_k_real = anomaly_detection.get_top_k_anomalies(sh_vector, k)
top_k_disp = anomaly_detection.get_top_k_displacement(sorted_d1, k)
anomaly_detection.get_top_k_performance(top_k_real, top_k_disp,
true_anomalies, pred_rrcf, N)
real = []
for index, row in sh_vector.iterrows():
if row['anom_count'] > 5000:
real.append(int(row['anom_count']) * 0.004)
else:
real.append(int(row['anom_count']) * 0.05)
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, sharex=True, sharey=True,
figsize=(10, 6))
ax1.plot(real, color='blue', linewidth=0.7)
ax1.plot(np.array(disp) * 0.5, color='red', linewidth=1)
ax1.set_title('SnapSketch', fontweight='bold', fontsize=8)
ax1.tick_params(axis='both', labelsize=6)
ax2.plot(real, color='blue', linewidth=0.7)
ax2.plot(np.array(sl_darpa) * 0.5, color='red', linewidth=1)
ax2.set_title('SpotLight', fontweight='bold', fontsize=8)
ax2.tick_params(axis='both', labelsize=6)
ax3.plot(real, color='blue', linewidth=0.7)
ax3.plot(np.array(ss_darpa) * 0.5, color='red', linewidth=1)
ax3.set_title('StreamSpot', fontweight='bold', fontsize=8)
ax3.tick_params(axis='both', labelsize=6)
plt.xlim([1200, 2600])
plt.ylim([0, 200])
plt.xlabel('Time', fontweight='bold', fontsize=6)
plt.ylabel('Anomaly Score', fontweight='bold', fontsize=6)
plt.tight_layout()
plt.savefig('results/darpaplot.pdf')
plt.show()
def visualize_iot():
anomaly_detection = AnomalyDetection()
sketch_file = 'sketches/iot/dim_32_n_3_k_256p_0.2_l_50p10q5.csv'
sh_vector = anomaly_detection.read_sketch(sketch_file)
print('Sketch File: ', sketch_file)
sh_vector.loc[sh_vector['anom_count'] >= 50, 'anomaly'] = 1
true_anomalies = np.array(sh_vector['anomaly'])
sl_total_hash = [0.0, 1.0000000000000007, 1.5200000000000011,
2.040000000000001, 1.910000000000001, 1.9350000000000012,
2.5449999999999995, 2.315, 2.9499999999999984, 2.7849999999999993,
3.89833333333333, 5.030833333333332, 5.389999999999997,
4.771666666666666, 4.824999999999997, 4.939499999999999,
7.968166666666666, 9.609333333333336, 8.426666666666668,
7.470833333333331, 10.100000000000001, 4.500166666666664,
7.260833333333329, 9.993166666666665, 6.825333333333333,
8.299261904761902, 7.549666666666667, 6.010499999999999, 8.34425,
2.3685277777777767, 7.453452380952375, 2.1427857142857127,
8.840499999999999, 5.5857261904761915, 9.547833333333328,
8.022940476190477, 8.995506854256854, 9.565772727272725,
14.310063852813851, 7.463338744588744, 9.922214285714288,
9.396590187590188, 12.81141666666667, 8.63265512265512,
4.9386277056277015, 2.4788333333333337, 12.741150793650796,
11.34468925518925, 13.031071428571423, 6.072166666666665,
6.815968253968248, 9.753902236652236, 7.688281912531911,
4.579552308802306, 15.257583333333342, 11.915132395382402,
7.997478715728716, 4.340111111111109, 7.981563492063491,
12.520726190476193, 10.64207538539891, 11.881075396825393,
12.839070346320339, 8.382726190476191, 14.908814489432139,
15.040856782106786, 6.970737267634322, 12.792952922077923,
11.989634698634697, 9.868615079365076, 15.096869047619045,
9.771920634920635, 8.824296218487394, 10.162924603174599,
12.123983405483404, 12.502298340548341, 8.35071611721611,
13.67055769230769, 15.885123015873008, 8.421123015873016,
8.837611844996434, 13.695330086580089, 10.84498153806977,
11.131603618603616, 17.294429197994987, 15.36783660783661,
16.86717081529581, 9.54105738705739, 11.565437857464167,
11.044768068887635, 12.045061237008607, 8.569439349539344,
3.9874482227345496, 9.6670439390331, 14.257423478482302,
16.960849927849928, 8.838148064495886, 9.73163816738816,
8.947979765403675, 7.660809736157563, 12.683369641796107,
11.364326479076478, 9.602993626743622, 6.912616766403531,
10.350631493506489, 11.90833531746031, 16.638106060606056,
20.079478687978686, 11.692355477855475, 6.446922327672323,
12.935004828504828, 11.15889274614274, 13.219127261627257,
15.531029390544095, 11.75360858585859, 10.88777564102564,
16.889138213981244, 11.368609231411863, 11.829447607947609,
9.649801976532236, 10.243709235209232, 6.926028215745601,
9.639434038476143, 10.043158730158728, 4.55204355366855,
12.576252261627255, 6.277979797979795, 11.390615911865911,
12.61961718836719, 13.82517892195524, 21.01147075147075,
12.09991388005838, 12.85642850334359, 10.692746312511018,
15.961849583423112, 6.026710127780715, 10.989923397985185,
15.115900706749397, 15.813263824840451, 15.098572798116916,
15.065998994469583, 21.615180293884237, 10.5956392659628,
11.07060025438584, 9.942979229104232, 9.967956655544894,
10.061437368187365, 2.9808877726659047, 20.069607149386563,
5.596657652324716, 9.873494553313902, 11.95747291434056,
7.327986707736705, 7.216033776795284, 12.465970668865017,
17.037541153291148, 12.04810969220837, 10.124884433205256,
9.650198848193426, 16.522870657085164, 14.591295267676342,
10.474009708297185, 20.797863678433632, 30.483203323723068,
12.2685559326388, 9.108504152737696, 12.571273576097102,
12.474292285414963, 7.580120945225744, 9.210636799252919,
13.868046260885658, 18.465385917187398, 14.847394358950147,
14.416545626089203, 8.451540777806645, 11.109228263568241,
10.414479371376816, 13.058494029273916, 15.33862789787761,
8.886395111768175, 15.42907009042729, 9.709224567486178,
14.875602319959093, 11.379371468005676, 12.997856485236223,
6.18236039786816, 18.252653055278053, 13.548163607963529,
15.150259799454071, 14.338007916508975, 13.687778925784366,
13.072724026202382, 14.619345782757547, 24.48042573222234,
15.28612068830722, 20.830274135684515, 14.035039516468776,
11.184922205171638, 11.132730121862796, 12.07848091648633,
15.847068398964295, 11.705474118726125, 9.302641447432853,
14.819597548693134, 16.48310627689304, 13.004734505649786,
3.810410668263627, 23.814508792508253, 10.848103676477027,
11.896245732767033, 14.864325926417623, 12.219535503149057,
7.7718429333640735, 12.516372978553232, 18.025979398285163,
16.111666271066156, 11.967626744714979, 25.13555079328996,
13.713982874684282, 9.244083281097986, 10.942876184321538,
12.662435486074806, 8.48561363360877, 12.85495403289521,
12.787459124836758, 10.043181544644371, 11.630393532327671,
7.222702346468929, 12.09825699382317, 10.765496016062885,
15.183752363370763, 10.550874248584607, 34.589417039329106,
10.991607511299366, 12.923168238950591, 17.649157104760278,
13.517545493511875, 9.989267495348466, 14.964039503640901,
11.654999042741816, 18.446485790321432, 10.993221283216265,
13.583325811275937, 11.386452531293605, 13.798483047410015,
10.382094492939462, 11.549051035502709, 17.545547313775227,
15.78166743683733, 10.98654968185409, 5.319946657022263,
14.434360536063137, 19.65145630071842, 14.517183818546412,
7.44150852477637, 9.104460242956371, 18.562987537935058,
9.292978950823638, 14.501876029835648, 14.282669255555295,
12.566730044003616, 12.830796881398907, 12.467705805932182,
13.643263208524544, 26.582495681551855, 46.24441250809099,
23.268883889760037, 10.962930943400261, 14.768651322439172,
18.154401468196397, 12.176217317098327, 17.47337651480892,
11.725951720103577, 11.7546494822007, 12.331764577983236,
14.653823628271427, 26.687428918322087, 10.853290715547818,
13.864074240936572, 12.753052483300122, 13.942820125109836,
9.516911775663946, 14.02211135597105, 11.473901457218489,
9.545227688103504, 14.260420303022233, 15.649636847286963,
13.457030988903359, 12.794822815399824, 19.778606077810217,
8.845460066209446, 11.045774811958825, 9.894838614739605,
12.376929251309246, 10.324285630720613, 10.280525895156629,
12.079127596912896, 10.52559631892971, 10.660508128586011,
14.962948377116444, 14.716836295370323, 12.49401685451108,
12.258759373431008, 9.96883766797468, 4.929313318614995,
11.629156279835687, 23.354051592500422, 13.165677301710415,
12.191832282068686, 32.56814473469892, 4.513373090882277,
7.6551439411122075, 28.36451590113103, 12.566251035882104,
12.742656796781539, 16.904395757149633, 12.05300624914015,
24.659889238410738, 12.509151785362588, 11.170633023230655,
12.286474831867473, 12.513379554816748, 9.721882093926814,
13.192858275932487, 20.60059683812919, 10.007686387314095,
13.604505433233554, 16.03765165586146, 12.05893128930242,
11.299410883073554, 10.084161427892028, 10.980642123343825,
8.797954981458206, 13.627328535742341, 15.82343459510619,
6.930158757196334, 13.151801215249376, 10.583603793066834,
10.070584266705081, 11.829309903968001, 9.264158145245466,
4.850432813262061, 6.599758156002351, 10.618567496191782,
9.059514010103639, 10.28849660767023, 5.4305259594250765,
11.272307874701797, 11.687286869881735, 11.306620140920911,
2.4928360131855576, 2.50863786341703, 11.168978062602905,
9.48007524748614, 9.660101514108227, 7.64617011634347,
11.397773579035583, 6.440589951178365, 7.586602878584321,
7.934611938545627, 9.212580135549302, 10.31261866146571,
5.7463901383711455, 9.586765946906718, 6.624105032220049,
11.44063267709711, 5.710452360167064, 6.137135992310425,
6.576521687554799, 21.063925943636182, 11.145622612535853,
10.61075568664775, 9.988019618584667, 9.42685399908465,
6.840372372598081, 10.811224792838475, 12.291490979431197,
11.74456146562922, 7.335267460989364, 9.465969223314522,
5.986744492695949, 9.216893216751066, 8.224732170062977,
11.510705016840632, 10.552612333694814, 11.416205394467935,
10.698495684874844, 2.39553546289595, 8.071759612447492,
8.07960221765112, 13.952443253742013, 10.621670520510131,
9.174322137892966, 6.958162959580833, 8.192727585950712,
8.173237865894706, 5.281692359027092, 10.538400147731574,
9.32924395982818, 11.701280692554233, 10.283743962911144,
15.313630421538337, 10.050639936907233, 9.009861977825317,
11.218979328218548, 14.766993329623595, 8.223208942938948,
14.15575037128048, 10.64365460700913, 7.456791416658677,
11.676617619147432, 15.613974729348055, 7.005190409547383,
9.463061484271524, 12.849208718651592, 10.40983462548057,
9.49457316157287, 11.094596681367786, 11.83407450485796,
10.318153582195407, 13.161307384747055, 11.44778238467615,
13.207599345587754, 9.602900725915612, 10.392233989292219,
10.854463262949013, 10.406278453219514, 11.833863626831292,
13.654324287518198, 5.463271860688648, 10.320881647705734,
12.267781294453492, 10.878071238338498, 7.878486890309873,
13.510832897393934, 8.841180189291883, 10.924972643508593,
18.161362993044108, 12.358050242222163, 6.520580025324834,
9.844635556077417, 10.986369602703896, 13.357134602026312,
13.857732674730185, 9.971122745095347, 14.077721590443105,
9.441817242681894, 9.756484692372057, 12.884420957074404,
8.71655585086868, 8.671763996942866, 10.710251197002425,
15.205465670004585, 4.65805932782175, 9.942207327675044,
9.388733620601965, 7.327148109451598, 8.828165653643445,
9.404216824841829, 6.349722880739916, 9.281637570742811,
13.908537657586654, 5.732908034736406, 11.084686592741555,
8.530687011984439, 7.745639297770371, 11.23530196492702,
22.03827021580931, 8.856085975308643, 9.420864704004842,
12.972863326860613, 10.286818810797318, 12.735987996901997,
6.001349506021301, 9.299868447705569, 10.613274101999789,
7.244426347751327, 9.01155893273753, 10.661937989499618,
7.3368609817775035, 13.568204570328296, 11.778216685949738,
9.762850117103655, 11.412395483120097, 16.459773995527442,
14.055422279830823, 8.61316888421957, 10.07474806106169,
5.259957217994107, 16.521317629800215, 8.390046417243948,
19.217554136164296, 17.11236587410929, 12.255366068097622,
13.4819510384782, 12.39565863379864, 12.528073915379235,
13.199982332183607, 15.00016062362778, 11.639750113846416,
17.657362551658377, 10.660504677550614, 17.854977596173445,
12.023951262858226, 10.826938663349026, 9.166500005786864,
9.34642000886033, 7.11394529206144, 7.997552932851012,
9.53297287353516, 11.590545356251756, 11.289757922074152,
12.628311121634828, 11.946383025442623, 16.4780662636958,
9.933129337410028, 11.464189720067207, 5.024517338115369,
8.830315718471752, 11.000010655880748, 9.325105062415242,
15.210630181514023, 20.0249073984683, 13.490197659862215,
33.23413081045201, 75.0253344677052, 31.01527379589795,
31.22920075651472, 44.02157306850319, 20.285046268879537,
54.298922561579026, 15.90723816919783, 11.64977639814105,
14.330469663887433, 7.916505545908438, 14.789737761489135,
11.044608676009265, 11.87709862870956, 14.249511854349597,
8.492633734864528, 9.510131403614123, 10.47878714644488,
13.529614726319531, 11.883770593576754, 11.609850577525238,
13.23566830428326, 15.44184372432791, 10.738355115547499,
9.332409941355364, 22.005092711890498, 11.607092137141723,
11.157643001468337, 16.985597461802197, 8.396475281723225,
12.48326264356571, 11.049412868422976, 3.8747468132819267,
11.389953215755991, 12.880123128072185, 11.841845824172452,
14.780033293274345, 16.962328579463275, 11.57015212875696,
13.22708098748122, 13.78568152721885, 8.531083573649537,
11.860251104470324, 6.928463157819973, 16.740129483517343,
8.914631113717462, 14.130156914980441, 14.317258569775596,
9.198145330855697, 9.054691845283509, 11.498156355681614,
16.96413971399202, 11.792199464289867, 13.09629073431669,
12.996533726548039, 12.77584437288146, 12.302802724088638,
12.450203061850635, 28.53686382223485, 12.041773141604484,
11.552456134999135, 10.068300008778209, 13.723871627132176,
8.077389958242035, 9.246826914469814, 9.540509983232166,
11.244606099536968, 20.81236293116173, 15.305942120114397,
15.718252989787219, 13.991067787510254, 12.284707000271938,
10.973563055722623, 10.46774068414044, 10.12037200360778,
16.87282328292622, 10.564619075448414, 10.227692539683037,
9.472327981730672, 12.751644891768985, 27.555046412138307,
85.43694072598372, 56.08802653127335, 23.66128547551483,
33.17584985478677, 67.28631244989101, 12.61227094463785,
14.726268586167194, 16.62931560481255, 14.911359213563834,
11.958171732479368, 13.71133988356006, 2.55964288748879,
9.858324417741642, 9.47145194266073, 10.480145894185153,
9.554108891465381, 15.2138744296639, 9.893545937803518,
11.440610203225516, 8.592703986782544, 8.436543701836646,
20.381453223216223, 12.42665063923577, 9.44086150063831,
9.01100650386427, 3.0216299624342637, 9.022816297089504,
11.266413968980688, 12.693713110209814, 11.998893112233928,
11.414920363600146, 11.411524474780858, 11.66297193943763,
10.624832217660124, 11.270443817199018, 8.633185310821471,
12.27232295989864, 8.629654441263881, 12.55155420358777,
7.674264325987024, 4.215214326381778, 8.519432384746139,
3.704289945681638, 9.692423464520962, 20.24128214232357,
9.495355738295645, 8.461117119375462, 8.222519601804896,
6.270856448576026, 12.59352119231331, 15.829601870111444,
10.997316851406064, 15.246424336514734, 8.743985116992656,
10.38077871808277, 12.024906005431983, 11.732132098650752,
16.23377850105429, 10.721177361666149, 11.713611435098143,
12.065810101238968, 7.987493397841775, 14.204145924938489,
17.37376384322795, 7.358973278767522, 8.486224692763733,
18.564958427964825, 5.3939599534836224, 11.81006931662247,
19.911618649311446, 18.92490889838332, 13.864733136772134,
7.756023664578203, 10.17245303827975, 6.191196639716306,
22.439839636357057, 9.547548205628994, 7.616376983168529,
10.545752049057567, 28.03242138246806, 27.615630546340377,
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15.776662968687486, 4.8219372726266645, 5.936965874078089,
8.383031773804413, 3.5528720165977674, 5.9581041742565555,
4.188658110611031, 3.920504226828131, 8.053123157367732,
4.668529739940333, 8.41590145173515, 3.6492106761835887,
4.656950310851449, 4.727182218523711, 9.738121408156632,
7.56547656631185, 4.5156682521285365, 6.426305932713351,
5.621130480623295, 7.5421604636783375, 4.657412403729312,
8.892899210206338, 5.163551873783113, 5.046241309743411,
9.219722334499348, 5.689805198476793, 4.752440741088711,
5.160585278510019, 4.618670921083705, 7.786209704206549,
6.309770264920504, 5.004721189405228, 6.908694263956865,
8.874429048978833, 4.942388223511625, 5.60091646689914,
5.267473455480562, 5.020882099279326, 6.764731325820961,
7.48902313208815, 5.093917779359989, 9.502668225621576,
3.7841151372351503, 4.587291422879952, 4.704452371210945,
4.704888813517309, 3.1647171365994886, 5.912495265677926,
10.828133522556133, 4.6289694592511275, 4.585820551791321,
10.603682775546957, 3.6611302972007147, 5.6969333402294025,
7.892222233520258, 4.8707119863738235, 12.768949521760167,
3.7454050236670673, 3.891410721341946, 7.104276184984107,
8.680172107820319, 4.080642353768982, 5.784738568169765,
6.900408796762545, 4.438949572159638, 3.773086171473992,
4.029547922319234, 5.438490529955845, 5.302302711385762,
4.461075322221949, 4.712363634866054, 5.546813928151976,
3.98453183666606, 6.714540149799347, 5.319514512822134,
11.36237559157177, 4.552022149152446, 7.156785147494494,
5.288562889581543, 5.904109387012734, 6.5831100538100475,
4.86876425880585, 4.870498362165367, 4.703307292426606,
3.5642690689331094, 3.025718932343065, 4.4620065984622235,
4.746831735399072, 5.917194043295348, 3.3374201095146203,
12.283095440867807, 4.659496073108648, 3.8781479310750444,
6.531958504341498, 4.421132817996439, 5.5750296718900225,
4.097888417384225, 5.057477409936974, 4.249051696490203,
5.8392091443212415, 3.922920121125648, 5.160174876720053,
16.183900660932007, 7.270916113093188, 4.053010491311951,
4.9803996535127695, 6.770116050891237, 6.7818346442614175,
6.967568633118546, 5.914101563211945, 4.380256770536154,
5.1814173838251705, 6.253818092578327, 4.422314575221432,
7.708296155915895, 5.192401403145199, 7.955853781011071,
5.638174256690393, 5.728373215176868, 6.342684739966915,
6.989658691082775, 4.887781929457405, 5.696999413189341,
6.055909608527767, 6.367848761419463, 5.81634857287425,
5.583309272912332, 6.218537593342466, 11.323709281711277,
6.092126853163, 5.394679136793757, 4.478248676510953,
5.652953468653985, 7.856802888720715, 3.6250683124934913,
5.000035892121128, 5.920869530871397, 4.3998786101028005,
6.32505776774248, 4.38175735462799, 3.718287043085531,
11.248670089341333, 6.178975484652476, 4.433129639845597,
5.998182418224579, 5.199652794316691, 5.704427737588539,
16.6353311652237, 6.284513692598696, 5.551214921670748,
7.125492703885347, 4.989500890302429, 6.57986820470262,
10.218872967101005, 4.351644420396437, 5.490953740155567,
212.3285714285715, 4.839548289998201, 5.299953999645585,
5.440232836061417, 5.596103284688051, 10.829980535626834,
7.824135060349622, 6.829418782013274, 5.778855577392975,
10.68623218122373, 5.723417350065048, 3.8043043683572235,
6.697167139579886, 6.200785955307497, 3.7657465681360383,
6.051100727274255, 4.617849110377838, 4.102702360029696,
9.08664754227254]
disp = hash_iot
d1 = pd.Series([s for s in disp])
sorted_d1 = d1.sort_values(ascending=False)
N = 1007
pred_rrcf = d1 > d1.quantile(0.9)
print('Displacement: ', d1.tolist())
print(metrics.classification_report(true_anomalies, pred_rrcf))
for k in [100, 200, 300, 400]:
top_k_real = anomaly_detection.get_top_k_anomalies(sh_vector, k)
top_k_disp = anomaly_detection.get_top_k_displacement(sorted_d1, k)
anomaly_detection.get_top_k_performance(top_k_real, top_k_disp,
true_anomalies, pred_rrcf, N)
real = []
for index, row in sh_vector.iterrows():
real.append(int(row['anom_count']) * 0.05)
plt.style.use('seaborn-darkgrid')
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, sharex=True, sharey=True,
figsize=(8, 5))
ax1.plot(real, color='blue', linewidth=0.5)
ax1.plot(hash_iot, color='red', linewidth=1)
ax1.set_title('SnapSketch', fontweight='bold', fontsize=8)
ax1.tick_params(axis='both', labelsize=6)
ax2.plot(real, color='blue', linewidth=0.5)
ax2.plot(sl_total_hash, color='red', linewidth=1)
ax2.set_title('SpotLight', fontweight='bold', fontsize=8)
ax2.tick_params(axis='both', labelsize=6)
ax3.plot(real, color='blue', linewidth=0.5)
ax3.plot(ss_total_hash, color='red', linewidth=1)
ax3.set_title('StreamSpot', fontweight='bold', fontsize=8)
ax3.tick_params(axis='both', labelsize=6)
plt.xlim([200, 2500])
plt.ylim([0, 120])
plt.xlabel('Time', fontweight='bold', fontsize=6)
plt.ylabel('Anomaly Score', fontweight='bold', fontsize=6)
plt.tight_layout()
plt.savefig('results/iotplot.pdf')
plt.show()
<mask token>
| import argparse
from sketching import Sketch
from anomaly_detection import AnomalyDetection
from graph_utils import GraphUtils
import glob
import numpy as np
import json
from collections import OrderedDict
from json import JSONDecoder
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.utils import shuffle
from sklearn.decomposition import PCA
def parse_args():
"""
Usual pythonic way of parsing command line arguments
:return: all command line arguments read
"""
args = argparse.ArgumentParser('GODIT')
args.add_argument('-d', '--sketch_size', default=256, type=int, help=
'Sketch Vector Size')
args.add_argument('-k', '--disc_shingle', default=32, type=int, help=
'# of Discriminative Shingle')
args.add_argument('-p', '--prob', default=0.2, type=int, help=
'Random Probability')
args.add_argument('--directed', dest='directed', action='store_true',
help='Graph is (un)directed. Default is undirected.')
args.add_argument('--undirected', dest='undirected', action='store_false')
args.add_argument('-l', '--walk_len', default=50, type=int, help=
'Length of random walk ')
args.add_argument('-n', '--n_shingle', default=3, type=int, help=
'Length of a shingle')
args.add_argument('-f', '--filename', default='vast', help=
'Dataset filename')
return args.parse_args()
def graph_sketching(args):
with open('graphs/graphs_' + args.filename + '.json') as jsonfile:
graphs = json.load(jsonfile)
print('[ ', len(graphs), ' ] graphs read successfully')
sketch = Sketch()
sketch_file = sketch.shingle_sketch(graphs, args)
print('\n Done Batch Sketching...')
return sketch_file
def main(args):
ad = AnomalyDetection()
files = ['graph2vec_mutag187_dim_128.csv']
for f in files:
print('\n\nFile: ', f)
sh_vector = ad.read_sketch_baseline('sketches/baseline/' + f)
ad.anomaly_detection(sh_vector, args)
def visualize_taxi():
anomaly_detection = AnomalyDetection()
if 1 == 1:
sketch_file = 'sketches/nyctaxi/dim_64_n_3_k_128p_0.2_l_50p10q5.csv'
print('Sketch File: ', sketch_file)
sh_vector = anomaly_detection.read_sketch(sketch_file)
disp = anomaly_detection.robust_random_cut(sh_vector)
sl_3month_24_24_1000tree = [0.0, 1.0000000000000007,
1.3069999999999837, 1.5749999999999693, 1.8219999999999728,
2.089499999999967, 2.1954999999999685, 2.4909999999999606,
2.805666666666629, 2.9281666666666295, 3.0729833333333003,
3.1683499999999607, 3.7842666666666394, 3.526116666666626,
3.547061904761867, 3.555507142857105, 3.844892857142829,
3.760817857142824, 3.6357361111110755, 3.715322619047586,
4.015897619047585, 4.13106785714283, 4.2710889971139725,
4.4617252525252225, 4.676477020201991, 4.354473448773429,
4.21600274170272, 4.040540223665191, 4.136821212121185,
4.377090945165925, 4.151008874458854, 4.3032929292929065,
3.893516883116856, 3.761827056277031, 3.848441341991319,
3.7394662337662092, 3.870903066378041, 3.839645707070682,
3.7634993145742905, 3.662225288600264, 3.557498268398248,
3.546423088023067, 3.437322943722922, 3.516349458874432,
3.7988536796536505, 3.5630521284271026, 3.5901415945165724,
3.820980303030273, 3.9180819624819345, 3.734498015872985,
3.5302974025973737, 3.6719216450216186, 3.8837171717171404,
3.758740548340518, 3.848997113997084, 3.8676169191918874,
4.496036760461739, 3.6177753607503376, 3.8619676406926144,
4.147067929292907, 3.5590136363636065, 3.6623922077921858,
3.7350548340548118, 3.729756782106761, 3.6178666305916067,
3.754875396825372, 3.587275613275586, 3.686499531024512,
3.3453290043289767, 3.561392027416999, 3.576660858585829,
3.56763726551224, 3.895365151515122, 3.4605695887445607,
3.6585208874458655, 3.594863852813823, 3.756620274170249,
3.622473376623352, 3.8295047258297004, 3.827783802308775,
3.887875072150057, 3.62666792929291, 3.827956782106755,
3.7893905122654874, 3.825775829725811, 3.6092622294372076,
3.589380772005751, 3.6329274170274, 3.6515268398268144,
3.8500302308802055, 3.561327489177468, 3.7184640692640496,
3.712509343434319, 3.546767243867226, 3.8106080447330215,
3.472430266955243, 3.753269372294346, 3.6059611832611607,
3.606277886002855, 3.5701953102452837, 3.7618476551226276,
3.82646277056274, 3.7214350288599967, 3.6715644660894373,
3.8966181818181496, 3.658271681096653, 3.8297926046175745,
3.5934492784992567, 3.9022642496392215, 3.698930735930712,
3.7655356060605834, 4.22597597402596, 3.6396637085136834,
3.729202633477606, 3.623097691197661, 3.959602344877324,
3.559363203463182, 3.7172894300144144, 3.3392061327561167,
3.755216738816722, 3.6067015512265224, 3.6207683261183017,
3.9007072510822227, 3.7254467532467253, 3.477940909090883,
3.8705008658008397, 3.5902923881673576, 3.900582287157263,
4.053705988455959, 3.796682323232295, 3.780787229437203,
3.9704607503607203, 3.7233017676767424, 3.697792171717144,
3.7598486291485975, 3.8793067821067546, 3.909909848484819,
3.7378263708513493, 3.8320799062048794, 3.9601289321789075,
4.308169300144279, 4.2086202380952304, 4.1125421356421175,
3.8586067821067593, 3.718154653679629, 3.809078679653647,
3.966138059163032, 3.951136435786407, 3.9802998917748615,
3.9425713203462953, 3.7856691197690924, 3.910652344877321,
3.9663066017315733, 3.977958946608921, 3.780931457431433,
3.820317316017289, 3.6599736291486047, 3.707707647907621,
3.725441883116858, 3.5673902958152763, 3.5991937590187337,
3.8827389249639017, 3.8220121572871335, 3.5044704545454297,
3.439539393939374, 3.3874304112553855, 3.3518844155843888,
3.3086836580086327, 2.9474969336219066, 2.8205874098123855,
3.2005816738816444, 3.1988915945165606, 3.125726515151481,
3.2303779220779036, 3.1010323232323014, 3.102419083694056,
3.068896789321767, 3.0816936147185885, 3.215689862914838,
3.385117207792184, 3.3956795093794767, 3.195732323232299,
3.265265007214983, 3.1392999639249446, 3.264929365079339,
3.4748344877344643, 3.208597294372267, 3.622541450216432,
3.4296585497835257, 3.5167829004328715, 3.7117974747474483,
3.6494369408369183, 3.6282344155843878, 3.7499116161615835,
3.72148538961036, 3.4892155844155557, 3.6508107864357613,
3.7926731601731336, 3.391973881673852, 3.887472186147164,
3.7511802308802067, 3.8429490620490383, 3.847867676767653,
3.7793133838383612, 3.801909920634893, 3.745292316017292,
4.041810064935048, 3.7616010822510555, 3.955913167388137,
3.963381349206331, 3.8228332611832396, 3.579587121212097,
3.4770942279942068, 3.7541075757575473, 3.79021580086578,
3.912270274170249, 4.066463924963898, 4.003379906204885,
4.0052485930735715, 4.156482323232299, 3.9260842352092147,
4.097521320346294, 4.045806673881648, 4.1314053751803534,
3.8381345959595765, 3.587890620490595, 3.9608605699855457,
3.668929040404014, 3.770212914862898, 3.5402694444444123,
3.585081060606036, 3.4611156204905957, 3.354173665223642,
3.4407172799422505, 3.515055014429991, 3.533650288600266,
3.619540079365053, 3.357846428571407, 3.2724475829725543,
3.3691640331890023, 3.688166269841241, 3.5040137806637435,
3.3259152236651937, 3.540546212121185, 3.5978412698412443,
3.5661549062048783, 3.4603155483405175, 3.381314105339083,
3.664481168831142, 3.559792316017284, 3.800688311688291,
4.76248813131311, 4.174080050505028, 3.781430158730134,
3.7634084415584184, 4.048151154401126, 3.7405865079364835,
4.0043095238094955, 3.739411219336193, 4.270551118326098,
3.8755366161615945, 3.7922791486291203, 3.971384379509358,
3.8612339466089214, 4.005365764790739, 4.140303354978328,
3.9664880591630367, 3.8060173520923275, 3.931700649350624,
3.862881168831139, 3.921066486291457, 3.756006782106756,
3.8823032828282575, 3.8607837301586994, 3.8950672438672154,
3.832847150072125, 3.527478751803729, 3.601055447330423,
3.698805663780636, 3.4474857864357644, 3.750525793650768,
3.599419264069234, 3.4653589466089207, 3.829073484848455,
3.447042424242396, 4.093282828282808, 3.6722832611832312,
3.564764105339074, 3.8253759379509096, 3.767274494949472,
3.939715187590156, 3.8363546897546583, 3.571977741702715,
3.8106083694083455, 3.6345401515151226, 3.924849278499249,
3.7938874458874174, 3.9271287157286885, 3.8956906565656326,
3.965942207792181, 3.9157770923520667, 3.9402582611832373,
4.490846067821058, 3.897951623376607, 4.0916613275613125,
3.7592698773448507, 3.903860461760436, 3.9230887085136796,
3.844913780663757, 3.574144877344853, 3.9054419552669337,
3.5425164141413803, 3.4547947691197436, 3.6863794372294136,
3.76660689033187, 3.787290584415554, 3.5852249278499024,
3.3435772727272455, 3.3772877344877075, 3.6352774170273934,
3.56096558441556, 3.701559415584389, 3.4648298701298454,
3.9327364718614426, 3.809838924963904, 3.6214967532467277,
3.820830266955253, 3.6022968614718383, 3.5950069985569777,
3.5158109307359133, 3.3681872655122373, 3.327917568542544,
3.257087301587282, 3.1492856782106555, 3.131523809523785,
2.8823072871572584, 2.9961978715728543, 2.838357828282802,
2.8527597763347474, 2.954603427128402, 3.085678823953801,
3.186112481962456, 3.1077302308802066, 2.861977813852789,
3.0699853535353263, 3.1257018759018496, 3.2181551948051696,
3.1954116883116623, 3.041358405483387, 2.951494877344863,
2.922761940836916, 3.036607972582944, 2.9529673160172902,
3.062448015872991, 3.335139718614696, 3.497888275613258,
3.656845670995646, 3.7393582972582684, 3.7651746031745774,
3.787149531024506, 3.913758333333307, 3.983562734487709,
3.7659469696969428, 4.034907251082223, 3.7833126984126695,
3.8380386724386404, 3.8034597763347424, 3.75308964646462,
3.8597727633477397, 4.166514935064909, 3.987993975468956,
3.981965331890302, 4.159145707070686, 3.9931064213563956,
4.164266666666647, 4.548593181818168, 4.005048953823927,
3.8002693722943457, 3.9042592352092123, 4.215628102453091,
3.837902813852799, 3.7273817460317153, 3.8264467171716903,
3.8019875541125288, 3.983259776334744, 3.8695760822510574,
3.5565028499278215, 3.675079292929267, 3.636276623376596,
3.5744255050504776, 3.6771806998556738, 3.5959114357864097,
3.660600649350634, 3.734209848484838, 3.714772546897526,
3.6122490620490395, 3.7443204545454254, 3.7384215728715473,
3.3945695887445693, 3.6578407287157058, 3.5878882395382132,
3.3685573593073377, 3.6852617243866987, 3.535026984126958,
3.432802922077896, 3.8117803391053258, 3.389550865800834,
3.407892893217866, 3.3898312049061787, 3.458620093795068,
3.4856037518037213, 3.4122480519480214, 3.3716331168830878,
3.5281458513708266, 3.480547835497807, 3.5022249639249368,
3.5978719336219136, 3.6419860028859747, 3.528142929292904,
3.6182410894660597, 3.5675144660894427, 3.5687557720057494,
3.4306428571428293, 3.6162410173159922, 3.387638924963901,
3.457994769119747, 3.6762860750360526, 3.4295958152957926,
3.3442378066377763, 3.3687195526695275, 3.442358982683957,
3.6152843434343236, 3.377038383838355, 3.246517243867219,
3.435268542568513, 3.3391543290043044, 3.363836291486265,
3.4306038961038663, 3.341835028860002, 3.396813564213544,
3.3936194805194546, 3.26737204184702, 3.243939610389581,
3.2504365800865607, 3.2946102813852636, 3.370105339105313,
3.4209319624819403, 3.453070382395357, 3.418980808080788,
3.5703095598845342, 3.430544083694061, 3.8854875541125358,
3.4231378787878497, 3.5119530303030078, 3.6576691919191675,
3.5083237734487493, 3.7449314574314316, 3.808185930735905,
4.129641089466064, 3.9677842712842377, 3.974645598845572,
4.1553882034631755, 3.900088816738788, 4.24138719336217,
4.111257683982658, 4.087929401154372, 4.049108008657985,
4.0701995310245085, 3.9891628427128145, 3.963061363636335,
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4.164157972582948, 4.178872871572849, 4.158167424242403,
4.276667496392467, 4.155344372294351, 4.321037193362181,
4.088869624819597, 4.249960209235189, 4.089403066378038,
4.140151587301561, 4.2251545815295515, 4.008581818181795,
3.9884704545454253, 4.110306457431437, 4.109988997113972,
3.7899787518037233, 3.713131565656544, 3.6306691197690926,
3.5057138888888604, 3.5010483405483135, 3.349766378066349,
3.3140680735930417, 3.1471725108224855, 3.509508838383807,
3.5799866522366273, 3.9397041486291378, 3.4254500721500447,
4.285101659451664, 3.371495743145715, 3.6270672799422505,
3.440475216450196, 3.939636760461752, 3.707285137085113,
3.6440493506493317, 3.6337319264069072, 3.4206152597402317,
3.4766953463203216, 3.645578391053363, 3.5364712842712787,
3.3782433621933396, 3.3004218975468627, 3.342176767676749,
3.1945509379509205, 3.4491018759018437, 3.508118831168803,
3.7443132034631774, 3.3601994227993894, 3.5442215367965004,
3.4196651515151246, 3.63979310966808, 3.424614466089444,
3.315955519480494, 3.378141955266924, 3.826569588744562,
3.3523027417027205, 3.433705194805169, 3.3523681818181554,
3.5237727272727, 4.023938961038932, 3.479545021644994,
3.4745572871572614, 3.5017689033188772, 3.716824386724349,
3.6588534271283986, 3.6964095959595684, 3.739368975468943,
3.5996099927849623, 3.613793975468953, 3.611695526695507,
3.704940404040386, 3.6758972943722688, 3.557323953823934,
3.4368426767676534, 3.2390504329004095, 3.526259704184679,
3.5329863997113713, 3.447791378066345, 3.7927203102452864,
3.5176984487734213, 3.441510966810936, 3.516128535353503,
3.5686651875901587, 3.496300144300112, 3.569259379509364,
3.65160790043287, 3.3929287157286967, 3.4763750360750105,
3.632346176046143, 3.625511327561301, 3.6000547979797726,
3.4161343434343197, 3.3325566378066145, 3.487288852813828,
3.4927690476190207, 3.246955627705599, 3.5028846681096413,
3.4991487734487468, 3.6180824675324432, 3.604977200577178,
3.665578427128402, 3.7045176046175747, 3.363262770562753,
3.7029059884559645, 3.5607610750360443, 3.4711203823953536,
3.64831006493504, 3.596419552669521, 3.3775670995670706,
3.4016603896103663, 3.4494856060605867, 3.1238439393939124,
3.40199494949492, 3.342463095238067, 3.334388131313099,
3.423255194805163, 3.3052384199133953, 3.456070923520902,
3.7392691919191714, 3.5142265873015615, 3.6688252164501858,
3.531621067821046, 3.461052380952353, 3.44930039682537,
3.315392784992763, 3.3503586940836665, 3.340679401154373,
3.5511273448773175, 3.064562121212096, 3.2278831890331623,
3.2610421717171456, 3.488687229437205, 3.2828787157286947,
3.355658694083674, 3.5122380952380756, 3.6231826118325867,
3.4042028860028624, 3.6339021284271085, 3.8676437950937617,
3.536569805194786, 3.7001546897546618, 3.346895238095218,
3.4263069624819398, 3.6255772366522026, 3.7808272366522058,
3.4349837662337435, 3.5832102453102155, 3.611504797979772,
3.4380056998556734, 3.5092514069263805, 3.517610750360727,
3.3764633116882967, 3.598326515151488, 3.6246312770562468,
3.4501836940836665, 3.474727164502134, 3.6527933982683662,
3.396318217893184, 3.5723413059162787, 3.4645700216449953,
3.5002759018758915, 3.4343788239538164, 3.2907455266955017,
3.5791827922077823, 3.5811041847041523, 3.2080687590187362,
3.395273845598816, 3.296913997113997, 3.3370277056276767,
3.4184201298701087, 3.5207560966810934, 3.385987085137057,
3.6008587301587056, 3.562529473304443, 3.4884353174602842,
3.6302541125540957, 3.655049206349201, 3.4293432900432617,
3.603386616161588, 3.5594039682539367, 3.4276949134198866,
3.5731476190476013, 3.205585569985554, 3.5695164141413787,
3.6405999639249353, 3.618561219336193, 3.660250360750329,
3.5690231240980976, 3.6049422799422612, 3.5725487012986776,
3.397205880230845, 3.442501334776311, 3.499210028859999,
3.5931566378066053, 3.5507364357864137, 3.5834439033188734,
3.479485028860012, 3.478391919191899, 3.310271031746012,
3.5914571067820766, 3.59273351370849, 3.6750512987012716,
3.607822258297232, 3.55760111832609, 3.7067989898989624,
3.362148917748913, 3.593859054834024, 3.4050120490620444,
3.5975595598845334, 3.3239717893217646, 3.3990839826839627,
3.3267614357864193, 3.509855303030277, 3.202276551226535,
3.4406770923520726, 3.6713771284271, 3.4813761183260956,
3.596760173160144, 3.6847953463203065, 3.716108116883086,
3.600093542568525, 3.6075520562770294, 3.595976875901851,
3.7360333333333013, 3.5165599927849662, 3.4992221861471586,
3.5285116522366247, 3.5480345959595785, 3.6082134920634688,
3.6220694805194547, 3.67156269841267, 3.6052178932178642,
3.4293011544011334, 3.508401695526665, 3.3518581529581355,
3.4332445165944865, 3.796077777777752, 3.43247968975467,
3.673125685425663, 3.6116721139970918, 3.69820508658006,
3.468771645021611, 3.539292676767656, 3.9654474386724057,
3.5753493506493186, 3.5659643217892913, 3.646480483405451,
3.7614234487734186, 3.743483910533883, 3.677483405483375,
3.6442661255410935, 3.6370542568542232, 3.6693554834054574,
3.8534254329003965, 3.6992376262625917, 3.6259961038960804,
3.6497096681096464, 3.7212052308802, 3.518662987012963,
3.626205266955236, 3.6703412337662047, 3.395637950937921,
3.3346030663780395, 3.438943542568505, 3.7228614357864007,
3.486473124098101, 3.6825029942279692, 3.3514910533910256,
3.528390476190444, 3.467328607503571, 3.527324206349173,
3.532494516594491, 3.204221861471843, 3.3100978715728555,
3.3343236652236454, 3.7396380591630316, 3.4669422799422485,
3.651525036075008, 3.5661218975468665, 3.2572421717171456,
3.3703782106781865, 3.4356599206348952, 3.4426006854256577,
3.52744747474745, 3.5517226911976643, 3.8167528138527906,
3.351922474747451, 3.56634711399708, 3.315893290043271,
3.3682616883116667, 3.5770427128426805, 3.6553499278498944,
3.6734190836940623, 3.3596617604617367, 3.4726179292929005,
3.421943759018734, 3.392463744588717, 3.4646640692640367,
3.5472951298701036, 3.4068395021644733, 3.591805627705602,
3.152540909090888, 3.3409378427128096, 3.6109486291486004,
3.552807106782076, 3.3760063492063237, 3.566610497835468,
3.4967387445887104, 3.322296428571404, 3.711784740259713,
3.2046276334776125, 3.587404473304442, 3.7483380591630366,
3.6487572871572604, 3.3524051948051667, 3.333438167388146,
3.4012370129870084, 3.536367929292898, 3.299234235209209,
3.498042893217867, 3.238408369408351, 3.534621139971104,
3.2058287878787657, 3.283614574314555, 3.20107579365077,
3.4033217171716883, 3.199304040404019, 3.4405079004328774,
3.3154903679653454, 3.3920802308802043, 3.2419352813852513,
3.320750216450199, 3.3448606421356177, 3.2142973304472995,
3.3397123015872685, 3.2660753246752994, 3.3130229437229266,
3.141910064935051, 3.3448118326118044, 3.4276056277055997,
3.3064205988455684, 3.451471825396798, 3.312898809523784,
3.342003174603147, 3.288666341991337, 3.4104757215006956,
3.2790400072150057, 3.1991698412698204, 3.384275901875886,
3.1948836940836647, 3.307539538239521, 3.3580050505050245,
3.1081702020201862, 3.1228773809523496, 3.307134054834026,
3.1510627344877142, 3.3209016233765976, 3.479646861471835,
3.4748538961038644, 3.474859776334752, 3.410531132756114,
3.6174274531024215, 3.5371584415584127, 3.3994079004328825,
3.514477272727245, 3.4363025252525023, 3.7218170274169977,
3.6358874098123852, 3.506348881673855, 3.4350543290043003,
3.617922871572844, 3.6134994588744322, 3.830190007214981,
3.6608829004328745, 3.545925937950904, 3.8678587662337347,
3.7508575396825052, 4.00299527417024, 4.1395118326118014,
3.8700648268398044, 3.697943614718585, 4.250540764790739,
3.6429310966810706, 3.6568993506493217, 4.122653643578626,
3.9118182178931953, 4.009558189033159, 3.8579217171716937,
3.910020021645, 3.9288633838383498, 3.6442356421356177,
3.572199711399689, 3.4879789321789265, 3.2705241702741468,
3.35403037518035, 3.2403025974025774, 3.248706240981215,
2.8923446608946417, 3.1936047258296987, 3.0712568181817965,
3.416106782106746, 3.1766304473304254, 2.994570887445865,
3.1402626262626065, 2.8315124458874257, 3.3640485569985255,
3.426519769119744, 3.510397113997085, 3.4856349567099265,
3.2369883116882923, 3.2682865800865604, 3.1922568181818,
3.285399314574291, 3.5574947691197365, 3.364213131313097,
3.551962842712816, 3.5838068542568213, 3.448910137085104,
3.524935461760437, 3.4287505772005535, 3.388999819624796,
3.4520884559884304, 3.486850180375154, 3.288185101010073,
3.298004004328981, 3.235140151515125, 3.7271895743145427,
3.4914124819624623, 3.7120948051947757, 3.715360497835467,
3.42893080808078, 3.4509375180374944, 3.5101240620490306,
3.4411117965367692, 3.5749021284271043, 3.72602968975466,
3.896334884559856, 3.641075072150055, 3.469741594516565,
3.499399278499258, 3.4940156565656286, 3.5175891414141134,
3.351470382395354, 3.7038156204905937, 3.4022489538239284,
3.6769700216449923, 3.602125757575725, 3.625870779220755,
3.6582954545454287, 3.5921053751803558, 3.5201286075035756,
3.513108405483382, 3.4777691558441317, 3.2880247474747195,
3.324983477633447, 3.3475782106781797, 3.2891756132755936,
3.107030844155823, 3.267221753246726, 3.327365909090883,
3.0455752886002667, 3.575517784992755, 3.4836657647907354,
3.235919805194784, 3.2626624819624555, 3.025963239538212,
3.3861622294371996, 3.1655125541125293, 3.3055551587301295,
3.3473224025973765, 3.304992171717144, 3.510782178932155,
3.380530663780633, 3.1728139249639007, 3.3474877344877116,
3.6445877705627407, 3.385356529581502, 3.660799314574289,
3.513949675324647, 3.2966316378066076, 3.65127427849925,
3.7438681096680755, 3.8357191558441364, 3.4628260461760263,
3.2598133838383583, 3.5265018759018565, 3.5011014430014122,
3.35850948773446, 3.6043847402597136, 3.4303568903318706,
3.6693863275613006, 3.494725721500695, 3.438726767676744,
3.4408869408369145, 3.4315762626262365, 3.5394078282828,
3.614963239538234, 3.479173015872981, 3.309512012986989,
3.2107059523809336, 3.539382215007182, 3.278323448773435,
3.405395310245277, 3.061225505050481, 3.246955411255387,
3.3028983405483157, 3.1950409451659194, 3.1305961038960817,
3.3173962121211824, 3.034025649350624, 3.228019624819602,
3.2047064213563847, 3.332253391053361, 3.2388595238094924,
3.314918795093765, 3.3618299783549497, 3.283075252525232,
3.465293434343408, 3.3088450216449994, 3.463889357864339,
3.419786219336196, 3.2087825396825203, 3.522573268398245,
3.3360270923520674, 3.424782034632006, 3.367450757575722,
3.4591977633477367, 3.2789356421356155, 3.6316588383838115,
3.6825156204905922, 3.9001475108224883, 3.862914790764771,
3.8830613275613057, 3.8035015512265207, 3.714767063492031,
3.8134426046175753, 3.587301262626236, 3.3655253246753007,
3.8412998917748764, 4.059842207792186, 4.284045707070687,
4.169291450216422, 4.293944264069251, 4.256984126984106,
3.843303968253942, 4.041025396825378, 3.9166680735930544,
3.8722202380952173, 4.049738059163038, 3.7550568903318675,
3.71240443722941, 3.483060064935031, 3.8624164141413884,
3.347439466089445, 3.5778279220778955, 3.8550708152957913,
4.248108008657984, 4.489894769119746, 4.396559848484826,
4.563660173160153, 4.342820598845582, 4.236970454545442,
4.13688614718613, 4.1565161255411045, 3.945583116883095,
4.237365836940814, 4.3258683261183055, 4.228977669552654,
4.346015656565634, 4.392648737373719, 4.485096789321772,
4.207221067821043, 4.504071753246733, 4.644336219336197,
4.558519841269825, 4.29503279220777, 4.283737518037495,
4.514020382395366, 4.218733802308783, 4.255114466089451,
4.0093888888888705, 3.7060284992784807, 3.902351406926384,
3.5842570707070514, 3.50645266955265, 3.5928034271284037,
3.558307756132728, 3.416949314574288, 3.4641007575757383,
3.208823881673855, 3.2894423160172797, 3.5185549062048813,
3.4904814213563964, 3.6075619769119456, 3.5799585497835196,
3.613853715728691, 3.7100060606060343, 4.105699891774864,
3.673746933621902, 3.9589537878787575, 3.8795498556998282,
3.77941114718612, 3.4756365079364815, 3.7126886002885744,
3.587670959595931, 3.687042424242398, 4.021024458874443,
3.8566215007214795, 3.738314249639221, 3.6091256132755936,
3.628927741702714, 3.5086377344877073, 3.472952200577178,
3.5387726911976634, 3.726934595959568, 3.692004725829703,
3.746876551226521, 3.577973124098105, 3.4958566017315786,
3.437313131313107, 3.4413020923520703, 3.656373051948025,
3.776435750360721, 3.842288311688287, 4.01849570707068,
3.7744726551226213, 3.7897423881673595, 3.5102832972582743,
3.5768917388167107, 3.292435606060583, 3.6728684704184476,
4.157170454545436, 3.5345207792207622, 3.4960542568542294,
3.599462049062024, 3.390737121212097, 3.5566846681096465,
3.5339489538239253, 3.7530128066377797, 3.538101046176021,
4.01714134199132, 3.724827092352062, 3.896990007214978,
3.778241702741681, 3.8946558080807834, 3.7854972222222023,
3.573991450216432, 3.5667772727272453, 3.8854299422799183,
3.8094425685425506, 3.947839826839798, 4.238178643578619,
3.7105190836940576, 3.7696475468975175, 3.611162373737343,
3.996808080808051, 3.989477597402566, 3.7724437950937713,
4.169715043290018, 3.9015271645021343, 3.758420598845573,
3.8888927849927564, 3.6642214285714023, 4.114003102453073,
3.7498011904761652, 3.87393214285712, 3.6473646825396546,
3.549368290043263, 3.5539690115439826, 3.5105417027416825,
3.71054325396823, 3.768528643578616, 3.658768867243839,
3.683383549783529, 3.6796175685425423, 3.651033333333306,
3.742247619047591, 3.7100461038960755, 3.81932330447328,
3.447403066378041, 3.7545474386724127, 3.4887262987012755,
3.6876364718614516, 3.795770670995648, 3.486950108225079,
3.4286481601731302, 3.630055808080781, 3.642051587301558,
3.64739563492061, 3.774097186147163, 3.608849314574286,
4.074280808080791, 3.777415584415552, 3.5965112914862636,
3.5197398629148338, 3.767559307359284, 3.7046252164501903,
4.113040512265486, 4.096717027417011, 3.9257302308801996,
3.5444796176045914, 3.746505375180352, 3.865215404040382,
3.98939765512263, 3.825869913419885, 3.721755699855674,
3.9998325757575564, 3.768903860028831, 3.9001917388167144,
3.765781565656535, 3.7642763347763073, 3.885239502164481,
3.9098973665223395, 3.449542532467517, 3.8661339466089184,
3.7893130591630375, 3.742495598845567, 3.557431204906183,
3.9245551587301395, 3.683747691197666, 3.6981939754689486,
3.3842016233765917, 3.588271356421329, 3.4919087301587046,
3.5403440836940563, 3.671433080808055, 3.8135924963924697,
3.600260678210655, 3.4482345959595735, 3.635264357864336,
3.6944480880230626, 3.6880526695526465, 3.581007611832589,
3.6345391053390834, 3.750940259740235, 3.776628607503576,
3.770705699855664, 3.778300252525232]
d1 = pd.Series([s for s in disp])
pred_rrcf = d1 > d1.quantile(0.9)
sorted_d1 = d1.sort_values(ascending=False)
anom = []
splt = []
sspot = []
for d in disp:
anom.append(d)
for d in sl_3month_24_24_1000tree:
splt.append(d)
for d in ss_3month_24_24_1000tree:
sspot.append(d)
events = {'Thanksgiving': (600, 648), 'Nov29': (672, 696), 'Nov30':
(697, 720), 'Christmas': (1296, 1320), 'NewYear': (1464, 1488),
'Blizzard': (1992, 2040)}
real = pd.DataFrame()
real['event'] = np.zeros(len(anom))
real_s = pd.DataFrame()
real_s['event'] = np.zeros(len(anom))
for event, duration in events.items():
start, end = duration
real.loc[start:end, 'event'] = 1
real_s.loc[start:end, 'event'] = 7
print('Displacement: ', d1.tolist())
print(metrics.classification_report(real, pred_rrcf))
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, sharex=True, sharey=
True, figsize=(8, 5))
ax1.plot(real_s, color='blue', linewidth=0.5)
ax1.plot(anom, color='red', linewidth=1)
ax1.set_title('SnapSketch', fontweight='bold', fontsize=8)
ax1.tick_params(axis='both', labelsize=6)
ax2.plot(real_s, color='blue', linewidth=0.5)
ax2.plot(splt, color='red', linewidth=1)
ax2.set_title('SpotLight', fontweight='bold', fontsize=8)
ax2.tick_params(axis='both', labelsize=6)
ax3.plot(real_s, color='blue', linewidth=0.5)
ax3.plot(sspot, color='red', linewidth=1)
ax3.set_title('StreamSpot', fontweight='bold', fontsize=8)
ax3.tick_params(axis='both', labelsize=6)
plt.xlim([4, 2180])
plt.xlabel('Time', fontweight='bold', fontsize=6)
plt.ylabel('Anomaly Score', fontweight='bold', fontsize=6)
plt.tight_layout()
plt.savefig('results/taxiplot.pdf')
plt.show()
def visualize_vast():
anomaly_detection = AnomalyDetection()
sketch_file = 'sketches/vast/dim_64_n_3_k_16p_0.2_l_50p10q5.csv'
sh_vector = anomaly_detection.read_sketch(sketch_file)
true_anomalies = np.array(sh_vector['anomaly'])
sl_vast_full = [0.0, 1.0000000000000007, 1.490000000000001,
1.7100000000000013, 1.7800000000000002, 1.455000000000001,
2.203333333333333, 2.5816666666666648, 3.0725000000000007,
2.468333333333332, 2.618333333333333, 3.1201666666666648,
4.885000000000001, 2.832666666666665, 2.8755952380952374,
2.147166666666667, 3.6497857142857155, 1.838214285714286,
5.315777777777774, 2.812238095238095, 3.2381547619047613,
3.078515873015871, 4.529853174603175, 3.7458189033189018,
4.538138888888887, 2.3045234487734487, 3.9079166666666647,
1.9696340603840605, 3.7305703463203455, 2.6001663336663334,
5.102301587301583, 4.11497619047619, 6.937190170940167,
3.3317508602508594, 5.671685637891517, 2.6808455139631597,
4.393103174603173, 3.5497279942279936, 3.46266989443305,
3.0899802741118507, 4.084215673215671, 3.6542838827838815,
9.099530483405484, 3.1833452870658756, 4.91520404432169,
3.5223060689310683, 3.513890062551827, 3.4316554556554526,
7.361717046238782, 3.034030247530244, 3.5310568729516087,
2.922128819280134, 7.7212221455445125, 4.079142108730322,
9.254576312576315, 2.432390327305543, 5.455857618301886,
2.1478042199748724, 5.717154465904462, 3.8056670117322278,
4.421209786945079, 8.25086314289575, 3.9806453075648105,
5.727092564298445, 2.2562179417804407, 4.726554874616326,
2.677858543417364, 4.647202617322312, 3.7667160478410473,
8.502338286713286, 5.371667172696582, 3.7220769479058933,
3.576872840746761, 5.672282716738598, 2.5096632245168564,
9.910175995573049, 3.7785037460976065, 4.679637417536024,
2.716194069819068, 8.629652946976746, 5.139533376711007,
5.80293475274725, 5.964897491397492, 4.9442456430043356,
2.580831972780479, 4.06149549146569, 2.8358605887099064,
7.453780982905984, 8.303637507045242, 8.939271899668952,
3.1272130815419152, 7.212484656361171, 6.0686778583559535,
6.4849147969677405, 1.9282294054311582, 9.1355835577466,
3.6393741215011333, 46.1344817251462, 10.535195634920635,
3.517713645901447, 13.903026048895066, 10.986628982128975,
46.87568816110287, 4.502153165109742, 2.3054975305856416,
3.9902068301925087, 67.18650000000004, 6.436401108076753,
2.9037945225053585, 26.276562895437905, 38.22192063492066,
7.659223675028898, 2.522062522103756, 39.35848469994057,
23.99845426824637, 29.993268808533507, 2.6173386692584395,
4.040253601184814, 69.4936666666667, 3.0327631390127134,
29.84829747540043, 50.814211376858445, 4.523049856432983,
43.74888023088023, 8.065274023338803, 2.5897766484663363,
9.986485255924695, 7.6404389382547295, 32.95801323340515,
26.934599137222552, 2.122800695507033, 22.719991302526008,
2.531943126662511, 2.1558683074106626, 2.5708741409260822,
1.9434624011492079, 2.0757465872643626, 2.112191638790354,
2.1096226232500697, 2.877354582108181, 2.018921542056945,
2.3010023212314104, 3.468095728869127, 2.9194415187548293,
2.192044953956719, 2.656430041857208, 2.247205654490438,
38.94106878306877, 23.100060904008263, 4.288507682349105,
22.36149772449771, 45.40354273504275, 39.483467352092354,
4.040192685218999, 2.062892591069062, 2.6459853032547698,
2.3301549219464825, 4.53012095596005, 2.9772097452807142,
2.8574584285347373, 2.548702471322061, 2.65944699872955,
2.3178856199551947, 3.534616098190533, 3.73302985152985,
1.515600041087059, 2.5456805194805203, 6.110558996558996,
4.519910499042486, 2.2248168402956487, 2.8929118477498887,
4.901385737844415, 3.0371780894668152, 1.8101252678667985,
4.101566830031821, 2.369464362967107, 3.3470473701906855,
2.427038507535658, 1.5092799924770854, 2.2393657326208065,
3.0204576584312086, 4.952721740884551, 4.545370277585918,
3.924783566704709, 5.679710899880955, 2.07507759482395,
3.689693986577186, 4.410048364585616, 2.395058360658565,
3.6105811351207198, 2.115178011484056, 2.8206351638239213,
3.853601344470662, 5.629191429184849, 2.688231487467939,
2.0287039034873398, 5.847567952148525, 2.335106430051789,
5.12541678257092, 3.117835198159749, 5.275337101555593,
4.574242697844625, 7.278482289017314, 7.196986561756102,
7.387243180155941, 5.759555818450555, 2.608319026470451,
4.323878181497967, 2.435695102799575, 2.5092351455577186,
2.4582267426944915, 2.5584739131116807, 4.855368125535241,
4.865108087486137, 4.122068797191757, 3.4873005866257736,
3.0433521286770286, 8.639741025911903, 2.201531830484797,
5.025303611525683, 3.755774402366179, 3.555147106457937,
2.5364964085130333, 3.722142841228145, 2.48247885796658,
4.271447546666749, 6.776819864615641, 4.277098861935588,
3.709109123203335, 7.183871606420256, 7.806880290238749,
5.138150428165338, 5.030142926030408, 2.7982305694827194,
7.09283679668314, 7.6988370345551225, 2.881321122862117,
4.163881733806376, 2.556985623498055, 3.528237524678694,
5.852479341137831, 4.109318090561147, 6.1545703356341805,
12.344342507710156, 4.131945865247535, 3.9870180327335487,
3.1779193852350267, 3.472430132464246, 7.8153322360666735,
3.620740995335953, 3.323551554097161, 10.810294973000854,
8.63308429742397, 3.632960134708154, 8.11147760808906,
4.336612368661363, 6.89291983150511, 5.82210924531265,
14.330259048456808, 3.494165167016899, 9.573348636018238,
3.7180761212323588, 11.202624465320335, 3.5557115359266405,
4.172923984092086, 4.500268075284338, 9.687788267998425,
4.140257106161449, 4.8787484102923475, 6.211739463108268,
7.372484195237485, 4.1178916374103975, 8.89759647073753,
11.326268564376145, 6.9458143337417075, 6.106456541856061,
9.170521105019516, 9.42727084425986, 8.40229245145132,
2.802653113145537, 4.141863946771091, 7.182149471621556,
3.7302128901799922, 7.247612138486243, 6.658189470038223,
5.815450303923129, 9.073282476112974, 3.5132360508852547,
8.799354414422949, 3.996735892005669, 5.791928514455155,
17.375807236064585, 5.716573824007423, 6.040451151859592,
13.252769128390991, 4.459342597781656, 8.304756656212426,
1.6980046914404392, 5.252022282785437, 2.697608278171014,
9.525614042146932, 9.649622163790943, 12.597816726256198,
12.087499700462937, 17.26588324235, 5.416809380839663,
6.744729129692225, 11.52585637934218, 9.038647712028743,
5.55738532923505, 7.148217616449185, 14.971559308625604,
5.484246597550578, 6.604505788028619, 4.706706504272274,
6.056393192282342, 14.422203111007722, 5.968753545314402,
8.43818908331422, 6.739394866846011, 8.70991038247342,
15.882690106243881, 9.519841064185808, 3.6689399466244503,
5.374653857232868, 7.285325948650889, 7.708996100034014,
5.522543807038958, 3.8149253799356084, 15.373093136038582,
6.125296988550094, 9.776068773617684, 15.77058670162355,
13.82363960521091, 247.6191666666668, 241.33333333333354,
234.42900000000017, 232.09750000000017, 210.64066666666673,
221.72516666666675, 232.2900000000001, 222.12333333333342, 195.435,
188.7107051282052, 212.0550000000001, 184.82833333333338, 174.603,
197.49654761904753, 201.08250000000007, 188.2996666666667,
168.91083333333341, 140.90283333333332, 182.10469047619037,
167.76266666666666, 201.27583333333334, 128.51128571428575,
163.6542142857143, 144.93942857142858, 165.571619047619,
126.74703968253968, 132.71890476190472, 132.64626190476187,
120.56686111111115, 133.08200000000002, 124.06377380952382,
109.44774603174604, 119.52338888888892, 132.30933333333337,
133.28711904761911, 123.43590476190475, 122.62227380952382,
119.69415476190476, 124.21578968253968, 101.97053427128431,
121.55083333333337, 97.77622222222222, 118.32983333333335,
85.58539105339106, 93.25142857142859, 92.86189430014429,
148.130974025974, 107.23876082251078, 99.64868109668106,
106.35083347208347, 124.41839774114771, 92.82343434343429,
79.42896667221666, 115.31892857142856, 102.09249603174604,
97.60596031746032, 98.39762049062051, 97.84232029082025,
74.7981453823954, 117.20717460317464, 82.88765778665778,
116.06709235209233, 112.63099350649348, 77.57687301587299,
68.50434188034187, 68.74346683871683, 66.55115656565658,
57.29339610389609, 82.01547757797759, 78.97707052669553,
59.293463203463205, 80.68080203130204, 68.20699583749587,
93.13353715728715, 82.54827380952378, 71.05218829373243,
94.86532683982684, 81.07937770562769, 58.239944319569325,
78.67548490398492, 77.73863247863247, 89.3007863158084,
60.59544094794097, 63.535196311858066, 54.066168898911535,
58.69705555555553, 62.112702227511036, 82.83547150072157,
76.60754390054392, 66.45232736707737, 69.87924711399714,
95.8731394993895, 60.590718475968465, 64.98781875580559,
40.28886784925603, 42.83568489190547, 63.44847224413014,
44.71849708624707, 57.9525066045066, 64.72960320480175,
53.59518587784764, 66.21941450216451, 56.599784756909735,
44.74083139199121, 53.34800285825286, 83.39110589410589,
61.71693729881226, 49.89878665044107, 45.469751470751476,
38.22292039496449, 63.3234285812227, 51.26063616397362,
48.50249020320923, 47.96220337995338, 41.76456023388377,
60.32749657042304, 78.21711122374357, 43.71192171218873,
74.69666076957098, 49.90576030376571, 39.539820534943225,
37.679378791057594, 40.80346641185612, 37.977084683290585,
32.41200153114859, 32.30609088602318, 45.93041749418451,
58.983918257009606, 61.89522984939897, 49.95603525567998,
52.72657009167304, 59.49734652748351, 45.7758046158348,
35.79614894337689, 56.338855168077544, 51.79309429154439,
50.17492246423167, 49.45681448535126, 94.87377564102566,
5.583283140835772, 3.9162921604302654, 59.28472619047617,
11.883705882494882, 6.915702521893311, 10.396591880341882,
6.91654627381261, 6.546112079441025, 11.451208101470254,
5.696588820477437, 11.162068886832122, 9.775125576986621,
6.667897129916862, 4.07249083943068, 3.427912597767718,
6.855468166852067, 5.262775517441072, 4.217759079737734,
8.438877241038531, 6.139497244793982, 4.155880007939896,
11.924343073593072, 4.112602314098305, 11.264307262182264,
5.586391976444605, 13.835801871822309, 10.366778818403816,
5.224708357337165, 13.664748223998217, 5.775802314352313,
3.7491118402825703, 12.640176046176046, 13.278944583194585,
10.60006447997059, 4.545913295038295, 4.137727793507116,
5.322219585969587, 4.137522028506548, 3.6467037770963953,
15.298778345837171, 19.10643365330322, 20.12315502144914,
16.2593566017316, 22.72560515873016, 13.387081353115406,
25.970498168498164, 7.15262736315175, 4.508708978752147,
23.648555707337977, 10.06831043895718, 22.33236464266728,
33.119184820081884, 8.204233006990385, 7.612921109806563,
21.478803054377366, 17.0805397935398, 6.8324731088767665,
15.51072815107598, 11.11415237452606, 19.32150271950271,
14.106476608187137, 16.141752961950328, 27.431247585747595,
12.988704068154068, 3.38169460598815, 4.0732080205394565,
10.804547558922552, 5.12622014341008, 17.40577338674397,
20.495269896769898, 22.278985181485194, 5.649780350182326,
15.86570876637982, 10.942273236567345, 14.617256521256516,
10.849919946599298, 16.393261994541408, 10.034207796451916,
5.222813611645463, 6.966476149667325, 18.843976190476184,
24.03774325674326, 7.157834376715055, 6.605692477654003,
12.482704532881002, 21.278275432900422, 9.492165812129052,
6.7409658798752705, 27.940258731464613, 5.186259425952376,
5.821990818508626, 14.840411772536765, 27.982968364968364,
7.2517002109144055, 26.41445592571679, 3.2213331698750385,
14.973314234511003, 5.891178387583598, 14.134004470202997,
15.104925575632095, 24.396750000000004, 18.53616784121676,
13.568361705545916, 6.126727585666205, 8.451839101716809,
22.887695669526547, 3.0808813504708805, 8.127122518909283,
12.862281399521528, 10.648651695526691, 10.34753681318681,
8.152438186209926, 14.650473216524126, 11.638633667956539,
5.927901170149454, 11.733014675520558, 7.434446923446925,
6.409333236938498, 19.368656813602644, 9.17803330796629,
10.765351883665227, 14.477716356107656, 13.71163424702459,
8.444197508947507, 5.251662244890185, 4.697624808198339,
4.8688349170587815, 5.3173791223396485, 9.202607416219262,
15.353170601650431, 18.94445810014359, 16.71226147670699,
12.653465651995052, 8.256661825103, 12.731945883743107,
11.498063149384548, 8.055364233332435, 8.451854445354789,
8.111012981159027, 15.732954315555835, 11.457314212642716,
11.153882807232547, 4.313494070269162, 8.952838488202627,
7.666871714714594, 5.964522730308335, 4.554589636001515,
8.313256336370884, 14.495610997786484, 16.115692661203532,
6.56816189202954, 13.195922991288455, 16.706598361820493,
5.705755287893645, 7.860273627688101, 2.475742251547555,
19.636313081703747, 6.293349770440286, 9.97330286380286,
13.916166379370408, 11.36112190907819, 12.218882859010904,
4.45602520842071, 2.9430648885164987, 9.025770072486543,
4.874773443840634, 3.8403056188378226, 2.8356793287432316,
2.855574242612899, 6.488899660764235, 4.486395485812943,
5.247979789309864, 8.037945376377852, 4.061050134918251,
4.754734366596591, 9.506047402509525, 3.6984312289018173,
4.560583411510789, 7.581555760018381, 27.29878766869413,
4.21898278916216, 10.802250230150907, 5.322705895268154,
13.011138349157132, 5.120299777237376, 2.5404489153808196,
4.138665206832909, 4.523924983014456, 10.399930040607897,
3.690950834342338, 7.718602409380349, 7.190240054659171,
9.394291920669092, 4.56354511183656, 7.22336542326033,
6.963257201059073, 4.663388387611124, 6.463243831952779,
4.957760749908201, 2.5029190324668145, 6.1018353564735905,
6.895868406921364, 4.432062112051939, 4.932937399202103,
2.6209314302551325, 7.091240085829241, 2.7871860363765397,
4.988536916009182, 12.238069121170813, 5.902388181856735,
2.6744604526694373, 7.069325425637308, 11.389871317161333,
2.4032967990235172, 3.187427619233651, 6.212646670287652,
4.425432562138935, 4.888203613274407, 6.156376357212045,
4.130593305332705, 4.145753257068871, 4.472086153368514,
3.406484848962027, 8.105159937284936, 9.513209739638093,
5.830413010819851, 4.5679915077027635, 9.370917975053096,
6.474360043387983, 5.161303263360733, 9.393186871476933,
9.212958309655408, 6.362967118111655, 5.4360638814985105,
5.37057641965138, 9.402097665722074, 6.2496566006257215,
6.718457246261504, 4.779065757174989, 5.801093469590185,
6.938687223226251, 6.641724404459532, 5.910398084692218,
5.8420188830094535, 13.003173856654671, 7.488764560626808,
6.499377551469278, 5.0326612993215925, 13.501693734180861,
14.79726454147782, 7.3638314988559666, 8.136374053949105,
10.157367763170786, 8.648649934721831, 8.964934614807692,
4.590364661220342, 10.598533660024756, 4.000223090379563,
7.573014177807931, 7.6784310654014485, 7.9139903781846055,
8.142755867503777, 7.142233541675048, 5.878573375073361,
5.670243636775541, 8.813096891169259, 6.734255940838059,
3.3130886984740853, 12.202141468242083, 6.387797699321444,
6.811708665110413, 5.554487659992712, 18.393146091997103,
2.994229282708331, 2.9695969357744114, 4.640360104551808,
3.682165193500811, 4.979289774586387, 8.858425964827523,
7.046054002189139, 7.98036349179714, 8.699491386212072,
4.419739449154545, 3.4725895947005783, 5.1913184024041055,
12.522598765542384, 6.117596536944922, 5.410794590563778,
9.785013886997014, 5.410251095625295, 10.010762259104451,
9.724111506361838, 4.967477974468255, 10.478704672652107,
7.491293348879519, 10.084161043651182, 4.4765761536025614,
3.466113433732224, 5.764284346867644, 5.4375955369737765,
3.5091357996576655, 12.567910943578498, 9.082663640804247,
6.690770057321501, 3.8115607700336396, 3.766753484055016,
3.9775284856755677, 4.575929096227209, 5.748117873545612,
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5.645947277064139, 14.416605908723346, 5.759299924145805,
8.300718831392524, 8.53781846373953, 4.074919947707334,
2.8273961399962686, 4.091206864365489, 11.36277172877854,
4.69069806063981, 5.503011297192813, 4.169436937792529,
3.227335979709522, 10.927397428988128, 4.651809062350412,
6.211176037265824, 8.202651425644643, 4.55140013939773,
9.276112935862553, 5.5793640821497394, 4.831738821825663,
14.335556364312636, 4.712004413959613, 4.131850094024241,
8.949034427005735, 15.09767539248144, 4.164010107842091,
3.0351288980277173, 3.7635277145792667, 3.9334441347729334,
8.1229969913426, 4.174933364580936, 5.469444242529065,
4.655265407435733, 5.115089494246537, 4.007535387839185,
11.038425542901994, 5.052376649140364, 6.011985646886391,
4.230650474998805, 4.713020261479946, 14.135825727913803,
4.524033167224263, 6.140640957898205, 3.367054764394021,
4.287068656591268, 33.50391164706565, 5.343432514636487,
3.2072694974904623, 9.46351477530971, 25.732486606692383,
6.6559933754946625, 5.326949710270922, 7.700448098163023,
9.623377196222025, 5.290016867006781, 10.318246630593064,
11.461276614000688, 4.899730136709521, 17.243929889510806,
5.7655490003853025, 7.7093964293703285, 6.71122268352426,
2.5904513597126537, 13.880125472166585, 11.93779872188574,
3.400180280000814, 16.709364926945955, 11.670224942230467,
4.184842382679839, 4.578218045197121, 4.582884649021197,
3.565759073710494, 14.524317514839522, 5.503138145637034,
6.238196683461711, 17.37220878781065, 20.434154485381526,
4.566787029804762, 13.975145735053681, 17.913545608559325,
6.3088131710986035, 12.210554128084466, 30.40746068494728,
4.827447692311119, 3.633284278162139, 4.1032110844421315,
3.31797008148174, 6.297678215654514, 3.2712620464394053,
3.709872068504734, 13.873046561745232, 7.323835385770356,
10.267732963177622, 5.524188548523757, 5.252260678752115,
4.010954034334723, 17.849252202690668, 2.717428598022562,
4.845309784015924, 3.588150991968713, 4.062358245225303,
6.5451953011362916, 4.594650998117036, 7.357212954984582,
3.1389851765506864, 8.155599887712313, 7.5305434677793395,
3.562559704028589, 3.8256866984521336, 4.161672991140334,
4.358439659411398, 8.175806749715456, 8.44925733109829,
2.5451409691372886, 124.2448057791355, 6.845275731725891,
10.342140409947344, 10.15561884780337, 46.86358071867744,
8.109758283078275, 4.728823941209188, 6.5954769007178395,
5.545256914810451, 9.85326934406742, 3.2157393937969823,
15.900778423566141, 3.1982417792285345, 4.289854078347183,
5.86527659162743, 11.544927404551617, 16.232418734444074,
3.057898031868858, 4.471957014796953, 5.206936491738273,
5.726270752686892, 5.8858024662886645, 8.990660740619703,
5.500586508383595, 5.264817521065193, 7.22545243886128,
4.265435581878583, 5.024075399314687, 4.382873514107971,
8.054286033925383, 8.365286052084514, 5.386371349005402,
4.84627035084442, 6.86109107089569, 3.9800194154174964,
120.42264703918288, 4.6910315210670435, 3.2779600617017404,
14.77103677143258, 76.04951159951159, 5.59717835438087,
7.208146610489702, 4.115273771682239, 4.921884396557897,
5.944808606025899, 4.195200296705362, 3.670358767437835,
5.145863145184387, 4.920670527026277, 6.279200579818427,
3.1012904123187117, 11.506139651028507, 3.1104296686970043,
6.245975221716039, 3.2316056706776695, 5.10035819455401,
7.53061693883638, 4.036234470283838, 14.563349377517,
5.267779411195879, 7.455462003840974, 4.430637090879873,
7.198560549183444, 5.248775198236952, 3.96895648524596,
3.6584862914483423, 5.650463593927018, 5.859260864850698,
9.108980264210008, 5.18051072642452, 17.305737697529732,
4.799317794891999, 3.1582815182487654, 2.7953608548807933,
7.240296823391461, 2.538957497910281, 6.651304218238559,
3.8882431343575825, 3.4505486491072968, 9.380542240066298,
3.369689205012699, 8.14420812869237, 3.8052677352809234,
3.0425601236589497, 5.702359888527926, 3.745680507246183,
7.0939399596565815, 4.84335022560825, 9.121713443449174,
6.793573370647204, 4.345075986194123, 4.524873022018129,
4.0978728158818996, 5.4709592216601335, 8.338998142586695,
5.264880013842203, 7.77800374494665, 4.711759300454508,
5.068280326085494, 5.293671002599666, 4.374937369959342,
4.283035196306944, 3.8280191538738966, 6.533628143028695,
4.324673648552489, 4.235564836752248, 3.5131775799173313,
9.465234097938572, 3.9624449844199057, 4.6570092578875855,
6.959980708774088, 5.199644453454408, 19.803562894236325,
4.375925742805079, 4.536184098255125, 4.586034209720847,
4.888118385738485, 5.23974726073579, 4.30720644872254,
7.320601213526189, 4.368802208014962, 7.7519187909365606,
4.762790099172769, 3.8753593657313172, 5.891853897183487,
4.722583725953089, 3.5466093412511914, 7.408643241285464,
6.82693110152808, 7.093303126162585, 4.680594262712849,
6.996386627101509, 7.327804297679037, 6.3415822151148316,
14.193108432314082, 4.030173909364596, 3.692230363050654,
7.8293511292817195, 5.784738608944195, 7.5889063293245655,
5.096278295534232, 3.746101786049061, 2.9919821734121177,
3.142188622251834, 7.358230595368869, 3.240346718412382,
9.11863385135738, 8.389137504029517, 3.4402517213808435,
4.702650682895258, 5.220843459233053, 4.517220453364481,
5.963155954281133, 28.550421333647698, 4.93714057863691,
4.670914274794189, 5.168806153082114, 7.16870761324864,
4.696063945402791, 6.584637115393755, 7.00269918912026,
5.130960764181455, 4.977492376617347, 3.251515420370515,
4.431062428217242, 7.682700802723183, 4.903494185488425,
3.528851521220606, 9.217290458047831, 4.18534470381575,
5.972318373472608, 5.2591364515332515, 3.8178467839019588,
7.679290012410782, 4.359930866965013, 6.209749921398044,
4.466098667334301, 4.35074835214217, 2.7613444616833083,
16.72524458456286, 4.402241542753284, 6.939970546380042,
8.297240870435441, 5.113116523281707, 4.740540751528764,
5.055747741686464, 4.022745917816791, 13.137256514943573,
3.6483731117599323, 3.4867979453521665, 10.222849232826183,
5.44526926404446, 7.77423201905466, 5.547698119559078,
4.52162109234846, 5.907769337511224, 5.722592273811731,
6.28274803071215, 20.23952127790497, 4.8575536643632775,
10.331131260798982, 5.279849215915478, 16.00043084572449,
6.899476437081788, 11.589425614503213, 4.242925522194997,
7.40819338515186, 3.5890713444554123, 4.9573216588424005,
3.7955871849022333, 6.141974232660044, 9.482789969172872,
5.143808107975955, 5.410408748598575, 11.67620107953851,
6.41779523791449, 3.4759992594037974, 4.635741323861855,
3.374276497853453, 3.076952914917783, 14.243249453802962,
8.550107804092484, 3.1644690646107003, 25.206608112607398,
8.514678531337816, 81.88144648208934, 4.03838253706934,
19.475949996762978, 8.69298425905638, 77.56302804140309,
16.99154529318515, 10.175701490182261, 12.235323949128562,
8.701438394142096, 7.053474190324598, 7.028903240835134,
98.16008881118881, 8.653968498412384, 6.140220718979678,
9.392051484642222, 78.7086210250804, 4.219458400907928,
8.090563413398275, 6.539354312376947, 6.993096866252306,
9.44147941190532, 9.376022306012919, 4.082741040407863,
4.496323206213832, 8.735737060263741, 4.855110825391274,
11.811151523313283, 10.798512248497625, 5.995242823780354,
9.667205604741838, 4.929436918620809, 8.012349600660778,
8.380470202977959, 4.895274133462526, 8.43796258605483,
4.6980071313934495, 5.605537416329118, 7.111768909317338,
4.837426710667193, 13.723565095581806, 4.237476278478476,
6.232069502267665, 6.95843319647167, 7.572192876082414,
3.8535901506152963, 8.945383848896428, 10.245353052702225,
5.773679264449326, 8.851156486611767, 5.18358587317758,
4.078695453583622, 5.926389033694539, 7.775306778288269,
14.701262168140024, 31.5559349203141, 6.3370981943614995,
6.084286502398295, 6.893686192349407, 5.85039978071514,
4.656068571549489, 3.9907279163162577, 5.803049172498916,
10.213612403392116, 8.802149038368533, 5.647589627061451,
5.682574509081085, 4.505895602651871, 4.7771723610001136,
4.954884489633116, 5.836035482551557, 8.48432459799846,
8.559520785689646, 5.080092088075714, 4.5143400922111,
3.2356411377139414, 4.35688397520755, 6.253646493330805,
3.2347681213577486, 4.1754490246288665, 4.246795169513299,
4.733331017365892, 5.399544811523383, 5.564378633087008,
3.451288489085621, 10.636772497444479, 14.134963848272129,
5.005722705038961, 4.15806604719205, 4.492440743943638,
5.057863665878178, 4.393856821746158, 5.811550332979167,
4.515961379221338, 2.7044037280266755, 3.586674866045954,
4.287671374082517, 4.519391614296786, 8.004253910390199,
4.383391110861225, 4.701812993778778, 25.22901575499826,
7.755497824386054, 4.959664943655632, 9.023873726302574,
4.227017026859467, 4.948121041718659, 5.353329291249265,
45.090020826509196, 3.566175953104494, 4.168202371194286,
3.0692349633064686, 7.960869020094999, 4.934509720541848,
6.410213167243775, 4.618053499871165, 9.478127760723714,
5.582944487523082, 5.704867829297357, 6.436000431445338,
6.841917236307939, 5.098072070748721, 6.3370368707862275,
4.553119367302885, 3.568697390609944, 28.031856254136947,
7.376789245554716, 5.038374893830336, 4.699700891705708,
16.019589659760044, 4.859629634401877, 4.41675014903738,
8.615531047423676, 33.254254357222635, 4.498705690337913,
4.209408988413425, 5.021513877849141, 6.154495252187022,
10.815950733178827, 4.898975986689111, 4.333347371161432,
3.3367879496291195, 11.119366957403923, 5.693470390689326,
10.093331611511582, 6.523024329671994, 5.313127910334534,
11.544399088207319, 4.640768235343204, 4.5906811013686735,
5.876647878104949, 10.032781710217716, 8.088181877012058,
5.007062806082465, 4.990841982428381, 10.381334674380781,
4.754486767388873, 5.145434397668821, 4.794402961816045,
4.5416664656796035, 5.497493323034263, 4.4240593880308,
10.072639246648427, 4.906330108661817, 3.9836192514115245,
5.573675306878233, 4.528693703895169, 7.15291368889014,
33.498251879291544, 6.056764760041608, 3.9349567796730116,
8.344128789379441, 4.233632936608567, 4.582922179948601,
4.363669173458646, 5.126811748713679, 8.053325017469811,
5.859751112097018, 3.6037688539479253, 5.018232307906261,
4.36824743891563, 4.074755624405861, 4.918462130933962,
6.8921597392245655, 5.631109247577801, 10.6405356300439,
5.74792698232398, 3.8744329683339616, 4.2451243679476285,
3.2206254165411092, 5.43161280107861, 8.272784850294613,
11.262044192385067, 13.34790269536158, 21.03973311226588,
6.637256912837035, 76.21368415356572, 4.170519658185719,
5.444735857901127, 3.511291366583334, 27.44086109313028,
17.2284553864111, 6.238288480830508, 93.42825235875235,
14.205268146312594, 9.370470424869858, 7.653869205092988,
84.90812245952397, 18.335954489820647, 3.9874612834410734,
24.94872355388109, 5.095030325030941, 6.2853069584719234,
2.8534773175150914, 4.067674711093241, 7.236127532729077,
12.702672836470757, 4.6294997878213815, 6.400526259198397,
7.002304109704079, 3.0079846147731684, 5.696283408470379,
9.285548003755759, 16.307335573979923, 4.79250981128249,
5.183219557314282, 4.271489314808509, 3.9649293523118487,
5.228027125352108, 7.931367896307529, 3.760562952298764,
4.9239291742590385, 21.847992503034092, 5.687764155336558,
6.7854367574116115, 4.053687054325117, 7.990017579965818,
13.541966545771945, 5.950335295074856, 4.898379498353713,
9.968785831023773, 7.319323979110153, 8.291096485429346,
6.019688618312564, 4.619755723667158, 6.037966108078858,
3.9786416038839345, 8.437999418080933, 5.066295655281356,
6.438209005367972, 3.614588178036152, 4.974102026431111,
7.513271924658028, 6.380559616320375, 6.214457905935818,
3.2273805194580456, 7.663188007219167, 7.38182560730083,
4.3737251770211305, 3.2269914229057757, 9.331663455275287,
4.815515498812364, 9.981319539439731, 3.003137153642821,
3.612970945213923, 2.9715983174974947, 4.978909250156718,
3.7546670177483916, 23.829626776856447, 4.80472166109154,
9.427197564202189, 6.893852204128162, 6.280011759944602,
3.672835019092789, 3.920436811705465, 4.904409218746314,
3.8965303558259428, 5.30437154108258, 4.519105958933951,
5.279126737501654, 10.447126806795405, 8.001379886800319,
5.479163464529284, 3.558483633961946, 20.173462483825126,
160.86820411935744, 141.5939125457876, 93.51120535836706,
68.51066008419684, 56.261066969213424, 25.035994011964995,
7.47022731347819, 89.10613414608267, 81.13326291417468,
3.959350977193689, 84.96572565811383, 77.96401073001466,
51.48801459792998]
disp = hash_d64_k16
d1 = pd.Series([s for s in disp])
sorted_d1 = d1.sort_values(ascending=False)
N = 140
pred_rrcf = d1 > d1.quantile(0.9)
print('Displacement: ', d1.tolist())
for k in [100, 200]:
top_k_real = anomaly_detection.get_top_k_anomalies(sh_vector, k)
top_k_disp = anomaly_detection.get_top_k_displacement(sorted_d1, k)
anomaly_detection.get_top_k_performance(top_k_real, top_k_disp,
true_anomalies, pred_rrcf, N)
real = []
for index, row in sh_vector.iterrows():
if row['anom_count'] > 100:
real.append(100)
else:
real.append(int(row['anom_count']))
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, sharex=True, sharey=True,
figsize=(8, 5))
ax1.plot(real, color='blue', linewidth=0.5)
ax1.plot(hash_d64_k16, color='red', linewidth=1)
ax1.set_title('SnapSketch', fontweight='bold', fontsize=8)
ax1.tick_params(axis='both', labelsize=6)
ax2.plot(real, color='blue', linewidth=0.5)
ax2.plot(sl_vast_full, color='red', linewidth=1)
ax2.set_title('SpotLight', fontweight='bold', fontsize=8)
ax2.tick_params(axis='both', labelsize=6)
ax3.plot(real, color='blue', linewidth=0.5)
ax3.plot(ss_vast_full, color='red', linewidth=1)
ax3.set_title('StreamSpot', fontweight='bold', fontsize=8)
ax3.tick_params(axis='both', labelsize=6)
plt.xlim([0, 1500])
plt.xlabel('Time', fontweight='bold', fontsize=6)
plt.ylabel('Anomaly Score', fontweight='bold', fontsize=6)
plt.tight_layout()
plt.savefig('results/vastplot.pdf')
plt.show()
def visualize_darpa():
if 1 == 1:
anomaly_detection = AnomalyDetection()
sketch_file = (
'sketches/darpa_10min/rdim_64_n_3_k_128p_0.2_l_50p10q5.csv')
sh_vector = anomaly_detection.read_sketch(sketch_file)
print('Sketch File: ', sketch_file)
true_anomalies = np.array(sh_vector['anomaly'])
sl_darpa = [0.0, 1.0000000000000004, 1.4200000000000008,
1.620000000000001, 1.8600000000000008, 2.4500000000000015,
2.660000000000001, 2.606666666666668, 2.163333333333334,
3.553333333333335, 4.0166666666666675, 3.8266666666666675,
2.5980000000000016, 2.075, 2.4666666666666677,
2.6790000000000003, 2.6266666666666674, 1.9041666666666677,
2.3625000000000007, 7.323666666666667, 2.3486666666666673,
3.3153333333333337, 3.063134920634921, 2.320262626262627,
5.969333333333335, 4.417166666666666, 6.199833333333332,
7.968666666666665, 3.1812142857142867, 6.162523809523808,
5.600134920634922, 8.386054112554113, 6.047714285714287,
4.551000000000001, 1.7697138972138982, 3.614246031746033,
2.480444444444445, 3.6538547008547004, 3.9801907308377915,
8.834181096681096, 5.261960317460318, 6.4440873015873015,
8.789714285714286, 4.714586219336219, 8.04961821511821,
2.5418132978132983, 7.096460317460318, 7.826078477078476,
2.5750670995671006, 7.68892007992008, 3.289951346039582,
13.420904761904762, 3.4483250670897743, 2.714681762681763,
4.418218118139173, 15.451951465201466, 6.739684550743376,
3.6353293650793663, 3.191541549953315, 4.221762570762571,
2.153847555952819, 3.109875457875458, 3.456950705727022,
2.0999702634677067, 1.7787056255054985, 1.764123928359223,
1.888398291251233, 2.1237179487179496, 2.3554988921579323,
27.04, 1.4998784545456214, 1.8871840829000228,
2.1192714078931822, 2.523507449178502, 2.239671578421579,
2.2529392409483693, 3.0643626177743837, 1.7397439836149518,
3.2541237553342834, 6.007199999999998, 4.979633699633699,
3.022961038961039, 5.0280000000000005, 3.695000000000001,
1.181985185185186, 5.990666666666666, 4.469416666666667,
5.625238095238096, 6.6290714285714305, 5.471904761904761,
8.187222222222223, 6.22639393939394, 5.630380952380951,
3.611518796992482, 3.755980392156863, 4.919714285714287,
5.687798346763989, 8.450095238095239, 13.118333333333334,
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13.02640797936521, 15.811316803455282, 14.36100254240862,
7.123728747433187, 6.8094696471805305, 6.80274296212756,
29.473624470345495, 15.56606551266405, 7.078460954121151,
6.286097749191182, 12.628836460727305, 10.972910341827081,
11.337576300187877, 7.422547695231272, 8.680527590503962,
6.889134126599722, 14.961641388964562, 5.150865483309711,
6.967325186233745, 9.387068315114968, 5.75737566739241,
6.781292729905152, 4.69866902216877, 7.959441085432782,
8.57698861669268, 7.41365315761497, 5.453339832300251,
6.829218190523225, 6.003049986883954, 5.787279372322578,
8.104651324118597, 7.035932273619949, 6.960746972199602,
6.190104177474828, 5.9656362159819185, 5.587974267291152,
5.3952485672350265, 5.197358799521997, 6.308841168814804,
5.770520133765092, 5.666563787417696, 5.093133769213094,
5.620794216200692, 9.48351513090556, 22.154731319580215,
8.720164931933521, 6.389975210089296, 6.489522864391742,
7.7234483024300875, 5.710480576425025, 6.896474243977068,
5.180801289778272, 7.52683689345378, 4.98790536383273,
8.279486809575708, 6.344842982575445, 6.346708427145821,
6.745636448046193, 4.367145081438637, 6.590269749096828,
4.597364100275982, 5.9114601906893425, 5.453606129566568,
6.310062388520356, 16.37593008385219, 6.8909111783528,
6.541159931285981, 7.579169159506998, 6.611496405315438,
5.696461729995997, 5.368917672172203, 6.402189145161245,
5.447100570432933, 6.681779511838211, 4.8794976690336656,
6.001353602118534, 6.335285287275466, 6.057808551901731,
6.145533480834151, 6.217694757019613, 7.650887840345928,
5.4401990060049075, 6.129073793095342, 5.1257031592254085,
6.292930805002428, 6.633253172305618, 5.363553301783041,
4.867259352477574, 4.433547601958522, 4.531206989778651,
6.425923090973049, 5.396312884886771, 4.468438094370893,
5.7340328755205165, 6.114518945165014, 6.47533299450849,
5.4559618597991975]
d1 = pd.Series([s for s in disp])
sorted_d1 = d1.sort_values(ascending=False)
N = 361
pred_rrcf = d1 > d1.quantile(0.9)
print('Displacement: ', d1.tolist())
for k in [100, 200, 300, 400]:
top_k_real = anomaly_detection.get_top_k_anomalies(sh_vector, k)
top_k_disp = anomaly_detection.get_top_k_displacement(sorted_d1, k)
anomaly_detection.get_top_k_performance(top_k_real, top_k_disp,
true_anomalies, pred_rrcf, N)
real = []
for index, row in sh_vector.iterrows():
if row['anom_count'] > 5000:
real.append(int(row['anom_count']) * 0.004)
else:
real.append(int(row['anom_count']) * 0.05)
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, sharex=True, sharey=True,
figsize=(10, 6))
ax1.plot(real, color='blue', linewidth=0.7)
ax1.plot(np.array(disp) * 0.5, color='red', linewidth=1)
ax1.set_title('SnapSketch', fontweight='bold', fontsize=8)
ax1.tick_params(axis='both', labelsize=6)
ax2.plot(real, color='blue', linewidth=0.7)
ax2.plot(np.array(sl_darpa) * 0.5, color='red', linewidth=1)
ax2.set_title('SpotLight', fontweight='bold', fontsize=8)
ax2.tick_params(axis='both', labelsize=6)
ax3.plot(real, color='blue', linewidth=0.7)
ax3.plot(np.array(ss_darpa) * 0.5, color='red', linewidth=1)
ax3.set_title('StreamSpot', fontweight='bold', fontsize=8)
ax3.tick_params(axis='both', labelsize=6)
plt.xlim([1200, 2600])
plt.ylim([0, 200])
plt.xlabel('Time', fontweight='bold', fontsize=6)
plt.ylabel('Anomaly Score', fontweight='bold', fontsize=6)
plt.tight_layout()
plt.savefig('results/darpaplot.pdf')
plt.show()
def visualize_iot():
anomaly_detection = AnomalyDetection()
sketch_file = 'sketches/iot/dim_32_n_3_k_256p_0.2_l_50p10q5.csv'
sh_vector = anomaly_detection.read_sketch(sketch_file)
print('Sketch File: ', sketch_file)
sh_vector.loc[sh_vector['anom_count'] >= 50, 'anomaly'] = 1
true_anomalies = np.array(sh_vector['anomaly'])
sl_total_hash = [0.0, 1.0000000000000007, 1.5200000000000011,
2.040000000000001, 1.910000000000001, 1.9350000000000012,
2.5449999999999995, 2.315, 2.9499999999999984, 2.7849999999999993,
3.89833333333333, 5.030833333333332, 5.389999999999997,
4.771666666666666, 4.824999999999997, 4.939499999999999,
7.968166666666666, 9.609333333333336, 8.426666666666668,
7.470833333333331, 10.100000000000001, 4.500166666666664,
7.260833333333329, 9.993166666666665, 6.825333333333333,
8.299261904761902, 7.549666666666667, 6.010499999999999, 8.34425,
2.3685277777777767, 7.453452380952375, 2.1427857142857127,
8.840499999999999, 5.5857261904761915, 9.547833333333328,
8.022940476190477, 8.995506854256854, 9.565772727272725,
14.310063852813851, 7.463338744588744, 9.922214285714288,
9.396590187590188, 12.81141666666667, 8.63265512265512,
4.9386277056277015, 2.4788333333333337, 12.741150793650796,
11.34468925518925, 13.031071428571423, 6.072166666666665,
6.815968253968248, 9.753902236652236, 7.688281912531911,
4.579552308802306, 15.257583333333342, 11.915132395382402,
7.997478715728716, 4.340111111111109, 7.981563492063491,
12.520726190476193, 10.64207538539891, 11.881075396825393,
12.839070346320339, 8.382726190476191, 14.908814489432139,
15.040856782106786, 6.970737267634322, 12.792952922077923,
11.989634698634697, 9.868615079365076, 15.096869047619045,
9.771920634920635, 8.824296218487394, 10.162924603174599,
12.123983405483404, 12.502298340548341, 8.35071611721611,
13.67055769230769, 15.885123015873008, 8.421123015873016,
8.837611844996434, 13.695330086580089, 10.84498153806977,
11.131603618603616, 17.294429197994987, 15.36783660783661,
16.86717081529581, 9.54105738705739, 11.565437857464167,
11.044768068887635, 12.045061237008607, 8.569439349539344,
3.9874482227345496, 9.6670439390331, 14.257423478482302,
16.960849927849928, 8.838148064495886, 9.73163816738816,
8.947979765403675, 7.660809736157563, 12.683369641796107,
11.364326479076478, 9.602993626743622, 6.912616766403531,
10.350631493506489, 11.90833531746031, 16.638106060606056,
20.079478687978686, 11.692355477855475, 6.446922327672323,
12.935004828504828, 11.15889274614274, 13.219127261627257,
15.531029390544095, 11.75360858585859, 10.88777564102564,
16.889138213981244, 11.368609231411863, 11.829447607947609,
9.649801976532236, 10.243709235209232, 6.926028215745601,
9.639434038476143, 10.043158730158728, 4.55204355366855,
12.576252261627255, 6.277979797979795, 11.390615911865911,
12.61961718836719, 13.82517892195524, 21.01147075147075,
12.09991388005838, 12.85642850334359, 10.692746312511018,
15.961849583423112, 6.026710127780715, 10.989923397985185,
15.115900706749397, 15.813263824840451, 15.098572798116916,
15.065998994469583, 21.615180293884237, 10.5956392659628,
11.07060025438584, 9.942979229104232, 9.967956655544894,
10.061437368187365, 2.9808877726659047, 20.069607149386563,
5.596657652324716, 9.873494553313902, 11.95747291434056,
7.327986707736705, 7.216033776795284, 12.465970668865017,
17.037541153291148, 12.04810969220837, 10.124884433205256,
9.650198848193426, 16.522870657085164, 14.591295267676342,
10.474009708297185, 20.797863678433632, 30.483203323723068,
12.2685559326388, 9.108504152737696, 12.571273576097102,
12.474292285414963, 7.580120945225744, 9.210636799252919,
13.868046260885658, 18.465385917187398, 14.847394358950147,
14.416545626089203, 8.451540777806645, 11.109228263568241,
10.414479371376816, 13.058494029273916, 15.33862789787761,
8.886395111768175, 15.42907009042729, 9.709224567486178,
14.875602319959093, 11.379371468005676, 12.997856485236223,
6.18236039786816, 18.252653055278053, 13.548163607963529,
15.150259799454071, 14.338007916508975, 13.687778925784366,
13.072724026202382, 14.619345782757547, 24.48042573222234,
15.28612068830722, 20.830274135684515, 14.035039516468776,
11.184922205171638, 11.132730121862796, 12.07848091648633,
15.847068398964295, 11.705474118726125, 9.302641447432853,
14.819597548693134, 16.48310627689304, 13.004734505649786,
3.810410668263627, 23.814508792508253, 10.848103676477027,
11.896245732767033, 14.864325926417623, 12.219535503149057,
7.7718429333640735, 12.516372978553232, 18.025979398285163,
16.111666271066156, 11.967626744714979, 25.13555079328996,
13.713982874684282, 9.244083281097986, 10.942876184321538,
12.662435486074806, 8.48561363360877, 12.85495403289521,
12.787459124836758, 10.043181544644371, 11.630393532327671,
7.222702346468929, 12.09825699382317, 10.765496016062885,
15.183752363370763, 10.550874248584607, 34.589417039329106,
10.991607511299366, 12.923168238950591, 17.649157104760278,
13.517545493511875, 9.989267495348466, 14.964039503640901,
11.654999042741816, 18.446485790321432, 10.993221283216265,
13.583325811275937, 11.386452531293605, 13.798483047410015,
10.382094492939462, 11.549051035502709, 17.545547313775227,
15.78166743683733, 10.98654968185409, 5.319946657022263,
14.434360536063137, 19.65145630071842, 14.517183818546412,
7.44150852477637, 9.104460242956371, 18.562987537935058,
9.292978950823638, 14.501876029835648, 14.282669255555295,
12.566730044003616, 12.830796881398907, 12.467705805932182,
13.643263208524544, 26.582495681551855, 46.24441250809099,
23.268883889760037, 10.962930943400261, 14.768651322439172,
18.154401468196397, 12.176217317098327, 17.47337651480892,
11.725951720103577, 11.7546494822007, 12.331764577983236,
14.653823628271427, 26.687428918322087, 10.853290715547818,
13.864074240936572, 12.753052483300122, 13.942820125109836,
9.516911775663946, 14.02211135597105, 11.473901457218489,
9.545227688103504, 14.260420303022233, 15.649636847286963,
13.457030988903359, 12.794822815399824, 19.778606077810217,
8.845460066209446, 11.045774811958825, 9.894838614739605,
12.376929251309246, 10.324285630720613, 10.280525895156629,
12.079127596912896, 10.52559631892971, 10.660508128586011,
14.962948377116444, 14.716836295370323, 12.49401685451108,
12.258759373431008, 9.96883766797468, 4.929313318614995,
11.629156279835687, 23.354051592500422, 13.165677301710415,
12.191832282068686, 32.56814473469892, 4.513373090882277,
7.6551439411122075, 28.36451590113103, 12.566251035882104,
12.742656796781539, 16.904395757149633, 12.05300624914015,
24.659889238410738, 12.509151785362588, 11.170633023230655,
12.286474831867473, 12.513379554816748, 9.721882093926814,
13.192858275932487, 20.60059683812919, 10.007686387314095,
13.604505433233554, 16.03765165586146, 12.05893128930242,
11.299410883073554, 10.084161427892028, 10.980642123343825,
8.797954981458206, 13.627328535742341, 15.82343459510619,
6.930158757196334, 13.151801215249376, 10.583603793066834,
10.070584266705081, 11.829309903968001, 9.264158145245466,
4.850432813262061, 6.599758156002351, 10.618567496191782,
9.059514010103639, 10.28849660767023, 5.4305259594250765,
11.272307874701797, 11.687286869881735, 11.306620140920911,
2.4928360131855576, 2.50863786341703, 11.168978062602905,
9.48007524748614, 9.660101514108227, 7.64617011634347,
11.397773579035583, 6.440589951178365, 7.586602878584321,
7.934611938545627, 9.212580135549302, 10.31261866146571,
5.7463901383711455, 9.586765946906718, 6.624105032220049,
11.44063267709711, 5.710452360167064, 6.137135992310425,
6.576521687554799, 21.063925943636182, 11.145622612535853,
10.61075568664775, 9.988019618584667, 9.42685399908465,
6.840372372598081, 10.811224792838475, 12.291490979431197,
11.74456146562922, 7.335267460989364, 9.465969223314522,
5.986744492695949, 9.216893216751066, 8.224732170062977,
11.510705016840632, 10.552612333694814, 11.416205394467935,
10.698495684874844, 2.39553546289595, 8.071759612447492,
8.07960221765112, 13.952443253742013, 10.621670520510131,
9.174322137892966, 6.958162959580833, 8.192727585950712,
8.173237865894706, 5.281692359027092, 10.538400147731574,
9.32924395982818, 11.701280692554233, 10.283743962911144,
15.313630421538337, 10.050639936907233, 9.009861977825317,
11.218979328218548, 14.766993329623595, 8.223208942938948,
14.15575037128048, 10.64365460700913, 7.456791416658677,
11.676617619147432, 15.613974729348055, 7.005190409547383,
9.463061484271524, 12.849208718651592, 10.40983462548057,
9.49457316157287, 11.094596681367786, 11.83407450485796,
10.318153582195407, 13.161307384747055, 11.44778238467615,
13.207599345587754, 9.602900725915612, 10.392233989292219,
10.854463262949013, 10.406278453219514, 11.833863626831292,
13.654324287518198, 5.463271860688648, 10.320881647705734,
12.267781294453492, 10.878071238338498, 7.878486890309873,
13.510832897393934, 8.841180189291883, 10.924972643508593,
18.161362993044108, 12.358050242222163, 6.520580025324834,
9.844635556077417, 10.986369602703896, 13.357134602026312,
13.857732674730185, 9.971122745095347, 14.077721590443105,
9.441817242681894, 9.756484692372057, 12.884420957074404,
8.71655585086868, 8.671763996942866, 10.710251197002425,
15.205465670004585, 4.65805932782175, 9.942207327675044,
9.388733620601965, 7.327148109451598, 8.828165653643445,
9.404216824841829, 6.349722880739916, 9.281637570742811,
13.908537657586654, 5.732908034736406, 11.084686592741555,
8.530687011984439, 7.745639297770371, 11.23530196492702,
22.03827021580931, 8.856085975308643, 9.420864704004842,
12.972863326860613, 10.286818810797318, 12.735987996901997,
6.001349506021301, 9.299868447705569, 10.613274101999789,
7.244426347751327, 9.01155893273753, 10.661937989499618,
7.3368609817775035, 13.568204570328296, 11.778216685949738,
9.762850117103655, 11.412395483120097, 16.459773995527442,
14.055422279830823, 8.61316888421957, 10.07474806106169,
5.259957217994107, 16.521317629800215, 8.390046417243948,
19.217554136164296, 17.11236587410929, 12.255366068097622,
13.4819510384782, 12.39565863379864, 12.528073915379235,
13.199982332183607, 15.00016062362778, 11.639750113846416,
17.657362551658377, 10.660504677550614, 17.854977596173445,
12.023951262858226, 10.826938663349026, 9.166500005786864,
9.34642000886033, 7.11394529206144, 7.997552932851012,
9.53297287353516, 11.590545356251756, 11.289757922074152,
12.628311121634828, 11.946383025442623, 16.4780662636958,
9.933129337410028, 11.464189720067207, 5.024517338115369,
8.830315718471752, 11.000010655880748, 9.325105062415242,
15.210630181514023, 20.0249073984683, 13.490197659862215,
33.23413081045201, 75.0253344677052, 31.01527379589795,
31.22920075651472, 44.02157306850319, 20.285046268879537,
54.298922561579026, 15.90723816919783, 11.64977639814105,
14.330469663887433, 7.916505545908438, 14.789737761489135,
11.044608676009265, 11.87709862870956, 14.249511854349597,
8.492633734864528, 9.510131403614123, 10.47878714644488,
13.529614726319531, 11.883770593576754, 11.609850577525238,
13.23566830428326, 15.44184372432791, 10.738355115547499,
9.332409941355364, 22.005092711890498, 11.607092137141723,
11.157643001468337, 16.985597461802197, 8.396475281723225,
12.48326264356571, 11.049412868422976, 3.8747468132819267,
11.389953215755991, 12.880123128072185, 11.841845824172452,
14.780033293274345, 16.962328579463275, 11.57015212875696,
13.22708098748122, 13.78568152721885, 8.531083573649537,
11.860251104470324, 6.928463157819973, 16.740129483517343,
8.914631113717462, 14.130156914980441, 14.317258569775596,
9.198145330855697, 9.054691845283509, 11.498156355681614,
16.96413971399202, 11.792199464289867, 13.09629073431669,
12.996533726548039, 12.77584437288146, 12.302802724088638,
12.450203061850635, 28.53686382223485, 12.041773141604484,
11.552456134999135, 10.068300008778209, 13.723871627132176,
8.077389958242035, 9.246826914469814, 9.540509983232166,
11.244606099536968, 20.81236293116173, 15.305942120114397,
15.718252989787219, 13.991067787510254, 12.284707000271938,
10.973563055722623, 10.46774068414044, 10.12037200360778,
16.87282328292622, 10.564619075448414, 10.227692539683037,
9.472327981730672, 12.751644891768985, 27.555046412138307,
85.43694072598372, 56.08802653127335, 23.66128547551483,
33.17584985478677, 67.28631244989101, 12.61227094463785,
14.726268586167194, 16.62931560481255, 14.911359213563834,
11.958171732479368, 13.71133988356006, 2.55964288748879,
9.858324417741642, 9.47145194266073, 10.480145894185153,
9.554108891465381, 15.2138744296639, 9.893545937803518,
11.440610203225516, 8.592703986782544, 8.436543701836646,
20.381453223216223, 12.42665063923577, 9.44086150063831,
9.01100650386427, 3.0216299624342637, 9.022816297089504,
11.266413968980688, 12.693713110209814, 11.998893112233928,
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4.171069326729994, 8.870677165734453, 4.074771194451311,
3.7434416310805037, 5.698674809975017, 4.501616093146417,
3.9159627210236305, 5.8730473556205975, 3.9903570993159367,
6.37423019007103, 5.172724756813935, 4.58515949857763,
7.9850717604634, 5.414395800499509, 5.915227069420454,
4.67997499348663, 15.651274203112605, 3.8319491698687753,
4.075266016201829, 5.919704658486967, 3.5188534123870943,
4.782542605087985, 6.328257747846602, 6.227897023906547,
4.48309764668923, 4.9671455336978765, 7.390168962902779,
4.98568965791647, 7.266773332876196, 8.412956231908932,
5.48375069406418, 7.304278638879564, 4.323423020170953,
11.157372426960256, 3.6996826438829453, 4.074172944502642,
4.83158867964644, 4.457618289219833, 4.651540069858034,
4.588892332484567, 5.3627602298130235, 3.5446623600241476,
6.897544922462426, 73.54592488230713, 3.581056998077067,
5.9238092721629725, 4.276748760979162, 4.287828005409622,
15.776662968687486, 4.8219372726266645, 5.936965874078089,
8.383031773804413, 3.5528720165977674, 5.9581041742565555,
4.188658110611031, 3.920504226828131, 8.053123157367732,
4.668529739940333, 8.41590145173515, 3.6492106761835887,
4.656950310851449, 4.727182218523711, 9.738121408156632,
7.56547656631185, 4.5156682521285365, 6.426305932713351,
5.621130480623295, 7.5421604636783375, 4.657412403729312,
8.892899210206338, 5.163551873783113, 5.046241309743411,
9.219722334499348, 5.689805198476793, 4.752440741088711,
5.160585278510019, 4.618670921083705, 7.786209704206549,
6.309770264920504, 5.004721189405228, 6.908694263956865,
8.874429048978833, 4.942388223511625, 5.60091646689914,
5.267473455480562, 5.020882099279326, 6.764731325820961,
7.48902313208815, 5.093917779359989, 9.502668225621576,
3.7841151372351503, 4.587291422879952, 4.704452371210945,
4.704888813517309, 3.1647171365994886, 5.912495265677926,
10.828133522556133, 4.6289694592511275, 4.585820551791321,
10.603682775546957, 3.6611302972007147, 5.6969333402294025,
7.892222233520258, 4.8707119863738235, 12.768949521760167,
3.7454050236670673, 3.891410721341946, 7.104276184984107,
8.680172107820319, 4.080642353768982, 5.784738568169765,
6.900408796762545, 4.438949572159638, 3.773086171473992,
4.029547922319234, 5.438490529955845, 5.302302711385762,
4.461075322221949, 4.712363634866054, 5.546813928151976,
3.98453183666606, 6.714540149799347, 5.319514512822134,
11.36237559157177, 4.552022149152446, 7.156785147494494,
5.288562889581543, 5.904109387012734, 6.5831100538100475,
4.86876425880585, 4.870498362165367, 4.703307292426606,
3.5642690689331094, 3.025718932343065, 4.4620065984622235,
4.746831735399072, 5.917194043295348, 3.3374201095146203,
12.283095440867807, 4.659496073108648, 3.8781479310750444,
6.531958504341498, 4.421132817996439, 5.5750296718900225,
4.097888417384225, 5.057477409936974, 4.249051696490203,
5.8392091443212415, 3.922920121125648, 5.160174876720053,
16.183900660932007, 7.270916113093188, 4.053010491311951,
4.9803996535127695, 6.770116050891237, 6.7818346442614175,
6.967568633118546, 5.914101563211945, 4.380256770536154,
5.1814173838251705, 6.253818092578327, 4.422314575221432,
7.708296155915895, 5.192401403145199, 7.955853781011071,
5.638174256690393, 5.728373215176868, 6.342684739966915,
6.989658691082775, 4.887781929457405, 5.696999413189341,
6.055909608527767, 6.367848761419463, 5.81634857287425,
5.583309272912332, 6.218537593342466, 11.323709281711277,
6.092126853163, 5.394679136793757, 4.478248676510953,
5.652953468653985, 7.856802888720715, 3.6250683124934913,
5.000035892121128, 5.920869530871397, 4.3998786101028005,
6.32505776774248, 4.38175735462799, 3.718287043085531,
11.248670089341333, 6.178975484652476, 4.433129639845597,
5.998182418224579, 5.199652794316691, 5.704427737588539,
16.6353311652237, 6.284513692598696, 5.551214921670748,
7.125492703885347, 4.989500890302429, 6.57986820470262,
10.218872967101005, 4.351644420396437, 5.490953740155567,
212.3285714285715, 4.839548289998201, 5.299953999645585,
5.440232836061417, 5.596103284688051, 10.829980535626834,
7.824135060349622, 6.829418782013274, 5.778855577392975,
10.68623218122373, 5.723417350065048, 3.8043043683572235,
6.697167139579886, 6.200785955307497, 3.7657465681360383,
6.051100727274255, 4.617849110377838, 4.102702360029696,
9.08664754227254]
disp = hash_iot
d1 = pd.Series([s for s in disp])
sorted_d1 = d1.sort_values(ascending=False)
N = 1007
pred_rrcf = d1 > d1.quantile(0.9)
print('Displacement: ', d1.tolist())
print(metrics.classification_report(true_anomalies, pred_rrcf))
for k in [100, 200, 300, 400]:
top_k_real = anomaly_detection.get_top_k_anomalies(sh_vector, k)
top_k_disp = anomaly_detection.get_top_k_displacement(sorted_d1, k)
anomaly_detection.get_top_k_performance(top_k_real, top_k_disp,
true_anomalies, pred_rrcf, N)
real = []
for index, row in sh_vector.iterrows():
real.append(int(row['anom_count']) * 0.05)
plt.style.use('seaborn-darkgrid')
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, sharex=True, sharey=True,
figsize=(8, 5))
ax1.plot(real, color='blue', linewidth=0.5)
ax1.plot(hash_iot, color='red', linewidth=1)
ax1.set_title('SnapSketch', fontweight='bold', fontsize=8)
ax1.tick_params(axis='both', labelsize=6)
ax2.plot(real, color='blue', linewidth=0.5)
ax2.plot(sl_total_hash, color='red', linewidth=1)
ax2.set_title('SpotLight', fontweight='bold', fontsize=8)
ax2.tick_params(axis='both', labelsize=6)
ax3.plot(real, color='blue', linewidth=0.5)
ax3.plot(ss_total_hash, color='red', linewidth=1)
ax3.set_title('StreamSpot', fontweight='bold', fontsize=8)
ax3.tick_params(axis='both', labelsize=6)
plt.xlim([200, 2500])
plt.ylim([0, 120])
plt.xlabel('Time', fontweight='bold', fontsize=6)
plt.ylabel('Anomaly Score', fontweight='bold', fontsize=6)
plt.tight_layout()
plt.savefig('results/iotplot.pdf')
plt.show()
if __name__ == '__main__':
args = parse_args()
sketch_file = graph_sketching(args)
| # ******************************************************************************
# main.py
#
# Date Name Description
# ======== ========= ========================================================
# 6/5/19 Paudel Initial version,
# ******************************************************************************
import argparse
from sketching import Sketch
from anomaly_detection import AnomalyDetection
from graph_utils import GraphUtils
import glob
import numpy as np
import json
from collections import OrderedDict
from json import JSONDecoder
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import metrics
from sklearn.utils import shuffle
from sklearn.decomposition import PCA
def parse_args():
'''
Usual pythonic way of parsing command line arguments
:return: all command line arguments read
'''
args = argparse.ArgumentParser("GODIT")
args.add_argument('-d', "--sketch_size", default=256, type=int, help="Sketch Vector Size")
args.add_argument('-k', "--disc_shingle", default=32, type=int, help="# of Discriminative Shingle")
args.add_argument('-p', "--prob", default=0.2, type=int, help="Random Probability")
args.add_argument('--directed', dest='directed',action='store_true',
help='Graph is (un)directed. Default is undirected.')
args.add_argument('--undirected', dest='undirected', action='store_false')
args.add_argument('-l', '--walk_len', default=50, type=int, help='Length of random walk ')
args.add_argument('-n', '--n_shingle', default=3, type=int, help='Length of a shingle')
args.add_argument('-f', '--filename', default='vast', help='Dataset filename')
return args.parse_args()
def graph_sketching(args):
with open('graphs/graphs_' + args.filename + '.json') as jsonfile: # graphs_nyctaxi_3month graphs_vast_total_30sec graphs_muta iot_1weekOct graphs_darpa_10min iot_june_01 sample_weight_3
graphs = json.load(jsonfile)
print("[ ", len(graphs), " ] graphs read successfully")
sketch = Sketch()
sketch_file =sketch.shingle_sketch(graphs, args)
print("\n Done Batch Sketching...")
return sketch_file
def main(args):
ad = AnomalyDetection()
files = ['graph2vec_mutag187_dim_128.csv']
# files = ['dim_32_n_3_k_256p_0.2_l_100.csv']# ['dim_16_n_3_k_128p_0.2_p100q50.csv','dim_32_n_3_k_128p_0.2_p100q50.csv','dim_64_n_3_k_128p_0.2_p100q50.csv','dim_128_n_3_k_128p_0.2_p100q50.csv','dim_256_n_3_k_128p_0.2_p100q50.csv','dim_512_n_3_k_128p_0.2_p100q50.csv','dim_1024_n_3_k_128p_0.2_p100q50.csv',]
for f in files:
print("\n\nFile: ", f)
# sh_vector = ad.read_sketch("sketches/nci1/"+f) #(args.sketch_vector)
sh_vector = ad.read_sketch_baseline("sketches/baseline/" + f) # (args.sketch_vector)
ad.anomaly_detection(sh_vector, args)
def visualize_taxi():
anomaly_detection = AnomalyDetection()
# sketch_file = "batch/traintaxi/25per_dim_256_walk_50_k_5_frecostp100q50.csv" #'batch/vastbfs/0_dim_256_walk_300_k_3_cost.csv' #"batch/trainvastbfs/25per_dim_1024_walk_300_k_4.csv" #25per_dim_512_walk_300_k_4 0_dim_64_walk_300_k_4
# sketch_file = "sketches/baseline/splt_taxi3month.csv"
# sketch_file = "dataset/streamspot_nyctaxi3month_vector.csv"
# print("Sketch File: ", sketch_file)
# for sketch_file in glob.glob('sketches/nyctaxi/*.csv'):
if 1 == 1:
sketch_file = "sketches/nyctaxi/dim_64_n_3_k_128p_0.2_l_50p10q5.csv" #use 24 24 for best result d256 k 256 (1024), dim 128 also works
print("Sketch File: ", sketch_file)
sh_vector = anomaly_detection.read_sketch(sketch_file)
# sh_vector = sh_vector[:1000]
# true_anomalies = np.array(sh_vector['anomaly'])
disp = anomaly_detection.robust_random_cut(sh_vector)
sl_3month_24_24_1000tree = [0.0, 1.0000000000000007, 1.3069999999999837, 1.5749999999999693, 1.8219999999999728, 2.089499999999967, 2.1954999999999685, 2.4909999999999606, 2.805666666666629, 2.9281666666666295, 3.0729833333333003, 3.1683499999999607, 3.7842666666666394, 3.526116666666626, 3.547061904761867, 3.555507142857105, 3.844892857142829, 3.760817857142824, 3.6357361111110755, 3.715322619047586, 4.015897619047585, 4.13106785714283, 4.2710889971139725, 4.4617252525252225, 4.676477020201991, 4.354473448773429, 4.21600274170272, 4.040540223665191, 4.136821212121185, 4.377090945165925, 4.151008874458854, 4.3032929292929065, 3.893516883116856, 3.761827056277031, 3.848441341991319, 3.7394662337662092, 3.870903066378041, 3.839645707070682, 3.7634993145742905, 3.662225288600264, 3.557498268398248, 3.546423088023067, 3.437322943722922, 3.516349458874432, 3.7988536796536505, 3.5630521284271026, 3.5901415945165724, 3.820980303030273, 3.9180819624819345, 3.734498015872985, 3.5302974025973737, 3.6719216450216186, 3.8837171717171404, 3.758740548340518, 3.848997113997084, 3.8676169191918874, 4.496036760461739, 3.6177753607503376, 3.8619676406926144, 4.147067929292907, 3.5590136363636065, 3.6623922077921858, 3.7350548340548118, 3.729756782106761, 3.6178666305916067, 3.754875396825372, 3.587275613275586, 3.686499531024512, 3.3453290043289767, 3.561392027416999, 3.576660858585829, 3.56763726551224, 3.895365151515122, 3.4605695887445607, 3.6585208874458655, 3.594863852813823, 3.756620274170249, 3.622473376623352, 3.8295047258297004, 3.827783802308775, 3.887875072150057, 3.62666792929291, 3.827956782106755, 3.7893905122654874, 3.825775829725811, 3.6092622294372076, 3.589380772005751, 3.6329274170274, 3.6515268398268144, 3.8500302308802055, 3.561327489177468, 3.7184640692640496, 3.712509343434319, 3.546767243867226, 3.8106080447330215, 3.472430266955243, 3.753269372294346, 3.6059611832611607, 3.606277886002855, 3.5701953102452837, 3.7618476551226276, 3.82646277056274, 3.7214350288599967, 3.6715644660894373, 3.8966181818181496, 3.658271681096653, 3.8297926046175745, 3.5934492784992567, 3.9022642496392215, 3.698930735930712, 3.7655356060605834, 4.22597597402596, 3.6396637085136834, 3.729202633477606, 3.623097691197661, 3.959602344877324, 3.559363203463182, 3.7172894300144144, 3.3392061327561167, 3.755216738816722, 3.6067015512265224, 3.6207683261183017, 3.9007072510822227, 3.7254467532467253, 3.477940909090883, 3.8705008658008397, 3.5902923881673576, 3.900582287157263, 4.053705988455959, 3.796682323232295, 3.780787229437203, 3.9704607503607203, 3.7233017676767424, 3.697792171717144, 3.7598486291485975, 3.8793067821067546, 3.909909848484819, 3.7378263708513493, 3.8320799062048794, 3.9601289321789075, 4.308169300144279, 4.2086202380952304, 4.1125421356421175, 3.8586067821067593, 3.718154653679629, 3.809078679653647, 3.966138059163032, 3.951136435786407, 3.9802998917748615, 3.9425713203462953, 3.7856691197690924, 3.910652344877321, 3.9663066017315733, 3.977958946608921, 3.780931457431433, 3.820317316017289, 3.6599736291486047, 3.707707647907621, 3.725441883116858, 3.5673902958152763, 3.5991937590187337, 3.8827389249639017, 3.8220121572871335, 3.5044704545454297, 3.439539393939374, 3.3874304112553855, 3.3518844155843888, 3.3086836580086327, 2.9474969336219066, 2.8205874098123855, 3.2005816738816444, 3.1988915945165606, 3.125726515151481, 3.2303779220779036, 3.1010323232323014, 3.102419083694056, 3.068896789321767, 3.0816936147185885, 3.215689862914838, 3.385117207792184, 3.3956795093794767, 3.195732323232299, 3.265265007214983, 3.1392999639249446, 3.264929365079339, 3.4748344877344643, 3.208597294372267, 3.622541450216432, 3.4296585497835257, 3.5167829004328715, 3.7117974747474483, 3.6494369408369183, 3.6282344155843878, 3.7499116161615835, 3.72148538961036, 3.4892155844155557, 3.6508107864357613, 3.7926731601731336, 3.391973881673852, 3.887472186147164, 3.7511802308802067, 3.8429490620490383, 3.847867676767653, 3.7793133838383612, 3.801909920634893, 3.745292316017292, 4.041810064935048, 3.7616010822510555, 3.955913167388137, 3.963381349206331, 3.8228332611832396, 3.579587121212097, 3.4770942279942068, 3.7541075757575473, 3.79021580086578, 3.912270274170249, 4.066463924963898, 4.003379906204885, 4.0052485930735715, 4.156482323232299, 3.9260842352092147, 4.097521320346294, 4.045806673881648, 4.1314053751803534, 3.8381345959595765, 3.587890620490595, 3.9608605699855457, 3.668929040404014, 3.770212914862898, 3.5402694444444123, 3.585081060606036, 3.4611156204905957, 3.354173665223642, 3.4407172799422505, 3.515055014429991, 3.533650288600266, 3.619540079365053, 3.357846428571407, 3.2724475829725543, 3.3691640331890023, 3.688166269841241, 3.5040137806637435, 3.3259152236651937, 3.540546212121185, 3.5978412698412443, 3.5661549062048783, 3.4603155483405175, 3.381314105339083, 3.664481168831142, 3.559792316017284, 3.800688311688291, 4.76248813131311, 4.174080050505028, 3.781430158730134, 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3.52744747474745, 3.5517226911976643, 3.8167528138527906, 3.351922474747451, 3.56634711399708, 3.315893290043271, 3.3682616883116667, 3.5770427128426805, 3.6553499278498944, 3.6734190836940623, 3.3596617604617367, 3.4726179292929005, 3.421943759018734, 3.392463744588717, 3.4646640692640367, 3.5472951298701036, 3.4068395021644733, 3.591805627705602, 3.152540909090888, 3.3409378427128096, 3.6109486291486004, 3.552807106782076, 3.3760063492063237, 3.566610497835468, 3.4967387445887104, 3.322296428571404, 3.711784740259713, 3.2046276334776125, 3.587404473304442, 3.7483380591630366, 3.6487572871572604, 3.3524051948051667, 3.333438167388146, 3.4012370129870084, 3.536367929292898, 3.299234235209209, 3.498042893217867, 3.238408369408351, 3.534621139971104, 3.2058287878787657, 3.283614574314555, 3.20107579365077, 3.4033217171716883, 3.199304040404019, 3.4405079004328774, 3.3154903679653454, 3.3920802308802043, 3.2419352813852513, 3.320750216450199, 3.3448606421356177, 3.2142973304472995, 3.3397123015872685, 3.2660753246752994, 3.3130229437229266, 3.141910064935051, 3.3448118326118044, 3.4276056277055997, 3.3064205988455684, 3.451471825396798, 3.312898809523784, 3.342003174603147, 3.288666341991337, 3.4104757215006956, 3.2790400072150057, 3.1991698412698204, 3.384275901875886, 3.1948836940836647, 3.307539538239521, 3.3580050505050245, 3.1081702020201862, 3.1228773809523496, 3.307134054834026, 3.1510627344877142, 3.3209016233765976, 3.479646861471835, 3.4748538961038644, 3.474859776334752, 3.410531132756114, 3.6174274531024215, 3.5371584415584127, 3.3994079004328825, 3.514477272727245, 3.4363025252525023, 3.7218170274169977, 3.6358874098123852, 3.506348881673855, 3.4350543290043003, 3.617922871572844, 3.6134994588744322, 3.830190007214981, 3.6608829004328745, 3.545925937950904, 3.8678587662337347, 3.7508575396825052, 4.00299527417024, 4.1395118326118014, 3.8700648268398044, 3.697943614718585, 4.250540764790739, 3.6429310966810706, 3.6568993506493217, 4.122653643578626, 3.9118182178931953, 4.009558189033159, 3.8579217171716937, 3.910020021645, 3.9288633838383498, 3.6442356421356177, 3.572199711399689, 3.4879789321789265, 3.2705241702741468, 3.35403037518035, 3.2403025974025774, 3.248706240981215, 2.8923446608946417, 3.1936047258296987, 3.0712568181817965, 3.416106782106746, 3.1766304473304254, 2.994570887445865, 3.1402626262626065, 2.8315124458874257, 3.3640485569985255, 3.426519769119744, 3.510397113997085, 3.4856349567099265, 3.2369883116882923, 3.2682865800865604, 3.1922568181818, 3.285399314574291, 3.5574947691197365, 3.364213131313097, 3.551962842712816, 3.5838068542568213, 3.448910137085104, 3.524935461760437, 3.4287505772005535, 3.388999819624796, 3.4520884559884304, 3.486850180375154, 3.288185101010073, 3.298004004328981, 3.235140151515125, 3.7271895743145427, 3.4914124819624623, 3.7120948051947757, 3.715360497835467, 3.42893080808078, 3.4509375180374944, 3.5101240620490306, 3.4411117965367692, 3.5749021284271043, 3.72602968975466, 3.896334884559856, 3.641075072150055, 3.469741594516565, 3.499399278499258, 3.4940156565656286, 3.5175891414141134, 3.351470382395354, 3.7038156204905937, 3.4022489538239284, 3.6769700216449923, 3.602125757575725, 3.625870779220755, 3.6582954545454287, 3.5921053751803558, 3.5201286075035756, 3.513108405483382, 3.4777691558441317, 3.2880247474747195, 3.324983477633447, 3.3475782106781797, 3.2891756132755936, 3.107030844155823, 3.267221753246726, 3.327365909090883, 3.0455752886002667, 3.575517784992755, 3.4836657647907354, 3.235919805194784, 3.2626624819624555, 3.025963239538212, 3.3861622294371996, 3.1655125541125293, 3.3055551587301295, 3.3473224025973765, 3.304992171717144, 3.510782178932155, 3.380530663780633, 3.1728139249639007, 3.3474877344877116, 3.6445877705627407, 3.385356529581502, 3.660799314574289, 3.513949675324647, 3.2966316378066076, 3.65127427849925, 3.7438681096680755, 3.8357191558441364, 3.4628260461760263, 3.2598133838383583, 3.5265018759018565, 3.5011014430014122, 3.35850948773446, 3.6043847402597136, 3.4303568903318706, 3.6693863275613006, 3.494725721500695, 3.438726767676744, 3.4408869408369145, 3.4315762626262365, 3.5394078282828, 3.614963239538234, 3.479173015872981, 3.309512012986989, 3.2107059523809336, 3.539382215007182, 3.278323448773435, 3.405395310245277, 3.061225505050481, 3.246955411255387, 3.3028983405483157, 3.1950409451659194, 3.1305961038960817, 3.3173962121211824, 3.034025649350624, 3.228019624819602, 3.2047064213563847, 3.332253391053361, 3.2388595238094924, 3.314918795093765, 3.3618299783549497, 3.283075252525232, 3.465293434343408, 3.3088450216449994, 3.463889357864339, 3.419786219336196, 3.2087825396825203, 3.522573268398245, 3.3360270923520674, 3.424782034632006, 3.367450757575722, 3.4591977633477367, 3.2789356421356155, 3.6316588383838115, 3.6825156204905922, 3.9001475108224883, 3.862914790764771, 3.8830613275613057, 3.8035015512265207, 3.714767063492031, 3.8134426046175753, 3.587301262626236, 3.3655253246753007, 3.8412998917748764, 4.059842207792186, 4.284045707070687, 4.169291450216422, 4.293944264069251, 4.256984126984106, 3.843303968253942, 4.041025396825378, 3.9166680735930544, 3.8722202380952173, 4.049738059163038, 3.7550568903318675, 3.71240443722941, 3.483060064935031, 3.8624164141413884, 3.347439466089445, 3.5778279220778955, 3.8550708152957913, 4.248108008657984, 4.489894769119746, 4.396559848484826, 4.563660173160153, 4.342820598845582, 4.236970454545442, 4.13688614718613, 4.1565161255411045, 3.945583116883095, 4.237365836940814, 4.3258683261183055, 4.228977669552654, 4.346015656565634, 4.392648737373719, 4.485096789321772, 4.207221067821043, 4.504071753246733, 4.644336219336197, 4.558519841269825, 4.29503279220777, 4.283737518037495, 4.514020382395366, 4.218733802308783, 4.255114466089451, 4.0093888888888705, 3.7060284992784807, 3.902351406926384, 3.5842570707070514, 3.50645266955265, 3.5928034271284037, 3.558307756132728, 3.416949314574288, 3.4641007575757383, 3.208823881673855, 3.2894423160172797, 3.5185549062048813, 3.4904814213563964, 3.6075619769119456, 3.5799585497835196, 3.613853715728691, 3.7100060606060343, 4.105699891774864, 3.673746933621902, 3.9589537878787575, 3.8795498556998282, 3.77941114718612, 3.4756365079364815, 3.7126886002885744, 3.587670959595931, 3.687042424242398, 4.021024458874443, 3.8566215007214795, 3.738314249639221, 3.6091256132755936, 3.628927741702714, 3.5086377344877073, 3.472952200577178, 3.5387726911976634, 3.726934595959568, 3.692004725829703, 3.746876551226521, 3.577973124098105, 3.4958566017315786, 3.437313131313107, 3.4413020923520703, 3.656373051948025, 3.776435750360721, 3.842288311688287, 4.01849570707068, 3.7744726551226213, 3.7897423881673595, 3.5102832972582743, 3.5768917388167107, 3.292435606060583, 3.6728684704184476, 4.157170454545436, 3.5345207792207622, 3.4960542568542294, 3.599462049062024, 3.390737121212097, 3.5566846681096465, 3.5339489538239253, 3.7530128066377797, 3.538101046176021, 4.01714134199132, 3.724827092352062, 3.896990007214978, 3.778241702741681, 3.8946558080807834, 3.7854972222222023, 3.573991450216432, 3.5667772727272453, 3.8854299422799183, 3.8094425685425506, 3.947839826839798, 4.238178643578619, 3.7105190836940576, 3.7696475468975175, 3.611162373737343, 3.996808080808051, 3.989477597402566, 3.7724437950937713, 4.169715043290018, 3.9015271645021343, 3.758420598845573, 3.8888927849927564, 3.6642214285714023, 4.114003102453073, 3.7498011904761652, 3.87393214285712, 3.6473646825396546, 3.549368290043263, 3.5539690115439826, 3.5105417027416825, 3.71054325396823, 3.768528643578616, 3.658768867243839, 3.683383549783529, 3.6796175685425423, 3.651033333333306, 3.742247619047591, 3.7100461038960755, 3.81932330447328, 3.447403066378041, 3.7545474386724127, 3.4887262987012755, 3.6876364718614516, 3.795770670995648, 3.486950108225079, 3.4286481601731302, 3.630055808080781, 3.642051587301558, 3.64739563492061, 3.774097186147163, 3.608849314574286, 4.074280808080791, 3.777415584415552, 3.5965112914862636, 3.5197398629148338, 3.767559307359284, 3.7046252164501903, 4.113040512265486, 4.096717027417011, 3.9257302308801996, 3.5444796176045914, 3.746505375180352, 3.865215404040382, 3.98939765512263, 3.825869913419885, 3.721755699855674, 3.9998325757575564, 3.768903860028831, 3.9001917388167144, 3.765781565656535, 3.7642763347763073, 3.885239502164481, 3.9098973665223395, 3.449542532467517, 3.8661339466089184, 3.7893130591630375, 3.742495598845567, 3.557431204906183, 3.9245551587301395, 3.683747691197666, 3.6981939754689486, 3.3842016233765917, 3.588271356421329, 3.4919087301587046, 3.5403440836940563, 3.671433080808055, 3.8135924963924697, 3.600260678210655, 3.4482345959595735, 3.635264357864336, 3.6944480880230626, 3.6880526695526465, 3.581007611832589, 3.6345391053390834, 3.750940259740235, 3.776628607503576, 3.770705699855664, 3.778300252525232]
d1 = pd.Series([s for s in disp])
pred_rrcf = d1 > d1.quantile(0.90)
sorted_d1 = d1.sort_values(ascending=False)
anom = []
splt = []
sspot = []
for d in disp:
anom.append(d)
for d in sl_3month_24_24_1000tree:
splt.append(d)
for d in ss_3month_24_24_1000tree:
sspot.append(d)
# Create events
events = {
'Thanksgiving': (600,648),
'Nov29': (672,696),
'Nov30': (697,720),
'Christmas': (1296, 1320),
'NewYear': (1464, 1488),
'Blizzard': (1992, 2040)
}
real = pd.DataFrame()
real['event'] = np.zeros(len(anom))
real_s = pd.DataFrame()
real_s['event'] = np.zeros(len(anom))
for event, duration in events.items():
start, end = duration
real.loc[start:end, 'event'] = 1
real_s.loc[start:end, 'event'] = 7
print("Displacement: ", d1.tolist())
print(metrics.classification_report(real, pred_rrcf))
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, sharex=True, sharey=True, figsize=(8, 5))
ax1.plot(real_s, color='blue', linewidth=0.5)
ax1.plot(anom, color='red', linewidth=1)
ax1.set_title("SnapSketch", fontweight="bold", fontsize=8)
ax1.tick_params(axis='both', labelsize=6)
ax2.plot(real_s, color='blue', linewidth=0.5)
ax2.plot(splt, color='red', linewidth=1)
ax2.set_title("SpotLight", fontweight="bold", fontsize=8)
ax2.tick_params(axis='both', labelsize=6)
ax3.plot(real_s, color='blue', linewidth=0.5)
ax3.plot(sspot, color='red', linewidth=1)
ax3.set_title("StreamSpot", fontweight="bold", fontsize=8)
ax3.tick_params(axis='both', labelsize=6)
plt.xlim([4, 2180])
# plt.ylim([0, 10])
plt.xlabel("Time", fontweight="bold", fontsize=6)
plt.ylabel("Anomaly Score", fontweight="bold", fontsize=6)
plt.tight_layout()
plt.savefig("results/taxiplot.pdf")
plt.show()
def visualize_vast():
anomaly_detection = AnomalyDetection()
sketch_file = "sketches/vast/dim_64_n_3_k_16p_0.2_l_50p10q5.csv"
sh_vector = anomaly_detection.read_sketch(sketch_file)
true_anomalies = np.array(sh_vector['anomaly'])
sl_vast_full = [0.0, 1.0000000000000007, 1.490000000000001, 1.7100000000000013, 1.7800000000000002, 1.455000000000001, 2.203333333333333, 2.5816666666666648, 3.0725000000000007, 2.468333333333332, 2.618333333333333, 3.1201666666666648, 4.885000000000001, 2.832666666666665, 2.8755952380952374, 2.147166666666667, 3.6497857142857155, 1.838214285714286, 5.315777777777774, 2.812238095238095, 3.2381547619047613, 3.078515873015871, 4.529853174603175, 3.7458189033189018, 4.538138888888887, 2.3045234487734487, 3.9079166666666647, 1.9696340603840605, 3.7305703463203455, 2.6001663336663334, 5.102301587301583, 4.11497619047619, 6.937190170940167, 3.3317508602508594, 5.671685637891517, 2.6808455139631597, 4.393103174603173, 3.5497279942279936, 3.46266989443305, 3.0899802741118507, 4.084215673215671, 3.6542838827838815, 9.099530483405484, 3.1833452870658756, 4.91520404432169, 3.5223060689310683, 3.513890062551827, 3.4316554556554526, 7.361717046238782, 3.034030247530244, 3.5310568729516087, 2.922128819280134, 7.7212221455445125, 4.079142108730322, 9.254576312576315, 2.432390327305543, 5.455857618301886, 2.1478042199748724, 5.717154465904462, 3.8056670117322278, 4.421209786945079, 8.25086314289575, 3.9806453075648105, 5.727092564298445, 2.2562179417804407, 4.726554874616326, 2.677858543417364, 4.647202617322312, 3.7667160478410473, 8.502338286713286, 5.371667172696582, 3.7220769479058933, 3.576872840746761, 5.672282716738598, 2.5096632245168564, 9.910175995573049, 3.7785037460976065, 4.679637417536024, 2.716194069819068, 8.629652946976746, 5.139533376711007, 5.80293475274725, 5.964897491397492, 4.9442456430043356, 2.580831972780479, 4.06149549146569, 2.8358605887099064, 7.453780982905984, 8.303637507045242, 8.939271899668952, 3.1272130815419152, 7.212484656361171, 6.0686778583559535, 6.4849147969677405, 1.9282294054311582, 9.1355835577466, 3.6393741215011333, 46.1344817251462, 10.535195634920635, 3.517713645901447, 13.903026048895066, 10.986628982128975, 46.87568816110287, 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4.585456349206349, 4.015780303030304, 3.097563852813851, 4.29775541125541, 4.037830808080808, 3.7625129870129865, 3.471087301587301, 4.3745573593073575, 3.0904895382395363, 3.880101731601729, 4.347519841269838, 3.954281746031747, 4.25192893217893, 5.670000000000003, 4.796579365079363, 3.1528333333333327, 3.594166666666667, 3.0534642857142855, 3.5776554834054832, 2.890468253968253, 2.988060245310243, 4.225345238095237, 3.535942640692641, 2.972294733044734, 3.1918888888888857, 3.2604920634920633, 3.9743412698412697, 4.154273809523809, 3.4798015873015857, 3.1546230158730144, 3.301274891774892, 4.055761904761903, 3.7194126984126985, 3.3725241702741706, 3.941639971139969, 3.12, 3.146588744588744, 3.284480158730157, 3.9908769841269836, 3.2807507215007217, 3.5214372294372303, 3.578777777777777, 3.800456349206348, 2.6509642857142848, 3.8218026695526692, 3.499309523809525, 3.6877499999999994, 3.1870003607503623, 3.40456746031746, 3.955810606060605, 3.7790992063492057, 3.550539682539683, 3.2897698412698406, 4.043266955266956, 3.7271111111111086, 3.447150793650793, 3.647996753246753, 3.4908033910533907, 3.3964574314574323, 3.346235209235207, 3.1201309523809524, 3.5201230158730166, 3.406321428571426, 2.8862182539682526, 3.3366111111111088, 3.6270198412698402, 2.7095714285714267, 3.0732864357864367, 3.082579365079362, 3.5792521645021655, 3.544476190476191, 3.670584054834056, 3.946797619047617, 4.255012626262627, 4.656556277056275, 4.101031746031745, 5.183503968253968, 4.241105339105335, 4.078734848484847, 4.463968253968256, 4.172846320346321, 4.042095238095237, 4.202060606060604, 4.114063492063489, 4.1924624819624805, 3.673417027417028, 3.649163780663779, 3.8136154401154374, 3.3855992063492053, 3.5941587301587297, 4.268492063492063, 3.725802669552671, 3.2893109668109664, 4.550440476190474, 5.889198412698411, 7.170984126984128, 5.765838383838384, 5.159047619047619, 6.018476190476187, 7.091330447330445, 5.461257936507938, 6.757686507936505, 6.592063492063488, 5.918611111111112, 6.10878391053391, 6.847358225108223, 7.709084415584415, 6.170103174603172, 6.763833333333329, 6.348095238095236, 6.909929653679654, 8.235727272727269, 6.672380952380952, 5.691219336219335, 7.2762619047619035, 5.912532828282827, 6.687500000000001, 6.4791785714285695, 4.751193722943723, 5.1812619047619, 4.744873015873013, 4.7950357142857145, 3.8895248917748915, 4.3000833333333315, 3.936747113997113, 4.261825396825396, 3.84615476190476, 3.8309011544011518, 3.814996031746032, 2.252976190476191, 2.7870833333333325, 2.371892857142857, 2.8493253968253955, 2.842639249639248, 2.713370129870129, 2.7820122655122645, 2.517551587301586, 2.8849657287157275, 3.508190476190474, 3.2860198412698414, 3.340607142857141, 2.8498481240981226, 3.112424963924965, 3.0266313131313116, 2.963670634920633, 2.871662698412697, 3.224969336219336, 3.3577673160173154, 4.804580447330447, 4.6198127705627705, 3.815047619047619, 3.421781746031744, 4.3499285714285705, 4.294416666666666, 4.237632034632035, 3.6272554112554123, 4.115920995670995, 4.248511904761903, 3.1585732323232327, 3.5358510101010094, 3.450044011544012, 3.7505753968253948, 4.78451587301587, 3.619904761904761, 3.0410725108225103, 3.7582348484848476, 3.9355284992784987, 4.013226911976911, 3.765028499278498, 3.097190476190475, 3.7097063492063485, 3.3913744588744574, 3.0087301587301587, 2.847099206349205, 3.7160656565656565, 3.4381493506493475, 3.046131673881673, 2.9550238095238104, 3.030914862914862, 3.034440476190475, 2.904599206349205, 3.557632034632033, 3.3122124819624807, 3.436511904761903, 3.3328095238095234, 3.537706349206349, 3.2628452380952377, 2.7690447330447308, 3.8342334054834057, 3.6402272727272726, 2.839845238095237, 2.9113394660894643, 3.72640476190476, 3.8679563492063473, 3.7769444444444424, 3.5105634920634907, 4.2451309523809515, 4.296757936507933, 3.903624098124096, 4.956500360750358, 3.983112193362191, 4.21193362193362, 4.2892777777777775, 3.571430375180375, 3.8660681818181817, 3.0336284271284253, 3.8172420634920603, 3.705104256854255, 3.788100288600289, 4.149253968253967, 3.6086598124098117, 3.8726230158730135, 3.536349206349206, 4.0738939393939395, 3.412541847041845, 3.480639971139971, 3.42527380952381, 3.367811688311691, 3.5947272727272725, 3.353501082251081, 3.521295454545453, 3.335726190476191, 3.938595238095235, 3.586182539682537, 3.6934325396825383, 3.575928571428572, 4.041722943722943, 3.364291847041846, 3.7595714285714283, 3.574777777777777, 3.4528134920634916, 3.242543650793649, 3.9069285714285686, 3.0500436507936493, 3.4892142857142834, 4.230464285714282, 3.7655324675324664, 3.406388888888887, 3.7108019480519467, 4.846434704184702, 3.8331388888888873, 4.153893939393938, 3.9900883838383847, 3.7023730158730155, 3.3251785714285718, 4.305285714285715, 3.0412301587301567, 3.4577153679653683, 4.437702380952381, 3.71976984126984, 3.6225447330447302, 3.554126984126982, 3.4300328282828274, 3.2811428571428563, 3.0351439393939375, 3.407773809523808, 3.0156926406926403, 3.8024801587301558, 2.9104166666666664, 2.968821428571429, 3.6308820346320347, 3.3340923520923513, 4.623809523809522, 4.493876984126984, 3.8821320346320336, 4.262999999999999, 4.383583333333333, 3.738769841269842, 4.290262987012986, 3.955785714285713, 5.410749999999996, 4.29165079365079, 4.10280266955267, 3.487261904761903, 4.158932539682538, 4.163509740259741, 3.775215367965369, 4.566647907647907, 4.436789682539682, 3.6880598845598835, 3.4868777056277045, 3.906519841269843, 3.530714285714284, 4.285666666666665, 3.8074999999999974, 2.800599206349206, 2.90329906204906, 3.469897546897545, 3.385167027417029, 6.365043650793647, 6.667230158730159, 5.929152597402597, 5.948111111111109, 7.2200952380952375, 5.735774891774888, 6.328325396825394, 6.919333333333331, 7.361488095238095, 7.41815151515152, 7.4267142857142865, 6.642952380952378, 7.813214285714288, 7.304134920634921, 8.023984126984127, 6.983999999999998, 6.804642857142856, 7.497103174603176, 7.807199134199135, 7.76875072150072, 8.34432142857143, 6.913666666666664, 6.110924603174599, 6.746880952380954, 5.182389971139969, 5.435797619047616, 4.317634920634919, 5.734261904761905, 4.990297619047616, 4.372392857142856, 4.907547619047619, 3.6778852813852785, 3.0706002886002874, 3.6560476190476168, 3.5317759740259733, 2.704349206349205, 2.720361111111111, 2.6086984126984123, 2.7178015873015857, 2.453916666666665, 2.8806071428571416, 2.504083333333333, 2.372704545454545, 3.2147233044733037, 2.479321428571428, 2.818830808080807, 3.3800090187590164, 2.5311547619047605, 2.5557103174603166, 3.108925685425684, 2.731865079365077, 3.3656269841269824, 3.2371468253968247, 3.5824404761904765, 4.070726190476189, 3.788892857142857, 3.785449494949493, 3.380397907647907, 3.5211161616161606, 3.335397907647908, 4.178905122655122, 3.29757251082251, 3.5249343434343436, 3.8863888888888876, 3.0946825396825393, 4.173023809523807, 3.6699769119769114, 3.046761904761903, 3.4730634920634915, 3.835464285714285, 4.125230158730158, 3.149472943722942, 3.5055086580086563, 3.2101388888888893, 3.2135992063492056, 3.449583333333331, 3.7082741702741693, 3.49490476190476, 4.014480158730159, 3.5960714285714293, 3.8185674603174604, 3.460046897546895, 3.2951111111111104, 3.1032034632034615, 3.4118492063492045, 3.33977380952381, 4.644646825396823, 3.8287341269841257, 3.7051240981240983, 12.35923520923521, 12.441630952380955, 11.346428571428579, 11.284107142857142, 12.190941558441564, 12.109083333333336, 11.942166666666669, 11.341928571428575, 9.052892857142863, 11.166666666666675, 9.7525753968254, 9.18716666666667, 8.875178571428574]#hash_d64_k16
d1 = pd.Series([s for s in disp])
sorted_d1 = d1.sort_values(ascending=False)
N = 140
pred_rrcf = d1 > d1.quantile(0.90)
print("Displacement: ", d1.tolist())
# print(metrics.classification_report(true_anomalies, pred_rrcf))
for k in [100, 200]:#, 200]:#, 300, 400]:
top_k_real = anomaly_detection.get_top_k_anomalies(sh_vector, k)
top_k_disp = anomaly_detection.get_top_k_displacement(sorted_d1, k)
anomaly_detection.get_top_k_performance(top_k_real, top_k_disp, true_anomalies, pred_rrcf, N)
real = []
for index, row in sh_vector.iterrows():
# real.append(int(row['anom_count'])*0.01)
if row['anom_count'] > 100:
real.append(100)
else:
real.append(int(row['anom_count']))
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, sharex=True, sharey=True, figsize=(8, 5))
ax1.plot(real, color='blue', linewidth=0.5)
ax1.plot(hash_d64_k16, color='red', linewidth=1)
ax1.set_title("SnapSketch", fontweight="bold", fontsize=8)
ax1.tick_params(axis='both', labelsize=6)
ax2.plot(real, color='blue', linewidth=0.5)
ax2.plot(sl_vast_full, color='red', linewidth=1)
ax2.set_title("SpotLight", fontweight="bold", fontsize=8)
ax2.tick_params(axis='both', labelsize=6)
ax3.plot(real, color='blue', linewidth=0.5)
ax3.plot(ss_vast_full, color='red', linewidth=1)
ax3.set_title("StreamSpot", fontweight="bold", fontsize=8)
ax3.tick_params(axis='both', labelsize=6)
plt.xlim([0, 1500])
# plt.ylim([0, 200])
plt.xlabel("Time", fontweight="bold", fontsize=6)
plt.ylabel("Anomaly Score", fontweight="bold", fontsize=6)
plt.tight_layout()
plt.savefig("results/vastplot.pdf")
plt.show()
def visualize_darpa():
# for sketch_file in glob.glob('sketches/darpa_10min/*.csv'):
if 1 ==1 :
# sketch_file = "sketches/nyctaxi/dim_32_n_3_k_1024p_0.2_l_50p10q5.csv" # use 24 24 for best result d256 k 256 (1024)
anomaly_detection = AnomalyDetection()
sketch_file = "sketches/darpa_10min/rdim_64_n_3_k_128p_0.2_l_50p10q5.csv" #"batch/traindarpabfs/25per_dim_4096_walk_300_k_4_costonlyp10q5.csv" # "batch/traindarpabfs/25per_dim_4096_walk_200_k_3_costonlyp10q5.csv" #25per_dim_32_walk_300_k_2 #50per_dim_512_walk_300_k_4 0_dim_4096_walk_300_k_3
# sketch_file = "sketches/baseline/streamspot_darpafull10min_vector.csv"
# sketch_file = "sketches/baseline/splt_darpa_10min.csv"
sh_vector = anomaly_detection.read_sketch(sketch_file)
print("Sketch File: ", sketch_file)
# sh_vector.loc[sh_vector['anom_count'] >= 100, 'anomaly'] = 1
true_anomalies = np.array(sh_vector['anomaly'])
# disp = anomaly_detection.robust_random_cut(sh_vector)
sl_darpa = [0.0, 1.0000000000000004, 1.4200000000000008, 1.620000000000001, 1.8600000000000008, 2.4500000000000015, 2.660000000000001, 2.606666666666668, 2.163333333333334, 3.553333333333335, 4.0166666666666675, 3.8266666666666675, 2.5980000000000016, 2.075, 2.4666666666666677, 2.6790000000000003, 2.6266666666666674, 1.9041666666666677, 2.3625000000000007, 7.323666666666667, 2.3486666666666673, 3.3153333333333337, 3.063134920634921, 2.320262626262627, 5.969333333333335, 4.417166666666666, 6.199833333333332, 7.968666666666665, 3.1812142857142867, 6.162523809523808, 5.600134920634922, 8.386054112554113, 6.047714285714287, 4.551000000000001, 1.7697138972138982, 3.614246031746033, 2.480444444444445, 3.6538547008547004, 3.9801907308377915, 8.834181096681096, 5.261960317460318, 6.4440873015873015, 8.789714285714286, 4.714586219336219, 8.04961821511821, 2.5418132978132983, 7.096460317460318, 7.826078477078476, 2.5750670995671006, 7.68892007992008, 3.289951346039582, 13.420904761904762, 3.4483250670897743, 2.714681762681763, 4.418218118139173, 15.451951465201466, 6.739684550743376, 3.6353293650793663, 3.191541549953315, 4.221762570762571, 2.153847555952819, 3.109875457875458, 3.456950705727022, 2.0999702634677067, 1.7787056255054985, 1.764123928359223, 1.888398291251233, 2.1237179487179496, 2.3554988921579323, 27.04, 1.4998784545456214, 1.8871840829000228, 2.1192714078931822, 2.523507449178502, 2.239671578421579, 2.2529392409483693, 3.0643626177743837, 1.7397439836149518, 3.2541237553342834, 6.007199999999998, 4.979633699633699, 3.022961038961039, 5.0280000000000005, 3.695000000000001, 1.181985185185186, 5.990666666666666, 4.469416666666667, 5.625238095238096, 6.6290714285714305, 5.471904761904761, 8.187222222222223, 6.22639393939394, 5.630380952380951, 3.611518796992482, 3.755980392156863, 4.919714285714287, 5.687798346763989, 8.450095238095239, 13.118333333333334, 10.500526315789475, 5.1608359859095145, 6.445960317460319, 4.468441225441227, 94.62000000000003, 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5.033472190450266, 4.0344106435058835, 6.0311233765278764, 4.908887522951813, 4.122736115502366, 4.721776054478353, 5.110335422566141, 5.457618141563391, 5.75670224040943, 14.886885463878246, 10.221891437058687, 7.813015729211129, 8.2715536339262, 4.811977618422307, 4.869194728365406, 7.9011026794363595, 9.046350981968367, 4.404884700885976, 7.743818024916072, 4.134901942672659, 6.5317586107045855, 4.895086314701115, 6.247884759958221, 5.037790118606422, 5.0859762237638355, 4.664874368094808, 2.4733913530990366, 3.797824852441488, 7.2911169200458685, 3.1927109719170383, 4.329637119775794, 4.469469560493093, 151.80679166666664, 4.7499367714751255, 6.1767339798406935, 4.416326012580518, 5.2695722177955, 8.047562555431387, 4.431262565263395, 5.249558983157615, 4.577808670715448, 6.407353709939479, 4.972916468712027, 7.802963965783831, 3.8860603333858834, 3.928728780339955, 3.6769457069605833, 5.425274564468319, 3.9902681004521807, 8.594185420061525, 5.285789325433516, 4.827162128381098, 5.237624604302334, 4.215993039482868, 4.598739204843459, 5.402611484467485, 6.671780950016793, 5.030508424912956, 4.686833788599542, 4.162046282549573, 5.522113135966277, 3.629783194609431, 6.305956427880355, 5.409909120016655, 5.139258186361698, 5.827743123593672, 3.6918481176852085, 4.139091932815936, 5.388556533656358, 4.289286030062015, 3.4625849116503202, 3.3636027729364186, 3.437616390122559, 3.6943902811799836, 3.286999923280424, 3.907688532216298, 3.912678082613576, 3.7650547140830746, 3.7166117300415635, 4.241771708412309, 3.3134866392162055, 16.480731141101415, 3.7120814983911408, 3.502942580881363, 4.333483640989162, 4.348967293415747, 3.5224189300967175, 4.366389810529792, 4.005746168390999, 3.416781106488308, 3.1141564846127494, 2.925638543580092, 3.1571328648828407, 4.741723956800506, 3.311316758629223, 3.9040029512762966, 4.417526151247758, 3.9516035421129945, 2.9713646739563107, 4.2613893980050985, 3.0225042084584923, 3.852412736177483, 4.82234623475484, 4.6771850667963175, 7.2064289554480006, 8.451851693325798, 5.378651714349072, 10.378371153611406, 8.591846990337334, 8.628776768114083, 7.450583564783365, 7.951145130557014, 8.608923572731879, 16.38713260606374, 11.295415933138566, 10.131944488060856, 7.267458124548903, 9.966697921253065, 7.644225146452233, 6.824782137039566, 7.291170093238506, 7.345905891781853, 7.227252315568842, 6.341705869734229, 5.186770810346664, 8.177501166088557, 11.143009661358514, 7.045935039819131, 5.823058390619455, 8.230306748717004, 6.323413163783719, 13.992886626374753, 5.426350286828656, 5.89685412943534, 5.626669744701722, 6.455971261087143, 6.586258555782774, 7.073786775889847, 5.117954665304739, 7.197341362589045, 6.742441778672439, 7.218528026328704, 4.9720180457408345, 5.06067986044219, 12.372573547400428, 11.787884617450242, 9.471721328854983, 6.896140283671081, 9.837847335632839, 5.944317504981653, 6.771258342057394, 8.971725246958941, 7.955791815132692, 4.820432389754627, 11.967569401248786, 13.079975246263096, 6.5813124687873845, 6.726533531061057, 6.836431700029519, 6.920583377506266, 8.814982227333777, 8.897623800324572, 8.421348478544118, 13.567743988055893, 16.213814339915977, 7.5981070393279415, 20.559047937714894, 10.529720567433506, 7.89163459494797, 7.204729947803644, 8.353697321835694, 6.434465401552552, 6.539655086789874, 12.279861731272286, 5.1666505155780404, 5.295889304480655, 5.944718003322571, 4.145790071475841, 5.808263920013532, 3.719977460310492, 3.356935851491742, 4.641838317102404, 5.507657754439067, 3.408166930590262, 5.433240607016858, 6.072029820566021, 7.137200625428808, 7.475588840278538, 8.798189912805457, 7.263185565684637, 6.53674557682283, 7.838920815189002, 5.934159590354037, 7.962016591515269, 6.9974606552769485, 7.327765271677789, 9.565263116518993, 8.117502670179539, 6.550459363693736, 6.969116542775713, 6.330751978892521, 5.27681953546291, 6.878871714781645, 7.75957010760448, 5.212777184593145, 5.818471122868557, 29.17933267942936, 5.491198367001468, 6.145534714281291, 7.542096160444845, 9.475012348742567, 7.570856502946661, 7.728812344226875, 5.420917515548322, 6.5089045202296445, 7.942533341111784, 8.181560232395094, 8.309765634975992, 8.630290358394548, 7.327637146325995, 6.828941333544249, 5.879913322385453, 10.800569276473654, 9.378383133335527, 9.957710870677726, 6.921627806017991, 7.489791972628446, 5.8552076368492205, 6.8399297181239325, 6.3549898299788, 6.688408327153296, 12.059044615546984, 5.748941440951698, 6.884646229756494, 9.400261679936984, 6.052826108887397, 7.187975520358613, 4.940287376699257, 5.427202175834721, 4.661110017144526, 7.138958882096568, 5.730438432438063, 8.028055628960143, 20.909747695561542, 9.28433670777088, 8.641705775732527, 6.244071519947334, 20.568172934992482, 5.187876218499282, 6.348197552297058, 7.364689388288148, 10.912815053361573, 23.431347178464698, 7.826647777077516, 14.80460131114221, 6.060105235732271, 9.537434712428098, 5.474382703171666, 6.140056764849204, 4.179456189663782, 20.634122970460215, 12.774240214941466, 4.827698091198126, 6.93240828824991, 6.434585204167808, 5.939755052440748, 4.898306294793826, 6.162771302106341, 14.172903971460471, 168.87063393421892, 237.37000000000026, 199.07200000000014, 179.67333333333337, 184.6490000000001, 163.80233333333337, 130.4204372405373, 19.46801316117082, 15.192308756343374, 34.20477756957798, 7.372710324977523, 8.148285913326967, 6.15012854479911, 4.340034819359692, 4.689536482827476, 5.402717808388374, 5.872866609012086, 4.102834987651392, 8.541709311429534, 6.849109348320388, 5.090925582995589, 5.350790231382896, 4.9847414641041565, 6.353496366590833, 4.164712311981808, 4.993348809751632, 5.072246832953704, 5.227149205373926, 7.428399182988344, 4.995780169172565, 4.984792859662108, 4.680655846065624, 6.559046443108163, 6.024103546906881, 4.273367924699345, 4.944780843202242, 4.418063207027579, 4.758902528412509, 4.407852152144423, 4.307244378520254, 3.760777003213636, 5.143770190081977, 9.73445764297778, 6.9474394611917125, 7.4888280316313764, 8.233662165597716, 5.60401733406029, 12.550582378915623, 14.942956773566799, 23.174882832647224, 20.02162097824465, 23.66280503191029, 16.403792124542125, 17.15405504686043, 22.610207935394556, 17.010586910472426, 20.290111762970575, 20.03127490668623, 4.956097746282726, 4.918310550839017, 3.8824843663219792, 3.7425728822219786, 6.564283382486792, 3.9803420946978934, 4.5363764099171195, 7.514933982294955, 5.678323321412732, 4.735698087492399, 5.543298153827893, 4.302258542041373, 20.048797266903154, 3.6578781198583656, 4.562044214387566, 6.086009839396536, 6.949717664221121, 5.9569980821323405, 4.791959668225559, 5.683663481030626, 3.0383386401806747, 4.734706626750323, 5.3884728535232345, 4.267781092035992, 3.025192794714916, 3.0069229104806485, 3.4920541283128514, 2.803092424329961, 3.449125718833545, 4.047419820000714, 3.495211617900202, 3.137751951323503, 4.078090606199659, 4.077435505060176, 5.2160359937424, 4.8074115488243345, 4.604037685343379, 4.996908906650311, 4.539803828104704, 3.5338922775672184, 4.823305868933567, 4.228161377852135, 3.9129818364529103, 10.588495373877244, 5.272565738889914, 7.798209848286381, 5.436529674226188, 5.066943325612405, 7.787481774822503, 10.191956840917008, 35.07420451744297, 26.566789050023143, 20.074649774725525, 11.733322219449764, 6.772244452058734, 9.047012164329804, 7.598105724768269, 80.38359488212099, 5.790025754236513, 6.11193515748542, 7.911940128458676, 7.04417085404567, 5.415872510086622, 6.15011516603868, 5.907509201815567, 9.901025446954954, 7.014242574992935, 7.898658887767696, 19.625230630850997, 5.6190295890363515, 8.724152538964779, 13.79839626669689, 7.07189342182275, 14.908638453973465, 10.00707229609724, 8.628499334176121, 9.086245736995089, 13.02640797936521, 15.811316803455282, 14.36100254240862, 7.123728747433187, 6.8094696471805305, 6.80274296212756, 29.473624470345495, 15.56606551266405, 7.078460954121151, 6.286097749191182, 12.628836460727305, 10.972910341827081, 11.337576300187877, 7.422547695231272, 8.680527590503962, 6.889134126599722, 14.961641388964562, 5.150865483309711, 6.967325186233745, 9.387068315114968, 5.75737566739241, 6.781292729905152, 4.69866902216877, 7.959441085432782, 8.57698861669268, 7.41365315761497, 5.453339832300251, 6.829218190523225, 6.003049986883954, 5.787279372322578, 8.104651324118597, 7.035932273619949, 6.960746972199602, 6.190104177474828, 5.9656362159819185, 5.587974267291152, 5.3952485672350265, 5.197358799521997, 6.308841168814804, 5.770520133765092, 5.666563787417696, 5.093133769213094, 5.620794216200692, 9.48351513090556, 22.154731319580215, 8.720164931933521, 6.389975210089296, 6.489522864391742, 7.7234483024300875, 5.710480576425025, 6.896474243977068, 5.180801289778272, 7.52683689345378, 4.98790536383273, 8.279486809575708, 6.344842982575445, 6.346708427145821, 6.745636448046193, 4.367145081438637, 6.590269749096828, 4.597364100275982, 5.9114601906893425, 5.453606129566568, 6.310062388520356, 16.37593008385219, 6.8909111783528, 6.541159931285981, 7.579169159506998, 6.611496405315438, 5.696461729995997, 5.368917672172203, 6.402189145161245, 5.447100570432933, 6.681779511838211, 4.8794976690336656, 6.001353602118534, 6.335285287275466, 6.057808551901731, 6.145533480834151, 6.217694757019613, 7.650887840345928, 5.4401990060049075, 6.129073793095342, 5.1257031592254085, 6.292930805002428, 6.633253172305618, 5.363553301783041, 4.867259352477574, 4.433547601958522, 4.531206989778651, 6.425923090973049, 5.396312884886771, 4.468438094370893, 5.7340328755205165, 6.114518945165014, 6.47533299450849, 5.4559618597991975]
d1 = pd.Series([s for s in disp])
sorted_d1 = d1.sort_values(ascending=False)
N = 361
pred_rrcf = d1 > d1.quantile(0.90)
print("Displacement: ", d1.tolist())
# print(metrics.classification_report(true_anomalies, pred_rrcf))
for k in [100, 200, 300, 400]:#, 300, 400]:
top_k_real = anomaly_detection.get_top_k_anomalies(sh_vector, k)
top_k_disp = anomaly_detection.get_top_k_displacement(sorted_d1, k)
anomaly_detection.get_top_k_performance(top_k_real, top_k_disp, true_anomalies, pred_rrcf, N)
#
# for i in top_k_real:
# print("Top K: ", i, " Score: ", d1[i], " Mean: ", median, pred_rrcf[i], true_anomalies[i])
# '''
real = []
for index, row in sh_vector.iterrows():
# real.append(int(row['anom_count'])*0.01)
if row['anom_count'] > 5000:
# real.append(300)
real.append(int(row['anom_count']) * 0.004)
# elif row['anom_count'] > 400 and row['anom_count'] < 10000:
# # real.append(300)
# real.append(int(row['anom_count']) * 0.002)
else:
real.append(int(row['anom_count'])*0.05)
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, sharex=True, sharey=True, figsize=(10,6))
ax1.plot(real, color='blue', linewidth=0.7)
ax1.plot(np.array(disp)*0.5, color='red', linewidth=1)
ax1.set_title("SnapSketch", fontweight="bold", fontsize=8)
ax1.tick_params(axis='both', labelsize=6)
ax2.plot(real, color='blue', linewidth=0.7)
ax2.plot(np.array(sl_darpa)*0.5, color='red', linewidth=1)
ax2.set_title("SpotLight", fontweight="bold", fontsize=8)
ax2.tick_params(axis='both', labelsize=6)
ax3.plot(real, color='blue', linewidth=0.7)
ax3.plot(np.array(ss_darpa)*0.5, color='red', linewidth=1)
ax3.set_title("StreamSpot", fontweight="bold", fontsize=8)
ax3.tick_params(axis='both', labelsize=6)
plt.xlim([1200, 2600])
plt.ylim([0, 200])
plt.xlabel("Time", fontweight="bold", fontsize=6)
plt.ylabel("Anomaly Score", fontweight="bold", fontsize=6)
plt.tight_layout()
plt.savefig("results/darpaplot.pdf")
plt.show()
# '''
def visualize_iot():
anomaly_detection = AnomalyDetection()
sketch_file = "sketches/iot/dim_32_n_3_k_256p_0.2_l_50p10q5.csv"
# sketch_file = "sketches/baseline/splt_IoT_oct1week.csv"
# sketch_file = "sketches/baseline/streamspot_iot_1weekfull.csv"
sh_vector = anomaly_detection.read_sketch(sketch_file)
# sh_vector = sh_vector.sort_values(by='graphid', ascending=False).reset_index()
print("Sketch File: ", sketch_file)
sh_vector.loc[sh_vector['anom_count'] >= 50, 'anomaly'] = 1
true_anomalies = np.array(sh_vector['anomaly'])
# true_anomalies = true_anomalies[256:]
# pred_rrcf, disp = anomaly_detection.robust_random_cut(sh_vector)
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5.414395800499509, 5.915227069420454, 4.67997499348663, 15.651274203112605, 3.8319491698687753, 4.075266016201829, 5.919704658486967, 3.5188534123870943, 4.782542605087985, 6.328257747846602, 6.227897023906547, 4.48309764668923, 4.9671455336978765, 7.390168962902779, 4.98568965791647, 7.266773332876196, 8.412956231908932, 5.48375069406418, 7.304278638879564, 4.323423020170953, 11.157372426960256, 3.6996826438829453, 4.074172944502642, 4.83158867964644, 4.457618289219833, 4.651540069858034, 4.588892332484567, 5.3627602298130235, 3.5446623600241476, 6.897544922462426, 73.54592488230713, 3.581056998077067, 5.9238092721629725, 4.276748760979162, 4.287828005409622, 15.776662968687486, 4.8219372726266645, 5.936965874078089, 8.383031773804413, 3.5528720165977674, 5.9581041742565555, 4.188658110611031, 3.920504226828131, 8.053123157367732, 4.668529739940333, 8.41590145173515, 3.6492106761835887, 4.656950310851449, 4.727182218523711, 9.738121408156632, 7.56547656631185, 4.5156682521285365, 6.426305932713351, 5.621130480623295, 7.5421604636783375, 4.657412403729312, 8.892899210206338, 5.163551873783113, 5.046241309743411, 9.219722334499348, 5.689805198476793, 4.752440741088711, 5.160585278510019, 4.618670921083705, 7.786209704206549, 6.309770264920504, 5.004721189405228, 6.908694263956865, 8.874429048978833, 4.942388223511625, 5.60091646689914, 5.267473455480562, 5.020882099279326, 6.764731325820961, 7.48902313208815, 5.093917779359989, 9.502668225621576, 3.7841151372351503, 4.587291422879952, 4.704452371210945, 4.704888813517309, 3.1647171365994886, 5.912495265677926, 10.828133522556133, 4.6289694592511275, 4.585820551791321, 10.603682775546957, 3.6611302972007147, 5.6969333402294025, 7.892222233520258, 4.8707119863738235, 12.768949521760167, 3.7454050236670673, 3.891410721341946, 7.104276184984107, 8.680172107820319, 4.080642353768982, 5.784738568169765, 6.900408796762545, 4.438949572159638, 3.773086171473992, 4.029547922319234, 5.438490529955845, 5.302302711385762, 4.461075322221949, 4.712363634866054, 5.546813928151976, 3.98453183666606, 6.714540149799347, 5.319514512822134, 11.36237559157177, 4.552022149152446, 7.156785147494494, 5.288562889581543, 5.904109387012734, 6.5831100538100475, 4.86876425880585, 4.870498362165367, 4.703307292426606, 3.5642690689331094, 3.025718932343065, 4.4620065984622235, 4.746831735399072, 5.917194043295348, 3.3374201095146203, 12.283095440867807, 4.659496073108648, 3.8781479310750444, 6.531958504341498, 4.421132817996439, 5.5750296718900225, 4.097888417384225, 5.057477409936974, 4.249051696490203, 5.8392091443212415, 3.922920121125648, 5.160174876720053, 16.183900660932007, 7.270916113093188, 4.053010491311951, 4.9803996535127695, 6.770116050891237, 6.7818346442614175, 6.967568633118546, 5.914101563211945, 4.380256770536154, 5.1814173838251705, 6.253818092578327, 4.422314575221432, 7.708296155915895, 5.192401403145199, 7.955853781011071, 5.638174256690393, 5.728373215176868, 6.342684739966915, 6.989658691082775, 4.887781929457405, 5.696999413189341, 6.055909608527767, 6.367848761419463, 5.81634857287425, 5.583309272912332, 6.218537593342466, 11.323709281711277, 6.092126853163, 5.394679136793757, 4.478248676510953, 5.652953468653985, 7.856802888720715, 3.6250683124934913, 5.000035892121128, 5.920869530871397, 4.3998786101028005, 6.32505776774248, 4.38175735462799, 3.718287043085531, 11.248670089341333, 6.178975484652476, 4.433129639845597, 5.998182418224579, 5.199652794316691, 5.704427737588539, 16.6353311652237, 6.284513692598696, 5.551214921670748, 7.125492703885347, 4.989500890302429, 6.57986820470262, 10.218872967101005, 4.351644420396437, 5.490953740155567, 212.3285714285715, 4.839548289998201, 5.299953999645585, 5.440232836061417, 5.596103284688051, 10.829980535626834, 7.824135060349622, 6.829418782013274, 5.778855577392975, 10.68623218122373, 5.723417350065048, 3.8043043683572235, 6.697167139579886, 6.200785955307497, 3.7657465681360383, 6.051100727274255, 4.617849110377838, 4.102702360029696, 9.08664754227254]
disp = hash_iot
d1 = pd.Series([s for s in disp])
sorted_d1 = d1.sort_values(ascending=False)
N = 1007 #404
pred_rrcf = d1 > d1.quantile(0.90)
print("Displacement: ", d1.tolist())
print(metrics.classification_report(true_anomalies, pred_rrcf))
for k in [100, 200, 300, 400]:
top_k_real = anomaly_detection.get_top_k_anomalies(sh_vector, k)
top_k_disp = anomaly_detection.get_top_k_displacement(sorted_d1, k)
anomaly_detection.get_top_k_performance(top_k_real, top_k_disp, true_anomalies, pred_rrcf, N)
real = []
for index, row in sh_vector.iterrows():
real.append(int(row['anom_count'])*0.05)
plt.style.use('seaborn-darkgrid')
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, sharex=True, sharey=True,figsize=(8,5))
ax1.plot(real, color='blue', linewidth=0.5)
ax1.plot(hash_iot, color='red', linewidth=1)
ax1.set_title("SnapSketch",fontweight="bold", fontsize=8)
ax1.tick_params(axis='both', labelsize=6)
ax2.plot(real, color='blue', linewidth=0.5)
ax2.plot(sl_total_hash, color='red', linewidth=1)
ax2.set_title("SpotLight",fontweight="bold", fontsize=8)
ax2.tick_params(axis='both', labelsize=6)
ax3.plot(real, color='blue', linewidth=0.5)
ax3.plot(ss_total_hash, color='red', linewidth=1)
ax3.set_title("StreamSpot",fontweight="bold", fontsize=8)
ax3.tick_params(axis='both', labelsize=6)
plt.xlim([200, 2500])
plt.ylim([0, 120])
plt.xlabel("Time",fontweight="bold", fontsize=6)
plt.ylabel("Anomaly Score",fontweight="bold", fontsize=6)
plt.tight_layout()
plt.savefig("results/iotplot.pdf")
plt.show()
if __name__=="__main__":
args = parse_args()
# main(args)
sketch_file = graph_sketching(args)
# ad = AnomalyDetection()
# ad.sub2vec_run(args)
# ad.spotlight_run(args)
# sh_vector = ad.read_sketch(sketch_file)
# ad.anomaly_detection(sh_vector, args)
# visualize_vast()
# visualize_iot()
# visualize_darpa()
# visualize_taxi()
| [
1,
2,
6,
9,
10
] |
204 | e5d7cc65041d65f915d4882b4fdad5bebf79a067 | <mask token>
class TokenizerPair(SourceTargetMixin):
def __init__(self, tokenizer_class=Tokenizer):
self.source = tokenizer_class()
self.target = tokenizer_class()
@property
def is_tokenized(self) ->bool:
return hasattr(self.source, 'word_index') and hasattr(self.target,
'word_index')
@cached_property
def target_index_word(self):
return {v: k for k, v in self.target.word_index.items()}
class TextDataset(BaseDataset):
def __init__(self, source_sentences: Union[Iterable, Sized],
target_sentences: Union[Iterable, Sized], shuffle: bool=True,
word_frequency_threshold: int=2):
super().__init__(source_sentences, target_sentences, shuffle)
self.word_frequency_threshold = word_frequency_threshold
self.tokenizer_pair = TokenizerPair()
@cached_property
def translation_references(self):
references = defaultdict(list)
for idx, sentence in enumerate(self.source):
split_sentence = text_to_word_sequence(self.target[idx])
references[sentence].append(split_sentence)
return references
@property
def source_max_sentence_length(self) ->int:
return self.max_sentence_length('source')
@property
def target_max_sentence_length(self) ->int:
return self.max_sentence_length('target')
@property
def source_vocab_size(self) ->int:
return self.tokenizer_pair.source.num_words
@property
def target_vocab_size(self) ->int:
return self.tokenizer_pair.target.num_words
def get_vocab_size(self, level: str) ->int:
if not self.tokenizer_pair.is_tokenized:
raise ValueError('Dataset has not been tokenized yet')
return len(self.tokenizer_pair[level].word_index) + 1
def max_sentence_length(self, level: str) ->int:
return max(len(line.split()) for line in self[level])
def tokenize(self) ->None:
if not self.tokenizer_pair.is_tokenized:
self.tokenizer_pair['source'].fit_on_texts(self.source)
self.tokenizer_pair['target'].fit_on_texts(self.target)
self.tokenizer_pair['source'].num_words = len([word for word,
count in self.tokenizer_pair['source'].word_counts.items() if
count > self.word_frequency_threshold - 1])
self.tokenizer_pair['target'].num_words = len([word for word,
count in self.tokenizer_pair['target'].word_counts.items() if
count > self.word_frequency_threshold - 1])
def get_sequences(self, level: str) ->np.ndarray:
if not self.tokenizer_pair.is_tokenized:
self.tokenize()
sentences = self.tokenizer_pair[level].texts_to_sequences(self[level])
return pad_sequences(sentences, maxlen=self.max_sentence_length(
level), padding='post')
def encode_output(self, sequences: np.array) ->np.array:
return to_categorical(sequences, self.target_vocab_size)
def sequence_to_sentence(self, sequence: Iterable) ->str:
target_sentence = [self.tokenizer_pair.target_index_word.get(
word_index, '') for word_index in sequence]
return ' '.join(target_sentence)
def sentence_to_sequence(self, sentence: str) ->np.ndarray:
return pad_sequences(self.tokenizer_pair['source'].
texts_to_sequences([sentence]), self.max_sentence_length(
'source'), padding='post')
| <mask token>
class BaseDataset(SourceTargetMixin):
<mask token>
<mask token>
<mask token>
class TokenizerPair(SourceTargetMixin):
def __init__(self, tokenizer_class=Tokenizer):
self.source = tokenizer_class()
self.target = tokenizer_class()
@property
def is_tokenized(self) ->bool:
return hasattr(self.source, 'word_index') and hasattr(self.target,
'word_index')
@cached_property
def target_index_word(self):
return {v: k for k, v in self.target.word_index.items()}
class TextDataset(BaseDataset):
def __init__(self, source_sentences: Union[Iterable, Sized],
target_sentences: Union[Iterable, Sized], shuffle: bool=True,
word_frequency_threshold: int=2):
super().__init__(source_sentences, target_sentences, shuffle)
self.word_frequency_threshold = word_frequency_threshold
self.tokenizer_pair = TokenizerPair()
@cached_property
def translation_references(self):
references = defaultdict(list)
for idx, sentence in enumerate(self.source):
split_sentence = text_to_word_sequence(self.target[idx])
references[sentence].append(split_sentence)
return references
@property
def source_max_sentence_length(self) ->int:
return self.max_sentence_length('source')
@property
def target_max_sentence_length(self) ->int:
return self.max_sentence_length('target')
@property
def source_vocab_size(self) ->int:
return self.tokenizer_pair.source.num_words
@property
def target_vocab_size(self) ->int:
return self.tokenizer_pair.target.num_words
def get_vocab_size(self, level: str) ->int:
if not self.tokenizer_pair.is_tokenized:
raise ValueError('Dataset has not been tokenized yet')
return len(self.tokenizer_pair[level].word_index) + 1
def max_sentence_length(self, level: str) ->int:
return max(len(line.split()) for line in self[level])
def tokenize(self) ->None:
if not self.tokenizer_pair.is_tokenized:
self.tokenizer_pair['source'].fit_on_texts(self.source)
self.tokenizer_pair['target'].fit_on_texts(self.target)
self.tokenizer_pair['source'].num_words = len([word for word,
count in self.tokenizer_pair['source'].word_counts.items() if
count > self.word_frequency_threshold - 1])
self.tokenizer_pair['target'].num_words = len([word for word,
count in self.tokenizer_pair['target'].word_counts.items() if
count > self.word_frequency_threshold - 1])
def get_sequences(self, level: str) ->np.ndarray:
if not self.tokenizer_pair.is_tokenized:
self.tokenize()
sentences = self.tokenizer_pair[level].texts_to_sequences(self[level])
return pad_sequences(sentences, maxlen=self.max_sentence_length(
level), padding='post')
def encode_output(self, sequences: np.array) ->np.array:
return to_categorical(sequences, self.target_vocab_size)
def sequence_to_sentence(self, sequence: Iterable) ->str:
target_sentence = [self.tokenizer_pair.target_index_word.get(
word_index, '') for word_index in sequence]
return ' '.join(target_sentence)
def sentence_to_sequence(self, sentence: str) ->np.ndarray:
return pad_sequences(self.tokenizer_pair['source'].
texts_to_sequences([sentence]), self.max_sentence_length(
'source'), padding='post')
| <mask token>
class SourceTargetMixin:
<mask token>
def __getitem__(self, item):
if item in ['source', 'target']:
return getattr(self, item)
raise TypeError(
'Subscription is available only with "source" and "target" keywords'
)
class BaseDataset(SourceTargetMixin):
def __init__(self, source: Union[Iterable, Sized], target: Union[
Iterable, Sized], shuffle: bool=True, seed: int=42):
self.source = source
self.target = target
self._validate()
if shuffle:
self.shuffle(seed)
def _validate(self) ->None:
src_len = len(self.source)
target_len = len(self.target)
if src_len != target_len:
raise TypeError(
'Number of source rows ({}) does not match the number of target rows ({})'
.format(src_len, target_len))
def shuffle(self, seed: int=42) ->None:
np.random.seed(seed)
shuffled_indexes = np.random.permutation(len(self.source))
self.source = self.source[shuffled_indexes]
self.target = self.target[shuffled_indexes]
class TokenizerPair(SourceTargetMixin):
def __init__(self, tokenizer_class=Tokenizer):
self.source = tokenizer_class()
self.target = tokenizer_class()
@property
def is_tokenized(self) ->bool:
return hasattr(self.source, 'word_index') and hasattr(self.target,
'word_index')
@cached_property
def target_index_word(self):
return {v: k for k, v in self.target.word_index.items()}
class TextDataset(BaseDataset):
def __init__(self, source_sentences: Union[Iterable, Sized],
target_sentences: Union[Iterable, Sized], shuffle: bool=True,
word_frequency_threshold: int=2):
super().__init__(source_sentences, target_sentences, shuffle)
self.word_frequency_threshold = word_frequency_threshold
self.tokenizer_pair = TokenizerPair()
@cached_property
def translation_references(self):
references = defaultdict(list)
for idx, sentence in enumerate(self.source):
split_sentence = text_to_word_sequence(self.target[idx])
references[sentence].append(split_sentence)
return references
@property
def source_max_sentence_length(self) ->int:
return self.max_sentence_length('source')
@property
def target_max_sentence_length(self) ->int:
return self.max_sentence_length('target')
@property
def source_vocab_size(self) ->int:
return self.tokenizer_pair.source.num_words
@property
def target_vocab_size(self) ->int:
return self.tokenizer_pair.target.num_words
def get_vocab_size(self, level: str) ->int:
if not self.tokenizer_pair.is_tokenized:
raise ValueError('Dataset has not been tokenized yet')
return len(self.tokenizer_pair[level].word_index) + 1
def max_sentence_length(self, level: str) ->int:
return max(len(line.split()) for line in self[level])
def tokenize(self) ->None:
if not self.tokenizer_pair.is_tokenized:
self.tokenizer_pair['source'].fit_on_texts(self.source)
self.tokenizer_pair['target'].fit_on_texts(self.target)
self.tokenizer_pair['source'].num_words = len([word for word,
count in self.tokenizer_pair['source'].word_counts.items() if
count > self.word_frequency_threshold - 1])
self.tokenizer_pair['target'].num_words = len([word for word,
count in self.tokenizer_pair['target'].word_counts.items() if
count > self.word_frequency_threshold - 1])
def get_sequences(self, level: str) ->np.ndarray:
if not self.tokenizer_pair.is_tokenized:
self.tokenize()
sentences = self.tokenizer_pair[level].texts_to_sequences(self[level])
return pad_sequences(sentences, maxlen=self.max_sentence_length(
level), padding='post')
def encode_output(self, sequences: np.array) ->np.array:
return to_categorical(sequences, self.target_vocab_size)
def sequence_to_sentence(self, sequence: Iterable) ->str:
target_sentence = [self.tokenizer_pair.target_index_word.get(
word_index, '') for word_index in sequence]
return ' '.join(target_sentence)
def sentence_to_sequence(self, sentence: str) ->np.ndarray:
return pad_sequences(self.tokenizer_pair['source'].
texts_to_sequences([sentence]), self.max_sentence_length(
'source'), padding='post')
| from collections import defaultdict
from typing import Union, Iterable, Sized
import numpy as np
from cached_property import cached_property
from keras.utils import to_categorical
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer, text_to_word_sequence
class SourceTargetMixin:
"""
Allows subscription with 'source' and 'target' keywords
"""
def __getitem__(self, item):
if item in ['source', 'target']:
return getattr(self, item)
raise TypeError(
'Subscription is available only with "source" and "target" keywords'
)
class BaseDataset(SourceTargetMixin):
def __init__(self, source: Union[Iterable, Sized], target: Union[
Iterable, Sized], shuffle: bool=True, seed: int=42):
self.source = source
self.target = target
self._validate()
if shuffle:
self.shuffle(seed)
def _validate(self) ->None:
src_len = len(self.source)
target_len = len(self.target)
if src_len != target_len:
raise TypeError(
'Number of source rows ({}) does not match the number of target rows ({})'
.format(src_len, target_len))
def shuffle(self, seed: int=42) ->None:
np.random.seed(seed)
shuffled_indexes = np.random.permutation(len(self.source))
self.source = self.source[shuffled_indexes]
self.target = self.target[shuffled_indexes]
class TokenizerPair(SourceTargetMixin):
def __init__(self, tokenizer_class=Tokenizer):
self.source = tokenizer_class()
self.target = tokenizer_class()
@property
def is_tokenized(self) ->bool:
return hasattr(self.source, 'word_index') and hasattr(self.target,
'word_index')
@cached_property
def target_index_word(self):
return {v: k for k, v in self.target.word_index.items()}
class TextDataset(BaseDataset):
def __init__(self, source_sentences: Union[Iterable, Sized],
target_sentences: Union[Iterable, Sized], shuffle: bool=True,
word_frequency_threshold: int=2):
super().__init__(source_sentences, target_sentences, shuffle)
self.word_frequency_threshold = word_frequency_threshold
self.tokenizer_pair = TokenizerPair()
@cached_property
def translation_references(self):
references = defaultdict(list)
for idx, sentence in enumerate(self.source):
split_sentence = text_to_word_sequence(self.target[idx])
references[sentence].append(split_sentence)
return references
@property
def source_max_sentence_length(self) ->int:
return self.max_sentence_length('source')
@property
def target_max_sentence_length(self) ->int:
return self.max_sentence_length('target')
@property
def source_vocab_size(self) ->int:
return self.tokenizer_pair.source.num_words
@property
def target_vocab_size(self) ->int:
return self.tokenizer_pair.target.num_words
def get_vocab_size(self, level: str) ->int:
if not self.tokenizer_pair.is_tokenized:
raise ValueError('Dataset has not been tokenized yet')
return len(self.tokenizer_pair[level].word_index) + 1
def max_sentence_length(self, level: str) ->int:
return max(len(line.split()) for line in self[level])
def tokenize(self) ->None:
if not self.tokenizer_pair.is_tokenized:
self.tokenizer_pair['source'].fit_on_texts(self.source)
self.tokenizer_pair['target'].fit_on_texts(self.target)
self.tokenizer_pair['source'].num_words = len([word for word,
count in self.tokenizer_pair['source'].word_counts.items() if
count > self.word_frequency_threshold - 1])
self.tokenizer_pair['target'].num_words = len([word for word,
count in self.tokenizer_pair['target'].word_counts.items() if
count > self.word_frequency_threshold - 1])
def get_sequences(self, level: str) ->np.ndarray:
if not self.tokenizer_pair.is_tokenized:
self.tokenize()
sentences = self.tokenizer_pair[level].texts_to_sequences(self[level])
return pad_sequences(sentences, maxlen=self.max_sentence_length(
level), padding='post')
def encode_output(self, sequences: np.array) ->np.array:
return to_categorical(sequences, self.target_vocab_size)
def sequence_to_sentence(self, sequence: Iterable) ->str:
target_sentence = [self.tokenizer_pair.target_index_word.get(
word_index, '') for word_index in sequence]
return ' '.join(target_sentence)
def sentence_to_sequence(self, sentence: str) ->np.ndarray:
return pad_sequences(self.tokenizer_pair['source'].
texts_to_sequences([sentence]), self.max_sentence_length(
'source'), padding='post')
| from collections import defaultdict
from typing import Union, Iterable, Sized
import numpy as np
from cached_property import cached_property
from keras.utils import to_categorical
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer, text_to_word_sequence
class SourceTargetMixin:
"""
Allows subscription with 'source' and 'target' keywords
"""
def __getitem__(self, item):
if item in ['source', 'target']:
return getattr(self, item)
raise TypeError('Subscription is available '
'only with "source" and "target" keywords')
class BaseDataset(SourceTargetMixin):
def __init__(self, source: Union[Iterable, Sized],
target: Union[Iterable, Sized],
shuffle: bool=True, seed: int=42):
self.source = source
self.target = target
self._validate()
if shuffle:
self.shuffle(seed)
def _validate(self) -> None:
src_len = len(self.source)
target_len = len(self.target)
if src_len != target_len:
raise TypeError('Number of source rows ({}) does not match '
'the number of target rows ({})'.format(src_len,
target_len))
def shuffle(self, seed: int=42) -> None:
np.random.seed(seed)
shuffled_indexes = np.random.permutation(len(self.source))
self.source = self.source[shuffled_indexes]
self.target = self.target[shuffled_indexes]
class TokenizerPair(SourceTargetMixin):
def __init__(self, tokenizer_class=Tokenizer):
self.source = tokenizer_class()
self.target = tokenizer_class()
@property
def is_tokenized(self) -> bool:
return hasattr(self.source, 'word_index') \
and hasattr(self.target, 'word_index')
@cached_property
def target_index_word(self):
return {v: k for k, v in self.target.word_index.items()}
class TextDataset(BaseDataset):
def __init__(self, source_sentences: Union[Iterable, Sized],
target_sentences: Union[Iterable, Sized],
shuffle: bool=True, word_frequency_threshold: int=2):
super().__init__(source_sentences, target_sentences, shuffle)
self.word_frequency_threshold = word_frequency_threshold
self.tokenizer_pair = TokenizerPair()
@cached_property
def translation_references(self):
references = defaultdict(list)
for idx, sentence in enumerate(self.source):
split_sentence = text_to_word_sequence(self.target[idx])
references[sentence].append(split_sentence)
return references
@property
def source_max_sentence_length(self) -> int:
return self.max_sentence_length('source')
@property
def target_max_sentence_length(self) -> int:
return self.max_sentence_length('target')
@property
def source_vocab_size(self) -> int:
return self.tokenizer_pair.source.num_words
@property
def target_vocab_size(self) -> int:
return self.tokenizer_pair.target.num_words
def get_vocab_size(self, level: str) -> int:
if not self.tokenizer_pair.is_tokenized:
raise ValueError('Dataset has not been tokenized yet')
return len(self.tokenizer_pair[level].word_index) + 1
def max_sentence_length(self, level: str) -> int:
return max(len(line.split()) for line in self[level])
def tokenize(self) -> None:
if not self.tokenizer_pair.is_tokenized:
self.tokenizer_pair['source'].fit_on_texts(self.source)
self.tokenizer_pair['target'].fit_on_texts(self.target)
# limit number of words returned from tokenizer
# according to frequency threshold
self.tokenizer_pair['source'].num_words = len(
[word for word, count
in self.tokenizer_pair['source'].word_counts.items()
if count > self.word_frequency_threshold - 1]
)
self.tokenizer_pair['target'].num_words = len(
[word for word, count
in self.tokenizer_pair['target'].word_counts.items()
if count > self.word_frequency_threshold - 1]
)
def get_sequences(self, level: str) -> np.ndarray:
if not self.tokenizer_pair.is_tokenized:
self.tokenize()
sentences = self.tokenizer_pair[level].texts_to_sequences(self[level])
return pad_sequences(
sentences, maxlen=self.max_sentence_length(level), padding='post'
)
def encode_output(self, sequences: np.array) -> np.array:
return to_categorical(sequences, self.target_vocab_size)
def sequence_to_sentence(self, sequence: Iterable) -> str:
target_sentence = [
self.tokenizer_pair.target_index_word.get(word_index, '')
for word_index in sequence
]
return ' '.join(target_sentence)
def sentence_to_sequence(self, sentence: str) -> np.ndarray:
return pad_sequences(
self.tokenizer_pair['source'].texts_to_sequences([sentence]),
self.max_sentence_length('source'), padding='post'
)
| [
18,
19,
24,
26,
27
] |
205 | e221553f866de8b3e175197a40982506bf8c1ef9 | <mask token>
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden)
self.predict = torch.nn.Linear(n_hidden, n_output)
def forward(self, x):
h1 = F.relu(self.hidden(x))
y = self.predict(h1)
return y
<mask token>
| <mask token>
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden)
self.predict = torch.nn.Linear(n_hidden, n_output)
def forward(self, x):
h1 = F.relu(self.hidden(x))
y = self.predict(h1)
return y
<mask token>
net.load_state_dict(torch.load('net_data_multi.pkl'))
<mask token>
file_out.write('caseid,midprice\n')
<mask token>
while case < 143:
line = file_test.readline().split(',')
if len(line) < 9:
case += 1
while case <= 153:
x = torch.FloatTensor(40).zero_()
y = torch.FloatTensor(20).zero_()
for ct in range(10):
line = file_test.readline()
if line == '':
break
line = line.split(',')
x[ct * 4] = float(line[6])
x[ct * 4 + 1] = float(line[7]) / 10000
x[ct * 4 + 2] = float(line[8])
x[ct * 4 + 3] = float(line[9]) / 10000
prediction = net(x)
average = 0
for k in range(10):
average += prediction.data.numpy()[k]
average = 1.0 * average / 10
file_out.write(str(case) + ',' + str(average) + '\n')
line = file_test.readline()
case += 1
file_test.close()
file_out.close()
print('test complete')
| <mask token>
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden)
self.predict = torch.nn.Linear(n_hidden, n_output)
def forward(self, x):
h1 = F.relu(self.hidden(x))
y = self.predict(h1)
return y
net = Net(n_feature=40, n_hidden=10, n_output=20)
net.load_state_dict(torch.load('net_data_multi.pkl'))
file_test = open('dataset/test_data.csv', 'r')
line = file_test.readline()
file_out = open('result_multi.csv', 'w')
file_out.write('caseid,midprice\n')
case = 1
while case < 143:
line = file_test.readline().split(',')
if len(line) < 9:
case += 1
while case <= 153:
x = torch.FloatTensor(40).zero_()
y = torch.FloatTensor(20).zero_()
for ct in range(10):
line = file_test.readline()
if line == '':
break
line = line.split(',')
x[ct * 4] = float(line[6])
x[ct * 4 + 1] = float(line[7]) / 10000
x[ct * 4 + 2] = float(line[8])
x[ct * 4 + 3] = float(line[9]) / 10000
prediction = net(x)
average = 0
for k in range(10):
average += prediction.data.numpy()[k]
average = 1.0 * average / 10
file_out.write(str(case) + ',' + str(average) + '\n')
line = file_test.readline()
case += 1
file_test.close()
file_out.close()
print('test complete')
| import torch
import torch.nn.functional as F
import csv
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden)
self.predict = torch.nn.Linear(n_hidden, n_output)
def forward(self, x):
h1 = F.relu(self.hidden(x))
y = self.predict(h1)
return y
net = Net(n_feature=40, n_hidden=10, n_output=20)
net.load_state_dict(torch.load('net_data_multi.pkl'))
file_test = open('dataset/test_data.csv', 'r')
line = file_test.readline()
file_out = open('result_multi.csv', 'w')
file_out.write('caseid,midprice\n')
case = 1
while case < 143:
line = file_test.readline().split(',')
if len(line) < 9:
case += 1
while case <= 153:
x = torch.FloatTensor(40).zero_()
y = torch.FloatTensor(20).zero_()
for ct in range(10):
line = file_test.readline()
if line == '':
break
line = line.split(',')
x[ct * 4] = float(line[6])
x[ct * 4 + 1] = float(line[7]) / 10000
x[ct * 4 + 2] = float(line[8])
x[ct * 4 + 3] = float(line[9]) / 10000
prediction = net(x)
average = 0
for k in range(10):
average += prediction.data.numpy()[k]
average = 1.0 * average / 10
file_out.write(str(case) + ',' + str(average) + '\n')
line = file_test.readline()
case += 1
file_test.close()
file_out.close()
print('test complete')
| import torch
import torch.nn.functional as F
import csv
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden)
self.predict = torch.nn.Linear(n_hidden, n_output)
def forward(self, x):
h1 = F.relu(self.hidden(x))
y = self.predict(h1)
return y
net = Net(n_feature=40, n_hidden=10, n_output=20)
net.load_state_dict(torch.load('net_data_multi.pkl'))
file_test = open('dataset/test_data.csv','r')
line = file_test.readline()
file_out = open('result_multi.csv','w')
file_out.write('caseid,midprice\n')
case = 1
while case < 143:
line = file_test.readline().split(',')
if len(line) < 9:
case += 1
while case <= 153:
x = torch.FloatTensor(40).zero_()
y = torch.FloatTensor(20).zero_()
for ct in range(10):
line = file_test.readline()
if line == '':
break
line = line.split(',')
x[ct*4] = float(line[6])
x[ct*4+1] = float(line[7])/10000
x[ct*4+2] = float(line[8])
x[ct*4+3] = float(line[9])/10000
prediction = net(x)
average = 0
for k in range(10):
average += prediction.data.numpy()[k]
average = 1.0*average/10
file_out.write(str(case)+','+str(average)+'\n')
#print(str(case)+','+str(average)+'\n')
line = file_test.readline()
case += 1
file_test.close()
file_out.close()
print('test complete')
| [
3,
4,
5,
6,
7
] |
206 | 39dda191ab2137b5f5538660f17e39b0a1358bf4 | <mask token>
| <mask token>
for i in range(nodes):
r, g, b = colorsys.hsv_to_rgb(float(i) / nodes, 1.0, 1.0)
R, G, B = int(255 * r), int(255 * g), int(255 * b)
color = [R, G, B]
print(color)
img[markers == i + 2] = list(color)
<mask token>
cv2.putText(img, text, (160, 20), font, 0.5, (0, 0, 0), 1, cv2.LINE_AA)
plt.subplot(321), plt.imshow(rgb_img)
plt.title('Input image'), plt.xticks([]), plt.yticks([])
plt.subplot(322), plt.imshow(thresh, 'gray')
plt.title('Binary threshold'), plt.xticks([]), plt.yticks([])
plt.subplot(323), plt.imshow(sure_bg, 'gray')
plt.title('Sure background'), plt.xticks([]), plt.yticks([])
plt.subplot(324), plt.imshow(sure_fg, 'gray')
plt.title('Sure foreground'), plt.xticks([]), plt.yticks([])
plt.subplot(325), plt.imshow(dist_transform, 'gray')
plt.title('Distance transform'), plt.xticks([]), plt.yticks([])
plt.subplot(326), plt.imshow(img, 'gray')
plt.title('Result from watershed'), plt.xticks([]), plt.yticks([])
plt.tight_layout()
plt.show()
| <mask token>
img = cv2.imread('coins.jpg')
b, g, r = cv2.split(img)
rgb_img = cv2.merge([r, g, b])
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
grayBlur = cv2.medianBlur(gray, 3)
ret, thresh = cv2.threshold(grayBlur, 200, 255, cv2.THRESH_BINARY_INV)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
sure_bg = cv2.dilate(opening, kernel, iterations=1)
dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 5)
ret, sure_fg = cv2.threshold(dist_transform, 0.6 * dist_transform.max(), 255, 0
)
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg, sure_fg)
ret, markers = cv2.connectedComponents(sure_fg)
markers = markers + 1
markers[unknown == 255] = 0
markers = cv2.watershed(img, markers)
img[markers == -1] = [0, 0, 0]
img[markers == 1] = [255, 255, 255]
nodes = np.amax(markers)
for i in range(nodes):
r, g, b = colorsys.hsv_to_rgb(float(i) / nodes, 1.0, 1.0)
R, G, B = int(255 * r), int(255 * g), int(255 * b)
color = [R, G, B]
print(color)
img[markers == i + 2] = list(color)
text = 'Coins: ' + str(np.amax(markers - 1))
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img, text, (160, 20), font, 0.5, (0, 0, 0), 1, cv2.LINE_AA)
plt.subplot(321), plt.imshow(rgb_img)
plt.title('Input image'), plt.xticks([]), plt.yticks([])
plt.subplot(322), plt.imshow(thresh, 'gray')
plt.title('Binary threshold'), plt.xticks([]), plt.yticks([])
plt.subplot(323), plt.imshow(sure_bg, 'gray')
plt.title('Sure background'), plt.xticks([]), plt.yticks([])
plt.subplot(324), plt.imshow(sure_fg, 'gray')
plt.title('Sure foreground'), plt.xticks([]), plt.yticks([])
plt.subplot(325), plt.imshow(dist_transform, 'gray')
plt.title('Distance transform'), plt.xticks([]), plt.yticks([])
plt.subplot(326), plt.imshow(img, 'gray')
plt.title('Result from watershed'), plt.xticks([]), plt.yticks([])
plt.tight_layout()
plt.show()
| import numpy as np
import cv2
import colorsys
from matplotlib import pyplot as plt
img = cv2.imread('coins.jpg')
b, g, r = cv2.split(img)
rgb_img = cv2.merge([r, g, b])
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
grayBlur = cv2.medianBlur(gray, 3)
ret, thresh = cv2.threshold(grayBlur, 200, 255, cv2.THRESH_BINARY_INV)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=2)
sure_bg = cv2.dilate(opening, kernel, iterations=1)
dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2, 5)
ret, sure_fg = cv2.threshold(dist_transform, 0.6 * dist_transform.max(), 255, 0
)
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg, sure_fg)
ret, markers = cv2.connectedComponents(sure_fg)
markers = markers + 1
markers[unknown == 255] = 0
markers = cv2.watershed(img, markers)
img[markers == -1] = [0, 0, 0]
img[markers == 1] = [255, 255, 255]
nodes = np.amax(markers)
for i in range(nodes):
r, g, b = colorsys.hsv_to_rgb(float(i) / nodes, 1.0, 1.0)
R, G, B = int(255 * r), int(255 * g), int(255 * b)
color = [R, G, B]
print(color)
img[markers == i + 2] = list(color)
text = 'Coins: ' + str(np.amax(markers - 1))
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img, text, (160, 20), font, 0.5, (0, 0, 0), 1, cv2.LINE_AA)
plt.subplot(321), plt.imshow(rgb_img)
plt.title('Input image'), plt.xticks([]), plt.yticks([])
plt.subplot(322), plt.imshow(thresh, 'gray')
plt.title('Binary threshold'), plt.xticks([]), plt.yticks([])
plt.subplot(323), plt.imshow(sure_bg, 'gray')
plt.title('Sure background'), plt.xticks([]), plt.yticks([])
plt.subplot(324), plt.imshow(sure_fg, 'gray')
plt.title('Sure foreground'), plt.xticks([]), plt.yticks([])
plt.subplot(325), plt.imshow(dist_transform, 'gray')
plt.title('Distance transform'), plt.xticks([]), plt.yticks([])
plt.subplot(326), plt.imshow(img, 'gray')
plt.title('Result from watershed'), plt.xticks([]), plt.yticks([])
plt.tight_layout()
plt.show()
| import numpy as np
import cv2
import colorsys
from matplotlib import pyplot as plt
img = cv2.imread('coins.jpg')
b,g,r = cv2.split(img)
rgb_img = cv2.merge([r,g,b])
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Blurring image
grayBlur = cv2.medianBlur(gray, 3)
# Binary threshold
ret, thresh = cv2.threshold(grayBlur, 200,255, cv2.THRESH_BINARY_INV)
# Noise removal
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel,iterations=2)
# Sure background area
sure_bg = cv2.dilate(opening, kernel, iterations=1)
# Finding sure foreground area
dist_transform = cv2.distanceTransform(opening, cv2.DIST_L2,5)
ret, sure_fg = cv2.threshold(dist_transform,0.6*dist_transform.max(),255,0)
# Finding unknown region
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg,sure_fg)
# Marker labelling
ret, markers = cv2.connectedComponents(sure_fg)
# Add one to all labels so that sure background is not 0, but 1
markers = markers+1
# Now, mark the region of unknown with zero
markers[unknown==255] = 0
markers = cv2.watershed(img,markers)
# Coloring borders black
img[markers == -1] = [0,0,0]
#Color background white
img[markers == 1] = [255, 255, 255]
# Color nodes
nodes = np.amax(markers)
for i in range(nodes):
(r, g, b) = colorsys.hsv_to_rgb(float(i) / nodes, 1.0, 1.0)
R, G, B = int(255 * r), int(255 * g), int(255 * b)
color = [R,G,B]
print(color)
img[markers == i+2] = list(color)
# Add text with coin count
text = 'Coins: ' + (str)(np.amax(markers-1))
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(img,text,(160,20), font, 0.5,(0,0,0),1,cv2.LINE_AA)
# Plotting
plt.subplot(321), plt.imshow(rgb_img )
plt.title('Input image'), plt.xticks([]), plt.yticks([])
plt.subplot(322),plt.imshow(thresh, 'gray')
plt.title("Binary threshold"), plt.xticks([]), plt.yticks([])
plt.subplot(323),plt.imshow(sure_bg, 'gray')
plt.title("Sure background"), plt.xticks([]), plt.yticks([])
plt.subplot(324),plt.imshow(sure_fg, 'gray')
plt.title("Sure foreground"), plt.xticks([]), plt.yticks([])
plt.subplot(325),plt.imshow(dist_transform, 'gray')
plt.title("Distance transform"), plt.xticks([]), plt.yticks([])
plt.subplot(326),plt.imshow(img, 'gray')
plt.title("Result from watershed"), plt.xticks([]), plt.yticks([])
plt.tight_layout()
plt.show()
| [
0,
1,
2,
3,
4
] |
207 | 0e6e84a31b626639e2aa149fd1ef89f3ef251cd7 | <mask token>
class Context(Base):
def __init__(self, dataset='', capsys=None):
super(Context, self).__init__(capsys=capsys)
self.dataset = ''
self.dataset = dataset
<mask token>
def set_dataset(self, dataset):
self.dataset = dataset
<mask token>
| <mask token>
class Context(Base):
def __init__(self, dataset='', capsys=None):
super(Context, self).__init__(capsys=capsys)
self.dataset = ''
self.dataset = dataset
def get_dataset(self):
return self.dataset
def set_dataset(self, dataset):
self.dataset = dataset
<mask token>
| <mask token>
class Context(Base):
def __init__(self, dataset='', capsys=None):
super(Context, self).__init__(capsys=capsys)
self.dataset = ''
self.dataset = dataset
def get_dataset(self):
return self.dataset
def set_dataset(self, dataset):
self.dataset = dataset
def validation_after_subcommand_execution(self):
captured = self.get_capsys().readouterr()
assert DATASET_EXAMPLE['version'] in captured.out
| from synda.tests.context.models import Context as Base
from synda.tests.tests.constants import DATASET_EXAMPLE
class Context(Base):
def __init__(self, dataset='', capsys=None):
super(Context, self).__init__(capsys=capsys)
self.dataset = ''
self.dataset = dataset
def get_dataset(self):
return self.dataset
def set_dataset(self, dataset):
self.dataset = dataset
def validation_after_subcommand_execution(self):
captured = self.get_capsys().readouterr()
assert DATASET_EXAMPLE['version'] in captured.out
| # -*- coding: utf-8 -*-
##################################
# @program synda
# @description climate models data transfer program
# @copyright Copyright "(c)2009 Centre National de la Recherche Scientifique CNRS.
# All Rights Reserved"
# @license CeCILL (https://raw.githubusercontent.com/Prodiguer/synda/master/sdt/doc/LICENSE)
##################################
from synda.tests.context.models import Context as Base
from synda.tests.tests.constants import DATASET_EXAMPLE
class Context(Base):
def __init__(self, dataset="", capsys=None):
super(Context, self).__init__(capsys=capsys)
# init
self.dataset = ""
# settings
self.dataset = dataset
def get_dataset(self):
return self.dataset
def set_dataset(self, dataset):
self.dataset = dataset
def validation_after_subcommand_execution(self):
captured = self.get_capsys().readouterr()
assert DATASET_EXAMPLE["version"] in captured.out
| [
3,
4,
5,
6,
7
] |
208 | c0d71d970b2632dbf182a5ee8bad27d3e41578f6 | <mask token>
def sumInput(text):
f = open(text, 'r')
sum = 0
count = 1
for line in f:
count += 1
line = line.strip()
if line[0] == '+':
sum += int(line[1:])
else:
sum -= int(line[1:])
f.close()
return sum
<mask token>
| <mask token>
def sumInput(text):
f = open(text, 'r')
sum = 0
count = 1
for line in f:
count += 1
line = line.strip()
if line[0] == '+':
sum += int(line[1:])
else:
sum -= int(line[1:])
f.close()
return sum
def main():
print(sumInput('input.txt'))
<mask token>
| <mask token>
def sumInput(text):
f = open(text, 'r')
sum = 0
count = 1
for line in f:
count += 1
line = line.strip()
if line[0] == '+':
sum += int(line[1:])
else:
sum -= int(line[1:])
f.close()
return sum
def main():
print(sumInput('input.txt'))
if __name__ == '__main__':
main()
| import sys
def sumInput(text):
f = open(text, 'r')
sum = 0
count = 1
for line in f:
count += 1
line = line.strip()
if line[0] == '+':
sum += int(line[1:])
else:
sum -= int(line[1:])
f.close()
return sum
def main():
print(sumInput('input.txt'))
if __name__ == '__main__':
main()
| #!/Library/Frameworks/Python.framework/Versions/3.7/bin/python3
import sys
def sumInput(text):
f = open(text, 'r')
sum = 0
count = 1
for line in f:
count += 1
line = line.strip()
if (line[0] == '+'):
sum += int(line[1:])
else:
sum -= int(line[1:])
f.close()
return sum
def main():
print(sumInput('input.txt'))
if __name__ == "__main__":
main()
| [
1,
2,
3,
4,
5
] |
209 | f14d46bedd5f6e0081a982251ad45e95860ef310 | class HashTable:
<mask token>
<mask token>
<mask token>
<mask token>
| class HashTable:
<mask token>
def hash(self, chave):
return int(chave)
<mask token>
<mask token>
| class HashTable:
<mask token>
def hash(self, chave):
return int(chave)
def __put(self, int, chave, valor):
self.dados.append({chave: valor})
<mask token>
| class HashTable:
def __init__(self):
self.dados = []
def hash(self, chave):
return int(chave)
def __put(self, int, chave, valor):
self.dados.append({chave: valor})
<mask token>
| class HashTable:
def __init__(self):
self.dados = []
def hash(self, chave):
return int(chave)
def __put(self, int, chave, valor):
self.dados.append({chave: valor})
"""
backup = dados
dados = novo_array(t * 2)
for elemento in backup:
hash = hash(elemento.chave)
__put(h % (t * 2), elemento.chave, elemento.valor)
""" | [
1,
2,
3,
4,
5
] |
210 | 21a41356fcedb36223498db0fe783e4a9e8e1ba6 | <mask token>
| with open('Book1.txt', 'r') as file1:
with open('20k.txt', 'r') as file2:
same = set(file1).intersection(file2)
same.discard('\n')
with open('notin20kforBook1.txt', 'w') as file_out:
for line in same:
file_out.write(line)
with open('Book2.txt', 'r') as file3:
with open('20k.txt', 'r') as file2:
same = set(file3).intersection(file2)
same.discard('\n')
with open('notin20kforBook2.txt', 'w') as file_out:
for line in same:
file_out.write(line)
with open('Book3.txt', 'r') as file4:
with open('20k.txt', 'r') as file2:
same = set(file4).intersection(file2)
same.discard('\n')
with open('notin20kforBook3.txt', 'w') as file_out:
for line in same:
file_out.write(line)
| # help from https://stackoverflow.com/questions/19007383/compare-two-different-files-line-by-line-in-python
with open('Book1.txt', 'r') as file1:
with open('20k.txt', 'r') as file2:
same = set(file1).intersection(file2)
same.discard('\n')
with open('notin20kforBook1.txt', 'w') as file_out:
for line in same:
file_out.write(line)
with open('Book2.txt', 'r') as file3:
with open('20k.txt', 'r') as file2:
same = set(file3).intersection(file2)
same.discard('\n')
with open('notin20kforBook2.txt', 'w') as file_out:
for line in same:
file_out.write(line)
with open('Book3.txt', 'r') as file4:
with open('20k.txt', 'r') as file2:
same = set(file4).intersection(file2)
same.discard('\n')
with open('notin20kforBook3.txt', 'w') as file_out:
for line in same:
file_out.write(line)
| null | null | [
0,
1,
2
] |
211 | de7b5e44c5c213e4ab70b0f8c0c402edaf4926e0 | <mask token>
| <mask token>
app_name = 'user'
urlpatterns = [url('^$', views.index, name='index'), url('login/', views.
login, name='login'), url('regist/', views.regist, name='regist'), url(
'^getuser\\w*/(?P<id>\\d*)', views.getUserById, name='getuser'), url(
'^sendmessage\\w*/(?P<user_telephone>\\d*)', views.sendMessage, name=
'sendmessage'), url('gettoken', views.getUserbyToken, name=
'getUserbyToken')]
| from django.conf.urls import url
from . import views
app_name = 'user'
urlpatterns = [url('^$', views.index, name='index'), url('login/', views.
login, name='login'), url('regist/', views.regist, name='regist'), url(
'^getuser\\w*/(?P<id>\\d*)', views.getUserById, name='getuser'), url(
'^sendmessage\\w*/(?P<user_telephone>\\d*)', views.sendMessage, name=
'sendmessage'), url('gettoken', views.getUserbyToken, name=
'getUserbyToken')]
|
from django.conf.urls import url
from .import views
app_name='user'
# user子路由
urlpatterns = [
# user首页
url(r'^$',views.index,name='index'),
# 用户登录
url('login/', views.login, name='login'),
# 用户注册
url('regist/', views.regist, name='regist'),
# 根据id判断用户是否存在
url(r'^getuser\w*/(?P<id>\d*)', views.getUserById, name='getuser'),
# 获取短信验证接口
url(r'^sendmessage\w*/(?P<user_telephone>\d*)',views.sendMessage,name='sendmessage'),
# 根据token查询一定的用户信息 postRight
url(r'gettoken', views.getUserbyToken, name='getUserbyToken'),
]
| null | [
0,
1,
2,
3
] |
212 | cc7f1f38efcd4d757c1d11e2bd53695fca44e15a | <mask token>
| <mask token>
with onto:
class Pizza(Thing):
pass
class MeatPizza(Pizza):
pass
class Topping(Thing):
pass
class has_Topping((Pizza >> Topping)):
pass
print(Pizza)
<mask token>
print(Pizza.subclasses())
<mask token>
print(MeatPizza.is_a)
<mask token>
print(MeatPizza.ancestors())
<mask token>
print(Pizza.iri)
| <mask token>
onto = get_ontology('http://test1.org/onto.owl')
with onto:
class Pizza(Thing):
pass
class MeatPizza(Pizza):
pass
class Topping(Thing):
pass
class has_Topping((Pizza >> Topping)):
pass
print(Pizza)
<mask token>
print(Pizza.subclasses())
<mask token>
print(MeatPizza.is_a)
<mask token>
print(MeatPizza.ancestors())
<mask token>
print(Pizza.iri)
| <mask token>
from owlready2 import *
onto = get_ontology('http://test1.org/onto.owl')
with onto:
class Pizza(Thing):
pass
class MeatPizza(Pizza):
pass
class Topping(Thing):
pass
class has_Topping((Pizza >> Topping)):
pass
print(Pizza)
<mask token>
print(Pizza.subclasses())
<mask token>
print(MeatPizza.is_a)
<mask token>
print(MeatPizza.ancestors())
<mask token>
print(Pizza.iri)
| 'For learning OWL and owlready2'
'From "https://qiita.com/sci-koke/items/a650c09bf77331f5537f"'
'From "https://owlready2.readthedocs.io/en/latest/class.html"'
'* Owlready2 * Warning: optimized Cython parser module "owlready2_optimized" is not available, defaulting to slower Python implementation'
'↑ This wartning mean is You can either install Cython (and a C compiler) and re-install Owlready to benefit from the optimized module, or continue using the non-optimized version'
'pip install Owlready2'
from owlready2 import *
onto = get_ontology("http://test1.org/onto.owl")
with onto:
#エンティティクラス
class Pizza(Thing):
pass
class MeatPizza(Pizza):
pass
class Topping(Thing):
pass
# プロパティクラス
class has_Topping(Pizza >> Topping):
pass
print(Pizza)
'The .subclasses() method returns the list of direct subclasses of a class.'
print(Pizza.subclasses())
'Owlready2 provides the .is_a attribute for getting the list of superclasses'
print(MeatPizza.is_a)
'The .descendants() and .ancestors() Class methods return a set of the descendant and ancestor Classes.'
print(MeatPizza.ancestors())
'The .iri attribute of the Class can be used to obtain the full IRI of the class.'
print(Pizza.iri)
| [
0,
1,
2,
3,
4
] |
213 | a2e2528f560f6117d4ceeb9cd20d3f6f6b2a30a7 | <mask token>
| def testeum():
a = 10
print(id(a))
<mask token>
| def testeum():
a = 10
print(id(a))
def testedois():
a = 10
print(id(a))
| # -*- coding: utf-8 -*-
def testeum():
a = 10
print(id(a))
def testedois():
a = 10
print(id(a)) | null | [
0,
1,
2,
3
] |
214 | e09af436f2fb37d16427aa0b1416d6f2d59ad6c4 | <mask token>
def append_log(log, message):
f = open(log, 'a+')
today = datetime.now()
f.write('%s %s \n' % (today.strftime('%Y-%m-%d %H:%M:%S'), message))
f.close()
def get_root_pass():
with open('/root/.my.cnf') as fp:
lines = fp.read().splitlines()
for line in lines:
grep = re.findall('password', line)
if grep:
pwrd = line.split('"')[1]
return pwrd
<mask token>
def check_ssh_conn(argv, remote_user, remote_host, remote_port, remote_pass):
cmd = ('sshpass -p "' + remote_pass +
'" ssh -o StrictHostKeyChecking=no -p ' + remote_port + ' -q ' +
remote_user + '@' + remote_host + ' exit;echo $?')
res = execute(cmd)
log = '/home/kusanagi/' + argv + '/log/backup.log'
if int(res) == 0:
pass
else:
append_log(log, 'Remote connection failed. Can not issue remote backup'
)
update_backup_record(argv, 1, 0)
sys.exit(1)
def remote_backup(argv, remote_user, remote_host, remote_port, remote_pass,
remote_dest):
log = '/home/kusanagi/' + argv + '/log/backup.log'
append_log(log, '--- Remote backup')
check_ssh_conn(argv, remote_user, remote_host, remote_port, remote_pass)
backup_db(argv)
tarname = compress_provision_dir(argv, '/home/kusanagi/')
conf_ssh = '/etc/ssh/ssh_config'
with open(conf_ssh) as fp:
lines = fp.read().splitlines()
for line in lines:
grep = re.findall(remote_host, line)
if grep:
break
if not grep:
f = open(conf_ssh, 'a+')
f.write('Host %s\n\tStrictHostKeyChecking no\n' % remote_host)
f.close()
cmd = ('sshpass -p "' + remote_pass +
'" rsync --remove-source-files -azhe \'ssh -p' + remote_port + "' " +
tarname + '.tar.gz ' + remote_user + '@' + remote_host + ':' +
remote_dest + ' 2>> ' + log + ' ; echo $?')
res = execute(cmd)
if int(res) == 0:
update_backup_record(argv, 1, 1)
else:
update_backup_record(argv, 1, 0)
def drive_backup(argv, drive_dir):
log = '/home/kusanagi/' + argv + '/log/backup.log'
append_log(log, '--- Backup to Google Drive')
backup_db(argv)
tarname = compress_provision_dir(argv, '/home/kusanagi/')
cmd = ('rclone copy ' + tarname + '.tar.gz GGD1:' + drive_dir + ' 2>> ' +
log + ' ; echo $?')
res = execute(cmd)
if int(res) == 0:
update_backup_record(argv, 2, 1)
else:
update_backup_record(argv, 2, 0)
os.remove(tarname + '.tar.gz')
def get_options(argv):
parser = argparse.ArgumentParser()
parser.add_argument('mode', type=str, choices=['local', 'remote', 'drive'])
parser.add_argument('options', nargs=argparse.REMAINDER)
return parser.parse_args(argv)
<mask token>
| <mask token>
def append_log(log, message):
f = open(log, 'a+')
today = datetime.now()
f.write('%s %s \n' % (today.strftime('%Y-%m-%d %H:%M:%S'), message))
f.close()
def get_root_pass():
with open('/root/.my.cnf') as fp:
lines = fp.read().splitlines()
for line in lines:
grep = re.findall('password', line)
if grep:
pwrd = line.split('"')[1]
return pwrd
<mask token>
def compress_provision_dir(argv, chdir=''):
date = datetime.now()
today = date.strftime('%Y-%m-%d')
if chdir:
tarname = chdir + argv + '.' + today
else:
tarname = '/home/kusanagi/backup/' + argv + '.' + today
source_dir = '/home/kusanagi/' + argv
shutil.make_archive(tarname, 'gztar', source_dir)
return tarname
def local_backup(argv):
append_log('/home/kusanagi/' + argv + '/log/backup.log', '--- Local backup'
)
backup_db(argv)
tarname = compress_provision_dir(argv)
tar_file = pathlib.Path(tarname + '.tar.gz')
if tar_file.exists():
update_backup_record(argv, 0, 1)
else:
update_backup_record(argv, 0, 0)
def check_ssh_conn(argv, remote_user, remote_host, remote_port, remote_pass):
cmd = ('sshpass -p "' + remote_pass +
'" ssh -o StrictHostKeyChecking=no -p ' + remote_port + ' -q ' +
remote_user + '@' + remote_host + ' exit;echo $?')
res = execute(cmd)
log = '/home/kusanagi/' + argv + '/log/backup.log'
if int(res) == 0:
pass
else:
append_log(log, 'Remote connection failed. Can not issue remote backup'
)
update_backup_record(argv, 1, 0)
sys.exit(1)
def remote_backup(argv, remote_user, remote_host, remote_port, remote_pass,
remote_dest):
log = '/home/kusanagi/' + argv + '/log/backup.log'
append_log(log, '--- Remote backup')
check_ssh_conn(argv, remote_user, remote_host, remote_port, remote_pass)
backup_db(argv)
tarname = compress_provision_dir(argv, '/home/kusanagi/')
conf_ssh = '/etc/ssh/ssh_config'
with open(conf_ssh) as fp:
lines = fp.read().splitlines()
for line in lines:
grep = re.findall(remote_host, line)
if grep:
break
if not grep:
f = open(conf_ssh, 'a+')
f.write('Host %s\n\tStrictHostKeyChecking no\n' % remote_host)
f.close()
cmd = ('sshpass -p "' + remote_pass +
'" rsync --remove-source-files -azhe \'ssh -p' + remote_port + "' " +
tarname + '.tar.gz ' + remote_user + '@' + remote_host + ':' +
remote_dest + ' 2>> ' + log + ' ; echo $?')
res = execute(cmd)
if int(res) == 0:
update_backup_record(argv, 1, 1)
else:
update_backup_record(argv, 1, 0)
def drive_backup(argv, drive_dir):
log = '/home/kusanagi/' + argv + '/log/backup.log'
append_log(log, '--- Backup to Google Drive')
backup_db(argv)
tarname = compress_provision_dir(argv, '/home/kusanagi/')
cmd = ('rclone copy ' + tarname + '.tar.gz GGD1:' + drive_dir + ' 2>> ' +
log + ' ; echo $?')
res = execute(cmd)
if int(res) == 0:
update_backup_record(argv, 2, 1)
else:
update_backup_record(argv, 2, 0)
os.remove(tarname + '.tar.gz')
def get_options(argv):
parser = argparse.ArgumentParser()
parser.add_argument('mode', type=str, choices=['local', 'remote', 'drive'])
parser.add_argument('options', nargs=argparse.REMAINDER)
return parser.parse_args(argv)
def main():
args = get_options(sys.argv[1:])
options = ' '.join(map(str, args.options))
if args.mode == 'local':
local_backup(*args.options)
elif args.mode == 'remote':
remote_backup(*args.options)
else:
drive_backup(*args.options)
<mask token>
| <mask token>
def append_log(log, message):
f = open(log, 'a+')
today = datetime.now()
f.write('%s %s \n' % (today.strftime('%Y-%m-%d %H:%M:%S'), message))
f.close()
def get_root_pass():
with open('/root/.my.cnf') as fp:
lines = fp.read().splitlines()
for line in lines:
grep = re.findall('password', line)
if grep:
pwrd = line.split('"')[1]
return pwrd
def get_db_name(argv):
try:
pwrd = get_root_pass()
db = pymysql.connect('localhost', 'root', pwrd, 'secure_vps')
cursor = db.cursor()
cursor.execute(
"select id,db_name from provision where provision_name='%s'" % argv
)
data = cursor.fetchone()
db.close()
return data
except pymysql.err.OperationalError as err:
print(' An error has occurred \n', err)
except pymysql.err.InternalError as err:
print(' An error has occurred \n', err)
def backup_db(argv):
data = get_db_name(argv)
db_name = data[1]
try:
sqldir = '/home/kusanagi/' + argv + '/sql_backup/'
p = pathlib.Path(sqldir)
if not p.exists():
p.mkdir(mode=493, parents=True, exist_ok=True)
shutil.chown(sqldir, 'kusanagi', 'kusanagi')
except BaseException as error:
print(error)
pwrd = get_root_pass()
log = '/home/kusanagi/' + argv + '/log/backup.log'
mess = 'Backed up database ' + db_name
append_log(log, mess)
cmd = ('mysqldump --single-transaction -p' + pwrd + ' --databases ' +
db_name + ' | gzip > ' + sqldir + db_name + '.sql.gz')
execute_outputfile(cmd, log)
def update_backup_record(argv, backup_type, result):
pwrd = get_root_pass()
data = get_db_name(argv)
provi_id = data[0]
log = '/home/kusanagi/' + argv + '/log/backup.log'
db = pymysql.connect('localhost', 'root', pwrd, 'secure_vps')
cursor = db.cursor()
cursor.execute(
'select id from logs where provision_id=%d and status=0 and backup_type=%d'
% (provi_id, backup_type))
res = cursor.fetchone()
record_id = res[0]
if result:
cursor.execute(
"update logs set status=1,message='Done' where provision_id=%d and id=%d"
% (provi_id, record_id))
else:
cursor.execute(
"update logs set status=-1,message='Failed. See %s' where provision_id=%d and id=%d"
% (log, provi_id, record_id))
db.commit()
db.close()
def compress_provision_dir(argv, chdir=''):
date = datetime.now()
today = date.strftime('%Y-%m-%d')
if chdir:
tarname = chdir + argv + '.' + today
else:
tarname = '/home/kusanagi/backup/' + argv + '.' + today
source_dir = '/home/kusanagi/' + argv
shutil.make_archive(tarname, 'gztar', source_dir)
return tarname
def local_backup(argv):
append_log('/home/kusanagi/' + argv + '/log/backup.log', '--- Local backup'
)
backup_db(argv)
tarname = compress_provision_dir(argv)
tar_file = pathlib.Path(tarname + '.tar.gz')
if tar_file.exists():
update_backup_record(argv, 0, 1)
else:
update_backup_record(argv, 0, 0)
def check_ssh_conn(argv, remote_user, remote_host, remote_port, remote_pass):
cmd = ('sshpass -p "' + remote_pass +
'" ssh -o StrictHostKeyChecking=no -p ' + remote_port + ' -q ' +
remote_user + '@' + remote_host + ' exit;echo $?')
res = execute(cmd)
log = '/home/kusanagi/' + argv + '/log/backup.log'
if int(res) == 0:
pass
else:
append_log(log, 'Remote connection failed. Can not issue remote backup'
)
update_backup_record(argv, 1, 0)
sys.exit(1)
def remote_backup(argv, remote_user, remote_host, remote_port, remote_pass,
remote_dest):
log = '/home/kusanagi/' + argv + '/log/backup.log'
append_log(log, '--- Remote backup')
check_ssh_conn(argv, remote_user, remote_host, remote_port, remote_pass)
backup_db(argv)
tarname = compress_provision_dir(argv, '/home/kusanagi/')
conf_ssh = '/etc/ssh/ssh_config'
with open(conf_ssh) as fp:
lines = fp.read().splitlines()
for line in lines:
grep = re.findall(remote_host, line)
if grep:
break
if not grep:
f = open(conf_ssh, 'a+')
f.write('Host %s\n\tStrictHostKeyChecking no\n' % remote_host)
f.close()
cmd = ('sshpass -p "' + remote_pass +
'" rsync --remove-source-files -azhe \'ssh -p' + remote_port + "' " +
tarname + '.tar.gz ' + remote_user + '@' + remote_host + ':' +
remote_dest + ' 2>> ' + log + ' ; echo $?')
res = execute(cmd)
if int(res) == 0:
update_backup_record(argv, 1, 1)
else:
update_backup_record(argv, 1, 0)
def drive_backup(argv, drive_dir):
log = '/home/kusanagi/' + argv + '/log/backup.log'
append_log(log, '--- Backup to Google Drive')
backup_db(argv)
tarname = compress_provision_dir(argv, '/home/kusanagi/')
cmd = ('rclone copy ' + tarname + '.tar.gz GGD1:' + drive_dir + ' 2>> ' +
log + ' ; echo $?')
res = execute(cmd)
if int(res) == 0:
update_backup_record(argv, 2, 1)
else:
update_backup_record(argv, 2, 0)
os.remove(tarname + '.tar.gz')
def get_options(argv):
parser = argparse.ArgumentParser()
parser.add_argument('mode', type=str, choices=['local', 'remote', 'drive'])
parser.add_argument('options', nargs=argparse.REMAINDER)
return parser.parse_args(argv)
def main():
args = get_options(sys.argv[1:])
options = ' '.join(map(str, args.options))
if args.mode == 'local':
local_backup(*args.options)
elif args.mode == 'remote':
remote_backup(*args.options)
else:
drive_backup(*args.options)
<mask token>
| <mask token>
def append_log(log, message):
f = open(log, 'a+')
today = datetime.now()
f.write('%s %s \n' % (today.strftime('%Y-%m-%d %H:%M:%S'), message))
f.close()
def get_root_pass():
with open('/root/.my.cnf') as fp:
lines = fp.read().splitlines()
for line in lines:
grep = re.findall('password', line)
if grep:
pwrd = line.split('"')[1]
return pwrd
def get_db_name(argv):
try:
pwrd = get_root_pass()
db = pymysql.connect('localhost', 'root', pwrd, 'secure_vps')
cursor = db.cursor()
cursor.execute(
"select id,db_name from provision where provision_name='%s'" % argv
)
data = cursor.fetchone()
db.close()
return data
except pymysql.err.OperationalError as err:
print(' An error has occurred \n', err)
except pymysql.err.InternalError as err:
print(' An error has occurred \n', err)
def backup_db(argv):
data = get_db_name(argv)
db_name = data[1]
try:
sqldir = '/home/kusanagi/' + argv + '/sql_backup/'
p = pathlib.Path(sqldir)
if not p.exists():
p.mkdir(mode=493, parents=True, exist_ok=True)
shutil.chown(sqldir, 'kusanagi', 'kusanagi')
except BaseException as error:
print(error)
pwrd = get_root_pass()
log = '/home/kusanagi/' + argv + '/log/backup.log'
mess = 'Backed up database ' + db_name
append_log(log, mess)
cmd = ('mysqldump --single-transaction -p' + pwrd + ' --databases ' +
db_name + ' | gzip > ' + sqldir + db_name + '.sql.gz')
execute_outputfile(cmd, log)
def update_backup_record(argv, backup_type, result):
pwrd = get_root_pass()
data = get_db_name(argv)
provi_id = data[0]
log = '/home/kusanagi/' + argv + '/log/backup.log'
db = pymysql.connect('localhost', 'root', pwrd, 'secure_vps')
cursor = db.cursor()
cursor.execute(
'select id from logs where provision_id=%d and status=0 and backup_type=%d'
% (provi_id, backup_type))
res = cursor.fetchone()
record_id = res[0]
if result:
cursor.execute(
"update logs set status=1,message='Done' where provision_id=%d and id=%d"
% (provi_id, record_id))
else:
cursor.execute(
"update logs set status=-1,message='Failed. See %s' where provision_id=%d and id=%d"
% (log, provi_id, record_id))
db.commit()
db.close()
def compress_provision_dir(argv, chdir=''):
date = datetime.now()
today = date.strftime('%Y-%m-%d')
if chdir:
tarname = chdir + argv + '.' + today
else:
tarname = '/home/kusanagi/backup/' + argv + '.' + today
source_dir = '/home/kusanagi/' + argv
shutil.make_archive(tarname, 'gztar', source_dir)
return tarname
def local_backup(argv):
append_log('/home/kusanagi/' + argv + '/log/backup.log', '--- Local backup'
)
backup_db(argv)
tarname = compress_provision_dir(argv)
tar_file = pathlib.Path(tarname + '.tar.gz')
if tar_file.exists():
update_backup_record(argv, 0, 1)
else:
update_backup_record(argv, 0, 0)
def check_ssh_conn(argv, remote_user, remote_host, remote_port, remote_pass):
cmd = ('sshpass -p "' + remote_pass +
'" ssh -o StrictHostKeyChecking=no -p ' + remote_port + ' -q ' +
remote_user + '@' + remote_host + ' exit;echo $?')
res = execute(cmd)
log = '/home/kusanagi/' + argv + '/log/backup.log'
if int(res) == 0:
pass
else:
append_log(log, 'Remote connection failed. Can not issue remote backup'
)
update_backup_record(argv, 1, 0)
sys.exit(1)
def remote_backup(argv, remote_user, remote_host, remote_port, remote_pass,
remote_dest):
log = '/home/kusanagi/' + argv + '/log/backup.log'
append_log(log, '--- Remote backup')
check_ssh_conn(argv, remote_user, remote_host, remote_port, remote_pass)
backup_db(argv)
tarname = compress_provision_dir(argv, '/home/kusanagi/')
conf_ssh = '/etc/ssh/ssh_config'
with open(conf_ssh) as fp:
lines = fp.read().splitlines()
for line in lines:
grep = re.findall(remote_host, line)
if grep:
break
if not grep:
f = open(conf_ssh, 'a+')
f.write('Host %s\n\tStrictHostKeyChecking no\n' % remote_host)
f.close()
cmd = ('sshpass -p "' + remote_pass +
'" rsync --remove-source-files -azhe \'ssh -p' + remote_port + "' " +
tarname + '.tar.gz ' + remote_user + '@' + remote_host + ':' +
remote_dest + ' 2>> ' + log + ' ; echo $?')
res = execute(cmd)
if int(res) == 0:
update_backup_record(argv, 1, 1)
else:
update_backup_record(argv, 1, 0)
def drive_backup(argv, drive_dir):
log = '/home/kusanagi/' + argv + '/log/backup.log'
append_log(log, '--- Backup to Google Drive')
backup_db(argv)
tarname = compress_provision_dir(argv, '/home/kusanagi/')
cmd = ('rclone copy ' + tarname + '.tar.gz GGD1:' + drive_dir + ' 2>> ' +
log + ' ; echo $?')
res = execute(cmd)
if int(res) == 0:
update_backup_record(argv, 2, 1)
else:
update_backup_record(argv, 2, 0)
os.remove(tarname + '.tar.gz')
def get_options(argv):
parser = argparse.ArgumentParser()
parser.add_argument('mode', type=str, choices=['local', 'remote', 'drive'])
parser.add_argument('options', nargs=argparse.REMAINDER)
return parser.parse_args(argv)
def main():
args = get_options(sys.argv[1:])
options = ' '.join(map(str, args.options))
if args.mode == 'local':
local_backup(*args.options)
elif args.mode == 'remote':
remote_backup(*args.options)
else:
drive_backup(*args.options)
if __name__ == '__main__':
main()
| #!/usr/bin/env python3
import argparse
import os
import sys,shutil
from shutil import make_archive
import pathlib
from phpManager import execute,execute_outputfile
from datetime import date,datetime
import re
import pymysql
import tarfile
def append_log(log,message):
f = open(log, "a+")
today = datetime.now()
f.write("%s %s \n" % (today.strftime("%Y-%m-%d %H:%M:%S"), message))
f.close()
def get_root_pass():
with open("/root/.my.cnf") as fp: lines = fp.read().splitlines()
for line in lines:
grep = re.findall(r'password', line)
if grep:
pwrd = line.split('"')[1]
return pwrd
def get_db_name(argv):
try:
pwrd = get_root_pass()
db = pymysql.connect("localhost","root",pwrd,"secure_vps")
cursor = db.cursor()
cursor.execute("select id,db_name from provision where provision_name='%s'" % argv)
data = cursor.fetchone()
db.close()
return data
except pymysql.err.OperationalError as err:
print (' An error has occurred \n', err)
except pymysql.err.InternalError as err:
print (' An error has occurred \n', err)
def backup_db(argv):
data = get_db_name(argv)
db_name = data[1]
try:
sqldir = '/home/kusanagi/'+argv+'/sql_backup/'
p = pathlib.Path(sqldir)
if not p.exists():
p.mkdir(mode=0o755, parents=True, exist_ok=True)
shutil.chown(sqldir,'kusanagi','kusanagi')
except BaseException as error:
print(error)
pwrd = get_root_pass()
log = '/home/kusanagi/'+argv+'/log/backup.log'
mess = 'Backed up database '+db_name
append_log(log,mess)
cmd = 'mysqldump --single-transaction -p'+pwrd+' --databases '+db_name+' | gzip > '+sqldir+db_name+'.sql.gz'
execute_outputfile(cmd,log)
def update_backup_record(argv,backup_type,result):
pwrd = get_root_pass()
data = get_db_name(argv)
provi_id = data[0]
log = '/home/kusanagi/'+argv+'/log/backup.log'
db = pymysql.connect("localhost","root",pwrd,"secure_vps")
cursor = db.cursor()
cursor.execute("select id from logs where provision_id=%d and status=0 and backup_type=%d" % (provi_id,backup_type))
res = cursor.fetchone()
record_id = res[0]
if result:
cursor.execute("update logs set status=1,message='Done' where provision_id=%d and id=%d" % (provi_id,record_id))
else:
cursor.execute("update logs set status=-1,message='Failed. See %s' where provision_id=%d and id=%d" % (log,provi_id,record_id))
db.commit()
db.close()
def compress_provision_dir(argv,chdir=''):
date = datetime.now()
today = date.strftime("%Y-%m-%d")
if chdir:
tarname = chdir+argv+'.'+today
else:
tarname = '/home/kusanagi/backup/'+argv+'.'+today
source_dir = '/home/kusanagi/'+argv
shutil.make_archive(tarname,"gztar",source_dir)
return tarname
def local_backup(argv):
append_log('/home/kusanagi/'+argv+'/log/backup.log', '--- Local backup')
backup_db(argv)
tarname = compress_provision_dir(argv)
tar_file=pathlib.Path(tarname+'.tar.gz')
if tar_file.exists():
update_backup_record(argv,0,1)
else:
update_backup_record(argv,0,0)
def check_ssh_conn(argv,remote_user,remote_host,remote_port,remote_pass):
cmd = 'sshpass -p "'+remote_pass+'" ssh -o StrictHostKeyChecking=no -p '+remote_port+' -q '+remote_user+'@'+remote_host+' exit;echo $?'
res = execute(cmd)
log = '/home/kusanagi/'+argv+'/log/backup.log'
if int(res) == 0:
#print('Connect OK \n')
pass
else:
append_log(log, 'Remote connection failed. Can not issue remote backup')
update_backup_record(argv,1,0)
sys.exit(1)
def remote_backup(argv, remote_user, remote_host, remote_port, remote_pass, remote_dest):
log = '/home/kusanagi/'+argv+'/log/backup.log'
append_log(log, '--- Remote backup')
check_ssh_conn(argv, remote_user, remote_host, remote_port, remote_pass)
backup_db(argv)
tarname = compress_provision_dir(argv,'/home/kusanagi/')
conf_ssh = '/etc/ssh/ssh_config'
with open(conf_ssh) as fp: lines = fp.read().splitlines()
for line in lines:
grep = re.findall(remote_host, line)
if grep:
break
if not grep:
#configure stricthostkey ssh
f = open(conf_ssh,"a+")
f.write('Host %s\n\tStrictHostKeyChecking no\n' % remote_host)
f.close()
cmd = 'sshpass -p "'+remote_pass+'" rsync --remove-source-files -azhe \'ssh -p'+remote_port+'\' '+tarname+'.tar.gz '+remote_user+'@'+remote_host+':'+remote_dest+' 2>> '+log+' ; echo $?'
res = execute(cmd)
if int(res) == 0:
update_backup_record(argv,1,1)
else:
update_backup_record(argv,1,0)
def drive_backup(argv,drive_dir):
log = '/home/kusanagi/'+argv+'/log/backup.log'
append_log(log,'--- Backup to Google Drive')
backup_db(argv)
tarname = compress_provision_dir(argv,'/home/kusanagi/')
cmd = 'rclone copy '+tarname+'.tar.gz GGD1:'+drive_dir+ ' 2>> '+log+' ; echo $?'
res = execute(cmd)
if int(res) == 0:
update_backup_record(argv,2,1)
else:
update_backup_record(argv,2,0)
os.remove(tarname+'.tar.gz')
def get_options(argv):
parser = argparse.ArgumentParser()
parser.add_argument('mode', type=str, choices=['local', 'remote', 'drive'])
parser.add_argument('options', nargs=argparse.REMAINDER)
return parser.parse_args(argv)
def main():
args=get_options(sys.argv[1:])
#pwrd = get_root_pass()
options = ' '.join(map(str, args.options))
if args.mode == 'local':
local_backup(*args.options)
elif args.mode == 'remote':
remote_backup(*args.options)
else:
drive_backup(*args.options)
if __name__ == '__main__':
main()
| [
6,
9,
12,
13,
15
] |
215 | 46adb1834f6013ca0f13a64f280182a805d76278 | <mask token>
| <mask token>
def parse_command_line(argv):
"""Parse command line argument. See -h option
:param argv: arguments on the command line must include caller file name.
"""
formatter_class = argparse.RawDescriptionHelpFormatter
parser = argparse.ArgumentParser(description=module, formatter_class=
formatter_class)
parser.add_argument('-n', '--name', dest='name', metavar='hostname',
nargs='?', type=str, required=True, help='Define a virtual hostname')
parser.add_argument('-o', '--os', dest='os', metavar='os', nargs='?',
required=True, type=str, choices=['debian', 'mint', 'centos', 'mac',
'w7'], help='Define a operating system')
parser.add_argument('--dry-run', help=
'Only print throughput, do not execute command.', action='store_true')
parser.add_argument('--legacy', help=
'Use legacy install (from official repository).', action='store_true')
parser.add_argument('--version', action='version', version=
'%(prog)s {}'.format(__version__))
parser.add_argument('-v', '--verbose', dest='verbose_count', action=
'count', default=0, help='increases log verbosity for each occurence.')
arguments = parser.parse_args(argv[1:])
log.setLevel(max(3 - arguments.verbose_count, 0) * 10)
return arguments
def main():
"""Main program. Sets up logging and do some work."""
logging.basicConfig(stream=sys.stderr, level=logging.DEBUG, format=
'%(name)s (%(levelname)s): %(message)s')
kvm = pathlib.Path('/disks/d/VMWare/KVM')
os = {'win7': '/disks/d/OS/Windows/Windows_7/Windows_7_LITE_X64.iso',
'win7_full':
'/disks/d/OS/Windows/Windows_7/fr_windows_7_ultimate_x64_dvd_x15-65928.iso'
, 'mint': '/disks/d/OS/Unix/Mint/linuxmint-18.3-cinnamon-64bit.iso',
'solaris': '/disks/d/OS/Unix/Solaris/11/sol-11_3-text-x86.iso'}
try:
arguments = parse_command_line(sys.argv)
server = arguments.name
os = arguments.os
legacy = arguments.legacy
dry = arguments.dry_run
if kvm.path.exists():
kvm_disk = kvm_path + server
command = (
'virt-install --ram 2048 --disk path=${DIR_HOST}/${HOST}.qcow2,size=8 --vcpus 2 --os-type linux --os-variant ubuntuquantal --network bridge=virbr0'
)
if dry:
print(command)
print(kvm_disk)
except KeyboardInterrupt:
log.error('Program interrupted!')
finally:
logging.shutdown()
<mask token>
| <mask token>
module = sys.modules['__main__'].__file__
__author__ = 'FFX'
__version__ = '1.0'
log = logging.getLogger(module)
def parse_command_line(argv):
"""Parse command line argument. See -h option
:param argv: arguments on the command line must include caller file name.
"""
formatter_class = argparse.RawDescriptionHelpFormatter
parser = argparse.ArgumentParser(description=module, formatter_class=
formatter_class)
parser.add_argument('-n', '--name', dest='name', metavar='hostname',
nargs='?', type=str, required=True, help='Define a virtual hostname')
parser.add_argument('-o', '--os', dest='os', metavar='os', nargs='?',
required=True, type=str, choices=['debian', 'mint', 'centos', 'mac',
'w7'], help='Define a operating system')
parser.add_argument('--dry-run', help=
'Only print throughput, do not execute command.', action='store_true')
parser.add_argument('--legacy', help=
'Use legacy install (from official repository).', action='store_true')
parser.add_argument('--version', action='version', version=
'%(prog)s {}'.format(__version__))
parser.add_argument('-v', '--verbose', dest='verbose_count', action=
'count', default=0, help='increases log verbosity for each occurence.')
arguments = parser.parse_args(argv[1:])
log.setLevel(max(3 - arguments.verbose_count, 0) * 10)
return arguments
def main():
"""Main program. Sets up logging and do some work."""
logging.basicConfig(stream=sys.stderr, level=logging.DEBUG, format=
'%(name)s (%(levelname)s): %(message)s')
kvm = pathlib.Path('/disks/d/VMWare/KVM')
os = {'win7': '/disks/d/OS/Windows/Windows_7/Windows_7_LITE_X64.iso',
'win7_full':
'/disks/d/OS/Windows/Windows_7/fr_windows_7_ultimate_x64_dvd_x15-65928.iso'
, 'mint': '/disks/d/OS/Unix/Mint/linuxmint-18.3-cinnamon-64bit.iso',
'solaris': '/disks/d/OS/Unix/Solaris/11/sol-11_3-text-x86.iso'}
try:
arguments = parse_command_line(sys.argv)
server = arguments.name
os = arguments.os
legacy = arguments.legacy
dry = arguments.dry_run
if kvm.path.exists():
kvm_disk = kvm_path + server
command = (
'virt-install --ram 2048 --disk path=${DIR_HOST}/${HOST}.qcow2,size=8 --vcpus 2 --os-type linux --os-variant ubuntuquantal --network bridge=virbr0'
)
if dry:
print(command)
print(kvm_disk)
except KeyboardInterrupt:
log.error('Program interrupted!')
finally:
logging.shutdown()
if __name__ == '__main__':
sys.exit(main())
| import sys
import argparse
import logging
from pathlib import Path
module = sys.modules['__main__'].__file__
__author__ = 'FFX'
__version__ = '1.0'
log = logging.getLogger(module)
def parse_command_line(argv):
"""Parse command line argument. See -h option
:param argv: arguments on the command line must include caller file name.
"""
formatter_class = argparse.RawDescriptionHelpFormatter
parser = argparse.ArgumentParser(description=module, formatter_class=
formatter_class)
parser.add_argument('-n', '--name', dest='name', metavar='hostname',
nargs='?', type=str, required=True, help='Define a virtual hostname')
parser.add_argument('-o', '--os', dest='os', metavar='os', nargs='?',
required=True, type=str, choices=['debian', 'mint', 'centos', 'mac',
'w7'], help='Define a operating system')
parser.add_argument('--dry-run', help=
'Only print throughput, do not execute command.', action='store_true')
parser.add_argument('--legacy', help=
'Use legacy install (from official repository).', action='store_true')
parser.add_argument('--version', action='version', version=
'%(prog)s {}'.format(__version__))
parser.add_argument('-v', '--verbose', dest='verbose_count', action=
'count', default=0, help='increases log verbosity for each occurence.')
arguments = parser.parse_args(argv[1:])
log.setLevel(max(3 - arguments.verbose_count, 0) * 10)
return arguments
def main():
"""Main program. Sets up logging and do some work."""
logging.basicConfig(stream=sys.stderr, level=logging.DEBUG, format=
'%(name)s (%(levelname)s): %(message)s')
kvm = pathlib.Path('/disks/d/VMWare/KVM')
os = {'win7': '/disks/d/OS/Windows/Windows_7/Windows_7_LITE_X64.iso',
'win7_full':
'/disks/d/OS/Windows/Windows_7/fr_windows_7_ultimate_x64_dvd_x15-65928.iso'
, 'mint': '/disks/d/OS/Unix/Mint/linuxmint-18.3-cinnamon-64bit.iso',
'solaris': '/disks/d/OS/Unix/Solaris/11/sol-11_3-text-x86.iso'}
try:
arguments = parse_command_line(sys.argv)
server = arguments.name
os = arguments.os
legacy = arguments.legacy
dry = arguments.dry_run
if kvm.path.exists():
kvm_disk = kvm_path + server
command = (
'virt-install --ram 2048 --disk path=${DIR_HOST}/${HOST}.qcow2,size=8 --vcpus 2 --os-type linux --os-variant ubuntuquantal --network bridge=virbr0'
)
if dry:
print(command)
print(kvm_disk)
except KeyboardInterrupt:
log.error('Program interrupted!')
finally:
logging.shutdown()
if __name__ == '__main__':
sys.exit(main())
| #!/usr/bin/python3
# encoding: utf-8
import sys
import argparse
import logging
from pathlib import Path
module = sys.modules['__main__'].__file__
__author__ = 'FFX'
__version__ = '1.0'
log = logging.getLogger(module)
def parse_command_line(argv):
"""Parse command line argument. See -h option
:param argv: arguments on the command line must include caller file name.
"""
formatter_class = argparse.RawDescriptionHelpFormatter
parser = argparse.ArgumentParser(description=module,formatter_class=formatter_class)
parser.add_argument('-n', '--name', dest="name", metavar="hostname",
nargs='?',
type=str,
required=True,
help="Define a virtual hostname")
parser.add_argument('-o', '--os', dest="os", metavar="os",
nargs='?',
required=True,
type=str,
choices=['debian','mint','centos','mac','w7'],
help="Define a operating system")
parser.add_argument("--dry-run", help="Only print throughput, do not execute command.",
action="store_true")
parser.add_argument("--legacy", help="Use legacy install (from official repository).",
action="store_true")
parser.add_argument("--version", action="version",
version="%(prog)s {}".format(__version__))
parser.add_argument("-v", "--verbose", dest="verbose_count",
action="count", default=0,
help="increases log verbosity for each occurence.")
arguments = parser.parse_args(argv[1:])
# Sets log level to WARN going more verbose for each new -v.
log.setLevel(max(3 - arguments.verbose_count, 0) * 10)
return arguments
def main():
"""Main program. Sets up logging and do some work."""
logging.basicConfig(stream=sys.stderr, level=logging.DEBUG,
format='%(name)s (%(levelname)s): %(message)s')
kvm = pathlib.Path("/disks/d/VMWare/KVM")
os = {
'win7':'/disks/d/OS/Windows/Windows_7/Windows_7_LITE_X64.iso',
'win7_full':'/disks/d/OS/Windows/Windows_7/fr_windows_7_ultimate_x64_dvd_x15-65928.iso',
'mint':'/disks/d/OS/Unix/Mint/linuxmint-18.3-cinnamon-64bit.iso',
'solaris':'/disks/d/OS/Unix/Solaris/11/sol-11_3-text-x86.iso'
}
try:
arguments = parse_command_line(sys.argv)
# Assign args to variables
server = arguments.name
os = arguments.os
legacy = arguments.legacy
dry = arguments.dry_run
if kvm.path.exists():
kvm_disk = kvm_path + server
command = "virt-install --ram 2048 --disk path=${DIR_HOST}/${HOST}.qcow2,size=8 --vcpus 2 --os-type linux --os-variant ubuntuquantal --network bridge=virbr0"
if dry:
print(command)
print(kvm_disk)
except KeyboardInterrupt:
log.error('Program interrupted!')
finally:
logging.shutdown()
if __name__ == "__main__":
sys.exit(main())
| [
0,
2,
4,
5,
6
] |
216 | c63e5a2178e82ec6e0e1e91a81145afb735bf7bf | <mask token>
class MyTestCase(unittest.TestCase):
def test_1(self):
a = t(2)
b = t(1)
a.left = b
self.assertEqual(sr.searchRange(a, 0, 4), [1, 2])
def test_2(self):
a = t(20)
b = t(1)
a.left = b
c = t(40)
a.right = c
d = t(35)
c.left = d
self.assertEqual(sr.searchRange(a, 17, 37), [20, 35])
<mask token>
| <mask token>
class MyTestCase(unittest.TestCase):
def test_1(self):
a = t(2)
b = t(1)
a.left = b
self.assertEqual(sr.searchRange(a, 0, 4), [1, 2])
def test_2(self):
a = t(20)
b = t(1)
a.left = b
c = t(40)
a.right = c
d = t(35)
c.left = d
self.assertEqual(sr.searchRange(a, 17, 37), [20, 35])
if __name__ == '__main__':
unittest.main()
| __author__ = 'lei'
<mask token>
class MyTestCase(unittest.TestCase):
def test_1(self):
a = t(2)
b = t(1)
a.left = b
self.assertEqual(sr.searchRange(a, 0, 4), [1, 2])
def test_2(self):
a = t(20)
b = t(1)
a.left = b
c = t(40)
a.right = c
d = t(35)
c.left = d
self.assertEqual(sr.searchRange(a, 17, 37), [20, 35])
if __name__ == '__main__':
unittest.main()
| __author__ = 'lei'
import unittest
from ch3.node import TreeNode as t
import ch3.searchRange as sr
class MyTestCase(unittest.TestCase):
def test_1(self):
a = t(2)
b = t(1)
a.left = b
self.assertEqual(sr.searchRange(a, 0, 4), [1, 2])
def test_2(self):
a = t(20)
b = t(1)
a.left = b
c = t(40)
a.right = c
d = t(35)
c.left = d
self.assertEqual(sr.searchRange(a, 17, 37), [20, 35])
if __name__ == '__main__':
unittest.main()
| __author__ = 'lei'
import unittest
from ch3.node import TreeNode as t
import ch3.searchRange as sr
class MyTestCase(unittest.TestCase):
def test_1(self):
a = t(2)
b=t(1)
a.left = b
self.assertEqual(sr.searchRange(a,0,4), [1,2])
def test_2(self):
a = t(20)
b = t(1)
a.left = b
c = t(40)
a.right = c
d = t(35)
c.left = d
self.assertEqual(sr.searchRange(a,17,37),[20,35])
if __name__ == '__main__':
unittest.main()
| [
3,
4,
5,
6,
7
] |
217 | b77da75b01e96ff89f873f4c5764a62cf68cd576 | <mask token>
class SeriesListSerializer(serializers.ModelSerializer):
class Meta:
model = Serie
fields = 'name',
class CatalogCoinListSerializer(serializers.ModelSerializer):
class Meta:
model = CatalogCoin
fields = ('id', 'face_value', 'currency', 'country', 'year',
'theme', 'mint', 'serie', 'collection', 'exchange', 'wishlist')
serie = serializers.SlugRelatedField(slug_field='name', read_only=True)
collection = serializers.IntegerField(read_only=True)
exchange = serializers.IntegerField(read_only=True)
wishlist = serializers.IntegerField(read_only=True)
class CatalogCoinSerializer(serializers.ModelSerializer):
class Meta:
model = CatalogCoin
fields = '__all__'
class CoinListSerializer(serializers.ModelSerializer):
class Meta:
model = Coin
fields = 'id', 'catalog_coin', 'owner', 'status'
catalog_coin = CatalogCoinListSerializer()
class CoinSerializer(serializers.ModelSerializer):
class Meta:
model = Coin
fields = '__all__'
| <mask token>
class CountriesListSerializer(serializers.ModelSerializer):
class Meta:
model = Country
fields = 'name', 'flag'
class SeriesListSerializer(serializers.ModelSerializer):
class Meta:
model = Serie
fields = 'name',
class CatalogCoinListSerializer(serializers.ModelSerializer):
class Meta:
model = CatalogCoin
fields = ('id', 'face_value', 'currency', 'country', 'year',
'theme', 'mint', 'serie', 'collection', 'exchange', 'wishlist')
serie = serializers.SlugRelatedField(slug_field='name', read_only=True)
collection = serializers.IntegerField(read_only=True)
exchange = serializers.IntegerField(read_only=True)
wishlist = serializers.IntegerField(read_only=True)
class CatalogCoinSerializer(serializers.ModelSerializer):
class Meta:
model = CatalogCoin
fields = '__all__'
class CoinListSerializer(serializers.ModelSerializer):
class Meta:
model = Coin
fields = 'id', 'catalog_coin', 'owner', 'status'
catalog_coin = CatalogCoinListSerializer()
class CoinSerializer(serializers.ModelSerializer):
class Meta:
model = Coin
fields = '__all__'
| <mask token>
__all__ = ('CatalogCoinListSerializer', 'CatalogCoinSerializer',
'SeriesListSerializer', 'CoinListSerializer', 'CoinSerializer',
'CountriesListSerializer')
class CountriesListSerializer(serializers.ModelSerializer):
class Meta:
model = Country
fields = 'name', 'flag'
class SeriesListSerializer(serializers.ModelSerializer):
class Meta:
model = Serie
fields = 'name',
class CatalogCoinListSerializer(serializers.ModelSerializer):
class Meta:
model = CatalogCoin
fields = ('id', 'face_value', 'currency', 'country', 'year',
'theme', 'mint', 'serie', 'collection', 'exchange', 'wishlist')
serie = serializers.SlugRelatedField(slug_field='name', read_only=True)
collection = serializers.IntegerField(read_only=True)
exchange = serializers.IntegerField(read_only=True)
wishlist = serializers.IntegerField(read_only=True)
class CatalogCoinSerializer(serializers.ModelSerializer):
class Meta:
model = CatalogCoin
fields = '__all__'
class CoinListSerializer(serializers.ModelSerializer):
class Meta:
model = Coin
fields = 'id', 'catalog_coin', 'owner', 'status'
catalog_coin = CatalogCoinListSerializer()
class CoinSerializer(serializers.ModelSerializer):
class Meta:
model = Coin
fields = '__all__'
| from rest_framework import serializers
from .models import *
__all__ = ('CatalogCoinListSerializer', 'CatalogCoinSerializer',
'SeriesListSerializer', 'CoinListSerializer', 'CoinSerializer',
'CountriesListSerializer')
class CountriesListSerializer(serializers.ModelSerializer):
class Meta:
model = Country
fields = 'name', 'flag'
class SeriesListSerializer(serializers.ModelSerializer):
class Meta:
model = Serie
fields = 'name',
class CatalogCoinListSerializer(serializers.ModelSerializer):
class Meta:
model = CatalogCoin
fields = ('id', 'face_value', 'currency', 'country', 'year',
'theme', 'mint', 'serie', 'collection', 'exchange', 'wishlist')
serie = serializers.SlugRelatedField(slug_field='name', read_only=True)
collection = serializers.IntegerField(read_only=True)
exchange = serializers.IntegerField(read_only=True)
wishlist = serializers.IntegerField(read_only=True)
class CatalogCoinSerializer(serializers.ModelSerializer):
class Meta:
model = CatalogCoin
fields = '__all__'
class CoinListSerializer(serializers.ModelSerializer):
class Meta:
model = Coin
fields = 'id', 'catalog_coin', 'owner', 'status'
catalog_coin = CatalogCoinListSerializer()
class CoinSerializer(serializers.ModelSerializer):
class Meta:
model = Coin
fields = '__all__'
| from rest_framework import serializers
from .models import *
__all__ = (
'CatalogCoinListSerializer', 'CatalogCoinSerializer', 'SeriesListSerializer', 'CoinListSerializer',
'CoinSerializer', 'CountriesListSerializer',
)
class CountriesListSerializer(serializers.ModelSerializer):
class Meta:
model = Country
fields = ('name', 'flag',)
class SeriesListSerializer(serializers.ModelSerializer):
class Meta:
model = Serie
fields = ('name',)
class CatalogCoinListSerializer(serializers.ModelSerializer):
class Meta:
model = CatalogCoin
fields = (
'id', 'face_value', 'currency', 'country', 'year', 'theme', 'mint', 'serie', 'collection', 'exchange',
'wishlist',
)
serie = serializers.SlugRelatedField(slug_field='name', read_only=True)
collection = serializers.IntegerField(read_only=True)
exchange = serializers.IntegerField(read_only=True)
wishlist = serializers.IntegerField(read_only=True)
class CatalogCoinSerializer(serializers.ModelSerializer):
class Meta:
model = CatalogCoin
fields = '__all__'
class CoinListSerializer(serializers.ModelSerializer):
class Meta:
model = Coin
fields = ('id', 'catalog_coin', 'owner', 'status',)
catalog_coin = CatalogCoinListSerializer()
class CoinSerializer(serializers.ModelSerializer):
class Meta:
model = Coin
fields = '__all__'
| [
7,
8,
9,
10,
11
] |
218 | 1f0695f0e9745912d8ee3a87e6c9b1272e9ebbae | <mask token>
| <mask token>
with open(filename, 'a') as handle:
handle.write(str(current_time))
handle.write('\n')
| <mask token>
filename = 'record_time.txt'
current_time = time.strftime('%a %H:%M:%S')
with open(filename, 'a') as handle:
handle.write(str(current_time))
handle.write('\n')
| <mask token>
import time
filename = 'record_time.txt'
current_time = time.strftime('%a %H:%M:%S')
with open(filename, 'a') as handle:
handle.write(str(current_time))
handle.write('\n')
| """
Writes day of the week and time to a file.
Script written for crontab tutorial.
Author: Jessica Yung 2016
"""
import time
filename = "record_time.txt"
# Records time in format Sun 10:00:00
current_time = time.strftime('%a %H:%M:%S')
# Append output to file. 'a' is append mode.
with open(filename, 'a') as handle:
# Write (Append) output to a line
handle.write(str(current_time))
# Newline to separate different lines of output
handle.write('\n')
| [
0,
1,
2,
3,
4
] |
219 | 142a2ba3ec2f6b35f4339ed9fffe7357c1a85fa0 | <mask token>
def search(url):
browser = webdriver.Chrome(executable_path=
'C:\\Users\\inaee\\Downloads\\chromedriver_win32\\chromedriver.exe')
browser.get(url)
time.sleep(1)
element = browser.find_element_by_tag_name('body')
for i in range(30):
element.send_keys(Keys.PAGE_DOWN)
time.sleep(0.2)
browser.find_element_by_id('smb').click()
for i in range(50):
element.send_keys(Keys.PAGE_DOWN)
time.sleep(0.2)
time.sleep(1)
source = browser.page_source
browser.close()
return source
<mask token>
| <mask token>
def search(url):
browser = webdriver.Chrome(executable_path=
'C:\\Users\\inaee\\Downloads\\chromedriver_win32\\chromedriver.exe')
browser.get(url)
time.sleep(1)
element = browser.find_element_by_tag_name('body')
for i in range(30):
element.send_keys(Keys.PAGE_DOWN)
time.sleep(0.2)
browser.find_element_by_id('smb').click()
for i in range(50):
element.send_keys(Keys.PAGE_DOWN)
time.sleep(0.2)
time.sleep(1)
source = browser.page_source
browser.close()
return source
def download_image(link):
headers = {'User-Agent': ua.random}
try:
r = requests.get('https://www.google.com' + link.get('href'),
headers=headers)
except:
print('Cannot get link.')
title = str(fromstring(r.content).findtext('.//title'))
link = title.split(' ')[-1]
print('At : ' + os.getcwd() + ', Downloading from ' + link)
try:
if link.split('.')[-1] == ('jpg' or 'png' or 'jpeg'):
urllib.request.urlretrieve(link, link.split('/')[-1])
except:
pass
<mask token>
| <mask token>
def search(url):
browser = webdriver.Chrome(executable_path=
'C:\\Users\\inaee\\Downloads\\chromedriver_win32\\chromedriver.exe')
browser.get(url)
time.sleep(1)
element = browser.find_element_by_tag_name('body')
for i in range(30):
element.send_keys(Keys.PAGE_DOWN)
time.sleep(0.2)
browser.find_element_by_id('smb').click()
for i in range(50):
element.send_keys(Keys.PAGE_DOWN)
time.sleep(0.2)
time.sleep(1)
source = browser.page_source
browser.close()
return source
def download_image(link):
headers = {'User-Agent': ua.random}
try:
r = requests.get('https://www.google.com' + link.get('href'),
headers=headers)
except:
print('Cannot get link.')
title = str(fromstring(r.content).findtext('.//title'))
link = title.split(' ')[-1]
print('At : ' + os.getcwd() + ', Downloading from ' + link)
try:
if link.split('.')[-1] == ('jpg' or 'png' or 'jpeg'):
urllib.request.urlretrieve(link, link.split('/')[-1])
except:
pass
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('keyword', help='the keyword to search')
args = parser.parse_args()
sys.setrecursionlimit(100000000)
query = args.keyword
url = ('https://www.google.com/search?as_st=y&tbm=isch&as_q=' + query +
'&as_epq=&as_oq=&as_eq=&cr=&as_sitesearch=&safe=images&tbs=isz:lt,islt:svga,itp:photo,ift:jpg'
)
source = search(url)
soup = BeautifulSoup(str(source), 'html.parser')
ua = UserAgent()
if not os.path.isdir(args.keyword):
os.makedirs(args.keyword)
os.chdir(str(os.getcwd()) + '/' + str(args.keyword))
links = soup.find_all('a', class_='rg_l')
with Pool() as pool:
pool.map(download_image, links)
| import requests
import time
import urllib
import argparse
from bs4 import BeautifulSoup
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from fake_useragent import UserAgent
from multiprocessing import Pool
from lxml.html import fromstring
import os, sys
def search(url):
browser = webdriver.Chrome(executable_path=
'C:\\Users\\inaee\\Downloads\\chromedriver_win32\\chromedriver.exe')
browser.get(url)
time.sleep(1)
element = browser.find_element_by_tag_name('body')
for i in range(30):
element.send_keys(Keys.PAGE_DOWN)
time.sleep(0.2)
browser.find_element_by_id('smb').click()
for i in range(50):
element.send_keys(Keys.PAGE_DOWN)
time.sleep(0.2)
time.sleep(1)
source = browser.page_source
browser.close()
return source
def download_image(link):
headers = {'User-Agent': ua.random}
try:
r = requests.get('https://www.google.com' + link.get('href'),
headers=headers)
except:
print('Cannot get link.')
title = str(fromstring(r.content).findtext('.//title'))
link = title.split(' ')[-1]
print('At : ' + os.getcwd() + ', Downloading from ' + link)
try:
if link.split('.')[-1] == ('jpg' or 'png' or 'jpeg'):
urllib.request.urlretrieve(link, link.split('/')[-1])
except:
pass
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('keyword', help='the keyword to search')
args = parser.parse_args()
sys.setrecursionlimit(100000000)
query = args.keyword
url = ('https://www.google.com/search?as_st=y&tbm=isch&as_q=' + query +
'&as_epq=&as_oq=&as_eq=&cr=&as_sitesearch=&safe=images&tbs=isz:lt,islt:svga,itp:photo,ift:jpg'
)
source = search(url)
soup = BeautifulSoup(str(source), 'html.parser')
ua = UserAgent()
if not os.path.isdir(args.keyword):
os.makedirs(args.keyword)
os.chdir(str(os.getcwd()) + '/' + str(args.keyword))
links = soup.find_all('a', class_='rg_l')
with Pool() as pool:
pool.map(download_image, links)
| import requests
import time
import urllib
import argparse
from bs4 import BeautifulSoup
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from fake_useragent import UserAgent
from multiprocessing import Pool
from lxml.html import fromstring
import os, sys
#text = 'chowchowbaby'
#url='https://www.google.co.kr/search?q=' + text + '&source=lnms&tbm=isch&sa=X&ved=0ahUKEwiF2fPLn7zdAhUBEbwKHSLWBowQ_AUICigB&biw=809&bih=868&dpr=1.13'
def search(url):
#Create a browser
browser=webdriver.Chrome(executable_path='C:\\Users\\inaee\\Downloads\\chromedriver_win32\\chromedriver.exe')
#Open the link
browser.get(url)
time.sleep(1)
element=browser.find_element_by_tag_name("body")
#Scroll down
for i in range(30):
element.send_keys(Keys.PAGE_DOWN)
time.sleep(0.2)
browser.find_element_by_id("smb").click()
for i in range(50):
element.send_keys(Keys.PAGE_DOWN)
time.sleep(0.2)
time.sleep(1)
#Get page source and close the browser
source=browser.page_source
browser.close()
return source
def download_image(link):
# Use a random user agent header
headers = {"User-Agent": ua.random}
# Get the image link
try:
r = requests.get("https://www.google.com" + link.get("href"), headers=headers)
except:
print("Cannot get link.")
title = str(fromstring(r.content).findtext(".//title"))
link = title.split(" ")[-1]
# Download the image
print("At : " + os.getcwd() + ", Downloading from " + link)
try:
if link.split(".")[-1] == ('jpg' or 'png' or 'jpeg'):
urllib.request.urlretrieve(link, link.split("/")[-1])
except:
pass
if __name__ == "__main__":
# parse command line options
parser = argparse.ArgumentParser()
parser.add_argument("keyword", help="the keyword to search")
args = parser.parse_args()
# set stack limit
sys.setrecursionlimit(100000000)
# get user input and search on google
query = args.keyword
#query = input("Enter the name you want to search")
url = "https://www.google.com/search?as_st=y&tbm=isch&as_q=" + query + \
"&as_epq=&as_oq=&as_eq=&cr=&as_sitesearch=&safe=images&tbs=isz:lt,islt:svga,itp:photo,ift:jpg"
source = search(url)
# Parse the page source and download pics
soup = BeautifulSoup(str(source), "html.parser")
ua = UserAgent()
# check directory and create if necessary
if not os.path.isdir(args.keyword):
os.makedirs(args.keyword)
os.chdir(str(os.getcwd()) + "/" + str(args.keyword))
# get the links
links = soup.find_all("a", class_="rg_l")
# open some processes to download
with Pool() as pool:
pool.map(download_image, links)
# 검색어
#search = 'chowchowbaby'
#url='https://www.google.co.kr/search?q=' + search + '&source=lnms&tbm=isch&sa=X&ved=0ahUKEwiF2fPLn7zdAhUBEbwKHSLWBowQ_AUICigB&biw=809&bih=868&dpr=1.13'
# url
#driver = webdriver.Chrome(executable_path="C:\\Users\\inaee\\Downloads\\chromedriver_win32\\chromedriver.exe")
#driver.get(url)
#driver.implicitly_wait(2)
#num_of_pagedowns = 50
#elem = driver.find_element_by_xpath('/html/body')
#i = 0
#count = 1
#img = driver.find_elements_by_tag_name("img")
#while i < num_of_pagedowns:
#for item in img:
# if(count>0 and count<502):
# elem.send_keys(Keys.DOWN)
# time.sleep(1)
# full_name = "C:\\Program Files\\Python35\\강아지크롤러\\chowchowbaby\\" + str(count) + "_chowchowbaby.jpg"
# try:
# urllib.request.urlretrieve(item.get_attribute('src'), full_name)
# tfp=open(full_name,url)
# print(item.get_attribute('src')[:30] + " : ")
# except:
# urllib.request.urlretrieve(item.get_attribute('data-src'), full_name)
# tfp=open(full_name,url)
# print(item.get_attribute('data-src')[:30] + " : ")
# count = count+1
# i =i+1
#driver.Quit()
#print("Done.")
| [
1,
2,
3,
4,
5
] |
220 | 2a19c2d6e51e9c123236c58f82de1a39e5db40f4 | import numpy as np
# Copyright 2011 University of Bonn
# Author: Hannes Schulz
def cnan(x):
""" check for not-a-number in parameter x """
if np.isnan(x).sum()>0:
import pdb
pdb.set_trace()
def get_curve_3D(eig, alpha=0.25,g23=0.5,g12=0.5): # renumerated according to sato et al: l3 is smallest
"""
return the vesselness measure based on eigenvalues of normalized hessians in eig
"""
#return sato(eig,alpha,g23, g12)
return frangi(eig)
def frangi_memory_hungry(eig):
print "Finding 3D curvilinear structures using Frangi et al..."
l1 = eig["lambda1"] # in fangi et al, we have |l1| <= |l2| <= |l3|
l2 = eig["lambda2"]
l3 = eig["lambda3"]
alpha = 0.5
beta = 0.5
RB = np.abs(l1) / np.sqrt(np.abs(l2 * l3))
RA = np.abs(l2) / np.abs(l3)
S = np.sqrt(l1**2+l2**2+l3**2)
c = np.max(S)/3. # half of maximum frobenius norm of hessian says fangi et al.
V = (1-np.exp(RA**2)/(-2. * alpha**2)) * np.exp(RB**2/(-2. * beta**2)) * (1-np.exp(S**2/(-2. * c**2)))
P = (l2 >= 0.) + (l3 >= 0.)
V[P] = 0
V[V<0] = 0
print "done."
return V
def frangi(eig):
print "Finding 3D curvilinear structures using Frangi et al..."
l1 = eig["lambda1"] # in fangi et al, we have |l1| <= |l2| <= |l3|
l2 = eig["lambda2"]
l3 = eig["lambda3"]
alpha = 0.5
beta = 0.5
RB = np.abs(l1)
RB /= np.sqrt(np.abs(l2*l3))
RA = np.abs(l2)
RA /= np.abs(l3)
S = l1.copy()
S **= 2
S += l2**2
S += l3**2
np.sqrt(S,S)
c = np.max(S)/3. # half of maximum frobenius norm of hessian says fangi et al.
V = 1-np.exp(RA**2)
V /= -2. * alpha**2
V *= np.exp(RB**2/(-2. * beta**2))
V *= 1-np.exp(S**2/(-2. * c**2))
#V = (1-np.exp(RA**2)/(-2. * alpha**2)) * np.exp(RB**2/(-2. * beta**2)) * (1-np.exp(S**2/(-2. * c**2)))
P = (l2 >= 0.) + (l3 >= 0.)
V[P] = 0
V[V<0] = 0
print "done."
return V
def sato(eig, alpha=0.25,g23=0.5,g12=0.5): # renumerated according to sato et al: l3 is smallest
print "Finding 3D curvilinear structures..."
l3 = eig["lambda1"] # in sato et al, l3 < l2 < l1; but lapack gives them in ascending order(!)
l2 = eig["lambda2"]
l1 = eig["lambda3"]
#gc.collect()
#alpha = 0.25 # 0.0: cannot deal well with curved lines. Sato: 0.25
#g23 = 0.5 # Sato: robust btw. 0.5,1.0. sharpness of
# # selectivity for cross-section isotropy
#g12 = 0.5 # Sato: robust btw. 0.5,1.0. gives w12
# # asymmetrical characteristic in neg/pos regions of l1
# # l1<0 and |l1| large: blob, not line!
l2a = np.abs(l2)
factA = np.abs(l3) * (l2/l3)**g23
factB = (1+l1/l2a)**g12
factC = (1-alpha*l1/l2a)**g12
A = (l3<l2) * (l2<l1) * (l1 <= 0)
B = (l3<l2) * (l2<0) * (0<l1) * (l1<l2a/alpha)
l123 = np.zeros(A.shape, dtype = "float32")
l123[A] = (factA*factB)[A]
l123[B] = (factA*factC)[B]
cnan(l123)
print "Done."
return l123
| null | null | null | null | [
0
] |
221 | 550f5ad4fef77d5795db0393ae0701f679143e72 | <mask token>
| <mask token>
if os.environ.get('MOCKPROGRAM_INOUT_FILE_OVERRIDE'):
mockProgramInOutFilePath = os.environ.get('MOCKPROGRAM_INOUT_FILE_OVERRIDE'
)
else:
mockProgramInOutFilePath = '.mockprogram_inout.txt'
if not os.path.exists(mockProgramInOutFilePath):
print('Error: ' + mockProgramInOutFilePath + ' is missing!')
sys.exit(1)
<mask token>
if len(mockprogramInoutArray) and mockprogramInoutArray[-1] == '':
mockprogramInoutArray = mockprogramInoutArray[:-1]
if len(mockprogramInoutArray) < 3:
print('Error: ' + mockProgramInOutFilePath +
' has less than three lines:\n-------------\n' + mockprogramInout +
'-------------')
sys.exit(2)
<mask token>
if expectedInputLine.find('MOCK_PROGRAM_INPUT:') != 0:
print("Error, first line = '" + expectedInputLine +
"', does not match ^MOCK_PROGRAM_INPUT:")
sys.exit(3)
<mask token>
if inputArgs != expectedInput:
print("Error, input args='" + inputArgs + "' does not match expected='" +
expectedInput + "'")
sys.exit(4)
<mask token>
if returnCodeLine.find('MOCK_PROGRAM_RETURN:') != 0:
print("Error, second line = '" + returnCodeLine +
"', does not match ^MOCK_PROGRAM_RETURN:")
sys.exit(5)
<mask token>
if outputLine.find('MOCK_PROGRAM_OUTPUT:') != 0:
print("Error, third line = '" + outputLine +
"', does not match ^MOCK_PROGRAM_OUTPUT:")
sys.exit(6)
<mask token>
if len(mockprogramInoutArray) > 3:
for line in mockprogramInoutArray[3:]:
if line.find('MOCK_PROGRAM_INPUT:') == 0:
break
outputStr = outputStr + '\n' + line
numLinesOuput = numLinesOuput + 1
print(outputStr)
<mask token>
if len(mockprogramInoutArray) > lineLineIndex:
open(mockProgramInOutFilePath, 'w').write('\n'.join(
mockprogramInoutArray[lineLineIndex:]) + '\n')
else:
open(mockProgramInOutFilePath, 'w').write('')
sys.exit(int(returnCode))
| <mask token>
inputArgs = ' '.join(sys.argv[1:])
if os.environ.get('MOCKPROGRAM_INOUT_FILE_OVERRIDE'):
mockProgramInOutFilePath = os.environ.get('MOCKPROGRAM_INOUT_FILE_OVERRIDE'
)
else:
mockProgramInOutFilePath = '.mockprogram_inout.txt'
if not os.path.exists(mockProgramInOutFilePath):
print('Error: ' + mockProgramInOutFilePath + ' is missing!')
sys.exit(1)
mockprogramInout = open(mockProgramInOutFilePath, 'r').read()
mockprogramInoutArray = mockprogramInout.splitlines()
if len(mockprogramInoutArray) and mockprogramInoutArray[-1] == '':
mockprogramInoutArray = mockprogramInoutArray[:-1]
if len(mockprogramInoutArray) < 3:
print('Error: ' + mockProgramInOutFilePath +
' has less than three lines:\n-------------\n' + mockprogramInout +
'-------------')
sys.exit(2)
expectedInputLine = mockprogramInoutArray[0]
if expectedInputLine.find('MOCK_PROGRAM_INPUT:') != 0:
print("Error, first line = '" + expectedInputLine +
"', does not match ^MOCK_PROGRAM_INPUT:")
sys.exit(3)
expectedInput = expectedInputLine.replace('MOCK_PROGRAM_INPUT:', '').strip()
if inputArgs != expectedInput:
print("Error, input args='" + inputArgs + "' does not match expected='" +
expectedInput + "'")
sys.exit(4)
returnCodeLine = mockprogramInoutArray[1]
if returnCodeLine.find('MOCK_PROGRAM_RETURN:') != 0:
print("Error, second line = '" + returnCodeLine +
"', does not match ^MOCK_PROGRAM_RETURN:")
sys.exit(5)
returnCode = returnCodeLine.replace('MOCK_PROGRAM_RETURN:', '').strip()
outputLine = mockprogramInoutArray[2]
if outputLine.find('MOCK_PROGRAM_OUTPUT:') != 0:
print("Error, third line = '" + outputLine +
"', does not match ^MOCK_PROGRAM_OUTPUT:")
sys.exit(6)
outputStr = outputLine.replace('MOCK_PROGRAM_OUTPUT: ', '')
numLinesOuput = 1
if len(mockprogramInoutArray) > 3:
for line in mockprogramInoutArray[3:]:
if line.find('MOCK_PROGRAM_INPUT:') == 0:
break
outputStr = outputStr + '\n' + line
numLinesOuput = numLinesOuput + 1
print(outputStr)
lineLineIndex = 2 + numLinesOuput
if len(mockprogramInoutArray) > lineLineIndex:
open(mockProgramInOutFilePath, 'w').write('\n'.join(
mockprogramInoutArray[lineLineIndex:]) + '\n')
else:
open(mockProgramInOutFilePath, 'w').write('')
sys.exit(int(returnCode))
| import sys
import os
inputArgs = ' '.join(sys.argv[1:])
if os.environ.get('MOCKPROGRAM_INOUT_FILE_OVERRIDE'):
mockProgramInOutFilePath = os.environ.get('MOCKPROGRAM_INOUT_FILE_OVERRIDE'
)
else:
mockProgramInOutFilePath = '.mockprogram_inout.txt'
if not os.path.exists(mockProgramInOutFilePath):
print('Error: ' + mockProgramInOutFilePath + ' is missing!')
sys.exit(1)
mockprogramInout = open(mockProgramInOutFilePath, 'r').read()
mockprogramInoutArray = mockprogramInout.splitlines()
if len(mockprogramInoutArray) and mockprogramInoutArray[-1] == '':
mockprogramInoutArray = mockprogramInoutArray[:-1]
if len(mockprogramInoutArray) < 3:
print('Error: ' + mockProgramInOutFilePath +
' has less than three lines:\n-------------\n' + mockprogramInout +
'-------------')
sys.exit(2)
expectedInputLine = mockprogramInoutArray[0]
if expectedInputLine.find('MOCK_PROGRAM_INPUT:') != 0:
print("Error, first line = '" + expectedInputLine +
"', does not match ^MOCK_PROGRAM_INPUT:")
sys.exit(3)
expectedInput = expectedInputLine.replace('MOCK_PROGRAM_INPUT:', '').strip()
if inputArgs != expectedInput:
print("Error, input args='" + inputArgs + "' does not match expected='" +
expectedInput + "'")
sys.exit(4)
returnCodeLine = mockprogramInoutArray[1]
if returnCodeLine.find('MOCK_PROGRAM_RETURN:') != 0:
print("Error, second line = '" + returnCodeLine +
"', does not match ^MOCK_PROGRAM_RETURN:")
sys.exit(5)
returnCode = returnCodeLine.replace('MOCK_PROGRAM_RETURN:', '').strip()
outputLine = mockprogramInoutArray[2]
if outputLine.find('MOCK_PROGRAM_OUTPUT:') != 0:
print("Error, third line = '" + outputLine +
"', does not match ^MOCK_PROGRAM_OUTPUT:")
sys.exit(6)
outputStr = outputLine.replace('MOCK_PROGRAM_OUTPUT: ', '')
numLinesOuput = 1
if len(mockprogramInoutArray) > 3:
for line in mockprogramInoutArray[3:]:
if line.find('MOCK_PROGRAM_INPUT:') == 0:
break
outputStr = outputStr + '\n' + line
numLinesOuput = numLinesOuput + 1
print(outputStr)
lineLineIndex = 2 + numLinesOuput
if len(mockprogramInoutArray) > lineLineIndex:
open(mockProgramInOutFilePath, 'w').write('\n'.join(
mockprogramInoutArray[lineLineIndex:]) + '\n')
else:
open(mockProgramInOutFilePath, 'w').write('')
sys.exit(int(returnCode))
| #!/usr/bin/env python
# @HEADER
# ************************************************************************
#
# TriBITS: Tribal Build, Integrate, and Test System
# Copyright 2013 Sandia Corporation
#
# Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation,
# the U.S. Government retains certain rights in this software.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the Corporation nor the names of the
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY SANDIA CORPORATION "AS IS" AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL SANDIA CORPORATION OR THE
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# ************************************************************************
# @HEADER
#
# Usage: mockprogram.py [any arguments]
#
# Mock program that takes input arguments and produces stdout by reading from
# a file .mockprogram_inout.txt in the current directory or the file specified
# by the env var MOCKPROGRAM_INOUT_FILE_OVERRIDE (which can be in any
# directory). This script is used to take the place of real commands during a
# test that involves calling commands on the commandline.
#
# The file .mockprogram_inout.txt (or pointed to by
# MOCKPROGRAM_INOUT_FILE_OVERRIDE) is of the form:
#
# MOCK_PROGRAM_INPUT: <args_1>
# MOCK_PROGRAM_RETURN: <rtn>
# MOCK_PROGRAM_OUTPUT: <outline_1_line_1>
# <outline_1_line_2>
# ...
# MOCK_PROGRAM_INPUT: <args_2>
#
# The program reads in the blocks starting at the time and removes the block
# from the file after it runs. After all of the blocks are read in, if run
# again it will error out with error code 2.
#
# This program can be used, for example, to simulate git command. For
# example, a couple of git commits might be simulated like:
#
# MOCK_PROGRAM_INPUT: log -1
# MOCK_PROGRAM_RETURN: 0
# MOCK_PROGRAM_OUTPUT: This is the summary line
#
# The is the body of the commit msg
# MOCK_PROGRAM_INPUT: diff --name-only HEAD --not @{u}
# MOCK_PROGRAM_RETURN: 0
# MOCK_PROGRAM_OUTPUT: file_name_1.txt
# file_name_2.txt
# file_name_3.txt
#
import sys
import os
inputArgs = ' '.join(sys.argv[1:])
#print("inputArgs = '" + inputArgs + "'"
if os.environ.get("MOCKPROGRAM_INOUT_FILE_OVERRIDE"):
mockProgramInOutFilePath=os.environ.get("MOCKPROGRAM_INOUT_FILE_OVERRIDE")
else:
mockProgramInOutFilePath='.mockprogram_inout.txt'
if not os.path.exists(mockProgramInOutFilePath):
print("Error: "+mockProgramInOutFilePath+" is missing!")
sys.exit(1)
mockprogramInout = open(mockProgramInOutFilePath, 'r').read()
mockprogramInoutArray = mockprogramInout.splitlines()
if len(mockprogramInoutArray) and mockprogramInoutArray[-1] == "":
mockprogramInoutArray = mockprogramInoutArray[:-1]
if len(mockprogramInoutArray) < 3:
print("Error: "+mockProgramInOutFilePath+" has less than three lines:\n"
"-------------\n" + mockprogramInout + "-------------")
sys.exit(2)
# Assert input
expectedInputLine = mockprogramInoutArray[0]
if expectedInputLine.find("MOCK_PROGRAM_INPUT:") != 0:
print("Error, first line = '" + expectedInputLine + "', does not match "
"^MOCK_PROGRAM_INPUT:")
sys.exit(3)
expectedInput = expectedInputLine.replace("MOCK_PROGRAM_INPUT:", "").strip()
if inputArgs != expectedInput:
print("Error, input args='" + inputArgs + "' does not match expected='" +
expectedInput + "'")
sys.exit(4)
# Get return code
returnCodeLine = mockprogramInoutArray[1]
if returnCodeLine.find("MOCK_PROGRAM_RETURN:") != 0:
print("Error, second line = '" + returnCodeLine + "', does not match "
"^MOCK_PROGRAM_RETURN:")
sys.exit(5)
returnCode = returnCodeLine.replace("MOCK_PROGRAM_RETURN:", "").strip()
# Get output (can be multi-line)
outputLine = mockprogramInoutArray[2]
if outputLine.find("MOCK_PROGRAM_OUTPUT:") != 0:
print("Error, third line = '" + outputLine + "', does not match "
"^MOCK_PROGRAM_OUTPUT:")
sys.exit(6)
outputStr = outputLine.replace("MOCK_PROGRAM_OUTPUT: ", "")
numLinesOuput = 1
if len(mockprogramInoutArray) > 3:
for line in mockprogramInoutArray[3:]:
if line.find("MOCK_PROGRAM_INPUT:") == 0:
break
outputStr = outputStr+"\n"+line
numLinesOuput = numLinesOuput + 1
print(outputStr)
# Write the remaining lines back into the file
lineLineIndex = 2 + numLinesOuput
if len(mockprogramInoutArray) > lineLineIndex:
open(mockProgramInOutFilePath, 'w').write(
('\n'.join(mockprogramInoutArray[lineLineIndex:]))+"\n" )
else:
open(mockProgramInOutFilePath, 'w').write("")
# Return exit code
sys.exit(int(returnCode))
| [
0,
1,
2,
3,
4
] |
222 | 4fa9d16f979acf3edce05a209e1c6636e50fc315 | <mask token>
class Menu:
<mask token>
<mask token>
<mask token>
<mask token>
| <mask token>
class Menu:
<mask token>
def get_menu(self, type, openid):
try:
if type == 'mine':
self.sql = (
"SELECT * FROM get_menu WHERE openid='%s' order by watch DESC "
% openid)
self.resql = self.mysqlClass.select_data(self.sql)
if self.resql['state'] != 'E':
return {'code': 1007, 'menu_list': self.resql['alldata']}
else:
return {'code': -1}
elif type == 'main':
self.sql = 'SELECT * FROM get_menu order by watch DESC'
self.resql = self.mysqlClass.select_data(self.sql)
if self.resql['state'] != 'E':
return {'code': 1007, 'top_list': self.resql['alldata']
[0:3], 'menu_list': self.resql['alldata'][3:-1]}
else:
return {'code': -1}
except Exception as e:
print(str(e))
return {'code': -1}
<mask token>
def add_menu(self, data, openid):
try:
self.create_time = self.timeClass.get_time()
self.sql = (
"""
INSERT INTO menu (openid,title,photo,material,accessories,ingredient,create_date)
VALUES ('%s','%s','%s','%s','%s','%s','%s')
"""
% (openid, data.title, data.photo, data.material, data.
accessories, data.ingredient, self.create_time))
self.resql = self.mysqlClass.add_insert(self.sql, '')
self.conn = self.resql['conn']
self.cur = self.resql['cur']
self.menu_id = self.cur.lastrowid
steps = json.loads(data.steps)
for step in steps:
print(step['num'])
self.sql2 = (
"""
INSERT INTO menu_step (menu_id,num,content,image,create_date)
VALUES (%s,%d,'%s','%s','%s')
"""
% (self.menu_id, step['num'], step['content'], step[
'image'], self.create_time))
self.resql2 = self.mysqlClass.add_insert(self.sql2, self.conn)
self.conn = self.resql2['conn']
self.resql = self.mysqlClass.commit_inserst(self.conn)
if self.resql['state'] != 'E':
return {'code': 1010}
else:
return {'code': -1}
except Exception as e:
print(str(e))
return {'code': -1}
| <mask token>
class Menu:
def __init__(self):
self.mysqlClass = Mysql.MySQL()
self.timeClass = Utils.Time()
def get_menu(self, type, openid):
try:
if type == 'mine':
self.sql = (
"SELECT * FROM get_menu WHERE openid='%s' order by watch DESC "
% openid)
self.resql = self.mysqlClass.select_data(self.sql)
if self.resql['state'] != 'E':
return {'code': 1007, 'menu_list': self.resql['alldata']}
else:
return {'code': -1}
elif type == 'main':
self.sql = 'SELECT * FROM get_menu order by watch DESC'
self.resql = self.mysqlClass.select_data(self.sql)
if self.resql['state'] != 'E':
return {'code': 1007, 'top_list': self.resql['alldata']
[0:3], 'menu_list': self.resql['alldata'][3:-1]}
else:
return {'code': -1}
except Exception as e:
print(str(e))
return {'code': -1}
def get_menu_info(self, menu_id):
try:
self.sql = 'SELECT * FROM menu WHERE id=%s' % menu_id
self.resql = self.mysqlClass.select_data(self.sql)
if self.resql['state'] != 'E':
self.menu = self.resql['alldata'][0]
self.sql3 = 'UPDATE menu SET watch=%s where id=%s' % (self.
menu['watch'] + 1, menu_id)
self.resql3 = self.mysqlClass.insert_data(self.sql3)
print(self.resql3)
self.sql2 = (
'SELECT * FROM menu_step WHERE menu_id=%s order by num ASC '
% menu_id)
self.resql2 = self.mysqlClass.select_data(self.sql2)
self.step_list = []
if self.resql2['state'] != 'E':
for ai_menu_log in self.resql2['alldata']:
self.step_list.append(ai_menu_log)
self.menu['menu_step'] = self.step_list
return {'code': 1008, 'menu_info': self.menu}
else:
return {'code': -1}
except Exception as e:
print(str(e))
return {'code': -1}
def add_menu(self, data, openid):
try:
self.create_time = self.timeClass.get_time()
self.sql = (
"""
INSERT INTO menu (openid,title,photo,material,accessories,ingredient,create_date)
VALUES ('%s','%s','%s','%s','%s','%s','%s')
"""
% (openid, data.title, data.photo, data.material, data.
accessories, data.ingredient, self.create_time))
self.resql = self.mysqlClass.add_insert(self.sql, '')
self.conn = self.resql['conn']
self.cur = self.resql['cur']
self.menu_id = self.cur.lastrowid
steps = json.loads(data.steps)
for step in steps:
print(step['num'])
self.sql2 = (
"""
INSERT INTO menu_step (menu_id,num,content,image,create_date)
VALUES (%s,%d,'%s','%s','%s')
"""
% (self.menu_id, step['num'], step['content'], step[
'image'], self.create_time))
self.resql2 = self.mysqlClass.add_insert(self.sql2, self.conn)
self.conn = self.resql2['conn']
self.resql = self.mysqlClass.commit_inserst(self.conn)
if self.resql['state'] != 'E':
return {'code': 1010}
else:
return {'code': -1}
except Exception as e:
print(str(e))
return {'code': -1}
| from jox_api import label_image, Mysql, Utils
from jox_config import api_base_url
import json
class Menu:
def __init__(self):
self.mysqlClass = Mysql.MySQL()
self.timeClass = Utils.Time()
def get_menu(self, type, openid):
try:
if type == 'mine':
self.sql = (
"SELECT * FROM get_menu WHERE openid='%s' order by watch DESC "
% openid)
self.resql = self.mysqlClass.select_data(self.sql)
if self.resql['state'] != 'E':
return {'code': 1007, 'menu_list': self.resql['alldata']}
else:
return {'code': -1}
elif type == 'main':
self.sql = 'SELECT * FROM get_menu order by watch DESC'
self.resql = self.mysqlClass.select_data(self.sql)
if self.resql['state'] != 'E':
return {'code': 1007, 'top_list': self.resql['alldata']
[0:3], 'menu_list': self.resql['alldata'][3:-1]}
else:
return {'code': -1}
except Exception as e:
print(str(e))
return {'code': -1}
def get_menu_info(self, menu_id):
try:
self.sql = 'SELECT * FROM menu WHERE id=%s' % menu_id
self.resql = self.mysqlClass.select_data(self.sql)
if self.resql['state'] != 'E':
self.menu = self.resql['alldata'][0]
self.sql3 = 'UPDATE menu SET watch=%s where id=%s' % (self.
menu['watch'] + 1, menu_id)
self.resql3 = self.mysqlClass.insert_data(self.sql3)
print(self.resql3)
self.sql2 = (
'SELECT * FROM menu_step WHERE menu_id=%s order by num ASC '
% menu_id)
self.resql2 = self.mysqlClass.select_data(self.sql2)
self.step_list = []
if self.resql2['state'] != 'E':
for ai_menu_log in self.resql2['alldata']:
self.step_list.append(ai_menu_log)
self.menu['menu_step'] = self.step_list
return {'code': 1008, 'menu_info': self.menu}
else:
return {'code': -1}
except Exception as e:
print(str(e))
return {'code': -1}
def add_menu(self, data, openid):
try:
self.create_time = self.timeClass.get_time()
self.sql = (
"""
INSERT INTO menu (openid,title,photo,material,accessories,ingredient,create_date)
VALUES ('%s','%s','%s','%s','%s','%s','%s')
"""
% (openid, data.title, data.photo, data.material, data.
accessories, data.ingredient, self.create_time))
self.resql = self.mysqlClass.add_insert(self.sql, '')
self.conn = self.resql['conn']
self.cur = self.resql['cur']
self.menu_id = self.cur.lastrowid
steps = json.loads(data.steps)
for step in steps:
print(step['num'])
self.sql2 = (
"""
INSERT INTO menu_step (menu_id,num,content,image,create_date)
VALUES (%s,%d,'%s','%s','%s')
"""
% (self.menu_id, step['num'], step['content'], step[
'image'], self.create_time))
self.resql2 = self.mysqlClass.add_insert(self.sql2, self.conn)
self.conn = self.resql2['conn']
self.resql = self.mysqlClass.commit_inserst(self.conn)
if self.resql['state'] != 'E':
return {'code': 1010}
else:
return {'code': -1}
except Exception as e:
print(str(e))
return {'code': -1}
| from jox_api import label_image,Mysql,Utils
from jox_config import api_base_url
import json
class Menu():
def __init__(self):
self.mysqlClass = Mysql.MySQL()
self.timeClass = Utils.Time()
def get_menu(self,type,openid):
try:
if type == 'mine':
self.sql = "SELECT * FROM get_menu WHERE openid=\'%s\' order by watch DESC " % (openid)
self.resql = self.mysqlClass.select_data(self.sql)
if self.resql['state'] != 'E':
return {'code': 1007, 'menu_list': self.resql['alldata'] }
else:
return {'code': -1}
elif type == 'main':
self.sql = "SELECT * FROM get_menu order by watch DESC"
self.resql = self.mysqlClass.select_data(self.sql)
if self.resql['state'] != 'E':
return {'code': 1007, 'top_list': self.resql['alldata'][0:3],'menu_list': self.resql['alldata'][3:-1]}
else:
return {'code': -1}
except Exception as e:
print(str(e))
return {'code': -1}
def get_menu_info(self,menu_id):
try:
self.sql = "SELECT * FROM menu WHERE id=%s" % (menu_id)
self.resql = self.mysqlClass.select_data(self.sql)
if self.resql['state'] != 'E':
self.menu= self.resql['alldata'][0]
self.sql3 = "UPDATE menu SET watch=%s where id=%s" %(self.menu['watch']+1,menu_id)
self.resql3 = self.mysqlClass.insert_data(self.sql3)
print(self.resql3)
self.sql2 = "SELECT * FROM menu_step WHERE menu_id=%s order by num ASC " % (menu_id)
self.resql2 = self.mysqlClass.select_data(self.sql2)
self.step_list = []
if self.resql2['state'] != 'E':
for ai_menu_log in self.resql2['alldata']:
self.step_list.append(ai_menu_log)
self.menu['menu_step'] = self.step_list
return {'code': 1008, 'menu_info': self.menu}
else:
return {'code': -1}
except Exception as e:
print(str(e))
return {'code': -1}
def add_menu(self,data,openid):
try:
self.create_time = self.timeClass.get_time()
self.sql ='''
INSERT INTO menu (openid,title,photo,material,accessories,ingredient,create_date)
VALUES (\'%s\',\'%s\',\'%s\',\'%s\',\'%s\',\'%s\',\'%s\')
'''% (openid, data.title,data.photo,data.material,data.accessories,data.ingredient, self.create_time)
self.resql = self.mysqlClass.add_insert(self.sql,"")
self.conn = self.resql['conn']
self.cur = self.resql['cur']
self.menu_id = self.cur.lastrowid
steps = json.loads(data.steps)
for step in steps:
print(step['num'])
self.sql2 = '''
INSERT INTO menu_step (menu_id,num,content,image,create_date)
VALUES (%s,%d,\'%s\',\'%s\',\'%s\')
'''% (self.menu_id, step['num'],step['content'],step['image'], self.create_time)
self.resql2 = self.mysqlClass.add_insert(self.sql2, self.conn)
self.conn = self.resql2['conn']
self.resql = self.mysqlClass.commit_inserst(self.conn)
if self.resql['state'] !='E':
return {'code': 1010}
else:
return {'code': -1}
except Exception as e:
print(str(e))
return {'code': -1} | [
1,
3,
5,
6,
7
] |
223 | e5e012e40a71dee9f4dbd9913590aef125b758df | <mask token>
class Visualiser:
<mask token>
<mask token>
<mask token>
def __build_map(self):
"""
Creates the array of the battlefield. Should never be used for logical operations
:return:
"""
columns = []
for i in range(self.__dimensions):
columns.append([])
for i in range(self.__dimensions):
self.map.append(columns)
def print(self, board: Board):
"""
Print the entire battlefield
:param board: The current board in play
:return: void
"""
row = [' ']
for x_marker in self.coordinate_map:
row.append(' ' + x_marker)
print(''.join(row))
for y, y_row in enumerate(self.map):
row = [str(8 - y) + ' ']
for x, square in enumerate(y_row):
anybody = board.who_is_in(*[x, y])
if anybody is not None:
row.append(anybody.name)
else:
row.append(' .')
print(''.join(row))
@staticmethod
def to_algebraic(x, y):
return Visualiser.coordinate_map[x] + str(abs(y - Visualiser.
__dimensions))
| <mask token>
class Visualiser:
<mask token>
<mask token>
def __init__(self):
self.map = []
self.__build_map()
def __build_map(self):
"""
Creates the array of the battlefield. Should never be used for logical operations
:return:
"""
columns = []
for i in range(self.__dimensions):
columns.append([])
for i in range(self.__dimensions):
self.map.append(columns)
def print(self, board: Board):
"""
Print the entire battlefield
:param board: The current board in play
:return: void
"""
row = [' ']
for x_marker in self.coordinate_map:
row.append(' ' + x_marker)
print(''.join(row))
for y, y_row in enumerate(self.map):
row = [str(8 - y) + ' ']
for x, square in enumerate(y_row):
anybody = board.who_is_in(*[x, y])
if anybody is not None:
row.append(anybody.name)
else:
row.append(' .')
print(''.join(row))
@staticmethod
def to_algebraic(x, y):
return Visualiser.coordinate_map[x] + str(abs(y - Visualiser.
__dimensions))
| <mask token>
class Visualiser:
coordinate_map = 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'
__dimensions = 8
def __init__(self):
self.map = []
self.__build_map()
def __build_map(self):
"""
Creates the array of the battlefield. Should never be used for logical operations
:return:
"""
columns = []
for i in range(self.__dimensions):
columns.append([])
for i in range(self.__dimensions):
self.map.append(columns)
def print(self, board: Board):
"""
Print the entire battlefield
:param board: The current board in play
:return: void
"""
row = [' ']
for x_marker in self.coordinate_map:
row.append(' ' + x_marker)
print(''.join(row))
for y, y_row in enumerate(self.map):
row = [str(8 - y) + ' ']
for x, square in enumerate(y_row):
anybody = board.who_is_in(*[x, y])
if anybody is not None:
row.append(anybody.name)
else:
row.append(' .')
print(''.join(row))
@staticmethod
def to_algebraic(x, y):
return Visualiser.coordinate_map[x] + str(abs(y - Visualiser.
__dimensions))
| from classes.Board import Board
class Visualiser:
coordinate_map = 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'
__dimensions = 8
def __init__(self):
self.map = []
self.__build_map()
def __build_map(self):
"""
Creates the array of the battlefield. Should never be used for logical operations
:return:
"""
columns = []
for i in range(self.__dimensions):
columns.append([])
for i in range(self.__dimensions):
self.map.append(columns)
def print(self, board: Board):
"""
Print the entire battlefield
:param board: The current board in play
:return: void
"""
row = [' ']
for x_marker in self.coordinate_map:
row.append(' ' + x_marker)
print(''.join(row))
for y, y_row in enumerate(self.map):
row = [str(8 - y) + ' ']
for x, square in enumerate(y_row):
anybody = board.who_is_in(*[x, y])
if anybody is not None:
row.append(anybody.name)
else:
row.append(' .')
print(''.join(row))
@staticmethod
def to_algebraic(x, y):
return Visualiser.coordinate_map[x] + str(abs(y - Visualiser.
__dimensions))
| from classes.Board import Board
class Visualiser:
coordinate_map = ("a", "b", "c", "d", "e", "f", "g", "h")
__dimensions = 8
def __init__(self):
self.map = []
self.__build_map()
def __build_map(self):
"""
Creates the array of the battlefield. Should never be used for logical operations
:return:
"""
columns = []
for i in range(self.__dimensions):
columns.append([])
for i in range(self.__dimensions):
self.map.append(columns)
def print(self, board: Board):
"""
Print the entire battlefield
:param board: The current board in play
:return: void
"""
# Render first horizontal alphabetical x-axis markers
row = [" "]
for x_marker in self.coordinate_map:
row.append(" " + x_marker)
print("".join(row))
# Render the rest of the cheese board
for y, y_row in enumerate(self.map):
# Render left side row numbers
row = [str((8-y)) + " "]
# Render battlefield
for x, square in enumerate(y_row):
# Check with Board if there is a piece on this coordinate
anybody = board.who_is_in(*[x, y])
# Anybody out there?
if anybody is not None:
# Oh hai
row.append(anybody.name)
else:
# Print a simple dot
row.append(" .")
# Print the entire row
print("".join(row))
@staticmethod
def to_algebraic(x, y):
return Visualiser.coordinate_map[x] + str(abs((y - Visualiser.__dimensions)))
| [
4,
5,
6,
7,
8
] |
224 | c4e4e54ac93c2acdbd3a1cd22b200341a6e45688 | import pyaudio
import numpy as np
from collections import OrderedDict
import utils
class MasterPlayer(object):
def __init__(self, volume=1., samplesPerSecond=44100):
self.p = pyaudio.PyAudio()
self.volume = volume
self.samplesPerSecond = samplesPerSecond
self.individual_callbacks = OrderedDict()
self.volumes = {}
def __del__(self):
self.p.terminate()
def play(self):
self.offset = 0
def callback(in_data, frame_count, time_info, status):
total_stereo = np.zeros((frame_count*2))
time = self.offset / float(self.samplesPerSecond)
for ic in self.individual_callbacks:
left, right = ic(self.offset, time, frame_count)
if left is None: # dead voice
continue
stereo = utils.to_stereo(left, right)
# Accumulate
total_stereo += stereo * self.volumes[ic]
self.offset += frame_count
output = utils.np_to_frames(total_stereo * self.volume)
return (output, pyaudio.paContinue)
self.stream = self.p.open(format=self.p.get_format_from_width(2),
channels=2,
rate=self.samplesPerSecond,
output=True,
stream_callback=callback)
self.stream.start_stream()
def stop(self):
self.stream.stop_stream()
def register(self, callback):
self.individual_callbacks[callback] = {}
self.volumes[callback] = 1.
def unregister(self, callback):
if callback in self.individual_callbacks:
del self.individual_callbacks[callback]
del self.volumes[callback]
def set_volume(self, callback, volume):
self.volumes[callback] = volume
MAXVOLUME = 32767.
def sawtooth(x):
return np.mod(x / (2*np.pi), 1.)
class ADSR(object):
def __init__(self, a=0.01, d=0.1, s=0.8, r=0.5, mode='linear'):
self.a = a
self.d = d
self.s = s
self.r = r
assert mode == 'linear'
def get_envelope_pressed(self, delta):
'''
:param delta: time after pressed
:return: envelope (between 0 and 1)
'''
delta = delta.astype(float)
#assert delta>0.
envelope = np.zeros(len(delta))
# attack
attack = delta < self.a
envelope[attack] = delta[attack] / self.a
# decay
decay = (delta < self.a + self.d) & (delta >= self.a)
envelope[decay] = 1 - (1 - self.s) * (delta[decay] - self.a) / self.d
# sustain
sustain = (delta >= self.a + self.d)
envelope[sustain] = self.s
return envelope
def get_envelope_released(self, delta):
'''
:param delta: time after released
:return: envelope (between 0 and 1)
'''
delta = delta.astype(float)
envelope = np.zeros(len(delta))
# release
release = delta < self.r
envelope[release] = self.s * (self.r - delta[release]) / self.r
# dead
dead = delta >= self.r
all_dead = np.all(dead)
return envelope, all_dead
class SineWavePlayer(object):
def __init__(self, freq, samplerate, adsr, motherwave=None):
self.freq = freq
self.samplerate = samplerate
self.pressed = False
self.volume = 0.3
#self.wave = np.sin
if motherwave is None:
motherwave = sawtooth()
self.wave = motherwave
self.adsr = adsr
self.dead = True
def __call__(self, offset, time, frame_count):
# Find out which state we are in
# Dead/NewPress/Pressed/NewRelease/Released/Dead
if self.pressed:
if self.new_press:
# Initialize phase to prevent clicking
self.onset = time
self.new_press = False
# Relative time after press
time_after_press = (time + np.arange(frame_count, dtype=float) / self.samplerate - self.onset)
left = self.volume * MAXVOLUME * self.wave(time_after_press * 2*np.pi * self.freq)
envelope = self.adsr.get_envelope_pressed(time_after_press)
left *= envelope
right = left
elif not self.dead:
if self.new_release:
self.new_release = False
self.release_time = time
# Relative time after release
time_after_press = (time + np.arange(frame_count, dtype=float) / self.samplerate - self.onset)
time_after_release = (time + np.arange(frame_count, dtype=float) / self.samplerate - self.release_time)
left = self.volume * MAXVOLUME * self.wave(time_after_press * 2*np.pi * self.freq)
envelope, self.dead = self.adsr.get_envelope_released(time_after_release)
left *= envelope
right = left
else:
left = right = None
return left, right
def press(self):
self.pressed = True
self.new_press = True
self.dead = False
def release(self):
self.pressed = False
self.new_release = True
def note_to_freq(note):
reference_a = 45
return np.exp(np.log(440) + (note - reference_a) / 12. * np.log(2))
class NaivePoly(object):
def __init__(self, octaves, samplerate, adsr, motherwave):
self.voices = []
self.octaves = octaves
for note in xrange(self.octaves*12):
# Compute frequency -> 440hz is note 45
freq = note_to_freq(note)
# Initialize voice
self.voices.append(SineWavePlayer(freq, samplerate, adsr, motherwave))
print 'note {} freq {}'.format(note, freq)
def register(self, master):
for voice in self.voices:
master.register(voice)
def unregister(self, master):
for voice in self.voices:
master.unregister(voice)
def press(self, key):
self.voices[key].press()
def release(self, key):
self.voices[key].release()
| null | null | null | null | [
0
] |
225 | 27e9e63338d422b5fca6f7a67fa3d255602a3358 | <mask token>
class V_test_abstract(V):
def __init__(self):
super(V_test_abstract, self).__init__()
<mask token>
def forward(self):
z = self.beta[:self.dim]
r1_local = self.beta[self.dim:2 * self.dim]
r2_local = self.beta[2 * self.dim:3 * self.dim]
r1_local_plus = self.beta[3 * self.dim:4 * self.dim]
r2_local_plus = self.beta[4 * self.dim:5 * self.dim]
r1_global = self.beta[5 * self.dim]
r2_global = self.beta[5 * self.dim + 1]
sigma = self.beta[5 * self.dim + 2]
w0 = self.beta[5 * self.dim + 3]
tau = r1_global * torch.sqrt(r2_global)
lamb = r1_local * torch.sqrt(r2_local)
lambda_plus = r1_local_plus * torch.sqrt(r2_local_plus)
w = z * lamb * lambda_plus * tau
outy = (self.y - (w0 + self.X.mv(w))) * (self.y - (w0 + self.X.mv(w))
) / (sigma * sigma) * 0.5
outz = torch.dot(z, z) * 0.5
outr1_local = torch.dot(r1_local, r1_local)
outr2_local = ((0.5 * self.nu + 1) * torch.log(r2_local) + 0.5 *
self.nu / r2_local).sum()
outr1_global = r1_global * r1_global * 0.5
outr2_global = 1.5 * torch.log(r2_global) + 0.5 / r2_global
outw0 = w0 * w0 / 25.0
out = (outy + outz + outr1_local + outr2_local + outr1_global +
outr2_global + outw0)
return out
<mask token>
| <mask token>
class V_test_abstract(V):
def __init__(self):
super(V_test_abstract, self).__init__()
def V_setup(self, y, X, nu):
self.explicit_gradient = False
self.need_higherorderderiv = True
self.dim = X.shape[1]
self.beta = nn.Parameter(torch.zeros(self.dim * 5 + 4),
requires_grad=True)
self.y = y
self.X = X
self.nu = nu
return ()
def forward(self):
z = self.beta[:self.dim]
r1_local = self.beta[self.dim:2 * self.dim]
r2_local = self.beta[2 * self.dim:3 * self.dim]
r1_local_plus = self.beta[3 * self.dim:4 * self.dim]
r2_local_plus = self.beta[4 * self.dim:5 * self.dim]
r1_global = self.beta[5 * self.dim]
r2_global = self.beta[5 * self.dim + 1]
sigma = self.beta[5 * self.dim + 2]
w0 = self.beta[5 * self.dim + 3]
tau = r1_global * torch.sqrt(r2_global)
lamb = r1_local * torch.sqrt(r2_local)
lambda_plus = r1_local_plus * torch.sqrt(r2_local_plus)
w = z * lamb * lambda_plus * tau
outy = (self.y - (w0 + self.X.mv(w))) * (self.y - (w0 + self.X.mv(w))
) / (sigma * sigma) * 0.5
outz = torch.dot(z, z) * 0.5
outr1_local = torch.dot(r1_local, r1_local)
outr2_local = ((0.5 * self.nu + 1) * torch.log(r2_local) + 0.5 *
self.nu / r2_local).sum()
outr1_global = r1_global * r1_global * 0.5
outr2_global = 1.5 * torch.log(r2_global) + 0.5 / r2_global
outw0 = w0 * w0 / 25.0
out = (outy + outz + outr1_local + outr2_local + outr1_global +
outr2_global + outw0)
return out
<mask token>
| <mask token>
class V_test_abstract(V):
def __init__(self):
super(V_test_abstract, self).__init__()
def V_setup(self, y, X, nu):
self.explicit_gradient = False
self.need_higherorderderiv = True
self.dim = X.shape[1]
self.beta = nn.Parameter(torch.zeros(self.dim * 5 + 4),
requires_grad=True)
self.y = y
self.X = X
self.nu = nu
return ()
def forward(self):
z = self.beta[:self.dim]
r1_local = self.beta[self.dim:2 * self.dim]
r2_local = self.beta[2 * self.dim:3 * self.dim]
r1_local_plus = self.beta[3 * self.dim:4 * self.dim]
r2_local_plus = self.beta[4 * self.dim:5 * self.dim]
r1_global = self.beta[5 * self.dim]
r2_global = self.beta[5 * self.dim + 1]
sigma = self.beta[5 * self.dim + 2]
w0 = self.beta[5 * self.dim + 3]
tau = r1_global * torch.sqrt(r2_global)
lamb = r1_local * torch.sqrt(r2_local)
lambda_plus = r1_local_plus * torch.sqrt(r2_local_plus)
w = z * lamb * lambda_plus * tau
outy = (self.y - (w0 + self.X.mv(w))) * (self.y - (w0 + self.X.mv(w))
) / (sigma * sigma) * 0.5
outz = torch.dot(z, z) * 0.5
outr1_local = torch.dot(r1_local, r1_local)
outr2_local = ((0.5 * self.nu + 1) * torch.log(r2_local) + 0.5 *
self.nu / r2_local).sum()
outr1_global = r1_global * r1_global * 0.5
outr2_global = 1.5 * torch.log(r2_global) + 0.5 / r2_global
outw0 = w0 * w0 / 25.0
out = (outy + outz + outr1_local + outr2_local + outr1_global +
outr2_global + outw0)
return out
def load_explcit_gradient(self):
return ()
| from abstract_class_V import V
import torch
import torch.nn as nn
class V_test_abstract(V):
def __init__(self):
super(V_test_abstract, self).__init__()
def V_setup(self, y, X, nu):
self.explicit_gradient = False
self.need_higherorderderiv = True
self.dim = X.shape[1]
self.beta = nn.Parameter(torch.zeros(self.dim * 5 + 4),
requires_grad=True)
self.y = y
self.X = X
self.nu = nu
return ()
def forward(self):
z = self.beta[:self.dim]
r1_local = self.beta[self.dim:2 * self.dim]
r2_local = self.beta[2 * self.dim:3 * self.dim]
r1_local_plus = self.beta[3 * self.dim:4 * self.dim]
r2_local_plus = self.beta[4 * self.dim:5 * self.dim]
r1_global = self.beta[5 * self.dim]
r2_global = self.beta[5 * self.dim + 1]
sigma = self.beta[5 * self.dim + 2]
w0 = self.beta[5 * self.dim + 3]
tau = r1_global * torch.sqrt(r2_global)
lamb = r1_local * torch.sqrt(r2_local)
lambda_plus = r1_local_plus * torch.sqrt(r2_local_plus)
w = z * lamb * lambda_plus * tau
outy = (self.y - (w0 + self.X.mv(w))) * (self.y - (w0 + self.X.mv(w))
) / (sigma * sigma) * 0.5
outz = torch.dot(z, z) * 0.5
outr1_local = torch.dot(r1_local, r1_local)
outr2_local = ((0.5 * self.nu + 1) * torch.log(r2_local) + 0.5 *
self.nu / r2_local).sum()
outr1_global = r1_global * r1_global * 0.5
outr2_global = 1.5 * torch.log(r2_global) + 0.5 / r2_global
outw0 = w0 * w0 / 25.0
out = (outy + outz + outr1_local + outr2_local + outr1_global +
outr2_global + outw0)
return out
def load_explcit_gradient(self):
return ()
| from abstract_class_V import V
import torch
import torch.nn as nn
class V_test_abstract(V):
def __init__(self):
super(V_test_abstract, self).__init__()
def V_setup(self,y,X,nu):
self.explicit_gradient = False
self.need_higherorderderiv = True
self.dim = X.shape[1]
self.beta = nn.Parameter(torch.zeros(self.dim*5+4),requires_grad=True)
self.y = y
self.X = X
self.nu = nu
# beta[:dim] = z
# beta[(dim):(2dim)] = r1_local
# beta[(2dim):(3dim)] = r2_local
# beta[(3dim):(4dim)] = r1_local_plus
# beta[(4dim):(5dim)] = r2_local_plus
# beta[5dim] = r1_global
# beta[5dim+1] = r2_global
# beta[5dim+2] = sigma
# beta[5dim+3] = w0
return()
def forward(self):
z = self.beta[:self.dim]
r1_local = self.beta[(self.dim):(2*self.dim)]
r2_local = self.beta[(2*self.dim):(3*self.dim)]
r1_local_plus = self.beta[(3*self.dim):(4*self.dim)]
r2_local_plus = self.beta[(4*self.dim):(5*self.dim)]
r1_global = self.beta[5*self.dim]
r2_global = self.beta[5*self.dim+1]
sigma = self.beta[5*self.dim+2]
w0 = self.beta[5*self.dim+3]
tau = r1_global * torch.sqrt(r2_global)
lamb = r1_local * torch.sqrt(r2_local)
lambda_plus = r1_local_plus * torch.sqrt(r2_local_plus)
w = z * lamb * lambda_plus * tau
outy = (self.y - (w0 + self.X.mv(w)))*(self.y - (w0 + self.X.mv(w)))/(sigma*sigma) * 0.5
outz = torch.dot(z,z) * 0.5
outr1_local = torch.dot(r1_local,r1_local)
outr2_local = ((0.5*self.nu+1)*torch.log(r2_local) + 0.5 * self.nu/r2_local).sum()
outr1_global = r1_global*r1_global * 0.5
outr2_global = 1.5 * torch.log(r2_global) + 0.5/r2_global
outw0 = w0*w0/(25.)
out = outy+outz+outr1_local+outr2_local+outr1_global+outr2_global+outw0
return(out)
def load_explcit_gradient(self):
return() | [
3,
4,
5,
6,
7
] |
226 | ec4348c61cd1c9130543bb20f9ca199399e1caff | class Solution(object):
def restoreIpAddresses(self, s):
"""
:type s: str
:rtype: List[str]
"""
def helper(sb, string, level):
if len(string) == 0:
if level == 4:
ans.append(sb[:-1])
return
if level == 4: return
for i in range(3):
if i < len(string):
part = string[:i + 1]
if valid(part):
helper(sb + part + '.', string[i + 1:], level + 1)
def valid(num):
if len(num) > 1 and num[0] == '0':
return False
if 0 <= int(num) <= 255:
return True
else:
return False
ans = []
sb = ''
helper(sb, s, 0)
return ans
solution = Solution()
print solution.restoreIpAddresses("010010") | null | null | null | null | [
0
] |
227 | d0a3f332e04627eb275168972bd92cd1ea9b9447 | <mask token>
class TestTTT(unittest.TestCase):
def test_mcts(self):
if 0 in skip:
print('Skipping ai self-play')
return
ttt = TTT()
for i in range(1000):
mcts = MCTS(ttt)
state = mcts.root.state
while not mcts.board.ending_state(state):
move = mcts.search()
print(move)
state = mcts.board.get_state(state, move)
mcts.board.print(state)
mcts.make_move(move)
self.assertEqual(mcts.board.ending_state(state), -1)
def test_play_mcts(self):
if 1 in skip:
print('Skipping human-ai play')
return
ttt = TTT()
mcts = MCTS(ttt)
state = mcts.root.state
my_player = 2
while not mcts.board.ending_state(state):
mcts.board.print(state)
move = mcts.search()
print(move)
if state[1] == my_player:
move = input('Make move!\n')
move = int(move[0]), int(move[1])
mcts.make_move(move)
state = mcts.root.state
mcts.board.print(state)
def test_positions(self):
move_sequence = [(1, 1), (2, 0), (0, 1)]
move_sequence = [(1, 1), (2, 2), (2, 1)]
move_sequence = [(1, 1), (2, 2), (2, 0), (0, 2), (1, 2), (2, 1)]
def from_position(self, move_sequence, expected_move, name):
ttt = TTT()
mcts = MCTS(ttt, searchtime=30)
mcts.board.print(mcts.root.state)
for move in move_sequence:
mcts.search()
mcts.make_move(move)
mcts.board.print(mcts.root.state)
move = mcts.search()
print('Testing {} block (that was lost before) on the following board'
.format(name))
self.assertEqual(move, expected_move)
def test_trick_win(self):
pass
<mask token>
| <mask token>
class TestTTT(unittest.TestCase):
def test_mcts(self):
if 0 in skip:
print('Skipping ai self-play')
return
ttt = TTT()
for i in range(1000):
mcts = MCTS(ttt)
state = mcts.root.state
while not mcts.board.ending_state(state):
move = mcts.search()
print(move)
state = mcts.board.get_state(state, move)
mcts.board.print(state)
mcts.make_move(move)
self.assertEqual(mcts.board.ending_state(state), -1)
def test_play_mcts(self):
if 1 in skip:
print('Skipping human-ai play')
return
ttt = TTT()
mcts = MCTS(ttt)
state = mcts.root.state
my_player = 2
while not mcts.board.ending_state(state):
mcts.board.print(state)
move = mcts.search()
print(move)
if state[1] == my_player:
move = input('Make move!\n')
move = int(move[0]), int(move[1])
mcts.make_move(move)
state = mcts.root.state
mcts.board.print(state)
def test_positions(self):
move_sequence = [(1, 1), (2, 0), (0, 1)]
move_sequence = [(1, 1), (2, 2), (2, 1)]
move_sequence = [(1, 1), (2, 2), (2, 0), (0, 2), (1, 2), (2, 1)]
def from_position(self, move_sequence, expected_move, name):
ttt = TTT()
mcts = MCTS(ttt, searchtime=30)
mcts.board.print(mcts.root.state)
for move in move_sequence:
mcts.search()
mcts.make_move(move)
mcts.board.print(mcts.root.state)
move = mcts.search()
print('Testing {} block (that was lost before) on the following board'
.format(name))
self.assertEqual(move, expected_move)
def test_trick_win(self):
pass
def test_defend_trick_win(self):
pass
| <mask token>
skip = [0]
class TestTTT(unittest.TestCase):
def test_mcts(self):
if 0 in skip:
print('Skipping ai self-play')
return
ttt = TTT()
for i in range(1000):
mcts = MCTS(ttt)
state = mcts.root.state
while not mcts.board.ending_state(state):
move = mcts.search()
print(move)
state = mcts.board.get_state(state, move)
mcts.board.print(state)
mcts.make_move(move)
self.assertEqual(mcts.board.ending_state(state), -1)
def test_play_mcts(self):
if 1 in skip:
print('Skipping human-ai play')
return
ttt = TTT()
mcts = MCTS(ttt)
state = mcts.root.state
my_player = 2
while not mcts.board.ending_state(state):
mcts.board.print(state)
move = mcts.search()
print(move)
if state[1] == my_player:
move = input('Make move!\n')
move = int(move[0]), int(move[1])
mcts.make_move(move)
state = mcts.root.state
mcts.board.print(state)
def test_positions(self):
move_sequence = [(1, 1), (2, 0), (0, 1)]
move_sequence = [(1, 1), (2, 2), (2, 1)]
move_sequence = [(1, 1), (2, 2), (2, 0), (0, 2), (1, 2), (2, 1)]
def from_position(self, move_sequence, expected_move, name):
ttt = TTT()
mcts = MCTS(ttt, searchtime=30)
mcts.board.print(mcts.root.state)
for move in move_sequence:
mcts.search()
mcts.make_move(move)
mcts.board.print(mcts.root.state)
move = mcts.search()
print('Testing {} block (that was lost before) on the following board'
.format(name))
self.assertEqual(move, expected_move)
def test_trick_win(self):
pass
def test_defend_trick_win(self):
pass
| from board.ttt import TTT
from mctsai.mcts import MCTS
import unittest
skip = [0]
class TestTTT(unittest.TestCase):
def test_mcts(self):
if 0 in skip:
print('Skipping ai self-play')
return
ttt = TTT()
for i in range(1000):
mcts = MCTS(ttt)
state = mcts.root.state
while not mcts.board.ending_state(state):
move = mcts.search()
print(move)
state = mcts.board.get_state(state, move)
mcts.board.print(state)
mcts.make_move(move)
self.assertEqual(mcts.board.ending_state(state), -1)
def test_play_mcts(self):
if 1 in skip:
print('Skipping human-ai play')
return
ttt = TTT()
mcts = MCTS(ttt)
state = mcts.root.state
my_player = 2
while not mcts.board.ending_state(state):
mcts.board.print(state)
move = mcts.search()
print(move)
if state[1] == my_player:
move = input('Make move!\n')
move = int(move[0]), int(move[1])
mcts.make_move(move)
state = mcts.root.state
mcts.board.print(state)
def test_positions(self):
move_sequence = [(1, 1), (2, 0), (0, 1)]
move_sequence = [(1, 1), (2, 2), (2, 1)]
move_sequence = [(1, 1), (2, 2), (2, 0), (0, 2), (1, 2), (2, 1)]
def from_position(self, move_sequence, expected_move, name):
ttt = TTT()
mcts = MCTS(ttt, searchtime=30)
mcts.board.print(mcts.root.state)
for move in move_sequence:
mcts.search()
mcts.make_move(move)
mcts.board.print(mcts.root.state)
move = mcts.search()
print('Testing {} block (that was lost before) on the following board'
.format(name))
self.assertEqual(move, expected_move)
def test_trick_win(self):
pass
def test_defend_trick_win(self):
pass
| from board.ttt import TTT
from mctsai.mcts import MCTS
import unittest
# skip = [0, 1]
skip = [0]
class TestTTT(unittest.TestCase):
def test_mcts(self):
if 0 in skip:
print("Skipping ai self-play")
return
ttt = TTT()
for i in range(1000):
mcts = MCTS(ttt)
state = mcts.root.state
while not mcts.board.ending_state(state):
move = mcts.search()
print(move)
state = mcts.board.get_state(state, move)
mcts.board.print(state)
mcts.make_move(move)
self.assertEqual(mcts.board.ending_state(state), -1)
def test_play_mcts(self):
if 1 in skip:
print("Skipping human-ai play")
return
ttt = TTT()
mcts = MCTS(ttt)
state = mcts.root.state
my_player = 2
while not mcts.board.ending_state(state):
mcts.board.print(state)
move = mcts.search()
print(move)
if state[1] == my_player:
move = input("Make move!\n")
move = (int(move[0]), int(move[1]))
mcts.make_move(move)
state = mcts.root.state
mcts.board.print(state)
# state = mcts.board.get_state(state, move)
# mcts = MCTS(ttt)
# mcts.root.state = state
# mcts.root.remaining_moves = mcts.board.get_legal_moves(mcts.root.state)
def test_positions(self):
# simple block
move_sequence = [(1, 1), (2, 0), (0, 1)]
# self.from_position(move_sequence, (2, 1), "Simple block 1")
# simple block 2
move_sequence = [(1, 1), (2, 2), (2, 1)]
# self.from_position(move_sequence, (0, 1), "Simple block 2")
# simple win 1
move_sequence = [(1, 1), (2, 2), (2, 0), (0, 2), (1, 2), (2, 1)]
# self.from_position(move_sequence, (1, 0), "Simple win")
def from_position(self, move_sequence, expected_move, name):
ttt = TTT()
mcts = MCTS(ttt, searchtime= 30)
mcts.board.print(mcts.root.state)
for move in move_sequence:
mcts.search()
mcts.make_move(move)
mcts.board.print(mcts.root.state)
move = mcts.search()
print("Testing {} block (that was lost before) on the following board".format(name))
self.assertEqual(move, expected_move)
def test_trick_win(self):
pass
# ttt = TTT()
# state = ttt.get_initial_state()
# state = ttt.get_state(state, (1, 1))
# state = ttt.get_state(state, (2, 2))
# state = ttt.get_state(state, (2, 0))
# print("Testing trick win on the following board")
# ttt.print(state)
# for _ in range(100):
# mcts = MCTS(ttt)
# mcts.set_root_state(state)
# move = mcts.search()
# self.assertEqual(move, (0, 2))
def test_defend_trick_win(self):
pass
| [
6,
7,
8,
9,
10
] |
228 | e95bda8be2294c295d89f1c035bc209128fa29c8 | def merge_the_tools(string, k):
# your code goes here
num_sub_strings = len(string)/k
#print num_sub_strings
for idx in range(num_sub_strings):
print "".join(set(list(string[idx * k : (idx + 1) * k])))
| null | null | null | null | [
0
] |
229 | f85a703b47d981397ed6048e941030a3fbee7b6d | <mask token>
| <mask token>
class Migration(migrations.Migration):
<mask token>
<mask token>
| <mask token>
class Migration(migrations.Migration):
dependencies = [('talk', '0023_auto_20180207_1121')]
operations = [migrations.AddField(model_name='talkmedia', name=
'codelink', field=models.CharField(blank=True, max_length=255,
verbose_name='Source code'))]
| from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [('talk', '0023_auto_20180207_1121')]
operations = [migrations.AddField(model_name='talkmedia', name=
'codelink', field=models.CharField(blank=True, max_length=255,
verbose_name='Source code'))]
| # -*- coding: utf-8 -*-
# Generated by Django 1.9.8 on 2018-04-27 08:05
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('talk', '0023_auto_20180207_1121'),
]
operations = [
migrations.AddField(
model_name='talkmedia',
name='codelink',
field=models.CharField(blank=True, max_length=255, verbose_name='Source code'),
),
]
| [
0,
1,
2,
3,
4
] |
230 | 23375760c0943ca177b7009031d9d17a91165c5c | #!/usr/bin/env python
#--coding: utf8--
import time
if __name__ == '__main__':
date = time.strftime('%m-%d')
if date == '03-08':
print '女神节'
elif date == '02-14':
print '情人节'
else:
print '发红包'
print '这是一个测试题' | null | null | null | null | [
0
] |
231 | f8d815bcdc74452b66a1b3b33bf0fbe976e728c8 | <mask token>
def dataX(features, set):
data_x = np.array([])
count = 0
for filepath in glob.iglob(set):
globpath = filepath + '\\*.jpg'
for filepath in glob.iglob('' + globpath):
count = count + 1
img = Image.open(filepath).convert('L')
data = list(img.getdata())
x = np.array(data)
data_x = np.append(data_x, x)
data_x = data_x.reshape(count, features)
return data_x.astype(int)
<mask token>
| <mask token>
def next_batch(num, data, labels):
"""
Return a total of `num` random samples and labels.
"""
idx = np.arange(0, len(data))
np.random.shuffle(idx)
idx = idx[:num]
data_shuffle = [data[i] for i in idx]
labels_shuffle = [labels[i] for i in idx]
return np.asarray(data_shuffle), np.asarray(labels_shuffle)
def dataX(features, set):
data_x = np.array([])
count = 0
for filepath in glob.iglob(set):
globpath = filepath + '\\*.jpg'
for filepath in glob.iglob('' + globpath):
count = count + 1
img = Image.open(filepath).convert('L')
data = list(img.getdata())
x = np.array(data)
data_x = np.append(data_x, x)
data_x = data_x.reshape(count, features)
return data_x.astype(int)
def dataY(categories, set):
data_y = np.array([])
count = 0
for filepath in glob.iglob(set):
path = filepath.split('\\')
globpath = filepath + '\\*.jpg'
for filepath in glob.iglob('' + globpath):
count = count + 1
y = np.array([])
t = [int(path[2])]
y = np.append(y, t)
data_y = np.append(data_y, y)
return data_y.astype(int)
def model():
print(' model')
batch_size = 50
features = 32 * 32
categories = 5
filter1 = 32
filter2 = 64
train_path = 'dataset\\train\\[0-4]'
test_path = 'dataset\\test\\[0-4]'
validation_path = 'dataset\\validation\\[0-4]'
data_x = dataX(features, train_path)
print('datax: ', data_x)
data_y = dataY(categories, train_path)
print('datay: ', data_y)
data_x_test = dataX(features, test_path)
data_y_test = dataY(categories, test_path)
data_x_validation = dataX(features, validation_path)
data_y_validation = dataY(categories, validation_path)
x_image = tf.reshape(data_x, [-1, 32, 32, 1])
x_image_test = tf.reshape(data_x_test, [-1, 32, 32, 1])
x_image_validation = tf.reshape(data_x_validation, [-1, 32, 32, 1])
model = models.Sequential()
model.add(layers.Conv2D(filter1, (5, 5), activation='relu', padding=
'SAME', input_shape=(32, 32, 1)))
model.add(layers.MaxPooling2D((2, 2), padding='SAME', strides=(2, 2)))
model.add(layers.Conv2D(filter2, (5, 5), activation='relu', padding=
'SAME', input_shape=(filter1, 16, 16, 1)))
model.add(layers.MaxPooling2D((2, 2), padding='SAME', strides=(2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(filter2, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(categories))
model.summary()
model.compile(optimizer='adam', loss=tf.keras.losses.
SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
history = model.fit(x=x_image, y=data_y, epochs=10, validation_data=(
x_image_validation, data_y_validation))
plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label='val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5, 1])
plt.legend(loc='lower right')
test_loss, test_acc = model.evaluate(x_image_test, data_y_test, verbose=2)
plt.show()
<mask token>
| <mask token>
def next_batch(num, data, labels):
"""
Return a total of `num` random samples and labels.
"""
idx = np.arange(0, len(data))
np.random.shuffle(idx)
idx = idx[:num]
data_shuffle = [data[i] for i in idx]
labels_shuffle = [labels[i] for i in idx]
return np.asarray(data_shuffle), np.asarray(labels_shuffle)
def dataX(features, set):
data_x = np.array([])
count = 0
for filepath in glob.iglob(set):
globpath = filepath + '\\*.jpg'
for filepath in glob.iglob('' + globpath):
count = count + 1
img = Image.open(filepath).convert('L')
data = list(img.getdata())
x = np.array(data)
data_x = np.append(data_x, x)
data_x = data_x.reshape(count, features)
return data_x.astype(int)
def dataY(categories, set):
data_y = np.array([])
count = 0
for filepath in glob.iglob(set):
path = filepath.split('\\')
globpath = filepath + '\\*.jpg'
for filepath in glob.iglob('' + globpath):
count = count + 1
y = np.array([])
t = [int(path[2])]
y = np.append(y, t)
data_y = np.append(data_y, y)
return data_y.astype(int)
def model():
print(' model')
batch_size = 50
features = 32 * 32
categories = 5
filter1 = 32
filter2 = 64
train_path = 'dataset\\train\\[0-4]'
test_path = 'dataset\\test\\[0-4]'
validation_path = 'dataset\\validation\\[0-4]'
data_x = dataX(features, train_path)
print('datax: ', data_x)
data_y = dataY(categories, train_path)
print('datay: ', data_y)
data_x_test = dataX(features, test_path)
data_y_test = dataY(categories, test_path)
data_x_validation = dataX(features, validation_path)
data_y_validation = dataY(categories, validation_path)
x_image = tf.reshape(data_x, [-1, 32, 32, 1])
x_image_test = tf.reshape(data_x_test, [-1, 32, 32, 1])
x_image_validation = tf.reshape(data_x_validation, [-1, 32, 32, 1])
model = models.Sequential()
model.add(layers.Conv2D(filter1, (5, 5), activation='relu', padding=
'SAME', input_shape=(32, 32, 1)))
model.add(layers.MaxPooling2D((2, 2), padding='SAME', strides=(2, 2)))
model.add(layers.Conv2D(filter2, (5, 5), activation='relu', padding=
'SAME', input_shape=(filter1, 16, 16, 1)))
model.add(layers.MaxPooling2D((2, 2), padding='SAME', strides=(2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(filter2, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(categories))
model.summary()
model.compile(optimizer='adam', loss=tf.keras.losses.
SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
history = model.fit(x=x_image, y=data_y, epochs=10, validation_data=(
x_image_validation, data_y_validation))
plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label='val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5, 1])
plt.legend(loc='lower right')
test_loss, test_acc = model.evaluate(x_image_test, data_y_test, verbose=2)
plt.show()
if __name__ == '__main__':
model()
| import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import glob
def next_batch(num, data, labels):
"""
Return a total of `num` random samples and labels.
"""
idx = np.arange(0, len(data))
np.random.shuffle(idx)
idx = idx[:num]
data_shuffle = [data[i] for i in idx]
labels_shuffle = [labels[i] for i in idx]
return np.asarray(data_shuffle), np.asarray(labels_shuffle)
def dataX(features, set):
data_x = np.array([])
count = 0
for filepath in glob.iglob(set):
globpath = filepath + '\\*.jpg'
for filepath in glob.iglob('' + globpath):
count = count + 1
img = Image.open(filepath).convert('L')
data = list(img.getdata())
x = np.array(data)
data_x = np.append(data_x, x)
data_x = data_x.reshape(count, features)
return data_x.astype(int)
def dataY(categories, set):
data_y = np.array([])
count = 0
for filepath in glob.iglob(set):
path = filepath.split('\\')
globpath = filepath + '\\*.jpg'
for filepath in glob.iglob('' + globpath):
count = count + 1
y = np.array([])
t = [int(path[2])]
y = np.append(y, t)
data_y = np.append(data_y, y)
return data_y.astype(int)
def model():
print(' model')
batch_size = 50
features = 32 * 32
categories = 5
filter1 = 32
filter2 = 64
train_path = 'dataset\\train\\[0-4]'
test_path = 'dataset\\test\\[0-4]'
validation_path = 'dataset\\validation\\[0-4]'
data_x = dataX(features, train_path)
print('datax: ', data_x)
data_y = dataY(categories, train_path)
print('datay: ', data_y)
data_x_test = dataX(features, test_path)
data_y_test = dataY(categories, test_path)
data_x_validation = dataX(features, validation_path)
data_y_validation = dataY(categories, validation_path)
x_image = tf.reshape(data_x, [-1, 32, 32, 1])
x_image_test = tf.reshape(data_x_test, [-1, 32, 32, 1])
x_image_validation = tf.reshape(data_x_validation, [-1, 32, 32, 1])
model = models.Sequential()
model.add(layers.Conv2D(filter1, (5, 5), activation='relu', padding=
'SAME', input_shape=(32, 32, 1)))
model.add(layers.MaxPooling2D((2, 2), padding='SAME', strides=(2, 2)))
model.add(layers.Conv2D(filter2, (5, 5), activation='relu', padding=
'SAME', input_shape=(filter1, 16, 16, 1)))
model.add(layers.MaxPooling2D((2, 2), padding='SAME', strides=(2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(filter2, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(categories))
model.summary()
model.compile(optimizer='adam', loss=tf.keras.losses.
SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
history = model.fit(x=x_image, y=data_y, epochs=10, validation_data=(
x_image_validation, data_y_validation))
plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label='val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5, 1])
plt.legend(loc='lower right')
test_loss, test_acc = model.evaluate(x_image_test, data_y_test, verbose=2)
plt.show()
if __name__ == '__main__':
model()
| # This is a sample Python script.
# Press Shift+F10 to execute it or replace it with your code.
# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
import tensorflow as tf
from tensorflow.keras import datasets, layers, models
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import glob
def next_batch(num, data, labels):
'''
Return a total of `num` random samples and labels.
'''
idx = np.arange(0 , len(data))
np.random.shuffle(idx)
idx = idx[:num]
data_shuffle = [data[ i] for i in idx]
labels_shuffle = [labels[ i] for i in idx]
return np.asarray(data_shuffle), np.asarray(labels_shuffle)
def dataX(features, set):
data_x = np.array([])
count = 0
for filepath in glob.iglob(set):
globpath = filepath + '\*.jpg'
for filepath in glob.iglob(r'' + globpath):
count = count + 1
img = Image.open(filepath).convert('L') # convert image to 8-bit grayscale
data = list(img.getdata())
x = np.array(data)
data_x = np.append(data_x, x)
data_x = data_x.reshape(count, features)
# return data_x
return data_x.astype(int)
def dataY(categories, set):
data_y = np.array([])
count = 0
for filepath in glob.iglob(set):
path = filepath.split("\\")
globpath = filepath + '\*.jpg'
for filepath in glob.iglob(r'' + globpath):
count = count + 1
y = np.array([])
t = [int(path[2])]
y = np.append(y, t)
data_y = np.append(data_y, y)
# data_y = data_y.reshape(count, categories)
# return data_y
return data_y.astype(int)
def model():
print(' model')
batch_size = 50
features = 32 * 32
categories = 5
filter1 = 32
filter2 = 64
train_path = r'dataset\train\[0-4]'
test_path = r'dataset\test\[0-4]'
validation_path = r'dataset\validation\[0-4]'
data_x = dataX(features, train_path)
print("datax: ", data_x)
data_y = dataY(categories, train_path)
print("datay: ", data_y)
data_x_test = dataX(features, test_path)
data_y_test = dataY(categories, test_path)
data_x_validation = dataX(features, validation_path)
data_y_validation = dataY(categories, validation_path)
x_image = tf.reshape(data_x, [-1, 32, 32, 1])
x_image_test = tf.reshape(data_x_test, [-1, 32, 32, 1])
x_image_validation = tf.reshape(data_x_validation, [-1, 32, 32, 1])
model = models.Sequential()
model.add(layers.Conv2D(filter1, (5, 5), activation='relu', padding='SAME', input_shape=(32, 32, 1)))
model.add(layers.MaxPooling2D((2, 2), padding='SAME', strides=(2, 2)))
model.add(layers.Conv2D(filter2, (5, 5), activation='relu', padding='SAME', input_shape=(filter1, 16, 16, 1)))
model.add(layers.MaxPooling2D((2, 2), padding='SAME', strides=(2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(filter2, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(categories))
model.summary()
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
history = model.fit(x=x_image, y=data_y, epochs=10,
validation_data=(x_image_validation, data_y_validation))
# Show results in graph view
plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label='val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5, 1])
plt.legend(loc='lower right')
# plt.show()
test_loss, test_acc = model.evaluate(x_image_test, data_y_test, verbose=2)
plt.show()
if __name__ == '__main__':
model()
| [
1,
4,
5,
6,
7
] |
232 | ef85f94282bfd7c9491c4e28bab61aaab5c792a5 | <mask token>
class Ui_Tab(object):
<mask token>
<mask token>
| <mask token>
class Ui_Tab(object):
<mask token>
def retranslateUi(self, Tab):
_translate = QtCore.QCoreApplication.translate
Tab.setWindowTitle(_translate('Tab', 'Form'))
self.btn_enterPassword.setText(_translate('Tab', 'Enter Password'))
| <mask token>
class Ui_Tab(object):
def setupUi(self, Tab):
Tab.setObjectName('Tab')
Tab.resize(762, 523)
self.verticalLayout = QtWidgets.QVBoxLayout(Tab)
self.verticalLayout.setObjectName('verticalLayout')
self.hLayout = QtWidgets.QHBoxLayout()
self.hLayout.setObjectName('hLayout')
self.btn_enterPassword = QtWidgets.QPushButton(Tab)
self.btn_enterPassword.setObjectName('btn_enterPassword')
self.hLayout.addWidget(self.btn_enterPassword)
spacerItem = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.
Expanding, QtWidgets.QSizePolicy.Minimum)
self.hLayout.addItem(spacerItem)
self.verticalLayout.addLayout(self.hLayout)
self.scrollArea = QtWidgets.QScrollArea(Tab)
self.scrollArea.setWidgetResizable(True)
self.scrollArea.setObjectName('scrollArea')
self.scrollAreaWidgetContents = QtWidgets.QWidget()
self.scrollAreaWidgetContents.setGeometry(QtCore.QRect(0, 0, 736, 455))
self.scrollAreaWidgetContents.setObjectName('scrollAreaWidgetContents')
self.gridLayout = QtWidgets.QGridLayout(self.scrollAreaWidgetContents)
self.gridLayout.setObjectName('gridLayout')
self.scrollArea.setWidget(self.scrollAreaWidgetContents)
self.verticalLayout.addWidget(self.scrollArea)
self.retranslateUi(Tab)
QtCore.QMetaObject.connectSlotsByName(Tab)
def retranslateUi(self, Tab):
_translate = QtCore.QCoreApplication.translate
Tab.setWindowTitle(_translate('Tab', 'Form'))
self.btn_enterPassword.setText(_translate('Tab', 'Enter Password'))
| from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_Tab(object):
def setupUi(self, Tab):
Tab.setObjectName('Tab')
Tab.resize(762, 523)
self.verticalLayout = QtWidgets.QVBoxLayout(Tab)
self.verticalLayout.setObjectName('verticalLayout')
self.hLayout = QtWidgets.QHBoxLayout()
self.hLayout.setObjectName('hLayout')
self.btn_enterPassword = QtWidgets.QPushButton(Tab)
self.btn_enterPassword.setObjectName('btn_enterPassword')
self.hLayout.addWidget(self.btn_enterPassword)
spacerItem = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.
Expanding, QtWidgets.QSizePolicy.Minimum)
self.hLayout.addItem(spacerItem)
self.verticalLayout.addLayout(self.hLayout)
self.scrollArea = QtWidgets.QScrollArea(Tab)
self.scrollArea.setWidgetResizable(True)
self.scrollArea.setObjectName('scrollArea')
self.scrollAreaWidgetContents = QtWidgets.QWidget()
self.scrollAreaWidgetContents.setGeometry(QtCore.QRect(0, 0, 736, 455))
self.scrollAreaWidgetContents.setObjectName('scrollAreaWidgetContents')
self.gridLayout = QtWidgets.QGridLayout(self.scrollAreaWidgetContents)
self.gridLayout.setObjectName('gridLayout')
self.scrollArea.setWidget(self.scrollAreaWidgetContents)
self.verticalLayout.addWidget(self.scrollArea)
self.retranslateUi(Tab)
QtCore.QMetaObject.connectSlotsByName(Tab)
def retranslateUi(self, Tab):
_translate = QtCore.QCoreApplication.translate
Tab.setWindowTitle(_translate('Tab', 'Form'))
self.btn_enterPassword.setText(_translate('Tab', 'Enter Password'))
| # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'src/ui_LibraryTab.ui'
#
# Created: Tue Jun 9 21:46:41 2015
# by: PyQt5 UI code generator 5.4
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_Tab(object):
def setupUi(self, Tab):
Tab.setObjectName("Tab")
Tab.resize(762, 523)
self.verticalLayout = QtWidgets.QVBoxLayout(Tab)
self.verticalLayout.setObjectName("verticalLayout")
self.hLayout = QtWidgets.QHBoxLayout()
self.hLayout.setObjectName("hLayout")
self.btn_enterPassword = QtWidgets.QPushButton(Tab)
self.btn_enterPassword.setObjectName("btn_enterPassword")
self.hLayout.addWidget(self.btn_enterPassword)
spacerItem = QtWidgets.QSpacerItem(40, 20, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Minimum)
self.hLayout.addItem(spacerItem)
self.verticalLayout.addLayout(self.hLayout)
self.scrollArea = QtWidgets.QScrollArea(Tab)
self.scrollArea.setWidgetResizable(True)
self.scrollArea.setObjectName("scrollArea")
self.scrollAreaWidgetContents = QtWidgets.QWidget()
self.scrollAreaWidgetContents.setGeometry(QtCore.QRect(0, 0, 736, 455))
self.scrollAreaWidgetContents.setObjectName("scrollAreaWidgetContents")
self.gridLayout = QtWidgets.QGridLayout(self.scrollAreaWidgetContents)
self.gridLayout.setObjectName("gridLayout")
self.scrollArea.setWidget(self.scrollAreaWidgetContents)
self.verticalLayout.addWidget(self.scrollArea)
self.retranslateUi(Tab)
QtCore.QMetaObject.connectSlotsByName(Tab)
def retranslateUi(self, Tab):
_translate = QtCore.QCoreApplication.translate
Tab.setWindowTitle(_translate("Tab", "Form"))
self.btn_enterPassword.setText(_translate("Tab", "Enter Password"))
| [
1,
2,
3,
4,
5
] |
233 | 8c8bbbc682889c8d79c893f27def76ad70e8bf8d | <mask token>
| DATABASE_NAME = 'user_db'
| DATABASE_NAME = "user_db" | null | null | [
0,
1,
2
] |
234 | 9d904225afd4f4d0cf338ae16f031f8ab41639ad | <mask token>
class FragmentMakoChain(Fragment):
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def __init__(self, content=None, base=None, lookup_dirs=None):
"""
Класс, позволяющий последовательно оборачивать экземпляры Fragment друг
в друга.
:param content: Содержимое фрагмента
:param base: Базовый фрагмент, тот, в который будет обёрнут этот фрагмент;
должен быть экземпляром FragmentMakoChain или None
:param lookup_dirs: Директории поиска шаблонов
:return:
"""
assert isinstance(base, FragmentMakoChain) or base is None
super(FragmentMakoChain, self).__init__(content=content)
self.base = base
self.lookup_dirs = lookup_dirs
def body_html(self):
template = self.build_chain()
return template.render(**self.context.get('render_context', {}))
def add_context(self, new_context):
deep_update(self.context, new_context)
def build_chain(self):
"""
Строит цепочку шаблонов.
В цепочке каждый шаблон наследуется от одного и того же ifmo_xblock_base,
поэтому порядок оборачивания не определён (точнее, его вычисляет
метод super()). Поскольку при рендере шаблона используется исключительно
lookup от шаблона, над которым он вызван, а не собственный Lookup для
каждого из шаблона в коллекции, необходимо добавить в коллекцию все
пути и шаблоны, использующиеся в шаблоне выше по цепочке. Более того,
необходимо изменить имена шаблонов (ifmo_xblock_base) на уникальные.
:param lookup: экземпляр TemplateLookup, в который будут записываться
новые пути и шаблоны, использующиеся как родительские
:return: tuple(template, lookup, base_template_id)
- template -- шаблон, который должен будет стать родителем
- lookup -- изменённый lookup
"""
def _build_chain(self, lookup=None):
old_base_name = 'ifmo_xblock_base'
new_base_name = None
if self.base is not None:
import uuid
new_base_name = '{name}_{rnd}'.format(name=old_base_name,
rnd=str(uuid.uuid4()))
if hasattr(self.base, 'build_chain'):
base_template, base_lookup = _build_chain(self.base, lookup
)
lookup.put_template(new_base_name, base_template)
else:
lookup.put_string(new_base_name, self.base.body_html())
lookup.append_dirs(self.base.lookup_dirs)
return Template(text=self._content.replace(old_base_name,
new_base_name) if new_base_name else self._content, lookup=
lookup), lookup
lookup = TemplateLookup(directories=self.lookup_dirs)
template, _ = _build_chain(self, lookup)
return template
<mask token>
@property
def content(self):
return self.body_html()
<mask token>
| <mask token>
class FragmentMakoChain(Fragment):
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def __init__(self, content=None, base=None, lookup_dirs=None):
"""
Класс, позволяющий последовательно оборачивать экземпляры Fragment друг
в друга.
:param content: Содержимое фрагмента
:param base: Базовый фрагмент, тот, в который будет обёрнут этот фрагмент;
должен быть экземпляром FragmentMakoChain или None
:param lookup_dirs: Директории поиска шаблонов
:return:
"""
assert isinstance(base, FragmentMakoChain) or base is None
super(FragmentMakoChain, self).__init__(content=content)
self.base = base
self.lookup_dirs = lookup_dirs
def body_html(self):
template = self.build_chain()
return template.render(**self.context.get('render_context', {}))
def add_context(self, new_context):
deep_update(self.context, new_context)
def build_chain(self):
"""
Строит цепочку шаблонов.
В цепочке каждый шаблон наследуется от одного и того же ifmo_xblock_base,
поэтому порядок оборачивания не определён (точнее, его вычисляет
метод super()). Поскольку при рендере шаблона используется исключительно
lookup от шаблона, над которым он вызван, а не собственный Lookup для
каждого из шаблона в коллекции, необходимо добавить в коллекцию все
пути и шаблоны, использующиеся в шаблоне выше по цепочке. Более того,
необходимо изменить имена шаблонов (ifmo_xblock_base) на уникальные.
:param lookup: экземпляр TemplateLookup, в который будут записываться
новые пути и шаблоны, использующиеся как родительские
:return: tuple(template, lookup, base_template_id)
- template -- шаблон, который должен будет стать родителем
- lookup -- изменённый lookup
"""
def _build_chain(self, lookup=None):
old_base_name = 'ifmo_xblock_base'
new_base_name = None
if self.base is not None:
import uuid
new_base_name = '{name}_{rnd}'.format(name=old_base_name,
rnd=str(uuid.uuid4()))
if hasattr(self.base, 'build_chain'):
base_template, base_lookup = _build_chain(self.base, lookup
)
lookup.put_template(new_base_name, base_template)
else:
lookup.put_string(new_base_name, self.base.body_html())
lookup.append_dirs(self.base.lookup_dirs)
return Template(text=self._content.replace(old_base_name,
new_base_name) if new_base_name else self._content, lookup=
lookup), lookup
lookup = TemplateLookup(directories=self.lookup_dirs)
template, _ = _build_chain(self, lookup)
return template
<mask token>
@property
def content(self):
return self.body_html()
@content.setter
def content(self, value):
self._content = value
| <mask token>
class FragmentMakoChain(Fragment):
"""
Класс, позволяющий последовательно оборачивать экземпляры Fragment друг в
друга.
Для того, чтобы цепочка отработала, шаблон должен наследоваться от шаблона
ifmo_xblock_base и определять блок block_body.
Порядок оборачивания не определён.
"""
base = None
context = {}
_content = None
lookup_dirs = None
def __init__(self, content=None, base=None, lookup_dirs=None):
"""
Класс, позволяющий последовательно оборачивать экземпляры Fragment друг
в друга.
:param content: Содержимое фрагмента
:param base: Базовый фрагмент, тот, в который будет обёрнут этот фрагмент;
должен быть экземпляром FragmentMakoChain или None
:param lookup_dirs: Директории поиска шаблонов
:return:
"""
assert isinstance(base, FragmentMakoChain) or base is None
super(FragmentMakoChain, self).__init__(content=content)
self.base = base
self.lookup_dirs = lookup_dirs
def body_html(self):
template = self.build_chain()
return template.render(**self.context.get('render_context', {}))
def add_context(self, new_context):
deep_update(self.context, new_context)
def build_chain(self):
"""
Строит цепочку шаблонов.
В цепочке каждый шаблон наследуется от одного и того же ifmo_xblock_base,
поэтому порядок оборачивания не определён (точнее, его вычисляет
метод super()). Поскольку при рендере шаблона используется исключительно
lookup от шаблона, над которым он вызван, а не собственный Lookup для
каждого из шаблона в коллекции, необходимо добавить в коллекцию все
пути и шаблоны, использующиеся в шаблоне выше по цепочке. Более того,
необходимо изменить имена шаблонов (ifmo_xblock_base) на уникальные.
:param lookup: экземпляр TemplateLookup, в который будут записываться
новые пути и шаблоны, использующиеся как родительские
:return: tuple(template, lookup, base_template_id)
- template -- шаблон, который должен будет стать родителем
- lookup -- изменённый lookup
"""
def _build_chain(self, lookup=None):
old_base_name = 'ifmo_xblock_base'
new_base_name = None
if self.base is not None:
import uuid
new_base_name = '{name}_{rnd}'.format(name=old_base_name,
rnd=str(uuid.uuid4()))
if hasattr(self.base, 'build_chain'):
base_template, base_lookup = _build_chain(self.base, lookup
)
lookup.put_template(new_base_name, base_template)
else:
lookup.put_string(new_base_name, self.base.body_html())
lookup.append_dirs(self.base.lookup_dirs)
return Template(text=self._content.replace(old_base_name,
new_base_name) if new_base_name else self._content, lookup=
lookup), lookup
lookup = TemplateLookup(directories=self.lookup_dirs)
template, _ = _build_chain(self, lookup)
return template
@property
def resources(self):
seen = set()
parent_res = self.base.resources if self.base else []
return [x for x in parent_res + self._resources if x not in seen and
not seen.add(x)]
@property
def content(self):
return self.body_html()
@content.setter
def content(self, value):
self._content = value
| from mako.template import Template
from xblock.fragment import Fragment
from .lookup import TemplateLookup
from .utils import deep_update
class FragmentMakoChain(Fragment):
"""
Класс, позволяющий последовательно оборачивать экземпляры Fragment друг в
друга.
Для того, чтобы цепочка отработала, шаблон должен наследоваться от шаблона
ifmo_xblock_base и определять блок block_body.
Порядок оборачивания не определён.
"""
base = None
context = {}
_content = None
lookup_dirs = None
def __init__(self, content=None, base=None, lookup_dirs=None):
"""
Класс, позволяющий последовательно оборачивать экземпляры Fragment друг
в друга.
:param content: Содержимое фрагмента
:param base: Базовый фрагмент, тот, в который будет обёрнут этот фрагмент;
должен быть экземпляром FragmentMakoChain или None
:param lookup_dirs: Директории поиска шаблонов
:return:
"""
assert isinstance(base, FragmentMakoChain) or base is None
super(FragmentMakoChain, self).__init__(content=content)
self.base = base
self.lookup_dirs = lookup_dirs
def body_html(self):
template = self.build_chain()
return template.render(**self.context.get('render_context', {}))
def add_context(self, new_context):
deep_update(self.context, new_context)
def build_chain(self):
"""
Строит цепочку шаблонов.
В цепочке каждый шаблон наследуется от одного и того же ifmo_xblock_base,
поэтому порядок оборачивания не определён (точнее, его вычисляет
метод super()). Поскольку при рендере шаблона используется исключительно
lookup от шаблона, над которым он вызван, а не собственный Lookup для
каждого из шаблона в коллекции, необходимо добавить в коллекцию все
пути и шаблоны, использующиеся в шаблоне выше по цепочке. Более того,
необходимо изменить имена шаблонов (ifmo_xblock_base) на уникальные.
:param lookup: экземпляр TemplateLookup, в который будут записываться
новые пути и шаблоны, использующиеся как родительские
:return: tuple(template, lookup, base_template_id)
- template -- шаблон, который должен будет стать родителем
- lookup -- изменённый lookup
"""
def _build_chain(self, lookup=None):
old_base_name = 'ifmo_xblock_base'
new_base_name = None
if self.base is not None:
import uuid
new_base_name = '{name}_{rnd}'.format(name=old_base_name,
rnd=str(uuid.uuid4()))
if hasattr(self.base, 'build_chain'):
base_template, base_lookup = _build_chain(self.base, lookup
)
lookup.put_template(new_base_name, base_template)
else:
lookup.put_string(new_base_name, self.base.body_html())
lookup.append_dirs(self.base.lookup_dirs)
return Template(text=self._content.replace(old_base_name,
new_base_name) if new_base_name else self._content, lookup=
lookup), lookup
lookup = TemplateLookup(directories=self.lookup_dirs)
template, _ = _build_chain(self, lookup)
return template
@property
def resources(self):
seen = set()
parent_res = self.base.resources if self.base else []
return [x for x in parent_res + self._resources if x not in seen and
not seen.add(x)]
@property
def content(self):
return self.body_html()
@content.setter
def content(self, value):
self._content = value
| # -*- coding=utf-8 -*-
from mako.template import Template
from xblock.fragment import Fragment
from .lookup import TemplateLookup # xblock_ifmo.lookup
from .utils import deep_update
class FragmentMakoChain(Fragment):
"""
Класс, позволяющий последовательно оборачивать экземпляры Fragment друг в
друга.
Для того, чтобы цепочка отработала, шаблон должен наследоваться от шаблона
ifmo_xblock_base и определять блок block_body.
Порядок оборачивания не определён.
"""
base = None
context = {}
_content = None
lookup_dirs = None
def __init__(self, content=None, base=None, lookup_dirs=None):
"""
Класс, позволяющий последовательно оборачивать экземпляры Fragment друг
в друга.
:param content: Содержимое фрагмента
:param base: Базовый фрагмент, тот, в который будет обёрнут этот фрагмент;
должен быть экземпляром FragmentMakoChain или None
:param lookup_dirs: Директории поиска шаблонов
:return:
"""
assert isinstance(base, FragmentMakoChain) or base is None
super(FragmentMakoChain, self).__init__(content=content)
self.base = base
self.lookup_dirs = lookup_dirs
def body_html(self):
template = self.build_chain()
return template.render(**self.context.get('render_context', {}))
def add_context(self, new_context):
deep_update(self.context, new_context)
def build_chain(self):
"""
Строит цепочку шаблонов.
В цепочке каждый шаблон наследуется от одного и того же ifmo_xblock_base,
поэтому порядок оборачивания не определён (точнее, его вычисляет
метод super()). Поскольку при рендере шаблона используется исключительно
lookup от шаблона, над которым он вызван, а не собственный Lookup для
каждого из шаблона в коллекции, необходимо добавить в коллекцию все
пути и шаблоны, использующиеся в шаблоне выше по цепочке. Более того,
необходимо изменить имена шаблонов (ifmo_xblock_base) на уникальные.
:param lookup: экземпляр TemplateLookup, в который будут записываться
новые пути и шаблоны, использующиеся как родительские
:return: tuple(template, lookup, base_template_id)
- template -- шаблон, который должен будет стать родителем
- lookup -- изменённый lookup
"""
def _build_chain(self, lookup=None):
old_base_name = "ifmo_xblock_base"
new_base_name = None
if self.base is not None:
import uuid
new_base_name = "{name}_{rnd}".format(name=old_base_name, rnd=str(uuid.uuid4()))
if hasattr(self.base, 'build_chain'):
base_template, base_lookup = _build_chain(self.base, lookup)
lookup.put_template(new_base_name, base_template)
else:
lookup.put_string(new_base_name, self.base.body_html())
lookup.append_dirs(self.base.lookup_dirs)
return Template(
text=self._content.replace(old_base_name, new_base_name) if new_base_name else self._content,
lookup=lookup
), lookup
lookup = TemplateLookup(directories=self.lookup_dirs)
template, _ = _build_chain(self, lookup)
return template
@property
def resources(self):
seen = set()
parent_res = self.base.resources if self.base else []
return [x for x in parent_res + self._resources if x not in seen and not seen.add(x)]
@property
def content(self):
return self.body_html()
@content.setter
def content(self, value):
self._content = value
| [
6,
7,
10,
11,
12
] |
235 | 8cc0393082448bb8f61068b5c96e89ef3aee77ed | <mask token>
class LdapSync(Thread):
def __init__(self, settings):
Thread.__init__(self)
self.settings = settings
def run(self):
if self.settings.enable_group_sync:
migrate_dn_pairs(settings=self.settings)
self.start_sync()
self.show_sync_result()
<mask token>
def start_sync(self):
data_ldap = self.get_data_from_ldap()
if data_ldap is None:
return
data_db = self.get_data_from_db()
if data_db is None:
return
self.sync_data(data_db, data_ldap)
<mask token>
def get_data_from_ldap(self):
ret = {}
for config in self.settings.ldap_configs:
cur_ret = self.get_data_from_ldap_by_server(config)
if cur_ret is None:
return None
for key in cur_ret.keys():
if key not in ret:
ret[key] = cur_ret[key]
ret[key].config = config
return ret
def get_data_from_ldap_by_server(self, config):
return None
def sync_data(self, data_db, data_ldap):
pass
| <mask token>
class LdapSync(Thread):
def __init__(self, settings):
Thread.__init__(self)
self.settings = settings
def run(self):
if self.settings.enable_group_sync:
migrate_dn_pairs(settings=self.settings)
self.start_sync()
self.show_sync_result()
<mask token>
def start_sync(self):
data_ldap = self.get_data_from_ldap()
if data_ldap is None:
return
data_db = self.get_data_from_db()
if data_db is None:
return
self.sync_data(data_db, data_ldap)
def get_data_from_db(self):
return None
def get_data_from_ldap(self):
ret = {}
for config in self.settings.ldap_configs:
cur_ret = self.get_data_from_ldap_by_server(config)
if cur_ret is None:
return None
for key in cur_ret.keys():
if key not in ret:
ret[key] = cur_ret[key]
ret[key].config = config
return ret
def get_data_from_ldap_by_server(self, config):
return None
def sync_data(self, data_db, data_ldap):
pass
| <mask token>
class LdapSync(Thread):
def __init__(self, settings):
Thread.__init__(self)
self.settings = settings
def run(self):
if self.settings.enable_group_sync:
migrate_dn_pairs(settings=self.settings)
self.start_sync()
self.show_sync_result()
def show_sync_result(self):
pass
def start_sync(self):
data_ldap = self.get_data_from_ldap()
if data_ldap is None:
return
data_db = self.get_data_from_db()
if data_db is None:
return
self.sync_data(data_db, data_ldap)
def get_data_from_db(self):
return None
def get_data_from_ldap(self):
ret = {}
for config in self.settings.ldap_configs:
cur_ret = self.get_data_from_ldap_by_server(config)
if cur_ret is None:
return None
for key in cur_ret.keys():
if key not in ret:
ret[key] = cur_ret[key]
ret[key].config = config
return ret
def get_data_from_ldap_by_server(self, config):
return None
def sync_data(self, data_db, data_ldap):
pass
| <mask token>
def migrate_dn_pairs(settings):
grp_dn_pairs = get_group_dn_pairs()
if grp_dn_pairs is None:
logger.warning(
'get group dn pairs from db failed when migrate dn pairs.')
return
grp_dn_pairs.reverse()
for grp_dn_pair in grp_dn_pairs:
for config in settings.ldap_configs:
search_filter = '(objectClass=*)'
ldap_conn = LdapConn(config.host, config.user_dn, config.passwd,
config.follow_referrals)
ldap_conn.create_conn()
if not ldap_conn.conn:
logger.warning('connect ldap server [%s] failed.' % config.
user_dn)
return
if config.use_page_result:
results = ldap_conn.paged_search(grp_dn_pair.dn, SCOPE_BASE,
search_filter, [config.group_uuid_attr])
else:
results = ldap_conn.search(grp_dn_pair.dn, SCOPE_BASE,
search_filter, [config.group_uuid_attr])
ldap_conn.unbind_conn()
results = bytes2str(results)
if not results:
continue
else:
uuid = results[0][1][config.group_uuid_attr][0]
add_group_uuid_pair(grp_dn_pair.group_id, uuid)
class LdapSync(Thread):
def __init__(self, settings):
Thread.__init__(self)
self.settings = settings
def run(self):
if self.settings.enable_group_sync:
migrate_dn_pairs(settings=self.settings)
self.start_sync()
self.show_sync_result()
def show_sync_result(self):
pass
def start_sync(self):
data_ldap = self.get_data_from_ldap()
if data_ldap is None:
return
data_db = self.get_data_from_db()
if data_db is None:
return
self.sync_data(data_db, data_ldap)
def get_data_from_db(self):
return None
def get_data_from_ldap(self):
ret = {}
for config in self.settings.ldap_configs:
cur_ret = self.get_data_from_ldap_by_server(config)
if cur_ret is None:
return None
for key in cur_ret.keys():
if key not in ret:
ret[key] = cur_ret[key]
ret[key].config = config
return ret
def get_data_from_ldap_by_server(self, config):
return None
def sync_data(self, data_db, data_ldap):
pass
| #coding: utf-8
import logging
from threading import Thread
from ldap import SCOPE_BASE
from seafevents.ldap_syncer.ldap_conn import LdapConn
from seafevents.ldap_syncer.utils import bytes2str, add_group_uuid_pair
from seaserv import get_group_dn_pairs
logger = logging.getLogger(__name__)
def migrate_dn_pairs(settings):
grp_dn_pairs = get_group_dn_pairs()
if grp_dn_pairs is None:
logger.warning('get group dn pairs from db failed when migrate dn pairs.')
return
grp_dn_pairs.reverse()
for grp_dn_pair in grp_dn_pairs:
for config in settings.ldap_configs:
search_filter = '(objectClass=*)'
ldap_conn = LdapConn(config.host, config.user_dn, config.passwd, config.follow_referrals)
ldap_conn.create_conn()
if not ldap_conn.conn:
logger.warning('connect ldap server [%s] failed.' % config.user_dn)
return
if config.use_page_result:
results = ldap_conn.paged_search(grp_dn_pair.dn, SCOPE_BASE,
search_filter,
[config.group_uuid_attr])
else:
results = ldap_conn.search(grp_dn_pair.dn, SCOPE_BASE,
search_filter,
[config.group_uuid_attr])
ldap_conn.unbind_conn()
results = bytes2str(results)
if not results:
continue
else:
uuid = results[0][1][config.group_uuid_attr][0]
add_group_uuid_pair(grp_dn_pair.group_id, uuid)
class LdapSync(Thread):
def __init__(self, settings):
Thread.__init__(self)
self.settings = settings
def run(self):
if self.settings.enable_group_sync:
migrate_dn_pairs(settings=self.settings)
self.start_sync()
self.show_sync_result()
def show_sync_result(self):
pass
def start_sync(self):
data_ldap = self.get_data_from_ldap()
if data_ldap is None:
return
data_db = self.get_data_from_db()
if data_db is None:
return
self.sync_data(data_db, data_ldap)
def get_data_from_db(self):
return None
def get_data_from_ldap(self):
ret = {}
for config in self.settings.ldap_configs:
cur_ret = self.get_data_from_ldap_by_server(config)
# If get data from one server failed, then the result is failed
if cur_ret is None:
return None
for key in cur_ret.keys():
if key not in ret:
ret[key] = cur_ret[key]
ret[key].config = config
return ret
def get_data_from_ldap_by_server(self, config):
return None
def sync_data(self, data_db, data_ldap):
pass
| [
7,
8,
9,
10,
13
] |
236 | 21af630bf383ee1bdd0f644283f0ddadde71620a | #!/bin/usr/python2.7.x
import os, re, urllib2
def main():
ip = raw_input(" Target IP : ")
check(ip)
def check(ip):
try:
print "Loading Check File Uploader...."
print 58*"-"
page = 1
while page <= 21:
bing = "http://www.bing.com/search?q=ip%3A" + \
ip + "+upload&count=50&first=" + str(page)
openbing = urllib2.urlopen(bing)
readbing = openbing.read()
findwebs = re.findall('<h2><a href="(.*?)"', readbing)
sites = findwebs
for i in sites:
try:
response = urllib2.urlopen(i).read()
checksqli(i)
except urllib2.HTTPError, e:
str(sites).strip(i)
page = page + 10
except:
pass
def checksqli(sqli):
responsetwo = urllib2.urlopen(sqli).read()
find = re.findall('type="file"', responsetwo)
if find:
print(" Found ---> " + sqli)
main() | null | null | null | null | [
0
] |
237 | eb853e430b996a81dc2ef20c320979a3e04d956a | <mask token>
class Beautyleg7Spider(scrapy.Spider):
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def start_requests(self):
mysql_host = self.crawler.settings.get('MYSQL_HOST')
mysql_port = self.crawler.settings.get('MYSQL_PORT')
mysql_user = self.crawler.settings.get('MYSQL_USER')
mysql_password = self.crawler.settings.get('MYSQL_PASSWORD')
mysql_db_name = self.crawler.settings.get('MYSQL_DB_NAME')
engine = create_engine('mysql+mysqlconnector://{}:{}@{}:{}/{}'.
format(mysql_user, mysql_password, mysql_host, mysql_port,
mysql_db_name), pool_recycle=180, echo=False)
session_maker = sessionmaker(bind=engine)
self.db_session = session_maker()
for url in self.start_urls:
yield scrapy.Request(url)
def parse(self, response):
if self.db_session is None:
self.logger.error('db_session is None')
return None
repeated_count = 0
if response is None:
self.logger.warn('响应为空，不做处理！')
else:
album_nodes = response.css('.pic .item')
category = response.css('.sitepath a')[1].css('a::text'
).extract_first().strip()
is_persisted_last_item = self.redis_cmd.get(self.
album_last_item_redis_unique_key)
is_last_item_finished = False
if is_persisted_last_item is not None and int(
is_persisted_last_item):
is_last_item_finished = True
self.logger.info('已持久化最后一页的最后主题：%s' % self.
album_last_item_redis_unique_key)
album_last_page_url = response.meta.get('album_last_page_url')
if album_last_page_url is not None:
album_last_page_url_last_item_redis_suffix = album_nodes[-1
].css('.p a::attr(href)').extract_first()
self.album_last_item_redis_unique_key = (self.
ALBUM_URL_REDIS_KEY_PREFIX + self.REDIS_LIMITER + self.
sub_url_scheme(album_last_page_url, '') + self.
REDIS_LIMITER + self.sub_url_scheme(
album_last_page_url_last_item_redis_suffix, ''))
self.redis_cmd.setnx(self.album_last_item_redis_unique_key, 0)
for album_node in album_nodes:
album_url = album_node.css('.p a::attr(href)').extract_first(
).strip()
is_persisted = self.redis_cmd.get(album_url)
if is_persisted is not None and int(is_persisted):
self.logger.info('Redis中该url album_url：%s已持久化' % album_url)
continue
album_url_object_id = self.get_md5(album_url)
self.redis_cmd.setnx(album_url, 0)
count = 0
try:
count = self.db_session.query(func.count()).filter(
Album.album_url_object_id == album_url_object_id
).first()
if count:
count = count[0]
except Exception as e:
self.logger.error('查询数据库异常，原因：{}'.format(e))
finally:
self.db_session.rollback()
if count:
self.logger.info('数据库已有该数据album_url_object_id：%s' %
album_url_object_id)
repeated_count += 1
self.redis_cmd.set(album_url, 1, xx=True)
continue
else:
album_item = self.parse_album_item(album_node,
album_url, album_url_object_id, category)
yield response.follow(url=album_url, meta={'AlbumItem':
album_item}, callback=self.parse_detail)
selector_list = response.css('.page li a::attr(href)')
if not is_last_item_finished:
if selector_list:
last_page_url = None
current_url_page = response.xpath(
'//li[@class="thisclass"]//text()').extract_first()
if current_url_page and int(current_url_page) == 1:
last_page_url = selector_list[-1].extract()
next_url = selector_list[-2].extract()
if next_url == last_page_url:
album_last_page_url = response.urljoin(last_page_url)
self.logger.info('Last page：%s' % album_last_page_url)
else:
self.logger.info('Next page：%s' % response.urljoin(
next_url))
yield response.follow(url=next_url, meta={
'album_last_page_url': album_last_page_url},
callback=self.parse)
else:
self.logger.info('selector_list is None')
self.logger.info('重复次数：%s' % repeated_count)
else:
self.logger.info('Stop crawler. None Next page!')
def parse_album_item(self, album_node, album_url, album_url_object_id,
category):
album_title = album_node.css('.p a img::attr(alt)').extract_first(
).strip()
cover_url = album_node.css('.p a img::attr(src)').extract_first(
).strip()
regex = '\\d+\\.\\d+.\\d+\\s+No\\.\\d+|\\d+\\-\\d+-\\d+\\s+No\\.\\d+'
number_group = re.findall(regex, album_title)
if len(number_group) > 0:
number = number_group[0]
else:
number = 'No.unknown'
create_date = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
album_item = AlbumItem()
album_item['category'] = category
album_item['album_url'] = album_url
album_item['album_url_object_id'] = album_url_object_id
album_item['album_title'] = album_title
album_item['cover_url'] = cover_url
album_item['number'] = number
album_item['create_date'] = create_date
return album_item
def parse_detail(self, response):
self.album_item = response.meta.get('AlbumItem')
self.album_image_relation_item['album_item'] = self.album_item
self.parse_album_image_item(response)
relative_next_page_list = response.css('.page li a::attr(href)'
).extract()
next_page_threads = [self.gevent_pool.spawn(self.
get_album_image_item_list, response.urljoin(relative_next_page)
) for relative_next_page in relative_next_page_list[2:-1]]
gevent.joinall(next_page_threads)
self.album_image_relation_item['album_image_item_list'
] = self.album_image_item_list
self.album_image_item_list = []
yield self.album_image_relation_item
def get_album_image_item_list(self, abs_next_page):
"""
使用下页绝对路径同步请求
:param abs_next_page:
:return:
"""
resp = requests.get(abs_next_page)
if resp.status_code == 200:
encoding = requests.utils.get_encodings_from_content(resp.text)
resp.encoding = encoding[0]
self.parse_album_image_item(etree.HTML(resp.text))
else:
self.logger.warn('下载此页{}失败,返回的状态码为{}'.format(abs_next_page,
resp.status_code))
def parse_album_image_item(self, response):
"""
解析item并返回给pipelines
:param response: 如果response类型是继承自scrapy的TextResponse类则使用scrapy的Selector来解析，否则使用lxml来解析
:return:
"""
if isinstance(response, HtmlResponse):
item_title = response.xpath('//div[@class="content"]/h1/text()'
).extract_first().strip()
publish_date = response.xpath('//div[@class="tit"]/span/text()'
).extract_first().split(':')[1]
image_link_list = response.css('.contents a img::attr(src)'
).extract()
else:
item_title = response.xpath('//div[@class="content"]/h1/text()')[0
].strip()
publish_date = response.xpath('//div[@class="tit"]/span/text()')[0
].split(':')[1]
image_link_list = response.xpath('//div[@class="contents"]/a/img')
image_link_list = [image_link.attrib['src'] for image_link in
image_link_list]
regex = '\\s?\\w+[^\\w]?'
regex_group = re.findall(regex, item_title)
stage_name = 'unknown'
if len(regex_group) > 0:
str = regex_group[-1]
if '[' in str:
stage_name = str.split('[')[0].strip()
elif '(' in str:
stage_name = str.split('(')[0].strip()
elif re.match('[^\\d*]', str):
stage_name = re.match('[^\\d*]', str).group()
for image_url in image_link_list:
album_image_item = AlbumImageItem()
album_image_item['item_url'] = image_url
album_image_item['item_url_object_id'] = self.get_md5(image_url)
item_url_list_json = '{}'
album_image_item['item_url_list_json'] = item_url_list_json
album_image_item['item_title'] = item_title
album_image_item['stage_name'] = stage_name
album_image_item['publish_date'] = publish_date
self.album_image_item_list.append(album_image_item)
return self.album_image_item_list
<mask token>
<mask token>
| <mask token>
class Beautyleg7Spider(scrapy.Spider):
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def start_requests(self):
mysql_host = self.crawler.settings.get('MYSQL_HOST')
mysql_port = self.crawler.settings.get('MYSQL_PORT')
mysql_user = self.crawler.settings.get('MYSQL_USER')
mysql_password = self.crawler.settings.get('MYSQL_PASSWORD')
mysql_db_name = self.crawler.settings.get('MYSQL_DB_NAME')
engine = create_engine('mysql+mysqlconnector://{}:{}@{}:{}/{}'.
format(mysql_user, mysql_password, mysql_host, mysql_port,
mysql_db_name), pool_recycle=180, echo=False)
session_maker = sessionmaker(bind=engine)
self.db_session = session_maker()
for url in self.start_urls:
yield scrapy.Request(url)
def parse(self, response):
if self.db_session is None:
self.logger.error('db_session is None')
return None
repeated_count = 0
if response is None:
self.logger.warn('响应为空，不做处理！')
else:
album_nodes = response.css('.pic .item')
category = response.css('.sitepath a')[1].css('a::text'
).extract_first().strip()
is_persisted_last_item = self.redis_cmd.get(self.
album_last_item_redis_unique_key)
is_last_item_finished = False
if is_persisted_last_item is not None and int(
is_persisted_last_item):
is_last_item_finished = True
self.logger.info('已持久化最后一页的最后主题：%s' % self.
album_last_item_redis_unique_key)
album_last_page_url = response.meta.get('album_last_page_url')
if album_last_page_url is not None:
album_last_page_url_last_item_redis_suffix = album_nodes[-1
].css('.p a::attr(href)').extract_first()
self.album_last_item_redis_unique_key = (self.
ALBUM_URL_REDIS_KEY_PREFIX + self.REDIS_LIMITER + self.
sub_url_scheme(album_last_page_url, '') + self.
REDIS_LIMITER + self.sub_url_scheme(
album_last_page_url_last_item_redis_suffix, ''))
self.redis_cmd.setnx(self.album_last_item_redis_unique_key, 0)
for album_node in album_nodes:
album_url = album_node.css('.p a::attr(href)').extract_first(
).strip()
is_persisted = self.redis_cmd.get(album_url)
if is_persisted is not None and int(is_persisted):
self.logger.info('Redis中该url album_url：%s已持久化' % album_url)
continue
album_url_object_id = self.get_md5(album_url)
self.redis_cmd.setnx(album_url, 0)
count = 0
try:
count = self.db_session.query(func.count()).filter(
Album.album_url_object_id == album_url_object_id
).first()
if count:
count = count[0]
except Exception as e:
self.logger.error('查询数据库异常，原因：{}'.format(e))
finally:
self.db_session.rollback()
if count:
self.logger.info('数据库已有该数据album_url_object_id：%s' %
album_url_object_id)
repeated_count += 1
self.redis_cmd.set(album_url, 1, xx=True)
continue
else:
album_item = self.parse_album_item(album_node,
album_url, album_url_object_id, category)
yield response.follow(url=album_url, meta={'AlbumItem':
album_item}, callback=self.parse_detail)
selector_list = response.css('.page li a::attr(href)')
if not is_last_item_finished:
if selector_list:
last_page_url = None
current_url_page = response.xpath(
'//li[@class="thisclass"]//text()').extract_first()
if current_url_page and int(current_url_page) == 1:
last_page_url = selector_list[-1].extract()
next_url = selector_list[-2].extract()
if next_url == last_page_url:
album_last_page_url = response.urljoin(last_page_url)
self.logger.info('Last page：%s' % album_last_page_url)
else:
self.logger.info('Next page：%s' % response.urljoin(
next_url))
yield response.follow(url=next_url, meta={
'album_last_page_url': album_last_page_url},
callback=self.parse)
else:
self.logger.info('selector_list is None')
self.logger.info('重复次数：%s' % repeated_count)
else:
self.logger.info('Stop crawler. None Next page!')
def parse_album_item(self, album_node, album_url, album_url_object_id,
category):
album_title = album_node.css('.p a img::attr(alt)').extract_first(
).strip()
cover_url = album_node.css('.p a img::attr(src)').extract_first(
).strip()
regex = '\\d+\\.\\d+.\\d+\\s+No\\.\\d+|\\d+\\-\\d+-\\d+\\s+No\\.\\d+'
number_group = re.findall(regex, album_title)
if len(number_group) > 0:
number = number_group[0]
else:
number = 'No.unknown'
create_date = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
album_item = AlbumItem()
album_item['category'] = category
album_item['album_url'] = album_url
album_item['album_url_object_id'] = album_url_object_id
album_item['album_title'] = album_title
album_item['cover_url'] = cover_url
album_item['number'] = number
album_item['create_date'] = create_date
return album_item
def parse_detail(self, response):
self.album_item = response.meta.get('AlbumItem')
self.album_image_relation_item['album_item'] = self.album_item
self.parse_album_image_item(response)
relative_next_page_list = response.css('.page li a::attr(href)'
).extract()
next_page_threads = [self.gevent_pool.spawn(self.
get_album_image_item_list, response.urljoin(relative_next_page)
) for relative_next_page in relative_next_page_list[2:-1]]
gevent.joinall(next_page_threads)
self.album_image_relation_item['album_image_item_list'
] = self.album_image_item_list
self.album_image_item_list = []
yield self.album_image_relation_item
def get_album_image_item_list(self, abs_next_page):
"""
使用下页绝对路径同步请求
:param abs_next_page:
:return:
"""
resp = requests.get(abs_next_page)
if resp.status_code == 200:
encoding = requests.utils.get_encodings_from_content(resp.text)
resp.encoding = encoding[0]
self.parse_album_image_item(etree.HTML(resp.text))
else:
self.logger.warn('下载此页{}失败,返回的状态码为{}'.format(abs_next_page,
resp.status_code))
def parse_album_image_item(self, response):
"""
解析item并返回给pipelines
:param response: 如果response类型是继承自scrapy的TextResponse类则使用scrapy的Selector来解析，否则使用lxml来解析
:return:
"""
if isinstance(response, HtmlResponse):
item_title = response.xpath('//div[@class="content"]/h1/text()'
).extract_first().strip()
publish_date = response.xpath('//div[@class="tit"]/span/text()'
).extract_first().split(':')[1]
image_link_list = response.css('.contents a img::attr(src)'
).extract()
else:
item_title = response.xpath('//div[@class="content"]/h1/text()')[0
].strip()
publish_date = response.xpath('//div[@class="tit"]/span/text()')[0
].split(':')[1]
image_link_list = response.xpath('//div[@class="contents"]/a/img')
image_link_list = [image_link.attrib['src'] for image_link in
image_link_list]
regex = '\\s?\\w+[^\\w]?'
regex_group = re.findall(regex, item_title)
stage_name = 'unknown'
if len(regex_group) > 0:
str = regex_group[-1]
if '[' in str:
stage_name = str.split('[')[0].strip()
elif '(' in str:
stage_name = str.split('(')[0].strip()
elif re.match('[^\\d*]', str):
stage_name = re.match('[^\\d*]', str).group()
for image_url in image_link_list:
album_image_item = AlbumImageItem()
album_image_item['item_url'] = image_url
album_image_item['item_url_object_id'] = self.get_md5(image_url)
item_url_list_json = '{}'
album_image_item['item_url_list_json'] = item_url_list_json
album_image_item['item_title'] = item_title
album_image_item['stage_name'] = stage_name
album_image_item['publish_date'] = publish_date
self.album_image_item_list.append(album_image_item)
return self.album_image_item_list
<mask token>
@staticmethod
def sub_url_scheme(website, replace_str):
scheme_regex = '^(http://|https://)'
return re.sub(scheme_regex, replace_str, website)
| <mask token>
class Beautyleg7Spider(scrapy.Spider):
name = 'Beautyleg7Spider'
category_list = ['siwameitui', 'xingganmeinv', 'weimeixiezhen',
'ribenmeinv']
start_urls = [('http://www.beautyleg7.com/' + category) for category in
category_list]
const.REPEATED_THRESHOLD = 10
def __init__(self, name=None, **kwargs):
super().__init__(name=None, **kwargs)
self.db_session = None
self.gevent_pool = Pool(32)
self.redis_cmd = get_redis_conn_from_pool()
self.ALBUM_URL_REDIS_KEY_PREFIX = 'album_url'
self.REDIS_LIMITER = ':'
self.album_last_item_redis_unique_key = ''
self.album_item = None
self.album_image_item_list = []
self.album_image_relation_item = AlbumImageRelationItem()
def start_requests(self):
mysql_host = self.crawler.settings.get('MYSQL_HOST')
mysql_port = self.crawler.settings.get('MYSQL_PORT')
mysql_user = self.crawler.settings.get('MYSQL_USER')
mysql_password = self.crawler.settings.get('MYSQL_PASSWORD')
mysql_db_name = self.crawler.settings.get('MYSQL_DB_NAME')
engine = create_engine('mysql+mysqlconnector://{}:{}@{}:{}/{}'.
format(mysql_user, mysql_password, mysql_host, mysql_port,
mysql_db_name), pool_recycle=180, echo=False)
session_maker = sessionmaker(bind=engine)
self.db_session = session_maker()
for url in self.start_urls:
yield scrapy.Request(url)
def parse(self, response):
if self.db_session is None:
self.logger.error('db_session is None')
return None
repeated_count = 0
if response is None:
self.logger.warn('响应为空，不做处理！')
else:
album_nodes = response.css('.pic .item')
category = response.css('.sitepath a')[1].css('a::text'
).extract_first().strip()
is_persisted_last_item = self.redis_cmd.get(self.
album_last_item_redis_unique_key)
is_last_item_finished = False
if is_persisted_last_item is not None and int(
is_persisted_last_item):
is_last_item_finished = True
self.logger.info('已持久化最后一页的最后主题：%s' % self.
album_last_item_redis_unique_key)
album_last_page_url = response.meta.get('album_last_page_url')
if album_last_page_url is not None:
album_last_page_url_last_item_redis_suffix = album_nodes[-1
].css('.p a::attr(href)').extract_first()
self.album_last_item_redis_unique_key = (self.
ALBUM_URL_REDIS_KEY_PREFIX + self.REDIS_LIMITER + self.
sub_url_scheme(album_last_page_url, '') + self.
REDIS_LIMITER + self.sub_url_scheme(
album_last_page_url_last_item_redis_suffix, ''))
self.redis_cmd.setnx(self.album_last_item_redis_unique_key, 0)
for album_node in album_nodes:
album_url = album_node.css('.p a::attr(href)').extract_first(
).strip()
is_persisted = self.redis_cmd.get(album_url)
if is_persisted is not None and int(is_persisted):
self.logger.info('Redis中该url album_url：%s已持久化' % album_url)
continue
album_url_object_id = self.get_md5(album_url)
self.redis_cmd.setnx(album_url, 0)
count = 0
try:
count = self.db_session.query(func.count()).filter(
Album.album_url_object_id == album_url_object_id
).first()
if count:
count = count[0]
except Exception as e:
self.logger.error('查询数据库异常，原因：{}'.format(e))
finally:
self.db_session.rollback()
if count:
self.logger.info('数据库已有该数据album_url_object_id：%s' %
album_url_object_id)
repeated_count += 1
self.redis_cmd.set(album_url, 1, xx=True)
continue
else:
album_item = self.parse_album_item(album_node,
album_url, album_url_object_id, category)
yield response.follow(url=album_url, meta={'AlbumItem':
album_item}, callback=self.parse_detail)
selector_list = response.css('.page li a::attr(href)')
if not is_last_item_finished:
if selector_list:
last_page_url = None
current_url_page = response.xpath(
'//li[@class="thisclass"]//text()').extract_first()
if current_url_page and int(current_url_page) == 1:
last_page_url = selector_list[-1].extract()
next_url = selector_list[-2].extract()
if next_url == last_page_url:
album_last_page_url = response.urljoin(last_page_url)
self.logger.info('Last page：%s' % album_last_page_url)
else:
self.logger.info('Next page：%s' % response.urljoin(
next_url))
yield response.follow(url=next_url, meta={
'album_last_page_url': album_last_page_url},
callback=self.parse)
else:
self.logger.info('selector_list is None')
self.logger.info('重复次数：%s' % repeated_count)
else:
self.logger.info('Stop crawler. None Next page!')
def parse_album_item(self, album_node, album_url, album_url_object_id,
category):
album_title = album_node.css('.p a img::attr(alt)').extract_first(
).strip()
cover_url = album_node.css('.p a img::attr(src)').extract_first(
).strip()
regex = '\\d+\\.\\d+.\\d+\\s+No\\.\\d+|\\d+\\-\\d+-\\d+\\s+No\\.\\d+'
number_group = re.findall(regex, album_title)
if len(number_group) > 0:
number = number_group[0]
else:
number = 'No.unknown'
create_date = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
album_item = AlbumItem()
album_item['category'] = category
album_item['album_url'] = album_url
album_item['album_url_object_id'] = album_url_object_id
album_item['album_title'] = album_title
album_item['cover_url'] = cover_url
album_item['number'] = number
album_item['create_date'] = create_date
return album_item
def parse_detail(self, response):
self.album_item = response.meta.get('AlbumItem')
self.album_image_relation_item['album_item'] = self.album_item
self.parse_album_image_item(response)
relative_next_page_list = response.css('.page li a::attr(href)'
).extract()
next_page_threads = [self.gevent_pool.spawn(self.
get_album_image_item_list, response.urljoin(relative_next_page)
) for relative_next_page in relative_next_page_list[2:-1]]
gevent.joinall(next_page_threads)
self.album_image_relation_item['album_image_item_list'
] = self.album_image_item_list
self.album_image_item_list = []
yield self.album_image_relation_item
def get_album_image_item_list(self, abs_next_page):
"""
使用下页绝对路径同步请求
:param abs_next_page:
:return:
"""
resp = requests.get(abs_next_page)
if resp.status_code == 200:
encoding = requests.utils.get_encodings_from_content(resp.text)
resp.encoding = encoding[0]
self.parse_album_image_item(etree.HTML(resp.text))
else:
self.logger.warn('下载此页{}失败,返回的状态码为{}'.format(abs_next_page,
resp.status_code))
def parse_album_image_item(self, response):
"""
解析item并返回给pipelines
:param response: 如果response类型是继承自scrapy的TextResponse类则使用scrapy的Selector来解析，否则使用lxml来解析
:return:
"""
if isinstance(response, HtmlResponse):
item_title = response.xpath('//div[@class="content"]/h1/text()'
).extract_first().strip()
publish_date = response.xpath('//div[@class="tit"]/span/text()'
).extract_first().split(':')[1]
image_link_list = response.css('.contents a img::attr(src)'
).extract()
else:
item_title = response.xpath('//div[@class="content"]/h1/text()')[0
].strip()
publish_date = response.xpath('//div[@class="tit"]/span/text()')[0
].split(':')[1]
image_link_list = response.xpath('//div[@class="contents"]/a/img')
image_link_list = [image_link.attrib['src'] for image_link in
image_link_list]
regex = '\\s?\\w+[^\\w]?'
regex_group = re.findall(regex, item_title)
stage_name = 'unknown'
if len(regex_group) > 0:
str = regex_group[-1]
if '[' in str:
stage_name = str.split('[')[0].strip()
elif '(' in str:
stage_name = str.split('(')[0].strip()
elif re.match('[^\\d*]', str):
stage_name = re.match('[^\\d*]', str).group()
for image_url in image_link_list:
album_image_item = AlbumImageItem()
album_image_item['item_url'] = image_url
album_image_item['item_url_object_id'] = self.get_md5(image_url)
item_url_list_json = '{}'
album_image_item['item_url_list_json'] = item_url_list_json
album_image_item['item_title'] = item_title
album_image_item['stage_name'] = stage_name
album_image_item['publish_date'] = publish_date
self.album_image_item_list.append(album_image_item)
return self.album_image_item_list
@staticmethod
def get_md5(param):
if isinstance(param, str):
param = param.encode()
m = hashlib.md5()
m.update(param)
return m.hexdigest()
@staticmethod
def sub_url_scheme(website, replace_str):
scheme_regex = '^(http://|https://)'
return re.sub(scheme_regex, replace_str, website)
| import hashlib
import re
from datetime import datetime
import gevent
import requests
import scrapy
from gevent.pool import Pool
from lxml import etree
from scrapy.http import HtmlResponse
from sqlalchemy import create_engine, func
from sqlalchemy.orm import sessionmaker
from ..items import Album, AlbumImageRelationItem, AlbumItem, AlbumImageItem
from ..utils.const import const
from ..utils.redis_util import get_redis_conn_from_pool
class Beautyleg7Spider(scrapy.Spider):
name = 'Beautyleg7Spider'
category_list = ['siwameitui', 'xingganmeinv', 'weimeixiezhen',
'ribenmeinv']
start_urls = [('http://www.beautyleg7.com/' + category) for category in
category_list]
const.REPEATED_THRESHOLD = 10
def __init__(self, name=None, **kwargs):
super().__init__(name=None, **kwargs)
self.db_session = None
self.gevent_pool = Pool(32)
self.redis_cmd = get_redis_conn_from_pool()
self.ALBUM_URL_REDIS_KEY_PREFIX = 'album_url'
self.REDIS_LIMITER = ':'
self.album_last_item_redis_unique_key = ''
self.album_item = None
self.album_image_item_list = []
self.album_image_relation_item = AlbumImageRelationItem()
def start_requests(self):
mysql_host = self.crawler.settings.get('MYSQL_HOST')
mysql_port = self.crawler.settings.get('MYSQL_PORT')
mysql_user = self.crawler.settings.get('MYSQL_USER')
mysql_password = self.crawler.settings.get('MYSQL_PASSWORD')
mysql_db_name = self.crawler.settings.get('MYSQL_DB_NAME')
engine = create_engine('mysql+mysqlconnector://{}:{}@{}:{}/{}'.
format(mysql_user, mysql_password, mysql_host, mysql_port,
mysql_db_name), pool_recycle=180, echo=False)
session_maker = sessionmaker(bind=engine)
self.db_session = session_maker()
for url in self.start_urls:
yield scrapy.Request(url)
def parse(self, response):
if self.db_session is None:
self.logger.error('db_session is None')
return None
repeated_count = 0
if response is None:
self.logger.warn('响应为空，不做处理！')
else:
album_nodes = response.css('.pic .item')
category = response.css('.sitepath a')[1].css('a::text'
).extract_first().strip()
is_persisted_last_item = self.redis_cmd.get(self.
album_last_item_redis_unique_key)
is_last_item_finished = False
if is_persisted_last_item is not None and int(
is_persisted_last_item):
is_last_item_finished = True
self.logger.info('已持久化最后一页的最后主题：%s' % self.
album_last_item_redis_unique_key)
album_last_page_url = response.meta.get('album_last_page_url')
if album_last_page_url is not None:
album_last_page_url_last_item_redis_suffix = album_nodes[-1
].css('.p a::attr(href)').extract_first()
self.album_last_item_redis_unique_key = (self.
ALBUM_URL_REDIS_KEY_PREFIX + self.REDIS_LIMITER + self.
sub_url_scheme(album_last_page_url, '') + self.
REDIS_LIMITER + self.sub_url_scheme(
album_last_page_url_last_item_redis_suffix, ''))
self.redis_cmd.setnx(self.album_last_item_redis_unique_key, 0)
for album_node in album_nodes:
album_url = album_node.css('.p a::attr(href)').extract_first(
).strip()
is_persisted = self.redis_cmd.get(album_url)
if is_persisted is not None and int(is_persisted):
self.logger.info('Redis中该url album_url：%s已持久化' % album_url)
continue
album_url_object_id = self.get_md5(album_url)
self.redis_cmd.setnx(album_url, 0)
count = 0
try:
count = self.db_session.query(func.count()).filter(
Album.album_url_object_id == album_url_object_id
).first()
if count:
count = count[0]
except Exception as e:
self.logger.error('查询数据库异常，原因：{}'.format(e))
finally:
self.db_session.rollback()
if count:
self.logger.info('数据库已有该数据album_url_object_id：%s' %
album_url_object_id)
repeated_count += 1
self.redis_cmd.set(album_url, 1, xx=True)
continue
else:
album_item = self.parse_album_item(album_node,
album_url, album_url_object_id, category)
yield response.follow(url=album_url, meta={'AlbumItem':
album_item}, callback=self.parse_detail)
selector_list = response.css('.page li a::attr(href)')
if not is_last_item_finished:
if selector_list:
last_page_url = None
current_url_page = response.xpath(
'//li[@class="thisclass"]//text()').extract_first()
if current_url_page and int(current_url_page) == 1:
last_page_url = selector_list[-1].extract()
next_url = selector_list[-2].extract()
if next_url == last_page_url:
album_last_page_url = response.urljoin(last_page_url)
self.logger.info('Last page：%s' % album_last_page_url)
else:
self.logger.info('Next page：%s' % response.urljoin(
next_url))
yield response.follow(url=next_url, meta={
'album_last_page_url': album_last_page_url},
callback=self.parse)
else:
self.logger.info('selector_list is None')
self.logger.info('重复次数：%s' % repeated_count)
else:
self.logger.info('Stop crawler. None Next page!')
def parse_album_item(self, album_node, album_url, album_url_object_id,
category):
album_title = album_node.css('.p a img::attr(alt)').extract_first(
).strip()
cover_url = album_node.css('.p a img::attr(src)').extract_first(
).strip()
regex = '\\d+\\.\\d+.\\d+\\s+No\\.\\d+|\\d+\\-\\d+-\\d+\\s+No\\.\\d+'
number_group = re.findall(regex, album_title)
if len(number_group) > 0:
number = number_group[0]
else:
number = 'No.unknown'
create_date = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
album_item = AlbumItem()
album_item['category'] = category
album_item['album_url'] = album_url
album_item['album_url_object_id'] = album_url_object_id
album_item['album_title'] = album_title
album_item['cover_url'] = cover_url
album_item['number'] = number
album_item['create_date'] = create_date
return album_item
def parse_detail(self, response):
self.album_item = response.meta.get('AlbumItem')
self.album_image_relation_item['album_item'] = self.album_item
self.parse_album_image_item(response)
relative_next_page_list = response.css('.page li a::attr(href)'
).extract()
next_page_threads = [self.gevent_pool.spawn(self.
get_album_image_item_list, response.urljoin(relative_next_page)
) for relative_next_page in relative_next_page_list[2:-1]]
gevent.joinall(next_page_threads)
self.album_image_relation_item['album_image_item_list'
] = self.album_image_item_list
self.album_image_item_list = []
yield self.album_image_relation_item
def get_album_image_item_list(self, abs_next_page):
"""
使用下页绝对路径同步请求
:param abs_next_page:
:return:
"""
resp = requests.get(abs_next_page)
if resp.status_code == 200:
encoding = requests.utils.get_encodings_from_content(resp.text)
resp.encoding = encoding[0]
self.parse_album_image_item(etree.HTML(resp.text))
else:
self.logger.warn('下载此页{}失败,返回的状态码为{}'.format(abs_next_page,
resp.status_code))
def parse_album_image_item(self, response):
"""
解析item并返回给pipelines
:param response: 如果response类型是继承自scrapy的TextResponse类则使用scrapy的Selector来解析，否则使用lxml来解析
:return:
"""
if isinstance(response, HtmlResponse):
item_title = response.xpath('//div[@class="content"]/h1/text()'
).extract_first().strip()
publish_date = response.xpath('//div[@class="tit"]/span/text()'
).extract_first().split(':')[1]
image_link_list = response.css('.contents a img::attr(src)'
).extract()
else:
item_title = response.xpath('//div[@class="content"]/h1/text()')[0
].strip()
publish_date = response.xpath('//div[@class="tit"]/span/text()')[0
].split(':')[1]
image_link_list = response.xpath('//div[@class="contents"]/a/img')
image_link_list = [image_link.attrib['src'] for image_link in
image_link_list]
regex = '\\s?\\w+[^\\w]?'
regex_group = re.findall(regex, item_title)
stage_name = 'unknown'
if len(regex_group) > 0:
str = regex_group[-1]
if '[' in str:
stage_name = str.split('[')[0].strip()
elif '(' in str:
stage_name = str.split('(')[0].strip()
elif re.match('[^\\d*]', str):
stage_name = re.match('[^\\d*]', str).group()
for image_url in image_link_list:
album_image_item = AlbumImageItem()
album_image_item['item_url'] = image_url
album_image_item['item_url_object_id'] = self.get_md5(image_url)
item_url_list_json = '{}'
album_image_item['item_url_list_json'] = item_url_list_json
album_image_item['item_title'] = item_title
album_image_item['stage_name'] = stage_name
album_image_item['publish_date'] = publish_date
self.album_image_item_list.append(album_image_item)
return self.album_image_item_list
@staticmethod
def get_md5(param):
if isinstance(param, str):
param = param.encode()
m = hashlib.md5()
m.update(param)
return m.hexdigest()
@staticmethod
def sub_url_scheme(website, replace_str):
scheme_regex = '^(http://|https://)'
return re.sub(scheme_regex, replace_str, website)
| #!/usr/bin/env python3
# -*- coding: UTF-8 -*-
import hashlib
import re
from datetime import datetime
import gevent
import requests
import scrapy
from gevent.pool import Pool
from lxml import etree
from scrapy.http import HtmlResponse
from sqlalchemy import create_engine, func
from sqlalchemy.orm import sessionmaker
from ..items import Album, AlbumImageRelationItem, AlbumItem, AlbumImageItem
from ..utils.const import const
from ..utils.redis_util import get_redis_conn_from_pool
class Beautyleg7Spider(scrapy.Spider):
name = 'Beautyleg7Spider'
category_list = ['siwameitui', 'xingganmeinv', 'weimeixiezhen', 'ribenmeinv']
start_urls = [('http://www.beautyleg7.com/' + category) for category in category_list]
const.REPEATED_THRESHOLD = 10
def __init__(self, name=None, **kwargs):
super().__init__(name=None, **kwargs)
self.db_session = None
self.gevent_pool = Pool(32)
self.redis_cmd = get_redis_conn_from_pool()
self.ALBUM_URL_REDIS_KEY_PREFIX = "album_url"
self.REDIS_LIMITER = ":"
self.album_last_item_redis_unique_key = ""
self.album_item = None
self.album_image_item_list = []
self.album_image_relation_item = AlbumImageRelationItem()
def start_requests(self):
mysql_host = self.crawler.settings.get("MYSQL_HOST")
mysql_port = self.crawler.settings.get("MYSQL_PORT")
mysql_user = self.crawler.settings.get("MYSQL_USER")
mysql_password = self.crawler.settings.get("MYSQL_PASSWORD")
mysql_db_name = self.crawler.settings.get("MYSQL_DB_NAME")
engine = create_engine('mysql+mysqlconnector://{}:{}@{}:{}/{}'.format(mysql_user, mysql_password,
mysql_host, mysql_port,
mysql_db_name),
pool_recycle=180, echo=False)
session_maker = sessionmaker(bind=engine)
self.db_session = session_maker()
for url in self.start_urls:
yield scrapy.Request(url)
def parse(self, response):
if self.db_session is None:
self.logger.error("db_session is None")
return None
repeated_count = 0
if response is None:
self.logger.warn("响应为空，不做处理！")
else:
album_nodes = response.css('.pic .item')
category = response.css('.sitepath a')[1].css('a::text').extract_first().strip()
# 判断最后一页的最后主题是否被持久化
is_persisted_last_item = self.redis_cmd.get(self.album_last_item_redis_unique_key)
is_last_item_finished = False
if is_persisted_last_item is not None and int(is_persisted_last_item):
is_last_item_finished = True
self.logger.info("已持久化最后一页的最后主题：%s" % self.album_last_item_redis_unique_key)
# 如果是最后一页则设置Redis存储key:“最后一页页码：最后一条主题url”，value：is_persisted（取值为0或1，默认为0）
album_last_page_url = response.meta.get("album_last_page_url")
if album_last_page_url is not None:
album_last_page_url_last_item_redis_suffix = album_nodes[-1].css('.p a::attr(href)').extract_first()
self.album_last_item_redis_unique_key = self.ALBUM_URL_REDIS_KEY_PREFIX + self.REDIS_LIMITER + \
self.sub_url_scheme(album_last_page_url,
"") + self.REDIS_LIMITER + \
self.sub_url_scheme(album_last_page_url_last_item_redis_suffix,
"")
self.redis_cmd.setnx(self.album_last_item_redis_unique_key, 0)
for album_node in album_nodes:
album_url = album_node.css('.p a::attr(href)').extract_first().strip()
# 判断当前主题url是否已持久化
is_persisted = self.redis_cmd.get(album_url)
if is_persisted is not None and int(is_persisted):
self.logger.info("Redis中该url album_url：%s已持久化" % album_url)
continue
album_url_object_id = self.get_md5(album_url)
# 只有name不存在时，当前set操作才执行
self.redis_cmd.setnx(album_url, 0)
count = 0
try:
count = self.db_session.query(func.count()).filter(
Album.album_url_object_id == album_url_object_id).first()
if count:
count = count[0]
except Exception as e:
self.logger.error("查询数据库异常，原因：{}".format(e))
finally:
self.db_session.rollback()
if count:
self.logger.info("数据库已有该数据album_url_object_id：%s" % album_url_object_id)
repeated_count += 1
# 只有name存在时，当前set操作才执行
self.redis_cmd.set(album_url, 1, xx=True)
continue
else:
album_item = self.parse_album_item(album_node, album_url, album_url_object_id, category)
yield response.follow(url=album_url,
meta={"AlbumItem": album_item},
callback=self.parse_detail)
# 提取下一页并交给scrapy下载
selector_list = response.css('.page li a::attr(href)')
# 如果最后一页的最后一个主题url未被持久化则继续爬取
if not is_last_item_finished:
if selector_list:
last_page_url = None
current_url_page = response.xpath('//li[@class="thisclass"]//text()').extract_first()
# 如果当前页是第一页则获取最后一页url
if current_url_page and int(current_url_page) == 1:
last_page_url = selector_list[-1].extract()
next_url = selector_list[-2].extract()
if next_url == last_page_url:
album_last_page_url = response.urljoin(last_page_url)
self.logger.info("Last page：%s" % album_last_page_url)
else:
self.logger.info("Next page：%s" % response.urljoin(next_url))
yield response.follow(url=next_url,
meta={"album_last_page_url": album_last_page_url},
callback=self.parse)
else:
self.logger.info("selector_list is None")
self.logger.info("重复次数：%s" % repeated_count)
else:
self.logger.info("Stop crawler. None Next page!")
def parse_album_item(self, album_node, album_url, album_url_object_id, category):
album_title = album_node.css('.p a img::attr(alt)').extract_first().strip()
cover_url = album_node.css('.p a img::attr(src)').extract_first().strip()
regex = "\d+\.\d+.\d+\s+No\.\d+|\d+\-\d+-\d+\s+No\.\d+"
number_group = re.findall(regex, album_title)
if len(number_group) > 0:
number = number_group[0]
else:
number = "No.unknown"
create_date = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
album_item = AlbumItem()
album_item['category'] = category
album_item['album_url'] = album_url
album_item['album_url_object_id'] = album_url_object_id
album_item['album_title'] = album_title
album_item['cover_url'] = cover_url
album_item['number'] = number
album_item['create_date'] = create_date
return album_item
def parse_detail(self, response):
self.album_item = response.meta.get("AlbumItem")
self.album_image_relation_item['album_item'] = self.album_item
self.parse_album_image_item(response)
# 详情页分页链接,循环生成所有子页面的请求
relative_next_page_list = response.css('.page li a::attr(href)').extract()
# 使用gevent协程池提升网络IO处理效率
next_page_threads = [
self.gevent_pool.spawn(self.get_album_image_item_list, response.urljoin(relative_next_page))
for relative_next_page in relative_next_page_list[2:-1]
]
gevent.joinall(next_page_threads)
self.album_image_relation_item['album_image_item_list'] = self.album_image_item_list
# 重新初始化
self.album_image_item_list = []
yield self.album_image_relation_item
def get_album_image_item_list(self, abs_next_page):
"""
使用下页绝对路径同步请求
:param abs_next_page:
:return:
"""
resp = requests.get(abs_next_page)
if resp.status_code == 200:
encoding = requests.utils.get_encodings_from_content(resp.text)
resp.encoding = encoding[0]
self.parse_album_image_item(etree.HTML(resp.text))
else:
self.logger.warn("下载此页{}失败,返回的状态码为{}".format(abs_next_page, resp.status_code))
def parse_album_image_item(self, response):
"""
解析item并返回给pipelines
:param response: 如果response类型是继承自scrapy的TextResponse类则使用scrapy的Selector来解析，否则使用lxml来解析
:return:
"""
if isinstance(response, HtmlResponse):
item_title = response.xpath('//div[@class="content"]/h1/text()').extract_first().strip()
publish_date = response.xpath('//div[@class="tit"]/span/text()').extract_first().split(":")[1]
image_link_list = response.css('.contents a img::attr(src)').extract()
else:
item_title = response.xpath('//div[@class="content"]/h1/text()')[0].strip()
publish_date = response.xpath('//div[@class="tit"]/span/text()')[0].split(":")[1]
image_link_list = response.xpath('//div[@class="contents"]/a/img')
image_link_list = [image_link.attrib['src'] for image_link in image_link_list]
regex = "\s?\w+[^\w]?"
regex_group = re.findall(regex, item_title)
stage_name = "unknown"
if len(regex_group) > 0:
str = regex_group[-1]
if "[" in str:
stage_name = str.split("[")[0].strip()
elif "(" in str:
stage_name = str.split("(")[0].strip()
elif re.match('[^\d*]', str):
stage_name = re.match('[^\d*]', str).group()
# 详情页多个图片链接
for image_url in image_link_list:
album_image_item = AlbumImageItem()
album_image_item['item_url'] = image_url
album_image_item['item_url_object_id'] = self.get_md5(image_url)
item_url_list_json = "{}"
album_image_item['item_url_list_json'] = item_url_list_json
album_image_item['item_title'] = item_title
album_image_item['stage_name'] = stage_name
album_image_item['publish_date'] = publish_date
self.album_image_item_list.append(album_image_item)
return self.album_image_item_list
@staticmethod
def get_md5(param):
if isinstance(param, str):
param = param.encode()
m = hashlib.md5()
m.update(param)
return m.hexdigest()
@staticmethod
def sub_url_scheme(website, replace_str):
scheme_regex = "^(http://|https://)"
return re.sub(scheme_regex, replace_str, website)
| [
7,
8,
11,
12,
13
] |
238 | bc4684d255a46427f708d8ce8bda2e12fb8c8ffe | <mask token>
def main():
r4m = Route4Me(API_KEY)
route = r4m.route
response = route.get_routes(limit=1, offset=0)
if isinstance(response, dict) and 'errors' in response.keys():
print('. '.join(response['errors']))
else:
route_id = response[0]['route_id']
print('Route ID: {}'.format(route_id))
response = route.get_route(route_id=route_id)
if isinstance(response, dict) and 'errors' in response.keys():
print('. '.join(response['errors']))
else:
print('Original Route')
print('Route ID: {}'.format(response['route_id']))
for i, address in enumerate(response['addresses']):
print('Address #{}'.format(i + 1))
print('\tAddress: {0}'.format(address['address']))
print('\tRoute Destination ID: {0}'.format(address[
'route_destination_id']))
route_destination_id = response['addresses'][1][
'route_destination_id']
route_destination_id2 = response['addresses'][2][
'route_destination_id']
data = {'route_destination_id': route_destination_id,
'route_id': route_id, 'addresses': [{'route_destination_id':
route_destination_id2, 'sequence_no': 6}]}
print('After Re-sequence Route')
response = route.resequence_route(**data)
print('Route ID: {}'.format(response['route_id']))
for i, address in enumerate(response['addresses']):
print('Address #{}'.format(i + 1))
print('\tAddress: {0}'.format(address['address']))
print('\tRoute Destination ID: {0}'.format(address[
'route_destination_id']))
<mask token>
| <mask token>
def main():
r4m = Route4Me(API_KEY)
route = r4m.route
response = route.get_routes(limit=1, offset=0)
if isinstance(response, dict) and 'errors' in response.keys():
print('. '.join(response['errors']))
else:
route_id = response[0]['route_id']
print('Route ID: {}'.format(route_id))
response = route.get_route(route_id=route_id)
if isinstance(response, dict) and 'errors' in response.keys():
print('. '.join(response['errors']))
else:
print('Original Route')
print('Route ID: {}'.format(response['route_id']))
for i, address in enumerate(response['addresses']):
print('Address #{}'.format(i + 1))
print('\tAddress: {0}'.format(address['address']))
print('\tRoute Destination ID: {0}'.format(address[
'route_destination_id']))
route_destination_id = response['addresses'][1][
'route_destination_id']
route_destination_id2 = response['addresses'][2][
'route_destination_id']
data = {'route_destination_id': route_destination_id,
'route_id': route_id, 'addresses': [{'route_destination_id':
route_destination_id2, 'sequence_no': 6}]}
print('After Re-sequence Route')
response = route.resequence_route(**data)
print('Route ID: {}'.format(response['route_id']))
for i, address in enumerate(response['addresses']):
print('Address #{}'.format(i + 1))
print('\tAddress: {0}'.format(address['address']))
print('\tRoute Destination ID: {0}'.format(address[
'route_destination_id']))
if __name__ == '__main__':
main()
| <mask token>
API_KEY = '11111111111111111111111111111111'
def main():
r4m = Route4Me(API_KEY)
route = r4m.route
response = route.get_routes(limit=1, offset=0)
if isinstance(response, dict) and 'errors' in response.keys():
print('. '.join(response['errors']))
else:
route_id = response[0]['route_id']
print('Route ID: {}'.format(route_id))
response = route.get_route(route_id=route_id)
if isinstance(response, dict) and 'errors' in response.keys():
print('. '.join(response['errors']))
else:
print('Original Route')
print('Route ID: {}'.format(response['route_id']))
for i, address in enumerate(response['addresses']):
print('Address #{}'.format(i + 1))
print('\tAddress: {0}'.format(address['address']))
print('\tRoute Destination ID: {0}'.format(address[
'route_destination_id']))
route_destination_id = response['addresses'][1][
'route_destination_id']
route_destination_id2 = response['addresses'][2][
'route_destination_id']
data = {'route_destination_id': route_destination_id,
'route_id': route_id, 'addresses': [{'route_destination_id':
route_destination_id2, 'sequence_no': 6}]}
print('After Re-sequence Route')
response = route.resequence_route(**data)
print('Route ID: {}'.format(response['route_id']))
for i, address in enumerate(response['addresses']):
print('Address #{}'.format(i + 1))
print('\tAddress: {0}'.format(address['address']))
print('\tRoute Destination ID: {0}'.format(address[
'route_destination_id']))
if __name__ == '__main__':
main()
| from route4me import Route4Me
API_KEY = '11111111111111111111111111111111'
def main():
r4m = Route4Me(API_KEY)
route = r4m.route
response = route.get_routes(limit=1, offset=0)
if isinstance(response, dict) and 'errors' in response.keys():
print('. '.join(response['errors']))
else:
route_id = response[0]['route_id']
print('Route ID: {}'.format(route_id))
response = route.get_route(route_id=route_id)
if isinstance(response, dict) and 'errors' in response.keys():
print('. '.join(response['errors']))
else:
print('Original Route')
print('Route ID: {}'.format(response['route_id']))
for i, address in enumerate(response['addresses']):
print('Address #{}'.format(i + 1))
print('\tAddress: {0}'.format(address['address']))
print('\tRoute Destination ID: {0}'.format(address[
'route_destination_id']))
route_destination_id = response['addresses'][1][
'route_destination_id']
route_destination_id2 = response['addresses'][2][
'route_destination_id']
data = {'route_destination_id': route_destination_id,
'route_id': route_id, 'addresses': [{'route_destination_id':
route_destination_id2, 'sequence_no': 6}]}
print('After Re-sequence Route')
response = route.resequence_route(**data)
print('Route ID: {}'.format(response['route_id']))
for i, address in enumerate(response['addresses']):
print('Address #{}'.format(i + 1))
print('\tAddress: {0}'.format(address['address']))
print('\tRoute Destination ID: {0}'.format(address[
'route_destination_id']))
if __name__ == '__main__':
main()
| # -*- coding: utf-8 -*-
from route4me import Route4Me
API_KEY = "11111111111111111111111111111111"
def main():
r4m = Route4Me(API_KEY)
route = r4m.route
response = route.get_routes(limit=1, offset=0)
if isinstance(response, dict) and 'errors' in response.keys():
print('. '.join(response['errors']))
else:
route_id = response[0]['route_id']
print('Route ID: {}'.format(route_id))
response = route.get_route(route_id=route_id)
if isinstance(response, dict) and 'errors' in response.keys():
print('. '.join(response['errors']))
else:
print('Original Route')
print('Route ID: {}'.format(response['route_id']))
for i, address in enumerate(response['addresses']):
print('Address #{}'.format(i + 1))
print('\tAddress: {0}'.format(address['address']))
print('\tRoute Destination ID: {0}'.format(
address['route_destination_id']))
route_destination_id = response['addresses'][1]['route_destination_id']
route_destination_id2 = response['addresses'][2]['route_destination_id']
data = {
"route_destination_id": route_destination_id,
"route_id": route_id,
"addresses": [{
"route_destination_id": route_destination_id2,
"sequence_no": 6,
}]
}
print('After Re-sequence Route')
response = route.resequence_route(**data)
print('Route ID: {}'.format(response['route_id']))
for i, address in enumerate(response['addresses']):
print('Address #{}'.format(i + 1))
print('\tAddress: {0}'.format(address['address']))
print('\tRoute Destination ID: {0}'.format(
address['route_destination_id']))
if __name__ == '__main__':
main()
| [
1,
2,
3,
4,
5
] |
239 | d015a1b27a3a9e7f5e6614da752137064000b905 | <mask token>
class my_model:
<mask token>
def make_model(self, param):
"""makes the model"""
self.lr = param[0][0]
dr = param[0][1]
layer_units0 = param[0][2]
layer_units1 = param[0][3]
layer_units2 = param[0][4]
def learning_rate(epoch):
"""The learning rate scheduler"""
self.lr = self.lr / 1.00000001
return self.lr
"""Do not touch from here..."""
(X, Y), (X_test, Y_test) = K.datasets.cifar10.load_data()
Y = K.utils.to_categorical(Y[:])
X = K.applications.xception.preprocess_input(X)
Y_test = K.utils.to_categorical(Y_test[:])
X_test = K.applications.xception.preprocess_input(X_test)
df = 'channels_last'
save_best = K.callbacks.ModelCheckpoint(filepath=
'model_lr{:.2f}_dr{:.2f}_l0{}_l1{}_l2{}.h5'.format(self.lr, dr,
layer_units0, layer_units1, layer_units2), monitor='val_loss',
save_best_only=True)
early_stop = K.callbacks.EarlyStopping(monitor='val_loss', patience=7)
learning_rate_0 = K.callbacks.LearningRateScheduler(learning_rate,
verbose=1)
try:
loaded_model = K.models.load_model('frozen_layers.h5')
print('Loaded frozen layers!')
except Exception as e:
if isinstance(e, OSError):
pass
else:
exit()
print('Failed to load frozen layers.')
inputs = K.Input(shape=(32, 32, 3))
l = K.layers.Lambda(lambda X: K.backend.resize_images(X,
height_factor=7, width_factor=7, data_format='channels_last'))(
inputs)
xception = K.applications.Xception(include_top=False,
input_tensor=l, weights='imagenet', pooling='max')
for layer in xception.layers:
layer.trainable = False
outputs = xception.layers[-1].output
outputs = K.layers.Dense(units=10, activation='softmax',
kernel_initializer=K.initializers.he_normal())(outputs)
model = K.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(X, Y, epochs=1, verbose=True, batch_size=128)
model.save('frozen_layers.h5')
loaded_model = K.models.load_model('frozen_layers.h5')
except MemoryError('Try lowering the batch size'):
exit()
if os.path.exists('X_inputs') and os.path.exists('X_test_inputs'):
with open('X_inputs', 'rb') as X_file:
X = pickle.load(X_file)
with open('X_test_inputs', 'rb') as X_test_file:
X_test = pickle.load(X_test_file)
else:
frozen_layers = K.Model(inputs=loaded_model.input, outputs=
loaded_model.layers[-2].output)
X = frozen_layers.predict(X, verbose=True)
X_test = frozen_layers.predict(X_test, verbose=True)
with open('X_inputs', 'wb') as X_file:
pickle.dump(X, X_file)
with open('X_test_inputs', 'wb') as X_test_file:
pickle.dump(X_test, X_test_file)
inputs = K.Input((2048,))
"""... to here!!!"""
layer = K.layers.Dense(units=layer_units0, activation='relu',
kernel_initializer=K.initializers.he_normal())(inputs)
layer = K.layers.Dropout(dr)(layer)
layer = K.layers.Dense(units=layer_units1, activation='relu',
kernel_initializer=K.initializers.he_normal())(layer)
layer = K.layers.Dense(units=layer_units2, activation='relu',
kernel_initializer=K.initializers.he_normal())(layer)
outputs = K.layers.Dense(units=10, activation='softmax',
kernel_initializer=K.initializers.he_normal())(layer)
model = K.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['accuracy'])
h = model.fit(X, Y, validation_data=(X_test, Y_test), epochs=64,
verbose=True, batch_size=128, shuffle=True, callbacks=[
early_stop, learning_rate_0, save_best])
val_accuracy = np.min(h.history['val_loss'])
return val_accuracy
def opt(self):
"""the optimization function"""
search_space = [{'name': 'lr', 'type': 'continuous', 'domain': (
0.01, 0.001)}, {'name': 'dr', 'type': 'continuous', 'domain': (
0.1, 0.3)}, {'name': 'layer_units0', 'type': 'discrete',
'domain': (32, 64, 128, 256, 512)}, {'name': 'layer_units1',
'type': 'discrete', 'domain': (32, 64, 128, 256, 512)}, {'name':
'layer_units2', 'type': 'discrete', 'domain': (32, 64, 128, 256,
512)}]
my_bayesian_opt = BayesianOptimization(self.make_model, domain=
search_space, model_type='GP', initial_design_numdata=1,
acquisition_type='EI', maximize=False, verbosity=True)
print('==============================')
my_bayesian_opt.run_optimization(max_iter=29, report_file='report',
evaluations_file='evaluation', models_file='models')
print('PLOTTING')
my_bayesian_opt.plot_acquisition()
my_bayesian_opt.plot_convergence()
print('==============================')
<mask token>
| <mask token>
class my_model:
"""A model bassed on xception"""
def make_model(self, param):
"""makes the model"""
self.lr = param[0][0]
dr = param[0][1]
layer_units0 = param[0][2]
layer_units1 = param[0][3]
layer_units2 = param[0][4]
def learning_rate(epoch):
"""The learning rate scheduler"""
self.lr = self.lr / 1.00000001
return self.lr
"""Do not touch from here..."""
(X, Y), (X_test, Y_test) = K.datasets.cifar10.load_data()
Y = K.utils.to_categorical(Y[:])
X = K.applications.xception.preprocess_input(X)
Y_test = K.utils.to_categorical(Y_test[:])
X_test = K.applications.xception.preprocess_input(X_test)
df = 'channels_last'
save_best = K.callbacks.ModelCheckpoint(filepath=
'model_lr{:.2f}_dr{:.2f}_l0{}_l1{}_l2{}.h5'.format(self.lr, dr,
layer_units0, layer_units1, layer_units2), monitor='val_loss',
save_best_only=True)
early_stop = K.callbacks.EarlyStopping(monitor='val_loss', patience=7)
learning_rate_0 = K.callbacks.LearningRateScheduler(learning_rate,
verbose=1)
try:
loaded_model = K.models.load_model('frozen_layers.h5')
print('Loaded frozen layers!')
except Exception as e:
if isinstance(e, OSError):
pass
else:
exit()
print('Failed to load frozen layers.')
inputs = K.Input(shape=(32, 32, 3))
l = K.layers.Lambda(lambda X: K.backend.resize_images(X,
height_factor=7, width_factor=7, data_format='channels_last'))(
inputs)
xception = K.applications.Xception(include_top=False,
input_tensor=l, weights='imagenet', pooling='max')
for layer in xception.layers:
layer.trainable = False
outputs = xception.layers[-1].output
outputs = K.layers.Dense(units=10, activation='softmax',
kernel_initializer=K.initializers.he_normal())(outputs)
model = K.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(X, Y, epochs=1, verbose=True, batch_size=128)
model.save('frozen_layers.h5')
loaded_model = K.models.load_model('frozen_layers.h5')
except MemoryError('Try lowering the batch size'):
exit()
if os.path.exists('X_inputs') and os.path.exists('X_test_inputs'):
with open('X_inputs', 'rb') as X_file:
X = pickle.load(X_file)
with open('X_test_inputs', 'rb') as X_test_file:
X_test = pickle.load(X_test_file)
else:
frozen_layers = K.Model(inputs=loaded_model.input, outputs=
loaded_model.layers[-2].output)
X = frozen_layers.predict(X, verbose=True)
X_test = frozen_layers.predict(X_test, verbose=True)
with open('X_inputs', 'wb') as X_file:
pickle.dump(X, X_file)
with open('X_test_inputs', 'wb') as X_test_file:
pickle.dump(X_test, X_test_file)
inputs = K.Input((2048,))
"""... to here!!!"""
layer = K.layers.Dense(units=layer_units0, activation='relu',
kernel_initializer=K.initializers.he_normal())(inputs)
layer = K.layers.Dropout(dr)(layer)
layer = K.layers.Dense(units=layer_units1, activation='relu',
kernel_initializer=K.initializers.he_normal())(layer)
layer = K.layers.Dense(units=layer_units2, activation='relu',
kernel_initializer=K.initializers.he_normal())(layer)
outputs = K.layers.Dense(units=10, activation='softmax',
kernel_initializer=K.initializers.he_normal())(layer)
model = K.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['accuracy'])
h = model.fit(X, Y, validation_data=(X_test, Y_test), epochs=64,
verbose=True, batch_size=128, shuffle=True, callbacks=[
early_stop, learning_rate_0, save_best])
val_accuracy = np.min(h.history['val_loss'])
return val_accuracy
def opt(self):
"""the optimization function"""
search_space = [{'name': 'lr', 'type': 'continuous', 'domain': (
0.01, 0.001)}, {'name': 'dr', 'type': 'continuous', 'domain': (
0.1, 0.3)}, {'name': 'layer_units0', 'type': 'discrete',
'domain': (32, 64, 128, 256, 512)}, {'name': 'layer_units1',
'type': 'discrete', 'domain': (32, 64, 128, 256, 512)}, {'name':
'layer_units2', 'type': 'discrete', 'domain': (32, 64, 128, 256,
512)}]
my_bayesian_opt = BayesianOptimization(self.make_model, domain=
search_space, model_type='GP', initial_design_numdata=1,
acquisition_type='EI', maximize=False, verbosity=True)
print('==============================')
my_bayesian_opt.run_optimization(max_iter=29, report_file='report',
evaluations_file='evaluation', models_file='models')
print('PLOTTING')
my_bayesian_opt.plot_acquisition()
my_bayesian_opt.plot_convergence()
print('==============================')
<mask token>
| <mask token>
class my_model:
"""A model bassed on xception"""
def make_model(self, param):
"""makes the model"""
self.lr = param[0][0]
dr = param[0][1]
layer_units0 = param[0][2]
layer_units1 = param[0][3]
layer_units2 = param[0][4]
def learning_rate(epoch):
"""The learning rate scheduler"""
self.lr = self.lr / 1.00000001
return self.lr
"""Do not touch from here..."""
(X, Y), (X_test, Y_test) = K.datasets.cifar10.load_data()
Y = K.utils.to_categorical(Y[:])
X = K.applications.xception.preprocess_input(X)
Y_test = K.utils.to_categorical(Y_test[:])
X_test = K.applications.xception.preprocess_input(X_test)
df = 'channels_last'
save_best = K.callbacks.ModelCheckpoint(filepath=
'model_lr{:.2f}_dr{:.2f}_l0{}_l1{}_l2{}.h5'.format(self.lr, dr,
layer_units0, layer_units1, layer_units2), monitor='val_loss',
save_best_only=True)
early_stop = K.callbacks.EarlyStopping(monitor='val_loss', patience=7)
learning_rate_0 = K.callbacks.LearningRateScheduler(learning_rate,
verbose=1)
try:
loaded_model = K.models.load_model('frozen_layers.h5')
print('Loaded frozen layers!')
except Exception as e:
if isinstance(e, OSError):
pass
else:
exit()
print('Failed to load frozen layers.')
inputs = K.Input(shape=(32, 32, 3))
l = K.layers.Lambda(lambda X: K.backend.resize_images(X,
height_factor=7, width_factor=7, data_format='channels_last'))(
inputs)
xception = K.applications.Xception(include_top=False,
input_tensor=l, weights='imagenet', pooling='max')
for layer in xception.layers:
layer.trainable = False
outputs = xception.layers[-1].output
outputs = K.layers.Dense(units=10, activation='softmax',
kernel_initializer=K.initializers.he_normal())(outputs)
model = K.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(X, Y, epochs=1, verbose=True, batch_size=128)
model.save('frozen_layers.h5')
loaded_model = K.models.load_model('frozen_layers.h5')
except MemoryError('Try lowering the batch size'):
exit()
if os.path.exists('X_inputs') and os.path.exists('X_test_inputs'):
with open('X_inputs', 'rb') as X_file:
X = pickle.load(X_file)
with open('X_test_inputs', 'rb') as X_test_file:
X_test = pickle.load(X_test_file)
else:
frozen_layers = K.Model(inputs=loaded_model.input, outputs=
loaded_model.layers[-2].output)
X = frozen_layers.predict(X, verbose=True)
X_test = frozen_layers.predict(X_test, verbose=True)
with open('X_inputs', 'wb') as X_file:
pickle.dump(X, X_file)
with open('X_test_inputs', 'wb') as X_test_file:
pickle.dump(X_test, X_test_file)
inputs = K.Input((2048,))
"""... to here!!!"""
layer = K.layers.Dense(units=layer_units0, activation='relu',
kernel_initializer=K.initializers.he_normal())(inputs)
layer = K.layers.Dropout(dr)(layer)
layer = K.layers.Dense(units=layer_units1, activation='relu',
kernel_initializer=K.initializers.he_normal())(layer)
layer = K.layers.Dense(units=layer_units2, activation='relu',
kernel_initializer=K.initializers.he_normal())(layer)
outputs = K.layers.Dense(units=10, activation='softmax',
kernel_initializer=K.initializers.he_normal())(layer)
model = K.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['accuracy'])
h = model.fit(X, Y, validation_data=(X_test, Y_test), epochs=64,
verbose=True, batch_size=128, shuffle=True, callbacks=[
early_stop, learning_rate_0, save_best])
val_accuracy = np.min(h.history['val_loss'])
return val_accuracy
def opt(self):
"""the optimization function"""
search_space = [{'name': 'lr', 'type': 'continuous', 'domain': (
0.01, 0.001)}, {'name': 'dr', 'type': 'continuous', 'domain': (
0.1, 0.3)}, {'name': 'layer_units0', 'type': 'discrete',
'domain': (32, 64, 128, 256, 512)}, {'name': 'layer_units1',
'type': 'discrete', 'domain': (32, 64, 128, 256, 512)}, {'name':
'layer_units2', 'type': 'discrete', 'domain': (32, 64, 128, 256,
512)}]
my_bayesian_opt = BayesianOptimization(self.make_model, domain=
search_space, model_type='GP', initial_design_numdata=1,
acquisition_type='EI', maximize=False, verbosity=True)
print('==============================')
my_bayesian_opt.run_optimization(max_iter=29, report_file='report',
evaluations_file='evaluation', models_file='models')
print('PLOTTING')
my_bayesian_opt.plot_acquisition()
my_bayesian_opt.plot_convergence()
print('==============================')
def preprocess_data(X, Y):
"""The data preprocessing"""
Y_p = K.utils.to_categorical(Y[:])
X_p = K.applications.xception.preprocess_input(X)
loaded_model = K.models.load_model('frozen_layers.h5')
frozen_layers = K.Model(inputs=loaded_model.input, outputs=loaded_model
.layers[-2].output)
X_p = frozen_layers.predict(X_p, verbose=True)
with open('Preprocessed_data_Xs', 'wb') as my_file0:
pickle.dump(X_p, my_file0)
with open('Preprocessed_data_Ys', 'wb') as my_file1:
pickle.dump(Y_p, my_file1)
return X_p, Y_p
| <mask token>
import tensorflow.keras as K
from GPyOpt.methods import BayesianOptimization
import pickle
import os
import numpy as np
class my_model:
"""A model bassed on xception"""
def make_model(self, param):
"""makes the model"""
self.lr = param[0][0]
dr = param[0][1]
layer_units0 = param[0][2]
layer_units1 = param[0][3]
layer_units2 = param[0][4]
def learning_rate(epoch):
"""The learning rate scheduler"""
self.lr = self.lr / 1.00000001
return self.lr
"""Do not touch from here..."""
(X, Y), (X_test, Y_test) = K.datasets.cifar10.load_data()
Y = K.utils.to_categorical(Y[:])
X = K.applications.xception.preprocess_input(X)
Y_test = K.utils.to_categorical(Y_test[:])
X_test = K.applications.xception.preprocess_input(X_test)
df = 'channels_last'
save_best = K.callbacks.ModelCheckpoint(filepath=
'model_lr{:.2f}_dr{:.2f}_l0{}_l1{}_l2{}.h5'.format(self.lr, dr,
layer_units0, layer_units1, layer_units2), monitor='val_loss',
save_best_only=True)
early_stop = K.callbacks.EarlyStopping(monitor='val_loss', patience=7)
learning_rate_0 = K.callbacks.LearningRateScheduler(learning_rate,
verbose=1)
try:
loaded_model = K.models.load_model('frozen_layers.h5')
print('Loaded frozen layers!')
except Exception as e:
if isinstance(e, OSError):
pass
else:
exit()
print('Failed to load frozen layers.')
inputs = K.Input(shape=(32, 32, 3))
l = K.layers.Lambda(lambda X: K.backend.resize_images(X,
height_factor=7, width_factor=7, data_format='channels_last'))(
inputs)
xception = K.applications.Xception(include_top=False,
input_tensor=l, weights='imagenet', pooling='max')
for layer in xception.layers:
layer.trainable = False
outputs = xception.layers[-1].output
outputs = K.layers.Dense(units=10, activation='softmax',
kernel_initializer=K.initializers.he_normal())(outputs)
model = K.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit(X, Y, epochs=1, verbose=True, batch_size=128)
model.save('frozen_layers.h5')
loaded_model = K.models.load_model('frozen_layers.h5')
except MemoryError('Try lowering the batch size'):
exit()
if os.path.exists('X_inputs') and os.path.exists('X_test_inputs'):
with open('X_inputs', 'rb') as X_file:
X = pickle.load(X_file)
with open('X_test_inputs', 'rb') as X_test_file:
X_test = pickle.load(X_test_file)
else:
frozen_layers = K.Model(inputs=loaded_model.input, outputs=
loaded_model.layers[-2].output)
X = frozen_layers.predict(X, verbose=True)
X_test = frozen_layers.predict(X_test, verbose=True)
with open('X_inputs', 'wb') as X_file:
pickle.dump(X, X_file)
with open('X_test_inputs', 'wb') as X_test_file:
pickle.dump(X_test, X_test_file)
inputs = K.Input((2048,))
"""... to here!!!"""
layer = K.layers.Dense(units=layer_units0, activation='relu',
kernel_initializer=K.initializers.he_normal())(inputs)
layer = K.layers.Dropout(dr)(layer)
layer = K.layers.Dense(units=layer_units1, activation='relu',
kernel_initializer=K.initializers.he_normal())(layer)
layer = K.layers.Dense(units=layer_units2, activation='relu',
kernel_initializer=K.initializers.he_normal())(layer)
outputs = K.layers.Dense(units=10, activation='softmax',
kernel_initializer=K.initializers.he_normal())(layer)
model = K.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['accuracy'])
h = model.fit(X, Y, validation_data=(X_test, Y_test), epochs=64,
verbose=True, batch_size=128, shuffle=True, callbacks=[
early_stop, learning_rate_0, save_best])
val_accuracy = np.min(h.history['val_loss'])
return val_accuracy
def opt(self):
"""the optimization function"""
search_space = [{'name': 'lr', 'type': 'continuous', 'domain': (
0.01, 0.001)}, {'name': 'dr', 'type': 'continuous', 'domain': (
0.1, 0.3)}, {'name': 'layer_units0', 'type': 'discrete',
'domain': (32, 64, 128, 256, 512)}, {'name': 'layer_units1',
'type': 'discrete', 'domain': (32, 64, 128, 256, 512)}, {'name':
'layer_units2', 'type': 'discrete', 'domain': (32, 64, 128, 256,
512)}]
my_bayesian_opt = BayesianOptimization(self.make_model, domain=
search_space, model_type='GP', initial_design_numdata=1,
acquisition_type='EI', maximize=False, verbosity=True)
print('==============================')
my_bayesian_opt.run_optimization(max_iter=29, report_file='report',
evaluations_file='evaluation', models_file='models')
print('PLOTTING')
my_bayesian_opt.plot_acquisition()
my_bayesian_opt.plot_convergence()
print('==============================')
def preprocess_data(X, Y):
"""The data preprocessing"""
Y_p = K.utils.to_categorical(Y[:])
X_p = K.applications.xception.preprocess_input(X)
loaded_model = K.models.load_model('frozen_layers.h5')
frozen_layers = K.Model(inputs=loaded_model.input, outputs=loaded_model
.layers[-2].output)
X_p = frozen_layers.predict(X_p, verbose=True)
with open('Preprocessed_data_Xs', 'wb') as my_file0:
pickle.dump(X_p, my_file0)
with open('Preprocessed_data_Ys', 'wb') as my_file1:
pickle.dump(Y_p, my_file1)
return X_p, Y_p
| #!/usr/bin/env python3
"""Transfer learning with xception"""
import tensorflow.keras as K
from GPyOpt.methods import BayesianOptimization
import pickle
import os
import numpy as np
class my_model():
"""A model bassed on xception"""
def make_model(self, param):
"""makes the model"""
self.lr = param[0][0]
dr = param[0][1]
layer_units0 = param[0][2]
layer_units1 = param[0][3]
layer_units2 = param[0][4]
def learning_rate(epoch):
"""The learning rate scheduler"""
self.lr = self.lr / 1.00000001
return self.lr
"""Do not touch from here..."""
# load data
(X, Y), (X_test, Y_test) = K.datasets.cifar10.load_data()
# uncomment for rapid test
# X = X[0:256, :, :, :]
# Y = Y[0:256, :]
# X_test = X_test[0:256, :, :, :]
# Y_test = Y_test[0:256, :]
# preprocessing
Y = K.utils.to_categorical(Y[:])
X = K.applications.xception.preprocess_input(X)
Y_test = K.utils.to_categorical(Y_test[:])
X_test = K.applications.xception.preprocess_input(X_test)
# data format
df = "channels_last"
# call backs
save_best = K.callbacks.ModelCheckpoint(filepath="model_lr{:.2f}_dr{:.2f}_l0{}_l1{}_l2{}.h5"
.format(self.lr,
dr,
layer_units0,
layer_units1,
layer_units2),
monitor="val_loss",
save_best_only=True,
)
early_stop = K.callbacks.EarlyStopping(monitor="val_loss",
patience=7
)
learning_rate_0 = K.callbacks.LearningRateScheduler(learning_rate,
verbose=1
)
# input layer and lambda layer save and load for faster training
try:
loaded_model = K.models.load_model("frozen_layers.h5")
print("Loaded frozen layers!")
except Exception as e:
if isinstance(e, OSError):
pass
else:
exit()
print("Failed to load frozen layers.")
inputs = K.Input(shape=(32, 32, 3))
l = K.layers.Lambda(lambda X:
K.backend.resize_images(X,
height_factor=7,
width_factor=7,
data_format="channels_last"
))(inputs)
# Transfer learning layers
xception = K.applications.Xception(include_top=False,
input_tensor=l,
weights="imagenet",
pooling="max"
)
# freeze the resnet50 layers
for layer in xception.layers:
layer.trainable = False
# get outputs
outputs = xception.layers[-1].output
outputs = K.layers.Dense(units=10,
activation="softmax",
kernel_initializer=K.initializers.he_normal()
)(outputs)
# compile frozen model
model = K.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer="adam",
loss="categorical_crossentropy",
metrics=["accuracy"])
model.fit(X,
Y,
epochs=1,
verbose=True,
batch_size=128
)
model.save("frozen_layers.h5")
loaded_model = K.models.load_model("frozen_layers.h5")
except MemoryError("Try lowering the batch size"):
exit()
# set up new model
if os.path.exists("X_inputs") and os.path.exists("X_test_inputs"):
with open("X_inputs", "rb") as X_file:
X = pickle.load(X_file)
with open("X_test_inputs", "rb") as X_test_file:
X_test = pickle.load(X_test_file)
else:
frozen_layers = K.Model(inputs=loaded_model.input,
outputs=loaded_model.layers[-2].output
)
X = frozen_layers.predict(X,
verbose=True
)
X_test = frozen_layers.predict(X_test,
verbose=True
)
with open("X_inputs", "wb") as X_file:
pickle.dump(X, X_file)
with open("X_test_inputs", "wb") as X_test_file:
pickle.dump(X_test, X_test_file)
# inputs
inputs = K.Input((2048,))
"""... to here!!!"""
# new layers here
layer = K.layers.Dense(units=layer_units0,
activation="relu",
kernel_initializer=K.initializers.he_normal()
)(inputs)
layer = K.layers.Dropout(dr)(layer)
layer = K.layers.Dense(units=layer_units1,
activation="relu",
kernel_initializer=K.initializers.he_normal()
)(layer)
# layer = K.layers.Dropout(dr)(layer)
layer = K.layers.Dense(units=layer_units2,
activation="relu",
kernel_initializer=K.initializers.he_normal()
)(layer)
# layer = K.layers.Dropout(dr)(layer)
outputs = K.layers.Dense(units=10,
activation="softmax",
kernel_initializer=K.initializers.he_normal()
)(layer)
model = K.Model(inputs=inputs, outputs=outputs)
model.compile(optimizer="adam",
loss="categorical_crossentropy",
metrics=["accuracy"])
# train
h = model.fit(X,
Y,
validation_data=(X_test, Y_test),
epochs=64,
verbose=True,
batch_size=128,
shuffle=True,
callbacks=[early_stop, learning_rate_0, save_best]
)
val_accuracy = np.min(h.history["val_loss"])
return val_accuracy
def opt(self):
"""the optimization function"""
search_space = [
{"name": "lr", "type": "continuous", "domain": (0.01, 0.001)},
{"name": "dr", "type": "continuous", "domain": (0.1, 0.3)},
{"name": "layer_units0", "type": "discrete", "domain": (32, 64, 128, 256, 512)},
{"name": "layer_units1", "type": "discrete", "domain": (32, 64, 128, 256, 512)},
{"name": "layer_units2", "type": "discrete", "domain": (32, 64, 128, 256, 512)}
]
my_bayesian_opt = BayesianOptimization(self.make_model,
domain=search_space,
model_type="GP",
initial_design_numdata=1,
acquisition_type="EI",
maximize=False,
verbosity=True
)
print("==============================")
my_bayesian_opt.run_optimization(max_iter=29,
report_file="report",
evaluations_file="evaluation",
models_file="models")
print("PLOTTING")
my_bayesian_opt.plot_acquisition()
my_bayesian_opt.plot_convergence()
print("==============================")
def preprocess_data(X, Y):
"""The data preprocessing"""
Y_p = K.utils.to_categorical(Y[:])
X_p = K.applications.xception.preprocess_input(X)
loaded_model = K.models.load_model("frozen_layers.h5")
frozen_layers = K.Model(inputs=loaded_model.input,
outputs=loaded_model.layers[-2].output
)
X_p = frozen_layers.predict(X_p,
verbose=True
)
with open("Preprocessed_data_Xs", "wb") as my_file0:
pickle.dump(X_p, my_file0)
with open("Preprocessed_data_Ys", "wb") as my_file1:
pickle.dump(Y_p, my_file1)
return X_p, Y_p
| [
3,
4,
5,
6,
7
] |
240 | ef6f55bf27982f53441215da6822cfcdc80706a5 | <mask token>
def display_meta(request):
context_dict = {'meta_dict': request.META}
return render_to_response('display_meta.html', context_dict)
| <mask token>
def current_datetime(request):
current_date = datetime.datetime.now()
locals_prams = {'locals': locals()}
return render_to_response('current_datetime.html', locals_prams)
def display_meta(request):
context_dict = {'meta_dict': request.META}
return render_to_response('display_meta.html', context_dict)
| __author__ = 'Yun'
__project__ = 'DjangoBookTest2'
<mask token>
def current_datetime(request):
current_date = datetime.datetime.now()
locals_prams = {'locals': locals()}
return render_to_response('current_datetime.html', locals_prams)
def display_meta(request):
context_dict = {'meta_dict': request.META}
return render_to_response('display_meta.html', context_dict)
| __author__ = 'Yun'
__project__ = 'DjangoBookTest2'
from django.shortcuts import render_to_response
import datetime
def current_datetime(request):
current_date = datetime.datetime.now()
locals_prams = {'locals': locals()}
return render_to_response('current_datetime.html', locals_prams)
def display_meta(request):
context_dict = {'meta_dict': request.META}
return render_to_response('display_meta.html', context_dict)
| # -*- coding: utf-8 -*-
__author__ = 'Yun'
__project__ = 'DjangoBookTest2'
# from django.template import Template, Context
# from django.template.loader import get_template
# from django.http import HttpResponse
from django.shortcuts import render_to_response
import datetime
def current_datetime(request):
# now = datetime.datetime.now()
# t = Template("<html><body>It is now {{ current_date }}.</body></html>")
# t = get_template('current_datetime.html')
# html = t.render(Context({'current_date': now}))
# return HttpResponse(html)
# return render_to_response('current_datetime.html', {'current_date': now})
current_date = datetime.datetime.now()
locals_prams = {'locals': locals(), }
# locals_prams = {'current_date': now, 'name': 'YZhu', 'list': {'name': 'Qiqi', 'age': '29', 'gender': 'man'}}
return render_to_response('current_datetime.html', locals_prams)
def display_meta(request):
context_dict = {'meta_dict': request.META, }
return render_to_response('display_meta.html', context_dict) | [
1,
2,
3,
4,
5
] |
241 | e8226ab6be5c21335d843cba720e66646a2dee4e | import os
import requests
import sqlite3
from models import analytics, jcanalytics
def populate():
url = 'https://api.clicky.com/api/stats/4?site_id=100716069&sitekey=93c104e29de28bd9&type=visitors-list'
date = '&date=last-30-days'
limit = '&limit=all'
output = '&output=json'
total = url+date+limit+output
r = requests.get(total)
print(total)
data = r.json()
# html = []
for item in data[0]['dates'][0]['items']:
si = item["session_id"]
ip = item["ip_address"]
time = item["time"]
timep = item["time_pretty"]
# geol = item["geolocation"]
# org = item["organization"]
if item.has_key("geolocation"):
geol = item["geolocation"]
else:
geol = ""
if item.has_key("organization"):
org = item["organization"]
else:
org = ""
add_entry(si,ip,org,time,timep,geol)
add_jcentry(org)
def add_entry(si,ip,org,time,timep,geol):
entry = analytics.objects.get_or_create(si=si,ip=ip,org=org,time=time,timep=timep,geol=geol)[0]
return entry
def add_jcentry(org):
jcentry = jcanalytics.objects.get_or_create(org=org)[0]
return jcentry
print "Starting population script..."
populate()
| null | null | null | null | [
0
] |
242 | e616d14827beaa08ab08219421cbf7990cf163fd | <mask token>
class AlipayInsSceneEcommerceInsureCheckModel(object):
def __init__(self):
self._insure_admit_dto_list = None
self._partner_org_id = None
self._product_code = None
self._scene_code = None
self._user_client = None
@property
def insure_admit_dto_list(self):
return self._insure_admit_dto_list
<mask token>
@property
def partner_org_id(self):
return self._partner_org_id
@partner_org_id.setter
def partner_org_id(self, value):
self._partner_org_id = value
@property
def product_code(self):
return self._product_code
@product_code.setter
def product_code(self, value):
self._product_code = value
<mask token>
<mask token>
@property
def user_client(self):
return self._user_client
<mask token>
<mask token>
<mask token>
| <mask token>
class AlipayInsSceneEcommerceInsureCheckModel(object):
def __init__(self):
self._insure_admit_dto_list = None
self._partner_org_id = None
self._product_code = None
self._scene_code = None
self._user_client = None
@property
def insure_admit_dto_list(self):
return self._insure_admit_dto_list
@insure_admit_dto_list.setter
def insure_admit_dto_list(self, value):
if isinstance(value, list):
self._insure_admit_dto_list = list()
for i in value:
if isinstance(i, InsureAdmitDTO):
self._insure_admit_dto_list.append(i)
else:
self._insure_admit_dto_list.append(InsureAdmitDTO.
from_alipay_dict(i))
@property
def partner_org_id(self):
return self._partner_org_id
@partner_org_id.setter
def partner_org_id(self, value):
self._partner_org_id = value
@property
def product_code(self):
return self._product_code
@product_code.setter
def product_code(self, value):
self._product_code = value
@property
def scene_code(self):
return self._scene_code
@scene_code.setter
def scene_code(self, value):
self._scene_code = value
@property
def user_client(self):
return self._user_client
<mask token>
<mask token>
@staticmethod
def from_alipay_dict(d):
if not d:
return None
o = AlipayInsSceneEcommerceInsureCheckModel()
if 'insure_admit_dto_list' in d:
o.insure_admit_dto_list = d['insure_admit_dto_list']
if 'partner_org_id' in d:
o.partner_org_id = d['partner_org_id']
if 'product_code' in d:
o.product_code = d['product_code']
if 'scene_code' in d:
o.scene_code = d['scene_code']
if 'user_client' in d:
o.user_client = d['user_client']
return o
| <mask token>
class AlipayInsSceneEcommerceInsureCheckModel(object):
def __init__(self):
self._insure_admit_dto_list = None
self._partner_org_id = None
self._product_code = None
self._scene_code = None
self._user_client = None
@property
def insure_admit_dto_list(self):
return self._insure_admit_dto_list
@insure_admit_dto_list.setter
def insure_admit_dto_list(self, value):
if isinstance(value, list):
self._insure_admit_dto_list = list()
for i in value:
if isinstance(i, InsureAdmitDTO):
self._insure_admit_dto_list.append(i)
else:
self._insure_admit_dto_list.append(InsureAdmitDTO.
from_alipay_dict(i))
@property
def partner_org_id(self):
return self._partner_org_id
@partner_org_id.setter
def partner_org_id(self, value):
self._partner_org_id = value
@property
def product_code(self):
return self._product_code
@product_code.setter
def product_code(self, value):
self._product_code = value
@property
def scene_code(self):
return self._scene_code
@scene_code.setter
def scene_code(self, value):
self._scene_code = value
@property
def user_client(self):
return self._user_client
<mask token>
def to_alipay_dict(self):
params = dict()
if self.insure_admit_dto_list:
if isinstance(self.insure_admit_dto_list, list):
for i in range(0, len(self.insure_admit_dto_list)):
element = self.insure_admit_dto_list[i]
if hasattr(element, 'to_alipay_dict'):
self.insure_admit_dto_list[i] = element.to_alipay_dict(
)
if hasattr(self.insure_admit_dto_list, 'to_alipay_dict'):
params['insure_admit_dto_list'
] = self.insure_admit_dto_list.to_alipay_dict()
else:
params['insure_admit_dto_list'] = self.insure_admit_dto_list
if self.partner_org_id:
if hasattr(self.partner_org_id, 'to_alipay_dict'):
params['partner_org_id'] = self.partner_org_id.to_alipay_dict()
else:
params['partner_org_id'] = self.partner_org_id
if self.product_code:
if hasattr(self.product_code, 'to_alipay_dict'):
params['product_code'] = self.product_code.to_alipay_dict()
else:
params['product_code'] = self.product_code
if self.scene_code:
if hasattr(self.scene_code, 'to_alipay_dict'):
params['scene_code'] = self.scene_code.to_alipay_dict()
else:
params['scene_code'] = self.scene_code
if self.user_client:
if hasattr(self.user_client, 'to_alipay_dict'):
params['user_client'] = self.user_client.to_alipay_dict()
else:
params['user_client'] = self.user_client
return params
@staticmethod
def from_alipay_dict(d):
if not d:
return None
o = AlipayInsSceneEcommerceInsureCheckModel()
if 'insure_admit_dto_list' in d:
o.insure_admit_dto_list = d['insure_admit_dto_list']
if 'partner_org_id' in d:
o.partner_org_id = d['partner_org_id']
if 'product_code' in d:
o.product_code = d['product_code']
if 'scene_code' in d:
o.scene_code = d['scene_code']
if 'user_client' in d:
o.user_client = d['user_client']
return o
| <mask token>
class AlipayInsSceneEcommerceInsureCheckModel(object):
def __init__(self):
self._insure_admit_dto_list = None
self._partner_org_id = None
self._product_code = None
self._scene_code = None
self._user_client = None
@property
def insure_admit_dto_list(self):
return self._insure_admit_dto_list
@insure_admit_dto_list.setter
def insure_admit_dto_list(self, value):
if isinstance(value, list):
self._insure_admit_dto_list = list()
for i in value:
if isinstance(i, InsureAdmitDTO):
self._insure_admit_dto_list.append(i)
else:
self._insure_admit_dto_list.append(InsureAdmitDTO.
from_alipay_dict(i))
@property
def partner_org_id(self):
return self._partner_org_id
@partner_org_id.setter
def partner_org_id(self, value):
self._partner_org_id = value
@property
def product_code(self):
return self._product_code
@product_code.setter
def product_code(self, value):
self._product_code = value
@property
def scene_code(self):
return self._scene_code
@scene_code.setter
def scene_code(self, value):
self._scene_code = value
@property
def user_client(self):
return self._user_client
@user_client.setter
def user_client(self, value):
self._user_client = value
def to_alipay_dict(self):
params = dict()
if self.insure_admit_dto_list:
if isinstance(self.insure_admit_dto_list, list):
for i in range(0, len(self.insure_admit_dto_list)):
element = self.insure_admit_dto_list[i]
if hasattr(element, 'to_alipay_dict'):
self.insure_admit_dto_list[i] = element.to_alipay_dict(
)
if hasattr(self.insure_admit_dto_list, 'to_alipay_dict'):
params['insure_admit_dto_list'
] = self.insure_admit_dto_list.to_alipay_dict()
else:
params['insure_admit_dto_list'] = self.insure_admit_dto_list
if self.partner_org_id:
if hasattr(self.partner_org_id, 'to_alipay_dict'):
params['partner_org_id'] = self.partner_org_id.to_alipay_dict()
else:
params['partner_org_id'] = self.partner_org_id
if self.product_code:
if hasattr(self.product_code, 'to_alipay_dict'):
params['product_code'] = self.product_code.to_alipay_dict()
else:
params['product_code'] = self.product_code
if self.scene_code:
if hasattr(self.scene_code, 'to_alipay_dict'):
params['scene_code'] = self.scene_code.to_alipay_dict()
else:
params['scene_code'] = self.scene_code
if self.user_client:
if hasattr(self.user_client, 'to_alipay_dict'):
params['user_client'] = self.user_client.to_alipay_dict()
else:
params['user_client'] = self.user_client
return params
@staticmethod
def from_alipay_dict(d):
if not d:
return None
o = AlipayInsSceneEcommerceInsureCheckModel()
if 'insure_admit_dto_list' in d:
o.insure_admit_dto_list = d['insure_admit_dto_list']
if 'partner_org_id' in d:
o.partner_org_id = d['partner_org_id']
if 'product_code' in d:
o.product_code = d['product_code']
if 'scene_code' in d:
o.scene_code = d['scene_code']
if 'user_client' in d:
o.user_client = d['user_client']
return o
| #!/usr/bin/env python
# -*- coding: utf-8 -*-
import json
from alipay.aop.api.constant.ParamConstants import *
from alipay.aop.api.domain.InsureAdmitDTO import InsureAdmitDTO
class AlipayInsSceneEcommerceInsureCheckModel(object):
def __init__(self):
self._insure_admit_dto_list = None
self._partner_org_id = None
self._product_code = None
self._scene_code = None
self._user_client = None
@property
def insure_admit_dto_list(self):
return self._insure_admit_dto_list
@insure_admit_dto_list.setter
def insure_admit_dto_list(self, value):
if isinstance(value, list):
self._insure_admit_dto_list = list()
for i in value:
if isinstance(i, InsureAdmitDTO):
self._insure_admit_dto_list.append(i)
else:
self._insure_admit_dto_list.append(InsureAdmitDTO.from_alipay_dict(i))
@property
def partner_org_id(self):
return self._partner_org_id
@partner_org_id.setter
def partner_org_id(self, value):
self._partner_org_id = value
@property
def product_code(self):
return self._product_code
@product_code.setter
def product_code(self, value):
self._product_code = value
@property
def scene_code(self):
return self._scene_code
@scene_code.setter
def scene_code(self, value):
self._scene_code = value
@property
def user_client(self):
return self._user_client
@user_client.setter
def user_client(self, value):
self._user_client = value
def to_alipay_dict(self):
params = dict()
if self.insure_admit_dto_list:
if isinstance(self.insure_admit_dto_list, list):
for i in range(0, len(self.insure_admit_dto_list)):
element = self.insure_admit_dto_list[i]
if hasattr(element, 'to_alipay_dict'):
self.insure_admit_dto_list[i] = element.to_alipay_dict()
if hasattr(self.insure_admit_dto_list, 'to_alipay_dict'):
params['insure_admit_dto_list'] = self.insure_admit_dto_list.to_alipay_dict()
else:
params['insure_admit_dto_list'] = self.insure_admit_dto_list
if self.partner_org_id:
if hasattr(self.partner_org_id, 'to_alipay_dict'):
params['partner_org_id'] = self.partner_org_id.to_alipay_dict()
else:
params['partner_org_id'] = self.partner_org_id
if self.product_code:
if hasattr(self.product_code, 'to_alipay_dict'):
params['product_code'] = self.product_code.to_alipay_dict()
else:
params['product_code'] = self.product_code
if self.scene_code:
if hasattr(self.scene_code, 'to_alipay_dict'):
params['scene_code'] = self.scene_code.to_alipay_dict()
else:
params['scene_code'] = self.scene_code
if self.user_client:
if hasattr(self.user_client, 'to_alipay_dict'):
params['user_client'] = self.user_client.to_alipay_dict()
else:
params['user_client'] = self.user_client
return params
@staticmethod
def from_alipay_dict(d):
if not d:
return None
o = AlipayInsSceneEcommerceInsureCheckModel()
if 'insure_admit_dto_list' in d:
o.insure_admit_dto_list = d['insure_admit_dto_list']
if 'partner_org_id' in d:
o.partner_org_id = d['partner_org_id']
if 'product_code' in d:
o.product_code = d['product_code']
if 'scene_code' in d:
o.scene_code = d['scene_code']
if 'user_client' in d:
o.user_client = d['user_client']
return o
| [
8,
12,
13,
14,
16
] |
243 | c4ca4b5c77c3c912b44a4853be30298ec845c4fd | <mask token>
| <mask token>
print(owog.find('e'))
print(owog.count('e'))
print(owog[2:10])
<mask token>
if a > b:
print('a too ih')
elif a == b:
print('tentsuu')
else:
print('b too ih')
<mask token>
for i in range(a, b + 1):
print(i)
| owog = 'Delger'
print(owog.find('e'))
print(owog.count('e'))
print(owog[2:10])
a = 21
b = 21
if a > b:
print('a too ih')
elif a == b:
print('tentsuu')
else:
print('b too ih')
a, b = input().split()
for i in range(a, b + 1):
print(i)
| #str
owog="Delger"
# len()- urt
# lower()- jijigruuleh
# upper()- tomruulah
# capitalize()- ehnii useg tomruulah
# replace()- temdegt solih
print(owog.find("e"))
print(owog.count("e"))
print(owog[2:10])
a=21
b=21
if a>b:
print("a too ih")
elif a==b:
print("tentsuu")
else:
print("b too ih")
a, b = input().split()
for i in range(a, b+1):
print(i) | null | [
0,
1,
2,
3
] |
244 | 050e2207ac7331444d39305869c4b25bcbc53907 | <mask token>
| <mask token>
print(
'Note: total_earnings values were scaled by multiplying by {:.10f} and adding {:.6f}'
.format(scaler.scale_[8], scaler.min_[8]))
<mask token>
scaled_training_df.to_csv('sales_data_training_scaled.csv', index=False)
scaled_training_df.to_csv('sales_data_test_scaled.csv', index=False)
| <mask token>
training_data_df = pd.read_csv('sales_data_training.csv')
test_data_df = pd.read_csv('sales_data_test.csv')
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_training = scaler.fit_transform(training_data_df)
scaled_testing = scaler.transform(test_data_df)
print(
'Note: total_earnings values were scaled by multiplying by {:.10f} and adding {:.6f}'
.format(scaler.scale_[8], scaler.min_[8]))
scaled_training_df = pd.DataFrame(scaled_training, columns=training_data_df
.columns.values)
scaled_testing_df = pd.DataFrame(scaled_testing, columns=test_data_df.
columns.values)
scaled_training_df.to_csv('sales_data_training_scaled.csv', index=False)
scaled_training_df.to_csv('sales_data_test_scaled.csv', index=False)
| import pandas as pd
from sklearn.preprocessing import MinMaxScaler
training_data_df = pd.read_csv('sales_data_training.csv')
test_data_df = pd.read_csv('sales_data_test.csv')
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_training = scaler.fit_transform(training_data_df)
scaled_testing = scaler.transform(test_data_df)
print(
'Note: total_earnings values were scaled by multiplying by {:.10f} and adding {:.6f}'
.format(scaler.scale_[8], scaler.min_[8]))
scaled_training_df = pd.DataFrame(scaled_training, columns=training_data_df
.columns.values)
scaled_testing_df = pd.DataFrame(scaled_testing, columns=test_data_df.
columns.values)
scaled_training_df.to_csv('sales_data_training_scaled.csv', index=False)
scaled_training_df.to_csv('sales_data_test_scaled.csv', index=False)
| import pandas as pd
from sklearn.preprocessing import MinMaxScaler
#loading data from CSV
training_data_df = pd.read_csv("sales_data_training.csv")
test_data_df = pd.read_csv("sales_data_test.csv")
#scaler
scaler = MinMaxScaler(feature_range=(0,1))
#scale both inputs and outputs
scaled_training = scaler.fit_transform(training_data_df)
scaled_testing = scaler.transform(test_data_df)
#to bring it back to the original values
print("Note: total_earnings values were scaled by multiplying by {:.10f} and adding {:.6f}".format(scaler.scale_[8], scaler.min_[8]))
#create a new scaled dataframe object
scaled_training_df = pd.DataFrame(scaled_training, columns=training_data_df.columns.values)
scaled_testing_df = pd.DataFrame(scaled_testing, columns=test_data_df.columns.values)
#save the scaled dataframe to new csv files
scaled_training_df.to_csv("sales_data_training_scaled.csv", index=False)
scaled_training_df.to_csv("sales_data_test_scaled.csv", index=False)
| [
0,
1,
2,
3,
4
] |
245 | df64d769ffba8cddac34282a526122e3c941249d | #!/usr/bin/env python
import os
import tempfile
import shutil
import math
import sys
import subprocess
from irank.config import IrankOptionParser, IrankApp
from irank import db as irank_db
STATUS = 0
def main():
p = IrankOptionParser('%prog -d DEST playlist_name [playlist_name ...]')
p.add_option('-d', '--dest', help='export destination', default=None)
p.add_option('-l', '--limit', type="int", help='per-playlist filesize limit', default=None)
p.add_option('--no-checksum', dest='checksum', action="store_false", default=True)
p.add_option('-i', '--interactive', action='store_true', help='Interactively resolve errors')
p.add_option('--rsync-opt', dest='rsync_opts', action='append', default=[], help='Add rsync option (can be used multiple times)')
opts, args = p.parse_args()
assert opts.dest, p.get_usage()
assert len(args) > 0, p.get_usage()
app = IrankApp(opts)
music_base = os.path.expanduser(opts.music)
irank_base = os.path.expanduser(opts.irank)
export_base = os.path.expanduser(opts.dest)
export_music = export_base # Used to be __music, but android 4+ doesn't like sub-folders
songs = {}
all_songs = set()
# we use hard-links, so the export_temp must be on the same device as our music!
# export_temp = tempfile.mkdtemp(prefix='irank-export-')
export_temp = os.path.join(irank_base, "__export_temp")
if os.path.exists(export_temp):
shutil.rmtree(export_temp)
else:
os.makedirs(export_temp)
shutil.copy(
os.path.join(irank_base, "irank.sqlite"),
os.path.join(export_temp, "irank.sqlite")
)
try:
for playlist in args:
playlist_songs = set(app.songs_for(playlist, relative=True))
songs[playlist] = playlist_songs
all_songs.update(playlist_songs)
write_m3u(export_temp, playlist, sorted(playlist_songs))
print "Generated playlist %s: %s files" % (playlist, len(playlist_songs))
print "linking into %r ..." % (export_temp,)
total_size = link_all_files(all_songs, export_temp=export_temp, music_base=music_base, limit=opts.limit)
print "Syncing %s files (%0.2fgb)" % (len(all_songs),total_size / (math.pow(1000, 3)))
extra_sync_opts = []
syncing = True
while syncing:
try:
sync(export_temp, export_music, additional_opts=opts.rsync_opts + extra_sync_opts, checksum=opts.checksum)
break
except (subprocess.CalledProcessError, OSError) as e:
if not opts.interactive:
raise
print >> sys.stderr, "Error syncing: %s\n" % (e,)
while True:
print >> sys.stderr, "Press Ctrl-C to abort, <return> to restart, 'k' to retry (skipping existing files) and 's' to skip to next step"
result = raw_input().strip().lower()
if result == 'k':
extra_sync_opts = ['--ignore-existing']
break
elif result == '':
extra_sync_opts = []
break
elif result == 's':
syncing = False
break
else:
print >> sys.stderr, "Eh?"
finally:
shutil.rmtree(export_temp)
def link_all_files(all_songs, export_temp, music_base, limit=None):
total_size = 0
def file_size(path):
try:
return os.stat(path).st_size
except OSError:
print >> sys.stderr, "couldn't get file size of file: %s" % (path,)
return None
for file in all_songs:
#if not os.path.isdir(os.path.dirname(
src_file = os.path.join(music_base, file)
src_file_size = file_size(src_file)
if src_file_size is None:
continue
if limit and (total_size + src_file_size) > limit:
return total_size
else:
total_size += src_file_size
link_dest = os.path.join(export_temp, file)
link_dest_dir = os.path.dirname(link_dest)
if not os.path.isdir(link_dest_dir):
os.makedirs(link_dest_dir)
os.link(src_file, link_dest)
return total_size
def sync(src, dest, additional_opts=[], checksum=True):
cmd = [
'rsync',
#'-n',
'--progress',
'--modify-window=5',
'-r',
#'-v',
'--delete-before']
if checksum:
cmd.append('-c')
cmd = cmd + additional_opts + [src + os.path.sep, dest]
print "running: %r" % (cmd,)
subprocess.check_call(cmd, stdin=subprocess.PIPE)
def write_m3u(dest, name, files):
global STATUS
encoding = sys.getfilesystemencoding()
with open(os.path.join(dest, name + '.m3u'), 'w') as output:
for name in files:
try:
print >> output, name.encode(encoding)
except (UnicodeEncodeError, UnicodeDecodeError) as err:
print "FAILED to write song: %r" % (name,)
STATUS = 1
if __name__ == '__main__':
main()
sys.exit(STATUS)
| null | null | null | null | [
0
] |
246 | 21b295e28a7e4443ea116df1b22ff5074dca955a | <mask token>
| <mask token>
for i in range(1, n + 1):
print(i)
| n = int(input('nhap gia tri'))
for i in range(1, n + 1):
print(i)
| n =int(input("nhap gia tri"))
for i in range(1,n+1):
print(i) | null | [
0,
1,
2,
3
] |
247 | 93737e4c409d0efb1ae2263cb60d4b03d9aad0d8 | <mask token>
| <mask token>
ap.add_argument('-D', '--dir', required=False, help='Directory to sort')
<mask token>
if args['dir'] == None:
DIR = os.getcwd()
elif os.path.exists(args['dir']):
DIR = args['dir']
for file in os.listdir(DIR):
if not os.path.isdir(os.path.join(DIR, file)):
name, ext = os.path.splitext(file)
ext = ext[::-1][:-1][::-1]
if os.path.exists(os.path.join(DIR, ext.upper())):
shutil.move(os.path.join(DIR, file), os.path.join(DIR, ext.
upper(), file))
else:
os.mkdir(os.path.join(DIR, ext.upper()))
shutil.move(os.path.join(DIR, file), os.path.join(DIR, ext.
upper(), file))
| <mask token>
ap = argparse.ArgumentParser()
ap.add_argument('-D', '--dir', required=False, help='Directory to sort')
args = vars(ap.parse_args())
if args['dir'] == None:
DIR = os.getcwd()
elif os.path.exists(args['dir']):
DIR = args['dir']
for file in os.listdir(DIR):
if not os.path.isdir(os.path.join(DIR, file)):
name, ext = os.path.splitext(file)
ext = ext[::-1][:-1][::-1]
if os.path.exists(os.path.join(DIR, ext.upper())):
shutil.move(os.path.join(DIR, file), os.path.join(DIR, ext.
upper(), file))
else:
os.mkdir(os.path.join(DIR, ext.upper()))
shutil.move(os.path.join(DIR, file), os.path.join(DIR, ext.
upper(), file))
| import os
import shutil
import argparse
ap = argparse.ArgumentParser()
ap.add_argument('-D', '--dir', required=False, help='Directory to sort')
args = vars(ap.parse_args())
if args['dir'] == None:
DIR = os.getcwd()
elif os.path.exists(args['dir']):
DIR = args['dir']
for file in os.listdir(DIR):
if not os.path.isdir(os.path.join(DIR, file)):
name, ext = os.path.splitext(file)
ext = ext[::-1][:-1][::-1]
if os.path.exists(os.path.join(DIR, ext.upper())):
shutil.move(os.path.join(DIR, file), os.path.join(DIR, ext.
upper(), file))
else:
os.mkdir(os.path.join(DIR, ext.upper()))
shutil.move(os.path.join(DIR, file), os.path.join(DIR, ext.
upper(), file))
| import os
import shutil
import argparse
ap = argparse.ArgumentParser()
ap.add_argument('-D','--dir', required=False, help='Directory to sort')
args = vars(ap.parse_args())
if args['dir'] == None:
DIR = os.getcwd()
elif os.path.exists(args['dir']):
DIR = args['dir']
for file in os.listdir(DIR):
if not os.path.isdir(os.path.join(DIR, file)):
name, ext = os.path.splitext(file)
ext = ext[::-1][:-1][::-1]
if os.path.exists(os.path.join(DIR, ext.upper())):
shutil.move(os.path.join(DIR, file), os.path.join(DIR, ext.upper(), file))
else:
os.mkdir(os.path.join(DIR, ext.upper()))
shutil.move(os.path.join(DIR, file), os.path.join(DIR, ext.upper(), file)) | [
0,
1,
2,
3,
4
] |
248 | b5611c668a40e1735c92d6d00867885023ad713f | <mask token>
def f(A):
if len(A) == 1:
return 0
else:
rightStart = len(A) // 2
leftArray = A[0:rightStart]
righArray = A[rightStart:]
B, b = count_and_sort(leftArray)
C, c = count_and_sort(righArray)
D, d = count_and_sort_split(B, C)
return b + c + d
<mask token>
def count_and_sort_split(B, C):
result = []
nums = 0
i = 0
j = 0
while i < len(B) or j < len(C):
if i >= len(B):
result = result + C[j:]
break
elif j >= len(C):
result = result + B[i:]
break
if B[i] < C[j]:
result.append(B[i])
i += 1
elif B[i] > C[j]:
result.append(C[j])
nums = nums + len(B[i:])
j += 1
return result, nums
<mask token>
| <mask token>
def f(A):
if len(A) == 1:
return 0
else:
rightStart = len(A) // 2
leftArray = A[0:rightStart]
righArray = A[rightStart:]
B, b = count_and_sort(leftArray)
C, c = count_and_sort(righArray)
D, d = count_and_sort_split(B, C)
return b + c + d
def count_and_sort(A):
if len(A) == 1:
return A, 0
elif len(A) == 2:
if A[0] < A[1]:
return A, 0
else:
temp = A[0]
A[0] = A[1]
A[1] = temp
return A, 1
else:
rightStart = len(A) // 2
leftArray = A[0:rightStart]
righArray = A[rightStart:]
B, b = count_and_sort(leftArray)
C, c = count_and_sort(righArray)
D, d = count_and_sort_split(B, C)
return D, b + c + d
def count_and_sort_split(B, C):
result = []
nums = 0
i = 0
j = 0
while i < len(B) or j < len(C):
if i >= len(B):
result = result + C[j:]
break
elif j >= len(C):
result = result + B[i:]
break
if B[i] < C[j]:
result.append(B[i])
i += 1
elif B[i] > C[j]:
result.append(C[j])
nums = nums + len(B[i:])
j += 1
return result, nums
<mask token>
| <mask token>
with open('IntegerArray.txt', 'r') as f:
target = f.readlines()
for x in range(len(target)):
target[x] = int(target[x])
def f(A):
if len(A) == 1:
return 0
else:
rightStart = len(A) // 2
leftArray = A[0:rightStart]
righArray = A[rightStart:]
B, b = count_and_sort(leftArray)
C, c = count_and_sort(righArray)
D, d = count_and_sort_split(B, C)
return b + c + d
def count_and_sort(A):
if len(A) == 1:
return A, 0
elif len(A) == 2:
if A[0] < A[1]:
return A, 0
else:
temp = A[0]
A[0] = A[1]
A[1] = temp
return A, 1
else:
rightStart = len(A) // 2
leftArray = A[0:rightStart]
righArray = A[rightStart:]
B, b = count_and_sort(leftArray)
C, c = count_and_sort(righArray)
D, d = count_and_sort_split(B, C)
return D, b + c + d
def count_and_sort_split(B, C):
result = []
nums = 0
i = 0
j = 0
while i < len(B) or j < len(C):
if i >= len(B):
result = result + C[j:]
break
elif j >= len(C):
result = result + B[i:]
break
if B[i] < C[j]:
result.append(B[i])
i += 1
elif B[i] > C[j]:
result.append(C[j])
nums = nums + len(B[i:])
j += 1
return result, nums
print(f(target))
| target = []
with open('IntegerArray.txt', 'r') as f:
target = f.readlines()
for x in range(len(target)):
target[x] = int(target[x])
def f(A):
if len(A) == 1:
return 0
else:
rightStart = len(A) // 2
leftArray = A[0:rightStart]
righArray = A[rightStart:]
B, b = count_and_sort(leftArray)
C, c = count_and_sort(righArray)
D, d = count_and_sort_split(B, C)
return b + c + d
def count_and_sort(A):
if len(A) == 1:
return A, 0
elif len(A) == 2:
if A[0] < A[1]:
return A, 0
else:
temp = A[0]
A[0] = A[1]
A[1] = temp
return A, 1
else:
rightStart = len(A) // 2
leftArray = A[0:rightStart]
righArray = A[rightStart:]
B, b = count_and_sort(leftArray)
C, c = count_and_sort(righArray)
D, d = count_and_sort_split(B, C)
return D, b + c + d
def count_and_sort_split(B, C):
result = []
nums = 0
i = 0
j = 0
while i < len(B) or j < len(C):
if i >= len(B):
result = result + C[j:]
break
elif j >= len(C):
result = result + B[i:]
break
if B[i] < C[j]:
result.append(B[i])
i += 1
elif B[i] > C[j]:
result.append(C[j])
nums = nums + len(B[i:])
j += 1
return result, nums
print(f(target))
| target=[]
with open('IntegerArray.txt','r') as f:
target=f.readlines()
for x in range(len(target)):
target[x]=int(target[x])
def f(A):
if len(A)==1:
return 0
else:
rightStart=len(A)//2
leftArray=A[0:rightStart]
righArray=A[rightStart:]
B,b=count_and_sort(leftArray)
C,c=count_and_sort(righArray)
D,d=count_and_sort_split(B,C)
return b+c+d
def count_and_sort(A):
if len(A)==1:
return A,0
elif len(A)==2:
if A[0]<A[1]:
return A,0
else:
temp=A[0]
A[0]=A[1]
A[1]=temp
return A,1
else:
rightStart=len(A)//2
leftArray=A[0:rightStart]
righArray=A[rightStart:]
B,b=count_and_sort(leftArray)
C,c=count_and_sort(righArray)
D,d=count_and_sort_split(B,C)
return D,b+c+d
def count_and_sort_split(B,C):
result=[]
nums=0
i=0
j=0
while i<len(B) or j<len(C):
if i>=len(B):
result=result+C[j:]
break
elif j>=len(C):
result=result+B[i:]
break
if B[i]<C[j]:
result.append(B[i])
i+=1
elif B[i]>C[j]:
result.append(C[j])
nums=nums+len(B[i:])
j+=1
return result,nums
print(f(target)) | [
2,
3,
4,
5,
6
] |
249 | 09f032301fa9389f6b07687e0ee13844e0b4ddf3 | from artichoke import DefaultManager, Config
from artichoke.helpers import read, prompt
from fabric.api import env, task, run
import os
chars = ''.join(chr(c) if chr(c).isupper() or chr(c).islower() else '_' for c in range(256))
class MagicDefaultManager(DefaultManager):
def __init__(self, env):
self.env = env
def SSH__user(self):
return env.user
def Global__project_root(self):
query = "Enter the project code root path"
default = "/home/%s/%s" % (self.env.user, self.config.project_name)
return read(query, default=default)
def Global__python_version(self):
major, minor, rev = tuple(run("python --version").split(' ')[1].split("."))
return "%s.%s" % (major, minor)
def Global__build(self):
query = "Select deployment mode"
options = ["development", "production"]
default = "development"
return read(query, options=options, default=default)
def Global__name(self):
query = "Enter deployment name"
return read(query)
def Global__project_name(self):
query = "Enter the project name"
return read(query)
def Global__branch(self):
query = "Enter the git branch for current project"
default = "master"
return read(query, default=default)
def Database__engine(self):
query = "Select database engine"
options = [
"postgresql_psycopg2", "postgresql",
"sqlite3", "oracle", "mysql"
]
default = "mysql"
return read(query, options=options, default=default)
def Database__config_name(self):
query = "Select configuration name"
default = "default"
return read(query, default=default)
def Database__name(self):
name = self.config.project_name.translate(chars)
if self.config.Database.engine == 'sqlite3':
query = "Select path to database"
default = "/tmp/%s.sqlite3" % name
else:
query = "Select database name"
default = name
return read(query, default=default)
def Database__user(self):
if self.config.Database.engine == 'sqlite3':
return ""
query = "Select database user"
default = self.config.project_name.translate(chars).lower()
return read(query, default=default)
def Database__password(self):
if self.config.Database.engine == 'sqlite3':
return ""
query = "Select database password"
default = self.config.project_name.translate(chars).lower()
return read(query, default=default)
def Database__host(self):
if self.config.Database.engine == 'sqlite3':
return ""
query = "Select database host"
default = "localhost"
return read(query, default=default)
def Database__port(self):
if self.config.Database.engine == 'sqlite3':
return ""
query = "Select database port"
default = "use %s default" % self.config.Database.engine
value = read(query, default=default)
return "" if value == default else value
def MySQL__root_password(self):
query = "Enter MySQL root password"
return read(query)
def GitHub__username(self):
query = "Enter Github username"
return read(query)
def GitHub__password(self):
query = "Enter Github password for user %s" % self.config.GitHub.username
return read(query)
def Django__static_url(self):
if self.config.build == "development":
return "/static/"
else:
if self.config.StaticServer.use_ssl:
return "https://%s/" % self.config.StaticServer.hostname
else:
return "http://%s/" % self.config.StaticServer.hostname
def StaticServer__document_root(self):
query = "Enter the document root path for the static server"
default = "/home/%s/klooff-static" % self.env.user
return read(query, default=default)
def StaticServer__hostname(self):
query = "Enter the static server hostname"
default = "static.klooff.com"
return read(query, default=default)
def ApiServer__hostname(self):
query = "Enter the api server hostname"
default = "api.klooff.com"
return read(query, default=default)
def ApiServer__ipaddress(self):
query = "Enter the api server IP address for apache hostname configuration"
default = "*"
return read(query, default=default)
def WebServer__hostname(self):
query = "Enter the web server hostname"
default = "www.klooff.com"
return read(query, default=default)
def ApiServer__use_ssl(self):
query = "Use SSL for Api?"
return prompt(query, default=True)
def StaticServer__use_ssl(self):
query = "Use SSL for Static?"
return prompt(query, default=True)
def StaticServer__ipaddress(self):
query = "Enter the static server IP address for apache hostname configuration"
default = "*"
return read(query, default=default)
def ApiServer__certificates_dir(self):
query = "Enter the certificates directory (relative to klooff-control root)"
default = os.path.join(self.env.local_code_root, "configs", "certs", "stage")
return read(query, default)
def StaticServer__certificates_dir(self):
query = "Enter the Static ssl certificates directory"
default = os.path.join(self.config.ApiServer.document_root, "configs", "certs", "static")
return read(query, default)
def ApiServer__document_root(self):
query = "Enter the document root path for the API server"
default = "/home/%s/klooff-server" % self.env.user
return read(query, default=default)
def Sentry__dsn(self):
query = "Enter the DSN where sentry logs are to be sent"
default = "http://b97f9ac6f98f48928afeed6a0ef91c0b:f8335b789f4945d59e93fa75aace5e85@localhost:9000/2"
return read(query, default=default)
class MagicConfig(Config):
def __init__(self, env, config_file=None):
Config.__init__(
self, config_file,
default_manager=MagicDefaultManager(env)
)
self.add_section("Database")
self.add_section("MySQL")
self.add_section("GitHub")
self.add_section("Django")
self.add_section("StaticServer")
self.add_section("ApiServer")
self.add_section("WebServer")
self.add_section("SSH")
self.add_section("Sentry")
self.add_section("Celery")
self.add_section("Redis")
self.add_section("Session")
self.add_section("Mongodb")
def set_config(name):
if name.endswith(".ini"):
name = name[:-4]
env.config_file = os.path.join(
env.local_code_root,
"configs",
"%s.ini" % name)
env.config = MagicConfig(env, env.config_file)
env.config.autosave(env.config_file)
#env.config.name = name
if env.config.SSH.is_set("user"):
env.user = env.config.SSH.user
if env.config.SSH.is_set("password"):
env.password = env.config.SSH.password
print "Setting user to ", env.user
if env.config.SSH.is_set("password"):
env.password = env.config.SSH.password
if env.config.SSH.is_set("hostname"):
env.hosts = [env.config.SSH.hostname]
print "Setting hosts to ", env.hosts
if env.config.SSH.is_set("identity_file"):
env.key_filename = os.path.join(
env.local_code_root,
"configs",
"keys",
env.config.SSH.identity_file)
print "Setting identity file to ", env.key_filename
@task
def save():
"""Saves the current config state."""
env.config.save(env.config_file)
@task
def set(name):
"""Loads a given configuration file from the configs directory"""
set_config(name)
| null | null | null | null | [
0
] |
250 | 68b967ecf18d576758cf05e889919944cfc34dcd | <mask token>
class Entity(Agent):
<mask token>
def __init__(self, unique_id, model):
super().__init__(unique_id, model)
self.type = ''
self.position = ''
self.log = []
self.move_probability = None
self.retire_probability = None
self._next_state = None
<mask token>
def get_next_position(self, next_level):
"""
randomly pick a position in some level and return its ID and the ID of its current occupant.
:param next_level: int
"""
next_positions = list(self.model.positions[int(next_level)].values())
shuffle(next_positions)
for p in next_positions:
if p.dual[1] != self.type:
self.model.desired_positions.append(p.unique_id)
return p.unique_id, p.dual[0]
def retire(self, other):
"""
swap with an agent and mark yourself as retired
:param other: an Entity-class object
"""
self.swap(other)
self.model.schedule.add(other)
self.model.schedule.remove(self)
self.model.retirees[self.type][self.model.schedule.steps] = self
self.model.per_step_movement[self.type] += 1
def swap(self, other):
"""
swap positions with an entity
:param other: an Entity-class object
"""
new_position = other.position
other.position = self.position
other.log.append(other.position)
your_old_level = int(self.position[0])
self.model.positions[your_old_level][self.position].dual = [other.
unique_id, other.type]
self.position = new_position
self.log.append(self.position)
if self.position != '':
your_new_level = int(self.position[0])
self.model.positions[your_new_level][self.position].dual = [self
.unique_id, self.type]
self.model.per_step_movement[self.type] += 1
def unmoving_update_log(self):
"""update own log if not moving."""
self.log.append(self.log[-1])
| <mask token>
class Entity(Agent):
<mask token>
def __init__(self, unique_id, model):
super().__init__(unique_id, model)
self.type = ''
self.position = ''
self.log = []
self.move_probability = None
self.retire_probability = None
self._next_state = None
def pick_move(self):
"""
given a vector of probabilities that sums to one, pick which level you'll go to
e.g. vector of probabilities = [0.3, 0.1, 0.3, 0.3]
:return: the draw, an int
"""
cum_sum = np.cumsum(self.move_probability)
cum_sum = np.insert(cum_sum, 0, 0)
rd = np.random.uniform(0.0, 1.0)
m = np.asarray(cum_sum < rd).nonzero()[0]
next_level = m[len(m) - 1]
return next_level
def get_next_position(self, next_level):
"""
randomly pick a position in some level and return its ID and the ID of its current occupant.
:param next_level: int
"""
next_positions = list(self.model.positions[int(next_level)].values())
shuffle(next_positions)
for p in next_positions:
if p.dual[1] != self.type:
self.model.desired_positions.append(p.unique_id)
return p.unique_id, p.dual[0]
def retire(self, other):
"""
swap with an agent and mark yourself as retired
:param other: an Entity-class object
"""
self.swap(other)
self.model.schedule.add(other)
self.model.schedule.remove(self)
self.model.retirees[self.type][self.model.schedule.steps] = self
self.model.per_step_movement[self.type] += 1
def swap(self, other):
"""
swap positions with an entity
:param other: an Entity-class object
"""
new_position = other.position
other.position = self.position
other.log.append(other.position)
your_old_level = int(self.position[0])
self.model.positions[your_old_level][self.position].dual = [other.
unique_id, other.type]
self.position = new_position
self.log.append(self.position)
if self.position != '':
your_new_level = int(self.position[0])
self.model.positions[your_new_level][self.position].dual = [self
.unique_id, self.type]
self.model.per_step_movement[self.type] += 1
def unmoving_update_log(self):
"""update own log if not moving."""
self.log.append(self.log[-1])
| <mask token>
class Entity(Agent):
"""
superclass for vacancy and actor agents
not intended to be used on its own, but to inherit its methods to multiple other agents
"""
def __init__(self, unique_id, model):
super().__init__(unique_id, model)
self.type = ''
self.position = ''
self.log = []
self.move_probability = None
self.retire_probability = None
self._next_state = None
def pick_move(self):
"""
given a vector of probabilities that sums to one, pick which level you'll go to
e.g. vector of probabilities = [0.3, 0.1, 0.3, 0.3]
:return: the draw, an int
"""
cum_sum = np.cumsum(self.move_probability)
cum_sum = np.insert(cum_sum, 0, 0)
rd = np.random.uniform(0.0, 1.0)
m = np.asarray(cum_sum < rd).nonzero()[0]
next_level = m[len(m) - 1]
return next_level
def get_next_position(self, next_level):
"""
randomly pick a position in some level and return its ID and the ID of its current occupant.
:param next_level: int
"""
next_positions = list(self.model.positions[int(next_level)].values())
shuffle(next_positions)
for p in next_positions:
if p.dual[1] != self.type:
self.model.desired_positions.append(p.unique_id)
return p.unique_id, p.dual[0]
def retire(self, other):
"""
swap with an agent and mark yourself as retired
:param other: an Entity-class object
"""
self.swap(other)
self.model.schedule.add(other)
self.model.schedule.remove(self)
self.model.retirees[self.type][self.model.schedule.steps] = self
self.model.per_step_movement[self.type] += 1
def swap(self, other):
"""
swap positions with an entity
:param other: an Entity-class object
"""
new_position = other.position
other.position = self.position
other.log.append(other.position)
your_old_level = int(self.position[0])
self.model.positions[your_old_level][self.position].dual = [other.
unique_id, other.type]
self.position = new_position
self.log.append(self.position)
if self.position != '':
your_new_level = int(self.position[0])
self.model.positions[your_new_level][self.position].dual = [self
.unique_id, self.type]
self.model.per_step_movement[self.type] += 1
def unmoving_update_log(self):
"""update own log if not moving."""
self.log.append(self.log[-1])
| <mask token>
from mesa import Agent
from random import shuffle
import numpy as np
class Entity(Agent):
"""
superclass for vacancy and actor agents
not intended to be used on its own, but to inherit its methods to multiple other agents
"""
def __init__(self, unique_id, model):
super().__init__(unique_id, model)
self.type = ''
self.position = ''
self.log = []
self.move_probability = None
self.retire_probability = None
self._next_state = None
def pick_move(self):
"""
given a vector of probabilities that sums to one, pick which level you'll go to
e.g. vector of probabilities = [0.3, 0.1, 0.3, 0.3]
:return: the draw, an int
"""
cum_sum = np.cumsum(self.move_probability)
cum_sum = np.insert(cum_sum, 0, 0)
rd = np.random.uniform(0.0, 1.0)
m = np.asarray(cum_sum < rd).nonzero()[0]
next_level = m[len(m) - 1]
return next_level
def get_next_position(self, next_level):
"""
randomly pick a position in some level and return its ID and the ID of its current occupant.
:param next_level: int
"""
next_positions = list(self.model.positions[int(next_level)].values())
shuffle(next_positions)
for p in next_positions:
if p.dual[1] != self.type:
self.model.desired_positions.append(p.unique_id)
return p.unique_id, p.dual[0]
def retire(self, other):
"""
swap with an agent and mark yourself as retired
:param other: an Entity-class object
"""
self.swap(other)
self.model.schedule.add(other)
self.model.schedule.remove(self)
self.model.retirees[self.type][self.model.schedule.steps] = self
self.model.per_step_movement[self.type] += 1
def swap(self, other):
"""
swap positions with an entity
:param other: an Entity-class object
"""
new_position = other.position
other.position = self.position
other.log.append(other.position)
your_old_level = int(self.position[0])
self.model.positions[your_old_level][self.position].dual = [other.
unique_id, other.type]
self.position = new_position
self.log.append(self.position)
if self.position != '':
your_new_level = int(self.position[0])
self.model.positions[your_new_level][self.position].dual = [self
.unique_id, self.type]
self.model.per_step_movement[self.type] += 1
def unmoving_update_log(self):
"""update own log if not moving."""
self.log.append(self.log[-1])
| """
generalised behaviour for actors and vacancies
"""
from mesa import Agent
from random import shuffle
import numpy as np
class Entity(Agent):
"""
superclass for vacancy and actor agents
not intended to be used on its own, but to inherit its methods to multiple other agents
"""
def __init__(self, unique_id, model):
super().__init__(unique_id, model)
self.type = '' # type of entity: vacancy, or actor
self.position = '' # ID of current position
self.log = [] # log of moves
self.move_probability = None # for in-system moves; float [0,1]
self.retire_probability = None # for leaving the system; float [0,1]
self._next_state = None
def pick_move(self):
"""
given a vector of probabilities that sums to one, pick which level you'll go to
e.g. vector of probabilities = [0.3, 0.1, 0.3, 0.3]
:return: the draw, an int
"""
cum_sum = np.cumsum(self.move_probability)
cum_sum = np.insert(cum_sum, 0, 0)
# throw random dart
rd = np.random.uniform(0.0, 1.0)
# see where dart hit
m = np.asarray(cum_sum < rd).nonzero()[0]
next_level = m[len(m) - 1]
return next_level
def get_next_position(self, next_level):
"""
randomly pick a position in some level and return its ID and the ID of its current occupant.
:param next_level: int
"""
next_positions = list(self.model.positions[int(next_level)].values())
shuffle(next_positions)
for p in next_positions:
if p.dual[1] != self.type: # vacancies only pick positions occupied by actors, and vice versa
self.model.desired_positions.append(p.unique_id) # mark position as desired
return p.unique_id, p.dual[0] # return positions ID and ID of current dual/occupant
def retire(self, other):
"""
swap with an agent and mark yourself as retired
:param other: an Entity-class object
"""
self.swap(other)
self.model.schedule.add(other) # put new entity into scheduler
self.model.schedule.remove(self) # take yourself out of it
self.model.retirees[self.type][self.model.schedule.steps] = self # mark yourself as retiree
self.model.per_step_movement[self.type] += 1
def swap(self, other):
"""
swap positions with an entity
:param other: an Entity-class object
"""
new_position = other.position # mark where you're going
other.position = self.position # put swapee in your position
other.log.append(other.position) # update swapee's log
# update your old position's dual
your_old_level = int(self.position[0])
self.model.positions[your_old_level][self.position].dual = [other.unique_id, other.type]
self.position = new_position # take your new position
self.log.append(self.position) # update your log
# if you have a new position, update its dual
if self.position != '':
your_new_level = int(self.position[0])
self.model.positions[your_new_level][self.position].dual = [self.unique_id, self.type]
# increment movement counters
self.model.per_step_movement[self.type] += 1
def unmoving_update_log(self):
"""update own log if not moving."""
self.log.append(self.log[-1])
| [
6,
7,
8,
9,
10
] |
251 | 46babde9c26a944c9d29121b6bbf89a32f242a81 | <mask token>
| <mask token>
def sun_prepare(xpoint, ypoint, radius, color, angle):
delta_list = []
radius_list = []
for delta in range(0, 360, angle):
delta_list.append(delta)
radius_list.append(random.randint(radius - 10, radius + 10))
return xpoint, ypoint, color, radius, delta_list, radius_list
<mask token>
| <mask token>
def sun_prepare(xpoint, ypoint, radius, color, angle):
delta_list = []
radius_list = []
for delta in range(0, 360, angle):
delta_list.append(delta)
radius_list.append(random.randint(radius - 10, radius + 10))
return xpoint, ypoint, color, radius, delta_list, radius_list
def sun(prepare_list):
xpoint = prepare_list[0]
ypoint = prepare_list[1]
color = prepare_list[2]
radius = prepare_list[3]
delta_list = prepare_list[4]
radius_list = prepare_list[5]
sd.start_drawing()
point = sd.get_point(xpoint, ypoint)
sd.circle(center_position=point, radius=radius * 3.9, color=sd.
background_color, width=0)
sd.circle(center_position=point, radius=radius, color=color, width=0)
for j, (delta, radius) in enumerate(zip(delta_list, radius_list)):
v = sd.get_vector(start_point=point, angle=delta, width=6, length=
random.randint(radius * 2, radius * 3))
v.draw(color)
sd.finish_drawing()
| import simple_draw as sd
import random
def sun_prepare(xpoint, ypoint, radius, color, angle):
delta_list = []
radius_list = []
for delta in range(0, 360, angle):
delta_list.append(delta)
radius_list.append(random.randint(radius - 10, radius + 10))
return xpoint, ypoint, color, radius, delta_list, radius_list
def sun(prepare_list):
xpoint = prepare_list[0]
ypoint = prepare_list[1]
color = prepare_list[2]
radius = prepare_list[3]
delta_list = prepare_list[4]
radius_list = prepare_list[5]
sd.start_drawing()
point = sd.get_point(xpoint, ypoint)
sd.circle(center_position=point, radius=radius * 3.9, color=sd.
background_color, width=0)
sd.circle(center_position=point, radius=radius, color=color, width=0)
for j, (delta, radius) in enumerate(zip(delta_list, radius_list)):
v = sd.get_vector(start_point=point, angle=delta, width=6, length=
random.randint(radius * 2, radius * 3))
v.draw(color)
sd.finish_drawing()
| import simple_draw as sd
import random
# sd.resolution = (1400, 900)
# Prepare data for the sun function
def sun_prepare(xpoint, ypoint, radius, color, angle):
delta_list = []
radius_list = []
for delta in range(0, 360, angle):
delta_list.append(delta)
radius_list.append(random.randint(radius - 10, radius + 10))
return xpoint, ypoint, color, radius, delta_list, radius_list
# Drawing the sun
def sun(prepare_list):
xpoint = prepare_list[0]
ypoint = prepare_list[1]
color = prepare_list[2]
radius = prepare_list[3]
delta_list = prepare_list[4]
radius_list = prepare_list[5]
sd.start_drawing()
point = sd.get_point(xpoint, ypoint)
sd.circle(center_position=point, radius=radius * 3.9, color=sd.background_color, width=0)
sd.circle(center_position=point, radius=radius, color=color, width=0)
for j, (delta, radius) in enumerate(zip(delta_list, radius_list)):
v = sd.get_vector(start_point=point, angle=delta, width=6,
length=random.randint(radius * 2, radius * 3))
v.draw(color)
sd.finish_drawing()
# sd.pause()
| [
0,
1,
2,
3,
4
] |
252 | e736991f364ba9ff709348e4b1f612b1e9673281 | <mask token>
| <mask token>
app = Flask(__name__)
<mask token>
| from flask import *
app = Flask(__name__)
from app import views
from app import admin_views
from app import usr_reg
from app import cookie
from app import db_connect
| from flask import *
app=Flask(__name__)
from app import views
from app import admin_views
from app import usr_reg
from app import cookie
from app import db_connect | null | [
0,
1,
2,
3
] |
253 | 07215403750be53994ae36727b6f790202b88697 | # Inspiration: [Fake Album Covers](https://fakealbumcovers.com/)
from IPython.display import Image as IPythonImage
from PIL import Image
from PIL import ImageFont
from PIL import ImageDraw
import requests
from xml.etree import ElementTree as ET
def display_cover(top,bottom ):
name='album_art_raw.png'
album_art_raw = requests.get('https://picsum.photos/500/500/?random')
with open(name,'wb') as album_art_raw_file:
album_art_raw_file.write(album_art_raw.content)
img = Image.open("album_art_raw.png")
draw = ImageDraw.Draw(img)
band_name_font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 25) #25pt font
album_name_font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf", 20) # 20pt font
band_x, band_y = 50, 50
album_x, album_y = 50, 400
outline_color ="black"
draw.text((band_x-1, band_y-1), top, font=band_name_font, fill=outline_color)
draw.text((band_x+1, band_y-1), top, font=band_name_font, fill=outline_color)
draw.text((band_x-1, band_y+1), top, font=band_name_font, fill=outline_color)
draw.text((band_x+1, band_y+1), top, font=band_name_font, fill=outline_color)
draw.text((album_x-1, album_y-1), bottom , font=album_name_font, fill=outline_color)
draw.text((album_x+1, album_y-1), bottom , font=album_name_font, fill=outline_color)
draw.text((album_x-1, album_y+1), bottom , font=album_name_font, fill=outline_color)
draw.text((album_x+1, album_y+1), bottom , font=album_name_font, fill=outline_color)
draw.text((band_x,band_y),top,(255,255,255),font=band_name_font)
draw.text((album_x, album_y),bottom,(255,255,255),font=album_name_font)
return img
wikipedia='https://en.wikipedia.org/wiki/Special:Random'
page = requests.get(wikipedia).text.strip()
file= ET.fromstring(page).find('head/title')
band_title = file.text.replace(' - Wikipedia','')
wikipedia='https://en.wikipedia.org/wiki/Special:Random'
page = requests.get(wikipedia).text.strip()p
file= ET.fromstring(page).find('head/title')
album_title = file.text.replace(' - Wikipedia','')
print(album_title)
print("Your band: ", band_title)
print("Your album: ", album_title)
img = display_cover(band_title,album_title)
img.save('sample-out.png')
IPythonImage(filename='sample-out.png') | null | null | null | null | [
0
] |
254 | 18d3f58048b7e5d792eb2494ecc62bb158ac7407 | <mask token>
@app.route('/')
def hello_world():
return render_template('index.html')
@app.route('/on/')
def on():
state = powerswitch.on()
return json.dumps(state)
@app.route('/off/')
def off():
state = powerswitch.off()
return json.dumps(state)
@app.route('/toggle/')
def toggle():
state = powerswitch.toggle()
return json.dumps(state)
@app.route('/state/')
def state():
state = powerswitch.state()
return json.dumps(state)
<mask token>
| <mask token>
@app.route('/')
def hello_world():
return render_template('index.html')
@app.route('/on/')
def on():
state = powerswitch.on()
return json.dumps(state)
@app.route('/off/')
def off():
state = powerswitch.off()
return json.dumps(state)
@app.route('/toggle/')
def toggle():
state = powerswitch.toggle()
return json.dumps(state)
@app.route('/state/')
def state():
state = powerswitch.state()
return json.dumps(state)
if __name__ == '__main__':
powerswitch.on()
app.run(host='0.0.0.0', port=80, debug=True)
| <mask token>
app = Flask(__name__)
@app.route('/')
def hello_world():
return render_template('index.html')
@app.route('/on/')
def on():
state = powerswitch.on()
return json.dumps(state)
@app.route('/off/')
def off():
state = powerswitch.off()
return json.dumps(state)
@app.route('/toggle/')
def toggle():
state = powerswitch.toggle()
return json.dumps(state)
@app.route('/state/')
def state():
state = powerswitch.state()
return json.dumps(state)
if __name__ == '__main__':
powerswitch.on()
app.run(host='0.0.0.0', port=80, debug=True)
| from flask import Flask
from flask import render_template
from flask import make_response
import json
from lib import powerswitch
app = Flask(__name__)
@app.route('/')
def hello_world():
return render_template('index.html')
@app.route('/on/')
def on():
state = powerswitch.on()
return json.dumps(state)
@app.route('/off/')
def off():
state = powerswitch.off()
return json.dumps(state)
@app.route('/toggle/')
def toggle():
state = powerswitch.toggle()
return json.dumps(state)
@app.route('/state/')
def state():
state = powerswitch.state()
return json.dumps(state)
if __name__ == '__main__':
powerswitch.on()
app.run(host='0.0.0.0', port=80, debug=True)
| from flask import Flask
from flask import render_template
from flask import make_response
import json
from lib import powerswitch
app = Flask(__name__)
@app.route('/')
def hello_world():
return render_template('index.html')
@app.route('/on/')
def on():
state = powerswitch.on()
return json.dumps(state)
@app.route('/off/')
def off():
state = powerswitch.off()
return json.dumps(state)
@app.route('/toggle/')
def toggle():
state = powerswitch.toggle()
return json.dumps(state)
@app.route('/state/')
def state():
state = powerswitch.state()
return json.dumps(state)
if __name__ == "__main__":
powerswitch.on()
app.run(host='0.0.0.0', port=80, debug=True)
| [
5,
6,
7,
8,
9
] |
255 | 869284fa531a93c1b9812ed90a560d0bb2f87e97 | <mask token>
| <mask token>
def fibonaci(n):
for i in range(0, n):
j = 1
i = i + j
j = i
return fibonaci
| def ep(m, h, el, g=9.8):
E = m * h * g
if E < el:
print('le plus grand est : el')
else:
print('le plus grand est : E')
<mask token>
def fibonaci(n):
for i in range(0, n):
j = 1
i = i + j
j = i
return fibonaci
| def ep(m, h, el, g=9.8):
E = m * h * g
if E < el:
print('le plus grand est : el')
else:
print('le plus grand est : E')
ep(3, 4, 5)
def fibonaci(n):
for i in range(0, n):
j = 1
i = i + j
j = i
return fibonaci
| # fonction pour voir quel est le plus grand entre l'energie limite et l'enerve potentiel
def ep (m,h,el,g=9.8):
E=m*h*g
if E<el:
print ("le plus grand est : el")
else:
print ("le plus grand est : E")
ep(3,4,5)
#fontion fibonaci 0 1 1 2 3 5 8 13
def fibonaci(n):
for i in range(0,n,):
j= 1
i = i + j
j=i
return fibonaci
| [
0,
1,
2,
3,
4
] |
256 | d126efa91b964a3a374d546bb860b39ae26dfa22 | <mask token>
class TestGetNumber(unittest.TestCase):
<mask token>
def test_fib(self):
self.assertEqual(Fib(5), 8)
<mask token>
| <mask token>
class TestGetNumber(unittest.TestCase):
def test_ok(self):
self.assertEqual(GetNumber(), 42)
def test_fib(self):
self.assertEqual(Fib(5), 8)
<mask token>
| <mask token>
class TestGetNumber(unittest.TestCase):
def test_ok(self):
self.assertEqual(GetNumber(), 42)
def test_fib(self):
self.assertEqual(Fib(5), 8)
if __name__ == '__main__':
unittest.main()
| <mask token>
import unittest
from bazel_tutorial.examples.py.lib import GetNumber
from bazel_tutorial.examples.py.fibonacci.fib import Fib
class TestGetNumber(unittest.TestCase):
def test_ok(self):
self.assertEqual(GetNumber(), 42)
def test_fib(self):
self.assertEqual(Fib(5), 8)
if __name__ == '__main__':
unittest.main()
| """A tiny example binary for the native Python rules of Bazel."""
import unittest
from bazel_tutorial.examples.py.lib import GetNumber
from bazel_tutorial.examples.py.fibonacci.fib import Fib
class TestGetNumber(unittest.TestCase):
def test_ok(self):
self.assertEqual(GetNumber(), 42)
def test_fib(self):
self.assertEqual(Fib(5), 8)
if __name__ == '__main__':
unittest.main()
| [
2,
3,
4,
5,
6
] |
257 | e582787a912f479830ed99575b2c6adb8088b4e5 | <mask token>
@app.route('/search_general', methods=['POST'])
def query():
message = None
searchQuery = request.json['searchQuery']
result = qp.generateQuery(searchQuery)
response = jsonify(result)
response.headers.add('Access-Control-Allow-Origin', '*')
return response
@app.route('/search_faceted', methods=['POST'])
def facQuery():
message = None
facQuery = request.json['facQuery']
result = qp.advancedQuery(facQuery)
response = jsonify(result)
response.headers.add('Access-Control-Allow-Origin', '*')
return response
<mask token>
| <mask token>
CORS(app)
<mask token>
@app.route('/search_general', methods=['POST'])
def query():
message = None
searchQuery = request.json['searchQuery']
result = qp.generateQuery(searchQuery)
response = jsonify(result)
response.headers.add('Access-Control-Allow-Origin', '*')
return response
@app.route('/search_faceted', methods=['POST'])
def facQuery():
message = None
facQuery = request.json['facQuery']
result = qp.advancedQuery(facQuery)
response = jsonify(result)
response.headers.add('Access-Control-Allow-Origin', '*')
return response
if __name__ == '__main__':
app.run(debug=True)
| <mask token>
app = Flask(__name__)
CORS(app)
qp = QueryProcessor()
@app.route('/search_general', methods=['POST'])
def query():
message = None
searchQuery = request.json['searchQuery']
result = qp.generateQuery(searchQuery)
response = jsonify(result)
response.headers.add('Access-Control-Allow-Origin', '*')
return response
@app.route('/search_faceted', methods=['POST'])
def facQuery():
message = None
facQuery = request.json['facQuery']
result = qp.advancedQuery(facQuery)
response = jsonify(result)
response.headers.add('Access-Control-Allow-Origin', '*')
return response
if __name__ == '__main__':
app.run(debug=True)
| from flask import Flask, request
from flask import jsonify
from preprocessing import QueryProcessor
from flask_cors import CORS
app = Flask(__name__)
CORS(app)
qp = QueryProcessor()
@app.route('/search_general', methods=['POST'])
def query():
message = None
searchQuery = request.json['searchQuery']
result = qp.generateQuery(searchQuery)
response = jsonify(result)
response.headers.add('Access-Control-Allow-Origin', '*')
return response
@app.route('/search_faceted', methods=['POST'])
def facQuery():
message = None
facQuery = request.json['facQuery']
result = qp.advancedQuery(facQuery)
response = jsonify(result)
response.headers.add('Access-Control-Allow-Origin', '*')
return response
if __name__ == '__main__':
app.run(debug=True)
| from flask import Flask, request
from flask import jsonify
from preprocessing import QueryProcessor
from flask_cors import CORS
app = Flask(__name__)
CORS(app)
qp = QueryProcessor()
@app.route('/search_general', methods=['POST'])
def query():
message = None
searchQuery = request.json['searchQuery']
result = qp.generateQuery(searchQuery)
response = jsonify(result)
response.headers.add('Access-Control-Allow-Origin', '*')
return response
@app.route('/search_faceted', methods=['POST'])
def facQuery():
message = None
facQuery = request.json['facQuery']
result = qp.advancedQuery(facQuery)
response = jsonify(result)
response.headers.add('Access-Control-Allow-Origin', '*')
return response
if __name__ == "__main__":
app.run(debug=True)
| [
2,
3,
4,
5,
6
] |
258 | cf0cf028d5f67e8deca8ebd3ad76d9c1e3563002 | #!/usr/bin/python2
import sys
import argparse
"""
This program generates an extract table having the following format:
<S1> <S2> <S3> ... <Sn> ||| <T1> <T2> <T3> ... <Tk> ||| 0-0
Each line is a mapping from a source sentence to target sentence
with special delimiter characters.
You can give the output of this script to extract2bin to generate
a binary phrase table.
"""
def main():
parser = argparse.ArgumentParser()
parser.add_argument("source_text", type=argparse.FileType("r"),
help="Tokenized sentences in the source language")
parser.add_argument("target_text", type=argparse.FileType("r"),
help="Tokenized sentences in the target language")
args = parser.parse_args()
src_lines = args.source_text.readlines()
len_src = len(src_lines)
tgt_lines = args.target_text.readlines()
len_tgt = len(tgt_lines)
if len_src != len_tgt:
print "Number of sentences doesn't match: %d - %d\n" % (len_src,len_tgt)
return 1
for s, t in zip(src_lines, tgt_lines):
print "%s ||| %s ||| 0-0" % (s.rstrip(), t.rstrip())
args.source_text.close()
args.target_text.close()
if __name__ == '__main__':
sys.exit(main())
| null | null | null | null | [
0
] |
259 | f8bf7e2d8f06bbd00f04047153833c07bf483fd3 | <mask token>
| <mask token>
class PyrpgConfig(AppConfig):
<mask token>
| <mask token>
class PyrpgConfig(AppConfig):
name = 'PyRPG'
| from django.apps import AppConfig
class PyrpgConfig(AppConfig):
name = 'PyRPG'
| null | [
0,
1,
2,
3
] |
260 | 48677d73f6489ce789884a9dff5d50c23f47d8b3 | <mask token>
class WindowFeatureExtractor(object):
<mask token>
<mask token>
def fit(self, X, y=None):
"""
X : list of list of str
list of word windows
y : ignored
returns : numpy array (sparse is sparse = True)
"""
feats = self.__extract_features_(X)
return self.vectorizer.fit(feats)
def transform(self, X, y=None):
return self.vectorizer.transform(X, y)
def fit_transform(self, X, y=None):
feats = self.__extract_features_(X)
return self.vectorizer.fit_transform(feats)
def __extract_features_(self, X):
if len(X) == 0:
raise Exception('Empty list passed to WindowFeatureExtractor.fit')
mid_ix = compute_middle_index(X[0])
all_feats = []
keys = []
for window in X:
d = {}
for fn in self.feature_extractors:
fts = fn(window, mid_ix, self.feature_val)
d.update(fts)
keys.extend(d.keys())
all_feats.append(d)
if self.min_feat_frequency <= 1:
return all_feats
""" Filter to at or above minimum feature frequency """
keyCnt = Counter(keys)
frequent = set([k for k, v in keyCnt.items() if v >= self.
min_feat_frequency])
freq_feats = []
for d in all_feats:
freq_d = dict([(k, v) for k, v in d.items() if k in frequent])
freq_feats.append(freq_d)
return freq_feats
| <mask token>
class WindowFeatureExtractor(object):
<mask token>
def __init__(self, feature_extractors, min_feat_frequency, sparse=True,
feature_val=1):
"""
feature_extractors : list of fns
feature extraction fns
min_feat_frequency : int
minimum frequency of features to retain
sparse : boolean
return a sparse numpy matrix or not
"""
self.feature_extractors = feature_extractors
self.min_feat_frequency = min_feat_frequency
self.vectorizer = DictVectorizer(sparse=sparse)
self.feature_val = feature_val
def fit(self, X, y=None):
"""
X : list of list of str
list of word windows
y : ignored
returns : numpy array (sparse is sparse = True)
"""
feats = self.__extract_features_(X)
return self.vectorizer.fit(feats)
def transform(self, X, y=None):
return self.vectorizer.transform(X, y)
def fit_transform(self, X, y=None):
feats = self.__extract_features_(X)
return self.vectorizer.fit_transform(feats)
def __extract_features_(self, X):
if len(X) == 0:
raise Exception('Empty list passed to WindowFeatureExtractor.fit')
mid_ix = compute_middle_index(X[0])
all_feats = []
keys = []
for window in X:
d = {}
for fn in self.feature_extractors:
fts = fn(window, mid_ix, self.feature_val)
d.update(fts)
keys.extend(d.keys())
all_feats.append(d)
if self.min_feat_frequency <= 1:
return all_feats
""" Filter to at or above minimum feature frequency """
keyCnt = Counter(keys)
frequent = set([k for k, v in keyCnt.items() if v >= self.
min_feat_frequency])
freq_feats = []
for d in all_feats:
freq_d = dict([(k, v) for k, v in d.items() if k in frequent])
freq_feats.append(freq_d)
return freq_feats
| __author__ = 'simon.hughes'
<mask token>
class WindowFeatureExtractor(object):
"""
A simple wrapper class that takes a number of window based feature extractor
functions and applies them to a dataset of windows, and then vectorizes with
the sklearn DictVectorizer class
"""
def __init__(self, feature_extractors, min_feat_frequency, sparse=True,
feature_val=1):
"""
feature_extractors : list of fns
feature extraction fns
min_feat_frequency : int
minimum frequency of features to retain
sparse : boolean
return a sparse numpy matrix or not
"""
self.feature_extractors = feature_extractors
self.min_feat_frequency = min_feat_frequency
self.vectorizer = DictVectorizer(sparse=sparse)
self.feature_val = feature_val
def fit(self, X, y=None):
"""
X : list of list of str
list of word windows
y : ignored
returns : numpy array (sparse is sparse = True)
"""
feats = self.__extract_features_(X)
return self.vectorizer.fit(feats)
def transform(self, X, y=None):
return self.vectorizer.transform(X, y)
def fit_transform(self, X, y=None):
feats = self.__extract_features_(X)
return self.vectorizer.fit_transform(feats)
def __extract_features_(self, X):
if len(X) == 0:
raise Exception('Empty list passed to WindowFeatureExtractor.fit')
mid_ix = compute_middle_index(X[0])
all_feats = []
keys = []
for window in X:
d = {}
for fn in self.feature_extractors:
fts = fn(window, mid_ix, self.feature_val)
d.update(fts)
keys.extend(d.keys())
all_feats.append(d)
if self.min_feat_frequency <= 1:
return all_feats
""" Filter to at or above minimum feature frequency """
keyCnt = Counter(keys)
frequent = set([k for k, v in keyCnt.items() if v >= self.
min_feat_frequency])
freq_feats = []
for d in all_feats:
freq_d = dict([(k, v) for k, v in d.items() if k in frequent])
freq_feats.append(freq_d)
return freq_feats
| __author__ = 'simon.hughes'
from sklearn.feature_extraction import DictVectorizer
from WindowFeatures import compute_middle_index
from collections import Counter
class WindowFeatureExtractor(object):
"""
A simple wrapper class that takes a number of window based feature extractor
functions and applies them to a dataset of windows, and then vectorizes with
the sklearn DictVectorizer class
"""
def __init__(self, feature_extractors, min_feat_frequency, sparse=True,
feature_val=1):
"""
feature_extractors : list of fns
feature extraction fns
min_feat_frequency : int
minimum frequency of features to retain
sparse : boolean
return a sparse numpy matrix or not
"""
self.feature_extractors = feature_extractors
self.min_feat_frequency = min_feat_frequency
self.vectorizer = DictVectorizer(sparse=sparse)
self.feature_val = feature_val
def fit(self, X, y=None):
"""
X : list of list of str
list of word windows
y : ignored
returns : numpy array (sparse is sparse = True)
"""
feats = self.__extract_features_(X)
return self.vectorizer.fit(feats)
def transform(self, X, y=None):
return self.vectorizer.transform(X, y)
def fit_transform(self, X, y=None):
feats = self.__extract_features_(X)
return self.vectorizer.fit_transform(feats)
def __extract_features_(self, X):
if len(X) == 0:
raise Exception('Empty list passed to WindowFeatureExtractor.fit')
mid_ix = compute_middle_index(X[0])
all_feats = []
keys = []
for window in X:
d = {}
for fn in self.feature_extractors:
fts = fn(window, mid_ix, self.feature_val)
d.update(fts)
keys.extend(d.keys())
all_feats.append(d)
if self.min_feat_frequency <= 1:
return all_feats
""" Filter to at or above minimum feature frequency """
keyCnt = Counter(keys)
frequent = set([k for k, v in keyCnt.items() if v >= self.
min_feat_frequency])
freq_feats = []
for d in all_feats:
freq_d = dict([(k, v) for k, v in d.items() if k in frequent])
freq_feats.append(freq_d)
return freq_feats
| __author__ = 'simon.hughes'
from sklearn.feature_extraction import DictVectorizer
from WindowFeatures import compute_middle_index
from collections import Counter
class WindowFeatureExtractor(object):
"""
A simple wrapper class that takes a number of window based feature extractor
functions and applies them to a dataset of windows, and then vectorizes with
the sklearn DictVectorizer class
"""
def __init__(self, feature_extractors, min_feat_frequency, sparse=True, feature_val=1):
"""
feature_extractors : list of fns
feature extraction fns
min_feat_frequency : int
minimum frequency of features to retain
sparse : boolean
return a sparse numpy matrix or not
"""
self.feature_extractors = feature_extractors
self.min_feat_frequency = min_feat_frequency
self.vectorizer = DictVectorizer(sparse=sparse)
self.feature_val = feature_val
def fit(self, X, y=None):
"""
X : list of list of str
list of word windows
y : ignored
returns : numpy array (sparse is sparse = True)
"""
feats = self.__extract_features_(X)
return self.vectorizer.fit(feats)
def transform(self, X, y=None):
return self.vectorizer.transform(X, y)
def fit_transform(self, X,y=None):
feats = self.__extract_features_(X)
return self.vectorizer.fit_transform(feats)
def __extract_features_(self, X):
if len(X) == 0:
raise Exception("Empty list passed to WindowFeatureExtractor.fit")
mid_ix = compute_middle_index(X[0])
all_feats = []
keys = []
for window in X:
d = {}
for fn in self.feature_extractors:
fts = fn(window, mid_ix, self.feature_val)
d.update(fts)
keys.extend(d.keys())
all_feats.append(d)
if self.min_feat_frequency <= 1:
return all_feats
""" Filter to at or above minimum feature frequency """
keyCnt = Counter(keys)
frequent = set([k for k,v in keyCnt.items() if v >= self.min_feat_frequency])
freq_feats = []
for d in all_feats:
freq_d = dict([(k,v) for k,v in d.items() if k in frequent])
freq_feats.append(freq_d)
return freq_feats | [
5,
6,
8,
9,
10
] |
261 | 65aa85675393efa1a0d8e5bab4b1dbf388018c58 |
indelCost = 1
swapCost = 13
subCost = 12
noOp = 0
def alignStrings(x,y):
nx = len(x)
ny = len(y)
S = matrix(nx+1, ny+1) #??
for i in range (nx+1)
for j in range (ny+1)
if i == 0: #if the string is empty
S[i][j] = j #this will put all the letters from j in i
elif j == 0: #if the second string is empy
S[i][j] = i #this will putj all the letter from i in j
elif
| null | null | null | null | [
0
] |
262 | 47c1ad4bd1ceffa38eef467ea8eb59dbd2fc2ebb | <mask token>
class PacketSender:
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def handle_ack(data):
global acked_packets
global seq_num
global acked_all_packets
global acked_packets_lock
p = Packet.from_bytes(data)
if not p.packet_type == PacketConstructor.ack_type:
return
print('received ack ' + str(p.seq_num))
acked_packets_lock.acquire()
if p.seq_num not in acked_packets:
print("it's a new ack")
acked_packets.append(p.seq_num)
if len(acked_packets) == seq_num:
print('got all acks')
acked_all_packets = True
else:
print('len: ' + str(len(acked_packets)))
print('seq_num: ' + str(seq_num))
acked_packets_lock.release()
def await_acks(conn):
print('awaiting acks')
while not PacketSender.acked_all_packets:
data, sender = conn.recvfrom(1024)
threading.Thread(target=PacketSender.handle_ack, args=(data,)
).start()
<mask token>
<mask token>
@staticmethod
def send_as_packets(data, conn, destination, peer_ip, peer_port):
global sent_packets
global acked_packets
global next_seq_num
global acked_all_packets
global seq_num
PacketSender.reset()
max_payload_length = Packet.MAX_LEN - Packet.MIN_LEN
curr = [0, 0]
def nbytes(n):
curr[0], curr[1] = curr[1], curr[1] + n
return data[curr[0]:curr[1]]
remaining_data = len(data)
if remaining_data > 0:
threading.Thread(target=PacketSender.await_acks, args=(conn,)
).start()
while remaining_data > 0:
while (sent_packets < PacketConstructor.window_size and
remaining_data > 0):
print('sending packet %d' % seq_num)
if remaining_data > max_payload_length:
p = Packet(packet_type=PacketConstructor.data_type,
seq_num=seq_num, peer_ip_addr=peer_ip, peer_port=
peer_port, is_last_packet=False, payload=nbytes(
max_payload_length))
conn.sendto(p.to_bytes(), destination)
sent_packets += 1
remaining_data -= max_payload_length
seq_num += 1
PacketSender.spawn_resend_thread(conn, p, destination)
print('not last packet')
else:
p = Packet(packet_type=PacketConstructor.data_type,
seq_num=seq_num, peer_ip_addr=peer_ip, peer_port=
peer_port, is_last_packet=True, payload=nbytes(
remaining_data))
conn.sendto(p.to_bytes(), destination)
sent_packets += 1
remaining_data -= remaining_data
seq_num += 1
print('remaining data ' + str(remaining_data))
print('is last packet')
PacketSender.spawn_resend_thread(conn, p, destination)
while next_seq_num in acked_packets:
next_seq_num += 1
sent_packets -= 1
print('Waiting for acks')
while not acked_all_packets:
pass
print('RECEIVED ALL ACKS')
PacketSender.was_reset = True
| <mask token>
class PacketSender:
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def reset(self):
global seq_num
global sent_packets
global next_seq_num
global acked_packets
global acked_all_packets
global acked_packets_lock
seq_num = 0
sent_packets = 0
next_seq_num = 0
acked_packets = []
acked_all_packets = False
acked_packets_lock = threading.Lock()
def handle_ack(data):
global acked_packets
global seq_num
global acked_all_packets
global acked_packets_lock
p = Packet.from_bytes(data)
if not p.packet_type == PacketConstructor.ack_type:
return
print('received ack ' + str(p.seq_num))
acked_packets_lock.acquire()
if p.seq_num not in acked_packets:
print("it's a new ack")
acked_packets.append(p.seq_num)
if len(acked_packets) == seq_num:
print('got all acks')
acked_all_packets = True
else:
print('len: ' + str(len(acked_packets)))
print('seq_num: ' + str(seq_num))
acked_packets_lock.release()
def await_acks(conn):
print('awaiting acks')
while not PacketSender.acked_all_packets:
data, sender = conn.recvfrom(1024)
threading.Thread(target=PacketSender.handle_ack, args=(data,)
).start()
<mask token>
def spawn_resend_thread(conn, packet, destination):
threading.Thread(target=PacketSender.resend_packet_if_needed, args=
(conn, packet, destination)).start()
@staticmethod
def send_as_packets(data, conn, destination, peer_ip, peer_port):
global sent_packets
global acked_packets
global next_seq_num
global acked_all_packets
global seq_num
PacketSender.reset()
max_payload_length = Packet.MAX_LEN - Packet.MIN_LEN
curr = [0, 0]
def nbytes(n):
curr[0], curr[1] = curr[1], curr[1] + n
return data[curr[0]:curr[1]]
remaining_data = len(data)
if remaining_data > 0:
threading.Thread(target=PacketSender.await_acks, args=(conn,)
).start()
while remaining_data > 0:
while (sent_packets < PacketConstructor.window_size and
remaining_data > 0):
print('sending packet %d' % seq_num)
if remaining_data > max_payload_length:
p = Packet(packet_type=PacketConstructor.data_type,
seq_num=seq_num, peer_ip_addr=peer_ip, peer_port=
peer_port, is_last_packet=False, payload=nbytes(
max_payload_length))
conn.sendto(p.to_bytes(), destination)
sent_packets += 1
remaining_data -= max_payload_length
seq_num += 1
PacketSender.spawn_resend_thread(conn, p, destination)
print('not last packet')
else:
p = Packet(packet_type=PacketConstructor.data_type,
seq_num=seq_num, peer_ip_addr=peer_ip, peer_port=
peer_port, is_last_packet=True, payload=nbytes(
remaining_data))
conn.sendto(p.to_bytes(), destination)
sent_packets += 1
remaining_data -= remaining_data
seq_num += 1
print('remaining data ' + str(remaining_data))
print('is last packet')
PacketSender.spawn_resend_thread(conn, p, destination)
while next_seq_num in acked_packets:
next_seq_num += 1
sent_packets -= 1
print('Waiting for acks')
while not acked_all_packets:
pass
print('RECEIVED ALL ACKS')
PacketSender.was_reset = True
| <mask token>
class PacketSender:
"""
Packet represents a simulated UDP packet.
"""
seq_num = 0
next_seq_num = 0
sent_packets = 0
acked_packets = []
acked_all_packets = False
acked_packets_lock = threading.Lock()
was_reset = False
def reset(self):
global seq_num
global sent_packets
global next_seq_num
global acked_packets
global acked_all_packets
global acked_packets_lock
seq_num = 0
sent_packets = 0
next_seq_num = 0
acked_packets = []
acked_all_packets = False
acked_packets_lock = threading.Lock()
def handle_ack(data):
global acked_packets
global seq_num
global acked_all_packets
global acked_packets_lock
p = Packet.from_bytes(data)
if not p.packet_type == PacketConstructor.ack_type:
return
print('received ack ' + str(p.seq_num))
acked_packets_lock.acquire()
if p.seq_num not in acked_packets:
print("it's a new ack")
acked_packets.append(p.seq_num)
if len(acked_packets) == seq_num:
print('got all acks')
acked_all_packets = True
else:
print('len: ' + str(len(acked_packets)))
print('seq_num: ' + str(seq_num))
acked_packets_lock.release()
def await_acks(conn):
print('awaiting acks')
while not PacketSender.acked_all_packets:
data, sender = conn.recvfrom(1024)
threading.Thread(target=PacketSender.handle_ack, args=(data,)
).start()
def resend_packet_if_needed(conn, packet, destination):
while (not packet.seq_num in PacketSender.acked_packets and not
PacketSender.was_reset):
print('starting resend loop')
time.sleep(0.5)
acked_packets_lock.acquire()
if (not packet.seq_num in PacketSender.acked_packets and not
PacketSender.was_reset):
print('resending packet ' + str(packet.seq_num))
conn.sendto(packet.to_bytes(), destination)
acked_packets_lock.release()
def spawn_resend_thread(conn, packet, destination):
threading.Thread(target=PacketSender.resend_packet_if_needed, args=
(conn, packet, destination)).start()
@staticmethod
def send_as_packets(data, conn, destination, peer_ip, peer_port):
global sent_packets
global acked_packets
global next_seq_num
global acked_all_packets
global seq_num
PacketSender.reset()
max_payload_length = Packet.MAX_LEN - Packet.MIN_LEN
curr = [0, 0]
def nbytes(n):
curr[0], curr[1] = curr[1], curr[1] + n
return data[curr[0]:curr[1]]
remaining_data = len(data)
if remaining_data > 0:
threading.Thread(target=PacketSender.await_acks, args=(conn,)
).start()
while remaining_data > 0:
while (sent_packets < PacketConstructor.window_size and
remaining_data > 0):
print('sending packet %d' % seq_num)
if remaining_data > max_payload_length:
p = Packet(packet_type=PacketConstructor.data_type,
seq_num=seq_num, peer_ip_addr=peer_ip, peer_port=
peer_port, is_last_packet=False, payload=nbytes(
max_payload_length))
conn.sendto(p.to_bytes(), destination)
sent_packets += 1
remaining_data -= max_payload_length
seq_num += 1
PacketSender.spawn_resend_thread(conn, p, destination)
print('not last packet')
else:
p = Packet(packet_type=PacketConstructor.data_type,
seq_num=seq_num, peer_ip_addr=peer_ip, peer_port=
peer_port, is_last_packet=True, payload=nbytes(
remaining_data))
conn.sendto(p.to_bytes(), destination)
sent_packets += 1
remaining_data -= remaining_data
seq_num += 1
print('remaining data ' + str(remaining_data))
print('is last packet')
PacketSender.spawn_resend_thread(conn, p, destination)
while next_seq_num in acked_packets:
next_seq_num += 1
sent_packets -= 1
print('Waiting for acks')
while not acked_all_packets:
pass
print('RECEIVED ALL ACKS')
PacketSender.was_reset = True
| from packet import Packet
from packetConstructor import PacketConstructor
import threading
import time
class PacketSender:
"""
Packet represents a simulated UDP packet.
"""
seq_num = 0
next_seq_num = 0
sent_packets = 0
acked_packets = []
acked_all_packets = False
acked_packets_lock = threading.Lock()
was_reset = False
def reset(self):
global seq_num
global sent_packets
global next_seq_num
global acked_packets
global acked_all_packets
global acked_packets_lock
seq_num = 0
sent_packets = 0
next_seq_num = 0
acked_packets = []
acked_all_packets = False
acked_packets_lock = threading.Lock()
def handle_ack(data):
global acked_packets
global seq_num
global acked_all_packets
global acked_packets_lock
p = Packet.from_bytes(data)
if not p.packet_type == PacketConstructor.ack_type:
return
print('received ack ' + str(p.seq_num))
acked_packets_lock.acquire()
if p.seq_num not in acked_packets:
print("it's a new ack")
acked_packets.append(p.seq_num)
if len(acked_packets) == seq_num:
print('got all acks')
acked_all_packets = True
else:
print('len: ' + str(len(acked_packets)))
print('seq_num: ' + str(seq_num))
acked_packets_lock.release()
def await_acks(conn):
print('awaiting acks')
while not PacketSender.acked_all_packets:
data, sender = conn.recvfrom(1024)
threading.Thread(target=PacketSender.handle_ack, args=(data,)
).start()
def resend_packet_if_needed(conn, packet, destination):
while (not packet.seq_num in PacketSender.acked_packets and not
PacketSender.was_reset):
print('starting resend loop')
time.sleep(0.5)
acked_packets_lock.acquire()
if (not packet.seq_num in PacketSender.acked_packets and not
PacketSender.was_reset):
print('resending packet ' + str(packet.seq_num))
conn.sendto(packet.to_bytes(), destination)
acked_packets_lock.release()
def spawn_resend_thread(conn, packet, destination):
threading.Thread(target=PacketSender.resend_packet_if_needed, args=
(conn, packet, destination)).start()
@staticmethod
def send_as_packets(data, conn, destination, peer_ip, peer_port):
global sent_packets
global acked_packets
global next_seq_num
global acked_all_packets
global seq_num
PacketSender.reset()
max_payload_length = Packet.MAX_LEN - Packet.MIN_LEN
curr = [0, 0]
def nbytes(n):
curr[0], curr[1] = curr[1], curr[1] + n
return data[curr[0]:curr[1]]
remaining_data = len(data)
if remaining_data > 0:
threading.Thread(target=PacketSender.await_acks, args=(conn,)
).start()
while remaining_data > 0:
while (sent_packets < PacketConstructor.window_size and
remaining_data > 0):
print('sending packet %d' % seq_num)
if remaining_data > max_payload_length:
p = Packet(packet_type=PacketConstructor.data_type,
seq_num=seq_num, peer_ip_addr=peer_ip, peer_port=
peer_port, is_last_packet=False, payload=nbytes(
max_payload_length))
conn.sendto(p.to_bytes(), destination)
sent_packets += 1
remaining_data -= max_payload_length
seq_num += 1
PacketSender.spawn_resend_thread(conn, p, destination)
print('not last packet')
else:
p = Packet(packet_type=PacketConstructor.data_type,
seq_num=seq_num, peer_ip_addr=peer_ip, peer_port=
peer_port, is_last_packet=True, payload=nbytes(
remaining_data))
conn.sendto(p.to_bytes(), destination)
sent_packets += 1
remaining_data -= remaining_data
seq_num += 1
print('remaining data ' + str(remaining_data))
print('is last packet')
PacketSender.spawn_resend_thread(conn, p, destination)
while next_seq_num in acked_packets:
next_seq_num += 1
sent_packets -= 1
print('Waiting for acks')
while not acked_all_packets:
pass
print('RECEIVED ALL ACKS')
PacketSender.was_reset = True
| from packet import Packet
from packetConstructor import PacketConstructor
import threading
import time
class PacketSender:
"""
Packet represents a simulated UDP packet.
"""
# The next seq num for sent packets
seq_num = 0
# The next seq num for acks that we're waiting for
next_seq_num = 0
sent_packets = 0
acked_packets = []
acked_all_packets = False
acked_packets_lock = threading.Lock()
was_reset = False
def reset(self):
global seq_num
global sent_packets
global next_seq_num
global acked_packets
global acked_all_packets
global acked_packets_lock
seq_num = 0
sent_packets = 0
next_seq_num = 0
acked_packets = []
acked_all_packets = False
acked_packets_lock = threading.Lock()
def handle_ack(data):
global acked_packets
global seq_num
global acked_all_packets
global acked_packets_lock
p = Packet.from_bytes(data)
if not p.packet_type == PacketConstructor.ack_type:
# TODO: handle NAKs here
return
print("received ack " + str(p.seq_num))
acked_packets_lock.acquire()
if p.seq_num not in acked_packets:
print("it's a new ack")
acked_packets.append(p.seq_num)
if len(acked_packets) == seq_num:
print("got all acks")
acked_all_packets = True
else:
print("len: " + str(len(acked_packets)))
print("seq_num: " + str(seq_num))
acked_packets_lock.release()
def await_acks(conn):
print("awaiting acks")
while not PacketSender.acked_all_packets:
data, sender = conn.recvfrom(1024)
threading.Thread(target=PacketSender.handle_ack, args=(data,)).start()
def resend_packet_if_needed(conn, packet, destination):
while not packet.seq_num in PacketSender.acked_packets and not PacketSender.was_reset:
print("starting resend loop")
time.sleep(0.5)
acked_packets_lock.acquire()
if not packet.seq_num in PacketSender.acked_packets and not PacketSender.was_reset:
print("resending packet " + str(packet.seq_num))
conn.sendto(packet.to_bytes(), destination)
acked_packets_lock.release()
def spawn_resend_thread(conn, packet, destination):
threading.Thread(target=PacketSender.resend_packet_if_needed, args=(conn, packet, destination)).start()
@staticmethod
def send_as_packets(data, conn, destination, peer_ip, peer_port):
global sent_packets
global acked_packets
global next_seq_num
global acked_all_packets
global seq_num
PacketSender.reset()
max_payload_length = Packet.MAX_LEN - Packet.MIN_LEN
curr = [0, 0]
def nbytes(n):
curr[0], curr[1] = curr[1], curr[1] + n
return data[curr[0]: curr[1]]
remaining_data = len(data)
if remaining_data > 0:
threading.Thread(target=PacketSender.await_acks, args=(conn,)).start()
# While there's still data to be sent
while remaining_data > 0:
# While there are less packets in transit than the window size
while (sent_packets < PacketConstructor.window_size and remaining_data > 0):
print("sending packet %d" % seq_num)
if remaining_data > max_payload_length:
p = Packet(packet_type=PacketConstructor.data_type,
seq_num=seq_num,
peer_ip_addr=peer_ip,
peer_port=peer_port,
is_last_packet=False,
payload=nbytes(max_payload_length))
conn.sendto(p.to_bytes(), destination)
sent_packets += 1
remaining_data -= max_payload_length
seq_num += 1
PacketSender.spawn_resend_thread(conn, p, destination)
print("not last packet")
else:
p = Packet(packet_type=PacketConstructor.data_type,
seq_num=seq_num,
peer_ip_addr=peer_ip,
peer_port=peer_port,
is_last_packet=True,
payload=nbytes(remaining_data))
conn.sendto(p.to_bytes(), destination)
sent_packets += 1
remaining_data -= remaining_data
seq_num += 1
print("remaining data " + str(remaining_data))
print("is last packet")
PacketSender.spawn_resend_thread(conn, p, destination)
# Update the number of packets still in transit
while next_seq_num in acked_packets:
next_seq_num += 1
sent_packets -= 1
print("Waiting for acks")
while not acked_all_packets:
# Wait here until all packets have been acked
pass
print("RECEIVED ALL ACKS")
PacketSender.was_reset = True
| [
4,
6,
9,
10,
11
] |
263 | 24cdbbadc8ff1c7ad5d42eeb518cb6c2b34724a2 | <mask token>
class EfficientDoubleExchange(AnsatzElement):
<mask token>
<mask token>
<mask token>
<mask token>
class EfficientDoubleExcitation2(AnsatzElement):
def __init__(self, qubit_pair_1, qubit_pair_2):
self.qubit_pair_1 = qubit_pair_1
self.qubit_pair_2 = qubit_pair_2
super(EfficientDoubleExcitation2, self).__init__(element=
'optimized_d_exc {}, {}'.format(qubit_pair_1, qubit_pair_2),
element_type=str(self), n_var_parameters=1)
@staticmethod
def efficient_double_excitation_2(angle, qubit_pair_1, qubit_pair_2):
qasm = ['']
theta = angle / 8
qasm.append('cx q[{}], q[{}];\n'.format(*qubit_pair_1))
qasm.append('x q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(*qubit_pair_2))
qasm.append('x q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_2[0]))
qasm.append('rz({}) q[{}];\n'.format(numpy.pi / 2, qubit_pair_1[0]))
qasm.append('rx({}) q[{}];\n'.format(theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_1[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('rx({}) q[{}];\n'.format(-theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_2[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('rx({}) q[{}];\n'.format(theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_1[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('rx({}) q[{}];\n'.format(-theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[0]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_2[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[0]))
qasm.append('rx({}) q[{}];\n'.format(theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_1[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('rx({}) q[{}];\n'.format(-theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_2[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('rx({}) q[{}];\n'.format(theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_1[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('rx({}) q[{}];\n'.format(-theta, qubit_pair_1[0]))
qasm.append('rz({}) q[{}];\n'.format(-numpy.pi / 2, qubit_pair_1[0]))
qasm.append(QasmUtils.controlled_xz(qubit_pair_1[0], qubit_pair_2[0
], reverse=True))
qasm.append('x q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(*qubit_pair_1))
qasm.append('x q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(*qubit_pair_2))
return ''.join(qasm)
def get_qasm(self, var_parameters):
assert len(var_parameters) == 1
parameter = var_parameters[0]
return self.efficient_double_excitation_2(parameter, self.
qubit_pair_1, self.qubit_pair_2)
| <mask token>
class EfficientDoubleExchange(AnsatzElement):
def __init__(self, qubit_pair_1, qubit_pair_2, rescaled_parameter=False,
parity_dependence=False, d_exc_correction=False):
self.qubit_pair_1 = qubit_pair_1
self.qubit_pair_2 = qubit_pair_2
self.rescaled_parameter = rescaled_parameter
self.parity_dependence = parity_dependence
self.d_exc_correction = d_exc_correction
super(EfficientDoubleExchange, self).__init__(element=
'd_exc {}, {}'.format(qubit_pair_1, qubit_pair_2), element_type
=str(self), n_var_parameters=1)
<mask token>
<mask token>
<mask token>
class EfficientDoubleExcitation2(AnsatzElement):
def __init__(self, qubit_pair_1, qubit_pair_2):
self.qubit_pair_1 = qubit_pair_1
self.qubit_pair_2 = qubit_pair_2
super(EfficientDoubleExcitation2, self).__init__(element=
'optimized_d_exc {}, {}'.format(qubit_pair_1, qubit_pair_2),
element_type=str(self), n_var_parameters=1)
@staticmethod
def efficient_double_excitation_2(angle, qubit_pair_1, qubit_pair_2):
qasm = ['']
theta = angle / 8
qasm.append('cx q[{}], q[{}];\n'.format(*qubit_pair_1))
qasm.append('x q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(*qubit_pair_2))
qasm.append('x q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_2[0]))
qasm.append('rz({}) q[{}];\n'.format(numpy.pi / 2, qubit_pair_1[0]))
qasm.append('rx({}) q[{}];\n'.format(theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_1[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('rx({}) q[{}];\n'.format(-theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_2[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('rx({}) q[{}];\n'.format(theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_1[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('rx({}) q[{}];\n'.format(-theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[0]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_2[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[0]))
qasm.append('rx({}) q[{}];\n'.format(theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_1[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('rx({}) q[{}];\n'.format(-theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_2[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('rx({}) q[{}];\n'.format(theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_1[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('rx({}) q[{}];\n'.format(-theta, qubit_pair_1[0]))
qasm.append('rz({}) q[{}];\n'.format(-numpy.pi / 2, qubit_pair_1[0]))
qasm.append(QasmUtils.controlled_xz(qubit_pair_1[0], qubit_pair_2[0
], reverse=True))
qasm.append('x q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(*qubit_pair_1))
qasm.append('x q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(*qubit_pair_2))
return ''.join(qasm)
def get_qasm(self, var_parameters):
assert len(var_parameters) == 1
parameter = var_parameters[0]
return self.efficient_double_excitation_2(parameter, self.
qubit_pair_1, self.qubit_pair_2)
| <mask token>
class EfficientDoubleExchange(AnsatzElement):
def __init__(self, qubit_pair_1, qubit_pair_2, rescaled_parameter=False,
parity_dependence=False, d_exc_correction=False):
self.qubit_pair_1 = qubit_pair_1
self.qubit_pair_2 = qubit_pair_2
self.rescaled_parameter = rescaled_parameter
self.parity_dependence = parity_dependence
self.d_exc_correction = d_exc_correction
super(EfficientDoubleExchange, self).__init__(element=
'd_exc {}, {}'.format(qubit_pair_1, qubit_pair_2), element_type
=str(self), n_var_parameters=1)
@staticmethod
def second_angle(x):
if x == 0:
return 0
else:
tan_x = numpy.tan(x)
tan_x_squared = tan_x ** 2
tan_y = (-tan_x_squared - 1 + numpy.sqrt(tan_x_squared ** 2 + 6 *
tan_x_squared + 1)) / (2 * tan_x)
return numpy.arctan(tan_y)
<mask token>
<mask token>
class EfficientDoubleExcitation2(AnsatzElement):
def __init__(self, qubit_pair_1, qubit_pair_2):
self.qubit_pair_1 = qubit_pair_1
self.qubit_pair_2 = qubit_pair_2
super(EfficientDoubleExcitation2, self).__init__(element=
'optimized_d_exc {}, {}'.format(qubit_pair_1, qubit_pair_2),
element_type=str(self), n_var_parameters=1)
@staticmethod
def efficient_double_excitation_2(angle, qubit_pair_1, qubit_pair_2):
qasm = ['']
theta = angle / 8
qasm.append('cx q[{}], q[{}];\n'.format(*qubit_pair_1))
qasm.append('x q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(*qubit_pair_2))
qasm.append('x q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_2[0]))
qasm.append('rz({}) q[{}];\n'.format(numpy.pi / 2, qubit_pair_1[0]))
qasm.append('rx({}) q[{}];\n'.format(theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_1[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('rx({}) q[{}];\n'.format(-theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_2[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('rx({}) q[{}];\n'.format(theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_1[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('rx({}) q[{}];\n'.format(-theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[0]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_2[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[0]))
qasm.append('rx({}) q[{}];\n'.format(theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_1[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('rx({}) q[{}];\n'.format(-theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_2[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('rx({}) q[{}];\n'.format(theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_1[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('rx({}) q[{}];\n'.format(-theta, qubit_pair_1[0]))
qasm.append('rz({}) q[{}];\n'.format(-numpy.pi / 2, qubit_pair_1[0]))
qasm.append(QasmUtils.controlled_xz(qubit_pair_1[0], qubit_pair_2[0
], reverse=True))
qasm.append('x q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(*qubit_pair_1))
qasm.append('x q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(*qubit_pair_2))
return ''.join(qasm)
def get_qasm(self, var_parameters):
assert len(var_parameters) == 1
parameter = var_parameters[0]
return self.efficient_double_excitation_2(parameter, self.
qubit_pair_1, self.qubit_pair_2)
| <mask token>
class EfficientDoubleExchange(AnsatzElement):
def __init__(self, qubit_pair_1, qubit_pair_2, rescaled_parameter=False,
parity_dependence=False, d_exc_correction=False):
self.qubit_pair_1 = qubit_pair_1
self.qubit_pair_2 = qubit_pair_2
self.rescaled_parameter = rescaled_parameter
self.parity_dependence = parity_dependence
self.d_exc_correction = d_exc_correction
super(EfficientDoubleExchange, self).__init__(element=
'd_exc {}, {}'.format(qubit_pair_1, qubit_pair_2), element_type
=str(self), n_var_parameters=1)
@staticmethod
def second_angle(x):
if x == 0:
return 0
else:
tan_x = numpy.tan(x)
tan_x_squared = tan_x ** 2
tan_y = (-tan_x_squared - 1 + numpy.sqrt(tan_x_squared ** 2 + 6 *
tan_x_squared + 1)) / (2 * tan_x)
return numpy.arctan(tan_y)
@staticmethod
def double_exchange(angle, qubit_pair_1, qubit_pair_2,
parity_dependence=False, d_exc_correction=False):
assert len(qubit_pair_1) == 2
assert len(qubit_pair_2) == 2
theta_1 = numpy.pi / 2 - angle
qasm = ['']
qasm.append(QasmUtils.controlled_xz(qubit_pair_2[0], qubit_pair_1[0]))
qasm.append('ry({}) q[{}];\n'.format(theta_1, qubit_pair_2[0]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_2[0]))
qasm.append('ry({}) q[{}];\n'.format(-theta_1, qubit_pair_2[0]))
qasm.append(QasmUtils.controlled_xz(qubit_pair_2[1], qubit_pair_1[1]))
qasm.append('ry({}) q[{}];\n'.format(theta_1, qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[1],
qubit_pair_2[1]))
qasm.append('ry({}) q[{}];\n'.format(-theta_1, qubit_pair_2[1]))
qasm.append('cz q[{}], q[{}];\n'.format(qubit_pair_2[0],
qubit_pair_2[1]))
if d_exc_correction:
angle_2 = DoubleExchange.second_angle(angle)
else:
angle_2 = angle
theta_2 = numpy.pi / 2 - angle_2
qasm.append('ry({}) q[{}];\n'.format(theta_2, qubit_pair_2[0]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_2[0]))
qasm.append('ry({}) q[{}];\n'.format(-theta_2, qubit_pair_2[0]))
qasm.append(QasmUtils.controlled_xz(qubit_pair_2[0], qubit_pair_1[0
], reverse=True))
qasm.append('ry({}) q[{}];\n'.format(theta_2, qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[1],
qubit_pair_2[1]))
qasm.append('ry({}) q[{}];\n'.format(-theta_2, qubit_pair_2[1]))
qasm.append(QasmUtils.controlled_xz(qubit_pair_2[1], qubit_pair_1[1
], reverse=True))
if parity_dependence:
parity_qubits = list(range(min(qubit_pair_1), max(qubit_pair_1))
) + list(range(min(qubit_pair_2) + 1, max(qubit_pair_2)))
cnot_ladder = ['']
for i in range(len(parity_qubits) - 1):
cnot_ladder.append('cx q[{}], q[{}];\n'.format(
parity_qubits[i], parity_qubits[i + 1]))
if angle > 0:
front = ['']
front.append('cz q[{}], q[{}];\n'.format(qubit_pair_2[0],
qubit_pair_2[1]))
front += cnot_ladder
front.append('x q[{}];\n'.format(parity_qubits[-1]))
front.append('cz q[{}], q[{}];\n'.format(parity_qubits[-1],
qubit_pair_2[0]))
front.append('x q[{}];\n'.format(parity_qubits[-1]))
front += cnot_ladder[::-1]
rear = ['']
rear.append('cz q[{}], q[{}];\n'.format(qubit_pair_2[0],
qubit_pair_2[1]))
rear += cnot_ladder
rear.append('cz q[{}], q[{}];\n'.format(parity_qubits[-1],
qubit_pair_2[0]))
rear += cnot_ladder[::-1]
rear.append('x q[{}];\n'.format(qubit_pair_2[1]))
rear.append('cz q[{}], q[{}];\n'.format(qubit_pair_2[0],
qubit_pair_2[1]))
rear.append('x q[{}];\n'.format(qubit_pair_2[1]))
qasm = front + qasm + rear
else:
front = ['']
front.append('cz q[{}], q[{}];\n'.format(qubit_pair_2[0],
qubit_pair_2[1]))
front += cnot_ladder
front.append('cz q[{}], q[{}];\n'.format(parity_qubits[-1],
qubit_pair_2[0]))
front += cnot_ladder[::-1]
rear = ['']
rear.append('cz q[{}], q[{}];\n'.format(qubit_pair_2[0],
qubit_pair_2[1]))
rear += cnot_ladder
rear.append('x q[{}];\n'.format(parity_qubits[-1]))
rear.append('cz q[{}], q[{}];\n'.format(parity_qubits[-1],
qubit_pair_2[0]))
rear.append('x q[{}];\n'.format(parity_qubits[-1]))
rear += cnot_ladder[::-1]
rear.append('x q[{}];\n'.format(qubit_pair_2[1]))
rear.append('cz q[{}], q[{}];\n'.format(qubit_pair_2[0],
qubit_pair_2[1]))
rear.append('x q[{}];\n'.format(qubit_pair_2[1]))
qasm = front + qasm + rear
elif angle > 0:
qasm.append('cz q[{}], q[{}];\n'.format(qubit_pair_2[0],
qubit_pair_2[1]))
else:
qasm = ['cz q[{}], q[{}];\n'.format(qubit_pair_2[0],
qubit_pair_2[1])] + qasm
return ''.join(qasm)
def get_qasm(self, var_parameters):
assert len(var_parameters) == 1
parameter = var_parameters[0]
if self.rescaled_parameter:
if var_parameters[0] > 0:
parameter = var_parameters[0] + numpy.tanh(var_parameters[0
] ** 0.5)
else:
parameter = var_parameters[0] + numpy.tanh(-(-
var_parameters[0]) ** 0.5)
return self.double_exchange(parameter, self.qubit_pair_1, self.
qubit_pair_2, parity_dependence=self.parity_dependence,
d_exc_correction=self.d_exc_correction)
class EfficientDoubleExcitation2(AnsatzElement):
def __init__(self, qubit_pair_1, qubit_pair_2):
self.qubit_pair_1 = qubit_pair_1
self.qubit_pair_2 = qubit_pair_2
super(EfficientDoubleExcitation2, self).__init__(element=
'optimized_d_exc {}, {}'.format(qubit_pair_1, qubit_pair_2),
element_type=str(self), n_var_parameters=1)
@staticmethod
def efficient_double_excitation_2(angle, qubit_pair_1, qubit_pair_2):
qasm = ['']
theta = angle / 8
qasm.append('cx q[{}], q[{}];\n'.format(*qubit_pair_1))
qasm.append('x q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(*qubit_pair_2))
qasm.append('x q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_2[0]))
qasm.append('rz({}) q[{}];\n'.format(numpy.pi / 2, qubit_pair_1[0]))
qasm.append('rx({}) q[{}];\n'.format(theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_1[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('rx({}) q[{}];\n'.format(-theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_2[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('rx({}) q[{}];\n'.format(theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_1[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('rx({}) q[{}];\n'.format(-theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[0]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_2[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[0]))
qasm.append('rx({}) q[{}];\n'.format(theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_1[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('rx({}) q[{}];\n'.format(-theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_2[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('rx({}) q[{}];\n'.format(theta, qubit_pair_1[0]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0],
qubit_pair_1[1]))
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('rx({}) q[{}];\n'.format(-theta, qubit_pair_1[0]))
qasm.append('rz({}) q[{}];\n'.format(-numpy.pi / 2, qubit_pair_1[0]))
qasm.append(QasmUtils.controlled_xz(qubit_pair_1[0], qubit_pair_2[0
], reverse=True))
qasm.append('x q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(*qubit_pair_1))
qasm.append('x q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(*qubit_pair_2))
return ''.join(qasm)
def get_qasm(self, var_parameters):
assert len(var_parameters) == 1
parameter = var_parameters[0]
return self.efficient_double_excitation_2(parameter, self.
qubit_pair_1, self.qubit_pair_2)
| from openfermion import QubitOperator, FermionOperator
from openfermion.transforms import jordan_wigner
from src.utils import QasmUtils, MatrixUtils
from src.ansatz_elements import AnsatzElement, DoubleExchange
import itertools
import numpy
class EfficientDoubleExchange(AnsatzElement):
def __init__(self, qubit_pair_1, qubit_pair_2, rescaled_parameter=False, parity_dependence=False, d_exc_correction=False):
self.qubit_pair_1 = qubit_pair_1
self.qubit_pair_2 = qubit_pair_2
self.rescaled_parameter = rescaled_parameter
self.parity_dependence = parity_dependence
self.d_exc_correction = d_exc_correction
super(EfficientDoubleExchange, self).__init__(element='d_exc {}, {}'.format(qubit_pair_1, qubit_pair_2),
element_type=str(self), n_var_parameters=1)
@staticmethod
def second_angle(x):
if x == 0:
return 0
else:
tan_x = numpy.tan(x)
tan_x_squared = tan_x**2
tan_y = ((-tan_x_squared - 1 + numpy.sqrt(tan_x_squared ** 2 + 6 * tan_x_squared + 1)) / (2*tan_x))
return numpy.arctan(tan_y)
# this method constructs an operation that acts approximately as a double partial exchange
@staticmethod
def double_exchange(angle, qubit_pair_1, qubit_pair_2, parity_dependence=False, d_exc_correction=False):
assert len(qubit_pair_1) == 2
assert len(qubit_pair_2) == 2
theta_1 = numpy.pi/2 - angle
qasm = ['']
# 1st exchange + 0-2
qasm.append(QasmUtils.controlled_xz(qubit_pair_2[0], qubit_pair_1[0]))
qasm.append('ry({}) q[{}];\n'.format(theta_1, qubit_pair_2[0]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0], qubit_pair_2[0]))
qasm.append('ry({}) q[{}];\n'.format(-theta_1, qubit_pair_2[0]))
# 2nd exchange + 1-3
qasm.append(QasmUtils.controlled_xz(qubit_pair_2[1], qubit_pair_1[1]))
qasm.append('ry({}) q[{}];\n'.format(theta_1, qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[1], qubit_pair_2[1]))
qasm.append('ry({}) q[{}];\n'.format(-theta_1, qubit_pair_2[1]))
# CZ gates
qasm.append('cz q[{}], q[{}];\n'.format(qubit_pair_2[0], qubit_pair_2[1]))
# qasm.append('cz q[{}], q[{}];\n'.format(qubit_pair_1[0], qubit_pair_1[1]))
# correction 3rd order terms approximates the operation of a double exchange
if d_exc_correction:
angle_2 = DoubleExchange.second_angle(angle)
# not correcting 3rd order terms approximates the operation of a double excitation (with 3rd order error terms)
else:
angle_2 = angle
theta_2 = numpy.pi / 2 - angle_2
# 3rd exchange - 0-2
qasm.append('ry({}) q[{}];\n'.format(theta_2, qubit_pair_2[0]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0], qubit_pair_2[0]))
qasm.append('ry({}) q[{}];\n'.format(-theta_2, qubit_pair_2[0]))
qasm.append(QasmUtils.controlled_xz(qubit_pair_2[0], qubit_pair_1[0], reverse=True))
# 4th exchange -1-3
qasm.append('ry({}) q[{}];\n'.format(theta_2, qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[1], qubit_pair_2[1]))
qasm.append('ry({}) q[{}];\n'.format(-theta_2, qubit_pair_2[1]))
qasm.append(QasmUtils.controlled_xz(qubit_pair_2[1], qubit_pair_1[1], reverse=True))
# correcting for parameter sign
if parity_dependence:
# do not include the first qubit of the second pair
parity_qubits = list(range(min(qubit_pair_1), max(qubit_pair_1))) + list(range(min(qubit_pair_2)+1, max(qubit_pair_2)))
# ladder of CNOT used to determine the parity
cnot_ladder = ['']
for i in range(len(parity_qubits) - 1):
cnot_ladder.append('cx q[{}], q[{}];\n'.format(parity_qubits[i], parity_qubits[i+1]))
if angle > 0:
# applies a CZ correction in front, to get a negative sign for the excitation term, if the parity is 1
# (or the parity of "parity_qubits" is 0)
front = ['']
# this is the CZ that determines the sign of the excitation term
front.append('cz q[{}], q[{}];\n'.format(qubit_pair_2[0], qubit_pair_2[1]))
# this bit determines the parity and applies a CZ to negate the correction if the parity is wrong
front += cnot_ladder
front.append('x q[{}];\n'.format(parity_qubits[-1]))
front.append('cz q[{}], q[{}];\n'.format(parity_qubits[-1], qubit_pair_2[0]))
front.append('x q[{}];\n'.format(parity_qubits[-1]))
front += cnot_ladder[::-1]
# .. positive sign for the excitation term, if the parity is 0 (or the parity of "parity_qubits" is 1)
rear = ['']
# .. sign correction
rear.append('cz q[{}], q[{}];\n'.format(qubit_pair_2[0], qubit_pair_2[1]))
# .. parity correction
rear += cnot_ladder
rear.append('cz q[{}], q[{}];\n'.format(parity_qubits[-1], qubit_pair_2[0]))
rear += cnot_ladder[::-1]
# additional correction of states 010 and 110
rear.append('x q[{}];\n'.format(qubit_pair_2[1]))
rear.append('cz q[{}], q[{}];\n'.format(qubit_pair_2[0], qubit_pair_2[1]))
rear.append('x q[{}];\n'.format(qubit_pair_2[1]))
qasm = front + qasm + rear
else:
front = ['']
# sign correction
front.append('cz q[{}], q[{}];\n'.format(qubit_pair_2[0], qubit_pair_2[1]))
# parity correction
front += cnot_ladder
front.append('cz q[{}], q[{}];\n'.format(parity_qubits[-1], qubit_pair_2[0]))
front += cnot_ladder[::-1]
rear = ['']
# sign correction
rear.append('cz q[{}], q[{}];\n'.format(qubit_pair_2[0], qubit_pair_2[1]))
# parity correction
rear += cnot_ladder
rear.append('x q[{}];\n'.format(parity_qubits[-1]))
rear.append('cz q[{}], q[{}];\n'.format(parity_qubits[-1], qubit_pair_2[0]))
rear.append('x q[{}];\n'.format(parity_qubits[-1]))
rear += cnot_ladder[::-1]
# 010 and 011 correction
rear.append('x q[{}];\n'.format(qubit_pair_2[1]))
rear.append('cz q[{}], q[{}];\n'.format(qubit_pair_2[0], qubit_pair_2[1]))
rear.append('x q[{}];\n'.format(qubit_pair_2[1]))
qasm = front + qasm + rear
else:
if angle > 0:
# adding a correcting CZ gate at the end will result in a minus sign
qasm.append('cz q[{}], q[{}];\n'.format(qubit_pair_2[0], qubit_pair_2[1]))
# qasm.append('cz q[{}], q[{}];\n'.format(qubit_pair_1[0], qubit_pair_1[1]))
else:
# adding a correcting CZ gate at the front will result in a plus sign
qasm = ['cz q[{}], q[{}];\n'.format(qubit_pair_2[0], qubit_pair_2[1]),
] \
+ qasm
# 'cz q[{}], q[{}];\n'.format(qubit_pair_1[0], qubit_pair_1[1])
return ''.join(qasm)
def get_qasm(self, var_parameters):
assert len(var_parameters) == 1
parameter = var_parameters[0]
# rescaled parameter (used for easier gradient optimization)
if self.rescaled_parameter:
if var_parameters[0] > 0:
parameter = var_parameters[0] + numpy.tanh(var_parameters[0]**0.5)
else:
parameter = var_parameters[0] + numpy.tanh(-(-var_parameters[0])**0.5)
return self.double_exchange(parameter, self.qubit_pair_1, self.qubit_pair_2,
parity_dependence=self.parity_dependence, d_exc_correction=self.d_exc_correction)
class EfficientDoubleExcitation2(AnsatzElement):
def __init__(self, qubit_pair_1, qubit_pair_2):
self.qubit_pair_1 = qubit_pair_1
self.qubit_pair_2 = qubit_pair_2
super(EfficientDoubleExcitation2, self).__init__(element='optimized_d_exc {}, {}'.format(qubit_pair_1, qubit_pair_2),
element_type=str(self), n_var_parameters=1)
@staticmethod
def efficient_double_excitation_2(angle, qubit_pair_1, qubit_pair_2):
qasm = ['']
theta = angle / 8
# determine the parity of the two pairs
qasm.append('cx q[{}], q[{}];\n'.format(*qubit_pair_1))
qasm.append('x q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(*qubit_pair_2))
qasm.append('x q[{}];\n'.format(qubit_pair_2[1]))
# apply a partial swap of qubits 0 and 2, controlled by 1 and 3 ##
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0], qubit_pair_2[0]))
# # partial ccc_y operation
qasm.append('rz({}) q[{}];\n'.format(numpy.pi/2, qubit_pair_1[0]))
qasm.append('rx({}) q[{}];\n'.format(theta, qubit_pair_1[0])) # +
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0], qubit_pair_1[1])) # 0 1
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('rx({}) q[{}];\n'.format(-theta, qubit_pair_1[0])) # -
qasm.append('h q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0], qubit_pair_2[1])) # 0 3
qasm.append('h q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('rx({}) q[{}];\n'.format(theta, qubit_pair_1[0])) # +
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0], qubit_pair_1[1])) # 0 1
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('rx({}) q[{}];\n'.format(-theta, qubit_pair_1[0])) # -
qasm.append('h q[{}];\n'.format(qubit_pair_2[0]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0], qubit_pair_2[0])) # 0 2
qasm.append('h q[{}];\n'.format(qubit_pair_2[0]))
qasm.append('rx({}) q[{}];\n'.format(theta, qubit_pair_1[0])) # +
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0], qubit_pair_1[1])) # 0 1
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('rx({}) q[{}];\n'.format(-theta, qubit_pair_1[0])) # -
qasm.append('h q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0], qubit_pair_2[1])) # 0 3
qasm.append('h q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('rx({}) q[{}];\n'.format(theta, qubit_pair_1[0])) # +
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0], qubit_pair_1[1])) # 0 1
qasm.append('h q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('rx({}) q[{}];\n'.format(-theta, qubit_pair_1[0])) # -
qasm.append('rz({}) q[{}];\n'.format(-numpy.pi / 2, qubit_pair_1[0]))
##### test #####
# qasm.append('h q[{}];\n'.format(qubit_pair_2[0]))
# qasm.append('cx q[{}], q[{}];\n'.format(qubit_pair_1[0], qubit_pair_2[0])) # 0 2
# qasm.append('h q[{}];\n'.format(qubit_pair_2[0]))
# ###############
# partial ccc_y operation ############ to here
qasm.append(QasmUtils.controlled_xz(qubit_pair_1[0], qubit_pair_2[0], reverse=True))
# correct for parity determination
qasm.append('x q[{}];\n'.format(qubit_pair_1[1]))
qasm.append('cx q[{}], q[{}];\n'.format(*qubit_pair_1))
qasm.append('x q[{}];\n'.format(qubit_pair_2[1]))
qasm.append('cx q[{}], q[{}];\n'.format(*qubit_pair_2))
return ''.join(qasm)
def get_qasm(self, var_parameters):
assert len(var_parameters) == 1
parameter = var_parameters[0]
return self.efficient_double_excitation_2(parameter, self.qubit_pair_1, self.qubit_pair_2)
| [
5,
6,
7,
9,
11
] |
264 | 2843845848747c723d670cd3a5fcb7127153ac7e | <mask token>
| <mask token>
@app.route('/<sensor_id>', methods=['GET'])
def sensor_details(sensor_id):
sensor_pos = search_index_by_id(sensor_id)
if sensor_pos >= 0:
return {'sensor': sensors[sensor_pos]}
else:
return {'kind': 'error', 'payload': f'Sensor {sensor_id} not found'}
@app.route('/<sensor_id>/toggle', methods=['PUT'])
def set_sensor_value(sensor_id: str):
sensor_pos = search_index_by_id(sensor_id)
if sensor_pos >= 0 and isinstance(sensors[sensor_pos], ToggleSensor):
sensors[sensor_pos].toggle()
return {'kind': 'error', 'payload': f'Sensor {sensor_id} not found'}
@app.route('/', methods=['GET'])
def hello_world():
for sensor in sensors:
sensor.update()
return {'sensors': sensors}
| <mask token>
def search_index_by_id(sensor_id: str):
for pos, sensor in enumerate(sensors):
if sensor.id == sensor_id:
return pos
return -1
@app.route('/<sensor_id>', methods=['GET'])
def sensor_details(sensor_id):
sensor_pos = search_index_by_id(sensor_id)
if sensor_pos >= 0:
return {'sensor': sensors[sensor_pos]}
else:
return {'kind': 'error', 'payload': f'Sensor {sensor_id} not found'}
@app.route('/<sensor_id>/toggle', methods=['PUT'])
def set_sensor_value(sensor_id: str):
sensor_pos = search_index_by_id(sensor_id)
if sensor_pos >= 0 and isinstance(sensors[sensor_pos], ToggleSensor):
sensors[sensor_pos].toggle()
return {'kind': 'error', 'payload': f'Sensor {sensor_id} not found'}
@app.route('/', methods=['GET'])
def hello_world():
for sensor in sensors:
sensor.update()
return {'sensors': sensors}
| <mask token>
app = Flask(__name__)
sensors = [ToggleSensor(id='s-01', description='lampadina'), ToggleSensor(
id='s-02', description='lampadina'), ToggleSensor(id='s-03',
description='allarme atomico'), ToggleSensor(id='s-04', description=
'porta aperta'), Sensor(id='temperature-01', description=
'sensore di temperatura'), Sensor(id='umidita-01', description=
'sensore di umiditá'), Sensor(id='cleancode-01', description=
'sensore di bellezza del codice'), Sensor(id='luce-01', description=
'sensore di luce')]
def search_index_by_id(sensor_id: str):
for pos, sensor in enumerate(sensors):
if sensor.id == sensor_id:
return pos
return -1
@app.route('/<sensor_id>', methods=['GET'])
def sensor_details(sensor_id):
sensor_pos = search_index_by_id(sensor_id)
if sensor_pos >= 0:
return {'sensor': sensors[sensor_pos]}
else:
return {'kind': 'error', 'payload': f'Sensor {sensor_id} not found'}
@app.route('/<sensor_id>/toggle', methods=['PUT'])
def set_sensor_value(sensor_id: str):
sensor_pos = search_index_by_id(sensor_id)
if sensor_pos >= 0 and isinstance(sensors[sensor_pos], ToggleSensor):
sensors[sensor_pos].toggle()
return {'kind': 'error', 'payload': f'Sensor {sensor_id} not found'}
@app.route('/', methods=['GET'])
def hello_world():
for sensor in sensors:
sensor.update()
return {'sensors': sensors}
| from flask import Flask
from sim.toggle import ToggleSensor
from sim.sensor import Sensor
app = Flask(__name__)
sensors = [
ToggleSensor(id="s-01", description="lampadina"),
ToggleSensor(id="s-02", description="lampadina"),
ToggleSensor(id="s-03", description="allarme atomico"),
ToggleSensor(id="s-04", description="porta aperta"),
Sensor(id="temperature-01", description="sensore di temperatura"),
Sensor(id="umidita-01", description="sensore di umiditá"),
Sensor(id="cleancode-01", description="sensore di bellezza del codice"),
Sensor(id="luce-01", description="sensore di luce"),
]
def search_index_by_id(sensor_id: str):
for pos, sensor in enumerate(sensors):
if sensor.id == sensor_id:
return pos
return -1
@app.route("/<sensor_id>", methods=["GET"])
def sensor_details(sensor_id):
sensor_pos = search_index_by_id(sensor_id)
if sensor_pos >= 0:
return {"sensor": sensors[sensor_pos]}
else:
return {"kind": "error", "payload": f"Sensor {sensor_id} not found"}
@app.route("/<sensor_id>/toggle", methods=["PUT"])
def set_sensor_value(sensor_id: str):
sensor_pos = search_index_by_id(sensor_id)
if sensor_pos >= 0 and isinstance(sensors[sensor_pos], ToggleSensor):
sensors[sensor_pos].toggle()
return {"kind": "error", "payload": f"Sensor {sensor_id} not found"}
@app.route("/", methods=["GET"])
def hello_world():
for sensor in sensors:
sensor.update()
return {"sensors": sensors}
| [
0,
3,
4,
5,
7
] |
265 | f5bd41f4aaff616a332d80ec44c364ffc91c58f0 | <mask token>
| <mask token>
def skewness_log(df):
df['SalePrice_New'] = np.log(df['SalePrice'])
df['GrLivArea_New'] = np.log(df['GrLivArea'])
skewed_slPri = skew(df['SalePrice_New'])
skewness_grLiv = skew(df['GrLivArea_New'])
return skewness_grLiv, skewed_slPri
| <mask token>
data = pd.read_csv('data/train.csv')
def skewness_log(df):
df['SalePrice_New'] = np.log(df['SalePrice'])
df['GrLivArea_New'] = np.log(df['GrLivArea'])
skewed_slPri = skew(df['SalePrice_New'])
skewness_grLiv = skew(df['GrLivArea_New'])
return skewness_grLiv, skewed_slPri
| from scipy.stats import skew
import pandas as pd
import numpy as np
data = pd.read_csv('data/train.csv')
def skewness_log(df):
df['SalePrice_New'] = np.log(df['SalePrice'])
df['GrLivArea_New'] = np.log(df['GrLivArea'])
skewed_slPri = skew(df['SalePrice_New'])
skewness_grLiv = skew(df['GrLivArea_New'])
return skewness_grLiv, skewed_slPri
| # %load q03_skewness_log/build.py
from scipy.stats import skew
import pandas as pd
import numpy as np
data = pd.read_csv('data/train.csv')
# Write code here:
def skewness_log(df):
df['SalePrice_New'] = np.log(df['SalePrice'])
df['GrLivArea_New'] = np.log(df['GrLivArea'])
skewed_slPri = skew(df['SalePrice_New'])
skewness_grLiv = skew(df['GrLivArea_New'])
return skewness_grLiv,skewed_slPri
| [
0,
1,
2,
3,
4
] |
266 | d65f858c3ad06226b83d2627f6d38e03eae5b36c | <mask token>
| <mask token>
def line_evaluation(param_list, param_eval, file_name='line evaluation', **
kwargs):
"""
Evaluates a list of parameter pairs across repeated trials and aggregates the result.
Parameters
----------
param_list : array_like
List of values to test for parameter of interest.
param_eval : callable
Must take a parameter instance and return an object that can be evaluated
by `aggr_meth` (see :func:`grid_evaluation`).
file_name : string, optional
Optional name for the file. (default is 'line evaluation')
Returns
-------
dict
A dictionary with the results of the experiment.
Notes
-----
You can also explicitely set the arguments in :func:`grid_evaluation` in this function
call.
"""
experiment = grid_evaluation(param_list_one=param_list, param_list_two=
[], param_eval=param_eval, file_name=file_name, **kwargs)
experiment['line'] = experiment.pop('grid')
experiment['cols'] = experiment.pop('rows')
return experiment
| <mask token>
def grid_evaluation(param_list_one, param_list_two, param_eval, n_trials=16,
aggr_method=np.mean, save_dir='data/', file_name='grid evaluation',
save_to_disk=True, save_each=1000, chunksize=1.0):
"""
Evaluates a grid of parameter pairs across repeated trials and aggregates the result.
Parameters
----------
param_list_one : array_like
List of values to test for the first parameter.
param_list_two : array_like, optional
List of values to test for the second parameter. Can be empty, in which case a
one-dimensional grid is evaluated.
param_eval : callable
Must take an instance of parameter values and return an object that can be evaluated
by `aggr_meth`. It should accept one input if `param_list_two` is empty, and two inputs
otherwise.
n_trials : int, optional
Number of trials to run for each parameter pair. (default is `16`)
aggr_method : callable, optional
The aggregation method for the values returned by `patam_eval` on different
trials for the same parameter pair. (default is :func:`numpy.mean`)
save_dir : string, optional
Directory onto which save the result. (default is 'data/')
file_name : string, optional
Optional name for the file. It is always prepended with the time stamp at the
end of the grid evaluation. (default is 'grid evaluation')
save_to_disk : bool, optional
Whether to save the experiment to disk (True) or not (False). (default is `True`)
save_each : int, optional
Save the experiment each time `save_each` grid points are computed. (default is `1000`)
chunksize : int
The size of the chunks of jobs sent to each parallel worker. (default is `1`)
Returns
-------
dict
A dictionary with the results of the experiment.
"""
if not list(param_list_two):
params = param_list_one
grid_shape = len(param_list_one),
is_really_grid = False
else:
params = list(itertools.product(param_list_one, param_list_two))
grid_shape = len(param_list_one), len(param_list_two)
is_really_grid = True
def grid_fun(point):
trial_out = np.nan * np.ones((n_trials,))
for i in np.arange(n_trials):
if is_really_grid:
trial_out[i] = param_eval(point[0], point[1])
else:
trial_out[i] = param_eval(point)
return aggr_method(trial_out)
n_grid_pts = len(params)
def record_experiment(grid):
now = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
save_path = save_dir + now + ' ' + file_name + '.pkl'
experiment = {'date': now, 'rows': param_list_one, 'cols':
param_list_two, 'n_trials': n_trials, 'grid': np.reshape(grid,
grid_shape), 'path': save_path}
if save_to_disk:
utils.save_obj(experiment, save_path)
return experiment
nb_workers = min(mp.cpu_count(), 24)
print('Working with {} processes.'.format(nb_workers))
pool = mp.Pool(nb_workers)
it = pool.imap(grid_fun, params, chunksize=chunksize)
grid = np.nan * np.ones((n_grid_pts,))
for idx, val in enumerate(tqdm(it, total=n_grid_pts)):
grid[idx] = val
if idx >= save_each and idx % save_each == 0:
experiment = record_experiment(grid)
pool.close()
pool.join()
experiment = record_experiment(grid)
return experiment
def line_evaluation(param_list, param_eval, file_name='line evaluation', **
kwargs):
"""
Evaluates a list of parameter pairs across repeated trials and aggregates the result.
Parameters
----------
param_list : array_like
List of values to test for parameter of interest.
param_eval : callable
Must take a parameter instance and return an object that can be evaluated
by `aggr_meth` (see :func:`grid_evaluation`).
file_name : string, optional
Optional name for the file. (default is 'line evaluation')
Returns
-------
dict
A dictionary with the results of the experiment.
Notes
-----
You can also explicitely set the arguments in :func:`grid_evaluation` in this function
call.
"""
experiment = grid_evaluation(param_list_one=param_list, param_list_two=
[], param_eval=param_eval, file_name=file_name, **kwargs)
experiment['line'] = experiment.pop('grid')
experiment['cols'] = experiment.pop('rows')
return experiment
| <mask token>
import utils
import datetime
import itertools
import numpy as np
import recovery as rec
import sampling as smp
import graphs_signals as gs
import pathos.multiprocessing as mp
from tqdm import tqdm
def grid_evaluation(param_list_one, param_list_two, param_eval, n_trials=16,
aggr_method=np.mean, save_dir='data/', file_name='grid evaluation',
save_to_disk=True, save_each=1000, chunksize=1.0):
"""
Evaluates a grid of parameter pairs across repeated trials and aggregates the result.
Parameters
----------
param_list_one : array_like
List of values to test for the first parameter.
param_list_two : array_like, optional
List of values to test for the second parameter. Can be empty, in which case a
one-dimensional grid is evaluated.
param_eval : callable
Must take an instance of parameter values and return an object that can be evaluated
by `aggr_meth`. It should accept one input if `param_list_two` is empty, and two inputs
otherwise.
n_trials : int, optional
Number of trials to run for each parameter pair. (default is `16`)
aggr_method : callable, optional
The aggregation method for the values returned by `patam_eval` on different
trials for the same parameter pair. (default is :func:`numpy.mean`)
save_dir : string, optional
Directory onto which save the result. (default is 'data/')
file_name : string, optional
Optional name for the file. It is always prepended with the time stamp at the
end of the grid evaluation. (default is 'grid evaluation')
save_to_disk : bool, optional
Whether to save the experiment to disk (True) or not (False). (default is `True`)
save_each : int, optional
Save the experiment each time `save_each` grid points are computed. (default is `1000`)
chunksize : int
The size of the chunks of jobs sent to each parallel worker. (default is `1`)
Returns
-------
dict
A dictionary with the results of the experiment.
"""
if not list(param_list_two):
params = param_list_one
grid_shape = len(param_list_one),
is_really_grid = False
else:
params = list(itertools.product(param_list_one, param_list_two))
grid_shape = len(param_list_one), len(param_list_two)
is_really_grid = True
def grid_fun(point):
trial_out = np.nan * np.ones((n_trials,))
for i in np.arange(n_trials):
if is_really_grid:
trial_out[i] = param_eval(point[0], point[1])
else:
trial_out[i] = param_eval(point)
return aggr_method(trial_out)
n_grid_pts = len(params)
def record_experiment(grid):
now = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
save_path = save_dir + now + ' ' + file_name + '.pkl'
experiment = {'date': now, 'rows': param_list_one, 'cols':
param_list_two, 'n_trials': n_trials, 'grid': np.reshape(grid,
grid_shape), 'path': save_path}
if save_to_disk:
utils.save_obj(experiment, save_path)
return experiment
nb_workers = min(mp.cpu_count(), 24)
print('Working with {} processes.'.format(nb_workers))
pool = mp.Pool(nb_workers)
it = pool.imap(grid_fun, params, chunksize=chunksize)
grid = np.nan * np.ones((n_grid_pts,))
for idx, val in enumerate(tqdm(it, total=n_grid_pts)):
grid[idx] = val
if idx >= save_each and idx % save_each == 0:
experiment = record_experiment(grid)
pool.close()
pool.join()
experiment = record_experiment(grid)
return experiment
def line_evaluation(param_list, param_eval, file_name='line evaluation', **
kwargs):
"""
Evaluates a list of parameter pairs across repeated trials and aggregates the result.
Parameters
----------
param_list : array_like
List of values to test for parameter of interest.
param_eval : callable
Must take a parameter instance and return an object that can be evaluated
by `aggr_meth` (see :func:`grid_evaluation`).
file_name : string, optional
Optional name for the file. (default is 'line evaluation')
Returns
-------
dict
A dictionary with the results of the experiment.
Notes
-----
You can also explicitely set the arguments in :func:`grid_evaluation` in this function
call.
"""
experiment = grid_evaluation(param_list_one=param_list, param_list_two=
[], param_eval=param_eval, file_name=file_name, **kwargs)
experiment['line'] = experiment.pop('grid')
experiment['cols'] = experiment.pop('rows')
return experiment
| #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Phase transition module
"""
import utils
import datetime
import itertools
import numpy as np
import recovery as rec
import sampling as smp
import graphs_signals as gs
import pathos.multiprocessing as mp
from tqdm import tqdm
## MAIN FUNCTIONS ##
def grid_evaluation(param_list_one, param_list_two, param_eval, n_trials=16,
aggr_method=np.mean, save_dir='data/', file_name='grid evaluation',
save_to_disk=True, save_each=1000, chunksize=1.):
r"""
Evaluates a grid of parameter pairs across repeated trials and aggregates the result.
Parameters
----------
param_list_one : array_like
List of values to test for the first parameter.
param_list_two : array_like, optional
List of values to test for the second parameter. Can be empty, in which case a
one-dimensional grid is evaluated.
param_eval : callable
Must take an instance of parameter values and return an object that can be evaluated
by `aggr_meth`. It should accept one input if `param_list_two` is empty, and two inputs
otherwise.
n_trials : int, optional
Number of trials to run for each parameter pair. (default is `16`)
aggr_method : callable, optional
The aggregation method for the values returned by `patam_eval` on different
trials for the same parameter pair. (default is :func:`numpy.mean`)
save_dir : string, optional
Directory onto which save the result. (default is 'data/')
file_name : string, optional
Optional name for the file. It is always prepended with the time stamp at the
end of the grid evaluation. (default is 'grid evaluation')
save_to_disk : bool, optional
Whether to save the experiment to disk (True) or not (False). (default is `True`)
save_each : int, optional
Save the experiment each time `save_each` grid points are computed. (default is `1000`)
chunksize : int
The size of the chunks of jobs sent to each parallel worker. (default is `1`)
Returns
-------
dict
A dictionary with the results of the experiment.
"""
if not list(param_list_two): # If `param_list_two` is empty
params = param_list_one
grid_shape = (len(param_list_one),)
is_really_grid = False
else:
params = list(itertools.product(param_list_one, param_list_two))
grid_shape = (len(param_list_one), len(param_list_two))
is_really_grid = True
def grid_fun(point): # Function to compute for each grid point
trial_out = np.nan * np.ones((n_trials,))
for i in np.arange(n_trials):
if is_really_grid:
trial_out[i] = param_eval(point[0], point[1])
else: # If `param_list_two` is empty
trial_out[i] = param_eval(point)
return aggr_method(trial_out)
n_grid_pts = len(params)
# Recording procedure
def record_experiment(grid):
now = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
save_path = save_dir + now + ' ' + file_name + '.pkl'
experiment = {
'date': now,
'rows': param_list_one,
'cols': param_list_two,
'n_trials': n_trials,
'grid': np.reshape(grid, grid_shape),
'path': save_path
}
if save_to_disk:
utils.save_obj(experiment, save_path)
return experiment
# Set a pool of workers
nb_workers = min(mp.cpu_count(), 24)
print('Working with {} processes.'.format(nb_workers))
pool = mp.Pool(nb_workers)
# Iterate `grid_fun` across workers
it = pool.imap(grid_fun, params, chunksize=chunksize)
grid = np.nan * np.ones((n_grid_pts,))
for idx, val in enumerate(tqdm(it, total=n_grid_pts)):
grid[idx] = val
# Make sure that we save after each couple of iterations
if (idx >= save_each) and (idx % save_each == 0):
experiment = record_experiment(grid)
# Close pool
pool.close()
pool.join()
experiment = record_experiment(grid)
return experiment
def line_evaluation(param_list, param_eval, file_name='line evaluation', **kwargs):
r"""
Evaluates a list of parameter pairs across repeated trials and aggregates the result.
Parameters
----------
param_list : array_like
List of values to test for parameter of interest.
param_eval : callable
Must take a parameter instance and return an object that can be evaluated
by `aggr_meth` (see :func:`grid_evaluation`).
file_name : string, optional
Optional name for the file. (default is 'line evaluation')
Returns
-------
dict
A dictionary with the results of the experiment.
Notes
-----
You can also explicitely set the arguments in :func:`grid_evaluation` in this function
call.
"""
experiment = grid_evaluation(param_list_one=param_list,
param_list_two=[],
param_eval=param_eval,
file_name=file_name,
**kwargs)
experiment['line'] = experiment.pop('grid')
experiment['cols'] = experiment.pop('rows')
return experiment
| [
0,
1,
2,
3,
4
] |
267 | f97a892e6e0aa258ad917c4a73a66e89b0dc3253 | <mask token>
| <mask token>
sys.path.extend(['detection', 'train'])
<mask token>
if test_mode in ['RNet', 'ONet']:
detectors[1] = Detector(R_Net, 24, batch_size[1], model_path[1])
if test_mode == 'ONet':
detectors[2] = Detector(O_Net, 48, batch_size[2], model_path[2])
<mask token>
if config.input_mode == '1':
path = config.test_dir
print(path)
for item in tqdm(os.listdir(path)):
img_path = os.path.join(path, item)
img = cv2.imread(img_path)
img_labeled = mtcnn_detector.detect_and_draw(img)
cv2.imwrite(out_path + item, img_labeled)
if config.input_mode == '2':
cap = cv2.VideoCapture(0)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(out_path + 'out.mp4', fourcc, 10, (640, 480))
while True:
t1 = cv2.getTickCount()
ret, frame = cap.read()
if ret == True:
boxes_c, landmarks = mtcnn_detector.detect(frame)
t2 = cv2.getTickCount()
t = (t2 - t1) / cv2.getTickFrequency()
fps = 1.0 / t
for i in range(boxes_c.shape[0]):
bbox = boxes_c[i, :4]
score = boxes_c[i, 4]
corpbbox = [int(bbox[0]), int(bbox[1]), int(bbox[2]), int(
bbox[3])]
cv2.rectangle(frame, (corpbbox[0], corpbbox[1]), (corpbbox[
2], corpbbox[3]), (255, 0, 0), 1)
cv2.putText(frame, '{:.2f}'.format(score), (corpbbox[0],
corpbbox[1] - 2), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0,
255), 2)
cv2.putText(frame, '{:.4f}'.format(t) + ' ' + '{:.3f}'.format(
fps), (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 255), 2
)
for i in range(landmarks.shape[0]):
for j in range(len(landmarks[i]) // 2):
cv2.circle(frame, (int(landmarks[i][2 * j]), int(int(
landmarks[i][2 * j + 1]))), 2, (0, 0, 255))
a = out.write(frame)
cv2.imshow('result', frame)
if cv2.waitKey(1) & 255 == ord('q'):
break
else:
break
cap.release()
out.release()
cv2.destroyAllWindows()
| <mask token>
sys.path.extend(['detection', 'train'])
<mask token>
test_mode = 'ONet'
thresh = [0.6, 0.7, 0.9]
min_face_size = 24
stride = 2
detectors = [None, None, None]
scale_factor = 0.79
model_path = ['model/PNet/', 'model/RNet/', 'model/ONet']
batch_size = config.batches
detectors[0] = FcnDetector(P_Net, model_path[0])
if test_mode in ['RNet', 'ONet']:
detectors[1] = Detector(R_Net, 24, batch_size[1], model_path[1])
if test_mode == 'ONet':
detectors[2] = Detector(O_Net, 48, batch_size[2], model_path[2])
mtcnn_detector = MtcnnDetector(detectors=detectors, min_face_size=
min_face_size, stride=stride, threshold=thresh, scale_factor=scale_factor)
out_path = join('validate', test_mode) + '/'
if config.input_mode == '1':
path = config.test_dir
print(path)
for item in tqdm(os.listdir(path)):
img_path = os.path.join(path, item)
img = cv2.imread(img_path)
img_labeled = mtcnn_detector.detect_and_draw(img)
cv2.imwrite(out_path + item, img_labeled)
if config.input_mode == '2':
cap = cv2.VideoCapture(0)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(out_path + 'out.mp4', fourcc, 10, (640, 480))
while True:
t1 = cv2.getTickCount()
ret, frame = cap.read()
if ret == True:
boxes_c, landmarks = mtcnn_detector.detect(frame)
t2 = cv2.getTickCount()
t = (t2 - t1) / cv2.getTickFrequency()
fps = 1.0 / t
for i in range(boxes_c.shape[0]):
bbox = boxes_c[i, :4]
score = boxes_c[i, 4]
corpbbox = [int(bbox[0]), int(bbox[1]), int(bbox[2]), int(
bbox[3])]
cv2.rectangle(frame, (corpbbox[0], corpbbox[1]), (corpbbox[
2], corpbbox[3]), (255, 0, 0), 1)
cv2.putText(frame, '{:.2f}'.format(score), (corpbbox[0],
corpbbox[1] - 2), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0,
255), 2)
cv2.putText(frame, '{:.4f}'.format(t) + ' ' + '{:.3f}'.format(
fps), (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 255), 2
)
for i in range(landmarks.shape[0]):
for j in range(len(landmarks[i]) // 2):
cv2.circle(frame, (int(landmarks[i][2 * j]), int(int(
landmarks[i][2 * j + 1]))), 2, (0, 0, 255))
a = out.write(frame)
cv2.imshow('result', frame)
if cv2.waitKey(1) & 255 == ord('q'):
break
else:
break
cap.release()
out.release()
cv2.destroyAllWindows()
| import sys
sys.path.extend(['detection', 'train'])
from MtcnnDetector import MtcnnDetector
from detector import Detector
from fcn_detector import FcnDetector
from model_factory import P_Net, R_Net, O_Net
import config as config
from preprocess.utils import iou
import cv2
import os
from os.path import join, split
import numpy as np
from tqdm import tqdm
test_mode = 'ONet'
thresh = [0.6, 0.7, 0.9]
min_face_size = 24
stride = 2
detectors = [None, None, None]
scale_factor = 0.79
model_path = ['model/PNet/', 'model/RNet/', 'model/ONet']
batch_size = config.batches
detectors[0] = FcnDetector(P_Net, model_path[0])
if test_mode in ['RNet', 'ONet']:
detectors[1] = Detector(R_Net, 24, batch_size[1], model_path[1])
if test_mode == 'ONet':
detectors[2] = Detector(O_Net, 48, batch_size[2], model_path[2])
mtcnn_detector = MtcnnDetector(detectors=detectors, min_face_size=
min_face_size, stride=stride, threshold=thresh, scale_factor=scale_factor)
out_path = join('validate', test_mode) + '/'
if config.input_mode == '1':
path = config.test_dir
print(path)
for item in tqdm(os.listdir(path)):
img_path = os.path.join(path, item)
img = cv2.imread(img_path)
img_labeled = mtcnn_detector.detect_and_draw(img)
cv2.imwrite(out_path + item, img_labeled)
if config.input_mode == '2':
cap = cv2.VideoCapture(0)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(out_path + 'out.mp4', fourcc, 10, (640, 480))
while True:
t1 = cv2.getTickCount()
ret, frame = cap.read()
if ret == True:
boxes_c, landmarks = mtcnn_detector.detect(frame)
t2 = cv2.getTickCount()
t = (t2 - t1) / cv2.getTickFrequency()
fps = 1.0 / t
for i in range(boxes_c.shape[0]):
bbox = boxes_c[i, :4]
score = boxes_c[i, 4]
corpbbox = [int(bbox[0]), int(bbox[1]), int(bbox[2]), int(
bbox[3])]
cv2.rectangle(frame, (corpbbox[0], corpbbox[1]), (corpbbox[
2], corpbbox[3]), (255, 0, 0), 1)
cv2.putText(frame, '{:.2f}'.format(score), (corpbbox[0],
corpbbox[1] - 2), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0,
255), 2)
cv2.putText(frame, '{:.4f}'.format(t) + ' ' + '{:.3f}'.format(
fps), (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 255), 2
)
for i in range(landmarks.shape[0]):
for j in range(len(landmarks[i]) // 2):
cv2.circle(frame, (int(landmarks[i][2 * j]), int(int(
landmarks[i][2 * j + 1]))), 2, (0, 0, 255))
a = out.write(frame)
cv2.imshow('result', frame)
if cv2.waitKey(1) & 255 == ord('q'):
break
else:
break
cap.release()
out.release()
cv2.destroyAllWindows()
|
# coding: utf-8
# In[1]:
import sys
sys.path.extend(['detection', 'train'])
# from detection folder
from MtcnnDetector import MtcnnDetector
from detector import Detector
from fcn_detector import FcnDetector
# from train folder
from model_factory import P_Net, R_Net, O_Net
import config as config
from preprocess.utils import iou
import cv2
import os
from os.path import join, split
import numpy as np
from tqdm import tqdm
# In[ ]:
# test_mode = config.test_mode
test_mode = 'ONet'
thresh = [0.6, 0.7, 0.9]
min_face_size = 24
stride = 2
detectors = [None, None, None]
scale_factor = 0.79
# 模型放置位置
model_path = ['model/PNet/', 'model/RNet/', 'model/ONet']
batch_size = config.batches
detectors[0] = FcnDetector(P_Net, model_path[0]) # detecotors for PNet
if test_mode in ['RNet', 'ONet']:
detectors[1] = Detector(R_Net, 24, batch_size[1], model_path[1])
if test_mode == 'ONet':
detectors[2] = Detector(O_Net, 48, batch_size[2], model_path[2])
# Use the three detectors to construct a
mtcnn_detector = MtcnnDetector(
detectors=detectors,
min_face_size=min_face_size,
stride=stride,
threshold=thresh,
scale_factor=scale_factor)
out_path = join('validate', test_mode) + '/'
if config.input_mode == '1':
#选用图片
path = config.test_dir
print(path)
for item in tqdm(os.listdir(path)):
img_path = os.path.join(path, item)
img = cv2.imread(img_path)
img_labeled = mtcnn_detector.detect_and_draw(img)
cv2.imwrite(out_path + item, img_labeled)
if config.input_mode == '2':
cap = cv2.VideoCapture(0)
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(out_path+'out.mp4', fourcc, 10, (640, 480))
while True:
t1 = cv2.getTickCount()
ret, frame = cap.read()
if ret == True:
boxes_c, landmarks = mtcnn_detector.detect(frame)
t2 = cv2.getTickCount()
t = (t2-t1)/cv2.getTickFrequency()
fps = 1.0/t
for i in range(boxes_c.shape[0]):
bbox = boxes_c[i, :4]
score = boxes_c[i, 4]
corpbbox = [int(bbox[0]), int(bbox[1]),
int(bbox[2]), int(bbox[3])]
#画人脸框
cv2.rectangle(frame, (corpbbox[0], corpbbox[1]),
(corpbbox[2], corpbbox[3]), (255, 0, 0), 1)
#画置信度
cv2.putText(frame, '{:.2f}'.format(score),
(corpbbox[0], corpbbox[1] - 2),
cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 0, 255), 2)
#画fps值
cv2.putText(frame, '{:.4f}'.format(t) + " " + '{:.3f}'.format(fps), (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 255), 2)
#画关键点
for i in range(landmarks.shape[0]):
for j in range(len(landmarks[i])//2):
cv2.circle(
frame, (int(landmarks[i][2*j]), int(int(landmarks[i][2*j+1]))), 2, (0, 0, 255))
a = out.write(frame)
cv2.imshow("result", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
break
cap.release()
out.release()
cv2.destroyAllWindows()
| [
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3,
4
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268 | f19d8aa2104240cc93a0146f1b14c635e7cd3a41 | #! /usr/bin/env python
import ldac
from numpy import *
import shearprofile as sp
import sys
import os, subprocess
import pylab
if len(sys.argv) != 6:
sys.stderr.write("wrong number of arguments!\n")
sys.exit(1)
catfile= sys.argv[1]
clusterz=float(sys.argv[2])
center= map(float,sys.argv[3].split(','))
pixscale=float(sys.argv[4]) # arcsec / pix
clustername=sys.argv[5]
catalog= ldac.openObjectFile(catfile)
r, E = sp.calcTangentialShear(catalog, center, pixscale)
beta=sp.beta(catalog["Z_BEST"],clusterz, calcAverage = False)
kappacut = sp.calcWLViolationCut(r, beta, sigma_v = 1300)
radiuscut = r > 60 #arcseconds
largeradiuscut = r < 500
zcut = logical_and(catalog['Z_BEST'] > 1.2*clusterz, catalog['Z_BEST'] < 1.2)
cleancut = logical_and(kappacut, logical_and(radiuscut, logical_and(largeradiuscut,zcut)))
cleancat = catalog.filter(cleancut)
samples = sp.simpleBootstrap(cleancat, clusterz, pixscale, center, beta[cleancut])
r500x = float(subprocess.Popen("grep %s /nfs/slac/g/ki/ki05/anja/SUBARU/clusters.r500x.dat | awk '{print $2}'" % clustername, stdout=subprocess.PIPE, shell=True).communicate()[0])
mass = lambda sigma2,r500x: 2*sigma2*r500x/4.3e-09
masses = [ mass(sigma2, r500x) for sigma2 in samples]
confidenceRegion = sp.ConfidenceRegion(masses)
filebase,ext=os.path.splitext(catfile)
#output = open(filebase+"_profile.dat", 'w')
#for i in xrange(len(r_as)):
# output.write("%f %f %f %f %f\n" % (r_as[i], E[i], Err[i], B[i], Berr[i]))
#output.close()
#veldisp = sqrt( confidenceRegion[0][0] * 4.3e-09 / (3*r500x) )
#veldisperr = (veldisp / 2) * ((confidenceRegion[1][0]+confidenceRegion[2][0])/confidenceRegion[0][0])
#
output = open(filebase+"_sisfit.ml.dat", 'w')
samples = array(samples)
samples[samples < 0] = 0.
veldispersions = sqrt(samples)
veldisp_confidenceregion = sp.ConfidenceRegion(veldispersions[veldispersions > 0])
output = open(filebase+"_sisfit.sigma.dat", 'w')
output.write("M500: %e -%e %e\n" % (confidenceRegion[0][0], confidenceRegion[1][0], confidenceRegion[2][0]))
output.write("sigma: %e -%e %e\n" % (veldisp_confidenceregion[0][0], veldisp_confidenceregion[1][0], veldisp_confidenceregion[2][0]))
output.close()
#sys.stderr.write("sigma: %e -%e %e\n" % (veldisp_confidenceregion[0][0], veldisp_confidenceregion[1][0], veldisp_confidenceregion[2][0]))
#
#pylab.hist(veldispersions[veldispersions > 0], bins=50)
#pylab.show()
#
print '%s %e -%e %e' % (clustername, confidenceRegion[0][0], confidenceRegion[1][0], confidenceRegion[2][0])
| null | null | null | null | [
0
] |
269 | 0e58834120c34b5152026bde6d089be19244e21a | import os
from MdApi import MdApi
class Adapter(MdApi):
def __init__(self):
super(Adapter, self).__init__()
def connect(self):
self.createFtdcMdApi(os.getcwd())
self.registerFront('tcp://180.168.146.187:10010')
def onFrontConnected(self):
print 'front success'
if __name__ == '__main__':
adapter = Adapter()
adapter.connect()
| null | null | null | null | [
0
] |
270 | df40b0628d6a180a98cd385145ee7c65ecb78256 | <mask token>
def bytes_feature(value):
assert isinstance(value, Iterable)
return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
<mask token>
class TFRecordProducer:
def remove_list(self, list1, list2):
i, j = 0, 0
tmp_list1 = []
tmp_list2 = []
while i < len(list1) and j < len(list2):
item1 = int(list1[i].split('/')[-1].split('-')[0])
item2 = int(list2[j].split('/')[-1].split('-')[0])
if item1 == item2:
tmp_list1.append(list1[i])
tmp_list2.append(list2[j])
i += 1
j += 1
elif item1 < item2:
i += 1
else:
j += 1
return tmp_list1, tmp_list2
def __init__(self, in_dir, hp, args, is_train):
def find_all(file_format):
return sorted(glob(os.path.join(self.data_dir, file_format)))
self.in_dir = in_dir
self.hp = hp
self.args = args
self.is_train = is_train
self.data_dir = in_dir
self.dvec_list = find_all(hp.form.dvec_npy)
self.target_wav_list = find_all(hp.form.target.wav)
self.mixed_wav_list = find_all(hp.form.mixed.wav)
self.target_mag_list = find_all(hp.form.target.mag)
self.mixed_mag_list = find_all(hp.form.mixed.mag)
self.target_phase_list = find_all(hp.form.target.phase)
self.mixed_phase_list = find_all(hp.form.mixed.phase)
self.target_mel_list = find_all(hp.form.target.mel)
self.mixed_mel_list = find_all(hp.form.mixed.mel)
_, self.target_wav_list = self.remove_list(self.dvec_list, self.
target_wav_list)
_, self.mixed_wav_list = self.remove_list(self.dvec_list, self.
mixed_wav_list)
_, self.target_mag_list = self.remove_list(self.dvec_list, self.
target_mag_list)
_, self.mixed_mag_list = self.remove_list(self.dvec_list, self.
mixed_mag_list)
_, self.target_phase_list = self.remove_list(self.dvec_list, self.
target_phase_list)
_, self.mixed_phase_list = self.remove_list(self.dvec_list, self.
mixed_phase_list)
_, self.target_mel_list = self.remove_list(self.dvec_list, self.
target_mel_list)
_, self.mixed_mel_list = self.remove_list(self.dvec_list, self.
mixed_mel_list)
print(len(self.dvec_list), len(self.target_wav_list), len(self.
mixed_wav_list), len(self.target_mag_list), len(self.
mixed_mag_list), len(self.target_phase_list), len(self.
mixed_phase_list), len(self.target_mel_list), len(self.
mixed_mel_list))
assert len(self.dvec_list) == len(self.target_wav_list) == len(self
.mixed_wav_list) == len(self.target_mag_list) == len(self.
mixed_mag_list), 'number of training files must match'
assert len(self.dvec_list) != 0, 'no training file found'
def write_training_examples_to_tfrecords(self, filename, need_phase=
False, need_mel=False, hp=None):
with tf.python_io.TFRecordWriter(filename) as writer:
for i in tqdm(range(len(self.dvec_list))):
target_mag = torch.load(self.target_mag_list[i]).numpy()
mixed_mag = torch.load(self.mixed_mag_list[i]).numpy()
dvec = np.load(self.dvec_list[i])
if need_phase:
target_phase = torch.load(self.target_phase_list[i]).numpy(
)
mixed_phase = torch.load(self.mixed_phase_list[i]).numpy()
target_mel = torch.load(self.target_mel_list[i]).numpy()
mixed_mel = torch.load(self.mixed_mel_list[i]).numpy()
writer.write(convert_to_example(target_mag, mixed_mag,
dvec, target_phase, mixed_phase, target_mel,
mixed_mel).SerializeToString())
elif need_mel:
target_mel = torch.load(self.target_mel_list[i]).numpy()
mixed_mel = torch.load(self.mixed_mel_list[i]).numpy()
writer.write(convert_to_example(target_mag, mixed_mag,
dvec, None, None, target_mel, mixed_mel).
SerializeToString())
else:
writer.write(convert_to_example(target_mag, mixed_mag,
dvec).SerializeToString())
<mask token>
| <mask token>
def bytes_feature(value):
assert isinstance(value, Iterable)
return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
def convert_to_example(target, mixed, speaker, target_phase=None,
mixed_phase=None, target_mel=None, mixed_mel=None):
raw_target = target.tostring()
raw_mixed = mixed.tostring()
raw_speaker = speaker.tostring()
raw_target_phase = target_phase.tostring(
) if target_phase is not None else None
raw_mixed_phase = mixed_phase.tostring(
) if mixed_phase is not None else None
raw_target_mel = target_mel.tostring() if target_mel is not None else None
raw_mixed_mel = mixed_mel.tostring() if mixed_mel is not None else None
if target_mel is not None and mixed_mel is not None:
if target_phase is not None and mixed_phase is not None:
example = tf.train.Example(features=tf.train.Features(feature={
'target': bytes_feature([raw_target]), 'mixed':
bytes_feature([raw_mixed]), 'speaker': bytes_feature([
raw_speaker]), 'target_phase': bytes_feature([
raw_target_phase]), 'mixed_phase': bytes_feature([
raw_mixed_phase]), 'target_mel': bytes_feature([
raw_target_mel]), 'mixed_mel': bytes_feature([raw_mixed_mel])})
)
else:
example = tf.train.Example(features=tf.train.Features(feature={
'target': bytes_feature([raw_target]), 'mixed':
bytes_feature([raw_mixed]), 'speaker': bytes_feature([
raw_speaker]), 'target_mel': bytes_feature([raw_target_mel]
), 'mixed_mel': bytes_feature([raw_mixed_mel])}))
else:
example = tf.train.Example(features=tf.train.Features(feature={
'target': bytes_feature([raw_target]), 'mixed': bytes_feature([
raw_mixed]), 'speaker': bytes_feature([raw_speaker])}))
return example
class TFRecordProducer:
def remove_list(self, list1, list2):
i, j = 0, 0
tmp_list1 = []
tmp_list2 = []
while i < len(list1) and j < len(list2):
item1 = int(list1[i].split('/')[-1].split('-')[0])
item2 = int(list2[j].split('/')[-1].split('-')[0])
if item1 == item2:
tmp_list1.append(list1[i])
tmp_list2.append(list2[j])
i += 1
j += 1
elif item1 < item2:
i += 1
else:
j += 1
return tmp_list1, tmp_list2
def __init__(self, in_dir, hp, args, is_train):
def find_all(file_format):
return sorted(glob(os.path.join(self.data_dir, file_format)))
self.in_dir = in_dir
self.hp = hp
self.args = args
self.is_train = is_train
self.data_dir = in_dir
self.dvec_list = find_all(hp.form.dvec_npy)
self.target_wav_list = find_all(hp.form.target.wav)
self.mixed_wav_list = find_all(hp.form.mixed.wav)
self.target_mag_list = find_all(hp.form.target.mag)
self.mixed_mag_list = find_all(hp.form.mixed.mag)
self.target_phase_list = find_all(hp.form.target.phase)
self.mixed_phase_list = find_all(hp.form.mixed.phase)
self.target_mel_list = find_all(hp.form.target.mel)
self.mixed_mel_list = find_all(hp.form.mixed.mel)
_, self.target_wav_list = self.remove_list(self.dvec_list, self.
target_wav_list)
_, self.mixed_wav_list = self.remove_list(self.dvec_list, self.
mixed_wav_list)
_, self.target_mag_list = self.remove_list(self.dvec_list, self.
target_mag_list)
_, self.mixed_mag_list = self.remove_list(self.dvec_list, self.
mixed_mag_list)
_, self.target_phase_list = self.remove_list(self.dvec_list, self.
target_phase_list)
_, self.mixed_phase_list = self.remove_list(self.dvec_list, self.
mixed_phase_list)
_, self.target_mel_list = self.remove_list(self.dvec_list, self.
target_mel_list)
_, self.mixed_mel_list = self.remove_list(self.dvec_list, self.
mixed_mel_list)
print(len(self.dvec_list), len(self.target_wav_list), len(self.
mixed_wav_list), len(self.target_mag_list), len(self.
mixed_mag_list), len(self.target_phase_list), len(self.
mixed_phase_list), len(self.target_mel_list), len(self.
mixed_mel_list))
assert len(self.dvec_list) == len(self.target_wav_list) == len(self
.mixed_wav_list) == len(self.target_mag_list) == len(self.
mixed_mag_list), 'number of training files must match'
assert len(self.dvec_list) != 0, 'no training file found'
def write_training_examples_to_tfrecords(self, filename, need_phase=
False, need_mel=False, hp=None):
with tf.python_io.TFRecordWriter(filename) as writer:
for i in tqdm(range(len(self.dvec_list))):
target_mag = torch.load(self.target_mag_list[i]).numpy()
mixed_mag = torch.load(self.mixed_mag_list[i]).numpy()
dvec = np.load(self.dvec_list[i])
if need_phase:
target_phase = torch.load(self.target_phase_list[i]).numpy(
)
mixed_phase = torch.load(self.mixed_phase_list[i]).numpy()
target_mel = torch.load(self.target_mel_list[i]).numpy()
mixed_mel = torch.load(self.mixed_mel_list[i]).numpy()
writer.write(convert_to_example(target_mag, mixed_mag,
dvec, target_phase, mixed_phase, target_mel,
mixed_mel).SerializeToString())
elif need_mel:
target_mel = torch.load(self.target_mel_list[i]).numpy()
mixed_mel = torch.load(self.mixed_mel_list[i]).numpy()
writer.write(convert_to_example(target_mag, mixed_mag,
dvec, None, None, target_mel, mixed_mel).
SerializeToString())
else:
writer.write(convert_to_example(target_mag, mixed_mag,
dvec).SerializeToString())
<mask token>
parser.add_argument('--in_dir', default='.', help='input glob str')
parser.add_argument('-c', '--config', type=str, default=
'config/config.yaml', help='yaml file for configuration')
parser.add_argument('--out', default='.', help='output prefix of tf record')
parser.add_argument('--gpu', default=0, help='Path to model checkpoint')
parser.add_argument('--need_phase', action='store_true')
parser.add_argument('--need_mel', action='store_true')
<mask token>
tp.write_training_examples_to_tfrecords(filename, args.need_phase, args.
need_mel, hp)
| <mask token>
def bytes_feature(value):
assert isinstance(value, Iterable)
return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
def convert_to_example(target, mixed, speaker, target_phase=None,
mixed_phase=None, target_mel=None, mixed_mel=None):
raw_target = target.tostring()
raw_mixed = mixed.tostring()
raw_speaker = speaker.tostring()
raw_target_phase = target_phase.tostring(
) if target_phase is not None else None
raw_mixed_phase = mixed_phase.tostring(
) if mixed_phase is not None else None
raw_target_mel = target_mel.tostring() if target_mel is not None else None
raw_mixed_mel = mixed_mel.tostring() if mixed_mel is not None else None
if target_mel is not None and mixed_mel is not None:
if target_phase is not None and mixed_phase is not None:
example = tf.train.Example(features=tf.train.Features(feature={
'target': bytes_feature([raw_target]), 'mixed':
bytes_feature([raw_mixed]), 'speaker': bytes_feature([
raw_speaker]), 'target_phase': bytes_feature([
raw_target_phase]), 'mixed_phase': bytes_feature([
raw_mixed_phase]), 'target_mel': bytes_feature([
raw_target_mel]), 'mixed_mel': bytes_feature([raw_mixed_mel])})
)
else:
example = tf.train.Example(features=tf.train.Features(feature={
'target': bytes_feature([raw_target]), 'mixed':
bytes_feature([raw_mixed]), 'speaker': bytes_feature([
raw_speaker]), 'target_mel': bytes_feature([raw_target_mel]
), 'mixed_mel': bytes_feature([raw_mixed_mel])}))
else:
example = tf.train.Example(features=tf.train.Features(feature={
'target': bytes_feature([raw_target]), 'mixed': bytes_feature([
raw_mixed]), 'speaker': bytes_feature([raw_speaker])}))
return example
class TFRecordProducer:
def remove_list(self, list1, list2):
i, j = 0, 0
tmp_list1 = []
tmp_list2 = []
while i < len(list1) and j < len(list2):
item1 = int(list1[i].split('/')[-1].split('-')[0])
item2 = int(list2[j].split('/')[-1].split('-')[0])
if item1 == item2:
tmp_list1.append(list1[i])
tmp_list2.append(list2[j])
i += 1
j += 1
elif item1 < item2:
i += 1
else:
j += 1
return tmp_list1, tmp_list2
def __init__(self, in_dir, hp, args, is_train):
def find_all(file_format):
return sorted(glob(os.path.join(self.data_dir, file_format)))
self.in_dir = in_dir
self.hp = hp
self.args = args
self.is_train = is_train
self.data_dir = in_dir
self.dvec_list = find_all(hp.form.dvec_npy)
self.target_wav_list = find_all(hp.form.target.wav)
self.mixed_wav_list = find_all(hp.form.mixed.wav)
self.target_mag_list = find_all(hp.form.target.mag)
self.mixed_mag_list = find_all(hp.form.mixed.mag)
self.target_phase_list = find_all(hp.form.target.phase)
self.mixed_phase_list = find_all(hp.form.mixed.phase)
self.target_mel_list = find_all(hp.form.target.mel)
self.mixed_mel_list = find_all(hp.form.mixed.mel)
_, self.target_wav_list = self.remove_list(self.dvec_list, self.
target_wav_list)
_, self.mixed_wav_list = self.remove_list(self.dvec_list, self.
mixed_wav_list)
_, self.target_mag_list = self.remove_list(self.dvec_list, self.
target_mag_list)
_, self.mixed_mag_list = self.remove_list(self.dvec_list, self.
mixed_mag_list)
_, self.target_phase_list = self.remove_list(self.dvec_list, self.
target_phase_list)
_, self.mixed_phase_list = self.remove_list(self.dvec_list, self.
mixed_phase_list)
_, self.target_mel_list = self.remove_list(self.dvec_list, self.
target_mel_list)
_, self.mixed_mel_list = self.remove_list(self.dvec_list, self.
mixed_mel_list)
print(len(self.dvec_list), len(self.target_wav_list), len(self.
mixed_wav_list), len(self.target_mag_list), len(self.
mixed_mag_list), len(self.target_phase_list), len(self.
mixed_phase_list), len(self.target_mel_list), len(self.
mixed_mel_list))
assert len(self.dvec_list) == len(self.target_wav_list) == len(self
.mixed_wav_list) == len(self.target_mag_list) == len(self.
mixed_mag_list), 'number of training files must match'
assert len(self.dvec_list) != 0, 'no training file found'
def write_training_examples_to_tfrecords(self, filename, need_phase=
False, need_mel=False, hp=None):
with tf.python_io.TFRecordWriter(filename) as writer:
for i in tqdm(range(len(self.dvec_list))):
target_mag = torch.load(self.target_mag_list[i]).numpy()
mixed_mag = torch.load(self.mixed_mag_list[i]).numpy()
dvec = np.load(self.dvec_list[i])
if need_phase:
target_phase = torch.load(self.target_phase_list[i]).numpy(
)
mixed_phase = torch.load(self.mixed_phase_list[i]).numpy()
target_mel = torch.load(self.target_mel_list[i]).numpy()
mixed_mel = torch.load(self.mixed_mel_list[i]).numpy()
writer.write(convert_to_example(target_mag, mixed_mag,
dvec, target_phase, mixed_phase, target_mel,
mixed_mel).SerializeToString())
elif need_mel:
target_mel = torch.load(self.target_mel_list[i]).numpy()
mixed_mel = torch.load(self.mixed_mel_list[i]).numpy()
writer.write(convert_to_example(target_mag, mixed_mag,
dvec, None, None, target_mel, mixed_mel).
SerializeToString())
else:
writer.write(convert_to_example(target_mag, mixed_mag,
dvec).SerializeToString())
<mask token>
parser = argparse.ArgumentParser()
parser.add_argument('--in_dir', default='.', help='input glob str')
parser.add_argument('-c', '--config', type=str, default=
'config/config.yaml', help='yaml file for configuration')
parser.add_argument('--out', default='.', help='output prefix of tf record')
parser.add_argument('--gpu', default=0, help='Path to model checkpoint')
parser.add_argument('--need_phase', action='store_true')
parser.add_argument('--need_mel', action='store_true')
args = parser.parse_args()
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
in_dir = args.in_dir
filename = args.out
hp = HParam(args.config)
tp = TFRecordProducer(in_dir, hp, args, True)
tp.write_training_examples_to_tfrecords(filename, args.need_phase, args.
need_mel, hp)
| import os
import tensorflow as tf
import torch
from tqdm import tqdm
from glob import glob
import numpy as np
from collections.abc import Iterable
from utils.hparams import HParam
def bytes_feature(value):
assert isinstance(value, Iterable)
return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
def convert_to_example(target, mixed, speaker, target_phase=None,
mixed_phase=None, target_mel=None, mixed_mel=None):
raw_target = target.tostring()
raw_mixed = mixed.tostring()
raw_speaker = speaker.tostring()
raw_target_phase = target_phase.tostring(
) if target_phase is not None else None
raw_mixed_phase = mixed_phase.tostring(
) if mixed_phase is not None else None
raw_target_mel = target_mel.tostring() if target_mel is not None else None
raw_mixed_mel = mixed_mel.tostring() if mixed_mel is not None else None
if target_mel is not None and mixed_mel is not None:
if target_phase is not None and mixed_phase is not None:
example = tf.train.Example(features=tf.train.Features(feature={
'target': bytes_feature([raw_target]), 'mixed':
bytes_feature([raw_mixed]), 'speaker': bytes_feature([
raw_speaker]), 'target_phase': bytes_feature([
raw_target_phase]), 'mixed_phase': bytes_feature([
raw_mixed_phase]), 'target_mel': bytes_feature([
raw_target_mel]), 'mixed_mel': bytes_feature([raw_mixed_mel])})
)
else:
example = tf.train.Example(features=tf.train.Features(feature={
'target': bytes_feature([raw_target]), 'mixed':
bytes_feature([raw_mixed]), 'speaker': bytes_feature([
raw_speaker]), 'target_mel': bytes_feature([raw_target_mel]
), 'mixed_mel': bytes_feature([raw_mixed_mel])}))
else:
example = tf.train.Example(features=tf.train.Features(feature={
'target': bytes_feature([raw_target]), 'mixed': bytes_feature([
raw_mixed]), 'speaker': bytes_feature([raw_speaker])}))
return example
class TFRecordProducer:
def remove_list(self, list1, list2):
i, j = 0, 0
tmp_list1 = []
tmp_list2 = []
while i < len(list1) and j < len(list2):
item1 = int(list1[i].split('/')[-1].split('-')[0])
item2 = int(list2[j].split('/')[-1].split('-')[0])
if item1 == item2:
tmp_list1.append(list1[i])
tmp_list2.append(list2[j])
i += 1
j += 1
elif item1 < item2:
i += 1
else:
j += 1
return tmp_list1, tmp_list2
def __init__(self, in_dir, hp, args, is_train):
def find_all(file_format):
return sorted(glob(os.path.join(self.data_dir, file_format)))
self.in_dir = in_dir
self.hp = hp
self.args = args
self.is_train = is_train
self.data_dir = in_dir
self.dvec_list = find_all(hp.form.dvec_npy)
self.target_wav_list = find_all(hp.form.target.wav)
self.mixed_wav_list = find_all(hp.form.mixed.wav)
self.target_mag_list = find_all(hp.form.target.mag)
self.mixed_mag_list = find_all(hp.form.mixed.mag)
self.target_phase_list = find_all(hp.form.target.phase)
self.mixed_phase_list = find_all(hp.form.mixed.phase)
self.target_mel_list = find_all(hp.form.target.mel)
self.mixed_mel_list = find_all(hp.form.mixed.mel)
_, self.target_wav_list = self.remove_list(self.dvec_list, self.
target_wav_list)
_, self.mixed_wav_list = self.remove_list(self.dvec_list, self.
mixed_wav_list)
_, self.target_mag_list = self.remove_list(self.dvec_list, self.
target_mag_list)
_, self.mixed_mag_list = self.remove_list(self.dvec_list, self.
mixed_mag_list)
_, self.target_phase_list = self.remove_list(self.dvec_list, self.
target_phase_list)
_, self.mixed_phase_list = self.remove_list(self.dvec_list, self.
mixed_phase_list)
_, self.target_mel_list = self.remove_list(self.dvec_list, self.
target_mel_list)
_, self.mixed_mel_list = self.remove_list(self.dvec_list, self.
mixed_mel_list)
print(len(self.dvec_list), len(self.target_wav_list), len(self.
mixed_wav_list), len(self.target_mag_list), len(self.
mixed_mag_list), len(self.target_phase_list), len(self.
mixed_phase_list), len(self.target_mel_list), len(self.
mixed_mel_list))
assert len(self.dvec_list) == len(self.target_wav_list) == len(self
.mixed_wav_list) == len(self.target_mag_list) == len(self.
mixed_mag_list), 'number of training files must match'
assert len(self.dvec_list) != 0, 'no training file found'
def write_training_examples_to_tfrecords(self, filename, need_phase=
False, need_mel=False, hp=None):
with tf.python_io.TFRecordWriter(filename) as writer:
for i in tqdm(range(len(self.dvec_list))):
target_mag = torch.load(self.target_mag_list[i]).numpy()
mixed_mag = torch.load(self.mixed_mag_list[i]).numpy()
dvec = np.load(self.dvec_list[i])
if need_phase:
target_phase = torch.load(self.target_phase_list[i]).numpy(
)
mixed_phase = torch.load(self.mixed_phase_list[i]).numpy()
target_mel = torch.load(self.target_mel_list[i]).numpy()
mixed_mel = torch.load(self.mixed_mel_list[i]).numpy()
writer.write(convert_to_example(target_mag, mixed_mag,
dvec, target_phase, mixed_phase, target_mel,
mixed_mel).SerializeToString())
elif need_mel:
target_mel = torch.load(self.target_mel_list[i]).numpy()
mixed_mel = torch.load(self.mixed_mel_list[i]).numpy()
writer.write(convert_to_example(target_mag, mixed_mag,
dvec, None, None, target_mel, mixed_mel).
SerializeToString())
else:
writer.write(convert_to_example(target_mag, mixed_mag,
dvec).SerializeToString())
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--in_dir', default='.', help='input glob str')
parser.add_argument('-c', '--config', type=str, default=
'config/config.yaml', help='yaml file for configuration')
parser.add_argument('--out', default='.', help='output prefix of tf record')
parser.add_argument('--gpu', default=0, help='Path to model checkpoint')
parser.add_argument('--need_phase', action='store_true')
parser.add_argument('--need_mel', action='store_true')
args = parser.parse_args()
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
in_dir = args.in_dir
filename = args.out
hp = HParam(args.config)
tp = TFRecordProducer(in_dir, hp, args, True)
tp.write_training_examples_to_tfrecords(filename, args.need_phase, args.
need_mel, hp)
| import os
import tensorflow as tf
import torch
from tqdm import tqdm
from glob import glob
import numpy as np
from collections.abc import Iterable
from utils.hparams import HParam
#from utils.audio import Audio
#import librosa
#python encoder_inference.py --in_dir training_libri_mel/train/ --gpu_str 5
#python tfrecord_producer.py --in_dir training_libri_mel/train/ --out xyz --gpu 6
#python tfrecord_producer.py --in_dir test_libri_mel/test/ --out xyz_mel_test --gpu 5 --need_mel --need_phase
def bytes_feature(value):
assert isinstance(value, Iterable)
return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))
def convert_to_example(target, mixed, speaker,
target_phase=None, mixed_phase=None, target_mel=None, mixed_mel=None):
raw_target = target.tostring()
raw_mixed = mixed.tostring()
raw_speaker = speaker.tostring()
raw_target_phase = target_phase.tostring() if target_phase is not None else None
raw_mixed_phase = mixed_phase.tostring() if mixed_phase is not None else None
raw_target_mel = target_mel.tostring() if target_mel is not None else None
raw_mixed_mel = mixed_mel.tostring() if mixed_mel is not None else None
#TODO: handle the case where only have phase info but no mel info
if target_mel is not None and mixed_mel is not None:
if target_phase is not None and mixed_phase is not None:
example = tf.train.Example(features=tf.train.Features(feature={
'target': bytes_feature([raw_target]),
'mixed': bytes_feature([raw_mixed]),
'speaker': bytes_feature([raw_speaker]),
'target_phase': bytes_feature([raw_target_phase]),
'mixed_phase': bytes_feature([raw_mixed_phase]),
'target_mel': bytes_feature([raw_target_mel]),
'mixed_mel': bytes_feature([raw_mixed_mel]),
}))
else:
example = tf.train.Example(features=tf.train.Features(feature={
'target': bytes_feature([raw_target]),
'mixed': bytes_feature([raw_mixed]),
'speaker': bytes_feature([raw_speaker]),
'target_mel': bytes_feature([raw_target_mel]),
'mixed_mel': bytes_feature([raw_mixed_mel]),
}))
else:
example = tf.train.Example(features=tf.train.Features(feature={
'target': bytes_feature([raw_target]),
'mixed': bytes_feature([raw_mixed]),
'speaker': bytes_feature([raw_speaker]),
}))
return example
class TFRecordProducer:
def remove_list(self, list1, list2):
i,j=0,0
tmp_list1=[]
tmp_list2=[]
while i<len(list1) and j<len(list2):
item1=int(list1[i].split('/')[-1].split('-')[0])
item2=int(list2[j].split('/')[-1].split('-')[0])
if item1==item2:
tmp_list1.append(list1[i])
tmp_list2.append(list2[j])
i+=1
j+=1
elif item1<item2:
i+=1
else:
j+=1
return tmp_list1, tmp_list2
def __init__(self, in_dir, hp, args, is_train):
def find_all(file_format):
return sorted(glob(os.path.join(self.data_dir, file_format)))
self.in_dir=in_dir
self.hp = hp
self.args = args
self.is_train = is_train
#self.data_dir = hp.data.train_dir if is_train else hp.data.test_dir
self.data_dir = in_dir
self.dvec_list = find_all(hp.form.dvec_npy)
self.target_wav_list = find_all(hp.form.target.wav)
self.mixed_wav_list = find_all(hp.form.mixed.wav)
self.target_mag_list = find_all(hp.form.target.mag)
self.mixed_mag_list = find_all(hp.form.mixed.mag)
self.target_phase_list = find_all(hp.form.target.phase)
self.mixed_phase_list = find_all(hp.form.mixed.phase)
self.target_mel_list = find_all(hp.form.target.mel)
self.mixed_mel_list = find_all(hp.form.mixed.mel)
_, self.target_wav_list=self.remove_list(self.dvec_list, self.target_wav_list)
_, self.mixed_wav_list=self.remove_list(self.dvec_list, self.mixed_wav_list)
_, self.target_mag_list=self.remove_list(self.dvec_list, self.target_mag_list)
_, self.mixed_mag_list=self.remove_list(self.dvec_list, self.mixed_mag_list)
_, self.target_phase_list=self.remove_list(self.dvec_list, self.target_phase_list)
_, self.mixed_phase_list=self.remove_list(self.dvec_list, self.mixed_phase_list)
_, self.target_mel_list=self.remove_list(self.dvec_list, self.target_mel_list)
_, self.mixed_mel_list=self.remove_list(self.dvec_list, self.mixed_mel_list)
print(len(self.dvec_list), len(self.target_wav_list), len(self.mixed_wav_list), \
len(self.target_mag_list), len(self.mixed_mag_list),
len(self.target_phase_list), len(self.mixed_phase_list),
len(self.target_mel_list), len(self.mixed_mel_list))
assert len(self.dvec_list) == len(self.target_wav_list) == len(self.mixed_wav_list) == \
len(self.target_mag_list) == len(self.mixed_mag_list), "number of training files must match"
assert len(self.dvec_list) != 0, \
"no training file found"
def write_training_examples_to_tfrecords(self, filename, need_phase=False, need_mel=False, hp=None):
#if hp is not None:
# audio=Audio(hp)
with tf.python_io.TFRecordWriter(filename) as writer:
for i in tqdm(range(len(self.dvec_list))):
target_mag = torch.load(self.target_mag_list[i]).numpy()
mixed_mag = torch.load(self.mixed_mag_list[i]).numpy()
dvec = np.load(self.dvec_list[i])
if need_phase:
target_phase = torch.load(self.target_phase_list[i]).numpy()
mixed_phase = torch.load(self.mixed_phase_list[i]).numpy()
target_mel = torch.load(self.target_mel_list[i]).numpy()
mixed_mel = torch.load(self.mixed_mel_list[i]).numpy()
#mixed_wav = audio.spec2wav(mixed_mag, mixed_phase)
#librosa.output.write_wav('mixed.wav', mixed_wav, 16000)
writer.write(convert_to_example(target_mag, mixed_mag, dvec, target_phase, mixed_phase, target_mel, mixed_mel).SerializeToString())
elif need_mel:
target_mel = torch.load(self.target_mel_list[i]).numpy()
mixed_mel = torch.load(self.mixed_mel_list[i]).numpy()
writer.write(convert_to_example(target_mag, mixed_mag, dvec, None, None, target_mel, mixed_mel).SerializeToString())
else:
writer.write(convert_to_example(target_mag, mixed_mag, dvec).SerializeToString())
import argparse
parser=argparse.ArgumentParser()
parser.add_argument('--in_dir', default='.', help='input glob str')
parser.add_argument('-c', '--config', type=str, default='config/config.yaml',
help="yaml file for configuration")
parser.add_argument('--out', default='.', help='output prefix of tf record')
parser.add_argument('--gpu', default=0,
help='Path to model checkpoint')
parser.add_argument('--need_phase', action='store_true')
parser.add_argument('--need_mel', action='store_true')
args=parser.parse_args()
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"]=str(args.gpu)
in_dir=args.in_dir
filename=args.out
hp = HParam(args.config)
tp=TFRecordProducer(in_dir, hp, args, True)
tp.write_training_examples_to_tfrecords(filename, args.need_phase, args.need_mel, hp)
| [
5,
7,
8,
9,
10
] |
271 | ed7b29a4d7f3a48884434373418c3528f2f397ac | <mask token>
def generate_script(seed_text, model, charset):
sys.stdout.write(seed_text)
sys.stdout.flush()
next_char = None
should_stop = False
while not should_stop:
prev_char = next_char
next_char = sample(model, seed_text, charset, temp=0.2)
sys.stdout.write(next_char)
sys.stdout.flush()
if prev_char == '\n' and prev_char == next_char:
should_stop = True
def sample(model, string, charset, temp=1.0):
inputs = [string_one_hot(string, charset)]
inputs = pad_sequences(inputs, padding='post', maxlen=64)
preds = model.predict(inputs)[0]
return charset[sample_preds(preds, temp)]
<mask token>
| <mask token>
def main():
print('Loading model...')
model, charset = load_model(MODEL_NAME)
print(charset)
seed_text = input('Enter a String: ').strip()
print()
generate_script(seed_text, model, charset)
def generate_script(seed_text, model, charset):
sys.stdout.write(seed_text)
sys.stdout.flush()
next_char = None
should_stop = False
while not should_stop:
prev_char = next_char
next_char = sample(model, seed_text, charset, temp=0.2)
sys.stdout.write(next_char)
sys.stdout.flush()
if prev_char == '\n' and prev_char == next_char:
should_stop = True
def sample(model, string, charset, temp=1.0):
inputs = [string_one_hot(string, charset)]
inputs = pad_sequences(inputs, padding='post', maxlen=64)
preds = model.predict(inputs)[0]
return charset[sample_preds(preds, temp)]
def sample_preds(results, temperature=1.0):
if temperature <= 0.0:
return np.argmax(results)
probs = np.exp(np.log(results) / temperature)
probs /= np.sum(probs)
return np.random.choice(len(results), p=probs)
<mask token>
| <mask token>
def main():
print('Loading model...')
model, charset = load_model(MODEL_NAME)
print(charset)
seed_text = input('Enter a String: ').strip()
print()
generate_script(seed_text, model, charset)
def generate_script(seed_text, model, charset):
sys.stdout.write(seed_text)
sys.stdout.flush()
next_char = None
should_stop = False
while not should_stop:
prev_char = next_char
next_char = sample(model, seed_text, charset, temp=0.2)
sys.stdout.write(next_char)
sys.stdout.flush()
if prev_char == '\n' and prev_char == next_char:
should_stop = True
def sample(model, string, charset, temp=1.0):
inputs = [string_one_hot(string, charset)]
inputs = pad_sequences(inputs, padding='post', maxlen=64)
preds = model.predict(inputs)[0]
return charset[sample_preds(preds, temp)]
def sample_preds(results, temperature=1.0):
if temperature <= 0.0:
return np.argmax(results)
probs = np.exp(np.log(results) / temperature)
probs /= np.sum(probs)
return np.random.choice(len(results), p=probs)
if __name__ == '__main__':
main()
| import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
os.environ['KERAS_BACKEND'] = 'tensorflow'
import numpy as np
import sys
from util import load_model
from keras.preprocessing.text import hashing_trick
from keras.preprocessing.sequence import pad_sequences
from southpark.southpark_generative import string_one_hot, char_one_hot
MODEL_NAME = 'script_gen_demo_model'
def main():
print('Loading model...')
model, charset = load_model(MODEL_NAME)
print(charset)
seed_text = input('Enter a String: ').strip()
print()
generate_script(seed_text, model, charset)
def generate_script(seed_text, model, charset):
sys.stdout.write(seed_text)
sys.stdout.flush()
next_char = None
should_stop = False
while not should_stop:
prev_char = next_char
next_char = sample(model, seed_text, charset, temp=0.2)
sys.stdout.write(next_char)
sys.stdout.flush()
if prev_char == '\n' and prev_char == next_char:
should_stop = True
def sample(model, string, charset, temp=1.0):
inputs = [string_one_hot(string, charset)]
inputs = pad_sequences(inputs, padding='post', maxlen=64)
preds = model.predict(inputs)[0]
return charset[sample_preds(preds, temp)]
def sample_preds(results, temperature=1.0):
if temperature <= 0.0:
return np.argmax(results)
probs = np.exp(np.log(results) / temperature)
probs /= np.sum(probs)
return np.random.choice(len(results), p=probs)
if __name__ == '__main__':
main()
| #!/usr/bin/python3
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # or any {'0', '1', '2'
os.environ['KERAS_BACKEND'] = 'tensorflow'
import numpy as np
import sys
from util import load_model
from keras.preprocessing.text import hashing_trick
from keras.preprocessing.sequence import pad_sequences
from southpark.southpark_generative import string_one_hot, char_one_hot
MODEL_NAME = "script_gen_demo_model"
def main():
print("Loading model...")
model, charset = load_model(MODEL_NAME)
print(charset)
seed_text = input("Enter a String: ").strip()
print()
generate_script(seed_text, model, charset)
def generate_script(seed_text, model, charset):
sys.stdout.write(seed_text)
sys.stdout.flush()
next_char = None
should_stop = False
while not should_stop:
prev_char = next_char
next_char = sample(model, seed_text, charset, temp = 0.2)
sys.stdout.write(next_char)
sys.stdout.flush()
if prev_char == '\n' and prev_char == next_char:
should_stop = True
def sample(model, string, charset, temp = 1.0):
inputs = [string_one_hot(string, charset)]
inputs = pad_sequences(inputs, padding = 'post', maxlen = 64)
preds = model.predict(inputs)[0]
return charset[sample_preds(preds, temp)]
def sample_preds(results, temperature = 1.0):
# helper function to sample an index from a probability array
if temperature <= 0.0:
return np.argmax(results)
#num_choices = results.shape[0] # (batch, outputs)
probs = np.exp(np.log(results) / temperature)
probs /= np.sum(probs)
return np.random.choice(len(results), p = probs)
#preds = np.asarray(preds).astype('float64')
#preds = np.log(preds) / temperature
#exp_preds = np.exp(preds)
#preds = exp_preds / np.sum(exp_preds)
#probas = np.random.multinomial(1, preds, 1)
#
#print(probas)
#return np.argmax(probas)
if __name__ == "__main__":
main()
| [
2,
4,
5,
7,
8
] |
272 | b6d8a918659f733919fe3bb4be9037e36ad32386 | <mask token>
def hwToidx(x: int, y: int, weight: int):
return y * weight + x
<mask token>
def idxToXY(idx, cellw: int):
curpoint = idxTohw(idx, cellw)
curpoint[0], curpoint[1] = curpoint[0] * 10, curpoint[1] * 10
return curpoint
class Graph:
def __init__(self, V: int, W: int):
self.V = V
self.E = 0
self.adj = []
self.W = W
for i in range(V):
nears = []
self.adj.append(nears)
def AddEdge(self, v: int, w: int):
self.adj[v].append(w)
self.E += 1
def __str__(self):
str = ''
for idx, nears in enumerate(self.adj):
str += 'idx: {} nears: {}\n'.format(idx, nears)
return str
class BreadthFirstPaths:
def __init__(self, g: Graph, s: int):
self.marked = [False] * g.V
self.edgeTo = [0] * g.V
self.s = s
self.bfs(g, self.s)
def bfs(self, g: Graph, s: int):
q = queue.Queue()
q.put(s)
while q.qsize() != 0:
v = q.get()
for w in g.adj[v]:
if not self.marked[w]:
self.edgeTo[w] = v
self.marked[w] = True
q.put(w)
def HasPathTo(self, v: int):
return self.marked[v]
def PathTo(self, v: int) ->[int]:
result = []
if not self.HasPathTo(v):
return result
x = v
while x != self.s:
result.append(x)
x = self.edgeTo[x]
result.append(self.s)
return result
<mask token>
class AStarPaths:
def __init__(self, g: Graph, start: int, end: int):
self.closedSet = []
self.openSet = [start]
self.start = start
self.end = end
self.edgeTo = [0] * g.V
self.marked = [False] * g.V
self.gScore = [sys.maxsize] * g.V
self.gScore[start] = 0
self.fScore = [sys.maxsize] * g.V
self.fScore[start] = manhattanDistance(idxTohw(start, g.W), idxTohw
(end, g.W))
self.astar(g)
def astar(self, g: Graph):
while len(self.openSet) > 0:
current = min(self.openSet, key=lambda s: self.fScore[s])
if current == self.end:
return
self.openSet.remove(current)
self.closedSet.append(current)
for w in g.adj[current]:
if w in self.closedSet:
continue
tentativegScore = self.gScore[current] + manhattanDistance(
idxTohw(current, g.W), idxTohw(w, g.W))
if tentativegScore < self.gScore[w]:
self.edgeTo[w] = current
self.marked[w] = True
print('edgeTo ({}) -> ({})'.format(idxTohw(current, g.W
), idxTohw(w, g.W)))
self.gScore[w] = tentativegScore
self.fScore[w] = self.gScore[w] + manhattanDistance(idxTohw
(w, g.W), idxTohw(self.end, g.W))
print('fScore[%d] manhattan: %d' % (w, self.fScore[w]))
if w not in self.openSet:
self.openSet.append(w)
def HasPathTo(self, v: int):
return self.marked[v]
def PathTo(self, v: int):
result = []
if not self.HasPathTo(v):
return result
x = v
while x != self.start:
result.append(x)
x = self.edgeTo[x]
result.append(self.start)
return result
<mask token>
| <mask token>
def hwToidx(x: int, y: int, weight: int):
return y * weight + x
def idxTohw(idx, weight: int):
return [idx % weight, idx // weight]
def idxToXY(idx, cellw: int):
curpoint = idxTohw(idx, cellw)
curpoint[0], curpoint[1] = curpoint[0] * 10, curpoint[1] * 10
return curpoint
class Graph:
def __init__(self, V: int, W: int):
self.V = V
self.E = 0
self.adj = []
self.W = W
for i in range(V):
nears = []
self.adj.append(nears)
def AddEdge(self, v: int, w: int):
self.adj[v].append(w)
self.E += 1
def __str__(self):
str = ''
for idx, nears in enumerate(self.adj):
str += 'idx: {} nears: {}\n'.format(idx, nears)
return str
class BreadthFirstPaths:
def __init__(self, g: Graph, s: int):
self.marked = [False] * g.V
self.edgeTo = [0] * g.V
self.s = s
self.bfs(g, self.s)
def bfs(self, g: Graph, s: int):
q = queue.Queue()
q.put(s)
while q.qsize() != 0:
v = q.get()
for w in g.adj[v]:
if not self.marked[w]:
self.edgeTo[w] = v
self.marked[w] = True
q.put(w)
def HasPathTo(self, v: int):
return self.marked[v]
def PathTo(self, v: int) ->[int]:
result = []
if not self.HasPathTo(v):
return result
x = v
while x != self.s:
result.append(x)
x = self.edgeTo[x]
result.append(self.s)
return result
<mask token>
def dist_between(a, b):
return (b[0] - a[0]) ** 2 + (b[1] - a[1]) ** 2
class AStarPaths:
def __init__(self, g: Graph, start: int, end: int):
self.closedSet = []
self.openSet = [start]
self.start = start
self.end = end
self.edgeTo = [0] * g.V
self.marked = [False] * g.V
self.gScore = [sys.maxsize] * g.V
self.gScore[start] = 0
self.fScore = [sys.maxsize] * g.V
self.fScore[start] = manhattanDistance(idxTohw(start, g.W), idxTohw
(end, g.W))
self.astar(g)
def astar(self, g: Graph):
while len(self.openSet) > 0:
current = min(self.openSet, key=lambda s: self.fScore[s])
if current == self.end:
return
self.openSet.remove(current)
self.closedSet.append(current)
for w in g.adj[current]:
if w in self.closedSet:
continue
tentativegScore = self.gScore[current] + manhattanDistance(
idxTohw(current, g.W), idxTohw(w, g.W))
if tentativegScore < self.gScore[w]:
self.edgeTo[w] = current
self.marked[w] = True
print('edgeTo ({}) -> ({})'.format(idxTohw(current, g.W
), idxTohw(w, g.W)))
self.gScore[w] = tentativegScore
self.fScore[w] = self.gScore[w] + manhattanDistance(idxTohw
(w, g.W), idxTohw(self.end, g.W))
print('fScore[%d] manhattan: %d' % (w, self.fScore[w]))
if w not in self.openSet:
self.openSet.append(w)
def HasPathTo(self, v: int):
return self.marked[v]
def PathTo(self, v: int):
result = []
if not self.HasPathTo(v):
return result
x = v
while x != self.start:
result.append(x)
x = self.edgeTo[x]
result.append(self.start)
return result
<mask token>
| <mask token>
def hwToidx(x: int, y: int, weight: int):
return y * weight + x
def idxTohw(idx, weight: int):
return [idx % weight, idx // weight]
def idxToXY(idx, cellw: int):
curpoint = idxTohw(idx, cellw)
curpoint[0], curpoint[1] = curpoint[0] * 10, curpoint[1] * 10
return curpoint
class Graph:
def __init__(self, V: int, W: int):
self.V = V
self.E = 0
self.adj = []
self.W = W
for i in range(V):
nears = []
self.adj.append(nears)
def AddEdge(self, v: int, w: int):
self.adj[v].append(w)
self.E += 1
def __str__(self):
str = ''
for idx, nears in enumerate(self.adj):
str += 'idx: {} nears: {}\n'.format(idx, nears)
return str
class BreadthFirstPaths:
def __init__(self, g: Graph, s: int):
self.marked = [False] * g.V
self.edgeTo = [0] * g.V
self.s = s
self.bfs(g, self.s)
def bfs(self, g: Graph, s: int):
q = queue.Queue()
q.put(s)
while q.qsize() != 0:
v = q.get()
for w in g.adj[v]:
if not self.marked[w]:
self.edgeTo[w] = v
self.marked[w] = True
q.put(w)
def HasPathTo(self, v: int):
return self.marked[v]
def PathTo(self, v: int) ->[int]:
result = []
if not self.HasPathTo(v):
return result
x = v
while x != self.s:
result.append(x)
x = self.edgeTo[x]
result.append(self.s)
return result
def manhattanDistance(x, y):
return sum(map(lambda i, j: abs(i - j), x, y))
def dist_between(a, b):
return (b[0] - a[0]) ** 2 + (b[1] - a[1]) ** 2
class AStarPaths:
def __init__(self, g: Graph, start: int, end: int):
self.closedSet = []
self.openSet = [start]
self.start = start
self.end = end
self.edgeTo = [0] * g.V
self.marked = [False] * g.V
self.gScore = [sys.maxsize] * g.V
self.gScore[start] = 0
self.fScore = [sys.maxsize] * g.V
self.fScore[start] = manhattanDistance(idxTohw(start, g.W), idxTohw
(end, g.W))
self.astar(g)
def astar(self, g: Graph):
while len(self.openSet) > 0:
current = min(self.openSet, key=lambda s: self.fScore[s])
if current == self.end:
return
self.openSet.remove(current)
self.closedSet.append(current)
for w in g.adj[current]:
if w in self.closedSet:
continue
tentativegScore = self.gScore[current] + manhattanDistance(
idxTohw(current, g.W), idxTohw(w, g.W))
if tentativegScore < self.gScore[w]:
self.edgeTo[w] = current
self.marked[w] = True
print('edgeTo ({}) -> ({})'.format(idxTohw(current, g.W
), idxTohw(w, g.W)))
self.gScore[w] = tentativegScore
self.fScore[w] = self.gScore[w] + manhattanDistance(idxTohw
(w, g.W), idxTohw(self.end, g.W))
print('fScore[%d] manhattan: %d' % (w, self.fScore[w]))
if w not in self.openSet:
self.openSet.append(w)
def HasPathTo(self, v: int):
return self.marked[v]
def PathTo(self, v: int):
result = []
if not self.HasPathTo(v):
return result
x = v
while x != self.start:
result.append(x)
x = self.edgeTo[x]
result.append(self.start)
return result
<mask token>
| import queue
import sys
import logging
from superai.common import InitLog
logger = logging.getLogger(__name__)
def hwToidx(x: int, y: int, weight: int):
return y * weight + x
def idxTohw(idx, weight: int):
return [idx % weight, idx // weight]
def idxToXY(idx, cellw: int):
curpoint = idxTohw(idx, cellw)
curpoint[0], curpoint[1] = curpoint[0] * 10, curpoint[1] * 10
return curpoint
class Graph:
def __init__(self, V: int, W: int):
self.V = V
self.E = 0
self.adj = []
self.W = W
for i in range(V):
nears = []
self.adj.append(nears)
def AddEdge(self, v: int, w: int):
self.adj[v].append(w)
self.E += 1
def __str__(self):
str = ''
for idx, nears in enumerate(self.adj):
str += 'idx: {} nears: {}\n'.format(idx, nears)
return str
class BreadthFirstPaths:
def __init__(self, g: Graph, s: int):
self.marked = [False] * g.V
self.edgeTo = [0] * g.V
self.s = s
self.bfs(g, self.s)
def bfs(self, g: Graph, s: int):
q = queue.Queue()
q.put(s)
while q.qsize() != 0:
v = q.get()
for w in g.adj[v]:
if not self.marked[w]:
self.edgeTo[w] = v
self.marked[w] = True
q.put(w)
def HasPathTo(self, v: int):
return self.marked[v]
def PathTo(self, v: int) ->[int]:
result = []
if not self.HasPathTo(v):
return result
x = v
while x != self.s:
result.append(x)
x = self.edgeTo[x]
result.append(self.s)
return result
def manhattanDistance(x, y):
return sum(map(lambda i, j: abs(i - j), x, y))
def dist_between(a, b):
return (b[0] - a[0]) ** 2 + (b[1] - a[1]) ** 2
class AStarPaths:
def __init__(self, g: Graph, start: int, end: int):
self.closedSet = []
self.openSet = [start]
self.start = start
self.end = end
self.edgeTo = [0] * g.V
self.marked = [False] * g.V
self.gScore = [sys.maxsize] * g.V
self.gScore[start] = 0
self.fScore = [sys.maxsize] * g.V
self.fScore[start] = manhattanDistance(idxTohw(start, g.W), idxTohw
(end, g.W))
self.astar(g)
def astar(self, g: Graph):
while len(self.openSet) > 0:
current = min(self.openSet, key=lambda s: self.fScore[s])
if current == self.end:
return
self.openSet.remove(current)
self.closedSet.append(current)
for w in g.adj[current]:
if w in self.closedSet:
continue
tentativegScore = self.gScore[current] + manhattanDistance(
idxTohw(current, g.W), idxTohw(w, g.W))
if tentativegScore < self.gScore[w]:
self.edgeTo[w] = current
self.marked[w] = True
print('edgeTo ({}) -> ({})'.format(idxTohw(current, g.W
), idxTohw(w, g.W)))
self.gScore[w] = tentativegScore
self.fScore[w] = self.gScore[w] + manhattanDistance(idxTohw
(w, g.W), idxTohw(self.end, g.W))
print('fScore[%d] manhattan: %d' % (w, self.fScore[w]))
if w not in self.openSet:
self.openSet.append(w)
def HasPathTo(self, v: int):
return self.marked[v]
def PathTo(self, v: int):
result = []
if not self.HasPathTo(v):
return result
x = v
while x != self.start:
result.append(x)
x = self.edgeTo[x]
result.append(self.start)
return result
def main():
InitLog()
graph = Graph(24, 6)
graph.AddEdge(hwToidx(2, 0, 6), hwToidx(2, 1, 6))
graph.AddEdge(hwToidx(1, 1, 6), hwToidx(1, 2, 6))
graph.AddEdge(hwToidx(2, 1, 6), hwToidx(2, 0, 6))
graph.AddEdge(hwToidx(2, 1, 6), hwToidx(3, 1, 6))
graph.AddEdge(hwToidx(3, 1, 6), hwToidx(2, 1, 6))
graph.AddEdge(hwToidx(3, 1, 6), hwToidx(4, 1, 6))
graph.AddEdge(hwToidx(4, 1, 6), hwToidx(4, 2, 6))
graph.AddEdge(hwToidx(4, 1, 6), hwToidx(3, 1, 6))
graph.AddEdge(hwToidx(0, 2, 6), hwToidx(1, 2, 6))
graph.AddEdge(hwToidx(1, 2, 6), hwToidx(1, 3, 6))
graph.AddEdge(hwToidx(1, 2, 6), hwToidx(0, 2, 6))
graph.AddEdge(hwToidx(1, 2, 6), hwToidx(1, 1, 6))
graph.AddEdge(hwToidx(1, 2, 6), hwToidx(2, 2, 6))
graph.AddEdge(hwToidx(2, 2, 6), hwToidx(2, 3, 6))
graph.AddEdge(hwToidx(2, 2, 6), hwToidx(1, 2, 6))
graph.AddEdge(hwToidx(2, 2, 6), hwToidx(3, 2, 6))
graph.AddEdge(hwToidx(3, 2, 6), hwToidx(3, 3, 6))
graph.AddEdge(hwToidx(3, 2, 6), hwToidx(2, 2, 6))
graph.AddEdge(hwToidx(3, 2, 6), hwToidx(4, 2, 6))
graph.AddEdge(hwToidx(4, 2, 6), hwToidx(4, 3, 6))
graph.AddEdge(hwToidx(4, 2, 6), hwToidx(3, 2, 6))
graph.AddEdge(hwToidx(4, 2, 6), hwToidx(4, 1, 6))
graph.AddEdge(hwToidx(4, 2, 6), hwToidx(5, 2, 6))
graph.AddEdge(hwToidx(5, 2, 6), hwToidx(5, 3, 6))
graph.AddEdge(hwToidx(5, 2, 6), hwToidx(4, 2, 6))
graph.AddEdge(hwToidx(1, 3, 6), hwToidx(1, 2, 6))
graph.AddEdge(hwToidx(2, 3, 6), hwToidx(2, 2, 6))
graph.AddEdge(hwToidx(2, 3, 6), hwToidx(3, 3, 6))
graph.AddEdge(hwToidx(3, 3, 6), hwToidx(2, 3, 6))
graph.AddEdge(hwToidx(3, 3, 6), hwToidx(3, 2, 6))
graph.AddEdge(hwToidx(4, 3, 6), hwToidx(5, 3, 6))
graph.AddEdge(hwToidx(4, 3, 6), hwToidx(4, 2, 6))
graph.AddEdge(hwToidx(5, 3, 6), hwToidx(4, 3, 6))
graph.AddEdge(hwToidx(5, 3, 6), hwToidx(5, 2, 6))
print('图:')
print(graph)
print('广度优先:')
bfs = BreadthFirstPaths(graph, 2)
paths = bfs.PathTo(hwToidx(1, 1, 6))
for v in reversed(paths):
print(v, idxTohw(v, 6))
print('\na*寻径')
astar = AStarPaths(graph, 2, 7)
paths = astar.PathTo(hwToidx(1, 1, 6))
for v in reversed(paths):
print(v, idxTohw(v, 6))
if __name__ == '__main__':
main()
| import queue
import sys
import logging
from superai.common import InitLog
logger = logging.getLogger(__name__)
# 2维到1维
def hwToidx(x: int, y: int, weight: int):
return y * weight + x
# 1维到2维
def idxTohw(idx, weight: int):
return [idx % weight, idx // weight]
# 10x10 cell idx 到 [x,y]
def idxToXY(idx, cellw: int):
curpoint = idxTohw(idx, cellw)
curpoint[0], curpoint[1] = curpoint[0] * 10, curpoint[1] * 10
return curpoint
# 有向图
class Graph:
def __init__(self, V: int, W: int):
# 顶点数量
self.V = V
# 边数量
self.E = 0
# 邻接表
self.adj = []
# 宽度 (虽然是一维的但是表示是二维的)
self.W = W
for i in range(V):
nears = []
self.adj.append(nears)
def AddEdge(self, v: int, w: int):
self.adj[v].append(w)
self.E += 1
def __str__(self):
str = ""
for idx, nears in enumerate(self.adj):
str += "idx: {} nears: {}\n".format(idx, nears)
return str
# bfs
class BreadthFirstPaths:
def __init__(self, g: Graph, s: int):
self.marked = [False] * g.V
self.edgeTo = [0] * g.V
self.s = s
self.bfs(g, self.s)
def bfs(self, g: Graph, s: int):
q = queue.Queue()
q.put(s)
while q.qsize() != 0:
v = q.get()
for w in g.adj[v]:
# 这个路径没有经过
if not self.marked[w]:
self.edgeTo[w] = v
self.marked[w] = True
q.put(w)
# print(w, idxTohw(w, 6))
def HasPathTo(self, v: int):
return self.marked[v]
def PathTo(self, v: int) -> [int]:
result = []
if not self.HasPathTo(v):
return result
x = v
while x != self.s:
result.append(x)
x = self.edgeTo[x]
result.append(self.s)
return result
# 曼哈顿距离
def manhattanDistance(x, y):
return sum(map(lambda i, j: abs(i - j), x, y))
# 欧几里得距离
def dist_between(a, b):
return (b[0] - a[0]) ** 2 + (b[1] - a[1]) ** 2
# a* 4方位
class AStarPaths:
def __init__(self, g: Graph, start: int, end: int):
self.closedSet = []
self.openSet = [start]
self.start = start
self.end = end
self.edgeTo = [0] * g.V
self.marked = [False] * g.V
# 实际距离
self.gScore = [sys.maxsize] * g.V
self.gScore[start] = 0
# 估算到终点的距离
self.fScore = [sys.maxsize] * g.V
self.fScore[start] = manhattanDistance(idxTohw(start, g.W), idxTohw(end, g.W))
self.astar(g)
def astar(self, g: Graph):
while len(self.openSet) > 0:
current = min(self.openSet, key=lambda s: self.fScore[s])
if current == self.end:
return
self.openSet.remove(current)
self.closedSet.append(current)
for w in g.adj[current]:
if w in self.closedSet:
continue
# 实际距离
tentativegScore = self.gScore[current] + manhattanDistance(idxTohw(current, g.W),
idxTohw(w, g.W))
if tentativegScore < self.gScore[w]:
self.edgeTo[w] = current
self.marked[w] = True
print("edgeTo ({}) -> ({})".format(idxTohw(current, g.W), idxTohw(w, g.W)))
self.gScore[w] = tentativegScore
self.fScore[w] = self.gScore[w] + manhattanDistance(idxTohw(w, g.W), idxTohw(self.end, g.W))
print("fScore[%d] manhattan: %d" % (w, self.fScore[w]))
if w not in self.openSet:
self.openSet.append(w)
def HasPathTo(self, v: int):
return self.marked[v]
def PathTo(self, v: int):
result = []
if not self.HasPathTo(v):
return result
x = v
while x != self.start:
result.append(x)
x = self.edgeTo[x]
result.append(self.start)
return result
def main():
InitLog()
# 0,1,2,3,4 ... 一共12个顶点. width=6,height=4
graph = Graph(24, 6)
graph.AddEdge(hwToidx(2, 0, 6), hwToidx(2, 1, 6))
graph.AddEdge(hwToidx(1, 1, 6), hwToidx(1, 2, 6))
graph.AddEdge(hwToidx(2, 1, 6), hwToidx(2, 0, 6))
graph.AddEdge(hwToidx(2, 1, 6), hwToidx(3, 1, 6))
graph.AddEdge(hwToidx(3, 1, 6), hwToidx(2, 1, 6))
graph.AddEdge(hwToidx(3, 1, 6), hwToidx(4, 1, 6))
graph.AddEdge(hwToidx(4, 1, 6), hwToidx(4, 2, 6))
graph.AddEdge(hwToidx(4, 1, 6), hwToidx(3, 1, 6))
graph.AddEdge(hwToidx(0, 2, 6), hwToidx(1, 2, 6))
graph.AddEdge(hwToidx(1, 2, 6), hwToidx(1, 3, 6))
graph.AddEdge(hwToidx(1, 2, 6), hwToidx(0, 2, 6))
graph.AddEdge(hwToidx(1, 2, 6), hwToidx(1, 1, 6))
graph.AddEdge(hwToidx(1, 2, 6), hwToidx(2, 2, 6))
graph.AddEdge(hwToidx(2, 2, 6), hwToidx(2, 3, 6))
graph.AddEdge(hwToidx(2, 2, 6), hwToidx(1, 2, 6))
graph.AddEdge(hwToidx(2, 2, 6), hwToidx(3, 2, 6))
graph.AddEdge(hwToidx(3, 2, 6), hwToidx(3, 3, 6))
graph.AddEdge(hwToidx(3, 2, 6), hwToidx(2, 2, 6))
graph.AddEdge(hwToidx(3, 2, 6), hwToidx(4, 2, 6))
graph.AddEdge(hwToidx(4, 2, 6), hwToidx(4, 3, 6))
graph.AddEdge(hwToidx(4, 2, 6), hwToidx(3, 2, 6))
graph.AddEdge(hwToidx(4, 2, 6), hwToidx(4, 1, 6))
graph.AddEdge(hwToidx(4, 2, 6), hwToidx(5, 2, 6))
graph.AddEdge(hwToidx(5, 2, 6), hwToidx(5, 3, 6))
graph.AddEdge(hwToidx(5, 2, 6), hwToidx(4, 2, 6))
graph.AddEdge(hwToidx(1, 3, 6), hwToidx(1, 2, 6))
graph.AddEdge(hwToidx(2, 3, 6), hwToidx(2, 2, 6))
graph.AddEdge(hwToidx(2, 3, 6), hwToidx(3, 3, 6))
graph.AddEdge(hwToidx(3, 3, 6), hwToidx(2, 3, 6))
graph.AddEdge(hwToidx(3, 3, 6), hwToidx(3, 2, 6))
graph.AddEdge(hwToidx(4, 3, 6), hwToidx(5, 3, 6))
graph.AddEdge(hwToidx(4, 3, 6), hwToidx(4, 2, 6))
graph.AddEdge(hwToidx(5, 3, 6), hwToidx(4, 3, 6))
graph.AddEdge(hwToidx(5, 3, 6), hwToidx(5, 2, 6))
print("图:")
print(graph)
print("广度优先:")
bfs = BreadthFirstPaths(graph, 2)
paths = bfs.PathTo(hwToidx(1, 1, 6))
for v in reversed(paths):
print(v, idxTohw(v, 6))
# print(manhattanDistance([0, 2], [1, 2]))
print("\na*寻径")
astar = AStarPaths(graph, 2, 7)
paths = astar.PathTo(hwToidx(1, 1, 6))
for v in reversed(paths):
print(v, idxTohw(v, 6))
if __name__ == "__main__":
main()
| [
16,
18,
19,
23,
24
] |
273 | 7930bb813bd546747c7c65b661900939f5ba93f1 | <mask token>
| <mask token>
for n in range(len(user_input)):
explosion_strength = 0
if user_input[n] == '>':
explosion_strength += int(user_input[n + 1])
if user_input[n + explosion_strength] != '>':
exploded_str = user_input[:n] + user_input[n +
explosion_strength + 1:]
user_input = exploded_str
else:
explosion_strength += user_input[n + explosion_strength + 1]
| user_input = input()
exploded_str = user_input
for n in range(len(user_input)):
explosion_strength = 0
if user_input[n] == '>':
explosion_strength += int(user_input[n + 1])
if user_input[n + explosion_strength] != '>':
exploded_str = user_input[:n] + user_input[n +
explosion_strength + 1:]
user_input = exploded_str
else:
explosion_strength += user_input[n + explosion_strength + 1]
| user_input = input() #abv>1>1>2>2asdasd
exploded_str = user_input
for n in range(len(user_input)):
explosion_strength = 0
if user_input[n] == ">":
explosion_strength += int(user_input[n+1])
if user_input[n+explosion_strength] != ">":
exploded_str = user_input[:n] + user_input[n+explosion_strength+1:]
user_input = exploded_str
else:
explosion_strength += user_input[n+explosion_strength+1] | null | [
0,
1,
2,
3
] |
274 | c6cf085330f47ffb139c5acc91d91e9758f5396a | <mask token>
class MainPage(PageObject):
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def __init__(self, webdriver, root_uri=None):
super(MainPage, self).__init__(webdriver, root_uri)
self.open_level_menu()
self.close_level_menu()
<mask token>
<mask token>
<mask token>
<mask token>
def open_level(self, level_number):
self.open_level_menu()
self.get_level_link(level_number).click()
def css_write(self, css):
self.css_input = css
self.enter_button.click()
<mask token>
| <mask token>
class MainPage(PageObject):
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def __init__(self, webdriver, root_uri=None):
super(MainPage, self).__init__(webdriver, root_uri)
self.open_level_menu()
self.close_level_menu()
<mask token>
<mask token>
def close_level_menu(self):
if self.level_menu_opened:
self.level_menu.click()
self.level_menu_opened = False
def get_level_link(self, level_number):
return PageElement(xpath=
'//span[@class="level-number" and text() = "{0}"]/..'.format(
level_number))
def open_level(self, level_number):
self.open_level_menu()
self.get_level_link(level_number).click()
def css_write(self, css):
self.css_input = css
self.enter_button.click()
<mask token>
| <mask token>
class MainPage(PageObject):
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
<mask token>
def __init__(self, webdriver, root_uri=None):
super(MainPage, self).__init__(webdriver, root_uri)
self.open_level_menu()
self.close_level_menu()
<mask token>
<mask token>
def close_level_menu(self):
if self.level_menu_opened:
self.level_menu.click()
self.level_menu_opened = False
def get_level_link(self, level_number):
return PageElement(xpath=
'//span[@class="level-number" and text() = "{0}"]/..'.format(
level_number))
def open_level(self, level_number):
self.open_level_menu()
self.get_level_link(level_number).click()
def css_write(self, css):
self.css_input = css
self.enter_button.click()
def do_level1(self):
self.open_level(1)
self.css_write('page')
self.level1_link.click()
| from page_objects import PageObject, PageElement
class MainPage(PageObject):
level_menu_opened = False
level_menu_created = False
css_input = PageElement(css='input.input-strobe')
level_text_span = PageElement(css='span.level-text')
instruction_h2 = PageElement(css='h2.order')
enter_button = PageElement(css='div.enter-button')
level_menu = PageElement(xpath='//div[@class="level-menu-toggle-wrapper"]')
def __init__(self, webdriver, root_uri=None):
super(MainPage, self).__init__(webdriver, root_uri)
self.open_level_menu()
self.close_level_menu()
def ensure_menu_created(self):
if not self.level_menu_created:
self.open_level_menu()
self.close_level_menu()
def open_level_menu(self):
if not self.level_menu_opened:
self.level_menu.click()
self.level_menu_opened = True
self.level_menu_created = True
def close_level_menu(self):
if self.level_menu_opened:
self.level_menu.click()
self.level_menu_opened = False
def get_level_link(self, level_number):
return PageElement(xpath=
'//span[@class="level-number" and text() = "{0}"]/..'.format(
level_number))
def open_level(self, level_number):
self.open_level_menu()
self.get_level_link(level_number).click()
def css_write(self, css):
self.css_input = css
self.enter_button.click()
def do_level1(self):
self.open_level(1)
self.css_write('page')
self.level1_link.click()
| from page_objects import PageObject, PageElement
class MainPage(PageObject):
level_menu_opened = False
level_menu_created = False
css_input = PageElement(css='input.input-strobe')
level_text_span = PageElement(css='span.level-text')
instruction_h2 = PageElement(css='h2.order')
enter_button = PageElement(css='div.enter-button')
level_menu = PageElement(
xpath='//div[@class="level-menu-toggle-wrapper"]')
def __init__(self, webdriver, root_uri=None):
super(MainPage, self).__init__(webdriver, root_uri)
# hack to initialize all the menu items
self.open_level_menu()
self.close_level_menu()
def ensure_menu_created(self):
if not self.level_menu_created:
self.open_level_menu()
self.close_level_menu()
def open_level_menu(self):
if not self.level_menu_opened:
self.level_menu.click()
self.level_menu_opened = True
self.level_menu_created = True
def close_level_menu(self):
if self.level_menu_opened:
self.level_menu.click()
self.level_menu_opened = False
def get_level_link(self, level_number):
return PageElement(
xpath='//span[@class="level-number" and text() = "{0}"]/..'
.format(level_number)
)
def open_level(self, level_number):
self.open_level_menu()
self.get_level_link(level_number).click()
def css_write(self, css):
self.css_input = css
self.enter_button.click()
def do_level1(self):
self.open_level(1)
self.css_write("page")
self.level1_link.click()
| [
4,
6,
7,
11,
12
] |
275 | c2ddf31bce4a5f3ae2b0d5455bbc9942f92bff40 | <mask token>
| <mask token>
with open(MODEL_LABELS_FILENAME, 'rb') as f:
lb = pickle.load(f)
<mask token>
for root, dirs, files in os.walk(CAPTCHA_IMAGE_FOLDER):
for name in tqdm(files, desc='Solving captchas'):
kernel = 5, 5
image = cv2.imread(os.path.join(root, name))
image = cv2.cvtColor(image, cv2.COLOR_RGBA2GRAY)
image = cv2.copyMakeBorder(image, 8, 8, 8, 8, cv2.BORDER_CONSTANT,
None, 255)
k = np.ones((5, 5), np.float32) / 25
image = cv2.filter2D(image, -1, k)
ret, image = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY_INV)
clear_chunks(image, 0, 50)
image = cv2.erode(image, kernel, iterations=1)
contours = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.
CHAIN_APPROX_SIMPLE)[-2]
letter_image_regions = []
contours = sorted(contours, key=lambda x: cv2.contourArea(x),
reverse=True)
contours = contours[:5]
for contour in contours:
if cv2.contourArea(contour) < 60:
continue
x, y, w, h = cv2.boundingRect(contour)
if w / h > 1.5:
half_width = int(w / 2)
letter_image_regions.append((x, y, half_width, h))
letter_image_regions.append((x + half_width, y, half_width, h))
else:
letter_image_regions.append((x, y, w, h))
if len(letter_image_regions) != 5:
incorrectly_segmented += 1
continue
print(
f'Found {len(letter_image_regions)} letter regions instead of 5 , the guess will likely be incorrect'
)
letter_image_regions = sorted(letter_image_regions, key=lambda x: x[0])
chars = []
i = 0
for x, y, w, h in letter_image_regions:
letter = image[y - 2:y + h + 2, x - 2:x + w + 2]
chars.append(letter)
i += 1
predictions = []
for letter in chars:
letter = resize_to_fit(letter, 20, 20)
letter = np.expand_dims(letter, axis=2)
letter = np.expand_dims(letter, axis=0)
prediction = model.predict(letter)
letter_text = lb.inverse_transform(prediction)[0]
predictions.append(letter_text)
gc1, gc2, gc3, gc4, gc5 = predictions
c1, c2, c3, c4, c5, e1, e2, e3, e4 = name
correct_guesses = 0
if c1 == gc1:
c1_correct += 1
correct_guesses += 1
if c2 == gc2:
c2_correct += 1
correct_guesses += 1
if c3 == gc3:
c3_correct += 1
correct_guesses += 1
if c4 == gc4:
c4_correct += 1
correct_guesses += 1
if c5 == gc5:
c5_correct += 1
correct_guesses += 1
if ''.join(predictions) == ''.join([c1, c2, c3, c4, c5]):
total_correct += 1
n = correct_guesses_dict.get(correct_guesses, 0) + 1
correct_guesses_dict[correct_guesses] = n
print(f"Prediction for {name}: {''.join(predictions)}")
print(f'correct c1: {c1_correct}')
print(f'correct c2: {c2_correct}')
print(f'correct c3: {c3_correct}')
print(f'correct c4: {c4_correct}')
print(f'correct c5: {c5_correct}')
print(f'correct total: {total_correct}')
print(f'correctly segmented: {10000 - incorrectly_segmented}')
print(correct_guesses_dict)
| <mask token>
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
<mask token>
c1_correct = 0
c2_correct = 0
c3_correct = 0
c4_correct = 0
c5_correct = 0
total_correct = 0
incorrectly_segmented = 0
correct_guesses_dict = {}
MODEL_FILENAME = 'captcha_model.hdf5'
MODEL_LABELS_FILENAME = 'model_labels.dat'
CAPTCHA_IMAGE_FOLDER = 'test captchas'
with open(MODEL_LABELS_FILENAME, 'rb') as f:
lb = pickle.load(f)
model = load_model(MODEL_FILENAME)
for root, dirs, files in os.walk(CAPTCHA_IMAGE_FOLDER):
for name in tqdm(files, desc='Solving captchas'):
kernel = 5, 5
image = cv2.imread(os.path.join(root, name))
image = cv2.cvtColor(image, cv2.COLOR_RGBA2GRAY)
image = cv2.copyMakeBorder(image, 8, 8, 8, 8, cv2.BORDER_CONSTANT,
None, 255)
k = np.ones((5, 5), np.float32) / 25
image = cv2.filter2D(image, -1, k)
ret, image = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY_INV)
clear_chunks(image, 0, 50)
image = cv2.erode(image, kernel, iterations=1)
contours = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.
CHAIN_APPROX_SIMPLE)[-2]
letter_image_regions = []
contours = sorted(contours, key=lambda x: cv2.contourArea(x),
reverse=True)
contours = contours[:5]
for contour in contours:
if cv2.contourArea(contour) < 60:
continue
x, y, w, h = cv2.boundingRect(contour)
if w / h > 1.5:
half_width = int(w / 2)
letter_image_regions.append((x, y, half_width, h))
letter_image_regions.append((x + half_width, y, half_width, h))
else:
letter_image_regions.append((x, y, w, h))
if len(letter_image_regions) != 5:
incorrectly_segmented += 1
continue
print(
f'Found {len(letter_image_regions)} letter regions instead of 5 , the guess will likely be incorrect'
)
letter_image_regions = sorted(letter_image_regions, key=lambda x: x[0])
chars = []
i = 0
for x, y, w, h in letter_image_regions:
letter = image[y - 2:y + h + 2, x - 2:x + w + 2]
chars.append(letter)
i += 1
predictions = []
for letter in chars:
letter = resize_to_fit(letter, 20, 20)
letter = np.expand_dims(letter, axis=2)
letter = np.expand_dims(letter, axis=0)
prediction = model.predict(letter)
letter_text = lb.inverse_transform(prediction)[0]
predictions.append(letter_text)
gc1, gc2, gc3, gc4, gc5 = predictions
c1, c2, c3, c4, c5, e1, e2, e3, e4 = name
correct_guesses = 0
if c1 == gc1:
c1_correct += 1
correct_guesses += 1
if c2 == gc2:
c2_correct += 1
correct_guesses += 1
if c3 == gc3:
c3_correct += 1
correct_guesses += 1
if c4 == gc4:
c4_correct += 1
correct_guesses += 1
if c5 == gc5:
c5_correct += 1
correct_guesses += 1
if ''.join(predictions) == ''.join([c1, c2, c3, c4, c5]):
total_correct += 1
n = correct_guesses_dict.get(correct_guesses, 0) + 1
correct_guesses_dict[correct_guesses] = n
print(f"Prediction for {name}: {''.join(predictions)}")
print(f'correct c1: {c1_correct}')
print(f'correct c2: {c2_correct}')
print(f'correct c3: {c3_correct}')
print(f'correct c4: {c4_correct}')
print(f'correct c5: {c5_correct}')
print(f'correct total: {total_correct}')
print(f'correctly segmented: {10000 - incorrectly_segmented}')
print(correct_guesses_dict)
| import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from keras.models import load_model
from utils import resize_to_fit, clear_chunks, stack_windows
from imutils import paths
import numpy as np
import imutils
import cv2 as cv2
import pickle
from tqdm import tqdm
c1_correct = 0
c2_correct = 0
c3_correct = 0
c4_correct = 0
c5_correct = 0
total_correct = 0
incorrectly_segmented = 0
correct_guesses_dict = {}
MODEL_FILENAME = 'captcha_model.hdf5'
MODEL_LABELS_FILENAME = 'model_labels.dat'
CAPTCHA_IMAGE_FOLDER = 'test captchas'
with open(MODEL_LABELS_FILENAME, 'rb') as f:
lb = pickle.load(f)
model = load_model(MODEL_FILENAME)
for root, dirs, files in os.walk(CAPTCHA_IMAGE_FOLDER):
for name in tqdm(files, desc='Solving captchas'):
kernel = 5, 5
image = cv2.imread(os.path.join(root, name))
image = cv2.cvtColor(image, cv2.COLOR_RGBA2GRAY)
image = cv2.copyMakeBorder(image, 8, 8, 8, 8, cv2.BORDER_CONSTANT,
None, 255)
k = np.ones((5, 5), np.float32) / 25
image = cv2.filter2D(image, -1, k)
ret, image = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY_INV)
clear_chunks(image, 0, 50)
image = cv2.erode(image, kernel, iterations=1)
contours = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.
CHAIN_APPROX_SIMPLE)[-2]
letter_image_regions = []
contours = sorted(contours, key=lambda x: cv2.contourArea(x),
reverse=True)
contours = contours[:5]
for contour in contours:
if cv2.contourArea(contour) < 60:
continue
x, y, w, h = cv2.boundingRect(contour)
if w / h > 1.5:
half_width = int(w / 2)
letter_image_regions.append((x, y, half_width, h))
letter_image_regions.append((x + half_width, y, half_width, h))
else:
letter_image_regions.append((x, y, w, h))
if len(letter_image_regions) != 5:
incorrectly_segmented += 1
continue
print(
f'Found {len(letter_image_regions)} letter regions instead of 5 , the guess will likely be incorrect'
)
letter_image_regions = sorted(letter_image_regions, key=lambda x: x[0])
chars = []
i = 0
for x, y, w, h in letter_image_regions:
letter = image[y - 2:y + h + 2, x - 2:x + w + 2]
chars.append(letter)
i += 1
predictions = []
for letter in chars:
letter = resize_to_fit(letter, 20, 20)
letter = np.expand_dims(letter, axis=2)
letter = np.expand_dims(letter, axis=0)
prediction = model.predict(letter)
letter_text = lb.inverse_transform(prediction)[0]
predictions.append(letter_text)
gc1, gc2, gc3, gc4, gc5 = predictions
c1, c2, c3, c4, c5, e1, e2, e3, e4 = name
correct_guesses = 0
if c1 == gc1:
c1_correct += 1
correct_guesses += 1
if c2 == gc2:
c2_correct += 1
correct_guesses += 1
if c3 == gc3:
c3_correct += 1
correct_guesses += 1
if c4 == gc4:
c4_correct += 1
correct_guesses += 1
if c5 == gc5:
c5_correct += 1
correct_guesses += 1
if ''.join(predictions) == ''.join([c1, c2, c3, c4, c5]):
total_correct += 1
n = correct_guesses_dict.get(correct_guesses, 0) + 1
correct_guesses_dict[correct_guesses] = n
print(f"Prediction for {name}: {''.join(predictions)}")
print(f'correct c1: {c1_correct}')
print(f'correct c2: {c2_correct}')
print(f'correct c3: {c3_correct}')
print(f'correct c4: {c4_correct}')
print(f'correct c5: {c5_correct}')
print(f'correct total: {total_correct}')
print(f'correctly segmented: {10000 - incorrectly_segmented}')
print(correct_guesses_dict)
| import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from keras.models import load_model
from utils import resize_to_fit, clear_chunks, stack_windows
from imutils import paths
import numpy as np
import imutils
import cv2 as cv2
import pickle
from tqdm import tqdm
c1_correct = 0
c2_correct = 0
c3_correct = 0
c4_correct = 0
c5_correct = 0
total_correct = 0
incorrectly_segmented = 0
correct_guesses_dict = {}
MODEL_FILENAME = "captcha_model.hdf5"
MODEL_LABELS_FILENAME = "model_labels.dat"
CAPTCHA_IMAGE_FOLDER = "test captchas"
# Load up the model labels (so we can translate model predictions to actual letters)
with open(MODEL_LABELS_FILENAME, "rb") as f:
lb = pickle.load(f)
# Load the trained neural network
model = load_model(MODEL_FILENAME)
for root, dirs, files in os.walk(CAPTCHA_IMAGE_FOLDER):
for name in tqdm(files, desc='Solving captchas'):
kernel = (5,5)
#load image
image = cv2.imread(os.path.join(root, name))
image = cv2.cvtColor(image, cv2.COLOR_RGBA2GRAY)
#add padding
image = cv2.copyMakeBorder(image, 8, 8, 8, 8, cv2.BORDER_CONSTANT, None, 255)
#blur
k = np.ones((5,5),np.float32)/25
image = cv2.filter2D(image,-1,k)
# threshhold image
ret, image = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY_INV)
# clear white dots
clear_chunks(image,0,50)
# erosion
image = cv2.erode(image, kernel, iterations=1)
# get contours
contours = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]
#segment letters
letter_image_regions = [] #(x, y, w ,h)
contours = sorted(contours, key=lambda x: cv2.contourArea(x), reverse=True)
contours = contours[:5]
for contour in contours:
if cv2.contourArea(contour) < 60:
continue
(x, y, w, h) = cv2.boundingRect(contour)
if w / h > 1.5:
half_width = int(w / 2)
letter_image_regions.append((x, y, half_width, h))
letter_image_regions.append((x + half_width, y, half_width, h))
else:
letter_image_regions.append((x, y, w, h))
if len(letter_image_regions) != 5:
incorrectly_segmented += 1
continue
print(f"Found {len(letter_image_regions)} letter regions instead of 5 , the guess will likely be incorrect")
letter_image_regions = sorted(letter_image_regions, key=lambda x: x[0])
chars = []
i=0
for (x,y,w,h) in letter_image_regions:
letter = image[y-2:y+h+2, x-2:x+w+2]
chars.append(letter)
i+=1
predictions = []
for letter in chars:
# Re-size the letter image to 20x20 pixels to match training data
letter = resize_to_fit(letter, 20, 20)
# Turn the single image into a 4d list of images to make Keras happy
letter = np.expand_dims(letter, axis=2)
letter = np.expand_dims(letter, axis=0)
# Ask the neural network to make a prediction
prediction = model.predict(letter)
# Convert the one-hot-encoded prediction back to a normal letter
letter_text = lb.inverse_transform(prediction)[0]
predictions.append(letter_text)
gc1, gc2, gc3, gc4, gc5 = predictions
c1, c2, c3, c4, c5, e1, e2, e3, e4 = name
correct_guesses = 0
if c1 == gc1:
c1_correct += 1
correct_guesses += 1
if c2 == gc2:
c2_correct += 1
correct_guesses += 1
if c3 == gc3:
c3_correct += 1
correct_guesses += 1
if c4 == gc4:
c4_correct += 1
correct_guesses += 1
if c5 == gc5:
c5_correct += 1
correct_guesses += 1
if ''.join(predictions) == ''.join([c1,c2,c3,c4,c5]):
total_correct += 1
n = correct_guesses_dict.get(correct_guesses, 0) + 1
correct_guesses_dict[correct_guesses] = n
print(f"Prediction for {name}: {''.join(predictions)}")
print(f"correct c1: {c1_correct}")
print(f"correct c2: {c2_correct}")
print(f"correct c3: {c3_correct}")
print(f"correct c4: {c4_correct}")
print(f"correct c5: {c5_correct}")
print(f"correct total: {total_correct}")
print(f"correctly segmented: {10000 - incorrectly_segmented}")
print(correct_guesses_dict)
| [
0,
1,
2,
3,
4
] |
276 | 75e6554ea3c327c87a2a65710a7f1d55e9933bb0 | <mask token>
def train():
args = get_args()
os.makedirs(args.model_path, exist_ok=True)
set_seed(args.seed)
"""
To follow this training routine you need a DataLoader that yields the tuples of the following format:
(Bx3xHxW FloatTensor x, BxHxW LongTensor y, BxN LongTensor y_cls) where
x - batch of input images,
y - batch of groung truth seg maps,
y_cls - batch of 1D tensors of dimensionality N: N total number of classes,
y_cls[i, T] = 1 if class T is present in image i, 0 otherwise
"""
traindata = HeadSegData(args.seg_data_path, args.train_txt, train=True)
train_loader = DataLoader(traindata, batch_size=args.seg_batch_size,
shuffle=True, num_workers=1)
net, _ = build_network(None, args.seg_backend)
seg_criterion = nn.NLLLoss().cuda(0)
cls_criterion = nn.BCEWithLogitsLoss().cuda(0)
optimizer = optim.Adam(net.parameters(), lr=args.seg_lr)
print('start training...')
net.train()
total_loss = 0.0
for epoch in range(args.seg_epochs):
if (epoch + 1) % 5 == 0:
for group in optimizer.param_groups:
group['lr'] *= 0.25
total_loss = 0.0
for i, (x, y, y_cls) in enumerate(train_loader):
x, y, y_cls = x.cuda(0), y.cuda(0).long(), y_cls.cuda(0).float()
out, out_cls = net(x)
seg_loss = seg_criterion(out, y)
cls_loss = cls_criterion(out_cls, y_cls)
loss = seg_loss + args.seg_alpha * cls_loss
total_loss += loss.item()
if i % 50 == 0:
status = '[batch:{0}/{1} epoch:{2}] loss = {3:0.5f}'.format(i,
len(traindata) // args.seg_batch_size, epoch + 1, loss.
item())
print(status)
optimizer.zero_grad()
loss.backward()
optimizer.step()
torch.save(net.state_dict(), os.path.join(args.model_path,
f"{'seg'}_{args.seg_model}_{args.seg_backend}_{epoch}.pth"))
print(f'epoch:{epoch} total_loss: {total_loss / len(traindata)}')
<mask token>
| <mask token>
def build_network(snapshot, backend):
epoch = 0
backend = backend.lower()
net = models[backend]()
if snapshot is not None:
_, epoch = os.path.basename(snapshot).split('_')
epoch = int(epoch)
net.load_state_dict(torch.load(snapshot))
logging.info('Snapshot for epoch {} loaded from {}'.format(epoch,
snapshot))
net = net.cuda(0)
return net, epoch
def train():
args = get_args()
os.makedirs(args.model_path, exist_ok=True)
set_seed(args.seed)
"""
To follow this training routine you need a DataLoader that yields the tuples of the following format:
(Bx3xHxW FloatTensor x, BxHxW LongTensor y, BxN LongTensor y_cls) where
x - batch of input images,
y - batch of groung truth seg maps,
y_cls - batch of 1D tensors of dimensionality N: N total number of classes,
y_cls[i, T] = 1 if class T is present in image i, 0 otherwise
"""
traindata = HeadSegData(args.seg_data_path, args.train_txt, train=True)
train_loader = DataLoader(traindata, batch_size=args.seg_batch_size,
shuffle=True, num_workers=1)
net, _ = build_network(None, args.seg_backend)
seg_criterion = nn.NLLLoss().cuda(0)
cls_criterion = nn.BCEWithLogitsLoss().cuda(0)
optimizer = optim.Adam(net.parameters(), lr=args.seg_lr)
print('start training...')
net.train()
total_loss = 0.0
for epoch in range(args.seg_epochs):
if (epoch + 1) % 5 == 0:
for group in optimizer.param_groups:
group['lr'] *= 0.25
total_loss = 0.0
for i, (x, y, y_cls) in enumerate(train_loader):
x, y, y_cls = x.cuda(0), y.cuda(0).long(), y_cls.cuda(0).float()
out, out_cls = net(x)
seg_loss = seg_criterion(out, y)
cls_loss = cls_criterion(out_cls, y_cls)
loss = seg_loss + args.seg_alpha * cls_loss
total_loss += loss.item()
if i % 50 == 0:
status = '[batch:{0}/{1} epoch:{2}] loss = {3:0.5f}'.format(i,
len(traindata) // args.seg_batch_size, epoch + 1, loss.
item())
print(status)
optimizer.zero_grad()
loss.backward()
optimizer.step()
torch.save(net.state_dict(), os.path.join(args.model_path,
f"{'seg'}_{args.seg_model}_{args.seg_backend}_{epoch}.pth"))
print(f'epoch:{epoch} total_loss: {total_loss / len(traindata)}')
if __name__ == '__main__':
train()
| __author__ = 'BeiYu'
<mask token>
models = {'squeezenet': lambda : PSPNet(sizes=(1, 2, 3, 6), psp_size=512,
deep_features_size=256, backend='squeezenet', n_classes=3), 'densenet':
lambda : PSPNet(sizes=(1, 2, 3, 6), psp_size=1024, deep_features_size=
512, backend='densenet', n_classes=3), 'resnet18': lambda : PSPNet(
sizes=(1, 2, 3, 6), psp_size=512, deep_features_size=256, backend=
'resnet18', n_classes=3), 'resnet34': lambda : PSPNet(sizes=(1, 2, 3, 6
), psp_size=512, deep_features_size=256, backend='resnet34', n_classes=
3), 'resnet50': lambda : PSPNet(sizes=(1, 2, 3, 6), psp_size=2048,
deep_features_size=1024, backend='resnet50', n_classes=3), 'resnet101':
lambda : PSPNet(sizes=(1, 2, 3, 6), psp_size=2048, deep_features_size=
1024, backend='resnet101', n_classes=3), 'resnet152': lambda : PSPNet(
sizes=(1, 2, 3, 6), psp_size=2048, deep_features_size=1024, backend=
'resnet152', n_classes=3)}
def build_network(snapshot, backend):
epoch = 0
backend = backend.lower()
net = models[backend]()
if snapshot is not None:
_, epoch = os.path.basename(snapshot).split('_')
epoch = int(epoch)
net.load_state_dict(torch.load(snapshot))
logging.info('Snapshot for epoch {} loaded from {}'.format(epoch,
snapshot))
net = net.cuda(0)
return net, epoch
def train():
args = get_args()
os.makedirs(args.model_path, exist_ok=True)
set_seed(args.seed)
"""
To follow this training routine you need a DataLoader that yields the tuples of the following format:
(Bx3xHxW FloatTensor x, BxHxW LongTensor y, BxN LongTensor y_cls) where
x - batch of input images,
y - batch of groung truth seg maps,
y_cls - batch of 1D tensors of dimensionality N: N total number of classes,
y_cls[i, T] = 1 if class T is present in image i, 0 otherwise
"""
traindata = HeadSegData(args.seg_data_path, args.train_txt, train=True)
train_loader = DataLoader(traindata, batch_size=args.seg_batch_size,
shuffle=True, num_workers=1)
net, _ = build_network(None, args.seg_backend)
seg_criterion = nn.NLLLoss().cuda(0)
cls_criterion = nn.BCEWithLogitsLoss().cuda(0)
optimizer = optim.Adam(net.parameters(), lr=args.seg_lr)
print('start training...')
net.train()
total_loss = 0.0
for epoch in range(args.seg_epochs):
if (epoch + 1) % 5 == 0:
for group in optimizer.param_groups:
group['lr'] *= 0.25
total_loss = 0.0
for i, (x, y, y_cls) in enumerate(train_loader):
x, y, y_cls = x.cuda(0), y.cuda(0).long(), y_cls.cuda(0).float()
out, out_cls = net(x)
seg_loss = seg_criterion(out, y)
cls_loss = cls_criterion(out_cls, y_cls)
loss = seg_loss + args.seg_alpha * cls_loss
total_loss += loss.item()
if i % 50 == 0:
status = '[batch:{0}/{1} epoch:{2}] loss = {3:0.5f}'.format(i,
len(traindata) // args.seg_batch_size, epoch + 1, loss.
item())
print(status)
optimizer.zero_grad()
loss.backward()
optimizer.step()
torch.save(net.state_dict(), os.path.join(args.model_path,
f"{'seg'}_{args.seg_model}_{args.seg_backend}_{epoch}.pth"))
print(f'epoch:{epoch} total_loss: {total_loss / len(traindata)}')
if __name__ == '__main__':
train()
| __author__ = 'BeiYu'
from utils.init_env import set_seed
from utils.options import *
import os
import logging
import torch
from torch import nn
from torch import optim
from torch.optim.lr_scheduler import MultiStepLR
from torch.autograd import Variable
from torch.utils.data import DataLoader
from modules.seg_dataset import *
from tqdm import tqdm
import click
import torch.nn.functional as F
import numpy as np
from modules.seg import PSPNet
models = {'squeezenet': lambda : PSPNet(sizes=(1, 2, 3, 6), psp_size=512,
deep_features_size=256, backend='squeezenet', n_classes=3), 'densenet':
lambda : PSPNet(sizes=(1, 2, 3, 6), psp_size=1024, deep_features_size=
512, backend='densenet', n_classes=3), 'resnet18': lambda : PSPNet(
sizes=(1, 2, 3, 6), psp_size=512, deep_features_size=256, backend=
'resnet18', n_classes=3), 'resnet34': lambda : PSPNet(sizes=(1, 2, 3, 6
), psp_size=512, deep_features_size=256, backend='resnet34', n_classes=
3), 'resnet50': lambda : PSPNet(sizes=(1, 2, 3, 6), psp_size=2048,
deep_features_size=1024, backend='resnet50', n_classes=3), 'resnet101':
lambda : PSPNet(sizes=(1, 2, 3, 6), psp_size=2048, deep_features_size=
1024, backend='resnet101', n_classes=3), 'resnet152': lambda : PSPNet(
sizes=(1, 2, 3, 6), psp_size=2048, deep_features_size=1024, backend=
'resnet152', n_classes=3)}
def build_network(snapshot, backend):
epoch = 0
backend = backend.lower()
net = models[backend]()
if snapshot is not None:
_, epoch = os.path.basename(snapshot).split('_')
epoch = int(epoch)
net.load_state_dict(torch.load(snapshot))
logging.info('Snapshot for epoch {} loaded from {}'.format(epoch,
snapshot))
net = net.cuda(0)
return net, epoch
def train():
args = get_args()
os.makedirs(args.model_path, exist_ok=True)
set_seed(args.seed)
"""
To follow this training routine you need a DataLoader that yields the tuples of the following format:
(Bx3xHxW FloatTensor x, BxHxW LongTensor y, BxN LongTensor y_cls) where
x - batch of input images,
y - batch of groung truth seg maps,
y_cls - batch of 1D tensors of dimensionality N: N total number of classes,
y_cls[i, T] = 1 if class T is present in image i, 0 otherwise
"""
traindata = HeadSegData(args.seg_data_path, args.train_txt, train=True)
train_loader = DataLoader(traindata, batch_size=args.seg_batch_size,
shuffle=True, num_workers=1)
net, _ = build_network(None, args.seg_backend)
seg_criterion = nn.NLLLoss().cuda(0)
cls_criterion = nn.BCEWithLogitsLoss().cuda(0)
optimizer = optim.Adam(net.parameters(), lr=args.seg_lr)
print('start training...')
net.train()
total_loss = 0.0
for epoch in range(args.seg_epochs):
if (epoch + 1) % 5 == 0:
for group in optimizer.param_groups:
group['lr'] *= 0.25
total_loss = 0.0
for i, (x, y, y_cls) in enumerate(train_loader):
x, y, y_cls = x.cuda(0), y.cuda(0).long(), y_cls.cuda(0).float()
out, out_cls = net(x)
seg_loss = seg_criterion(out, y)
cls_loss = cls_criterion(out_cls, y_cls)
loss = seg_loss + args.seg_alpha * cls_loss
total_loss += loss.item()
if i % 50 == 0:
status = '[batch:{0}/{1} epoch:{2}] loss = {3:0.5f}'.format(i,
len(traindata) // args.seg_batch_size, epoch + 1, loss.
item())
print(status)
optimizer.zero_grad()
loss.backward()
optimizer.step()
torch.save(net.state_dict(), os.path.join(args.model_path,
f"{'seg'}_{args.seg_model}_{args.seg_backend}_{epoch}.pth"))
print(f'epoch:{epoch} total_loss: {total_loss / len(traindata)}')
if __name__ == '__main__':
train()
| # Author: BeiYu
# Github: https://github.com/beiyuouo
# Date : 2021/2/21 21:57
# Description:
__author__ = "BeiYu"
from utils.init_env import set_seed
from utils.options import *
import os
import logging
import torch
from torch import nn
from torch import optim
from torch.optim.lr_scheduler import MultiStepLR
from torch.autograd import Variable
from torch.utils.data import DataLoader
from modules.seg_dataset import *
from tqdm import tqdm
import click
import torch.nn.functional as F
import numpy as np
from modules.seg import PSPNet
models = {
'squeezenet': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=512, deep_features_size=256, backend='squeezenet', n_classes=3),
'densenet': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=1024, deep_features_size=512, backend='densenet', n_classes=3),
'resnet18': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=512, deep_features_size=256, backend='resnet18', n_classes=3),
'resnet34': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=512, deep_features_size=256, backend='resnet34', n_classes=3),
'resnet50': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=2048, deep_features_size=1024, backend='resnet50', n_classes=3),
'resnet101': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=2048, deep_features_size=1024, backend='resnet101', n_classes=3),
'resnet152': lambda: PSPNet(sizes=(1, 2, 3, 6), psp_size=2048, deep_features_size=1024, backend='resnet152', n_classes=3)
}
def build_network(snapshot, backend):
epoch = 0
backend = backend.lower()
net = models[backend]()
# net = nn.DataParallel(net)
if snapshot is not None:
_, epoch = os.path.basename(snapshot).split('_')
epoch = int(epoch)
net.load_state_dict(torch.load(snapshot))
logging.info("Snapshot for epoch {} loaded from {}".format(epoch, snapshot))
net = net.cuda(0)
return net, epoch
def train():
args = get_args()
# os.environ["CUDA_VISIBLE_DEVICES"] = gpu
# net, starting_epoch = build_network(snapshot, backend)
# data_path = os.path.abspath(os.path.expanduser(data_path))
# models_path = os.path.abspath(os.path.expanduser(models_path))
os.makedirs(args.model_path, exist_ok=True)
set_seed(args.seed)
'''
To follow this training routine you need a DataLoader that yields the tuples of the following format:
(Bx3xHxW FloatTensor x, BxHxW LongTensor y, BxN LongTensor y_cls) where
x - batch of input images,
y - batch of groung truth seg maps,
y_cls - batch of 1D tensors of dimensionality N: N total number of classes,
y_cls[i, T] = 1 if class T is present in image i, 0 otherwise
'''
traindata = HeadSegData(args.seg_data_path, args.train_txt, train=True)
train_loader = DataLoader(traindata, batch_size=args.seg_batch_size, shuffle=True, num_workers=1)
net, _ = build_network(None, args.seg_backend)
seg_criterion = nn.NLLLoss().cuda(0)
cls_criterion = nn.BCEWithLogitsLoss().cuda(0)
optimizer = optim.Adam(net.parameters(), lr=args.seg_lr)
# scheduler = MultiStepLR(optimizer, milestones=[int(x) for x in milestones.split(',')])
print("start training...")
net.train()
total_loss = 0.0
for epoch in range(args.seg_epochs):
if (epoch+1) % 5 == 0:
for group in optimizer.param_groups:
group['lr'] *= 0.25
total_loss = 0.0
for i, (x, y, y_cls) in enumerate(train_loader):
x, y, y_cls = x.cuda(0), y.cuda(0).long(), y_cls.cuda(0).float()
out, out_cls = net(x)
# print(x.shape, out.shape, out_cls.shape, y.shape, y_cls.shape)
seg_loss = seg_criterion(out, y)
cls_loss = cls_criterion(out_cls, y_cls)
loss = seg_loss + args.seg_alpha * cls_loss
total_loss += loss.item()
if i % 50 == 0:
status = '[batch:{0}/{1} epoch:{2}] loss = {3:0.5f}'.format(i, len(traindata) // args.seg_batch_size,
epoch + 1,
loss.item())
print(status)
optimizer.zero_grad()
loss.backward()
optimizer.step()
torch.save(net.state_dict(), os.path.join(args.model_path,
f'{"seg"}_{args.seg_model}_{args.seg_backend}_{epoch}.pth'))
print(f'epoch:{epoch} total_loss: {total_loss / len(traindata)}')
if __name__ == '__main__':
train()
| [
1,
3,
4,
5,
6
] |
277 | 58f3b8c5470c765c81f27d39d9c28751a8c2b719 | <mask token>
| <mask token>
print(f'Sua frase tem {n_a} letras a')
print(f'A letra A aparece pela primeira vez na {f_a}° posição')
print(f'A letra A apaerece pela ultima vez na {l_a}° posição')
| <mask token>
frase = str(input('Digite uma frase: ')).strip().lower()
n_a = frase.count('a')
f_a = frase.find('a') + 1
l_a = frase.rfind('a') - 1
print(f'Sua frase tem {n_a} letras a')
print(f'A letra A aparece pela primeira vez na {f_a}° posição')
print(f'A letra A apaerece pela ultima vez na {l_a}° posição')
| """Ex026 Faça um programa que leia uma frase pelo teclado e mostre:
Quantas vezes aparece a letra "A".
Em que posição ela aparece a primeira vez.
Em que posição ela aparece pela última vez."""
frase = str(input('Digite uma frase: ')).strip().lower()
n_a = frase.count('a')
f_a = frase.find('a')+1
l_a= frase.rfind('a')-1
print(f'Sua frase tem {n_a} letras a')
print(f'A letra A aparece pela primeira vez na {f_a}° posição')
print(f'A letra A apaerece pela ultima vez na {l_a}° posição')
| null | [
0,
1,
2,
3
] |
278 | d83f2d9bb25a46bc7344b420ce65bf729165e6b9 | <mask token>
| <mask token>
class FosAppConfig(AppConfig):
<mask token>
| <mask token>
class FosAppConfig(AppConfig):
name = 'fos_app'
| from django.apps import AppConfig
class FosAppConfig(AppConfig):
name = 'fos_app'
| from django.apps import AppConfig
class FosAppConfig(AppConfig):
name = 'fos_app'
| [
0,
1,
2,
3,
4
] |
279 | d2b05c5653ca6c6b7219f6c0393e81c9425b5977 | <mask token>
| <mask token>
print(bucket)
if bucket is None:
raise Exception('No Input Bucket set')
def handler(event: Dict, context: Dict):
"""AWS Lambda handler."""
granule = event.get('granule')
prefix = granule[0:-6]
print(prefix)
response = s3.list_objects_v2(Bucket=bucket, Prefix=prefix)
print(response)
granules = []
contents = response['Contents']
for obj in contents:
granules.append(obj['Key'][0:-4])
granule_str = ','.join(granules)
output = {'granule': granule_str}
return output
| <mask token>
s3 = boto3.client('s3')
bucket = os.getenv('SENTINEL_INPUT_BUCKET', None)
print(bucket)
if bucket is None:
raise Exception('No Input Bucket set')
def handler(event: Dict, context: Dict):
"""AWS Lambda handler."""
granule = event.get('granule')
prefix = granule[0:-6]
print(prefix)
response = s3.list_objects_v2(Bucket=bucket, Prefix=prefix)
print(response)
granules = []
contents = response['Contents']
for obj in contents:
granules.append(obj['Key'][0:-4])
granule_str = ','.join(granules)
output = {'granule': granule_str}
return output
| <mask token>
from typing import Dict
import os
import re
import boto3
from botocore.errorfactory import ClientError
from datetime import date
s3 = boto3.client('s3')
bucket = os.getenv('SENTINEL_INPUT_BUCKET', None)
print(bucket)
if bucket is None:
raise Exception('No Input Bucket set')
def handler(event: Dict, context: Dict):
"""AWS Lambda handler."""
granule = event.get('granule')
prefix = granule[0:-6]
print(prefix)
response = s3.list_objects_v2(Bucket=bucket, Prefix=prefix)
print(response)
granules = []
contents = response['Contents']
for obj in contents:
granules.append(obj['Key'][0:-4])
granule_str = ','.join(granules)
output = {'granule': granule_str}
return output
| """
HLS: Check if Twin Granule Exists
"""
from typing import Dict
import os
import re
import boto3
from botocore.errorfactory import ClientError
from datetime import date
s3 = boto3.client("s3")
bucket = os.getenv("SENTINEL_INPUT_BUCKET", None)
print(bucket)
if bucket is None:
raise Exception("No Input Bucket set")
def handler(event: Dict, context: Dict):
"""AWS Lambda handler."""
granule = event.get("granule")
prefix = granule[0:-6]
print(prefix)
response = s3.list_objects_v2(Bucket=bucket, Prefix=prefix,)
print(response)
granules = []
contents = response["Contents"]
for obj in contents:
granules.append(obj["Key"][0:-4])
granule_str = ",".join(granules)
output = {
"granule": granule_str,
}
return output
| [
0,
2,
3,
4,
5
] |
280 | c73a199d1c1c1867f3d53ceebf614bc9b65c0d5e | <mask token>
@admin.register(UserTicket)
class UserTicketAdmin(admin.ModelAdmin):
pass
| <mask token>
@admin.register(AuxiliaryTicket)
class AuxiliaryTicketAdmin(admin.ModelAdmin):
pass
@admin.register(UserTicket)
class UserTicketAdmin(admin.ModelAdmin):
pass
| <mask token>
@admin.register(Ticket)
class TicketAdmin(admin.ModelAdmin):
pass
@admin.register(AuxiliaryTicket)
class AuxiliaryTicketAdmin(admin.ModelAdmin):
pass
@admin.register(UserTicket)
class UserTicketAdmin(admin.ModelAdmin):
pass
| from django.contrib import admin
from ticket.models import Ticket, UserTicket, AuxiliaryTicket
@admin.register(Ticket)
class TicketAdmin(admin.ModelAdmin):
pass
@admin.register(AuxiliaryTicket)
class AuxiliaryTicketAdmin(admin.ModelAdmin):
pass
@admin.register(UserTicket)
class UserTicketAdmin(admin.ModelAdmin):
pass
| from django.contrib import admin
from ticket.models import Ticket, UserTicket, AuxiliaryTicket
@admin.register(Ticket)
class TicketAdmin(admin.ModelAdmin):
pass
@admin.register(AuxiliaryTicket)
class AuxiliaryTicketAdmin(admin.ModelAdmin):
pass
@admin.register(UserTicket)
class UserTicketAdmin(admin.ModelAdmin):
pass
| [
1,
2,
3,
4,
5
] |
281 | e564e0d05c3c0e60f356422722803df510d9dd0b | <mask token>
@njit(parallel=True)
def parallel_test(subject_array, typeII_error, typeI_error, num):
test_result = np.zeros(subject_array.shape, dtype=int)
random_table = np.random.uniform(0, 1, (subject_array.shape[0], num))
for i in range(len(subject_array)):
subject = subject_array[i, 1]
if subject == 1:
temp = 1 if max(random_table[i, :]) > typeII_error else 0
elif subject == 0:
temp = 1 if min(random_table[i, :]) < typeI_error else 0
test_result[i, 0] = subject_array[i, 0]
test_result[i, 1] = temp
return test_result, len(subject_array) * num, len(subject_array) * num
def infection_rate_on_negative_batch(p, batch_size, typeII_error, typeI_error):
"""
Given infection rate, batch size, prob of type II error and prob of type I error, this
function gives the infection rate on the negative batch.
Input:
p (float): the infection rate
batch_size (int): the batch size
typeII_error (float): the prob of type II error
typeI_error (float): the prob of type I error
Output:
(float): the infection rate on the negative batch
"""
q = 1 - p
r = typeII_error * (1 - q ** batch_size) / ((1 - typeI_error) * q **
batch_size + typeII_error * (1 - q ** batch_size))
return p * r / (1 - q ** batch_size)
def infection_rate_on_positive_batch(p, batch_size, typeII_error, typeI_error):
"""
Given infection rate, batch size, prob of type II error and prob of type I error, this
function gives the infection rate on the positive batch.
Input:
p (float): the infection rate
batch_size (int): the batch size
typeII_error (float): the prob of type II error
typeI_error (float): the prob of type I error
Output:
(float): the infection rate on the positive batch
"""
q = 1 - p
r = (1 - typeII_error) * (1 - q ** batch_size) / (typeI_error * q **
batch_size + (1 - typeII_error) * (1 - q ** batch_size))
return p * r / (1 - q ** batch_size)
def one_batch_test_solver(prevalence_rate, typeII_error, typeI_error,
n_initial_guess=2):
"""
A function gives (float) the best batch size for one batch test given the infection rate
Inputs:
prevalence_rate(float): infection rate
typeII_error(float): the prob of type II error
typeI_error(float): the prob of type I error
n_initial_guess(float): the initial guess
Output:
(float): the optimal batch size
"""
q = 1 - prevalence_rate
func = lambda n: n * q ** (n / 2) - (-(1 - typeII_error - typeI_error) *
np.log(q)) ** (-1 / 2)
n_solution = fsolve(func, n_initial_guess)
return float(n_solution)
<mask token>
def neg_pos_batch_split(subject_array, batch_size, typeII_error, typeI_error):
"""
A function gives a list of sujects on the negative batch(es),
a list of subjects on the postive batch(es) and the test-kit
consumption given the probability of type II error, the
probability of Type I error.
Input:
subject_array (Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
Output:
neg_batch (Numpy Array): an array of subjects on the negative batch(es)
pos_batch (Numpy Array): an array of subjects on the postive batch(es)
test_consum (int): the number of test-kit consumptions
"""
neg_batch = []
pos_batch = []
test_consum = np.ceil(len(subject_array) / batch_size)
random_table = np.random.uniform(0, 1, int(test_consum))
i = 0
for temp_batch in np.array_split(subject_array, test_consum):
if 1 in temp_batch[:, 1]:
if random_table[i] > typeII_error:
pos_batch.append(temp_batch)
else:
neg_batch.append(temp_batch)
elif random_table[i] > typeI_error:
neg_batch.append(temp_batch)
else:
pos_batch.append(temp_batch)
i += 1
neg_batch = np.concatenate(neg_batch) if len(neg_batch) > 0 else np.array([
])
pos_batch = np.concatenate(pos_batch) if len(pos_batch) > 0 else np.array([
])
return neg_batch, pos_batch, test_consum
def helpfunction(subject_array, p, batch_size, typeII_error, typeI_error,
batch_limit):
"""
The helpfunction is a handy function to give the list of subjects on the
negative batch(es), the list of subjects on the postive batch(es), the
test-kit consumption, the infection rate on the negative batches, the
infection rate on the positive batches, the optimal batch size for
negative batches and the optimal batch size for positive batches.
Input:
subject_array (Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
p (float): Infection rate
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
batch_limit (int): batch size upper limit
Output:
temp0 (Numpy Array): an array of subjects on the negative batch(es)
temp1 (Numpy Array): an array of subjects on the postive batch(es)
temp_con (int): the number of test-kit consumptions
p0 (float): the infection rate on the negative batches
p1 (float): the infection rate on the positive batches
n0 (float): the optimal batch size for the negative batches
n1 (float): the optimal batch size for the positive batches
"""
batch_size = min(batch_size, batch_limit)
p0 = infection_rate_on_negative_batch(p, batch_size, typeII_error,
typeI_error)
p1 = infection_rate_on_positive_batch(p, batch_size, typeII_error,
typeI_error)
n0 = one_batch_test_int_solver(p0, typeII_error, typeI_error, batch_limit)
n1 = one_batch_test_int_solver(p1, typeII_error, typeI_error, batch_limit)
if subject_array == np.array([]):
return np.array([]), np.array([]), p0, p1, n0, n1
temp0, temp1, temp_con = neg_pos_batch_split(subject_array, batch_size,
typeII_error, typeI_error)
return temp0, temp1, temp_con, p0, p1, n0, n1
def seq_test(subject_array, stop_rule, p, batch_size, typeII_error,
typeI_error, repeat=1, prob_threshold=1, seq=True, batch_limit=32):
"""
A function gives the test results to a subject array and the total number of
test-kit consumption and the individual testing number given the subject array,
the stop rule, the batch size, the probability of type II error, the probability of
Type I error, and the number of repeatition, the probability threshold, and
setting of sequence testing or not.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
stop_rule (int): the number of postive batches to enter individual testing
p (float): infection rate
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
repeat (int): the number of repetition
prob_threshold (float): if the infection rate of a batch is beyond prob_threshold,
the subjects on that batch will enter individual testing phase
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
batch_limit (int):
Output:
result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
individual_con (int): the test consumption for individual testings
"""
temp_list = []
neg_list = []
pos_list = []
consum = 0
temp = {'data': subject_array, 'NB_Num': 0, 'PB_Num': 0, 'p': p,
'batch_size': batch_size}
temp_list.append(temp)
new_list = []
neg_array = []
pos_array = []
while len(temp_list) > 0:
for i in temp_list:
temp0, temp1, temp_con, p0, p1, n0, n1 = helpfunction(i['data'],
i['p'], i['batch_size'], typeII_error, typeI_error,
batch_limit=batch_limit)
temp0 = {'data': temp0, 'NB_Num': i['NB_Num'] + 1, 'PB_Num': i[
'PB_Num'], 'p': p0, 'batch_size': n0}
temp1 = {'data': temp1, 'NB_Num': i['NB_Num'], 'PB_Num': i[
'PB_Num'] + 1, 'p': p1, 'batch_size': n1}
if len(temp0['data']) > 0:
if temp0['NB_Num'] >= stop_rule:
neg_list.append(temp0)
else:
new_list.append(temp0)
if len(temp1['data']) > 0:
if temp1['PB_Num'] >= stop_rule or temp1['p'
] >= prob_threshold:
pos_list.append(temp1)
else:
new_list.append(temp1)
consum += temp_con
temp_list = new_list
new_list = []
for j in neg_list:
neg_array.append(j['data'])
neg_array = np.concatenate(neg_array)
for k in pos_list:
pos_array.append(k['data'])
pos_array = np.concatenate(pos_array)
neg_array[:, 1] = 0
individual_test, individual_con = conventional_test(pos_array,
typeII_error, typeI_error, repeat, seq)
pos_array = individual_test
consum += individual_con
result = np.concatenate((pos_array, neg_array))
result = result[result[:, 0].argsort()]
result = result.astype('int64')
return result, consum, individual_con
<mask token>
@jit(parallel=True)
def data_gen(size, p):
"""
data_gen provides a faster way to generate a random population with
infection rate p.
Input:
size (int): the size of population
p (float): the infection rate
Output:
test_array (array): the first column is for id and the second column
is the condition, where 1 stands for infection and 0 stands for uninfection
"""
random_table = np.random.binomial(size=size, p=p, n=1)
test_array = np.zeros((size, 2), dtype=int)
for i in range(size):
test_array[i, 0] = i
test_array[i, 1] = random_table[i]
return test_array
<mask token>
def fixed_batch_seq_test(subject_array, stop_rule, p, batch_size,
typeII_error, typeI_error, repeat, prob_threshold=0.3, seq=True):
"""
This function provides the parallel batch testing results for a given subject array.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
stop_rule (int): the number of positive batches to enter the individual testing phase
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
repeat (int): the number of potential individual testing for the positive crossings
prob_threshold (float): if the infection rate of a batch is beyond prob_threshold,
the subjects on that batch will enter individual testing phase
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
Output:
result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
individual_con (int): the test consumption for individual testings
"""
temp_list = []
neg_list = []
pos_list = []
consum = 0
temp = {'data': subject_array, 'NB_Num': 0, 'PB_Num': 0, 'p': p,
'batch_size': batch_size}
temp_list.append(temp)
new_list = []
neg_array = []
pos_array = []
while len(temp_list) > 0:
for i in temp_list:
temp0, temp1, temp_con, p0, p1, n0, n1 = helpfunction(i['data'],
i['p'], i['batch_size'], typeII_error, typeI_error)
temp0 = {'data': np.random.permutation(temp0), 'NB_Num': i[
'NB_Num'] + 1, 'PB_Num': i['PB_Num'], 'p': p0, 'batch_size':
batch_size}
temp1 = {'data': np.random.permutation(temp1), 'NB_Num': i[
'NB_Num'], 'PB_Num': i['PB_Num'] + 1, 'p': p1, 'batch_size':
batch_size}
if len(temp0['data']) > 0:
if temp0['NB_Num'] >= stop_rule:
neg_list.append(temp0)
else:
new_list.append(temp0)
if len(temp1['data']) > 0:
if temp1['PB_Num'] >= stop_rule or temp1['p'
] >= prob_threshold:
pos_list.append(temp1)
else:
new_list.append(temp1)
consum += temp_con
temp_list = new_list
new_list = []
for j in neg_list:
neg_array.append(j['data'])
neg_array = np.concatenate(neg_array)
for k in pos_list:
pos_array.append(k['data'])
pos_array = np.concatenate(pos_array)
neg_array[:, 1] = 0
individual_test, individual_con = conventional_test(pos_array,
typeII_error, typeI_error, repeat, seq)
pos_array = individual_test
consum += individual_con
result = np.concatenate((pos_array, neg_array))
result = result[result[:, 0].argsort()]
result = result.astype('int64')
return result, consum, individual_con
def name_fun(n):
"""
input: stopping rule
output: finish nodes
"""
output = []
temp = ['']
for i in range(2 * n - 1):
temp_cur = []
for j in temp:
candidate_pos = j + '+'
candidate_neg = j + '-'
if str.count(candidate_pos, '+') >= n:
output.append(candidate_pos)
else:
temp_cur.append(candidate_pos)
if str.count(candidate_neg, '-') >= n:
output.append(candidate_neg)
else:
temp_cur.append(candidate_neg)
temp = temp_cur
neg_symbol = [x for x in output if str.count(x, '-') == n]
pos_symbol = [x for x in output if str.count(x, '+') == n]
return output, neg_symbol, pos_symbol
def seq_test_with_node(subject_array, stop_rule, p, batch_size,
typeII_error, typeI_error, repeat=1, prob_threshold=1, seq=True,
batch_limit=32):
"""
A function gives the test results to a subject array and the total number of
test-kit consumption and the individual testing number given the subject array,
the stop rule, the batch size, the probability of type II error, the probability of
Type I error, and the number of repeatition, the probability threshold, and
setting of sequence testing or not.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
stop_rule (int): the number of postive batches to enter individual testing
p (float): infection rate
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
repeat (int): the number of repetition
prob_threshold (float): if the infection rate of a batch is beyond prob_threshold,
the subjects on that batch will enter individual testing phase
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
batch_limit (int):
Output:
result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
individual_con (int): the test consumption for individual testings
"""
temp_list = []
neg_list = []
pos_list = []
batch_num_list = []
consum = 0
temp = {'data': subject_array, 'NB_Num': 0, 'PB_Num': 0, 'p': p,
'batch_size': batch_size, 'node': ''}
temp_list.append(temp)
new_list = []
neg_array = []
neg_node = []
pos_node = []
pos_array = []
while len(temp_list) > 0:
for i in temp_list:
temp0, temp1, temp_con, p0, p1, n0, n1 = helpfunction(i['data'],
i['p'], i['batch_size'], typeII_error, typeI_error,
batch_limit=batch_limit)
temp0 = {'data': temp0, 'NB_Num': i['NB_Num'] + 1, 'PB_Num': i[
'PB_Num'], 'p': p0, 'batch_size': n0, 'node': i['node'] + '-'}
temp1 = {'data': temp1, 'NB_Num': i['NB_Num'], 'PB_Num': i[
'PB_Num'] + 1, 'p': p1, 'batch_size': n1, 'node': i['node'] +
'+'}
if len(temp0['data']) > 0:
if temp0['NB_Num'] >= stop_rule:
neg_list.append(temp0)
else:
new_list.append(temp0)
if len(temp1['data']) > 0:
if temp1['PB_Num'] >= stop_rule or temp1['p'
] >= prob_threshold:
pos_list.append(temp1)
else:
new_list.append(temp1)
consum += temp_con
batch_num_list.append(consum)
temp_list = new_list
new_list = []
for j in neg_list:
neg_array.append(j['data'])
temp = [[x, j['node']] for x in j['data'][:, 0]]
neg_node.append(temp)
neg_array = np.concatenate(neg_array)
for k in pos_list:
pos_array.append(k['data'])
temp = [[x, k['node']] for x in k['data'][:, 0]]
pos_node.append(temp)
pos_array = np.concatenate(pos_array)
neg_array[:, 1] = 0
individual_test, individual_con = conventional_test(pos_array,
typeII_error, typeI_error, repeat, seq)
pos_array = individual_test
consum += individual_con
result = np.concatenate((pos_array, neg_array))
pos_node.extend(neg_node)
node = pos_node
node = sum(node, [])
node.sort()
node = [x[1] for x in node]
result = result[result[:, 0].argsort()]
result = result.astype('int64')
return result, consum, individual_con, node, batch_num_list
| <mask token>
@njit(parallel=True)
def parallel_test(subject_array, typeII_error, typeI_error, num):
test_result = np.zeros(subject_array.shape, dtype=int)
random_table = np.random.uniform(0, 1, (subject_array.shape[0], num))
for i in range(len(subject_array)):
subject = subject_array[i, 1]
if subject == 1:
temp = 1 if max(random_table[i, :]) > typeII_error else 0
elif subject == 0:
temp = 1 if min(random_table[i, :]) < typeI_error else 0
test_result[i, 0] = subject_array[i, 0]
test_result[i, 1] = temp
return test_result, len(subject_array) * num, len(subject_array) * num
def infection_rate_on_negative_batch(p, batch_size, typeII_error, typeI_error):
"""
Given infection rate, batch size, prob of type II error and prob of type I error, this
function gives the infection rate on the negative batch.
Input:
p (float): the infection rate
batch_size (int): the batch size
typeII_error (float): the prob of type II error
typeI_error (float): the prob of type I error
Output:
(float): the infection rate on the negative batch
"""
q = 1 - p
r = typeII_error * (1 - q ** batch_size) / ((1 - typeI_error) * q **
batch_size + typeII_error * (1 - q ** batch_size))
return p * r / (1 - q ** batch_size)
def infection_rate_on_positive_batch(p, batch_size, typeII_error, typeI_error):
"""
Given infection rate, batch size, prob of type II error and prob of type I error, this
function gives the infection rate on the positive batch.
Input:
p (float): the infection rate
batch_size (int): the batch size
typeII_error (float): the prob of type II error
typeI_error (float): the prob of type I error
Output:
(float): the infection rate on the positive batch
"""
q = 1 - p
r = (1 - typeII_error) * (1 - q ** batch_size) / (typeI_error * q **
batch_size + (1 - typeII_error) * (1 - q ** batch_size))
return p * r / (1 - q ** batch_size)
def one_batch_test_solver(prevalence_rate, typeII_error, typeI_error,
n_initial_guess=2):
"""
A function gives (float) the best batch size for one batch test given the infection rate
Inputs:
prevalence_rate(float): infection rate
typeII_error(float): the prob of type II error
typeI_error(float): the prob of type I error
n_initial_guess(float): the initial guess
Output:
(float): the optimal batch size
"""
q = 1 - prevalence_rate
func = lambda n: n * q ** (n / 2) - (-(1 - typeII_error - typeI_error) *
np.log(q)) ** (-1 / 2)
n_solution = fsolve(func, n_initial_guess)
return float(n_solution)
<mask token>
def neg_pos_batch_split(subject_array, batch_size, typeII_error, typeI_error):
"""
A function gives a list of sujects on the negative batch(es),
a list of subjects on the postive batch(es) and the test-kit
consumption given the probability of type II error, the
probability of Type I error.
Input:
subject_array (Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
Output:
neg_batch (Numpy Array): an array of subjects on the negative batch(es)
pos_batch (Numpy Array): an array of subjects on the postive batch(es)
test_consum (int): the number of test-kit consumptions
"""
neg_batch = []
pos_batch = []
test_consum = np.ceil(len(subject_array) / batch_size)
random_table = np.random.uniform(0, 1, int(test_consum))
i = 0
for temp_batch in np.array_split(subject_array, test_consum):
if 1 in temp_batch[:, 1]:
if random_table[i] > typeII_error:
pos_batch.append(temp_batch)
else:
neg_batch.append(temp_batch)
elif random_table[i] > typeI_error:
neg_batch.append(temp_batch)
else:
pos_batch.append(temp_batch)
i += 1
neg_batch = np.concatenate(neg_batch) if len(neg_batch) > 0 else np.array([
])
pos_batch = np.concatenate(pos_batch) if len(pos_batch) > 0 else np.array([
])
return neg_batch, pos_batch, test_consum
def helpfunction(subject_array, p, batch_size, typeII_error, typeI_error,
batch_limit):
"""
The helpfunction is a handy function to give the list of subjects on the
negative batch(es), the list of subjects on the postive batch(es), the
test-kit consumption, the infection rate on the negative batches, the
infection rate on the positive batches, the optimal batch size for
negative batches and the optimal batch size for positive batches.
Input:
subject_array (Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
p (float): Infection rate
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
batch_limit (int): batch size upper limit
Output:
temp0 (Numpy Array): an array of subjects on the negative batch(es)
temp1 (Numpy Array): an array of subjects on the postive batch(es)
temp_con (int): the number of test-kit consumptions
p0 (float): the infection rate on the negative batches
p1 (float): the infection rate on the positive batches
n0 (float): the optimal batch size for the negative batches
n1 (float): the optimal batch size for the positive batches
"""
batch_size = min(batch_size, batch_limit)
p0 = infection_rate_on_negative_batch(p, batch_size, typeII_error,
typeI_error)
p1 = infection_rate_on_positive_batch(p, batch_size, typeII_error,
typeI_error)
n0 = one_batch_test_int_solver(p0, typeII_error, typeI_error, batch_limit)
n1 = one_batch_test_int_solver(p1, typeII_error, typeI_error, batch_limit)
if subject_array == np.array([]):
return np.array([]), np.array([]), p0, p1, n0, n1
temp0, temp1, temp_con = neg_pos_batch_split(subject_array, batch_size,
typeII_error, typeI_error)
return temp0, temp1, temp_con, p0, p1, n0, n1
def seq_test(subject_array, stop_rule, p, batch_size, typeII_error,
typeI_error, repeat=1, prob_threshold=1, seq=True, batch_limit=32):
"""
A function gives the test results to a subject array and the total number of
test-kit consumption and the individual testing number given the subject array,
the stop rule, the batch size, the probability of type II error, the probability of
Type I error, and the number of repeatition, the probability threshold, and
setting of sequence testing or not.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
stop_rule (int): the number of postive batches to enter individual testing
p (float): infection rate
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
repeat (int): the number of repetition
prob_threshold (float): if the infection rate of a batch is beyond prob_threshold,
the subjects on that batch will enter individual testing phase
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
batch_limit (int):
Output:
result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
individual_con (int): the test consumption for individual testings
"""
temp_list = []
neg_list = []
pos_list = []
consum = 0
temp = {'data': subject_array, 'NB_Num': 0, 'PB_Num': 0, 'p': p,
'batch_size': batch_size}
temp_list.append(temp)
new_list = []
neg_array = []
pos_array = []
while len(temp_list) > 0:
for i in temp_list:
temp0, temp1, temp_con, p0, p1, n0, n1 = helpfunction(i['data'],
i['p'], i['batch_size'], typeII_error, typeI_error,
batch_limit=batch_limit)
temp0 = {'data': temp0, 'NB_Num': i['NB_Num'] + 1, 'PB_Num': i[
'PB_Num'], 'p': p0, 'batch_size': n0}
temp1 = {'data': temp1, 'NB_Num': i['NB_Num'], 'PB_Num': i[
'PB_Num'] + 1, 'p': p1, 'batch_size': n1}
if len(temp0['data']) > 0:
if temp0['NB_Num'] >= stop_rule:
neg_list.append(temp0)
else:
new_list.append(temp0)
if len(temp1['data']) > 0:
if temp1['PB_Num'] >= stop_rule or temp1['p'
] >= prob_threshold:
pos_list.append(temp1)
else:
new_list.append(temp1)
consum += temp_con
temp_list = new_list
new_list = []
for j in neg_list:
neg_array.append(j['data'])
neg_array = np.concatenate(neg_array)
for k in pos_list:
pos_array.append(k['data'])
pos_array = np.concatenate(pos_array)
neg_array[:, 1] = 0
individual_test, individual_con = conventional_test(pos_array,
typeII_error, typeI_error, repeat, seq)
pos_array = individual_test
consum += individual_con
result = np.concatenate((pos_array, neg_array))
result = result[result[:, 0].argsort()]
result = result.astype('int64')
return result, consum, individual_con
<mask token>
def specificity_score(y_true, y_pred):
"""
A function provides specificty given the prediction and the truth
"""
tn, fp, _, _ = confusion_matrix(y_true=y_true, y_pred=y_pred).ravel()
return tn / (tn + fp)
@jit(parallel=True)
def data_gen(size, p):
"""
data_gen provides a faster way to generate a random population with
infection rate p.
Input:
size (int): the size of population
p (float): the infection rate
Output:
test_array (array): the first column is for id and the second column
is the condition, where 1 stands for infection and 0 stands for uninfection
"""
random_table = np.random.binomial(size=size, p=p, n=1)
test_array = np.zeros((size, 2), dtype=int)
for i in range(size):
test_array[i, 0] = i
test_array[i, 1] = random_table[i]
return test_array
<mask token>
def fixed_batch_seq_test(subject_array, stop_rule, p, batch_size,
typeII_error, typeI_error, repeat, prob_threshold=0.3, seq=True):
"""
This function provides the parallel batch testing results for a given subject array.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
stop_rule (int): the number of positive batches to enter the individual testing phase
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
repeat (int): the number of potential individual testing for the positive crossings
prob_threshold (float): if the infection rate of a batch is beyond prob_threshold,
the subjects on that batch will enter individual testing phase
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
Output:
result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
individual_con (int): the test consumption for individual testings
"""
temp_list = []
neg_list = []
pos_list = []
consum = 0
temp = {'data': subject_array, 'NB_Num': 0, 'PB_Num': 0, 'p': p,
'batch_size': batch_size}
temp_list.append(temp)
new_list = []
neg_array = []
pos_array = []
while len(temp_list) > 0:
for i in temp_list:
temp0, temp1, temp_con, p0, p1, n0, n1 = helpfunction(i['data'],
i['p'], i['batch_size'], typeII_error, typeI_error)
temp0 = {'data': np.random.permutation(temp0), 'NB_Num': i[
'NB_Num'] + 1, 'PB_Num': i['PB_Num'], 'p': p0, 'batch_size':
batch_size}
temp1 = {'data': np.random.permutation(temp1), 'NB_Num': i[
'NB_Num'], 'PB_Num': i['PB_Num'] + 1, 'p': p1, 'batch_size':
batch_size}
if len(temp0['data']) > 0:
if temp0['NB_Num'] >= stop_rule:
neg_list.append(temp0)
else:
new_list.append(temp0)
if len(temp1['data']) > 0:
if temp1['PB_Num'] >= stop_rule or temp1['p'
] >= prob_threshold:
pos_list.append(temp1)
else:
new_list.append(temp1)
consum += temp_con
temp_list = new_list
new_list = []
for j in neg_list:
neg_array.append(j['data'])
neg_array = np.concatenate(neg_array)
for k in pos_list:
pos_array.append(k['data'])
pos_array = np.concatenate(pos_array)
neg_array[:, 1] = 0
individual_test, individual_con = conventional_test(pos_array,
typeII_error, typeI_error, repeat, seq)
pos_array = individual_test
consum += individual_con
result = np.concatenate((pos_array, neg_array))
result = result[result[:, 0].argsort()]
result = result.astype('int64')
return result, consum, individual_con
def name_fun(n):
"""
input: stopping rule
output: finish nodes
"""
output = []
temp = ['']
for i in range(2 * n - 1):
temp_cur = []
for j in temp:
candidate_pos = j + '+'
candidate_neg = j + '-'
if str.count(candidate_pos, '+') >= n:
output.append(candidate_pos)
else:
temp_cur.append(candidate_pos)
if str.count(candidate_neg, '-') >= n:
output.append(candidate_neg)
else:
temp_cur.append(candidate_neg)
temp = temp_cur
neg_symbol = [x for x in output if str.count(x, '-') == n]
pos_symbol = [x for x in output if str.count(x, '+') == n]
return output, neg_symbol, pos_symbol
def seq_test_with_node(subject_array, stop_rule, p, batch_size,
typeII_error, typeI_error, repeat=1, prob_threshold=1, seq=True,
batch_limit=32):
"""
A function gives the test results to a subject array and the total number of
test-kit consumption and the individual testing number given the subject array,
the stop rule, the batch size, the probability of type II error, the probability of
Type I error, and the number of repeatition, the probability threshold, and
setting of sequence testing or not.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
stop_rule (int): the number of postive batches to enter individual testing
p (float): infection rate
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
repeat (int): the number of repetition
prob_threshold (float): if the infection rate of a batch is beyond prob_threshold,
the subjects on that batch will enter individual testing phase
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
batch_limit (int):
Output:
result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
individual_con (int): the test consumption for individual testings
"""
temp_list = []
neg_list = []
pos_list = []
batch_num_list = []
consum = 0
temp = {'data': subject_array, 'NB_Num': 0, 'PB_Num': 0, 'p': p,
'batch_size': batch_size, 'node': ''}
temp_list.append(temp)
new_list = []
neg_array = []
neg_node = []
pos_node = []
pos_array = []
while len(temp_list) > 0:
for i in temp_list:
temp0, temp1, temp_con, p0, p1, n0, n1 = helpfunction(i['data'],
i['p'], i['batch_size'], typeII_error, typeI_error,
batch_limit=batch_limit)
temp0 = {'data': temp0, 'NB_Num': i['NB_Num'] + 1, 'PB_Num': i[
'PB_Num'], 'p': p0, 'batch_size': n0, 'node': i['node'] + '-'}
temp1 = {'data': temp1, 'NB_Num': i['NB_Num'], 'PB_Num': i[
'PB_Num'] + 1, 'p': p1, 'batch_size': n1, 'node': i['node'] +
'+'}
if len(temp0['data']) > 0:
if temp0['NB_Num'] >= stop_rule:
neg_list.append(temp0)
else:
new_list.append(temp0)
if len(temp1['data']) > 0:
if temp1['PB_Num'] >= stop_rule or temp1['p'
] >= prob_threshold:
pos_list.append(temp1)
else:
new_list.append(temp1)
consum += temp_con
batch_num_list.append(consum)
temp_list = new_list
new_list = []
for j in neg_list:
neg_array.append(j['data'])
temp = [[x, j['node']] for x in j['data'][:, 0]]
neg_node.append(temp)
neg_array = np.concatenate(neg_array)
for k in pos_list:
pos_array.append(k['data'])
temp = [[x, k['node']] for x in k['data'][:, 0]]
pos_node.append(temp)
pos_array = np.concatenate(pos_array)
neg_array[:, 1] = 0
individual_test, individual_con = conventional_test(pos_array,
typeII_error, typeI_error, repeat, seq)
pos_array = individual_test
consum += individual_con
result = np.concatenate((pos_array, neg_array))
pos_node.extend(neg_node)
node = pos_node
node = sum(node, [])
node.sort()
node = [x[1] for x in node]
result = result[result[:, 0].argsort()]
result = result.astype('int64')
return result, consum, individual_con, node, batch_num_list
| <mask token>
@jit(parallel=True)
def conventional_test(subject_array, typeII_error, typeI_error, repeat=1,
seq=True):
"""
A function gives the test results to a subject array given the probability of
type II error, the probability of Type I error, and the number of repeatition,
and setting of sequence testing or not.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
repeat (int): the number of repetition
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
Output:
test_result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
"""
if seq == True:
consum = 0
test_result = np.zeros(subject_array.shape, dtype=int)
random_table = np.random.uniform(0, 1, (subject_array.shape[0], repeat)
)
for i in range(len(subject_array)):
temp = 0
j = 0
subject = subject_array[i, 1]
while j < repeat and temp == 0:
random_num = random_table[i, j]
consum += 1
if subject == 1:
temp = 1 if random_num > typeII_error else 0
else:
temp = 1 if random_num < typeI_error else 0
j += 1
test_result[i, 0] = subject_array[i, 0]
test_result[i, 1] = temp
return test_result, consum
else:
test_result = np.zeros(subject_array.shape, dtype=int)
random_table = np.random.uniform(0, 1, (subject_array.shape[0], repeat)
)
for i in range(len(subject_array)):
temp = 0
for j in range(repeat):
temp_random = random_table[i, j]
if subject_array[i, 1] == 1:
temp_1 = 1 if temp_random > typeII_error else 0
elif subject_array[i, 1] == 0:
temp_1 = 1 if temp_random < typeI_error else 0
temp += temp_1
temp = 1 if temp >= repeat / 2 else 0
test_result[i, 0] = subject_array[i, 0]
test_result[i, 1] = temp
return test_result, len(subject_array) * repeat
@njit(parallel=True)
def parallel_test(subject_array, typeII_error, typeI_error, num):
test_result = np.zeros(subject_array.shape, dtype=int)
random_table = np.random.uniform(0, 1, (subject_array.shape[0], num))
for i in range(len(subject_array)):
subject = subject_array[i, 1]
if subject == 1:
temp = 1 if max(random_table[i, :]) > typeII_error else 0
elif subject == 0:
temp = 1 if min(random_table[i, :]) < typeI_error else 0
test_result[i, 0] = subject_array[i, 0]
test_result[i, 1] = temp
return test_result, len(subject_array) * num, len(subject_array) * num
def infection_rate_on_negative_batch(p, batch_size, typeII_error, typeI_error):
"""
Given infection rate, batch size, prob of type II error and prob of type I error, this
function gives the infection rate on the negative batch.
Input:
p (float): the infection rate
batch_size (int): the batch size
typeII_error (float): the prob of type II error
typeI_error (float): the prob of type I error
Output:
(float): the infection rate on the negative batch
"""
q = 1 - p
r = typeII_error * (1 - q ** batch_size) / ((1 - typeI_error) * q **
batch_size + typeII_error * (1 - q ** batch_size))
return p * r / (1 - q ** batch_size)
def infection_rate_on_positive_batch(p, batch_size, typeII_error, typeI_error):
"""
Given infection rate, batch size, prob of type II error and prob of type I error, this
function gives the infection rate on the positive batch.
Input:
p (float): the infection rate
batch_size (int): the batch size
typeII_error (float): the prob of type II error
typeI_error (float): the prob of type I error
Output:
(float): the infection rate on the positive batch
"""
q = 1 - p
r = (1 - typeII_error) * (1 - q ** batch_size) / (typeI_error * q **
batch_size + (1 - typeII_error) * (1 - q ** batch_size))
return p * r / (1 - q ** batch_size)
def one_batch_test_solver(prevalence_rate, typeII_error, typeI_error,
n_initial_guess=2):
"""
A function gives (float) the best batch size for one batch test given the infection rate
Inputs:
prevalence_rate(float): infection rate
typeII_error(float): the prob of type II error
typeI_error(float): the prob of type I error
n_initial_guess(float): the initial guess
Output:
(float): the optimal batch size
"""
q = 1 - prevalence_rate
func = lambda n: n * q ** (n / 2) - (-(1 - typeII_error - typeI_error) *
np.log(q)) ** (-1 / 2)
n_solution = fsolve(func, n_initial_guess)
return float(n_solution)
def one_batch_test_int_solver(prevalence_rate, typeII_error, typeI_error,
batch_limit, n_initial_guess=2):
"""
A function gives (int) the best batch size for one batch test given the infection rate
Inputs:
prevalence_rate(float): infection rate
n_initial_guess(float): the initial guess
typeII_error(float): the prob of type II error
typeI_error(float): the prob of type I error
n_initial_guess:
batch_limit (int): the upper limit of batch size
Output:
(int): the optimal batch size
"""
sol_float = one_batch_test_solver(prevalence_rate, typeII_error,
typeI_error, n_initial_guess)
floor, ceil = np.floor(sol_float), np.ceil(sol_float)
func = lambda batch_size: 1 / batch_size + 1 - typeII_error - (1 -
typeII_error - typeI_error) * (1 - prevalence_rate) ** batch_size
if func(floor) < func(ceil):
temp = int(floor)
else:
temp = int(ceil)
if temp <= batch_limit:
return temp
else:
return int(batch_limit)
def neg_pos_batch_split(subject_array, batch_size, typeII_error, typeI_error):
"""
A function gives a list of sujects on the negative batch(es),
a list of subjects on the postive batch(es) and the test-kit
consumption given the probability of type II error, the
probability of Type I error.
Input:
subject_array (Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
Output:
neg_batch (Numpy Array): an array of subjects on the negative batch(es)
pos_batch (Numpy Array): an array of subjects on the postive batch(es)
test_consum (int): the number of test-kit consumptions
"""
neg_batch = []
pos_batch = []
test_consum = np.ceil(len(subject_array) / batch_size)
random_table = np.random.uniform(0, 1, int(test_consum))
i = 0
for temp_batch in np.array_split(subject_array, test_consum):
if 1 in temp_batch[:, 1]:
if random_table[i] > typeII_error:
pos_batch.append(temp_batch)
else:
neg_batch.append(temp_batch)
elif random_table[i] > typeI_error:
neg_batch.append(temp_batch)
else:
pos_batch.append(temp_batch)
i += 1
neg_batch = np.concatenate(neg_batch) if len(neg_batch) > 0 else np.array([
])
pos_batch = np.concatenate(pos_batch) if len(pos_batch) > 0 else np.array([
])
return neg_batch, pos_batch, test_consum
def helpfunction(subject_array, p, batch_size, typeII_error, typeI_error,
batch_limit):
"""
The helpfunction is a handy function to give the list of subjects on the
negative batch(es), the list of subjects on the postive batch(es), the
test-kit consumption, the infection rate on the negative batches, the
infection rate on the positive batches, the optimal batch size for
negative batches and the optimal batch size for positive batches.
Input:
subject_array (Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
p (float): Infection rate
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
batch_limit (int): batch size upper limit
Output:
temp0 (Numpy Array): an array of subjects on the negative batch(es)
temp1 (Numpy Array): an array of subjects on the postive batch(es)
temp_con (int): the number of test-kit consumptions
p0 (float): the infection rate on the negative batches
p1 (float): the infection rate on the positive batches
n0 (float): the optimal batch size for the negative batches
n1 (float): the optimal batch size for the positive batches
"""
batch_size = min(batch_size, batch_limit)
p0 = infection_rate_on_negative_batch(p, batch_size, typeII_error,
typeI_error)
p1 = infection_rate_on_positive_batch(p, batch_size, typeII_error,
typeI_error)
n0 = one_batch_test_int_solver(p0, typeII_error, typeI_error, batch_limit)
n1 = one_batch_test_int_solver(p1, typeII_error, typeI_error, batch_limit)
if subject_array == np.array([]):
return np.array([]), np.array([]), p0, p1, n0, n1
temp0, temp1, temp_con = neg_pos_batch_split(subject_array, batch_size,
typeII_error, typeI_error)
return temp0, temp1, temp_con, p0, p1, n0, n1
def seq_test(subject_array, stop_rule, p, batch_size, typeII_error,
typeI_error, repeat=1, prob_threshold=1, seq=True, batch_limit=32):
"""
A function gives the test results to a subject array and the total number of
test-kit consumption and the individual testing number given the subject array,
the stop rule, the batch size, the probability of type II error, the probability of
Type I error, and the number of repeatition, the probability threshold, and
setting of sequence testing or not.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
stop_rule (int): the number of postive batches to enter individual testing
p (float): infection rate
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
repeat (int): the number of repetition
prob_threshold (float): if the infection rate of a batch is beyond prob_threshold,
the subjects on that batch will enter individual testing phase
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
batch_limit (int):
Output:
result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
individual_con (int): the test consumption for individual testings
"""
temp_list = []
neg_list = []
pos_list = []
consum = 0
temp = {'data': subject_array, 'NB_Num': 0, 'PB_Num': 0, 'p': p,
'batch_size': batch_size}
temp_list.append(temp)
new_list = []
neg_array = []
pos_array = []
while len(temp_list) > 0:
for i in temp_list:
temp0, temp1, temp_con, p0, p1, n0, n1 = helpfunction(i['data'],
i['p'], i['batch_size'], typeII_error, typeI_error,
batch_limit=batch_limit)
temp0 = {'data': temp0, 'NB_Num': i['NB_Num'] + 1, 'PB_Num': i[
'PB_Num'], 'p': p0, 'batch_size': n0}
temp1 = {'data': temp1, 'NB_Num': i['NB_Num'], 'PB_Num': i[
'PB_Num'] + 1, 'p': p1, 'batch_size': n1}
if len(temp0['data']) > 0:
if temp0['NB_Num'] >= stop_rule:
neg_list.append(temp0)
else:
new_list.append(temp0)
if len(temp1['data']) > 0:
if temp1['PB_Num'] >= stop_rule or temp1['p'
] >= prob_threshold:
pos_list.append(temp1)
else:
new_list.append(temp1)
consum += temp_con
temp_list = new_list
new_list = []
for j in neg_list:
neg_array.append(j['data'])
neg_array = np.concatenate(neg_array)
for k in pos_list:
pos_array.append(k['data'])
pos_array = np.concatenate(pos_array)
neg_array[:, 1] = 0
individual_test, individual_con = conventional_test(pos_array,
typeII_error, typeI_error, repeat, seq)
pos_array = individual_test
consum += individual_con
result = np.concatenate((pos_array, neg_array))
result = result[result[:, 0].argsort()]
result = result.astype('int64')
return result, consum, individual_con
<mask token>
def specificity_score(y_true, y_pred):
"""
A function provides specificty given the prediction and the truth
"""
tn, fp, _, _ = confusion_matrix(y_true=y_true, y_pred=y_pred).ravel()
return tn / (tn + fp)
@jit(parallel=True)
def data_gen(size, p):
"""
data_gen provides a faster way to generate a random population with
infection rate p.
Input:
size (int): the size of population
p (float): the infection rate
Output:
test_array (array): the first column is for id and the second column
is the condition, where 1 stands for infection and 0 stands for uninfection
"""
random_table = np.random.binomial(size=size, p=p, n=1)
test_array = np.zeros((size, 2), dtype=int)
for i in range(size):
test_array[i, 0] = i
test_array[i, 1] = random_table[i]
return test_array
def test_result(data, seq_test, **kwargs):
"""
a helper function provides convenient results for a given test method with its **kwargs
Input:
data (array or list of arrays)
seq_test (test_method object): could be seq_test, matrix_test and other test_method objects
Output:
result (DataFrame): a dataframe contains important evaluation metrics for the test method
"""
if isinstance(data, list) == False:
pred, consum, ind_con = seq_test(data, **kwargs)
result = {'acc': np.mean(pred[:, 1] == data[:, 1]), 'sens':
recall_score(data[:, 1], pred[:, 1]), 'spec': specificity_score
(data[:, 1], pred[:, 1]), 'PPV': precision_score(data[:, 1],
pred[:, 1]), 'NPV': npv_score(data[:, 1], pred[:, 1]),
'test_consum': consum, 'ind_consum': ind_con, 'batch_consum':
consum - ind_con}
return result
else:
length = len(data)
acc = np.zeros(length)
sens = np.zeros(length)
spec = np.zeros(length)
ppv = np.zeros(length)
npv = np.zeros(length)
test_consum = np.zeros(length)
ind_consum = np.zeros(length)
batch_consum = np.zeros(length)
for i in range(length):
pred, consum, ind_con = seq_test(data[i], **kwargs)
acc[i] = np.mean(pred[:, 1] == data[i][:, 1])
sens[i] = recall_score(data[i][:, 1], pred[:, 1])
spec[i] = specificity_score(data[i][:, 1], pred[:, 1])
ppv[i] = precision_score(data[i][:, 1], pred[:, 1])
npv[i] = npv_score(data[i][:, 1], pred[:, 1])
test_consum[i] = consum
ind_consum[i] = ind_con
batch_consum[i] = consum - ind_con
result = {'acc': acc, 'sens': sens, 'spec': spec, 'PPV': ppv, 'NPV':
npv, 'test_consum': test_consum, 'ind_consum': ind_consum,
'batch_consum': batch_consum}
return pd.DataFrame(result)
def matrix_test(subject_array, side_length, typeII_error, typeI_error,
sq_repeat=1, ind_repeat=1, seq=True):
"""
This function provides the matrix testing results for a given subject array.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
side_length (int): the side length of the matrix testing
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
sq_repeat (int): the number of parallel testing for the column/row batch testing
ind_repeat (int): the number of potential individual testing for the positive crossings
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
Output:
result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
individual_con (int): the test consumption for individual testings
"""
matrix_test_num = len(subject_array) // side_length ** 2
matrix_test_array = subject_array[0:matrix_test_num * side_length ** 2, :]
ind_test_array = subject_array[matrix_test_num * side_length ** 2:, :]
ind_idx = []
for temp_batch in np.array_split(matrix_test_array, matrix_test_num):
temp_batch = temp_batch.reshape(side_length, side_length, 2)
temp_row = []
temp_col = []
random_num_row = np.random.uniform(0, 1, sq_repeat)
random_num_col = np.random.uniform(0, 1, sq_repeat)
for i in range(side_length):
if 1 in temp_batch[i, :, 1]:
if max(random_num_row) > typeII_error:
temp_row.append(temp_batch[i, :, 0])
elif min(random_num_row) < typeI_error:
temp_row.append(temp_batch[i, :, 0])
if 1 in temp_batch[:, i, 1]:
if max(random_num_col) > typeII_error:
temp_col.append(temp_batch[:, i, 0])
elif min(random_num_col) < typeI_error:
temp_col.append(temp_batch[:, i, 0])
ind_idx.append(np.intersect1d(temp_row, temp_col))
ind_idx = np.concatenate(ind_idx)
ind_idx = ind_idx.astype('int')
if len(ind_idx) == 0:
neg_array = matrix_test_array
else:
mask = np.zeros(subject_array.shape[0], dtype=bool)
mask[ind_idx] = True
mask[matrix_test_num * side_length ** 2:] = True
ind_test_array = subject_array[mask, :]
neg_array = subject_array[~mask, :]
neg_array[:, 1] = 0
ind_test, ind_con = conventional_test(ind_test_array, typeII_error,
typeI_error, repeat=ind_repeat, seq=seq)
batch_test_num = matrix_test_num * 2 * side_length * sq_repeat
result = np.concatenate((neg_array, ind_test))
result = result[result[:, 0].argsort()]
return result, batch_test_num + ind_con, ind_con
def parallel_batch_testing(subject_array, batch_size, typeII_error,
typeI_error, parallel_num, ind_repeat, seq):
"""
This function provides the parallel batch testing results for a given subject array.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
parallel_num (int): the number of parallel testing for the batch testing
ind_repeat (int): the number of potential individual testing for the positive batches
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
Output:
result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
individual_con (int): the test consumption for individual testings
"""
neg_batch = []
pos_batch = []
batch_consum = np.ceil(len(subject_array) / batch_size) * parallel_num
for temp_batch in np.array_split(subject_array, np.ceil(len(
subject_array) / batch_size)):
random_table = np.random.uniform(0, 1, (1, parallel_num))
if 1 in temp_batch[:, 1]:
if random_table.max() > typeII_error:
pos_batch.append(temp_batch)
else:
neg_batch.append(temp_batch)
elif random_table.min() < typeI_error:
pos_batch.append(temp_batch)
else:
neg_batch.append(temp_batch)
neg_batch = np.concatenate(neg_batch) if len(neg_batch) > 0 else np.array([
])
pos_batch = np.concatenate(pos_batch) if len(pos_batch) > 0 else np.array([
])
neg_batch[:, 1] = 0
individual_test, individual_con = conventional_test(pos_batch,
typeII_error, typeI_error, repeat=ind_repeat, seq=seq)
result = np.concatenate((individual_test, neg_batch))
result = result[result[:, 0].argsort()]
result = result.astype('int64')
return result, batch_consum + individual_con, individual_con
def fixed_batch_seq_test(subject_array, stop_rule, p, batch_size,
typeII_error, typeI_error, repeat, prob_threshold=0.3, seq=True):
"""
This function provides the parallel batch testing results for a given subject array.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
stop_rule (int): the number of positive batches to enter the individual testing phase
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
repeat (int): the number of potential individual testing for the positive crossings
prob_threshold (float): if the infection rate of a batch is beyond prob_threshold,
the subjects on that batch will enter individual testing phase
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
Output:
result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
individual_con (int): the test consumption for individual testings
"""
temp_list = []
neg_list = []
pos_list = []
consum = 0
temp = {'data': subject_array, 'NB_Num': 0, 'PB_Num': 0, 'p': p,
'batch_size': batch_size}
temp_list.append(temp)
new_list = []
neg_array = []
pos_array = []
while len(temp_list) > 0:
for i in temp_list:
temp0, temp1, temp_con, p0, p1, n0, n1 = helpfunction(i['data'],
i['p'], i['batch_size'], typeII_error, typeI_error)
temp0 = {'data': np.random.permutation(temp0), 'NB_Num': i[
'NB_Num'] + 1, 'PB_Num': i['PB_Num'], 'p': p0, 'batch_size':
batch_size}
temp1 = {'data': np.random.permutation(temp1), 'NB_Num': i[
'NB_Num'], 'PB_Num': i['PB_Num'] + 1, 'p': p1, 'batch_size':
batch_size}
if len(temp0['data']) > 0:
if temp0['NB_Num'] >= stop_rule:
neg_list.append(temp0)
else:
new_list.append(temp0)
if len(temp1['data']) > 0:
if temp1['PB_Num'] >= stop_rule or temp1['p'
] >= prob_threshold:
pos_list.append(temp1)
else:
new_list.append(temp1)
consum += temp_con
temp_list = new_list
new_list = []
for j in neg_list:
neg_array.append(j['data'])
neg_array = np.concatenate(neg_array)
for k in pos_list:
pos_array.append(k['data'])
pos_array = np.concatenate(pos_array)
neg_array[:, 1] = 0
individual_test, individual_con = conventional_test(pos_array,
typeII_error, typeI_error, repeat, seq)
pos_array = individual_test
consum += individual_con
result = np.concatenate((pos_array, neg_array))
result = result[result[:, 0].argsort()]
result = result.astype('int64')
return result, consum, individual_con
def name_fun(n):
"""
input: stopping rule
output: finish nodes
"""
output = []
temp = ['']
for i in range(2 * n - 1):
temp_cur = []
for j in temp:
candidate_pos = j + '+'
candidate_neg = j + '-'
if str.count(candidate_pos, '+') >= n:
output.append(candidate_pos)
else:
temp_cur.append(candidate_pos)
if str.count(candidate_neg, '-') >= n:
output.append(candidate_neg)
else:
temp_cur.append(candidate_neg)
temp = temp_cur
neg_symbol = [x for x in output if str.count(x, '-') == n]
pos_symbol = [x for x in output if str.count(x, '+') == n]
return output, neg_symbol, pos_symbol
def seq_test_with_node(subject_array, stop_rule, p, batch_size,
typeII_error, typeI_error, repeat=1, prob_threshold=1, seq=True,
batch_limit=32):
"""
A function gives the test results to a subject array and the total number of
test-kit consumption and the individual testing number given the subject array,
the stop rule, the batch size, the probability of type II error, the probability of
Type I error, and the number of repeatition, the probability threshold, and
setting of sequence testing or not.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
stop_rule (int): the number of postive batches to enter individual testing
p (float): infection rate
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
repeat (int): the number of repetition
prob_threshold (float): if the infection rate of a batch is beyond prob_threshold,
the subjects on that batch will enter individual testing phase
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
batch_limit (int):
Output:
result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
individual_con (int): the test consumption for individual testings
"""
temp_list = []
neg_list = []
pos_list = []
batch_num_list = []
consum = 0
temp = {'data': subject_array, 'NB_Num': 0, 'PB_Num': 0, 'p': p,
'batch_size': batch_size, 'node': ''}
temp_list.append(temp)
new_list = []
neg_array = []
neg_node = []
pos_node = []
pos_array = []
while len(temp_list) > 0:
for i in temp_list:
temp0, temp1, temp_con, p0, p1, n0, n1 = helpfunction(i['data'],
i['p'], i['batch_size'], typeII_error, typeI_error,
batch_limit=batch_limit)
temp0 = {'data': temp0, 'NB_Num': i['NB_Num'] + 1, 'PB_Num': i[
'PB_Num'], 'p': p0, 'batch_size': n0, 'node': i['node'] + '-'}
temp1 = {'data': temp1, 'NB_Num': i['NB_Num'], 'PB_Num': i[
'PB_Num'] + 1, 'p': p1, 'batch_size': n1, 'node': i['node'] +
'+'}
if len(temp0['data']) > 0:
if temp0['NB_Num'] >= stop_rule:
neg_list.append(temp0)
else:
new_list.append(temp0)
if len(temp1['data']) > 0:
if temp1['PB_Num'] >= stop_rule or temp1['p'
] >= prob_threshold:
pos_list.append(temp1)
else:
new_list.append(temp1)
consum += temp_con
batch_num_list.append(consum)
temp_list = new_list
new_list = []
for j in neg_list:
neg_array.append(j['data'])
temp = [[x, j['node']] for x in j['data'][:, 0]]
neg_node.append(temp)
neg_array = np.concatenate(neg_array)
for k in pos_list:
pos_array.append(k['data'])
temp = [[x, k['node']] for x in k['data'][:, 0]]
pos_node.append(temp)
pos_array = np.concatenate(pos_array)
neg_array[:, 1] = 0
individual_test, individual_con = conventional_test(pos_array,
typeII_error, typeI_error, repeat, seq)
pos_array = individual_test
consum += individual_con
result = np.concatenate((pos_array, neg_array))
pos_node.extend(neg_node)
node = pos_node
node = sum(node, [])
node.sort()
node = [x[1] for x in node]
result = result[result[:, 0].argsort()]
result = result.astype('int64')
return result, consum, individual_con, node, batch_num_list
| <mask token>
@jit(parallel=True)
def conventional_test(subject_array, typeII_error, typeI_error, repeat=1,
seq=True):
"""
A function gives the test results to a subject array given the probability of
type II error, the probability of Type I error, and the number of repeatition,
and setting of sequence testing or not.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
repeat (int): the number of repetition
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
Output:
test_result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
"""
if seq == True:
consum = 0
test_result = np.zeros(subject_array.shape, dtype=int)
random_table = np.random.uniform(0, 1, (subject_array.shape[0], repeat)
)
for i in range(len(subject_array)):
temp = 0
j = 0
subject = subject_array[i, 1]
while j < repeat and temp == 0:
random_num = random_table[i, j]
consum += 1
if subject == 1:
temp = 1 if random_num > typeII_error else 0
else:
temp = 1 if random_num < typeI_error else 0
j += 1
test_result[i, 0] = subject_array[i, 0]
test_result[i, 1] = temp
return test_result, consum
else:
test_result = np.zeros(subject_array.shape, dtype=int)
random_table = np.random.uniform(0, 1, (subject_array.shape[0], repeat)
)
for i in range(len(subject_array)):
temp = 0
for j in range(repeat):
temp_random = random_table[i, j]
if subject_array[i, 1] == 1:
temp_1 = 1 if temp_random > typeII_error else 0
elif subject_array[i, 1] == 0:
temp_1 = 1 if temp_random < typeI_error else 0
temp += temp_1
temp = 1 if temp >= repeat / 2 else 0
test_result[i, 0] = subject_array[i, 0]
test_result[i, 1] = temp
return test_result, len(subject_array) * repeat
@njit(parallel=True)
def parallel_test(subject_array, typeII_error, typeI_error, num):
test_result = np.zeros(subject_array.shape, dtype=int)
random_table = np.random.uniform(0, 1, (subject_array.shape[0], num))
for i in range(len(subject_array)):
subject = subject_array[i, 1]
if subject == 1:
temp = 1 if max(random_table[i, :]) > typeII_error else 0
elif subject == 0:
temp = 1 if min(random_table[i, :]) < typeI_error else 0
test_result[i, 0] = subject_array[i, 0]
test_result[i, 1] = temp
return test_result, len(subject_array) * num, len(subject_array) * num
def infection_rate_on_negative_batch(p, batch_size, typeII_error, typeI_error):
"""
Given infection rate, batch size, prob of type II error and prob of type I error, this
function gives the infection rate on the negative batch.
Input:
p (float): the infection rate
batch_size (int): the batch size
typeII_error (float): the prob of type II error
typeI_error (float): the prob of type I error
Output:
(float): the infection rate on the negative batch
"""
q = 1 - p
r = typeII_error * (1 - q ** batch_size) / ((1 - typeI_error) * q **
batch_size + typeII_error * (1 - q ** batch_size))
return p * r / (1 - q ** batch_size)
def infection_rate_on_positive_batch(p, batch_size, typeII_error, typeI_error):
"""
Given infection rate, batch size, prob of type II error and prob of type I error, this
function gives the infection rate on the positive batch.
Input:
p (float): the infection rate
batch_size (int): the batch size
typeII_error (float): the prob of type II error
typeI_error (float): the prob of type I error
Output:
(float): the infection rate on the positive batch
"""
q = 1 - p
r = (1 - typeII_error) * (1 - q ** batch_size) / (typeI_error * q **
batch_size + (1 - typeII_error) * (1 - q ** batch_size))
return p * r / (1 - q ** batch_size)
def one_batch_test_solver(prevalence_rate, typeII_error, typeI_error,
n_initial_guess=2):
"""
A function gives (float) the best batch size for one batch test given the infection rate
Inputs:
prevalence_rate(float): infection rate
typeII_error(float): the prob of type II error
typeI_error(float): the prob of type I error
n_initial_guess(float): the initial guess
Output:
(float): the optimal batch size
"""
q = 1 - prevalence_rate
func = lambda n: n * q ** (n / 2) - (-(1 - typeII_error - typeI_error) *
np.log(q)) ** (-1 / 2)
n_solution = fsolve(func, n_initial_guess)
return float(n_solution)
def one_batch_test_int_solver(prevalence_rate, typeII_error, typeI_error,
batch_limit, n_initial_guess=2):
"""
A function gives (int) the best batch size for one batch test given the infection rate
Inputs:
prevalence_rate(float): infection rate
n_initial_guess(float): the initial guess
typeII_error(float): the prob of type II error
typeI_error(float): the prob of type I error
n_initial_guess:
batch_limit (int): the upper limit of batch size
Output:
(int): the optimal batch size
"""
sol_float = one_batch_test_solver(prevalence_rate, typeII_error,
typeI_error, n_initial_guess)
floor, ceil = np.floor(sol_float), np.ceil(sol_float)
func = lambda batch_size: 1 / batch_size + 1 - typeII_error - (1 -
typeII_error - typeI_error) * (1 - prevalence_rate) ** batch_size
if func(floor) < func(ceil):
temp = int(floor)
else:
temp = int(ceil)
if temp <= batch_limit:
return temp
else:
return int(batch_limit)
def neg_pos_batch_split(subject_array, batch_size, typeII_error, typeI_error):
"""
A function gives a list of sujects on the negative batch(es),
a list of subjects on the postive batch(es) and the test-kit
consumption given the probability of type II error, the
probability of Type I error.
Input:
subject_array (Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
Output:
neg_batch (Numpy Array): an array of subjects on the negative batch(es)
pos_batch (Numpy Array): an array of subjects on the postive batch(es)
test_consum (int): the number of test-kit consumptions
"""
neg_batch = []
pos_batch = []
test_consum = np.ceil(len(subject_array) / batch_size)
random_table = np.random.uniform(0, 1, int(test_consum))
i = 0
for temp_batch in np.array_split(subject_array, test_consum):
if 1 in temp_batch[:, 1]:
if random_table[i] > typeII_error:
pos_batch.append(temp_batch)
else:
neg_batch.append(temp_batch)
elif random_table[i] > typeI_error:
neg_batch.append(temp_batch)
else:
pos_batch.append(temp_batch)
i += 1
neg_batch = np.concatenate(neg_batch) if len(neg_batch) > 0 else np.array([
])
pos_batch = np.concatenate(pos_batch) if len(pos_batch) > 0 else np.array([
])
return neg_batch, pos_batch, test_consum
def helpfunction(subject_array, p, batch_size, typeII_error, typeI_error,
batch_limit):
"""
The helpfunction is a handy function to give the list of subjects on the
negative batch(es), the list of subjects on the postive batch(es), the
test-kit consumption, the infection rate on the negative batches, the
infection rate on the positive batches, the optimal batch size for
negative batches and the optimal batch size for positive batches.
Input:
subject_array (Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
p (float): Infection rate
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
batch_limit (int): batch size upper limit
Output:
temp0 (Numpy Array): an array of subjects on the negative batch(es)
temp1 (Numpy Array): an array of subjects on the postive batch(es)
temp_con (int): the number of test-kit consumptions
p0 (float): the infection rate on the negative batches
p1 (float): the infection rate on the positive batches
n0 (float): the optimal batch size for the negative batches
n1 (float): the optimal batch size for the positive batches
"""
batch_size = min(batch_size, batch_limit)
p0 = infection_rate_on_negative_batch(p, batch_size, typeII_error,
typeI_error)
p1 = infection_rate_on_positive_batch(p, batch_size, typeII_error,
typeI_error)
n0 = one_batch_test_int_solver(p0, typeII_error, typeI_error, batch_limit)
n1 = one_batch_test_int_solver(p1, typeII_error, typeI_error, batch_limit)
if subject_array == np.array([]):
return np.array([]), np.array([]), p0, p1, n0, n1
temp0, temp1, temp_con = neg_pos_batch_split(subject_array, batch_size,
typeII_error, typeI_error)
return temp0, temp1, temp_con, p0, p1, n0, n1
def seq_test(subject_array, stop_rule, p, batch_size, typeII_error,
typeI_error, repeat=1, prob_threshold=1, seq=True, batch_limit=32):
"""
A function gives the test results to a subject array and the total number of
test-kit consumption and the individual testing number given the subject array,
the stop rule, the batch size, the probability of type II error, the probability of
Type I error, and the number of repeatition, the probability threshold, and
setting of sequence testing or not.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
stop_rule (int): the number of postive batches to enter individual testing
p (float): infection rate
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
repeat (int): the number of repetition
prob_threshold (float): if the infection rate of a batch is beyond prob_threshold,
the subjects on that batch will enter individual testing phase
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
batch_limit (int):
Output:
result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
individual_con (int): the test consumption for individual testings
"""
temp_list = []
neg_list = []
pos_list = []
consum = 0
temp = {'data': subject_array, 'NB_Num': 0, 'PB_Num': 0, 'p': p,
'batch_size': batch_size}
temp_list.append(temp)
new_list = []
neg_array = []
pos_array = []
while len(temp_list) > 0:
for i in temp_list:
temp0, temp1, temp_con, p0, p1, n0, n1 = helpfunction(i['data'],
i['p'], i['batch_size'], typeII_error, typeI_error,
batch_limit=batch_limit)
temp0 = {'data': temp0, 'NB_Num': i['NB_Num'] + 1, 'PB_Num': i[
'PB_Num'], 'p': p0, 'batch_size': n0}
temp1 = {'data': temp1, 'NB_Num': i['NB_Num'], 'PB_Num': i[
'PB_Num'] + 1, 'p': p1, 'batch_size': n1}
if len(temp0['data']) > 0:
if temp0['NB_Num'] >= stop_rule:
neg_list.append(temp0)
else:
new_list.append(temp0)
if len(temp1['data']) > 0:
if temp1['PB_Num'] >= stop_rule or temp1['p'
] >= prob_threshold:
pos_list.append(temp1)
else:
new_list.append(temp1)
consum += temp_con
temp_list = new_list
new_list = []
for j in neg_list:
neg_array.append(j['data'])
neg_array = np.concatenate(neg_array)
for k in pos_list:
pos_array.append(k['data'])
pos_array = np.concatenate(pos_array)
neg_array[:, 1] = 0
individual_test, individual_con = conventional_test(pos_array,
typeII_error, typeI_error, repeat, seq)
pos_array = individual_test
consum += individual_con
result = np.concatenate((pos_array, neg_array))
result = result[result[:, 0].argsort()]
result = result.astype('int64')
return result, consum, individual_con
def npv_score(y_true, y_pred):
"""
A function provides npv given the prediction and the truth
"""
tn, _, fn, _ = confusion_matrix(y_true=y_true, y_pred=y_pred).ravel()
return tn / (tn + fn)
def specificity_score(y_true, y_pred):
"""
A function provides specificty given the prediction and the truth
"""
tn, fp, _, _ = confusion_matrix(y_true=y_true, y_pred=y_pred).ravel()
return tn / (tn + fp)
@jit(parallel=True)
def data_gen(size, p):
"""
data_gen provides a faster way to generate a random population with
infection rate p.
Input:
size (int): the size of population
p (float): the infection rate
Output:
test_array (array): the first column is for id and the second column
is the condition, where 1 stands for infection and 0 stands for uninfection
"""
random_table = np.random.binomial(size=size, p=p, n=1)
test_array = np.zeros((size, 2), dtype=int)
for i in range(size):
test_array[i, 0] = i
test_array[i, 1] = random_table[i]
return test_array
def test_result(data, seq_test, **kwargs):
"""
a helper function provides convenient results for a given test method with its **kwargs
Input:
data (array or list of arrays)
seq_test (test_method object): could be seq_test, matrix_test and other test_method objects
Output:
result (DataFrame): a dataframe contains important evaluation metrics for the test method
"""
if isinstance(data, list) == False:
pred, consum, ind_con = seq_test(data, **kwargs)
result = {'acc': np.mean(pred[:, 1] == data[:, 1]), 'sens':
recall_score(data[:, 1], pred[:, 1]), 'spec': specificity_score
(data[:, 1], pred[:, 1]), 'PPV': precision_score(data[:, 1],
pred[:, 1]), 'NPV': npv_score(data[:, 1], pred[:, 1]),
'test_consum': consum, 'ind_consum': ind_con, 'batch_consum':
consum - ind_con}
return result
else:
length = len(data)
acc = np.zeros(length)
sens = np.zeros(length)
spec = np.zeros(length)
ppv = np.zeros(length)
npv = np.zeros(length)
test_consum = np.zeros(length)
ind_consum = np.zeros(length)
batch_consum = np.zeros(length)
for i in range(length):
pred, consum, ind_con = seq_test(data[i], **kwargs)
acc[i] = np.mean(pred[:, 1] == data[i][:, 1])
sens[i] = recall_score(data[i][:, 1], pred[:, 1])
spec[i] = specificity_score(data[i][:, 1], pred[:, 1])
ppv[i] = precision_score(data[i][:, 1], pred[:, 1])
npv[i] = npv_score(data[i][:, 1], pred[:, 1])
test_consum[i] = consum
ind_consum[i] = ind_con
batch_consum[i] = consum - ind_con
result = {'acc': acc, 'sens': sens, 'spec': spec, 'PPV': ppv, 'NPV':
npv, 'test_consum': test_consum, 'ind_consum': ind_consum,
'batch_consum': batch_consum}
return pd.DataFrame(result)
def matrix_test(subject_array, side_length, typeII_error, typeI_error,
sq_repeat=1, ind_repeat=1, seq=True):
"""
This function provides the matrix testing results for a given subject array.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
side_length (int): the side length of the matrix testing
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
sq_repeat (int): the number of parallel testing for the column/row batch testing
ind_repeat (int): the number of potential individual testing for the positive crossings
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
Output:
result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
individual_con (int): the test consumption for individual testings
"""
matrix_test_num = len(subject_array) // side_length ** 2
matrix_test_array = subject_array[0:matrix_test_num * side_length ** 2, :]
ind_test_array = subject_array[matrix_test_num * side_length ** 2:, :]
ind_idx = []
for temp_batch in np.array_split(matrix_test_array, matrix_test_num):
temp_batch = temp_batch.reshape(side_length, side_length, 2)
temp_row = []
temp_col = []
random_num_row = np.random.uniform(0, 1, sq_repeat)
random_num_col = np.random.uniform(0, 1, sq_repeat)
for i in range(side_length):
if 1 in temp_batch[i, :, 1]:
if max(random_num_row) > typeII_error:
temp_row.append(temp_batch[i, :, 0])
elif min(random_num_row) < typeI_error:
temp_row.append(temp_batch[i, :, 0])
if 1 in temp_batch[:, i, 1]:
if max(random_num_col) > typeII_error:
temp_col.append(temp_batch[:, i, 0])
elif min(random_num_col) < typeI_error:
temp_col.append(temp_batch[:, i, 0])
ind_idx.append(np.intersect1d(temp_row, temp_col))
ind_idx = np.concatenate(ind_idx)
ind_idx = ind_idx.astype('int')
if len(ind_idx) == 0:
neg_array = matrix_test_array
else:
mask = np.zeros(subject_array.shape[0], dtype=bool)
mask[ind_idx] = True
mask[matrix_test_num * side_length ** 2:] = True
ind_test_array = subject_array[mask, :]
neg_array = subject_array[~mask, :]
neg_array[:, 1] = 0
ind_test, ind_con = conventional_test(ind_test_array, typeII_error,
typeI_error, repeat=ind_repeat, seq=seq)
batch_test_num = matrix_test_num * 2 * side_length * sq_repeat
result = np.concatenate((neg_array, ind_test))
result = result[result[:, 0].argsort()]
return result, batch_test_num + ind_con, ind_con
def parallel_batch_testing(subject_array, batch_size, typeII_error,
typeI_error, parallel_num, ind_repeat, seq):
"""
This function provides the parallel batch testing results for a given subject array.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
parallel_num (int): the number of parallel testing for the batch testing
ind_repeat (int): the number of potential individual testing for the positive batches
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
Output:
result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
individual_con (int): the test consumption for individual testings
"""
neg_batch = []
pos_batch = []
batch_consum = np.ceil(len(subject_array) / batch_size) * parallel_num
for temp_batch in np.array_split(subject_array, np.ceil(len(
subject_array) / batch_size)):
random_table = np.random.uniform(0, 1, (1, parallel_num))
if 1 in temp_batch[:, 1]:
if random_table.max() > typeII_error:
pos_batch.append(temp_batch)
else:
neg_batch.append(temp_batch)
elif random_table.min() < typeI_error:
pos_batch.append(temp_batch)
else:
neg_batch.append(temp_batch)
neg_batch = np.concatenate(neg_batch) if len(neg_batch) > 0 else np.array([
])
pos_batch = np.concatenate(pos_batch) if len(pos_batch) > 0 else np.array([
])
neg_batch[:, 1] = 0
individual_test, individual_con = conventional_test(pos_batch,
typeII_error, typeI_error, repeat=ind_repeat, seq=seq)
result = np.concatenate((individual_test, neg_batch))
result = result[result[:, 0].argsort()]
result = result.astype('int64')
return result, batch_consum + individual_con, individual_con
def fixed_batch_seq_test(subject_array, stop_rule, p, batch_size,
typeII_error, typeI_error, repeat, prob_threshold=0.3, seq=True):
"""
This function provides the parallel batch testing results for a given subject array.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
stop_rule (int): the number of positive batches to enter the individual testing phase
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
repeat (int): the number of potential individual testing for the positive crossings
prob_threshold (float): if the infection rate of a batch is beyond prob_threshold,
the subjects on that batch will enter individual testing phase
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
Output:
result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
individual_con (int): the test consumption for individual testings
"""
temp_list = []
neg_list = []
pos_list = []
consum = 0
temp = {'data': subject_array, 'NB_Num': 0, 'PB_Num': 0, 'p': p,
'batch_size': batch_size}
temp_list.append(temp)
new_list = []
neg_array = []
pos_array = []
while len(temp_list) > 0:
for i in temp_list:
temp0, temp1, temp_con, p0, p1, n0, n1 = helpfunction(i['data'],
i['p'], i['batch_size'], typeII_error, typeI_error)
temp0 = {'data': np.random.permutation(temp0), 'NB_Num': i[
'NB_Num'] + 1, 'PB_Num': i['PB_Num'], 'p': p0, 'batch_size':
batch_size}
temp1 = {'data': np.random.permutation(temp1), 'NB_Num': i[
'NB_Num'], 'PB_Num': i['PB_Num'] + 1, 'p': p1, 'batch_size':
batch_size}
if len(temp0['data']) > 0:
if temp0['NB_Num'] >= stop_rule:
neg_list.append(temp0)
else:
new_list.append(temp0)
if len(temp1['data']) > 0:
if temp1['PB_Num'] >= stop_rule or temp1['p'
] >= prob_threshold:
pos_list.append(temp1)
else:
new_list.append(temp1)
consum += temp_con
temp_list = new_list
new_list = []
for j in neg_list:
neg_array.append(j['data'])
neg_array = np.concatenate(neg_array)
for k in pos_list:
pos_array.append(k['data'])
pos_array = np.concatenate(pos_array)
neg_array[:, 1] = 0
individual_test, individual_con = conventional_test(pos_array,
typeII_error, typeI_error, repeat, seq)
pos_array = individual_test
consum += individual_con
result = np.concatenate((pos_array, neg_array))
result = result[result[:, 0].argsort()]
result = result.astype('int64')
return result, consum, individual_con
def name_fun(n):
"""
input: stopping rule
output: finish nodes
"""
output = []
temp = ['']
for i in range(2 * n - 1):
temp_cur = []
for j in temp:
candidate_pos = j + '+'
candidate_neg = j + '-'
if str.count(candidate_pos, '+') >= n:
output.append(candidate_pos)
else:
temp_cur.append(candidate_pos)
if str.count(candidate_neg, '-') >= n:
output.append(candidate_neg)
else:
temp_cur.append(candidate_neg)
temp = temp_cur
neg_symbol = [x for x in output if str.count(x, '-') == n]
pos_symbol = [x for x in output if str.count(x, '+') == n]
return output, neg_symbol, pos_symbol
def seq_test_with_node(subject_array, stop_rule, p, batch_size,
typeII_error, typeI_error, repeat=1, prob_threshold=1, seq=True,
batch_limit=32):
"""
A function gives the test results to a subject array and the total number of
test-kit consumption and the individual testing number given the subject array,
the stop rule, the batch size, the probability of type II error, the probability of
Type I error, and the number of repeatition, the probability threshold, and
setting of sequence testing or not.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
stop_rule (int): the number of postive batches to enter individual testing
p (float): infection rate
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
repeat (int): the number of repetition
prob_threshold (float): if the infection rate of a batch is beyond prob_threshold,
the subjects on that batch will enter individual testing phase
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
batch_limit (int):
Output:
result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
individual_con (int): the test consumption for individual testings
"""
temp_list = []
neg_list = []
pos_list = []
batch_num_list = []
consum = 0
temp = {'data': subject_array, 'NB_Num': 0, 'PB_Num': 0, 'p': p,
'batch_size': batch_size, 'node': ''}
temp_list.append(temp)
new_list = []
neg_array = []
neg_node = []
pos_node = []
pos_array = []
while len(temp_list) > 0:
for i in temp_list:
temp0, temp1, temp_con, p0, p1, n0, n1 = helpfunction(i['data'],
i['p'], i['batch_size'], typeII_error, typeI_error,
batch_limit=batch_limit)
temp0 = {'data': temp0, 'NB_Num': i['NB_Num'] + 1, 'PB_Num': i[
'PB_Num'], 'p': p0, 'batch_size': n0, 'node': i['node'] + '-'}
temp1 = {'data': temp1, 'NB_Num': i['NB_Num'], 'PB_Num': i[
'PB_Num'] + 1, 'p': p1, 'batch_size': n1, 'node': i['node'] +
'+'}
if len(temp0['data']) > 0:
if temp0['NB_Num'] >= stop_rule:
neg_list.append(temp0)
else:
new_list.append(temp0)
if len(temp1['data']) > 0:
if temp1['PB_Num'] >= stop_rule or temp1['p'
] >= prob_threshold:
pos_list.append(temp1)
else:
new_list.append(temp1)
consum += temp_con
batch_num_list.append(consum)
temp_list = new_list
new_list = []
for j in neg_list:
neg_array.append(j['data'])
temp = [[x, j['node']] for x in j['data'][:, 0]]
neg_node.append(temp)
neg_array = np.concatenate(neg_array)
for k in pos_list:
pos_array.append(k['data'])
temp = [[x, k['node']] for x in k['data'][:, 0]]
pos_node.append(temp)
pos_array = np.concatenate(pos_array)
neg_array[:, 1] = 0
individual_test, individual_con = conventional_test(pos_array,
typeII_error, typeI_error, repeat, seq)
pos_array = individual_test
consum += individual_con
result = np.concatenate((pos_array, neg_array))
pos_node.extend(neg_node)
node = pos_node
node = sum(node, [])
node.sort()
node = [x[1] for x in node]
result = result[result[:, 0].argsort()]
result = result.astype('int64')
return result, consum, individual_con, node, batch_num_list
| import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from sklearn.metrics import precision_score, recall_score, f1_score
from scipy.optimize import fsolve
import numba
from numba import njit,jit
#
@jit(parallel = True)
def conventional_test(subject_array, typeII_error, typeI_error, repeat = 1,
seq = True):
"""
A function gives the test results to a subject array given the probability of
type II error, the probability of Type I error, and the number of repeatition,
and setting of sequence testing or not.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
repeat (int): the number of repetition
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
Output:
test_result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
"""
# Sequential Testing
if seq == True:
consum = 0
test_result = np.zeros(subject_array.shape, dtype = int)
random_table = np.random.uniform(0, 1, (subject_array.shape[0], repeat))
for i in range(len(subject_array)):
temp = 0
j = 0
subject = subject_array[i,1]
while j < repeat and temp == 0:
random_num = random_table[i, j]
consum += 1
if subject == 1:
temp = 1 if random_num > typeII_error else 0
else:
temp = 1 if random_num < typeI_error else 0
j += 1
test_result[i,0] = subject_array[i,0]
test_result[i,1] = temp
return test_result, consum
# Simultanous Testing
else:
test_result = np.zeros(subject_array.shape, dtype = int)
random_table = np.random.uniform(0, 1, (subject_array.shape[0], repeat))
for i in range(len(subject_array)):
temp = 0
for j in range(repeat):
temp_random = random_table[i, j]
if subject_array[i, 1] == 1:
temp_1 = 1 if temp_random > typeII_error else 0
elif subject_array[i, 1] == 0:
temp_1 = 1 if temp_random < typeI_error else 0
temp += temp_1
temp = 1 if temp >= repeat/2 else 0
test_result[i,0] = subject_array[i,0]
test_result[i,1] = temp
return test_result, len(subject_array)*repeat
@njit(parallel = True)
def parallel_test(subject_array, typeII_error, typeI_error, num):
test_result = np.zeros(subject_array.shape, dtype = int)
random_table = np.random.uniform(0, 1, (subject_array.shape[0], num))
for i in range(len(subject_array)):
subject = subject_array[i, 1]
if subject == 1:
temp = 1 if max(random_table[i,:]) > typeII_error else 0
elif subject == 0:
temp = 1 if min(random_table[i,:]) < typeI_error else 0
test_result[i,0] = subject_array[i,0]
test_result[i,1] = temp
return test_result,len(subject_array)*num,len(subject_array)*num
def infection_rate_on_negative_batch(p,batch_size,typeII_error, typeI_error):
"""
Given infection rate, batch size, prob of type II error and prob of type I error, this
function gives the infection rate on the negative batch.
Input:
p (float): the infection rate
batch_size (int): the batch size
typeII_error (float): the prob of type II error
typeI_error (float): the prob of type I error
Output:
(float): the infection rate on the negative batch
"""
q = 1-p
r = typeII_error * (1 - q ** batch_size)/((1 - typeI_error) * q ** batch_size + typeII_error *(1 - q**batch_size))
return p*r/(1-q**batch_size)
def infection_rate_on_positive_batch(p, batch_size, typeII_error, typeI_error):
"""
Given infection rate, batch size, prob of type II error and prob of type I error, this
function gives the infection rate on the positive batch.
Input:
p (float): the infection rate
batch_size (int): the batch size
typeII_error (float): the prob of type II error
typeI_error (float): the prob of type I error
Output:
(float): the infection rate on the positive batch
"""
q = 1-p
r = (1 - typeII_error) * (1 - q ** batch_size)/(typeI_error * q ** batch_size + (1 - typeII_error) * (1 - q **batch_size))
return p*r/(1 - q** batch_size)
def one_batch_test_solver(prevalence_rate,typeII_error, typeI_error,n_initial_guess = 2):
"""
A function gives (float) the best batch size for one batch test given the infection rate
Inputs:
prevalence_rate(float): infection rate
typeII_error(float): the prob of type II error
typeI_error(float): the prob of type I error
n_initial_guess(float): the initial guess
Output:
(float): the optimal batch size
"""
q = 1- prevalence_rate # To consistent with the notation of our document
func = lambda n : n*q**(n/2) - (-(1-typeII_error - typeI_error)*np.log(q))**(-1/2)
# print(func(n_initial_guess))
n_solution = fsolve(func, n_initial_guess)
return float(n_solution)
def one_batch_test_int_solver(prevalence_rate,typeII_error, typeI_error,batch_limit,n_initial_guess = 2):
"""
A function gives (int) the best batch size for one batch test given the infection rate
Inputs:
prevalence_rate(float): infection rate
n_initial_guess(float): the initial guess
typeII_error(float): the prob of type II error
typeI_error(float): the prob of type I error
n_initial_guess:
batch_limit (int): the upper limit of batch size
Output:
(int): the optimal batch size
"""
sol_float = one_batch_test_solver(prevalence_rate,typeII_error, typeI_error, n_initial_guess)
floor, ceil = np.floor(sol_float), np.ceil(sol_float)
func = lambda batch_size: 1/batch_size + 1 - typeII_error -(1 - typeII_error - typeI_error)*(1-prevalence_rate)**batch_size
if func(floor) < func(ceil):
temp = int(floor)
else:
temp = int(ceil)
if temp <= batch_limit:
return temp
else:
return int(batch_limit)
def neg_pos_batch_split(subject_array, batch_size, typeII_error, typeI_error):
"""
A function gives a list of sujects on the negative batch(es),
a list of subjects on the postive batch(es) and the test-kit
consumption given the probability of type II error, the
probability of Type I error.
Input:
subject_array (Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
Output:
neg_batch (Numpy Array): an array of subjects on the negative batch(es)
pos_batch (Numpy Array): an array of subjects on the postive batch(es)
test_consum (int): the number of test-kit consumptions
"""
neg_batch = []
pos_batch = []
test_consum = np.ceil(len(subject_array)/batch_size)
random_table = np.random.uniform(0, 1, int(test_consum))
i = 0
for temp_batch in np.array_split(subject_array, test_consum):
if 1 in (temp_batch[:,1]):
if random_table[i] > typeII_error:
pos_batch.append(temp_batch)
else:
neg_batch.append(temp_batch)
else:
if random_table[i] > typeI_error:
neg_batch.append(temp_batch)
else:
pos_batch.append(temp_batch)
i += 1
neg_batch = np.concatenate(neg_batch) if len(neg_batch) > 0 else np.array([])
pos_batch = np.concatenate(pos_batch) if len(pos_batch) > 0 else np.array([])
return (neg_batch, pos_batch, test_consum)
def helpfunction(subject_array, p, batch_size ,typeII_error, typeI_error, batch_limit):
"""
The helpfunction is a handy function to give the list of subjects on the
negative batch(es), the list of subjects on the postive batch(es), the
test-kit consumption, the infection rate on the negative batches, the
infection rate on the positive batches, the optimal batch size for
negative batches and the optimal batch size for positive batches.
Input:
subject_array (Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
p (float): Infection rate
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
batch_limit (int): batch size upper limit
Output:
temp0 (Numpy Array): an array of subjects on the negative batch(es)
temp1 (Numpy Array): an array of subjects on the postive batch(es)
temp_con (int): the number of test-kit consumptions
p0 (float): the infection rate on the negative batches
p1 (float): the infection rate on the positive batches
n0 (float): the optimal batch size for the negative batches
n1 (float): the optimal batch size for the positive batches
"""
batch_size = min(batch_size, batch_limit)
p0 = infection_rate_on_negative_batch(p, batch_size, typeII_error, typeI_error)
p1 = infection_rate_on_positive_batch(p, batch_size, typeII_error, typeI_error)
n0= one_batch_test_int_solver(p0, typeII_error, typeI_error, batch_limit)
n1 = one_batch_test_int_solver(p1, typeII_error, typeI_error, batch_limit)
if subject_array == np.array([]):
return (np.array([]), np.array([]), p0, p1, n0, n1)
temp0, temp1, temp_con = neg_pos_batch_split(subject_array,batch_size,typeII_error, typeI_error)
return(temp0, temp1, temp_con, p0, p1, n0, n1)
def seq_test(subject_array,stop_rule,p, batch_size, typeII_error, typeI_error, repeat = 1,
prob_threshold = 1, seq = True, batch_limit = 32):
"""
A function gives the test results to a subject array and the total number of
test-kit consumption and the individual testing number given the subject array,
the stop rule, the batch size, the probability of type II error, the probability of
Type I error, and the number of repeatition, the probability threshold, and
setting of sequence testing or not.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
stop_rule (int): the number of postive batches to enter individual testing
p (float): infection rate
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
repeat (int): the number of repetition
prob_threshold (float): if the infection rate of a batch is beyond prob_threshold,
the subjects on that batch will enter individual testing phase
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
batch_limit (int):
Output:
result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
individual_con (int): the test consumption for individual testings
"""
temp_list = []
neg_list = [] #renamed to negativeInfoList
pos_list = [] #renamed to positiveInfoList
consum = 0
temp = {'data': subject_array,
'NB_Num': 0,
'PB_Num': 0,
'p': p,
'batch_size': batch_size}
temp_list.append(temp)
new_list = []
neg_array = [] #renamed to negativeBatches
pos_array = [] #renamed to positiveBatches
while len(temp_list) > 0:
for i in temp_list:
temp0, temp1, temp_con, p0, p1, n0, n1 = helpfunction(i['data'], i['p'], i['batch_size'],
typeII_error, typeI_error,
batch_limit = batch_limit)
temp0 = {'data': temp0,
'NB_Num': i['NB_Num'] + 1,
'PB_Num': i['PB_Num'],
'p': p0,
'batch_size': n0}
temp1 = {'data': temp1,
'NB_Num': i['NB_Num'],
'PB_Num': i['PB_Num'] + 1,
'p': p1,
'batch_size': n1}
if len(temp0['data']) > 0:
if temp0['NB_Num'] >= stop_rule:
neg_list.append(temp0)
else:
new_list.append(temp0)
if len(temp1['data'])>0:
if temp1['PB_Num'] >= stop_rule or temp1['p']>=prob_threshold:
pos_list.append(temp1)
else:
new_list.append(temp1)
consum += temp_con
temp_list = new_list
new_list = []
for j in neg_list:
neg_array.append(j['data'])
neg_array = np.concatenate(neg_array)
for k in pos_list:
pos_array.append(k['data'])
pos_array = np.concatenate(pos_array)
neg_array[:,1] = 0
individual_test, individual_con = conventional_test(pos_array, typeII_error, typeI_error, repeat, seq)
pos_array = individual_test
consum += individual_con
result = np.concatenate((pos_array, neg_array))
result = result[result[:,0].argsort()]
result = result.astype('int64')
return (result, consum, individual_con)
def npv_score(y_true, y_pred):
"""
A function provides npv given the prediction and the truth
"""
tn, _, fn, _ = confusion_matrix(y_true = y_true,
y_pred = y_pred).ravel()
return tn/(tn + fn)
def specificity_score(y_true, y_pred):
"""
A function provides specificty given the prediction and the truth
"""
tn, fp, _, _ = confusion_matrix(y_true = y_true,
y_pred = y_pred).ravel()
return tn/(tn + fp)
@jit(parallel = True)
def data_gen(size, p):
"""
data_gen provides a faster way to generate a random population with
infection rate p.
Input:
size (int): the size of population
p (float): the infection rate
Output:
test_array (array): the first column is for id and the second column
is the condition, where 1 stands for infection and 0 stands for uninfection
"""
#print(np.random.get_state()[1][0])
random_table = np.random.binomial(size = size, p = p, n = 1)
test_array = np.zeros((size, 2), dtype = int)
for i in range(size):
test_array[i,0] = i
test_array[i,1] = random_table[i]
return test_array
def test_result(data, seq_test, **kwargs):
"""
a helper function provides convenient results for a given test method with its **kwargs
Input:
data (array or list of arrays)
seq_test (test_method object): could be seq_test, matrix_test and other test_method objects
Output:
result (DataFrame): a dataframe contains important evaluation metrics for the test method
"""
if isinstance(data, list) == False:
pred,consum, ind_con = seq_test(data, **kwargs)
result = {'acc': np.mean(pred[:,1] == data[:,1]),
'sens': recall_score(data[:,1], pred[:,1]),
'spec': specificity_score(data[:,1], pred[:,1]),
'PPV': precision_score(data[:, 1], pred[:,1]),
'NPV': npv_score(data[:, 1], pred[:,1]),
'test_consum': consum,
'ind_consum': ind_con,
'batch_consum': consum - ind_con}
return result
else:
length = len(data)
acc = np.zeros(length)
sens = np.zeros(length)
spec = np.zeros(length)
ppv = np.zeros(length)
npv = np.zeros(length)
test_consum = np.zeros(length)
ind_consum = np.zeros(length)
batch_consum = np.zeros(length)
for i in range(length):
pred,consum, ind_con = seq_test(data[i], **kwargs)
acc[i] = np.mean(pred[:,1] == data[i][:,1])
sens[i] = recall_score(data[i][:,1], pred[:,1])
spec[i] = specificity_score(data[i][:,1], pred[:,1])
ppv[i] = precision_score(data[i][:,1], pred[:,1])
npv[i] = npv_score(data[i][:,1], pred[:,1])
test_consum[i] = consum
ind_consum[i] = ind_con
batch_consum[i] = consum-ind_con
result = {'acc': acc,
'sens': sens,
'spec': spec,
'PPV': ppv,
'NPV': npv,
'test_consum': test_consum,
'ind_consum': ind_consum,
'batch_consum': batch_consum}
return pd.DataFrame(result)
def matrix_test(subject_array, side_length, typeII_error, typeI_error, sq_repeat = 1 ,ind_repeat = 1, seq = True):
"""
This function provides the matrix testing results for a given subject array.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
side_length (int): the side length of the matrix testing
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
sq_repeat (int): the number of parallel testing for the column/row batch testing
ind_repeat (int): the number of potential individual testing for the positive crossings
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
Output:
result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
individual_con (int): the test consumption for individual testings
"""
matrix_test_num = len(subject_array)//(side_length**2)
matrix_test_array = subject_array[0:matrix_test_num*side_length**2, :]
ind_test_array = subject_array[matrix_test_num*side_length**2:, :]
ind_idx = []
for temp_batch in np.array_split(matrix_test_array, matrix_test_num):
temp_batch = temp_batch.reshape(side_length, side_length, 2)
temp_row = []
temp_col = []
random_num_row = np.random.uniform(0, 1, sq_repeat)
random_num_col = np.random.uniform(0, 1, sq_repeat)
for i in range(side_length):
if 1 in (temp_batch[i,:,1]):
if max(random_num_row) > typeII_error:
temp_row.append(temp_batch[i,:,0])
else:
if min(random_num_row) < typeI_error:
temp_row.append(temp_batch[i, :, 0])
if 1 in (temp_batch[:,i,1]):
if max(random_num_col) > typeII_error:
temp_col.append(temp_batch[:,i,0])
else:
if min(random_num_col) < typeI_error:
temp_col.append(temp_batch[:, i, 0])
ind_idx.append(np.intersect1d(temp_row, temp_col))
ind_idx = np.concatenate(ind_idx)
ind_idx = ind_idx.astype('int')
if len(ind_idx) == 0:
neg_array = matrix_test_array
else:
mask = np.zeros(subject_array.shape[0], dtype = bool)
mask[ind_idx] = True
mask[matrix_test_num*side_length**2:] = True
ind_test_array = subject_array[mask,:]
neg_array = subject_array[~mask, :]
neg_array[:, 1] = 0
ind_test, ind_con = conventional_test(ind_test_array,
typeII_error, typeI_error, repeat = ind_repeat, seq = seq)
batch_test_num = matrix_test_num * 2 * side_length * sq_repeat
result = np.concatenate((neg_array, ind_test))
result = result[result[:, 0].argsort()]
return (result, batch_test_num + ind_con, ind_con)
def parallel_batch_testing(subject_array, batch_size, typeII_error, typeI_error, parallel_num, ind_repeat, seq):
"""
This function provides the parallel batch testing results for a given subject array.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
parallel_num (int): the number of parallel testing for the batch testing
ind_repeat (int): the number of potential individual testing for the positive batches
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
Output:
result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
individual_con (int): the test consumption for individual testings
"""
neg_batch = []
pos_batch = []
batch_consum = np.ceil(len(subject_array)/batch_size)* parallel_num
for temp_batch in np.array_split(subject_array, np.ceil(len(subject_array)/batch_size)):
random_table = np.random.uniform(0, 1, (1, parallel_num))
if 1 in (temp_batch[:, 1]):
if random_table.max() > typeII_error:
pos_batch.append(temp_batch)
else:
neg_batch.append(temp_batch)
else:
if random_table.min() < typeI_error:
pos_batch.append(temp_batch)
else:
neg_batch.append(temp_batch)
neg_batch = np.concatenate(neg_batch) if len(neg_batch) > 0 else np.array([])
pos_batch = np.concatenate(pos_batch) if len(pos_batch) > 0 else np.array([])
neg_batch[:, 1] = 0
individual_test, individual_con = conventional_test(pos_batch, typeII_error, typeI_error,
repeat = ind_repeat, seq = seq)
result = np.concatenate((individual_test, neg_batch))
result = result[result[:,0].argsort()]
result = result.astype('int64')
return (result, batch_consum+individual_con, individual_con)
def fixed_batch_seq_test(subject_array,stop_rule, p, batch_size, typeII_error, typeI_error, repeat, prob_threshold = 0.3, seq = True):
"""
This function provides the parallel batch testing results for a given subject array.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
stop_rule (int): the number of positive batches to enter the individual testing phase
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
repeat (int): the number of potential individual testing for the positive crossings
prob_threshold (float): if the infection rate of a batch is beyond prob_threshold,
the subjects on that batch will enter individual testing phase
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
Output:
result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
individual_con (int): the test consumption for individual testings
"""
temp_list = []
neg_list = []
pos_list = []
consum = 0
temp = {'data': subject_array,
'NB_Num': 0,
'PB_Num': 0,
'p': p,
'batch_size': batch_size}
temp_list.append(temp)
new_list = []
neg_array = []
pos_array = []
while len(temp_list) > 0:
for i in temp_list:
temp0, temp1, temp_con, p0, p1, n0, n1 = helpfunction(i['data'], i['p'], i['batch_size'],
typeII_error, typeI_error)
temp0 = {'data': np.random.permutation(temp0),
'NB_Num': i['NB_Num'] + 1,
'PB_Num': i['PB_Num'],
'p': p0,
'batch_size': batch_size}
temp1 = {'data': np.random.permutation(temp1),
'NB_Num': i['NB_Num'],
'PB_Num': i['PB_Num'] + 1,
'p': p1,
'batch_size': batch_size}
if len(temp0['data']) > 0:
if temp0['NB_Num'] >= stop_rule:
neg_list.append(temp0)
else:
new_list.append(temp0)
if len(temp1['data'])>0:
if temp1['PB_Num'] >= stop_rule or temp1['p']>=prob_threshold:
pos_list.append(temp1)
else:
new_list.append(temp1)
consum += temp_con
temp_list = new_list
new_list = []
for j in neg_list:
neg_array.append(j['data'])
neg_array = np.concatenate(neg_array)
for k in pos_list:
pos_array.append(k['data'])
pos_array = np.concatenate(pos_array)
neg_array[:,1] = 0
individual_test, individual_con = conventional_test(pos_array, typeII_error, typeI_error, repeat, seq)
pos_array = individual_test
consum += individual_con
result = np.concatenate((pos_array, neg_array))
result = result[result[:,0].argsort()]
result = result.astype('int64')
return (result, consum, individual_con)
def name_fun(n):
"""
input: stopping rule
output: finish nodes
"""
output = []
temp = ['']
for i in range(2*n-1):
temp_cur = []
for j in temp:
candidate_pos = j + '+'
candidate_neg = j + '-'
if str.count(candidate_pos, '+') >= n:
output.append(candidate_pos)
else:
temp_cur.append(candidate_pos)
if str.count(candidate_neg, '-') >= n:
output.append(candidate_neg)
else:
temp_cur.append(candidate_neg)
temp = temp_cur
neg_symbol = [x for x in output if str.count(x, '-') == n]
pos_symbol = [x for x in output if str.count(x, '+') == n]
return output, neg_symbol, pos_symbol
def seq_test_with_node(subject_array,stop_rule,p, batch_size, typeII_error, typeI_error, repeat = 1,
prob_threshold = 1, seq = True, batch_limit = 32):
"""
A function gives the test results to a subject array and the total number of
test-kit consumption and the individual testing number given the subject array,
the stop rule, the batch size, the probability of type II error, the probability of
Type I error, and the number of repeatition, the probability threshold, and
setting of sequence testing or not.
Input:
subject_array(Numpy Array): an array contains subject id and subject's
condition (1 stands for infection and 0 stands for uninfection)
stop_rule (int): the number of postive batches to enter individual testing
p (float): infection rate
batch_size (int): batch size
typeII_error (float): probability of type II error
typeI_error (float): probability of type I error
repeat (int): the number of repetition
prob_threshold (float): if the infection rate of a batch is beyond prob_threshold,
the subjects on that batch will enter individual testing phase
seq (boolean): True stands for sequential testing. The test will end
when the test result is positive or run up the number of repetition.
False stands for simutanlous testing with majority voting.
batch_limit (int):
Output:
result (Numpy Array): an array contains subjects' id and test results
consum (int): the total test consumption
individual_con (int): the test consumption for individual testings
"""
temp_list = []
neg_list = []
pos_list = []
batch_num_list = []
consum = 0
temp = {'data': subject_array,
'NB_Num': 0,
'PB_Num': 0,
'p': p,
'batch_size': batch_size,
'node': ''}
temp_list.append(temp)
new_list = []
neg_array = []
neg_node = []
pos_node = []
pos_array = []
while len(temp_list) > 0:
for i in temp_list:
temp0, temp1, temp_con, p0, p1, n0, n1 = helpfunction(i['data'], i['p'], i['batch_size'],
typeII_error, typeI_error,
batch_limit = batch_limit)
temp0 = {'data': temp0,
'NB_Num': i['NB_Num'] + 1,
'PB_Num': i['PB_Num'],
'p': p0,
'batch_size': n0,
'node': i['node'] + '-'}
temp1 = {'data': temp1,
'NB_Num': i['NB_Num'],
'PB_Num': i['PB_Num'] + 1,
'p': p1,
'batch_size': n1,
'node': i['node'] + '+'}
if len(temp0['data']) > 0:
if temp0['NB_Num'] >= stop_rule:
neg_list.append(temp0)
else:
new_list.append(temp0)
if len(temp1['data'])>0:
if temp1['PB_Num'] >= stop_rule or temp1['p']>=prob_threshold:
pos_list.append(temp1)
else:
new_list.append(temp1)
consum += temp_con
batch_num_list.append(consum)
temp_list = new_list
new_list = []
for j in neg_list:
neg_array.append(j['data'])
temp = [[x, j['node']] for x in j['data'][:,0]]
neg_node.append(temp)
neg_array = np.concatenate(neg_array)
#print(neg_array)
#print(neg_node)
#neg_node = np.concatenate(neg_node)
for k in pos_list:
pos_array.append(k['data'])
#pos_node.append(k['node'])
#pos_node.append(np.column_stack((k['data'][:,0],np.repeat(k['node'], len(k['data'])))))
temp = [[x, k['node']] for x in k['data'][:,0]]
pos_node.append(temp)
pos_array = np.concatenate(pos_array)
#pos_node = np.concatenate(pos_node)
neg_array[:,1] = 0
individual_test, individual_con = conventional_test(pos_array, typeII_error, typeI_error, repeat, seq)
pos_array = individual_test
consum += individual_con
result = np.concatenate((pos_array, neg_array))
#node = np.concatenate((pos_node, neg_node))
pos_node.extend(neg_node)
node = pos_node
node = sum(node, [])
node.sort()
node = [x[1] for x in node]
#node = node[node[:,0].argsort()]
result = result[result[:,0].argsort()]
result = result.astype('int64')
return (result, consum, individual_con, node, batch_num_list)
| [
11,
12,
17,
18,
20
] |
282 | 9bc13c608c079cbf23ed04f29edd1fd836214cde | <mask token>
class CommentViewSet(viewsets.GenericViewSet, mixins.ListModelMixin, mixins
.RetrieveModelMixin):
<mask token>
def get_serializer_class(self):
if self.action == 'retrieve':
if self.get_object().level < 3:
return CommentSerializer
return AllCommentSerializer
| <mask token>
class CommentViewSet(viewsets.GenericViewSet, mixins.ListModelMixin, mixins
.RetrieveModelMixin):
queryset = Comment.objects.all()
def get_serializer_class(self):
if self.action == 'retrieve':
if self.get_object().level < 3:
return CommentSerializer
return AllCommentSerializer
| <mask token>
class PostViewSet(viewsets.ModelViewSet):
<mask token>
<mask token>
class CommentViewSet(viewsets.GenericViewSet, mixins.ListModelMixin, mixins
.RetrieveModelMixin):
queryset = Comment.objects.all()
def get_serializer_class(self):
if self.action == 'retrieve':
if self.get_object().level < 3:
return CommentSerializer
return AllCommentSerializer
| from rest_framework import viewsets, mixins
from .models import Comment, Post
from .serializer import CommentSerializer, PostSerializer, AllCommentSerializer
class PostViewSet(viewsets.ModelViewSet):
serializer_class = PostSerializer
queryset = Post.objects.all()
class CommentViewSet(viewsets.GenericViewSet, mixins.ListModelMixin, mixins
.RetrieveModelMixin):
queryset = Comment.objects.all()
def get_serializer_class(self):
if self.action == 'retrieve':
if self.get_object().level < 3:
return CommentSerializer
return AllCommentSerializer
| from rest_framework import viewsets, mixins
from .models import Comment, Post
from .serializer import CommentSerializer, PostSerializer, AllCommentSerializer
class PostViewSet(viewsets.ModelViewSet):
serializer_class = PostSerializer
queryset = Post.objects.all()
class CommentViewSet(viewsets.GenericViewSet, mixins.ListModelMixin, mixins.RetrieveModelMixin):
queryset = Comment.objects.all()
def get_serializer_class(self):
if self.action == 'retrieve':
if self.get_object().level < 3:
return CommentSerializer
return AllCommentSerializer
| [
2,
3,
4,
6,
7
] |
283 | b11e2837d3ba9c14770b8039186a2175adc41ea1 | <mask token>
def http_server(file: str=None, host: str='localhost', port: int=5050
) ->CanvasServer:
"""Creates a new HTTP server for displaying the network, using WebSockets to
transmit data. The server will only start once its
:meth:`~server.CanvasServer.start` method is called. After the server has started,
the network can be viewed by opening a browser and navigating to the address
``http://localhost:5050/`` (change the port as necessary).
:file: (Optional) The path to the HTML file which the server should display,
relative to the current runtime directory. If unspecified, the default HTML file
will be used. When creating a custom HTML interface, use the default file as a
guide.
:type file: str
:port: (Optional) The port on which the server should start, defaulting to to 5050.
Note that the next port (by default 5051) will also be used to transmit data
through WebSockets.
:type port: int
"""
return CanvasServer(file, host, port)
<mask token>
| <mask token>
def http_server(file: str=None, host: str='localhost', port: int=5050
) ->CanvasServer:
"""Creates a new HTTP server for displaying the network, using WebSockets to
transmit data. The server will only start once its
:meth:`~server.CanvasServer.start` method is called. After the server has started,
the network can be viewed by opening a browser and navigating to the address
``http://localhost:5050/`` (change the port as necessary).
:file: (Optional) The path to the HTML file which the server should display,
relative to the current runtime directory. If unspecified, the default HTML file
will be used. When creating a custom HTML interface, use the default file as a
guide.
:type file: str
:port: (Optional) The port on which the server should start, defaulting to to 5050.
Note that the next port (by default 5051) will also be used to transmit data
through WebSockets.
:type port: int
"""
return CanvasServer(file, host, port)
def jupyter_canvas(buttons: bool=False) ->JupyterCanvas:
"""Creates a new :class:`~api.Canvas` which will dispatch and receive
events through a Jupyter widget, and which can be displayed using the IPython
``display`` function.
By default, the canvas size is (400, 250), and requires the ``ctrl``/``cmd`` to be
held down while zooming."""
if HAS_JUPYTER:
return create_jupyter_canvas(buttons=buttons)
else:
raise Exception('Jupyter is not installed')
| <mask token>
try:
from .jupyter import JupyterCanvas, create_jupyter_canvas
HAS_JUPYTER = True
except:
HAS_JUPYTER = False
JupyterCanvas = None
def http_server(file: str=None, host: str='localhost', port: int=5050
) ->CanvasServer:
"""Creates a new HTTP server for displaying the network, using WebSockets to
transmit data. The server will only start once its
:meth:`~server.CanvasServer.start` method is called. After the server has started,
the network can be viewed by opening a browser and navigating to the address
``http://localhost:5050/`` (change the port as necessary).
:file: (Optional) The path to the HTML file which the server should display,
relative to the current runtime directory. If unspecified, the default HTML file
will be used. When creating a custom HTML interface, use the default file as a
guide.
:type file: str
:port: (Optional) The port on which the server should start, defaulting to to 5050.
Note that the next port (by default 5051) will also be used to transmit data
through WebSockets.
:type port: int
"""
return CanvasServer(file, host, port)
def jupyter_canvas(buttons: bool=False) ->JupyterCanvas:
"""Creates a new :class:`~api.Canvas` which will dispatch and receive
events through a Jupyter widget, and which can be displayed using the IPython
``display`` function.
By default, the canvas size is (400, 250), and requires the ``ctrl``/``cmd`` to be
held down while zooming."""
if HAS_JUPYTER:
return create_jupyter_canvas(buttons=buttons)
else:
raise Exception('Jupyter is not installed')
| from .server import CanvasServer
try:
from .jupyter import JupyterCanvas, create_jupyter_canvas
HAS_JUPYTER = True
except:
HAS_JUPYTER = False
JupyterCanvas = None
def http_server(file: str=None, host: str='localhost', port: int=5050
) ->CanvasServer:
"""Creates a new HTTP server for displaying the network, using WebSockets to
transmit data. The server will only start once its
:meth:`~server.CanvasServer.start` method is called. After the server has started,
the network can be viewed by opening a browser and navigating to the address
``http://localhost:5050/`` (change the port as necessary).
:file: (Optional) The path to the HTML file which the server should display,
relative to the current runtime directory. If unspecified, the default HTML file
will be used. When creating a custom HTML interface, use the default file as a
guide.
:type file: str
:port: (Optional) The port on which the server should start, defaulting to to 5050.
Note that the next port (by default 5051) will also be used to transmit data
through WebSockets.
:type port: int
"""
return CanvasServer(file, host, port)
def jupyter_canvas(buttons: bool=False) ->JupyterCanvas:
"""Creates a new :class:`~api.Canvas` which will dispatch and receive
events through a Jupyter widget, and which can be displayed using the IPython
``display`` function.
By default, the canvas size is (400, 250), and requires the ``ctrl``/``cmd`` to be
held down while zooming."""
if HAS_JUPYTER:
return create_jupyter_canvas(buttons=buttons)
else:
raise Exception('Jupyter is not installed')
| from .server import CanvasServer
try:
from .jupyter import JupyterCanvas, create_jupyter_canvas
HAS_JUPYTER = True
except:
HAS_JUPYTER = False
JupyterCanvas = None # type: ignore
def http_server(
file: str = None, host: str = "localhost", port: int = 5050
) -> CanvasServer:
"""Creates a new HTTP server for displaying the network, using WebSockets to
transmit data. The server will only start once its
:meth:`~server.CanvasServer.start` method is called. After the server has started,
the network can be viewed by opening a browser and navigating to the address
``http://localhost:5050/`` (change the port as necessary).
:file: (Optional) The path to the HTML file which the server should display,
relative to the current runtime directory. If unspecified, the default HTML file
will be used. When creating a custom HTML interface, use the default file as a
guide.
:type file: str
:port: (Optional) The port on which the server should start, defaulting to to 5050.
Note that the next port (by default 5051) will also be used to transmit data
through WebSockets.
:type port: int
"""
return CanvasServer(file, host, port)
def jupyter_canvas(buttons: bool = False) -> JupyterCanvas:
"""Creates a new :class:`~api.Canvas` which will dispatch and receive
events through a Jupyter widget, and which can be displayed using the IPython
``display`` function.
By default, the canvas size is (400, 250), and requires the ``ctrl``/``cmd`` to be
held down while zooming."""
if HAS_JUPYTER:
return create_jupyter_canvas(buttons=buttons)
else:
raise Exception("Jupyter is not installed")
| [
1,
2,
3,
4,
5
] |
284 | 2da7892722afde5a6f87e3bd6d5763c895ac96c9 | <mask token>
class Lang:
def __init__(self):
super(Lang, self).__init__()
self.word2index = {}
self.word2count = {}
self.index2word = {}
self.n_words = 0
def index_words(self, sentence):
for word in sentence:
self.index_word(word)
def index_word(self, word):
if word not in self.word2index:
self.word2index[word] = self.n_words
self.word2count[word] = 1
self.index2word[self.n_words] = word
self.n_words += 1
else:
self.word2count[word] += 1
<mask token>
| <mask token>
class Lang:
def __init__(self):
super(Lang, self).__init__()
self.word2index = {}
self.word2count = {}
self.index2word = {}
self.n_words = 0
def index_words(self, sentence):
for word in sentence:
self.index_word(word)
def index_word(self, word):
if word not in self.word2index:
self.word2index[word] = self.n_words
self.word2count[word] = 1
self.index2word[self.n_words] = word
self.n_words += 1
else:
self.word2count[word] += 1
def gen_embeddings(vocab, file, emb_size, emb_dim):
"""
Generate an initial embedding matrix for word_dict.
If an embedding file is not given or a word is not in the embedding file,
a randomly initialized vector will be used.
"""
embeddings = np.zeros((vocab.n_words, emb_size))
print('Embeddings: %d x %d' % (vocab.n_words, emb_size))
if file is not None:
print('Loading embedding file: %s' % file)
pre_trained = 0
for line in open(file).readlines():
sp = line.split()
if len(sp) == emb_dim + 1:
if sp[0] in vocab.word2index:
pre_trained += 1
embeddings[vocab.word2index[sp[0]]] = [float(x) for x in
sp[1:]]
else:
print(sp[0])
print('Pre-trained: %d (%.2f%%)' % (pre_trained, pre_trained *
100.0 / vocab.n_words))
return embeddings
def process_raw_abstracts(vocab):
with open(raw_save_path, 'r', encoding='utf8') as f:
for line in tqdm(f, total=13304586):
tokens = nltk.tokenize.word_tokenize(line)
tokens = [token for token in tokens if not token in nltk_stopwords]
vocab.index_words(tokens)
<mask token>
def check_graph(G):
total_nodes = len(G.nodes)
no_emb_nodes = 0
nodes_to_delete = []
for node_str in G.nodes:
try:
emb = G.node[node_str]['emb']
except:
no_emb_nodes += 1
nodes_to_delete.append(node_str)
print('%d Nodes and %d missing nodes in G ' % (total_nodes, no_emb_nodes))
G.remove_nodes_from(nodes_to_delete)
return G
def process_line(G, line, vocab=None):
try:
fos = data['fos']
abstract = data['indexed_abstract']
paper_id = data['id']
references_id = data['references']
text = list(abstract['InvertedIndex'].keys())
text = ' '.join(text)
if args.process_raw:
with open(raw_save_path, 'a+') as f:
f.write(text)
f.write('\n')
"""Create Node Embedding if Node doesn't exist """
if vocab is not None:
tokens = nltk.tokenize.word_tokenize(text)
tokens = [token for token in tokens if not token in nltk_stopwords]
node_emb = get_node_embed(tokens, vocab)
for field in fos:
name = field['name']
for ref in references_id:
G.add_edge(paper_id, ref)
G.node[paper_id]['emb'] = node_emb
except:
return G
return G
<mask token>
| <mask token>
nltk.download('stopwords')
<mask token>
class Lang:
def __init__(self):
super(Lang, self).__init__()
self.word2index = {}
self.word2count = {}
self.index2word = {}
self.n_words = 0
def index_words(self, sentence):
for word in sentence:
self.index_word(word)
def index_word(self, word):
if word not in self.word2index:
self.word2index[word] = self.n_words
self.word2count[word] = 1
self.index2word[self.n_words] = word
self.n_words += 1
else:
self.word2count[word] += 1
def gen_embeddings(vocab, file, emb_size, emb_dim):
"""
Generate an initial embedding matrix for word_dict.
If an embedding file is not given or a word is not in the embedding file,
a randomly initialized vector will be used.
"""
embeddings = np.zeros((vocab.n_words, emb_size))
print('Embeddings: %d x %d' % (vocab.n_words, emb_size))
if file is not None:
print('Loading embedding file: %s' % file)
pre_trained = 0
for line in open(file).readlines():
sp = line.split()
if len(sp) == emb_dim + 1:
if sp[0] in vocab.word2index:
pre_trained += 1
embeddings[vocab.word2index[sp[0]]] = [float(x) for x in
sp[1:]]
else:
print(sp[0])
print('Pre-trained: %d (%.2f%%)' % (pre_trained, pre_trained *
100.0 / vocab.n_words))
return embeddings
def process_raw_abstracts(vocab):
with open(raw_save_path, 'r', encoding='utf8') as f:
for line in tqdm(f, total=13304586):
tokens = nltk.tokenize.word_tokenize(line)
tokens = [token for token in tokens if not token in nltk_stopwords]
vocab.index_words(tokens)
def get_node_embed(text, vocab):
sum_embed = 0
for word in text:
embed = embeddings[vocab.word2index[word]]
sum_embed += embed
return sum_embed
def check_graph(G):
total_nodes = len(G.nodes)
no_emb_nodes = 0
nodes_to_delete = []
for node_str in G.nodes:
try:
emb = G.node[node_str]['emb']
except:
no_emb_nodes += 1
nodes_to_delete.append(node_str)
print('%d Nodes and %d missing nodes in G ' % (total_nodes, no_emb_nodes))
G.remove_nodes_from(nodes_to_delete)
return G
def process_line(G, line, vocab=None):
try:
fos = data['fos']
abstract = data['indexed_abstract']
paper_id = data['id']
references_id = data['references']
text = list(abstract['InvertedIndex'].keys())
text = ' '.join(text)
if args.process_raw:
with open(raw_save_path, 'a+') as f:
f.write(text)
f.write('\n')
"""Create Node Embedding if Node doesn't exist """
if vocab is not None:
tokens = nltk.tokenize.word_tokenize(text)
tokens = [token for token in tokens if not token in nltk_stopwords]
node_emb = get_node_embed(tokens, vocab)
for field in fos:
name = field['name']
for ref in references_id:
G.add_edge(paper_id, ref)
G.node[paper_id]['emb'] = node_emb
except:
return G
return G
if __name__ == '__main__':
"""
Create Aminer-Citation v-11 Graphs
"""
parser = argparse.ArgumentParser()
parser.add_argument('--topk', type=int, default='100')
parser.add_argument('--process_raw', action='store_true', default=False,
help='Process Raw Data')
parser.add_argument('--make_vocab', action='store_true', default=False,
help='Create Vocab from the raw abstract data')
args = parser.parse_args()
onlyfiles = [f for f in listdir(load_path_rank_base) if isfile(join(
load_path_rank_base, f))]
vocab = Lang()
if args.make_vocab:
process_raw_abstracts(vocab)
joblib.dump(vocab, 'aminer_100_vocab.pkl')
print('Done generating vocab')
embeddings = gen_embeddings(vocab, file=glove_path, emb_size=300,
emb_dim=300)
joblib.dump(embeddings, 'aminer_100_embed.pkl')
print('Done')
exit()
else:
vocab = joblib.load('aminer_100_vocab.pkl')
embeddings = joblib.load('aminer_100_embed.pkl')
for i, file_ in tqdm(enumerate(onlyfiles), total=len(onlyfiles)):
file_path = load_path_rank_base + file_
G = nx.Graph()
with open(file_path, 'r', encoding='utf8') as f:
for line in f:
data = json.loads(line)
G = process_line(G, data, vocab)
G = check_graph(G)
print('%s has %d Nodes and %d edges' % (file_, len(G), len(G.edges)))
if not os.path.exists(save_path_graph_base):
os.mkdir(save_path_graph_base)
save_path_graph = save_path_graph_base + file_.split('.')[0
] + '_graph.pkl'
nx.write_gpickle(G, save_path_graph)
| <mask token>
nltk.download('stopwords')
nltk_stopwords = nltk.corpus.stopwords.words('english')
data_path = '/home/joey.bose/dblp_papers_v11.txt'
save_path_base = '/home/joey.bose/aminer_data/'
load_path_rank_base = '/home/joey.bose/aminer_data_ranked/fos/'
save_path_graph_base = '/home/joey.bose/aminer_data_ranked/graphs/'
raw_save_path = '/home/joey.bose/aminer_data_ranked/aminer_raw.txt'
spacy_nlp = spacy.load('en_core_web_sm')
glove_path = (
'/home/joey.bose/docker_temp/meta-graph/meta-graph/glove.840B.300d.txt')
class Lang:
def __init__(self):
super(Lang, self).__init__()
self.word2index = {}
self.word2count = {}
self.index2word = {}
self.n_words = 0
def index_words(self, sentence):
for word in sentence:
self.index_word(word)
def index_word(self, word):
if word not in self.word2index:
self.word2index[word] = self.n_words
self.word2count[word] = 1
self.index2word[self.n_words] = word
self.n_words += 1
else:
self.word2count[word] += 1
def gen_embeddings(vocab, file, emb_size, emb_dim):
"""
Generate an initial embedding matrix for word_dict.
If an embedding file is not given or a word is not in the embedding file,
a randomly initialized vector will be used.
"""
embeddings = np.zeros((vocab.n_words, emb_size))
print('Embeddings: %d x %d' % (vocab.n_words, emb_size))
if file is not None:
print('Loading embedding file: %s' % file)
pre_trained = 0
for line in open(file).readlines():
sp = line.split()
if len(sp) == emb_dim + 1:
if sp[0] in vocab.word2index:
pre_trained += 1
embeddings[vocab.word2index[sp[0]]] = [float(x) for x in
sp[1:]]
else:
print(sp[0])
print('Pre-trained: %d (%.2f%%)' % (pre_trained, pre_trained *
100.0 / vocab.n_words))
return embeddings
def process_raw_abstracts(vocab):
with open(raw_save_path, 'r', encoding='utf8') as f:
for line in tqdm(f, total=13304586):
tokens = nltk.tokenize.word_tokenize(line)
tokens = [token for token in tokens if not token in nltk_stopwords]
vocab.index_words(tokens)
def get_node_embed(text, vocab):
sum_embed = 0
for word in text:
embed = embeddings[vocab.word2index[word]]
sum_embed += embed
return sum_embed
def check_graph(G):
total_nodes = len(G.nodes)
no_emb_nodes = 0
nodes_to_delete = []
for node_str in G.nodes:
try:
emb = G.node[node_str]['emb']
except:
no_emb_nodes += 1
nodes_to_delete.append(node_str)
print('%d Nodes and %d missing nodes in G ' % (total_nodes, no_emb_nodes))
G.remove_nodes_from(nodes_to_delete)
return G
def process_line(G, line, vocab=None):
try:
fos = data['fos']
abstract = data['indexed_abstract']
paper_id = data['id']
references_id = data['references']
text = list(abstract['InvertedIndex'].keys())
text = ' '.join(text)
if args.process_raw:
with open(raw_save_path, 'a+') as f:
f.write(text)
f.write('\n')
"""Create Node Embedding if Node doesn't exist """
if vocab is not None:
tokens = nltk.tokenize.word_tokenize(text)
tokens = [token for token in tokens if not token in nltk_stopwords]
node_emb = get_node_embed(tokens, vocab)
for field in fos:
name = field['name']
for ref in references_id:
G.add_edge(paper_id, ref)
G.node[paper_id]['emb'] = node_emb
except:
return G
return G
if __name__ == '__main__':
"""
Create Aminer-Citation v-11 Graphs
"""
parser = argparse.ArgumentParser()
parser.add_argument('--topk', type=int, default='100')
parser.add_argument('--process_raw', action='store_true', default=False,
help='Process Raw Data')
parser.add_argument('--make_vocab', action='store_true', default=False,
help='Create Vocab from the raw abstract data')
args = parser.parse_args()
onlyfiles = [f for f in listdir(load_path_rank_base) if isfile(join(
load_path_rank_base, f))]
vocab = Lang()
if args.make_vocab:
process_raw_abstracts(vocab)
joblib.dump(vocab, 'aminer_100_vocab.pkl')
print('Done generating vocab')
embeddings = gen_embeddings(vocab, file=glove_path, emb_size=300,
emb_dim=300)
joblib.dump(embeddings, 'aminer_100_embed.pkl')
print('Done')
exit()
else:
vocab = joblib.load('aminer_100_vocab.pkl')
embeddings = joblib.load('aminer_100_embed.pkl')
for i, file_ in tqdm(enumerate(onlyfiles), total=len(onlyfiles)):
file_path = load_path_rank_base + file_
G = nx.Graph()
with open(file_path, 'r', encoding='utf8') as f:
for line in f:
data = json.loads(line)
G = process_line(G, data, vocab)
G = check_graph(G)
print('%s has %d Nodes and %d edges' % (file_, len(G), len(G.edges)))
if not os.path.exists(save_path_graph_base):
os.mkdir(save_path_graph_base)
save_path_graph = save_path_graph_base + file_.split('.')[0
] + '_graph.pkl'
nx.write_gpickle(G, save_path_graph)
| import json
import os
import ipdb
from tqdm import tqdm
import argparse
from os import listdir
from os.path import isfile, join
import pickle
import joblib
from collections import Counter
from shutil import copyfile
import networkx as nx
import spacy
import nltk
import numpy as np
nltk.download('stopwords')
nltk_stopwords = nltk.corpus.stopwords.words('english')
data_path = '/home/joey.bose/dblp_papers_v11.txt'
save_path_base = '/home/joey.bose/aminer_data/'
load_path_rank_base = '/home/joey.bose/aminer_data_ranked/fos/'
save_path_graph_base = '/home/joey.bose/aminer_data_ranked/graphs/'
raw_save_path = '/home/joey.bose/aminer_data_ranked/aminer_raw.txt'
spacy_nlp = spacy.load('en_core_web_sm')
glove_path = '/home/joey.bose/docker_temp/meta-graph/meta-graph/glove.840B.300d.txt'
class Lang:
def __init__(self):
super(Lang, self).__init__()
self.word2index = {}
self.word2count = {}
self.index2word = {}
self.n_words = 0 # Count default tokens
def index_words(self, sentence):
for word in sentence:
self.index_word(word)
def index_word(self, word):
if word not in self.word2index:
self.word2index[word] = self.n_words
self.word2count[word] = 1
self.index2word[self.n_words] = word
self.n_words += 1
else:
self.word2count[word] += 1
def gen_embeddings(vocab, file, emb_size, emb_dim):
"""
Generate an initial embedding matrix for word_dict.
If an embedding file is not given or a word is not in the embedding file,
a randomly initialized vector will be used.
"""
# embeddings = np.random.randn(vocab.n_words, emb_size) * 0.01
embeddings = np.zeros((vocab.n_words, emb_size))
print('Embeddings: %d x %d' % (vocab.n_words, emb_size))
if file is not None:
print('Loading embedding file: %s' % file)
pre_trained = 0
for line in open(file).readlines():
sp = line.split()
if(len(sp) == emb_dim + 1):
if sp[0] in vocab.word2index:
pre_trained += 1
embeddings[vocab.word2index[sp[0]]] = [float(x) for x in sp[1:]]
else:
print(sp[0])
print('Pre-trained: %d (%.2f%%)' % (pre_trained, pre_trained * 100.0 / vocab.n_words))
return embeddings
def process_raw_abstracts(vocab):
with open(raw_save_path,"r",encoding="utf8") as f:
for line in tqdm(f,total=13304586):
tokens = nltk.tokenize.word_tokenize(line)
tokens = [token for token in tokens if not token in nltk_stopwords]
vocab.index_words(tokens)
def get_node_embed(text,vocab):
sum_embed = 0
for word in text:
embed = embeddings[vocab.word2index[word]]
sum_embed += embed
return sum_embed
def check_graph(G):
total_nodes = len(G.nodes)
no_emb_nodes = 0
nodes_to_delete = []
for node_str in G.nodes:
try:
emb = G.node[node_str]['emb']
except:
no_emb_nodes += 1
nodes_to_delete.append(node_str)
print("%d Nodes and %d missing nodes in G " %(total_nodes, no_emb_nodes))
G.remove_nodes_from(nodes_to_delete)
return G
def process_line(G, line, vocab=None):
try:
fos = data['fos']
abstract = data['indexed_abstract']
paper_id = data['id']
references_id = data['references']
text = list(abstract['InvertedIndex'].keys())
text =" ".join(text)
if args.process_raw:
with open(raw_save_path,"a+") as f:
f.write(text)
f.write('\n')
'''Create Node Embedding if Node doesn't exist '''
if vocab is not None:
tokens = nltk.tokenize.word_tokenize(text)
tokens = [token for token in tokens if not token in nltk_stopwords]
node_emb = get_node_embed(tokens,vocab)
for field in fos:
name = field['name']
for ref in references_id:
G.add_edge(paper_id, ref)
G.node[paper_id]['emb'] = node_emb
except:
return G
return G
if __name__ == '__main__':
"""
Create Aminer-Citation v-11 Graphs
"""
parser = argparse.ArgumentParser()
parser.add_argument('--topk', type=int, default='100')
parser.add_argument("--process_raw", action="store_true", default=False,
help='Process Raw Data')
parser.add_argument("--make_vocab", action="store_true", default=False,
help='Create Vocab from the raw abstract data')
args = parser.parse_args()
onlyfiles = [f for f in listdir(load_path_rank_base) if isfile(join(load_path_rank_base, f))]
vocab = Lang()
if args.make_vocab:
process_raw_abstracts(vocab)
joblib.dump(vocab, "aminer_100_vocab.pkl")
print("Done generating vocab")
embeddings = gen_embeddings(vocab,file=glove_path,emb_size=300,emb_dim=300)
joblib.dump(embeddings, "aminer_100_embed.pkl")
print("Done")
exit()
else:
vocab = joblib.load("aminer_100_vocab.pkl")
embeddings = joblib.load("aminer_100_embed.pkl")
for i, file_ in tqdm(enumerate(onlyfiles),total=len(onlyfiles)):
file_path = load_path_rank_base + file_
G = nx.Graph()
with open(file_path,'r', encoding="utf8") as f:
for line in f:
data = json.loads(line)
G = process_line(G,data,vocab)
G = check_graph(G)
print("%s has %d Nodes and %d edges" %(file_,len(G),len(G.edges)))
if not os.path.exists(save_path_graph_base):
os.mkdir(save_path_graph_base)
save_path_graph = save_path_graph_base + file_.split('.')[0] + '_graph.pkl'
nx.write_gpickle(G,save_path_graph)
| [
4,
8,
10,
11,
13
] |
285 | e38ae7f91deed1be00e60b7516210ea1feefe23e | <mask token>
def folders_with_documents(pat_ids, main_dir_name, doc_prog_folder):
str_pat_ids = [str(pat_id) for pat_id in pat_ids]
str_pat_folder_names = [os.path.join(main_dir_name, os.path.join(
str_pat_id, doc_prog_folder)) for str_pat_id in str_pat_ids]
for pid, folder in zip(str_pat_ids, str_pat_folder_names):
if os.path.isdir(folder) and len(os.listdir(folder)) > 1:
yield pid, folder
def df_chunksof_100(df):
lendf = len(df)
top100 = df.iloc[:, :100]
df = df.iloc[:, 100:]
yield top100, df
def getvalid_src_dest_filepaths(config, pat_ids, main_dir_name, doctype_info):
idx_chkr_cls = doctype_info['class'].initialize(config)
log, indx_filename = doctype_info['log'], idx_chkr_cls.__name__ + '.csv'
for pid, pid_src_folder in folders_with_documents(pat_ids,
main_dir_name, doctype_info['folder']):
index_file_path = os.path.join(pid_src_folder, indx_filename)
idxfile_data, built_src_filenames = idx_chkr_cls.get_idxfile_data(pid,
index_file_path)
if not idxfile_data:
logger.debug(f'{pid}{log} | Issue(s) with IndexFile. Skipping..')
continue
not_in_indexbuilt_files, not_in_folderlisting = source_files.validate(
pid, built_src_filenames, pid_src_folder, log, indx_filename)
if any([*not_in_indexbuilt_files, *not_in_folderlisting]):
logger.debug(f'{pid}{log} | invalid folder. skipping')
continue
for idxfile_row, built_src_file in zip(idxfile_data,
built_src_filenames):
destn_filename = idx_chkr_cls.get_destination_filename(idxfile_row)
if not destn_filename:
logger.error(
f'{pid}{log} | Could not form the destination filename string'
)
continue
logger.info(
f'{pid}{log} | src: {built_src_file} dest:{destn_filename}')
yield pid, built_src_file, destn_filename
def main():
config = setup_cli(sys.argv[1:])
logger.info(
f'{sys.argv[0]} -f {config.file_type} -s {config.start_range} -e {config.end_range} -d {config.base_directory}'
)
docinfo = DOC_TYPE[config.file_type]
main_dir_name = os.path.join(config.base_directory, 'Patients')
results_doc = os.path.join(os.path.join(config.base_directory,
'results'), docinfo['folder'])
logger.debug(f'results doc : {results_doc}')
parser = ConfigParser()
parser.read('./config/settings.indexfile.ini')
outfile = os.path.join('output',
f"{docinfo['folder']}.paths_pids.{config.start_range}-{config.end_range}.csv"
)
if not os.path.isfile(outfile):
with open(outfile, 'w', newline='') as csv_file:
write_to_file('pid', 'src', 'dest', csv_file, 'size')
with open(outfile, 'a', newline='') as csv_file:
for p, s, d in getvalid_src_dest_filepaths(parser, range(config.
start_range, config.end_range), main_dir_name, docinfo):
full_src_path = (
f"{main_dir_name}{os.sep}{p}{os.sep}{docinfo['folder']}{os.sep}{s}"
)
write_to_file(p, s, d, csv_file, os.path.getsize(full_src_path))
<mask token>
| <mask token>
def folders_with_documents(pat_ids, main_dir_name, doc_prog_folder):
str_pat_ids = [str(pat_id) for pat_id in pat_ids]
str_pat_folder_names = [os.path.join(main_dir_name, os.path.join(
str_pat_id, doc_prog_folder)) for str_pat_id in str_pat_ids]
for pid, folder in zip(str_pat_ids, str_pat_folder_names):
if os.path.isdir(folder) and len(os.listdir(folder)) > 1:
yield pid, folder
def df_chunksof_100(df):
lendf = len(df)
top100 = df.iloc[:, :100]
df = df.iloc[:, 100:]
yield top100, df
def getvalid_src_dest_filepaths(config, pat_ids, main_dir_name, doctype_info):
idx_chkr_cls = doctype_info['class'].initialize(config)
log, indx_filename = doctype_info['log'], idx_chkr_cls.__name__ + '.csv'
for pid, pid_src_folder in folders_with_documents(pat_ids,
main_dir_name, doctype_info['folder']):
index_file_path = os.path.join(pid_src_folder, indx_filename)
idxfile_data, built_src_filenames = idx_chkr_cls.get_idxfile_data(pid,
index_file_path)
if not idxfile_data:
logger.debug(f'{pid}{log} | Issue(s) with IndexFile. Skipping..')
continue
not_in_indexbuilt_files, not_in_folderlisting = source_files.validate(
pid, built_src_filenames, pid_src_folder, log, indx_filename)
if any([*not_in_indexbuilt_files, *not_in_folderlisting]):
logger.debug(f'{pid}{log} | invalid folder. skipping')
continue
for idxfile_row, built_src_file in zip(idxfile_data,
built_src_filenames):
destn_filename = idx_chkr_cls.get_destination_filename(idxfile_row)
if not destn_filename:
logger.error(
f'{pid}{log} | Could not form the destination filename string'
)
continue
logger.info(
f'{pid}{log} | src: {built_src_file} dest:{destn_filename}')
yield pid, built_src_file, destn_filename
def main():
config = setup_cli(sys.argv[1:])
logger.info(
f'{sys.argv[0]} -f {config.file_type} -s {config.start_range} -e {config.end_range} -d {config.base_directory}'
)
docinfo = DOC_TYPE[config.file_type]
main_dir_name = os.path.join(config.base_directory, 'Patients')
results_doc = os.path.join(os.path.join(config.base_directory,
'results'), docinfo['folder'])
logger.debug(f'results doc : {results_doc}')
parser = ConfigParser()
parser.read('./config/settings.indexfile.ini')
outfile = os.path.join('output',
f"{docinfo['folder']}.paths_pids.{config.start_range}-{config.end_range}.csv"
)
if not os.path.isfile(outfile):
with open(outfile, 'w', newline='') as csv_file:
write_to_file('pid', 'src', 'dest', csv_file, 'size')
with open(outfile, 'a', newline='') as csv_file:
for p, s, d in getvalid_src_dest_filepaths(parser, range(config.
start_range, config.end_range), main_dir_name, docinfo):
full_src_path = (
f"{main_dir_name}{os.sep}{p}{os.sep}{docinfo['folder']}{os.sep}{s}"
)
write_to_file(p, s, d, csv_file, os.path.getsize(full_src_path))
if __name__ == '__main__':
main()
| <mask token>
logger = logging.getLogger(__name__)
DOCUMENTS = 1
PROGRESS_NOTES = 2
DOC_TYPE = {DOCUMENTS: {'file_type': 'Document', 'folder': 'Documents',
'class': DocumentIndex, 'log': 'd', 'dates': ['CREATED_TIMESTAMP',
'POST_DATE'], 'converters': {'FILENAME': strip, 'DISPLAY_DESC': strip,
'DOC_COMMENT': strip, 'DOCUMENT_TYPE': strip}, 'staff_field':
'displayname'}, PROGRESS_NOTES: {'file_type': 'ProgressNote', 'folder':
'ProgressNotes', 'class': ProgressNoteIndex, 'log': 'p', 'dates': [
'CREATED_TIMESTAMP'], 'converters': {}, 'staff_field': 'login'}}
def folders_with_documents(pat_ids, main_dir_name, doc_prog_folder):
str_pat_ids = [str(pat_id) for pat_id in pat_ids]
str_pat_folder_names = [os.path.join(main_dir_name, os.path.join(
str_pat_id, doc_prog_folder)) for str_pat_id in str_pat_ids]
for pid, folder in zip(str_pat_ids, str_pat_folder_names):
if os.path.isdir(folder) and len(os.listdir(folder)) > 1:
yield pid, folder
def df_chunksof_100(df):
lendf = len(df)
top100 = df.iloc[:, :100]
df = df.iloc[:, 100:]
yield top100, df
def getvalid_src_dest_filepaths(config, pat_ids, main_dir_name, doctype_info):
idx_chkr_cls = doctype_info['class'].initialize(config)
log, indx_filename = doctype_info['log'], idx_chkr_cls.__name__ + '.csv'
for pid, pid_src_folder in folders_with_documents(pat_ids,
main_dir_name, doctype_info['folder']):
index_file_path = os.path.join(pid_src_folder, indx_filename)
idxfile_data, built_src_filenames = idx_chkr_cls.get_idxfile_data(pid,
index_file_path)
if not idxfile_data:
logger.debug(f'{pid}{log} | Issue(s) with IndexFile. Skipping..')
continue
not_in_indexbuilt_files, not_in_folderlisting = source_files.validate(
pid, built_src_filenames, pid_src_folder, log, indx_filename)
if any([*not_in_indexbuilt_files, *not_in_folderlisting]):
logger.debug(f'{pid}{log} | invalid folder. skipping')
continue
for idxfile_row, built_src_file in zip(idxfile_data,
built_src_filenames):
destn_filename = idx_chkr_cls.get_destination_filename(idxfile_row)
if not destn_filename:
logger.error(
f'{pid}{log} | Could not form the destination filename string'
)
continue
logger.info(
f'{pid}{log} | src: {built_src_file} dest:{destn_filename}')
yield pid, built_src_file, destn_filename
def main():
config = setup_cli(sys.argv[1:])
logger.info(
f'{sys.argv[0]} -f {config.file_type} -s {config.start_range} -e {config.end_range} -d {config.base_directory}'
)
docinfo = DOC_TYPE[config.file_type]
main_dir_name = os.path.join(config.base_directory, 'Patients')
results_doc = os.path.join(os.path.join(config.base_directory,
'results'), docinfo['folder'])
logger.debug(f'results doc : {results_doc}')
parser = ConfigParser()
parser.read('./config/settings.indexfile.ini')
outfile = os.path.join('output',
f"{docinfo['folder']}.paths_pids.{config.start_range}-{config.end_range}.csv"
)
if not os.path.isfile(outfile):
with open(outfile, 'w', newline='') as csv_file:
write_to_file('pid', 'src', 'dest', csv_file, 'size')
with open(outfile, 'a', newline='') as csv_file:
for p, s, d in getvalid_src_dest_filepaths(parser, range(config.
start_range, config.end_range), main_dir_name, docinfo):
full_src_path = (
f"{main_dir_name}{os.sep}{p}{os.sep}{docinfo['folder']}{os.sep}{s}"
)
write_to_file(p, s, d, csv_file, os.path.getsize(full_src_path))
if __name__ == '__main__':
main()
| import sys
import os
from configparser import ConfigParser
import logging
from mod_argparse import setup_cli
from checkers.IndexFile import DocumentIndex, ProgressNoteIndex
from checkers import source_files
from utilities import write_to_file, strip
logger = logging.getLogger(__name__)
DOCUMENTS = 1
PROGRESS_NOTES = 2
DOC_TYPE = {DOCUMENTS: {'file_type': 'Document', 'folder': 'Documents',
'class': DocumentIndex, 'log': 'd', 'dates': ['CREATED_TIMESTAMP',
'POST_DATE'], 'converters': {'FILENAME': strip, 'DISPLAY_DESC': strip,
'DOC_COMMENT': strip, 'DOCUMENT_TYPE': strip}, 'staff_field':
'displayname'}, PROGRESS_NOTES: {'file_type': 'ProgressNote', 'folder':
'ProgressNotes', 'class': ProgressNoteIndex, 'log': 'p', 'dates': [
'CREATED_TIMESTAMP'], 'converters': {}, 'staff_field': 'login'}}
def folders_with_documents(pat_ids, main_dir_name, doc_prog_folder):
str_pat_ids = [str(pat_id) for pat_id in pat_ids]
str_pat_folder_names = [os.path.join(main_dir_name, os.path.join(
str_pat_id, doc_prog_folder)) for str_pat_id in str_pat_ids]
for pid, folder in zip(str_pat_ids, str_pat_folder_names):
if os.path.isdir(folder) and len(os.listdir(folder)) > 1:
yield pid, folder
def df_chunksof_100(df):
lendf = len(df)
top100 = df.iloc[:, :100]
df = df.iloc[:, 100:]
yield top100, df
def getvalid_src_dest_filepaths(config, pat_ids, main_dir_name, doctype_info):
idx_chkr_cls = doctype_info['class'].initialize(config)
log, indx_filename = doctype_info['log'], idx_chkr_cls.__name__ + '.csv'
for pid, pid_src_folder in folders_with_documents(pat_ids,
main_dir_name, doctype_info['folder']):
index_file_path = os.path.join(pid_src_folder, indx_filename)
idxfile_data, built_src_filenames = idx_chkr_cls.get_idxfile_data(pid,
index_file_path)
if not idxfile_data:
logger.debug(f'{pid}{log} | Issue(s) with IndexFile. Skipping..')
continue
not_in_indexbuilt_files, not_in_folderlisting = source_files.validate(
pid, built_src_filenames, pid_src_folder, log, indx_filename)
if any([*not_in_indexbuilt_files, *not_in_folderlisting]):
logger.debug(f'{pid}{log} | invalid folder. skipping')
continue
for idxfile_row, built_src_file in zip(idxfile_data,
built_src_filenames):
destn_filename = idx_chkr_cls.get_destination_filename(idxfile_row)
if not destn_filename:
logger.error(
f'{pid}{log} | Could not form the destination filename string'
)
continue
logger.info(
f'{pid}{log} | src: {built_src_file} dest:{destn_filename}')
yield pid, built_src_file, destn_filename
def main():
config = setup_cli(sys.argv[1:])
logger.info(
f'{sys.argv[0]} -f {config.file_type} -s {config.start_range} -e {config.end_range} -d {config.base_directory}'
)
docinfo = DOC_TYPE[config.file_type]
main_dir_name = os.path.join(config.base_directory, 'Patients')
results_doc = os.path.join(os.path.join(config.base_directory,
'results'), docinfo['folder'])
logger.debug(f'results doc : {results_doc}')
parser = ConfigParser()
parser.read('./config/settings.indexfile.ini')
outfile = os.path.join('output',
f"{docinfo['folder']}.paths_pids.{config.start_range}-{config.end_range}.csv"
)
if not os.path.isfile(outfile):
with open(outfile, 'w', newline='') as csv_file:
write_to_file('pid', 'src', 'dest', csv_file, 'size')
with open(outfile, 'a', newline='') as csv_file:
for p, s, d in getvalid_src_dest_filepaths(parser, range(config.
start_range, config.end_range), main_dir_name, docinfo):
full_src_path = (
f"{main_dir_name}{os.sep}{p}{os.sep}{docinfo['folder']}{os.sep}{s}"
)
write_to_file(p, s, d, csv_file, os.path.getsize(full_src_path))
if __name__ == '__main__':
main()
| import sys
import os
from configparser import ConfigParser
import logging
from mod_argparse import setup_cli
from checkers.IndexFile import DocumentIndex, ProgressNoteIndex
from checkers import source_files
from utilities import write_to_file, strip # , write_to_db_isok
# import pandas as pd
logger = logging.getLogger(__name__)
DOCUMENTS = 1
PROGRESS_NOTES = 2
DOC_TYPE = {
DOCUMENTS: {
'file_type': 'Document',
'folder': 'Documents',
'class': DocumentIndex,
'log': 'd',
'dates': ['CREATED_TIMESTAMP', 'POST_DATE'],
'converters': {'FILENAME': strip,
'DISPLAY_DESC': strip,
'DOC_COMMENT': strip,
'DOCUMENT_TYPE': strip},
'staff_field': 'displayname'
},
PROGRESS_NOTES: {
'file_type': 'ProgressNote',
'folder': 'ProgressNotes',
'class': ProgressNoteIndex,
'log': 'p',
'dates': ['CREATED_TIMESTAMP'],
'converters': {},
'staff_field': 'login'
}
}
def folders_with_documents(pat_ids, main_dir_name, doc_prog_folder):
str_pat_ids = [str(pat_id) for pat_id in pat_ids]
str_pat_folder_names = [os.path.join(main_dir_name, os.path.join(str_pat_id, doc_prog_folder))
for str_pat_id in str_pat_ids] # Patients/*/Documents
for pid, folder in zip(str_pat_ids, str_pat_folder_names):
if os.path.isdir(folder) and len(os.listdir(folder)) > 1:
yield pid, folder
#
# def write_db_isok(list, pid):
# df = pd.concat(list)
# return write_to_db_isok(df, pid)
def df_chunksof_100(df):
lendf = len(df)
top100 = df.iloc[:, :100]
df = df.iloc[:, 100:]
yield top100, df
#
# def indxfiles_todb(config, pat_ids, main_dir_name, doctype_info):
# idx_chkr_cls = doctype_info['class'].initialize(config)
# log, indx_filename = doctype_info['log'], idx_chkr_cls.__name__ + '.csv'
#
# # df = pd.DataFrame()
# list_ = []
# pid_start = 0
# pid_end = 0
# df_rows = 0
# # https://stackoverflow.com/questions/50689082/to-sql-pyodbc-count-field-incorrect-or-syntax-error
# skipped_list = []
# for pid, pid_src_folder in folders_with_documents(pat_ids, main_dir_name, doctype_info['folder']):
# index_file_path = os.path.join(pid_src_folder, indx_filename)
# #int_pid = int(pid)
# pid_end = int(pid)
# try:
# df = pd.read_csv(index_file_path, index_col=None, header=0,
# converters=doctype_info['converters'],
# # date_parser=pd.core.tools.datetimes.to_datetime,
# # parse_dates=doctype_info['dates']
# )
# df['PID'] = pid
#
# #len_df = len(df)
#
# # if len_df > 50:
# # df_1 = pd.concat(list_)
# # if write_to_db_isok(df_1, f'{pid_start} - {pid_end}'):
# # logger.info(f'Wrote to DB..upto pid: {pid} dfrows: {df_rows}')
# # else:
# # logger.error(f'Skipping batch because of errors : {pid_start} - {pid_end} dfrows: {df_rows} ')
# # skipped_list.extend(range(pid_start, pid_end))
# # pid_start = pid_end + 1
# #
# # else:
# # list_.append(df)
# # df_rows += len_df
#
# list_.append(df)
# df_rows += len(df)
# if len(list_) > 5 or df_rows > 30:
# df = pd.concat(list_)
# if write_to_db_isok(df, f'{pid_start} - {pid_end}'):
# logger.info(f'Wrote to DB..upto pid: {pid} dfrows: {df_rows}')
# else:
# logger.error(f'Skipping batch because of errors : {pid_start} - {pid_end} dfrows: {df_rows} ')
# skipped_list.extend(range(pid_start, pid_end))
# list_ = []
# pid_start = pid_end+1
# df_rows = 0
#
# except pd.errors.ParserError as pe:
# logger.error(f"Something went wrong (ParseError) pid:{pid}. skipping..")
# list_ = []
# pid_start = pid_end + 1
# df_rows = 0
# continue
#
# if list_:
# df = pd.concat(list_)
# if write_to_db_isok(df, {pid_start} - {pid_end}):
# logger.info(f'Wrote to DB..upto pid: {pid}')
#
# logger.warning(f'Skipped list of pids: {",".join([str(i) for i in skipped_list])}')
def getvalid_src_dest_filepaths(config, pat_ids, main_dir_name, doctype_info):
idx_chkr_cls = doctype_info['class'].initialize(config)
log, indx_filename = doctype_info['log'], idx_chkr_cls.__name__ + '.csv'
for pid, pid_src_folder in folders_with_documents(pat_ids, main_dir_name, doctype_info['folder']):
index_file_path = os.path.join(pid_src_folder, indx_filename)
idxfile_data, built_src_filenames = idx_chkr_cls.get_idxfile_data(pid, index_file_path)
if not idxfile_data:
logger.debug(f'{pid}{log} | Issue(s) with IndexFile. Skipping..')
continue
not_in_indexbuilt_files, not_in_folderlisting = source_files.validate(pid, built_src_filenames,
pid_src_folder, log, indx_filename)
if any([*not_in_indexbuilt_files, *not_in_folderlisting]):
logger.debug(f'{pid}{log} | invalid folder. skipping')
continue
# . . . . . . . . . . . folder listing should match the file names built from index file . . . . . . . .
for idxfile_row, built_src_file in zip(idxfile_data, built_src_filenames):
destn_filename = idx_chkr_cls.get_destination_filename(idxfile_row)
if not destn_filename:
logger.error(f'{pid}{log} | Could not form the destination filename string')
continue
logger.info(f'{pid}{log} | src: {built_src_file} dest:{destn_filename}')
yield pid, built_src_file, destn_filename
def main():
config = setup_cli(sys.argv[1:])
logger.info(f'{sys.argv[0]} -f {config.file_type} -s {config.start_range}'
f' -e {config.end_range} -d {config.base_directory}')
docinfo = DOC_TYPE[config.file_type]
main_dir_name = os.path.join(config.base_directory, 'Patients')
results_doc = os.path.join(os.path.join(config.base_directory, 'results'), docinfo['folder'])
logger.debug(f'results doc : {results_doc}')
parser = ConfigParser()
parser.read('./config/settings.indexfile.ini')
outfile = os.path.join('output', f"{docinfo['folder']}.paths_pids.{config.start_range}-{config.end_range}.csv")
if not os.path.isfile(outfile):
with open(outfile, 'w', newline='') as csv_file:
write_to_file("pid", "src", "dest", csv_file, "size")
#indxfiles_todb(parser, range(config.start_range, config.end_range),
# main_dir_name, doctype_info)
with open(outfile, 'a', newline='') as csv_file:
for p, s, d in getvalid_src_dest_filepaths(parser, range(config.start_range, config.end_range),
main_dir_name, docinfo):
full_src_path = f"{main_dir_name}{os.sep}{p}{os.sep}{docinfo['folder']}{os.sep}{s}"
write_to_file(p, s, d, csv_file, os.path.getsize(full_src_path))
if __name__ == "__main__":
main()
# from checkers import sanitize
# print(sanitize.fix_altdoctitle('None'))
# print(sanitize.fix_doctitle('None'))
# print(sanitize.fix_timestamp('None'))
# print(sanitize.fix_pid('None'))
# print(sanitize.fix_fileno('None'))
# print(sanitize.fix_ext('...'))
# print(sanitize.fix_altdoctitle(None))
# print(sanitize.fix_doctitle(None))
# print(sanitize.fix_timestamp(None))
# print(sanitize.fix_pid(None))
# print(sanitize.fix_fileno(None))
# print(sanitize.fix_ext('aa'))
# print(sanitize.fix_ext('asd'))
# print(sanitize.fix_ext('bbbb'))
# print(sanitize.fix_ext('ccccc'))
# print(sanitize.fix_pid('asdf'))
# print(sanitize.fix_fileno(3.4))
| [
4,
5,
6,
7,
8
] |
286 | 069338b188f3cf16357b2502cbb3130b69918bd9 | <mask token>
| <mask token>
if __name__ == '__main__':
exit(cli.main(prog_name='htmap'))
| from .cli import cli
if __name__ == '__main__':
exit(cli.main(prog_name='htmap'))
| from .cli import cli
if __name__ == "__main__":
exit(cli.main(prog_name="htmap"))
| null | [
0,
1,
2,
3
] |
287 | b52269237d66ea50c453395b9536f25f1310bf2e | #!/usr/bin/env python2.7
# -*- coding: utf-8 -*-
import re
from blessings import Terminal
from validate_email import validate_email
import requests
import sys
_site_ = sys.argv[1]
_saida_ = sys.argv[2]
_file_ = open(_saida_, "w")
t = Terminal()
r = requests.get(_site_, headers={'User-Agent': 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36'})
conteudo = r.content
_file_ = open(_saida_, "w")
_filtro_ = re.findall(r'[\w\.-]+@[\w\.-]+', conteudo)
for line in _filtro_:
if validate_email(line, verify=True)==False:
print line, t.red("INVALID!")
else:
print line, t.green("OK!")
_file_.write(line)
_file_.write("\n")
| null | null | null | null | [
0
] |
288 | 8c539dbbb762717393b9a71ddca8eb3872890854 | <mask token>
| <mask token>
def process_names():
"""
Opening, reading name file and building name array.
"""
with open(input_names_file, 'r') as data:
plaintext = data.read()
name_array = plaintext.split('\n')
final_name_list = []
for name in name_array:
if len(name.split(',')) == 2:
temp_name_list = re.split(reg_ex, name)
last_name = temp_name_list.pop()
first_name = temp_name_list.pop()
final_name_list.append(last_name + ',' + first_name)
elif len(name.split(' ')) == 2:
final_name_list.append(name.replace(' ', ','))
elif len(name.split(' ')) == 3:
temp_name_list = re.split(' ', name)
last_name = temp_name_list.pop()
middle_name = temp_name_list.pop()
first_name = temp_name_list.pop()
final_name_list.append(first_name + ',' + middle_name + ' ' +
last_name)
else:
final_name_list.append(name)
with open(output_names_file, 'w') as txt_file:
txt_file.write('first_name,last_name' + '\n')
for name in final_name_list:
txt_file.write(name + '\n')
names_df = pd.read_csv(output_names_file, names=name_header, sep=',',
engine='python')
| <mask token>
instruments_file = os.path.abspath('instruments.csv')
input_names_file = os.path.abspath('names.txt')
output_names_file = os.path.abspath('names.csv')
inst_name_file = os.path.abspath('name_instrument.csv')
reg_ex = '; |, |\\*|\n'
name_header = ['first_name', 'last_name']
def process_names():
"""
Opening, reading name file and building name array.
"""
with open(input_names_file, 'r') as data:
plaintext = data.read()
name_array = plaintext.split('\n')
final_name_list = []
for name in name_array:
if len(name.split(',')) == 2:
temp_name_list = re.split(reg_ex, name)
last_name = temp_name_list.pop()
first_name = temp_name_list.pop()
final_name_list.append(last_name + ',' + first_name)
elif len(name.split(' ')) == 2:
final_name_list.append(name.replace(' ', ','))
elif len(name.split(' ')) == 3:
temp_name_list = re.split(' ', name)
last_name = temp_name_list.pop()
middle_name = temp_name_list.pop()
first_name = temp_name_list.pop()
final_name_list.append(first_name + ',' + middle_name + ' ' +
last_name)
else:
final_name_list.append(name)
with open(output_names_file, 'w') as txt_file:
txt_file.write('first_name,last_name' + '\n')
for name in final_name_list:
txt_file.write(name + '\n')
names_df = pd.read_csv(output_names_file, names=name_header, sep=',',
engine='python')
| import re
import os
import pandas as pd
instruments_file = os.path.abspath('instruments.csv')
input_names_file = os.path.abspath('names.txt')
output_names_file = os.path.abspath('names.csv')
inst_name_file = os.path.abspath('name_instrument.csv')
reg_ex = '; |, |\\*|\n'
name_header = ['first_name', 'last_name']
def process_names():
"""
Opening, reading name file and building name array.
"""
with open(input_names_file, 'r') as data:
plaintext = data.read()
name_array = plaintext.split('\n')
final_name_list = []
for name in name_array:
if len(name.split(',')) == 2:
temp_name_list = re.split(reg_ex, name)
last_name = temp_name_list.pop()
first_name = temp_name_list.pop()
final_name_list.append(last_name + ',' + first_name)
elif len(name.split(' ')) == 2:
final_name_list.append(name.replace(' ', ','))
elif len(name.split(' ')) == 3:
temp_name_list = re.split(' ', name)
last_name = temp_name_list.pop()
middle_name = temp_name_list.pop()
first_name = temp_name_list.pop()
final_name_list.append(first_name + ',' + middle_name + ' ' +
last_name)
else:
final_name_list.append(name)
with open(output_names_file, 'w') as txt_file:
txt_file.write('first_name,last_name' + '\n')
for name in final_name_list:
txt_file.write(name + '\n')
names_df = pd.read_csv(output_names_file, names=name_header, sep=',',
engine='python')
| import re
import os
import pandas as pd
instruments_file = os.path.abspath("instruments.csv")
input_names_file = os.path.abspath("names.txt")
output_names_file = os.path.abspath("names.csv")
inst_name_file = os.path.abspath("name_instrument.csv")
reg_ex = '; |, |\\*|\n'
name_header = ["first_name", "last_name"]
def process_names():
"""
Opening, reading name file and building name array.
"""
with open(input_names_file, 'r') as data:
plaintext = data.read()
name_array = plaintext.split('\n')
# Final name list
final_name_list = []
# Parsing different name formats and standardizing to create csv
for name in name_array:
if len(name.split(',')) == 2:
temp_name_list = re.split(reg_ex, name)
last_name = temp_name_list.pop()
first_name = temp_name_list.pop()
final_name_list.append(last_name + ',' + first_name)
elif len(name.split(' ')) == 2:
final_name_list.append(name.replace(' ', ','))
elif len(name.split(' ')) == 3:
temp_name_list = re.split(' ', name)
last_name = temp_name_list.pop()
middle_name = temp_name_list.pop()
first_name = temp_name_list.pop()
final_name_list.append(first_name + ',' + middle_name + ' ' + last_name)
else:
final_name_list.append(name)
# Writing final name list to a file
with open(output_names_file, "w") as txt_file:
txt_file.write("first_name,last_name" + "\n")
for name in final_name_list:
txt_file.write(name + "\n") # works with any number of elements in a line
names_df = pd.read_csv(output_names_file, names=name_header, sep=',', engine='python')
| [
0,
1,
2,
3,
4
] |
289 | 0db0daf9bea254cffaec1280cd13b2d70368cd94 | <mask token>
| <mask token>
for i in range(B):
p1 = 0.0
for j in range(N1):
if rnd.uniform(0, 1) < p1mle:
p1 += 1
p1 /= N1
p2 = 0.0
for j in range(N2):
if rnd.uniform(0, 1) < p2mle:
p2 += 1
p2 /= N2
estimate.append(p2 - p1)
<mask token>
for t in allt:
cur = np.mean(np.sqrt(N1 + N2) * (estimate - taumle) < t)
if np.abs(target - cur) < tol:
print(t)
print(cur)
break
| <mask token>
B = 100000
N1 = 50
N2 = 50
p1mle = 0.3
p2mle = 0.4
taumle = p2mle - p1mle
estimate = []
for i in range(B):
p1 = 0.0
for j in range(N1):
if rnd.uniform(0, 1) < p1mle:
p1 += 1
p1 /= N1
p2 = 0.0
for j in range(N2):
if rnd.uniform(0, 1) < p2mle:
p2 += 1
p2 /= N2
estimate.append(p2 - p1)
t = -10
estimate = np.array(estimate)
allt = [(0.01 * t) for t in xrange(-5000, 5000)]
target = 0.95
tol = 0.01
for t in allt:
cur = np.mean(np.sqrt(N1 + N2) * (estimate - taumle) < t)
if np.abs(target - cur) < tol:
print(t)
print(cur)
break
| import numpy.random as rnd
import numpy as np
B = 100000
N1 = 50
N2 = 50
p1mle = 0.3
p2mle = 0.4
taumle = p2mle - p1mle
estimate = []
for i in range(B):
p1 = 0.0
for j in range(N1):
if rnd.uniform(0, 1) < p1mle:
p1 += 1
p1 /= N1
p2 = 0.0
for j in range(N2):
if rnd.uniform(0, 1) < p2mle:
p2 += 1
p2 /= N2
estimate.append(p2 - p1)
t = -10
estimate = np.array(estimate)
allt = [(0.01 * t) for t in xrange(-5000, 5000)]
target = 0.95
tol = 0.01
for t in allt:
cur = np.mean(np.sqrt(N1 + N2) * (estimate - taumle) < t)
if np.abs(target - cur) < tol:
print(t)
print(cur)
break
| import numpy.random as rnd
import numpy as np
B=100000
N1=50
N2=50
p1mle=0.3
p2mle=0.4
taumle=p2mle-p1mle
estimate=[]
for i in range(B):
p1=0.0
for j in range(N1):
if(rnd.uniform(0,1)<p1mle):
p1+=1
p1/=N1
p2=0.0
for j in range(N2):
if(rnd.uniform(0,1)<p2mle):
p2+=1
p2/=N2
estimate.append(p2-p1)
t=-10
estimate=np.array(estimate)
allt=[0.01*t for t in xrange(-5000,5000)]
target=0.95
tol=0.01
for t in allt:
cur=np.mean(np.sqrt(N1+N2)*(estimate-taumle)<t)
if(np.abs(target-cur)<tol):
print(t)
print(cur)
break | [
0,
1,
2,
3,
4
] |
290 | a90b7e44cc54d4f96a13e5e6e2d15b632d3c4983 | <mask token>
class GroupSignature:
def __init__(self, groupObj):
global util, group
util = SecretUtil(groupObj, debug)
self.group = groupObj
def pkGen(self, h1str):
gstr = (
'[6172776968119684165170291368128433652817636448173749093457023424948260385279837018774774149930982188956916913145008943931711059687988096415181819433817738, 8687587692191287108886119971783525001480020593934954052605681527814232399216375005546606067382536684351686344089456732201641997200939472924879001214689004]'
)
g2str = (
'[7648994551207171188393784904797547917038803147671542540175090956205316897431443264058433935237605598252399113847934759009659621851760599508222321653067284, 922489308494109901795721463782161260386164061515796674638135394871842997698175772871045949554746517321480649326465484116060959631197509151923296896589720]'
)
u0str = (
'[180015966842918451436547451263180245588308971597733548673037049536176684754209695288737508087729924028686259002375511049961436438196866049956546630518033, 1295050197915669955783867959538729894307963685491173858450359845766785488725907727220684060845012524740394664162328817669422178637925195059862486690053923]'
)
u1str = (
'[2555472719769037960206282327195096320915753855199743796256065902544200822503613205017219993060986152240852358189992579821797745072366030183800897743028220, 7573705235093543416041007636313631591000596820214067724084077929638801811700093589294454562385664531190678890366928407286293582994146887505184778221562373]'
)
u2str = (
'[6876276970903121931083294698771200898345396507892092532649392211995185517437159402176975528760594250374462299539306423347676182899798006533425047523984724, 5323739238507219125881988073888745575030677585404965990610324901624530474522642705344792075909082041735695801098770187248023797265998906693745587936574078]'
)
u3str = (
'[6628726193389375981104409894060310698729022957801238449570622103067828518416602275957863668289683360250722835022304456841105526036470008237775051984811323, 862537748555943361001122447731987661405436458862545177179548603003392540530328380518694788420155531238391922289886044667763424887444361610972254938158280]'
)
u4str = (
'[8157254219580822599577995921928211211847392705248772673869189421041858895589817404931780741226510985762564598862965174380020566416411083236239871342674775, 4736677719200783513058679582227494204159737596114643136852532046080608159561620208171676599501713934575216178076006396924589443776642926902969084668055006]'
)
hstr = (
'[6248393417805371388321299785844751688345516419281230263497475615452026459314582553252281068616984105757749673095320346188725995701858182333525688832492249, 351368339412205819108519989143352052898751906937356995136442397753142226531384069336237369861919799955237545207977716196031001184146017796598836939617335]'
)
nstr = (
'[75201312764006187596691102237923705656296213254701583615255122742135170369075831428394751330697143847448434841509551532135632624530360013837581615049543, 3886258599652934715331576083899336629981754505948456216299528998628273512432828729344158706718479567056972375128622026273382126529171409058157562418608963]'
)
g = self.group.fromstr(gstr, 10, G1)
g2 = self.group.fromstr(g2str, 10, G2)
u0 = self.group.fromstr(u0str, 10, G2)
u1 = self.group.fromstr(u1str, 10, G2)
u2 = self.group.fromstr(u2str, 10, G2)
u3 = self.group.fromstr(u3str, 10, G2)
u4 = self.group.fromstr(u4str, 10, G2)
h = self.group.fromstr(hstr, 10, G1)
n = self.group.fromstr(nstr, 10, GT)
h1 = self.group.fromstr(h1str, 10, G1)
pk = {'g': g, 'g2': g2, 'u0': u0, 'u1': u1, 'u2': u2, 'u3': u3,
'u4': u4, 'h': h, 'n': n, 'h1': h1}
return pk
def uskGen(self, usklist, pk, GID, UID, L, k):
t1 = time()
b0 = self.group.gen1_0(1)
b3 = self.group.gen1_0(1)
b4 = self.group.gen1_0(1)
b5 = self.group.gen1_0(1)
r2 = self.group.random(ZR)
for i in range(k):
b0 = b0 * usklist[i]['b0'] ** L[i]
b3 = b3 * usklist[i]['b3'] ** L[i]
b4 = b4 * usklist[i]['b4'] ** L[i]
b5 = b5 * usklist[i]['b5'] ** L[i]
b0 = b0 * (pk['u0'] * pk['u1'] ** GID * pk['u2'] ** UID) ** r2
b3 = b3 * pk['u3'] ** r2
b4 = b4 * pk['u4'] ** r2
b5 = b5 * pk['g'] ** r2
usk = {'b0': b0, 'b3': b3, 'b4': b4, 'b5': b5}
t2 = time()
with open('extracttime.txt', 'a') as f:
f.write(str(t2 - t1))
f.write('\n')
return usk
def LGen(self, n, k):
L = []
I = self.group.random(ZR)
J = self.group.random(ZR)
for i in range(n):
L.append(self.group.random(ZR))
L[i].set(1)
I.set(i + 1)
for j in range(1, k + 1):
print(j)
J.set(j)
if i + 1 != j:
L[i] = L[i] * (J / (J - I))
return L
def verifyUsk(self, usk, vk, pk, GID, UID):
g = pk['g']
g2 = pk['g2']
u0 = pk['u0']
u1 = pk['u1']
u2 = pk['u2']
u3 = pk['u3']
u4 = pk['u4']
b0 = usk['b0']
b5 = usk['b5']
b3 = usk['b3']
b4 = usk['b4']
return pair(g, b0) == pair(vk, g2) * pair(b5, u0) * pair(b5, u1 ** GID
) * pair(b5, u2 ** UID) and pair(g, b3) == pair(b5, u3) and pair(g,
b4) == pair(b5, u4)
def sign(self, title, usk, pk, GID, UID, groupID):
t1 = time()
m = self.group.hash(title)
b0 = usk['b0']
b3 = usk['b3']
b4 = usk['b4']
b5 = usk['b5']
r4 = self.group.random(ZR)
r3 = self.group.random(ZR)
k = self.group.random(ZR)
c0 = b0 * b3 ** m * b4 ** r4 * (pk['u0'] * pk['u1'] ** GID * pk[
'u2'] ** UID * pk['u3'] ** m * pk['u4'] ** r4) ** r3
c5 = b5 * pk['g'] ** r3
c6 = pk['u2'] ** UID * pk['u4'] ** r4
e1 = pk['g'] ** k
e2 = (pk['u0'] * pk['u1'] ** GID) ** k
e3 = pk['n'] ** UID * pair(pk['h1'], pk['g2']) ** k
f = pk['u0'] * pk['u1'] ** GID
gp = pair(pk['h1'], pk['g2'])
k1 = self.group.random(ZR)
k2 = self.group.random(ZR)
k3 = self.group.random(ZR)
r1 = pk['u2'] ** k1 * pk['u4'] ** k2
r2 = pk['g'] ** k3
r3 = f ** k3
t4 = pk['n'] ** k1 * gp ** k3
hashstr = str(r1) + str(r2) + str(r3) + str(t4)
c = self.group.hash(hashstr)
s1 = k1 + c * UID
s2 = k2 + c * r4
s3 = k3 + c * k
signature = {'c0': c0, 'c5': c5, 'c6': c6, 'e1': e1, 'e2': e2, 'e3':
e3, 'c': c, 's1': s1, 's2': s2, 's3': s3}
t2 = time()
with open('gssigntime.txt', 'a') as f:
f.write(str(t2 - t1))
f.write('\n')
print('gs time', t2 - t1)
return signature
def open(self, okliststr, L, k):
t1 = time()
oklist = []
for ok in okliststr:
oklist.append({'ok1': self.group.fromstr(ok['ok1'], 10, GT),
'ok2': self.group.fromstr(ok['ok2'], 10, GT)})
ok1 = self.group.gen1_0(1)
ok2 = self.group.gen1_0(1)
for i in range(k):
ok1 = ok1 * oklist[i]['ok1'] ** L[i]
ok2 = ok2 * oklist[i]['ok2'] ** L[i]
t2 = time()
with open('opentime.txt', 'a') as f:
f.write(str(t2 - t1))
f.write('\n')
print('open time', t2 - t1)
return ok1 / ok2
<mask token>
def get_lam(sig):
okliststr = []
i = 0
for client in clientlist:
okstr = client.get_ok(str(sig['e1']), str(sig['e2']))
print(okstr)
okliststr.append(okstr)
i = i + 1
if i < k:
print('the number of ok is not enough\n')
return
lam = group_signature.open(okliststr, L, k)
return lam
def tx_build_broad(op, steemd_instance, wallet_instance, account):
tx = TransactionBuilder(steemd_instance=steemd_instance,
wallet_instance=wallet_instance, no_broadcast=False)
tx.appendOps(op)
tx.appendSigner(account, 'posting')
tx.sign()
re = tx.broadcast()
return re
<mask token>
def annoy_commit(account, usk, pk, GID, UID, title='paper_title', body=
'paper_body', groupID='computer'):
annoy_author = 'nya'
sig = group_signature.sign(title, usk, pk, GID, UID, groupID)
permlink = ''.join(random.choices(string.digits, k=7))
print('permlink is ' + permlink)
op = operations.CommitPaper(**{'account': account, 'author':
annoy_author, 'permlink': permlink, 'title': title, 'body': body,
'json_metadata': '', 'c0': str(sig['c0']), 'c5': str(sig['c5']),
'c6': str(sig['c6']), 'e1': str(sig['e1']), 'e2': str(sig['e2']),
'e3': str(sig['e3']), 'c': str(sig['c']), 's1': str(sig['s1']),
's2': str(sig['s2']), 's3': str(sig['s3'])})
print('commitop', op)
return op, sig, permlink
<mask token>
def annoy_commit_tx(account, usk, pk, GID, UID, steemd_instance,
wallet_instance, title='paper_title', body='paper_body'):
commitop, ssig, permlink = annoy_commit(account, usk, pk, GID, UID,
title='paper_title', body='paper_body', groupID='computer')
re = tx_build_broad(commitop, steemd_instance, wallet_instance, account)
print('commit-re', re)
return ssig, permlink
<mask token>
def one_mul_annoy_tx(account, usk, pk, UID, steemd, wallet):
ssiglistone = []
permlinklistone = []
threads = []
for i in range(nodeTX):
t = MyThread(annoy_commit_tx, args=(account, usk, pk, GID, UID,
steemd, wallet))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
ssig, permlink = t.get_result()
ssiglistone.append(ssig)
permlinklistone.append(permlink)
return ssiglistone, permlinklistone
def one_mul_open_tx(account, ssiglistone, userID, permlinklistone, steemd,
wallet):
threads = []
for i in range(nodeTX):
t = MyThread(open_tx, args=(account, ssiglistone[i], userID,
permlinklistone[i], steemd, wallet))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
def mul_annoy_tx(usk, pk, UID):
ssiglist = []
permlinklist = []
threads = []
for i in range(n):
t = MyThread(one_mul_annoy_tx, args=(accountlist[i], usk, pk, UID,
clientlist[i].steemd, clientlist[i].wallet))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
ssig, permlink = t.get_result()
ssiglist.append(ssig)
permlinklist.append(permlink)
return ssiglist, permlinklist
def mul_open_tx(ssiglist, permlinklist, userID):
threads = []
for i in range(n):
t = MyThread(one_mul_open_tx, args=(accountlist[i], ssiglist[i],
userID, permlinklist[i], clientlist[i].steemd, clientlist[i].
wallet))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
def creat_commit_tx(account, usk, pk, GID, UID, steemd_instance,
wallet_instance, title='paper_title', body='paper_body'):
commitop, ssig, permlink = annoy_commit(account, usk, pk, GID, UID,
title, body, groupID='computer')
commit_tx = tx_build(commitop, steemd_instance, wallet_instance, account)
return ssig, permlink, commit_tx
def creat_num_commit_tx(num, account, usk, pk, GID, UID, steemd_instance,
wallet_instance, ttitle='paper_title', tbody='paper_body'):
ssiglist = []
permlinklist = []
txlist = []
threads = []
for i in range(num):
t = MyThread(creat_commit_tx, args=(account, usk, pk, GID, UID,
steemd_instance, wallet_instance, ttitle, tbody))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
ssig, permlink, commit_tx = t.get_result()
ssiglist.append(ssig)
permlinklist.append(permlink)
txlist.append(commit_tx)
return ssiglist, permlinklist, txlist
def creat_open_tx(account, ssig, userID, permlink, steemd_instance,
wallet_instance):
openop = open_op(account, ssig, userID, permlink)
open_tx = tx_build(openop, steemd_instance, wallet_instance, account)
return open_tx
def creat_num_open_tx(num, account, ssiglist, userID, permlinklist,
steemd_instance, wallet_instance):
opentxlist = []
threads = []
for i in range(num):
t = MyThread(creat_open_tx, args=(account, ssiglist[i], userID,
permlinklist[i], steemd_instance, wallet_instance))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
opentx = t.get_result()
opentxlist.append(opentx)
return opentxlist
def tx_broad(tx):
tx.broadcast()
def mul_tx_broad(txlist):
threads = []
for tx in txlist:
t = MyThread(tx_broad, args=(tx,))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
<mask token>
| <mask token>
class GroupSignature:
def __init__(self, groupObj):
global util, group
util = SecretUtil(groupObj, debug)
self.group = groupObj
def pkGen(self, h1str):
gstr = (
'[6172776968119684165170291368128433652817636448173749093457023424948260385279837018774774149930982188956916913145008943931711059687988096415181819433817738, 8687587692191287108886119971783525001480020593934954052605681527814232399216375005546606067382536684351686344089456732201641997200939472924879001214689004]'
)
g2str = (
'[7648994551207171188393784904797547917038803147671542540175090956205316897431443264058433935237605598252399113847934759009659621851760599508222321653067284, 922489308494109901795721463782161260386164061515796674638135394871842997698175772871045949554746517321480649326465484116060959631197509151923296896589720]'
)
u0str = (
'[180015966842918451436547451263180245588308971597733548673037049536176684754209695288737508087729924028686259002375511049961436438196866049956546630518033, 1295050197915669955783867959538729894307963685491173858450359845766785488725907727220684060845012524740394664162328817669422178637925195059862486690053923]'
)
u1str = (
'[2555472719769037960206282327195096320915753855199743796256065902544200822503613205017219993060986152240852358189992579821797745072366030183800897743028220, 7573705235093543416041007636313631591000596820214067724084077929638801811700093589294454562385664531190678890366928407286293582994146887505184778221562373]'
)
u2str = (
'[6876276970903121931083294698771200898345396507892092532649392211995185517437159402176975528760594250374462299539306423347676182899798006533425047523984724, 5323739238507219125881988073888745575030677585404965990610324901624530474522642705344792075909082041735695801098770187248023797265998906693745587936574078]'
)
u3str = (
'[6628726193389375981104409894060310698729022957801238449570622103067828518416602275957863668289683360250722835022304456841105526036470008237775051984811323, 862537748555943361001122447731987661405436458862545177179548603003392540530328380518694788420155531238391922289886044667763424887444361610972254938158280]'
)
u4str = (
'[8157254219580822599577995921928211211847392705248772673869189421041858895589817404931780741226510985762564598862965174380020566416411083236239871342674775, 4736677719200783513058679582227494204159737596114643136852532046080608159561620208171676599501713934575216178076006396924589443776642926902969084668055006]'
)
hstr = (
'[6248393417805371388321299785844751688345516419281230263497475615452026459314582553252281068616984105757749673095320346188725995701858182333525688832492249, 351368339412205819108519989143352052898751906937356995136442397753142226531384069336237369861919799955237545207977716196031001184146017796598836939617335]'
)
nstr = (
'[75201312764006187596691102237923705656296213254701583615255122742135170369075831428394751330697143847448434841509551532135632624530360013837581615049543, 3886258599652934715331576083899336629981754505948456216299528998628273512432828729344158706718479567056972375128622026273382126529171409058157562418608963]'
)
g = self.group.fromstr(gstr, 10, G1)
g2 = self.group.fromstr(g2str, 10, G2)
u0 = self.group.fromstr(u0str, 10, G2)
u1 = self.group.fromstr(u1str, 10, G2)
u2 = self.group.fromstr(u2str, 10, G2)
u3 = self.group.fromstr(u3str, 10, G2)
u4 = self.group.fromstr(u4str, 10, G2)
h = self.group.fromstr(hstr, 10, G1)
n = self.group.fromstr(nstr, 10, GT)
h1 = self.group.fromstr(h1str, 10, G1)
pk = {'g': g, 'g2': g2, 'u0': u0, 'u1': u1, 'u2': u2, 'u3': u3,
'u4': u4, 'h': h, 'n': n, 'h1': h1}
return pk
def uskGen(self, usklist, pk, GID, UID, L, k):
t1 = time()
b0 = self.group.gen1_0(1)
b3 = self.group.gen1_0(1)
b4 = self.group.gen1_0(1)
b5 = self.group.gen1_0(1)
r2 = self.group.random(ZR)
for i in range(k):
b0 = b0 * usklist[i]['b0'] ** L[i]
b3 = b3 * usklist[i]['b3'] ** L[i]
b4 = b4 * usklist[i]['b4'] ** L[i]
b5 = b5 * usklist[i]['b5'] ** L[i]
b0 = b0 * (pk['u0'] * pk['u1'] ** GID * pk['u2'] ** UID) ** r2
b3 = b3 * pk['u3'] ** r2
b4 = b4 * pk['u4'] ** r2
b5 = b5 * pk['g'] ** r2
usk = {'b0': b0, 'b3': b3, 'b4': b4, 'b5': b5}
t2 = time()
with open('extracttime.txt', 'a') as f:
f.write(str(t2 - t1))
f.write('\n')
return usk
def LGen(self, n, k):
L = []
I = self.group.random(ZR)
J = self.group.random(ZR)
for i in range(n):
L.append(self.group.random(ZR))
L[i].set(1)
I.set(i + 1)
for j in range(1, k + 1):
print(j)
J.set(j)
if i + 1 != j:
L[i] = L[i] * (J / (J - I))
return L
def verifyUsk(self, usk, vk, pk, GID, UID):
g = pk['g']
g2 = pk['g2']
u0 = pk['u0']
u1 = pk['u1']
u2 = pk['u2']
u3 = pk['u3']
u4 = pk['u4']
b0 = usk['b0']
b5 = usk['b5']
b3 = usk['b3']
b4 = usk['b4']
return pair(g, b0) == pair(vk, g2) * pair(b5, u0) * pair(b5, u1 ** GID
) * pair(b5, u2 ** UID) and pair(g, b3) == pair(b5, u3) and pair(g,
b4) == pair(b5, u4)
def sign(self, title, usk, pk, GID, UID, groupID):
t1 = time()
m = self.group.hash(title)
b0 = usk['b0']
b3 = usk['b3']
b4 = usk['b4']
b5 = usk['b5']
r4 = self.group.random(ZR)
r3 = self.group.random(ZR)
k = self.group.random(ZR)
c0 = b0 * b3 ** m * b4 ** r4 * (pk['u0'] * pk['u1'] ** GID * pk[
'u2'] ** UID * pk['u3'] ** m * pk['u4'] ** r4) ** r3
c5 = b5 * pk['g'] ** r3
c6 = pk['u2'] ** UID * pk['u4'] ** r4
e1 = pk['g'] ** k
e2 = (pk['u0'] * pk['u1'] ** GID) ** k
e3 = pk['n'] ** UID * pair(pk['h1'], pk['g2']) ** k
f = pk['u0'] * pk['u1'] ** GID
gp = pair(pk['h1'], pk['g2'])
k1 = self.group.random(ZR)
k2 = self.group.random(ZR)
k3 = self.group.random(ZR)
r1 = pk['u2'] ** k1 * pk['u4'] ** k2
r2 = pk['g'] ** k3
r3 = f ** k3
t4 = pk['n'] ** k1 * gp ** k3
hashstr = str(r1) + str(r2) + str(r3) + str(t4)
c = self.group.hash(hashstr)
s1 = k1 + c * UID
s2 = k2 + c * r4
s3 = k3 + c * k
signature = {'c0': c0, 'c5': c5, 'c6': c6, 'e1': e1, 'e2': e2, 'e3':
e3, 'c': c, 's1': s1, 's2': s2, 's3': s3}
t2 = time()
with open('gssigntime.txt', 'a') as f:
f.write(str(t2 - t1))
f.write('\n')
print('gs time', t2 - t1)
return signature
def open(self, okliststr, L, k):
t1 = time()
oklist = []
for ok in okliststr:
oklist.append({'ok1': self.group.fromstr(ok['ok1'], 10, GT),
'ok2': self.group.fromstr(ok['ok2'], 10, GT)})
ok1 = self.group.gen1_0(1)
ok2 = self.group.gen1_0(1)
for i in range(k):
ok1 = ok1 * oklist[i]['ok1'] ** L[i]
ok2 = ok2 * oklist[i]['ok2'] ** L[i]
t2 = time()
with open('opentime.txt', 'a') as f:
f.write(str(t2 - t1))
f.write('\n')
print('open time', t2 - t1)
return ok1 / ok2
def get_usk(userID, GID, UID, h1str='', count=0):
pk = {}
for i in range(n):
vkliststr.append(clientlist[i].get_vk()['vk'])
vklist.append(group_signature.group.fromstr(vkliststr[i], 10, G1))
uskliststr.append(clientlist[i].user_extract(userID))
usklist.append({})
usklist[i]['b0'] = group_signature.group.fromstr(uskliststr[i]['b0'
], 10, G2)
usklist[i]['b3'] = group_signature.group.fromstr(uskliststr[i]['b3'
], 10, G2)
usklist[i]['b4'] = group_signature.group.fromstr(uskliststr[i]['b4'
], 10, G2)
usklist[i]['b5'] = group_signature.group.fromstr(uskliststr[i]['b5'
], 10, G1)
print(usklist[i])
if h1str == '' or h1str == '0' or h1str == 0:
h1str = clientlist[i].get_pk()['pk']
print('h1str', h1str)
pk = group_signature.pkGen(h1str)
print('pk---------------\n', pk)
if group_signature.verifyUsk(usklist[i], vklist[i], pk, GID, UID):
count = count + 1
else:
print('key is invalide\n\n')
usk = group_signature.uskGen(usklist, pk, GID, UID, L, k)
print('usk---------------\n', usk)
return pk, usk
def get_lam(sig):
okliststr = []
i = 0
for client in clientlist:
okstr = client.get_ok(str(sig['e1']), str(sig['e2']))
print(okstr)
okliststr.append(okstr)
i = i + 1
if i < k:
print('the number of ok is not enough\n')
return
lam = group_signature.open(okliststr, L, k)
return lam
def tx_build_broad(op, steemd_instance, wallet_instance, account):
tx = TransactionBuilder(steemd_instance=steemd_instance,
wallet_instance=wallet_instance, no_broadcast=False)
tx.appendOps(op)
tx.appendSigner(account, 'posting')
tx.sign()
re = tx.broadcast()
return re
<mask token>
def annoy_commit(account, usk, pk, GID, UID, title='paper_title', body=
'paper_body', groupID='computer'):
annoy_author = 'nya'
sig = group_signature.sign(title, usk, pk, GID, UID, groupID)
permlink = ''.join(random.choices(string.digits, k=7))
print('permlink is ' + permlink)
op = operations.CommitPaper(**{'account': account, 'author':
annoy_author, 'permlink': permlink, 'title': title, 'body': body,
'json_metadata': '', 'c0': str(sig['c0']), 'c5': str(sig['c5']),
'c6': str(sig['c6']), 'e1': str(sig['e1']), 'e2': str(sig['e2']),
'e3': str(sig['e3']), 'c': str(sig['c']), 's1': str(sig['s1']),
's2': str(sig['s2']), 's3': str(sig['s3'])})
print('commitop', op)
return op, sig, permlink
def open_op(account, sig, userID, permlink):
lam = get_lam(sig)
op = operations.ApplyOpen(**{'account': account, 'author': userID,
'lambda': str(lam), 'permlink': permlink, 'json_metadata': ''})
return op
def annoy_commit_tx(account, usk, pk, GID, UID, steemd_instance,
wallet_instance, title='paper_title', body='paper_body'):
commitop, ssig, permlink = annoy_commit(account, usk, pk, GID, UID,
title='paper_title', body='paper_body', groupID='computer')
re = tx_build_broad(commitop, steemd_instance, wallet_instance, account)
print('commit-re', re)
return ssig, permlink
def open_tx(account, ssig, userID, permlink, steemd_instance, wallet_instance):
openop = open_op(account, ssig, userID, permlink)
re = tx_build_broad(openop, steemd_instance, wallet_instance, account)
print('open-re', re)
def one_mul_annoy_tx(account, usk, pk, UID, steemd, wallet):
ssiglistone = []
permlinklistone = []
threads = []
for i in range(nodeTX):
t = MyThread(annoy_commit_tx, args=(account, usk, pk, GID, UID,
steemd, wallet))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
ssig, permlink = t.get_result()
ssiglistone.append(ssig)
permlinklistone.append(permlink)
return ssiglistone, permlinklistone
def one_mul_open_tx(account, ssiglistone, userID, permlinklistone, steemd,
wallet):
threads = []
for i in range(nodeTX):
t = MyThread(open_tx, args=(account, ssiglistone[i], userID,
permlinklistone[i], steemd, wallet))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
def mul_annoy_tx(usk, pk, UID):
ssiglist = []
permlinklist = []
threads = []
for i in range(n):
t = MyThread(one_mul_annoy_tx, args=(accountlist[i], usk, pk, UID,
clientlist[i].steemd, clientlist[i].wallet))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
ssig, permlink = t.get_result()
ssiglist.append(ssig)
permlinklist.append(permlink)
return ssiglist, permlinklist
def mul_open_tx(ssiglist, permlinklist, userID):
threads = []
for i in range(n):
t = MyThread(one_mul_open_tx, args=(accountlist[i], ssiglist[i],
userID, permlinklist[i], clientlist[i].steemd, clientlist[i].
wallet))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
def creat_commit_tx(account, usk, pk, GID, UID, steemd_instance,
wallet_instance, title='paper_title', body='paper_body'):
commitop, ssig, permlink = annoy_commit(account, usk, pk, GID, UID,
title, body, groupID='computer')
commit_tx = tx_build(commitop, steemd_instance, wallet_instance, account)
return ssig, permlink, commit_tx
def creat_num_commit_tx(num, account, usk, pk, GID, UID, steemd_instance,
wallet_instance, ttitle='paper_title', tbody='paper_body'):
ssiglist = []
permlinklist = []
txlist = []
threads = []
for i in range(num):
t = MyThread(creat_commit_tx, args=(account, usk, pk, GID, UID,
steemd_instance, wallet_instance, ttitle, tbody))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
ssig, permlink, commit_tx = t.get_result()
ssiglist.append(ssig)
permlinklist.append(permlink)
txlist.append(commit_tx)
return ssiglist, permlinklist, txlist
def creat_open_tx(account, ssig, userID, permlink, steemd_instance,
wallet_instance):
openop = open_op(account, ssig, userID, permlink)
open_tx = tx_build(openop, steemd_instance, wallet_instance, account)
return open_tx
def creat_num_open_tx(num, account, ssiglist, userID, permlinklist,
steemd_instance, wallet_instance):
opentxlist = []
threads = []
for i in range(num):
t = MyThread(creat_open_tx, args=(account, ssiglist[i], userID,
permlinklist[i], steemd_instance, wallet_instance))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
opentx = t.get_result()
opentxlist.append(opentx)
return opentxlist
def tx_broad(tx):
tx.broadcast()
def mul_tx_broad(txlist):
threads = []
for tx in txlist:
t = MyThread(tx_broad, args=(tx,))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
<mask token>
def main():
userID = 'zhou'
UID = group_signature.group.hash(userID)
print('uid', UID)
pk, usk = get_usk(userID, GID, UID)
ssig, permlink = annoy_commit_tx(accountlist[0], usk, pk, GID, UID,
clientlist[0].steemd, clientlist[0].wallet, title='paper_title',
body='paper_body')
sleep(3)
open_tx(accountlist[0], ssig, userID, permlink, clientlist[0].steemd,
clientlist[0].wallet)
return
<mask token>
| <mask token>
class GroupSignature:
def __init__(self, groupObj):
global util, group
util = SecretUtil(groupObj, debug)
self.group = groupObj
def pkGen(self, h1str):
gstr = (
'[6172776968119684165170291368128433652817636448173749093457023424948260385279837018774774149930982188956916913145008943931711059687988096415181819433817738, 8687587692191287108886119971783525001480020593934954052605681527814232399216375005546606067382536684351686344089456732201641997200939472924879001214689004]'
)
g2str = (
'[7648994551207171188393784904797547917038803147671542540175090956205316897431443264058433935237605598252399113847934759009659621851760599508222321653067284, 922489308494109901795721463782161260386164061515796674638135394871842997698175772871045949554746517321480649326465484116060959631197509151923296896589720]'
)
u0str = (
'[180015966842918451436547451263180245588308971597733548673037049536176684754209695288737508087729924028686259002375511049961436438196866049956546630518033, 1295050197915669955783867959538729894307963685491173858450359845766785488725907727220684060845012524740394664162328817669422178637925195059862486690053923]'
)
u1str = (
'[2555472719769037960206282327195096320915753855199743796256065902544200822503613205017219993060986152240852358189992579821797745072366030183800897743028220, 7573705235093543416041007636313631591000596820214067724084077929638801811700093589294454562385664531190678890366928407286293582994146887505184778221562373]'
)
u2str = (
'[6876276970903121931083294698771200898345396507892092532649392211995185517437159402176975528760594250374462299539306423347676182899798006533425047523984724, 5323739238507219125881988073888745575030677585404965990610324901624530474522642705344792075909082041735695801098770187248023797265998906693745587936574078]'
)
u3str = (
'[6628726193389375981104409894060310698729022957801238449570622103067828518416602275957863668289683360250722835022304456841105526036470008237775051984811323, 862537748555943361001122447731987661405436458862545177179548603003392540530328380518694788420155531238391922289886044667763424887444361610972254938158280]'
)
u4str = (
'[8157254219580822599577995921928211211847392705248772673869189421041858895589817404931780741226510985762564598862965174380020566416411083236239871342674775, 4736677719200783513058679582227494204159737596114643136852532046080608159561620208171676599501713934575216178076006396924589443776642926902969084668055006]'
)
hstr = (
'[6248393417805371388321299785844751688345516419281230263497475615452026459314582553252281068616984105757749673095320346188725995701858182333525688832492249, 351368339412205819108519989143352052898751906937356995136442397753142226531384069336237369861919799955237545207977716196031001184146017796598836939617335]'
)
nstr = (
'[75201312764006187596691102237923705656296213254701583615255122742135170369075831428394751330697143847448434841509551532135632624530360013837581615049543, 3886258599652934715331576083899336629981754505948456216299528998628273512432828729344158706718479567056972375128622026273382126529171409058157562418608963]'
)
g = self.group.fromstr(gstr, 10, G1)
g2 = self.group.fromstr(g2str, 10, G2)
u0 = self.group.fromstr(u0str, 10, G2)
u1 = self.group.fromstr(u1str, 10, G2)
u2 = self.group.fromstr(u2str, 10, G2)
u3 = self.group.fromstr(u3str, 10, G2)
u4 = self.group.fromstr(u4str, 10, G2)
h = self.group.fromstr(hstr, 10, G1)
n = self.group.fromstr(nstr, 10, GT)
h1 = self.group.fromstr(h1str, 10, G1)
pk = {'g': g, 'g2': g2, 'u0': u0, 'u1': u1, 'u2': u2, 'u3': u3,
'u4': u4, 'h': h, 'n': n, 'h1': h1}
return pk
def uskGen(self, usklist, pk, GID, UID, L, k):
t1 = time()
b0 = self.group.gen1_0(1)
b3 = self.group.gen1_0(1)
b4 = self.group.gen1_0(1)
b5 = self.group.gen1_0(1)
r2 = self.group.random(ZR)
for i in range(k):
b0 = b0 * usklist[i]['b0'] ** L[i]
b3 = b3 * usklist[i]['b3'] ** L[i]
b4 = b4 * usklist[i]['b4'] ** L[i]
b5 = b5 * usklist[i]['b5'] ** L[i]
b0 = b0 * (pk['u0'] * pk['u1'] ** GID * pk['u2'] ** UID) ** r2
b3 = b3 * pk['u3'] ** r2
b4 = b4 * pk['u4'] ** r2
b5 = b5 * pk['g'] ** r2
usk = {'b0': b0, 'b3': b3, 'b4': b4, 'b5': b5}
t2 = time()
with open('extracttime.txt', 'a') as f:
f.write(str(t2 - t1))
f.write('\n')
return usk
def LGen(self, n, k):
L = []
I = self.group.random(ZR)
J = self.group.random(ZR)
for i in range(n):
L.append(self.group.random(ZR))
L[i].set(1)
I.set(i + 1)
for j in range(1, k + 1):
print(j)
J.set(j)
if i + 1 != j:
L[i] = L[i] * (J / (J - I))
return L
def verifyUsk(self, usk, vk, pk, GID, UID):
g = pk['g']
g2 = pk['g2']
u0 = pk['u0']
u1 = pk['u1']
u2 = pk['u2']
u3 = pk['u3']
u4 = pk['u4']
b0 = usk['b0']
b5 = usk['b5']
b3 = usk['b3']
b4 = usk['b4']
return pair(g, b0) == pair(vk, g2) * pair(b5, u0) * pair(b5, u1 ** GID
) * pair(b5, u2 ** UID) and pair(g, b3) == pair(b5, u3) and pair(g,
b4) == pair(b5, u4)
def sign(self, title, usk, pk, GID, UID, groupID):
t1 = time()
m = self.group.hash(title)
b0 = usk['b0']
b3 = usk['b3']
b4 = usk['b4']
b5 = usk['b5']
r4 = self.group.random(ZR)
r3 = self.group.random(ZR)
k = self.group.random(ZR)
c0 = b0 * b3 ** m * b4 ** r4 * (pk['u0'] * pk['u1'] ** GID * pk[
'u2'] ** UID * pk['u3'] ** m * pk['u4'] ** r4) ** r3
c5 = b5 * pk['g'] ** r3
c6 = pk['u2'] ** UID * pk['u4'] ** r4
e1 = pk['g'] ** k
e2 = (pk['u0'] * pk['u1'] ** GID) ** k
e3 = pk['n'] ** UID * pair(pk['h1'], pk['g2']) ** k
f = pk['u0'] * pk['u1'] ** GID
gp = pair(pk['h1'], pk['g2'])
k1 = self.group.random(ZR)
k2 = self.group.random(ZR)
k3 = self.group.random(ZR)
r1 = pk['u2'] ** k1 * pk['u4'] ** k2
r2 = pk['g'] ** k3
r3 = f ** k3
t4 = pk['n'] ** k1 * gp ** k3
hashstr = str(r1) + str(r2) + str(r3) + str(t4)
c = self.group.hash(hashstr)
s1 = k1 + c * UID
s2 = k2 + c * r4
s3 = k3 + c * k
signature = {'c0': c0, 'c5': c5, 'c6': c6, 'e1': e1, 'e2': e2, 'e3':
e3, 'c': c, 's1': s1, 's2': s2, 's3': s3}
t2 = time()
with open('gssigntime.txt', 'a') as f:
f.write(str(t2 - t1))
f.write('\n')
print('gs time', t2 - t1)
return signature
def open(self, okliststr, L, k):
t1 = time()
oklist = []
for ok in okliststr:
oklist.append({'ok1': self.group.fromstr(ok['ok1'], 10, GT),
'ok2': self.group.fromstr(ok['ok2'], 10, GT)})
ok1 = self.group.gen1_0(1)
ok2 = self.group.gen1_0(1)
for i in range(k):
ok1 = ok1 * oklist[i]['ok1'] ** L[i]
ok2 = ok2 * oklist[i]['ok2'] ** L[i]
t2 = time()
with open('opentime.txt', 'a') as f:
f.write(str(t2 - t1))
f.write('\n')
print('open time', t2 - t1)
return ok1 / ok2
def get_usk(userID, GID, UID, h1str='', count=0):
pk = {}
for i in range(n):
vkliststr.append(clientlist[i].get_vk()['vk'])
vklist.append(group_signature.group.fromstr(vkliststr[i], 10, G1))
uskliststr.append(clientlist[i].user_extract(userID))
usklist.append({})
usklist[i]['b0'] = group_signature.group.fromstr(uskliststr[i]['b0'
], 10, G2)
usklist[i]['b3'] = group_signature.group.fromstr(uskliststr[i]['b3'
], 10, G2)
usklist[i]['b4'] = group_signature.group.fromstr(uskliststr[i]['b4'
], 10, G2)
usklist[i]['b5'] = group_signature.group.fromstr(uskliststr[i]['b5'
], 10, G1)
print(usklist[i])
if h1str == '' or h1str == '0' or h1str == 0:
h1str = clientlist[i].get_pk()['pk']
print('h1str', h1str)
pk = group_signature.pkGen(h1str)
print('pk---------------\n', pk)
if group_signature.verifyUsk(usklist[i], vklist[i], pk, GID, UID):
count = count + 1
else:
print('key is invalide\n\n')
usk = group_signature.uskGen(usklist, pk, GID, UID, L, k)
print('usk---------------\n', usk)
return pk, usk
def get_lam(sig):
okliststr = []
i = 0
for client in clientlist:
okstr = client.get_ok(str(sig['e1']), str(sig['e2']))
print(okstr)
okliststr.append(okstr)
i = i + 1
if i < k:
print('the number of ok is not enough\n')
return
lam = group_signature.open(okliststr, L, k)
return lam
def tx_build_broad(op, steemd_instance, wallet_instance, account):
tx = TransactionBuilder(steemd_instance=steemd_instance,
wallet_instance=wallet_instance, no_broadcast=False)
tx.appendOps(op)
tx.appendSigner(account, 'posting')
tx.sign()
re = tx.broadcast()
return re
def tx_build(op, steemd_instance, wallet_instance, account):
tx = TransactionBuilder(steemd_instance=steemd_instance,
wallet_instance=wallet_instance, no_broadcast=False)
tx.appendOps(op)
tx.appendSigner(account, 'posting')
tx.sign()
return tx
def annoy_commit(account, usk, pk, GID, UID, title='paper_title', body=
'paper_body', groupID='computer'):
annoy_author = 'nya'
sig = group_signature.sign(title, usk, pk, GID, UID, groupID)
permlink = ''.join(random.choices(string.digits, k=7))
print('permlink is ' + permlink)
op = operations.CommitPaper(**{'account': account, 'author':
annoy_author, 'permlink': permlink, 'title': title, 'body': body,
'json_metadata': '', 'c0': str(sig['c0']), 'c5': str(sig['c5']),
'c6': str(sig['c6']), 'e1': str(sig['e1']), 'e2': str(sig['e2']),
'e3': str(sig['e3']), 'c': str(sig['c']), 's1': str(sig['s1']),
's2': str(sig['s2']), 's3': str(sig['s3'])})
print('commitop', op)
return op, sig, permlink
def open_op(account, sig, userID, permlink):
lam = get_lam(sig)
op = operations.ApplyOpen(**{'account': account, 'author': userID,
'lambda': str(lam), 'permlink': permlink, 'json_metadata': ''})
return op
def annoy_commit_tx(account, usk, pk, GID, UID, steemd_instance,
wallet_instance, title='paper_title', body='paper_body'):
commitop, ssig, permlink = annoy_commit(account, usk, pk, GID, UID,
title='paper_title', body='paper_body', groupID='computer')
re = tx_build_broad(commitop, steemd_instance, wallet_instance, account)
print('commit-re', re)
return ssig, permlink
def open_tx(account, ssig, userID, permlink, steemd_instance, wallet_instance):
openop = open_op(account, ssig, userID, permlink)
re = tx_build_broad(openop, steemd_instance, wallet_instance, account)
print('open-re', re)
def one_mul_annoy_tx(account, usk, pk, UID, steemd, wallet):
ssiglistone = []
permlinklistone = []
threads = []
for i in range(nodeTX):
t = MyThread(annoy_commit_tx, args=(account, usk, pk, GID, UID,
steemd, wallet))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
ssig, permlink = t.get_result()
ssiglistone.append(ssig)
permlinklistone.append(permlink)
return ssiglistone, permlinklistone
def one_mul_open_tx(account, ssiglistone, userID, permlinklistone, steemd,
wallet):
threads = []
for i in range(nodeTX):
t = MyThread(open_tx, args=(account, ssiglistone[i], userID,
permlinklistone[i], steemd, wallet))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
def mul_annoy_tx(usk, pk, UID):
ssiglist = []
permlinklist = []
threads = []
for i in range(n):
t = MyThread(one_mul_annoy_tx, args=(accountlist[i], usk, pk, UID,
clientlist[i].steemd, clientlist[i].wallet))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
ssig, permlink = t.get_result()
ssiglist.append(ssig)
permlinklist.append(permlink)
return ssiglist, permlinklist
def mul_open_tx(ssiglist, permlinklist, userID):
threads = []
for i in range(n):
t = MyThread(one_mul_open_tx, args=(accountlist[i], ssiglist[i],
userID, permlinklist[i], clientlist[i].steemd, clientlist[i].
wallet))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
def creat_commit_tx(account, usk, pk, GID, UID, steemd_instance,
wallet_instance, title='paper_title', body='paper_body'):
commitop, ssig, permlink = annoy_commit(account, usk, pk, GID, UID,
title, body, groupID='computer')
commit_tx = tx_build(commitop, steemd_instance, wallet_instance, account)
return ssig, permlink, commit_tx
def creat_num_commit_tx(num, account, usk, pk, GID, UID, steemd_instance,
wallet_instance, ttitle='paper_title', tbody='paper_body'):
ssiglist = []
permlinklist = []
txlist = []
threads = []
for i in range(num):
t = MyThread(creat_commit_tx, args=(account, usk, pk, GID, UID,
steemd_instance, wallet_instance, ttitle, tbody))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
ssig, permlink, commit_tx = t.get_result()
ssiglist.append(ssig)
permlinklist.append(permlink)
txlist.append(commit_tx)
return ssiglist, permlinklist, txlist
def creat_open_tx(account, ssig, userID, permlink, steemd_instance,
wallet_instance):
openop = open_op(account, ssig, userID, permlink)
open_tx = tx_build(openop, steemd_instance, wallet_instance, account)
return open_tx
def creat_num_open_tx(num, account, ssiglist, userID, permlinklist,
steemd_instance, wallet_instance):
opentxlist = []
threads = []
for i in range(num):
t = MyThread(creat_open_tx, args=(account, ssiglist[i], userID,
permlinklist[i], steemd_instance, wallet_instance))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
opentx = t.get_result()
opentxlist.append(opentx)
return opentxlist
def tx_broad(tx):
tx.broadcast()
def mul_tx_broad(txlist):
threads = []
for tx in txlist:
t = MyThread(tx_broad, args=(tx,))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
<mask token>
for i in range(n):
clientlist.append(steem.Steem(nodes=[nodelist[i]], keys=keylist[i]))
<mask token>
def main():
userID = 'zhou'
UID = group_signature.group.hash(userID)
print('uid', UID)
pk, usk = get_usk(userID, GID, UID)
ssig, permlink = annoy_commit_tx(accountlist[0], usk, pk, GID, UID,
clientlist[0].steemd, clientlist[0].wallet, title='paper_title',
body='paper_body')
sleep(3)
open_tx(accountlist[0], ssig, userID, permlink, clientlist[0].steemd,
clientlist[0].wallet)
return
if __name__ == '__main__':
main()
print('end')
| import random
import string
import steembase
import struct
import steem
from time import sleep
from time import time
from steem.transactionbuilder import TransactionBuilder
from steembase import operations
from steembase.transactions import SignedTransaction
from resultthread import MyThread
from charm.toolbox.pairinggroup import PairingGroup, ZR, G1, G2, GT, pair
from charm.toolbox.secretutil import SecretUtil
class GroupSignature:
def __init__(self, groupObj):
global util, group
util = SecretUtil(groupObj, debug)
self.group = groupObj
def pkGen(self, h1str):
gstr = (
'[6172776968119684165170291368128433652817636448173749093457023424948260385279837018774774149930982188956916913145008943931711059687988096415181819433817738, 8687587692191287108886119971783525001480020593934954052605681527814232399216375005546606067382536684351686344089456732201641997200939472924879001214689004]'
)
g2str = (
'[7648994551207171188393784904797547917038803147671542540175090956205316897431443264058433935237605598252399113847934759009659621851760599508222321653067284, 922489308494109901795721463782161260386164061515796674638135394871842997698175772871045949554746517321480649326465484116060959631197509151923296896589720]'
)
u0str = (
'[180015966842918451436547451263180245588308971597733548673037049536176684754209695288737508087729924028686259002375511049961436438196866049956546630518033, 1295050197915669955783867959538729894307963685491173858450359845766785488725907727220684060845012524740394664162328817669422178637925195059862486690053923]'
)
u1str = (
'[2555472719769037960206282327195096320915753855199743796256065902544200822503613205017219993060986152240852358189992579821797745072366030183800897743028220, 7573705235093543416041007636313631591000596820214067724084077929638801811700093589294454562385664531190678890366928407286293582994146887505184778221562373]'
)
u2str = (
'[6876276970903121931083294698771200898345396507892092532649392211995185517437159402176975528760594250374462299539306423347676182899798006533425047523984724, 5323739238507219125881988073888745575030677585404965990610324901624530474522642705344792075909082041735695801098770187248023797265998906693745587936574078]'
)
u3str = (
'[6628726193389375981104409894060310698729022957801238449570622103067828518416602275957863668289683360250722835022304456841105526036470008237775051984811323, 862537748555943361001122447731987661405436458862545177179548603003392540530328380518694788420155531238391922289886044667763424887444361610972254938158280]'
)
u4str = (
'[8157254219580822599577995921928211211847392705248772673869189421041858895589817404931780741226510985762564598862965174380020566416411083236239871342674775, 4736677719200783513058679582227494204159737596114643136852532046080608159561620208171676599501713934575216178076006396924589443776642926902969084668055006]'
)
hstr = (
'[6248393417805371388321299785844751688345516419281230263497475615452026459314582553252281068616984105757749673095320346188725995701858182333525688832492249, 351368339412205819108519989143352052898751906937356995136442397753142226531384069336237369861919799955237545207977716196031001184146017796598836939617335]'
)
nstr = (
'[75201312764006187596691102237923705656296213254701583615255122742135170369075831428394751330697143847448434841509551532135632624530360013837581615049543, 3886258599652934715331576083899336629981754505948456216299528998628273512432828729344158706718479567056972375128622026273382126529171409058157562418608963]'
)
g = self.group.fromstr(gstr, 10, G1)
g2 = self.group.fromstr(g2str, 10, G2)
u0 = self.group.fromstr(u0str, 10, G2)
u1 = self.group.fromstr(u1str, 10, G2)
u2 = self.group.fromstr(u2str, 10, G2)
u3 = self.group.fromstr(u3str, 10, G2)
u4 = self.group.fromstr(u4str, 10, G2)
h = self.group.fromstr(hstr, 10, G1)
n = self.group.fromstr(nstr, 10, GT)
h1 = self.group.fromstr(h1str, 10, G1)
pk = {'g': g, 'g2': g2, 'u0': u0, 'u1': u1, 'u2': u2, 'u3': u3,
'u4': u4, 'h': h, 'n': n, 'h1': h1}
return pk
def uskGen(self, usklist, pk, GID, UID, L, k):
t1 = time()
b0 = self.group.gen1_0(1)
b3 = self.group.gen1_0(1)
b4 = self.group.gen1_0(1)
b5 = self.group.gen1_0(1)
r2 = self.group.random(ZR)
for i in range(k):
b0 = b0 * usklist[i]['b0'] ** L[i]
b3 = b3 * usklist[i]['b3'] ** L[i]
b4 = b4 * usklist[i]['b4'] ** L[i]
b5 = b5 * usklist[i]['b5'] ** L[i]
b0 = b0 * (pk['u0'] * pk['u1'] ** GID * pk['u2'] ** UID) ** r2
b3 = b3 * pk['u3'] ** r2
b4 = b4 * pk['u4'] ** r2
b5 = b5 * pk['g'] ** r2
usk = {'b0': b0, 'b3': b3, 'b4': b4, 'b5': b5}
t2 = time()
with open('extracttime.txt', 'a') as f:
f.write(str(t2 - t1))
f.write('\n')
return usk
def LGen(self, n, k):
L = []
I = self.group.random(ZR)
J = self.group.random(ZR)
for i in range(n):
L.append(self.group.random(ZR))
L[i].set(1)
I.set(i + 1)
for j in range(1, k + 1):
print(j)
J.set(j)
if i + 1 != j:
L[i] = L[i] * (J / (J - I))
return L
def verifyUsk(self, usk, vk, pk, GID, UID):
g = pk['g']
g2 = pk['g2']
u0 = pk['u0']
u1 = pk['u1']
u2 = pk['u2']
u3 = pk['u3']
u4 = pk['u4']
b0 = usk['b0']
b5 = usk['b5']
b3 = usk['b3']
b4 = usk['b4']
return pair(g, b0) == pair(vk, g2) * pair(b5, u0) * pair(b5, u1 ** GID
) * pair(b5, u2 ** UID) and pair(g, b3) == pair(b5, u3) and pair(g,
b4) == pair(b5, u4)
def sign(self, title, usk, pk, GID, UID, groupID):
t1 = time()
m = self.group.hash(title)
b0 = usk['b0']
b3 = usk['b3']
b4 = usk['b4']
b5 = usk['b5']
r4 = self.group.random(ZR)
r3 = self.group.random(ZR)
k = self.group.random(ZR)
c0 = b0 * b3 ** m * b4 ** r4 * (pk['u0'] * pk['u1'] ** GID * pk[
'u2'] ** UID * pk['u3'] ** m * pk['u4'] ** r4) ** r3
c5 = b5 * pk['g'] ** r3
c6 = pk['u2'] ** UID * pk['u4'] ** r4
e1 = pk['g'] ** k
e2 = (pk['u0'] * pk['u1'] ** GID) ** k
e3 = pk['n'] ** UID * pair(pk['h1'], pk['g2']) ** k
f = pk['u0'] * pk['u1'] ** GID
gp = pair(pk['h1'], pk['g2'])
k1 = self.group.random(ZR)
k2 = self.group.random(ZR)
k3 = self.group.random(ZR)
r1 = pk['u2'] ** k1 * pk['u4'] ** k2
r2 = pk['g'] ** k3
r3 = f ** k3
t4 = pk['n'] ** k1 * gp ** k3
hashstr = str(r1) + str(r2) + str(r3) + str(t4)
c = self.group.hash(hashstr)
s1 = k1 + c * UID
s2 = k2 + c * r4
s3 = k3 + c * k
signature = {'c0': c0, 'c5': c5, 'c6': c6, 'e1': e1, 'e2': e2, 'e3':
e3, 'c': c, 's1': s1, 's2': s2, 's3': s3}
t2 = time()
with open('gssigntime.txt', 'a') as f:
f.write(str(t2 - t1))
f.write('\n')
print('gs time', t2 - t1)
return signature
def open(self, okliststr, L, k):
t1 = time()
oklist = []
for ok in okliststr:
oklist.append({'ok1': self.group.fromstr(ok['ok1'], 10, GT),
'ok2': self.group.fromstr(ok['ok2'], 10, GT)})
ok1 = self.group.gen1_0(1)
ok2 = self.group.gen1_0(1)
for i in range(k):
ok1 = ok1 * oklist[i]['ok1'] ** L[i]
ok2 = ok2 * oklist[i]['ok2'] ** L[i]
t2 = time()
with open('opentime.txt', 'a') as f:
f.write(str(t2 - t1))
f.write('\n')
print('open time', t2 - t1)
return ok1 / ok2
def get_usk(userID, GID, UID, h1str='', count=0):
pk = {}
for i in range(n):
vkliststr.append(clientlist[i].get_vk()['vk'])
vklist.append(group_signature.group.fromstr(vkliststr[i], 10, G1))
uskliststr.append(clientlist[i].user_extract(userID))
usklist.append({})
usklist[i]['b0'] = group_signature.group.fromstr(uskliststr[i]['b0'
], 10, G2)
usklist[i]['b3'] = group_signature.group.fromstr(uskliststr[i]['b3'
], 10, G2)
usklist[i]['b4'] = group_signature.group.fromstr(uskliststr[i]['b4'
], 10, G2)
usklist[i]['b5'] = group_signature.group.fromstr(uskliststr[i]['b5'
], 10, G1)
print(usklist[i])
if h1str == '' or h1str == '0' or h1str == 0:
h1str = clientlist[i].get_pk()['pk']
print('h1str', h1str)
pk = group_signature.pkGen(h1str)
print('pk---------------\n', pk)
if group_signature.verifyUsk(usklist[i], vklist[i], pk, GID, UID):
count = count + 1
else:
print('key is invalide\n\n')
usk = group_signature.uskGen(usklist, pk, GID, UID, L, k)
print('usk---------------\n', usk)
return pk, usk
def get_lam(sig):
okliststr = []
i = 0
for client in clientlist:
okstr = client.get_ok(str(sig['e1']), str(sig['e2']))
print(okstr)
okliststr.append(okstr)
i = i + 1
if i < k:
print('the number of ok is not enough\n')
return
lam = group_signature.open(okliststr, L, k)
return lam
def tx_build_broad(op, steemd_instance, wallet_instance, account):
tx = TransactionBuilder(steemd_instance=steemd_instance,
wallet_instance=wallet_instance, no_broadcast=False)
tx.appendOps(op)
tx.appendSigner(account, 'posting')
tx.sign()
re = tx.broadcast()
return re
def tx_build(op, steemd_instance, wallet_instance, account):
tx = TransactionBuilder(steemd_instance=steemd_instance,
wallet_instance=wallet_instance, no_broadcast=False)
tx.appendOps(op)
tx.appendSigner(account, 'posting')
tx.sign()
return tx
def annoy_commit(account, usk, pk, GID, UID, title='paper_title', body=
'paper_body', groupID='computer'):
annoy_author = 'nya'
sig = group_signature.sign(title, usk, pk, GID, UID, groupID)
permlink = ''.join(random.choices(string.digits, k=7))
print('permlink is ' + permlink)
op = operations.CommitPaper(**{'account': account, 'author':
annoy_author, 'permlink': permlink, 'title': title, 'body': body,
'json_metadata': '', 'c0': str(sig['c0']), 'c5': str(sig['c5']),
'c6': str(sig['c6']), 'e1': str(sig['e1']), 'e2': str(sig['e2']),
'e3': str(sig['e3']), 'c': str(sig['c']), 's1': str(sig['s1']),
's2': str(sig['s2']), 's3': str(sig['s3'])})
print('commitop', op)
return op, sig, permlink
def open_op(account, sig, userID, permlink):
lam = get_lam(sig)
op = operations.ApplyOpen(**{'account': account, 'author': userID,
'lambda': str(lam), 'permlink': permlink, 'json_metadata': ''})
return op
def annoy_commit_tx(account, usk, pk, GID, UID, steemd_instance,
wallet_instance, title='paper_title', body='paper_body'):
commitop, ssig, permlink = annoy_commit(account, usk, pk, GID, UID,
title='paper_title', body='paper_body', groupID='computer')
re = tx_build_broad(commitop, steemd_instance, wallet_instance, account)
print('commit-re', re)
return ssig, permlink
def open_tx(account, ssig, userID, permlink, steemd_instance, wallet_instance):
openop = open_op(account, ssig, userID, permlink)
re = tx_build_broad(openop, steemd_instance, wallet_instance, account)
print('open-re', re)
def one_mul_annoy_tx(account, usk, pk, UID, steemd, wallet):
ssiglistone = []
permlinklistone = []
threads = []
for i in range(nodeTX):
t = MyThread(annoy_commit_tx, args=(account, usk, pk, GID, UID,
steemd, wallet))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
ssig, permlink = t.get_result()
ssiglistone.append(ssig)
permlinklistone.append(permlink)
return ssiglistone, permlinklistone
def one_mul_open_tx(account, ssiglistone, userID, permlinklistone, steemd,
wallet):
threads = []
for i in range(nodeTX):
t = MyThread(open_tx, args=(account, ssiglistone[i], userID,
permlinklistone[i], steemd, wallet))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
def mul_annoy_tx(usk, pk, UID):
ssiglist = []
permlinklist = []
threads = []
for i in range(n):
t = MyThread(one_mul_annoy_tx, args=(accountlist[i], usk, pk, UID,
clientlist[i].steemd, clientlist[i].wallet))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
ssig, permlink = t.get_result()
ssiglist.append(ssig)
permlinklist.append(permlink)
return ssiglist, permlinklist
def mul_open_tx(ssiglist, permlinklist, userID):
threads = []
for i in range(n):
t = MyThread(one_mul_open_tx, args=(accountlist[i], ssiglist[i],
userID, permlinklist[i], clientlist[i].steemd, clientlist[i].
wallet))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
def creat_commit_tx(account, usk, pk, GID, UID, steemd_instance,
wallet_instance, title='paper_title', body='paper_body'):
commitop, ssig, permlink = annoy_commit(account, usk, pk, GID, UID,
title, body, groupID='computer')
commit_tx = tx_build(commitop, steemd_instance, wallet_instance, account)
return ssig, permlink, commit_tx
def creat_num_commit_tx(num, account, usk, pk, GID, UID, steemd_instance,
wallet_instance, ttitle='paper_title', tbody='paper_body'):
ssiglist = []
permlinklist = []
txlist = []
threads = []
for i in range(num):
t = MyThread(creat_commit_tx, args=(account, usk, pk, GID, UID,
steemd_instance, wallet_instance, ttitle, tbody))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
ssig, permlink, commit_tx = t.get_result()
ssiglist.append(ssig)
permlinklist.append(permlink)
txlist.append(commit_tx)
return ssiglist, permlinklist, txlist
def creat_open_tx(account, ssig, userID, permlink, steemd_instance,
wallet_instance):
openop = open_op(account, ssig, userID, permlink)
open_tx = tx_build(openop, steemd_instance, wallet_instance, account)
return open_tx
def creat_num_open_tx(num, account, ssiglist, userID, permlinklist,
steemd_instance, wallet_instance):
opentxlist = []
threads = []
for i in range(num):
t = MyThread(creat_open_tx, args=(account, ssiglist[i], userID,
permlinklist[i], steemd_instance, wallet_instance))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
opentx = t.get_result()
opentxlist.append(opentx)
return opentxlist
def tx_broad(tx):
tx.broadcast()
def mul_tx_broad(txlist):
threads = []
for tx in txlist:
t = MyThread(tx_broad, args=(tx,))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
nodeTX = 5
k = 2
n = 3
nodelist = ['http://101.76.208.83:8090', 'http://101.76.208.83:8094',
'http://101.76.208.83:8098']
accountlist = ['initminer2', 'zy1', 'zy2', 'zy3', 'zy4', 'zy5', 'zy6',
'zy7', 'zy8', 'zy9', 'zy10', 'zy11', 'zy12', 'zy13', 'zy14', 'zy15',
'zy16', 'zy17', 'zy18', 'zy19', 'zy20']
keylist = ['5J3yMruND2TADZ7cZc6Cnp4VePrnehei2wvGdnLgf3aEj2nDGhc',
'5Hs4jcm5X4sanCnUKNFCjrq2irN8sH1Krzsb13Qd6DHqutZbhqu',
'5KPLLsQ3MuWgKvNYqAFRjziWZenBqefDhSe4K1uYuj8hT3zQoKv']
debug = True
groupobj = PairingGroup('SS512')
group_signature = GroupSignature(groupobj)
L = group_signature.LGen(n, k)
clientlist = []
for i in range(n):
clientlist.append(steem.Steem(nodes=[nodelist[i]], keys=keylist[i]))
vkliststr = []
uskliststr = []
vklist = []
usklist = []
steembase.chains.known_chains['TEST'] = {'chain_id':
'18dcf0a285365fc58b71f18b3d3fec954aa0c141c44e4e5cb4cf777b9eab274e',
'prefix': 'TST', 'steem_symbol': 'TESTS', 'sbd_symbol': 'TBD',
'vests_symbol': 'VESTS'}
groupID = 'computer'
GID = group_signature.group.hash(groupID)
def main():
userID = 'zhou'
UID = group_signature.group.hash(userID)
print('uid', UID)
pk, usk = get_usk(userID, GID, UID)
ssig, permlink = annoy_commit_tx(accountlist[0], usk, pk, GID, UID,
clientlist[0].steemd, clientlist[0].wallet, title='paper_title',
body='paper_body')
sleep(3)
open_tx(accountlist[0], ssig, userID, permlink, clientlist[0].steemd,
clientlist[0].wallet)
return
if __name__ == '__main__':
main()
print('end')
| import random
import string
import steembase
import struct
import steem
from time import sleep
from time import time
from steem.transactionbuilder import TransactionBuilder
from steembase import operations
from steembase.transactions import SignedTransaction
from resultthread import MyThread
from charm.toolbox.pairinggroup import PairingGroup, ZR, G1, G2, GT, pair
from charm.toolbox.secretutil import SecretUtil
class GroupSignature():
def __init__(self, groupObj):
global util, group
util = SecretUtil(groupObj, debug)
self.group = groupObj
def pkGen(self, h1str):
gstr = "[6172776968119684165170291368128433652817636448173749093457023424948260385279837018774774149930982188956916913145008943931711059687988096415181819433817738, 8687587692191287108886119971783525001480020593934954052605681527814232399216375005546606067382536684351686344089456732201641997200939472924879001214689004]"
g2str = "[7648994551207171188393784904797547917038803147671542540175090956205316897431443264058433935237605598252399113847934759009659621851760599508222321653067284, 922489308494109901795721463782161260386164061515796674638135394871842997698175772871045949554746517321480649326465484116060959631197509151923296896589720]"
u0str = "[180015966842918451436547451263180245588308971597733548673037049536176684754209695288737508087729924028686259002375511049961436438196866049956546630518033, 1295050197915669955783867959538729894307963685491173858450359845766785488725907727220684060845012524740394664162328817669422178637925195059862486690053923]"
u1str = "[2555472719769037960206282327195096320915753855199743796256065902544200822503613205017219993060986152240852358189992579821797745072366030183800897743028220, 7573705235093543416041007636313631591000596820214067724084077929638801811700093589294454562385664531190678890366928407286293582994146887505184778221562373]"
u2str = "[6876276970903121931083294698771200898345396507892092532649392211995185517437159402176975528760594250374462299539306423347676182899798006533425047523984724, 5323739238507219125881988073888745575030677585404965990610324901624530474522642705344792075909082041735695801098770187248023797265998906693745587936574078]"
u3str = "[6628726193389375981104409894060310698729022957801238449570622103067828518416602275957863668289683360250722835022304456841105526036470008237775051984811323, 862537748555943361001122447731987661405436458862545177179548603003392540530328380518694788420155531238391922289886044667763424887444361610972254938158280]"
u4str = "[8157254219580822599577995921928211211847392705248772673869189421041858895589817404931780741226510985762564598862965174380020566416411083236239871342674775, 4736677719200783513058679582227494204159737596114643136852532046080608159561620208171676599501713934575216178076006396924589443776642926902969084668055006]"
hstr = "[6248393417805371388321299785844751688345516419281230263497475615452026459314582553252281068616984105757749673095320346188725995701858182333525688832492249, 351368339412205819108519989143352052898751906937356995136442397753142226531384069336237369861919799955237545207977716196031001184146017796598836939617335]"
nstr = "[75201312764006187596691102237923705656296213254701583615255122742135170369075831428394751330697143847448434841509551532135632624530360013837581615049543, 3886258599652934715331576083899336629981754505948456216299528998628273512432828729344158706718479567056972375128622026273382126529171409058157562418608963]"
g = self.group.fromstr(gstr, 10, G1)
g2 = self.group.fromstr(g2str, 10, G2)
u0 = self.group.fromstr(u0str, 10, G2)
u1 = self.group.fromstr(u1str, 10, G2)
u2 = self.group.fromstr(u2str, 10, G2)
u3 = self.group.fromstr(u3str, 10, G2)
u4 = self.group.fromstr(u4str, 10, G2)
h = self.group.fromstr(hstr, 10, G1)
n = self.group.fromstr(nstr, 10, GT)
h1 = self.group.fromstr(h1str, 10, G1)
pk = {'g': g, 'g2': g2, 'u0': u0, 'u1': u1, 'u2': u2, 'u3': u3, 'u4': u4, 'h': h, 'n': n, 'h1': h1}
return pk
def uskGen(self, usklist, pk, GID, UID, L, k):
t1 = time()
b0 = self.group.gen1_0(1)
b3 = self.group.gen1_0(1)
b4 = self.group.gen1_0(1)
b5 = self.group.gen1_0(1)
r2 = self.group.random(ZR)
for i in range(k):
b0 = b0 * (usklist[i]['b0'] ** L[i])
b3 = b3 * (usklist[i]['b3'] ** L[i])
b4 = b4 * (usklist[i]['b4'] ** L[i])
b5 = b5 * (usklist[i]['b5'] ** L[i])
b0 = b0 * (pk['u0'] * (pk['u1'] ** GID) * (pk['u2'] ** UID)) ** r2
b3 = b3 * (pk['u3'] ** r2)
b4 = b4 * (pk['u4'] ** r2)
b5 = b5 * (pk['g'] ** r2)
usk = {'b0': b0, 'b3': b3, 'b4': b4, 'b5': b5}
t2 = time()
with open("extracttime.txt", 'a') as f:
f.write(str(t2 - t1))
f.write('\n')
return usk
def LGen(self, n, k):
L = []
I = self.group.random(ZR)
J = self.group.random(ZR)
for i in range(n):
L.append(self.group.random(ZR))
L[i].set(1)
I.set(i + 1)
for j in range(1, k + 1):
print(j)
J.set(j)
if (i + 1) != j:
L[i] = L[i] * ((J) / (J - I))
return L
def verifyUsk(self, usk, vk, pk, GID, UID):
g = pk['g']
g2 = pk['g2']
u0 = pk['u0']
u1 = pk['u1']
u2 = pk['u2']
u3 = pk['u3']
u4 = pk['u4']
b0 = usk['b0']
b5 = usk['b5']
b3 = usk['b3']
b4 = usk['b4']
return pair(g, b0) == (pair(vk, g2) * pair(b5, u0) * pair(b5, u1 ** GID) * pair(b5, u2 ** UID)) and pair(g,
b3) == pair(
b5, u3) and pair(g, b4) == pair(b5, u4)
def sign(self, title, usk, pk, GID, UID, groupID):
t1 = time()
m = self.group.hash(title)
b0 = usk['b0']
b3 = usk['b3']
b4 = usk['b4']
b5 = usk['b5']
r4 = self.group.random(ZR)
r3 = self.group.random(ZR)
k = self.group.random(ZR)
c0 = b0 * (b3 ** m) * (b4 ** r4) * (
(pk['u0'] * (pk['u1'] ** GID) * (pk['u2'] ** UID) * (pk['u3'] ** m) * (pk['u4'] ** r4)) ** r3)
c5 = b5 * (pk['g'] ** r3)
c6 = (pk['u2'] ** UID) * (pk['u4'] ** r4)
e1 = pk['g'] ** k
e2 = (pk['u0'] * (pk['u1'] ** GID)) ** k
e3 = (pk['n'] ** UID) * (pair(pk['h1'], pk['g2']) ** k)
# 产生pok
f = pk['u0'] * (pk['u1'] ** GID)
gp = pair(pk['h1'], pk['g2'])
k1 = self.group.random(ZR)
k2 = self.group.random(ZR)
k3 = self.group.random(ZR)
r1 = (pk['u2'] ** k1) * (pk['u4'] ** k2)
r2 = pk['g'] ** k3
r3 = f ** k3
t4 = (pk['n'] ** k1) * (gp ** k3)
hashstr = str(r1) + str(r2) + str(r3) + str(t4)
c = self.group.hash(hashstr)
s1 = k1 + c * UID
s2 = k2 + c * r4
s3 = k3 + c * k
signature = {'c0': c0, 'c5': c5, 'c6': c6, 'e1': e1, 'e2': e2, 'e3': e3, 'c': c, 's1': s1, 's2': s2, 's3': s3}
t2 = time()
with open("gssigntime.txt", 'a') as f:
f.write(str(t2 - t1))
f.write('\n')
print("gs time", t2 - t1)
return signature
def open(self, okliststr, L, k):
t1 = time()
oklist = []
for ok in okliststr:
oklist.append({'ok1': self.group.fromstr(ok['ok1'], 10, GT), 'ok2': self.group.fromstr(ok['ok2'], 10, GT)})
ok1 = self.group.gen1_0(1)
ok2 = self.group.gen1_0(1)
for i in range(k):
ok1 = ok1 * (oklist[i]['ok1'] ** L[i])
ok2 = ok2 * (oklist[i]['ok2'] ** L[i])
t2 = time()
with open("opentime.txt", 'a') as f:
f.write(str(t2 - t1))
f.write('\n')
print("open time", t2 - t1)
return ok1 / ok2
def get_usk(userID, GID, UID, h1str="", count=0):
pk = {}
for i in range(n):
vkliststr.append(clientlist[i].get_vk()['vk'])
vklist.append(group_signature.group.fromstr(vkliststr[i], 10, G1))
uskliststr.append(clientlist[i].user_extract(userID))
usklist.append({})
usklist[i]['b0'] = group_signature.group.fromstr(uskliststr[i]['b0'], 10, G2)
usklist[i]['b3'] = group_signature.group.fromstr(uskliststr[i]['b3'], 10, G2)
usklist[i]['b4'] = group_signature.group.fromstr(uskliststr[i]['b4'], 10, G2)
usklist[i]['b5'] = group_signature.group.fromstr(uskliststr[i]['b5'], 10, G1)
print(usklist[i])
if h1str == "" or h1str == "0" or h1str == 0:
h1str = clientlist[i].get_pk()['pk']
print("h1str", h1str)
pk = group_signature.pkGen(h1str)
print("pk---------------\n", pk)
if (group_signature.verifyUsk(usklist[i], vklist[i], pk, GID, UID)):
count = count + 1
else:
print("key is invalide\n\n")
usk = group_signature.uskGen(usklist, pk, GID, UID, L, k)
print("usk---------------\n", usk)
return pk, usk
def get_lam(sig):
okliststr = []
i = 0
for client in clientlist:
okstr = client.get_ok(str(sig['e1']), str(sig['e2']))
print(okstr)
okliststr.append(okstr)
i = i + 1
if i < k:
print("the number of ok is not enough\n")
return
lam = group_signature.open(okliststr, L, k)
return lam
def tx_build_broad(op, steemd_instance, wallet_instance, account):
tx = TransactionBuilder(steemd_instance=steemd_instance, wallet_instance=wallet_instance,
no_broadcast=False)
tx.appendOps(op)
tx.appendSigner(account, 'posting')
tx.sign()
# print("txsign",tx)
re = tx.broadcast()
return re
def tx_build(op, steemd_instance, wallet_instance, account):
tx = TransactionBuilder(steemd_instance=steemd_instance, wallet_instance=wallet_instance,
no_broadcast=False)
tx.appendOps(op)
tx.appendSigner(account, 'posting')
tx.sign()
# print("txsign",tx)
# re = tx.broadcast()
return tx
def annoy_commit(account, usk, pk, GID, UID, title="paper_title", body="paper_body", groupID="computer"):
annoy_author = 'nya'
# group signature ------title 必须 这里面是对title进行hash 然后使用usk对hash进行签名
sig = group_signature.sign(title, usk, pk, GID, UID, groupID)
permlink = ''.join(random.choices(string.digits, k=7))
print("permlink is " + permlink)
op = operations.CommitPaper(
**{
"account": account,
"author": annoy_author,
"permlink": permlink,
"title": title,
"body": body,
"json_metadata": "",
"c0": str(sig['c0']),
"c5": str(sig['c5']),
"c6": str(sig['c6']),
"e1": str(sig['e1']),
"e2": str(sig['e2']),
"e3": str(sig['e3']),
"c": str(sig['c']),
"s1": str(sig['s1']),
"s2": str(sig['s2']),
"s3": str(sig['s3'])
}
)
print("commitop", op)
return op, sig, permlink
def open_op(account, sig, userID, permlink):
lam = get_lam(sig)
# E = (pk['n'] ** UID) * lam #计算出e3 即签名的e3 判断是否相等
op = operations.ApplyOpen(
**{
'account': account,
'author': userID,
'lambda': str(lam),
'permlink': permlink,
'json_metadata': ""
}
)
return op
def annoy_commit_tx(account, usk, pk, GID, UID, steemd_instance, wallet_instance, title="paper_title",
body="paper_body"):
commitop, ssig, permlink = annoy_commit(account, usk, pk, GID, UID, title="paper_title", body="paper_body",
groupID="computer")
re = tx_build_broad(commitop, steemd_instance, wallet_instance, account)
print("commit-re", re)
return ssig, permlink
def open_tx(account, ssig, userID, permlink, steemd_instance, wallet_instance):
openop = open_op(account, ssig, userID, permlink)
re = tx_build_broad(openop, steemd_instance, wallet_instance, account)
print("open-re", re)
# 一个节点的 并发产生交易
def one_mul_annoy_tx(account, usk, pk, UID, steemd, wallet):
ssiglistone = []
permlinklistone = []
threads = []
for i in range(nodeTX):
t = MyThread(annoy_commit_tx, args=(account, usk, pk, GID, UID, steemd, wallet))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
ssig, permlink = t.get_result()
ssiglistone.append(ssig)
permlinklistone.append(permlink)
return ssiglistone, permlinklistone
def one_mul_open_tx(account, ssiglistone, userID, permlinklistone, steemd, wallet):
threads = []
for i in range(nodeTX):
t = MyThread(open_tx,
args=(account, ssiglistone[i], userID, permlinklistone[i], steemd, wallet))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
def mul_annoy_tx(usk, pk, UID):
ssiglist = []
permlinklist = []
threads = []
for i in range(n):
# t = MyThread(annoy_commit_tx, args=(accountlist[i], usk, pk, GID, UID, clientlist[i].steemd, clientlist[i].wallet))
t = MyThread(one_mul_annoy_tx,
args=(accountlist[i], usk, pk, UID, clientlist[i].steemd, clientlist[i].wallet))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
ssig, permlink = t.get_result()
ssiglist.append(ssig)
permlinklist.append(permlink)
return ssiglist, permlinklist
# 多个节点， 每个节点并发
def mul_open_tx(ssiglist, permlinklist, userID):
threads = []
for i in range(n):
# t = MyThread(open_tx,
# args=(accountlist[i], ssiglist[i], userID, permlinklist[i], clientlist[i].steemd, clientlist[i].wallet))
t = MyThread(one_mul_open_tx,
args=(
accountlist[i], ssiglist[i], userID, permlinklist[i], clientlist[i].steemd, clientlist[i].wallet))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
# for t in threads:
# t.get_result()
# 仅创造tx 不广播
def creat_commit_tx(account, usk, pk, GID, UID, steemd_instance, wallet_instance, title="paper_title",
body="paper_body"):
commitop, ssig, permlink = annoy_commit(account, usk, pk, GID, UID, title, body, groupID="computer")
commit_tx = tx_build(commitop, steemd_instance, wallet_instance, account)
return ssig, permlink, commit_tx
def creat_num_commit_tx(num, account, usk, pk, GID, UID, steemd_instance, wallet_instance, ttitle="paper_title",
tbody="paper_body"):
ssiglist = []
permlinklist = []
txlist = []
threads = []
for i in range(num):
t = MyThread(creat_commit_tx, args=(account, usk, pk, GID, UID, steemd_instance, wallet_instance, ttitle,
tbody))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
ssig, permlink, commit_tx = t.get_result()
ssiglist.append(ssig)
permlinklist.append(permlink)
txlist.append(commit_tx)
return ssiglist, permlinklist, txlist
def creat_open_tx(account, ssig, userID, permlink, steemd_instance, wallet_instance):
openop = open_op(account, ssig, userID, permlink)
open_tx = tx_build(openop, steemd_instance, wallet_instance, account)
return open_tx
def creat_num_open_tx(num, account, ssiglist, userID, permlinklist, steemd_instance, wallet_instance):
opentxlist = []
threads = []
for i in range(num):
t = MyThread(creat_open_tx,
args=(account, ssiglist[i], userID, permlinklist[i], steemd_instance,
wallet_instance))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
for t in threads:
opentx = t.get_result()
opentxlist.append(opentx)
return opentxlist
def tx_broad(tx):
tx.broadcast()
def mul_tx_broad(txlist):
threads = []
for tx in txlist:
t = MyThread(tx_broad, args=(tx,))
threads.append(t)
for t in threads:
t.start()
for t in threads:
t.join()
# public parma
nodeTX = 5
k = 2
n = 3 # (k,n)
# 节点地址
nodelist = [
'http://101.76.208.83:8090',
'http://101.76.208.83:8094',
'http://101.76.208.83:8098'
]
accountlist = ["initminer2", "zy1", "zy2", "zy3", "zy4", "zy5", "zy6", "zy7", "zy8", "zy9", "zy10", "zy11", "zy12",
"zy13", "zy14", "zy15", "zy16", "zy17", "zy18", "zy19", "zy20"]
# 除了第一个 其他的都是posting key 5Hs4jcm5X4sanCnUKNFCjrq2irN8sH1Krzsb13Qd6DHqutZbhqu
keylist = ['5J3yMruND2TADZ7cZc6Cnp4VePrnehei2wvGdnLgf3aEj2nDGhc', '5Hs4jcm5X4sanCnUKNFCjrq2irN8sH1Krzsb13Qd6DHqutZbhqu', "5KPLLsQ3MuWgKvNYqAFRjziWZenBqefDhSe4K1uYuj8hT3zQoKv"]
debug = True
# 群签名相关
groupobj = PairingGroup('SS512')
group_signature = GroupSignature(groupobj)
L = group_signature.LGen(n, k)
# 密钥相关
clientlist = []
for i in range(n):
clientlist.append(steem.Steem(nodes=[nodelist[i]], keys=keylist[i]))
vkliststr = []
uskliststr = []
vklist = []
usklist = []
# steem testchain信息
steembase.chains.known_chains['TEST'] = {
'chain_id': '18dcf0a285365fc58b71f18b3d3fec954aa0c141c44e4e5cb4cf777b9eab274e',
'prefix': 'TST', 'steem_symbol': 'TESTS', 'sbd_symbol': 'TBD', 'vests_symbol': 'VESTS'
}
groupID = "computer"
GID = group_signature.group.hash(groupID)
def main():
# 假设不存在不可用节点(无法判断节点状态)
userID = "zhou"
UID = group_signature.group.hash(userID)
print("uid", UID)
# 获取usk
pk, usk = get_usk(userID, GID, UID)
ssig, permlink = annoy_commit_tx(accountlist[0], usk, pk, GID, UID, clientlist[0].steemd, clientlist[0].wallet, title="paper_title",
body="paper_body")
sleep(3)
open_tx(accountlist[0], ssig, userID, permlink, clientlist[0].steemd, clientlist[0].wallet)
return
if __name__ == "__main__":
main()
print("end")
| [
22,
26,
28,
30,
31
] |
291 | ee80169afd4741854eff8619822a857bbf757575 | <mask token>
class Test(unittest.TestCase):
def test_take(self):
x = np.linspace(0, 100)
idx = np.random.random_integers(0, 50, 20)
result = indexing.take(x, idx)
expected = np.take(x, idx)
np.testing.assert_array_equal(expected, result)
def test_take_comparison(self):
x = np.arange(1000000.0)
idx = np.random.random_integers(0, 100000.0, 1000000.0)
indexing.take(x, idx)
np.take(x, idx)
with Timer('numba') as nbtimer:
indexing.take(x, idx)
with Timer('numpy') as nptimer:
np.take(x, idx)
ratio = nbtimer.interval / nptimer.interval
print('numba version of take took %0.2f as long as numpy' % ratio)
def test_square_take(self):
X = np.random.random_integers(0, 50, 25).reshape(5, 5)
idx = np.arange(0, 4, 2)
result = np.empty((idx.shape[0], idx.shape[0]))
indexing.square_take_to_out(X, idx, result)
print(result)
expected = X.take(idx, axis=0).take(idx, axis=1)
print(expected)
np.testing.assert_array_equal(expected, result)
def test_square_take_to_out(self):
X = np.arange(25).reshape(5, 5)
idx = np.arange(0, 4, 2)
result = np.empty((idx.shape[0], idx.shape[0]))
indexing.square_take_to_out(X, idx, result)
print(result)
expected = X.take(idx, axis=0).take(idx, axis=1)
print(expected)
np.testing.assert_array_equal(expected, result)
<mask token>
<mask token>
def test_get_resample_indices(self):
raw_index = np.arange(10)
resampled_index = np.arange(1, 10, 2)
result = indexing.get_resample_indices(raw_index, resampled_index)
expected = np.arange(0, 10, 2)
np.testing.assert_array_equal(expected, result)
<mask token>
<mask token>
| <mask token>
class Test(unittest.TestCase):
def test_take(self):
x = np.linspace(0, 100)
idx = np.random.random_integers(0, 50, 20)
result = indexing.take(x, idx)
expected = np.take(x, idx)
np.testing.assert_array_equal(expected, result)
def test_take_comparison(self):
x = np.arange(1000000.0)
idx = np.random.random_integers(0, 100000.0, 1000000.0)
indexing.take(x, idx)
np.take(x, idx)
with Timer('numba') as nbtimer:
indexing.take(x, idx)
with Timer('numpy') as nptimer:
np.take(x, idx)
ratio = nbtimer.interval / nptimer.interval
print('numba version of take took %0.2f as long as numpy' % ratio)
def test_square_take(self):
X = np.random.random_integers(0, 50, 25).reshape(5, 5)
idx = np.arange(0, 4, 2)
result = np.empty((idx.shape[0], idx.shape[0]))
indexing.square_take_to_out(X, idx, result)
print(result)
expected = X.take(idx, axis=0).take(idx, axis=1)
print(expected)
np.testing.assert_array_equal(expected, result)
def test_square_take_to_out(self):
X = np.arange(25).reshape(5, 5)
idx = np.arange(0, 4, 2)
result = np.empty((idx.shape[0], idx.shape[0]))
indexing.square_take_to_out(X, idx, result)
print(result)
expected = X.take(idx, axis=0).take(idx, axis=1)
print(expected)
np.testing.assert_array_equal(expected, result)
<mask token>
<mask token>
def test_get_resample_indices(self):
raw_index = np.arange(10)
resampled_index = np.arange(1, 10, 2)
result = indexing.get_resample_indices(raw_index, resampled_index)
expected = np.arange(0, 10, 2)
np.testing.assert_array_equal(expected, result)
def test_take_upper_off_diagonal(self):
X = np.array([[1, 2, 3], [np.nan, 5, 6], [np.nan, np.nan, 9]])
idx = np.array([0, 1])
expected = np.array([2])
actual = indexing.take_upper_off_diagonal(X, idx)
np.testing.assert_array_equal(actual, expected)
idx = np.array([1, 2])
expected = np.array([6])
actual = indexing.take_upper_off_diagonal(X, idx)
np.testing.assert_array_equal(actual, expected)
idx = np.array([0, 2])
expected = np.array([3])
actual = indexing.take_upper_off_diagonal(X, idx)
np.testing.assert_array_equal(actual, expected)
idx = np.array([0, 1, 2])
expected = np.array([2, 3, 6])
actual = indexing.take_upper_off_diagonal(X, idx)
np.testing.assert_array_equal(actual, expected)
<mask token>
| <mask token>
class Test(unittest.TestCase):
def test_take(self):
x = np.linspace(0, 100)
idx = np.random.random_integers(0, 50, 20)
result = indexing.take(x, idx)
expected = np.take(x, idx)
np.testing.assert_array_equal(expected, result)
def test_take_comparison(self):
x = np.arange(1000000.0)
idx = np.random.random_integers(0, 100000.0, 1000000.0)
indexing.take(x, idx)
np.take(x, idx)
with Timer('numba') as nbtimer:
indexing.take(x, idx)
with Timer('numpy') as nptimer:
np.take(x, idx)
ratio = nbtimer.interval / nptimer.interval
print('numba version of take took %0.2f as long as numpy' % ratio)
def test_square_take(self):
X = np.random.random_integers(0, 50, 25).reshape(5, 5)
idx = np.arange(0, 4, 2)
result = np.empty((idx.shape[0], idx.shape[0]))
indexing.square_take_to_out(X, idx, result)
print(result)
expected = X.take(idx, axis=0).take(idx, axis=1)
print(expected)
np.testing.assert_array_equal(expected, result)
def test_square_take_to_out(self):
X = np.arange(25).reshape(5, 5)
idx = np.arange(0, 4, 2)
result = np.empty((idx.shape[0], idx.shape[0]))
indexing.square_take_to_out(X, idx, result)
print(result)
expected = X.take(idx, axis=0).take(idx, axis=1)
print(expected)
np.testing.assert_array_equal(expected, result)
def test_square_take_performance(self):
X = np.arange(25).reshape(5, 5)
idx = np.arange(0, 4, 2)
result = np.empty((idx.shape[0], idx.shape[0]))
indexing.square_take_to_out(X, idx, result)
result2 = indexing.square_take(X, idx)
np.testing.assert_array_equal(result, result2)
num_tests = 1000
nbts = []
nbts2 = []
npts = []
ms = 10, 20, 40, 80, 160
for m in ms:
X = np.arange(m * m).reshape(m, m)
idx = np.random.random_integers(0, m - 1, m // 2)
result = np.empty((idx.shape[0], idx.shape[0]))
with Timer('numba') as nbt:
for _ in range(num_tests):
indexing.square_take_to_out(X, idx, result)
nbts.append(nbt.interval)
with Timer('numba2') as nbt:
for _ in range(num_tests):
r = indexing.square_take(X, idx)
nbts2.append(nbt.interval)
with Timer('numpy') as npt:
for _ in range(num_tests):
X.take(idx, axis=0).take(idx, axis=1)
npts.append(npt.interval)
plt.plot(ms, nbts, label='nb to out')
plt.plot(ms, nbts2, label='nb new result')
plt.plot(ms, npts, label='np')
plt.title('square_take_to_out performance test')
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.show()
<mask token>
def test_get_resample_indices(self):
raw_index = np.arange(10)
resampled_index = np.arange(1, 10, 2)
result = indexing.get_resample_indices(raw_index, resampled_index)
expected = np.arange(0, 10, 2)
np.testing.assert_array_equal(expected, result)
def test_take_upper_off_diagonal(self):
X = np.array([[1, 2, 3], [np.nan, 5, 6], [np.nan, np.nan, 9]])
idx = np.array([0, 1])
expected = np.array([2])
actual = indexing.take_upper_off_diagonal(X, idx)
np.testing.assert_array_equal(actual, expected)
idx = np.array([1, 2])
expected = np.array([6])
actual = indexing.take_upper_off_diagonal(X, idx)
np.testing.assert_array_equal(actual, expected)
idx = np.array([0, 2])
expected = np.array([3])
actual = indexing.take_upper_off_diagonal(X, idx)
np.testing.assert_array_equal(actual, expected)
idx = np.array([0, 1, 2])
expected = np.array([2, 3, 6])
actual = indexing.take_upper_off_diagonal(X, idx)
np.testing.assert_array_equal(actual, expected)
<mask token>
| <mask token>
import matplotlib.pyplot as plt
from numerical_functions import Timer
import numerical_functions.numba_funcs.indexing as indexing
import numpy as np
import unittest
class Test(unittest.TestCase):
def test_take(self):
x = np.linspace(0, 100)
idx = np.random.random_integers(0, 50, 20)
result = indexing.take(x, idx)
expected = np.take(x, idx)
np.testing.assert_array_equal(expected, result)
def test_take_comparison(self):
x = np.arange(1000000.0)
idx = np.random.random_integers(0, 100000.0, 1000000.0)
indexing.take(x, idx)
np.take(x, idx)
with Timer('numba') as nbtimer:
indexing.take(x, idx)
with Timer('numpy') as nptimer:
np.take(x, idx)
ratio = nbtimer.interval / nptimer.interval
print('numba version of take took %0.2f as long as numpy' % ratio)
def test_square_take(self):
X = np.random.random_integers(0, 50, 25).reshape(5, 5)
idx = np.arange(0, 4, 2)
result = np.empty((idx.shape[0], idx.shape[0]))
indexing.square_take_to_out(X, idx, result)
print(result)
expected = X.take(idx, axis=0).take(idx, axis=1)
print(expected)
np.testing.assert_array_equal(expected, result)
def test_square_take_to_out(self):
X = np.arange(25).reshape(5, 5)
idx = np.arange(0, 4, 2)
result = np.empty((idx.shape[0], idx.shape[0]))
indexing.square_take_to_out(X, idx, result)
print(result)
expected = X.take(idx, axis=0).take(idx, axis=1)
print(expected)
np.testing.assert_array_equal(expected, result)
def test_square_take_performance(self):
X = np.arange(25).reshape(5, 5)
idx = np.arange(0, 4, 2)
result = np.empty((idx.shape[0], idx.shape[0]))
indexing.square_take_to_out(X, idx, result)
result2 = indexing.square_take(X, idx)
np.testing.assert_array_equal(result, result2)
num_tests = 1000
nbts = []
nbts2 = []
npts = []
ms = 10, 20, 40, 80, 160
for m in ms:
X = np.arange(m * m).reshape(m, m)
idx = np.random.random_integers(0, m - 1, m // 2)
result = np.empty((idx.shape[0], idx.shape[0]))
with Timer('numba') as nbt:
for _ in range(num_tests):
indexing.square_take_to_out(X, idx, result)
nbts.append(nbt.interval)
with Timer('numba2') as nbt:
for _ in range(num_tests):
r = indexing.square_take(X, idx)
nbts2.append(nbt.interval)
with Timer('numpy') as npt:
for _ in range(num_tests):
X.take(idx, axis=0).take(idx, axis=1)
npts.append(npt.interval)
plt.plot(ms, nbts, label='nb to out')
plt.plot(ms, nbts2, label='nb new result')
plt.plot(ms, npts, label='np')
plt.title('square_take_to_out performance test')
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.show()
def test_square_and_rect_take_to_out(self):
X = np.arange(100).reshape((10, 10))
idx0 = np.arange(0, 4, 2)
idx1 = np.arange(4, 6)
result = np.empty((idx0.shape[0], idx0.shape[0] + idx1.shape[0]))
indexing.square_and_rect_take_to_out(X, idx0, idx1, result)
np.testing.assert_array_equal(result[:, :2], indexing.square_take(X,
idx0))
r2 = np.array([[4, 5], [24, 25]])
np.testing.assert_array_equal(r2, result[:, 2:])
def test_get_resample_indices(self):
raw_index = np.arange(10)
resampled_index = np.arange(1, 10, 2)
result = indexing.get_resample_indices(raw_index, resampled_index)
expected = np.arange(0, 10, 2)
np.testing.assert_array_equal(expected, result)
def test_take_upper_off_diagonal(self):
X = np.array([[1, 2, 3], [np.nan, 5, 6], [np.nan, np.nan, 9]])
idx = np.array([0, 1])
expected = np.array([2])
actual = indexing.take_upper_off_diagonal(X, idx)
np.testing.assert_array_equal(actual, expected)
idx = np.array([1, 2])
expected = np.array([6])
actual = indexing.take_upper_off_diagonal(X, idx)
np.testing.assert_array_equal(actual, expected)
idx = np.array([0, 2])
expected = np.array([3])
actual = indexing.take_upper_off_diagonal(X, idx)
np.testing.assert_array_equal(actual, expected)
idx = np.array([0, 1, 2])
expected = np.array([2, 3, 6])
actual = indexing.take_upper_off_diagonal(X, idx)
np.testing.assert_array_equal(actual, expected)
if __name__ == '__main__':
unittest.main()
| '''
Created on 27 Mar 2015
@author: Jon
'''
import matplotlib.pyplot as plt
from numerical_functions import Timer
import numerical_functions.numba_funcs.indexing as indexing
import numpy as np
import unittest
class Test(unittest.TestCase):
def test_take(self):
x = np.linspace( 0, 100 )
idx = np.random.random_integers( 0, 50, 20 )
result = indexing.take( x, idx )
expected = np.take( x, idx )
np.testing.assert_array_equal( expected, result )
def test_take_comparison(self):
x = np.arange( 1e6 )
idx = np.random.random_integers( 0, 1e5, 1e6 )
indexing.take( x, idx )
np.take( x, idx )
with Timer( 'numba' ) as nbtimer:
indexing.take( x, idx )
with Timer( 'numpy' ) as nptimer:
np.take( x, idx )
ratio = nbtimer.interval / nptimer.interval
print( 'numba version of take took %0.2f as long as numpy'%ratio)
def test_square_take(self):
X = np.random.random_integers( 0, 50, 25 ).reshape( 5, 5 )
idx = np.arange( 0, 4, 2 )
result = np.empty( ( idx.shape[0], idx.shape[0] ) )
indexing.square_take_to_out( X, idx, result )
print( result )
expected = X.take( idx, axis=0 ).take( idx, axis=1 )
print( expected )
np.testing.assert_array_equal( expected, result )
def test_square_take_to_out(self):
X = np.arange(25).reshape(5,5)
idx = np.arange( 0, 4, 2 )
result = np.empty( ( idx.shape[0], idx.shape[0] ) )
indexing.square_take_to_out( X, idx, result )
print( result )
expected = X.take( idx, axis=0 ).take( idx, axis=1 )
print( expected )
np.testing.assert_array_equal( expected, result )
def test_square_take_performance(self):
X = np.arange(25).reshape(5,5)
idx = np.arange( 0, 4, 2 )
result = np.empty( ( idx.shape[0], idx.shape[0] ) )
indexing.square_take_to_out( X, idx, result )
result2 = indexing.square_take( X, idx )
np.testing.assert_array_equal( result, result2 )
num_tests = 1000
nbts = []
nbts2 = []
npts = []
ms = ( 10, 20, 40, 80, 160 )#, 320, 640 )
for m in ms:
X = np.arange(m*m).reshape(m,m)
idx = np.random.random_integers( 0, m-1, m//2 )
result = np.empty( ( idx.shape[0], idx.shape[0] ) )
with Timer( 'numba' ) as nbt:
for _ in range( num_tests ):
indexing.square_take_to_out( X, idx, result )
nbts.append( nbt.interval )
with Timer( 'numba2' ) as nbt:
for _ in range( num_tests ):
r=indexing.square_take( X, idx )
nbts2.append( nbt.interval )
with Timer( 'numpy') as npt:
for _ in range(num_tests):
X.take( idx, axis=0 ).take( idx, axis=1 )
npts.append( npt.interval )
plt.plot( ms, nbts, label='nb to out' )
plt.plot( ms, nbts2, label='nb new result')
plt.plot( ms, npts, label='np' )
plt.title( 'square_take_to_out performance test')
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.show()
def test_square_and_rect_take_to_out(self):
X = np.arange( 100 ).reshape( (10, 10 ) )
idx0 = np.arange( 0, 4, 2 )
idx1 = np.arange( 4, 6 )
result = np.empty( ( idx0.shape[0], idx0.shape[0]+idx1.shape[0] ) )
indexing.square_and_rect_take_to_out( X, idx0, idx1, result )
np.testing.assert_array_equal( result[:,:2], indexing.square_take( X, idx0 ) )
r2 = np.array( [ [ 4, 5 ], [24, 25 ] ] )
np.testing.assert_array_equal( r2, result[:,2:])
def test_get_resample_indices(self):
raw_index = np.arange( 10 )
resampled_index = np.arange( 1, 10, 2 )
result = indexing.get_resample_indices(raw_index, resampled_index)
expected = np.arange( 0, 10, 2 )
np.testing.assert_array_equal( expected, result )
def test_take_upper_off_diagonal(self):
X = np.array( [[ 1, 2, 3],
[ np.nan, 5, 6],
[ np.nan, np.nan, 9]])
idx = np.array( [ 0, 1 ] )
expected = np.array( [ 2 ] )
actual = indexing.take_upper_off_diagonal( X, idx )
np.testing.assert_array_equal( actual, expected )
idx = np.array( [ 1, 2 ] )
expected = np.array( [ 6 ] )
actual = indexing.take_upper_off_diagonal( X, idx )
np.testing.assert_array_equal( actual, expected )
idx = np.array( [ 0, 2 ] )
expected = np.array( [ 3 ] )
actual = indexing.take_upper_off_diagonal( X, idx )
np.testing.assert_array_equal( actual, expected )
idx = np.array( [ 0, 1, 2 ] )
expected = np.array( [ 2, 3, 6 ] )
actual = indexing.take_upper_off_diagonal( X, idx )
np.testing.assert_array_equal( actual, expected )
if __name__ == "__main__":
#import sys;sys.argv = ['', 'Test.testName']
unittest.main() | [
6,
7,
8,
11,
12
] |
292 | ce6dba2f682b091249f3bbf362bead4b95fee1f4 | <mask token>
| <mask token>
from .book import book
from . import style, to, read, dist
| """
.. currentmodule:: jotting
.. automodule:: jotting.book
:members:
.. automodule:: jotting.to
:members:
.. automodule:: jotting.read
:members:
.. automodule:: jotting.style
:members:
"""
from .book import book
from . import style, to, read, dist
| null | null | [
0,
1,
2
] |
293 | 99c839eddcbe985c81e709878d03c59e3be3c909 | #coding=utf-8
#########################################
# dbscan：
# 用法说明：读取文件
# 生成路径文件及簇文件，输出分类准确率
#########################################
from matplotlib.pyplot import *
import matplotlib.pyplot as plt
from collections import defaultdict
import random
from math import *
import numpy
import datetime
from dateutil.parser import parse
import datetime
import time
def dataset(filename):
#读取原始文件
lines = open(filename,'r').readlines()
l = len(lines)
all_points = []
for i in range(l):
if lines[i].strip():
line = lines[i].split()
time = line[0] +' '+ line[1]
lat = float(line[4])
lon = float(line[6])
all_points.append([lat,lon,time])
return all_points
def datarevise(all_points):
#数据平滑处理
point_new = []
all_points1 = np.array(all_points)
l = len(all_points)
for i in range(2,l-3):
lat_lon = np.array(all_points1[i-2:i+3,:-1],dtype = float).mean(0)
point_new.append([lat_lon[0],lat_lon[1],all_points1[i][-1]])
return point_new
def dist(p1, p2):
#计算亮点之间的距离
a = cos(p1[0])*cos(p2[0])
b = sin(p1[0])*sin(p2[0])*cos(p2[1]-p1[1])
if a+b >=1:
return 0
return acos(float(a+b))*6371*pi/180
def find_core(all_points,E,minPts):
#查找核心点
#输出：核心点，要绘制的点，非核心点
other_points =[]
core_points=[]
plotted_points=[]
for point in all_points:
point.append(0) # 初始点标号为0
total = 0 #计数：对每个点周围大于给定距离的点的个数
for otherPoint in all_points:
distance = dist(otherPoint,point)
if distance <= E:
total += 1
if total > minPts:
core_points.append(point)
plotted_points.append(point)
else:
other_points.append(point)
return core_points,plotted_points,other_points
def find_border(core_points,plotted_points,other_points,E):
#在非核心点查找边界点
#输出：边界点，要绘制的点
border_points=[]
for core in core_points:
for other in other_points:
if dist(core,other) <= E:#边界点的与核心点的距离小于E
border_points.append(other)
plotted_points.append(other)
return border_points,plotted_points
def algorithm(all_points,core_points,border_points,plotted_points,E):
# 返回簇，噪声点
#将所有的核心点分成不同的簇
cluster_label = 0
for point in core_points:
if point[-1] == 0:
cluster_label += 1
point[-1] = cluster_label
for point2 in plotted_points:
distance = dist(point2,point)
if point2[-1] ==0 and distance <= E:
point2[-1] =point[-1]
#将点集标号类型写成字典格式
cluster_dict = {}
for point in plotted_points:
if cluster_dict.get(point[-1]) is None:
cluster_dict[point[-1]] = [point[0:-1]]
else:
cluster_dict[point[-1]].append(point[0:-1])
#将簇中各个点按时间排序
cluster_dict_sort = {}
for lable in cluster_dict:
cluster_dict_sort.setdefault(lable,[])
cl = np.array(cluster_dict[lable])
cl_sort = cl[cl[:,-1].argsort()]
cluster_dict_sort[lable] = cl_sort
#噪声点，既不在边界点也不在核心点中
noise_points=[]
for point in all_points:
if point not in core_points and point not in border_points:
noise_points.append(point[0:-1])
return cluster_dict_sort,noise_points
def durtime(noise_points,difftime):
# 输入：噪声点，时间间隔
# 功能：分成不同的路径
# 输出：路径点[[],[]]
no = np.array(noise_points)
no_sort = no[no[:,-1].argsort()]
l = len(no_sort)
k = [0]
for i in range(l-1):
diff_time = (no_sort[i+1][-1] - no_sort[i][-1]).seconds
if diff_time > difftime:
k.append(i+1)
k.append(l)
no_split = []
for i in range(len(k)-1):
no_split.append(no_sort[k[i]:k[i+1]])
return no_split
def matplotshow(cluster_dict,no_split,name):
#画出各个簇
markers = ['or', 'ob', 'og', 'ok', '^r', '+r', 'sr', 'dr', '<r', 'pr']
i=0
for lable in cluster_dict:
for j in cluster_dict[lable]:
plot(j[0], j[1],markers[i])
i += 1
i = i%10
print i
#画出路径
markers = ['r', 'b', 'g', 'k', 'c', 'y', 'm',]
l =len(no_split)
for i in range(l):
path = np.array(no_split[i])
plt.plot(path[:,0],path[:,1],markers[i%7])
print i
title(" clusters created with E ="+str(E)+" Min Points="+str(minPts)+" total points="+str(len(all_points))+" noise Points = "+ str(len(noise_points)))
savefig(name)
show()
def datewrite(no_split,filename,mark):
f = open(filename,'w+')
for path in no_split:
f.write( str(mark) +'\n')
for no_path in path:
f.write(str(list(no_path))+'\n')
f.close()
def datewrite1(no_split,filename,mark):
f = open(filename,'w+')
for path in no_split:
for no_path in path:
f.write( str(mark) +'\n')
for j in no_path:
f.write(str(list(j))+'\n')
f.close()
if __name__ == '__main__':
filename = 'D:/sensor_data/sensor/gps/location_zh0710.txt'
all_points_old = dataset(filename)
all_points = datarevise(all_points_old)
E,minPts = 0.1,10
core_points,plotted_points,other_points = find_core(all_points,E,minPts)
border_points,plotted_points = find_border(core_points,plotted_points,other_points,E)
cluster_dict,noise_points = algorithm(all_points,border_points,core_points,plotted_points,E)
difftime = 1200
no_split = durtime(noise_points,difftime)
matplotshow(cluster_dict,no_split,"location_zh0710.png")
filename = 'D:/sensor_data/sensor/gps/location_zh0710_no_split.txt'
datewrite(no_split,filename,'path')
filename = 'D:/sensor_data/sensor/gps/location_zh0710_cluster.txt'
datewrite(cluster_dict.values(),filename,'lable')
| null | null | null | null | [
0
] |
294 | bf8bbeb408cb75af314ef9f3907456036e731c0b | <mask token>
| def solution(S):
log_sep = ','
num_sep = '-'
time_sep = ':'
from collections import defaultdict
bill = defaultdict(int)
total = defaultdict(int)
calls = S.splitlines()
maximal = 0
free_number = 0
for call in calls:
hhmmss, number = call.split(log_sep)
hh, mm, ss = hhmmss.split(time_sep)
hh, mm, ss = int(hh), int(mm), int(ss)
number = int(number.replace(num_sep, ''))
minutes = mm + hh * 60
seconds = ss + minutes * 60
total[number] += seconds
if total[number] > maximal:
maximal = total[number]
free_number = number
elif total[number] == maximal:
free_number = min(number, free_number)
if minutes < 5:
bill[number] += seconds * 3
else:
if ss > 0:
started = 1
else:
started = 0
bill[number] += (minutes + started) * 150
bill[free_number] = 0
return sum(bill.values())
| def solution(S):
# write your code in Python 3.6
# Definitions
log_sep = ','
num_sep = '-'
time_sep = ':'
# Initialization
from collections import defaultdict
# defaultdict initialize missing key to default value -> 0
bill = defaultdict(int)
total = defaultdict(int)
calls = S.splitlines()
maximal = 0
free_number = 0
for call in calls:
# Parsing values
hhmmss, number = call.split(log_sep)
hh, mm, ss = hhmmss.split(time_sep)
hh, mm, ss = int(hh), int(mm), int(ss)
number = int(number.replace(num_sep,''))
# Call duration calculations
minutes = mm + hh * 60
seconds = ss + minutes * 60
# Free number Rule
total[number] += seconds
if total[number] > maximal:
# new maximal
maximal = total[number]
free_number = number
elif total[number] == maximal:
# in case of a tie...
free_number = min(number,free_number)
# Billing Rule
if minutes < 5:
bill[number] += seconds * 3
else:
if ss > 0:
started = 1
else:
started = 0
bill[number] += (minutes + started) * 150
# Free number Rule enforcement
bill[free_number] = 0
return sum(bill.values())
| null | null | [
0,
1,
2
] |
295 | 35ae9c86594b50bbe4a67d2cc6b20efc6f6fdc64 | <mask token>
| <mask token>
sys.path.append(dir_path)
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'gulishop.settings')
<mask token>
django.setup()
<mask token>
for lev1 in row_data:
cat1 = GoodsCategory()
cat1.name = lev1['name']
cat1.code = lev1['code'] if lev1['code'] else ''
cat1.category_type = 1
cat1.save()
for lev2 in lev1['sub_categorys']:
cat2 = GoodsCategory()
cat2.name = lev2['name']
cat2.code = lev2['code'] if lev2['code'] else ''
cat2.category_type = 2
cat2.parent_category = cat1
cat2.save()
for lev3 in lev2['sub_categorys']:
cat3 = GoodsCategory()
cat3.name = lev3['name']
cat3.code = lev3['code'] if lev3['code'] else ''
cat3.category_type = 3
cat3.parent_category = cat2
cat3.save()
| <mask token>
file_path = os.path.abspath(__file__)
dir_path = os.path.dirname(file_path)
sys.path.append(dir_path)
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'gulishop.settings')
<mask token>
django.setup()
<mask token>
for lev1 in row_data:
cat1 = GoodsCategory()
cat1.name = lev1['name']
cat1.code = lev1['code'] if lev1['code'] else ''
cat1.category_type = 1
cat1.save()
for lev2 in lev1['sub_categorys']:
cat2 = GoodsCategory()
cat2.name = lev2['name']
cat2.code = lev2['code'] if lev2['code'] else ''
cat2.category_type = 2
cat2.parent_category = cat1
cat2.save()
for lev3 in lev2['sub_categorys']:
cat3 = GoodsCategory()
cat3.name = lev3['name']
cat3.code = lev3['code'] if lev3['code'] else ''
cat3.category_type = 3
cat3.parent_category = cat2
cat3.save()
| import os, sys
file_path = os.path.abspath(__file__)
dir_path = os.path.dirname(file_path)
sys.path.append(dir_path)
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'gulishop.settings')
import django
django.setup()
from goods.models import GoodsCategory
from db_tools.data.category_data import row_data
for lev1 in row_data:
cat1 = GoodsCategory()
cat1.name = lev1['name']
cat1.code = lev1['code'] if lev1['code'] else ''
cat1.category_type = 1
cat1.save()
for lev2 in lev1['sub_categorys']:
cat2 = GoodsCategory()
cat2.name = lev2['name']
cat2.code = lev2['code'] if lev2['code'] else ''
cat2.category_type = 2
cat2.parent_category = cat1
cat2.save()
for lev3 in lev2['sub_categorys']:
cat3 = GoodsCategory()
cat3.name = lev3['name']
cat3.code = lev3['code'] if lev3['code'] else ''
cat3.category_type = 3
cat3.parent_category = cat2
cat3.save()
| #配置我们文件所在目录的搜寻环境
import os,sys
#第一步先拿到当前文件的路径
file_path = os.path.abspath(__file__)
#第二步 根据这个路径去拿到这个文件所在目录的路径
dir_path = os.path.dirname(file_path)
#第三步：讲这个目录的路径添加到我们的搜寻环境当中
sys.path.append(dir_path)
#第四步，动态设置我们的setting文件
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "gulishop.settings")
#第五步，让设置好的环境初始化生效
import django
django.setup()
#这一行，不能放在上面
from goods.models import GoodsCategory
from db_tools.data.category_data import row_data
for lev1 in row_data:
cat1 = GoodsCategory()
cat1.name = lev1['name']
cat1.code = lev1['code'] if lev1['code'] else ''
cat1.category_type = 1
cat1.save()
for lev2 in lev1['sub_categorys']:
cat2 = GoodsCategory()
cat2.name = lev2['name']
cat2.code = lev2['code'] if lev2['code'] else ''
cat2.category_type = 2
cat2.parent_category = cat1
cat2.save()
for lev3 in lev2['sub_categorys']:
cat3 = GoodsCategory()
cat3.name = lev3['name']
cat3.code = lev3['code'] if lev3['code'] else ''
cat3.category_type = 3
cat3.parent_category = cat2
cat3.save()
| [
0,
1,
2,
3,
4
] |
296 | d34159536e860719094a36cfc30ffb5fcae72a9a | <mask token>
| <mask token>
print('Retriving', url)
<mask token>
print('Retrived', len(data), 'characters')
<mask token>
print(json.dumps(js, indent=4))
print('Place id', js['results'][0]['place_id'])
<mask token>
| <mask token>
target = 'http://py4e-data.dr-chuck.net/json?'
local = input('Enter location: ')
url = target + urllib.parse.urlencode({'address': local, 'key': 42})
print('Retriving', url)
data = urllib.request.urlopen(url).read()
print('Retrived', len(data), 'characters')
js = json.loads(data)
print(json.dumps(js, indent=4))
print('Place id', js['results'][0]['place_id'])
<mask token>
| import urllib.error, urllib.request, urllib.parse
import json
target = 'http://py4e-data.dr-chuck.net/json?'
local = input('Enter location: ')
url = target + urllib.parse.urlencode({'address': local, 'key': 42})
print('Retriving', url)
data = urllib.request.urlopen(url).read()
print('Retrived', len(data), 'characters')
js = json.loads(data)
print(json.dumps(js, indent=4))
print('Place id', js['results'][0]['place_id'])
<mask token>
| #API End Points by Mitul
import urllib.error, urllib.request, urllib.parse
import json
target = 'http://py4e-data.dr-chuck.net/json?'
local = input('Enter location: ')
url = target + urllib.parse.urlencode({'address': local, 'key' : 42})
print('Retriving', url)
data = urllib.request.urlopen(url).read()
print('Retrived', len(data), 'characters')
js = json.loads(data)
print(json.dumps(js, indent = 4))
print('Place id', js['results'][0]['place_id'])
'''Output:
Enter location: >? UIUC
Retriving http://py4e-data.dr-chuck.net/json?address=UIUC&key=42
Retrived 1720 characters
{
"results": [
{
"access_points": [],
"address_components": [
{
"long_name": "Champaign",
"short_name": "Champaign",
"types": [
"locality",
"political"
]
},
{
"long_name": "Champaign County",
"short_name": "Champaign County",
"types": [
"administrative_area_level_2",
"political"
]
},
{
"long_name": "Illinois",
"short_name": "IL",
"types": [
"administrative_area_level_1",
"political"
]
},
{
"long_name": "United States",
"short_name": "US",
"types": [
"country",
"political"
]
}
],
"formatted_address": "Champaign, IL, USA",
"geometry": {
"location": {
"lat": 40.1019523,
"lng": -88.2271615
},
"location_type": "GEOMETRIC_CENTER",
"viewport": {
"northeast": {
"lat": 40.1033012802915,
"lng": -88.22581251970848
},
"southwest": {
"lat": 40.1006033197085,
"lng": -88.2285104802915
}
}
},
"place_id": "ChIJ6VUmqSTXDIgR-iZoBCUFPKU",
"plus_code": {
"compound_code": "4Q2F+Q4 Champaign, Champaign City Township, IL, United States",
"global_code": "86GH4Q2F+Q4"
},
"types": [
"establishment",
"point_of_interest",
"university"
]
}
],
"status": "OK"
}
Place id ChIJ6VUmqSTXDIgR-iZoBCUFPKU
'''
| [
0,
1,
2,
3,
4
] |
297 | c382b298cce8d7045d6ce8a84f90b3800dba7717 | <mask token>
| <mask token>
class Migration(migrations.Migration):
<mask token>
<mask token>
| <mask token>
class Migration(migrations.Migration):
dependencies = [('products', '0003_auto_20200615_1225')]
operations = [migrations.AlterField(model_name='product', name=
'harmonizacao', field=models.TextField(null=True)), migrations.
AlterField(model_name='product', name='history', field=models.
TextField(null=True)), migrations.AlterField(model_name='product',
name='premios', field=models.TextField(null=True))]
| from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [('products', '0003_auto_20200615_1225')]
operations = [migrations.AlterField(model_name='product', name=
'harmonizacao', field=models.TextField(null=True)), migrations.
AlterField(model_name='product', name='history', field=models.
TextField(null=True)), migrations.AlterField(model_name='product',
name='premios', field=models.TextField(null=True))]
| # Generated by Django 3.0.7 on 2020-06-15 15:26
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('products', '0003_auto_20200615_1225'),
]
operations = [
migrations.AlterField(
model_name='product',
name='harmonizacao',
field=models.TextField(null=True),
),
migrations.AlterField(
model_name='product',
name='history',
field=models.TextField(null=True),
),
migrations.AlterField(
model_name='product',
name='premios',
field=models.TextField(null=True),
),
]
| [
0,
1,
2,
3,
4
] |
298 | 3372d98ff91d90558a87293d4032820b1662d60b | <mask token>
| <mask token>
urlpatterns = patterns('', url('appmanagement', views.appmanagement, name=
'appmanagement'), url('^.*', views.index, name='index'))
| from django.conf.urls import patterns, url
from riskDashboard2 import views
urlpatterns = patterns('', url('appmanagement', views.appmanagement, name=
'appmanagement'), url('^.*', views.index, name='index'))
| from django.conf.urls import patterns, url
from riskDashboard2 import views
urlpatterns = patterns('',
#url(r'getdata', views.vulnData, name='getdata'),
url(r'appmanagement', views.appmanagement, name='appmanagement'),
url(r'^.*', views.index, name='index'),
)
| null | [
0,
1,
2,
3
] |
299 | 0465e33d65c2ce47ebffeec38db6908826bf4934 | <mask token>
| <mask token>
if people < cats:
print('Too many cats')
elif people > cats:
print('Not many cats')
else:
print('we cannnot decide')
| people = 20
cats = 30
dogs = 15
if people < cats:
print('Too many cats')
elif people > cats:
print('Not many cats')
else:
print('we cannnot decide')
| people = 20
cats = 30
dogs = 15
if people < cats:
print("Too many cats")
elif people > cats:
print("Not many cats")
else:
print("we cannnot decide") | null | [
0,
1,
2,
3
] |
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