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@property
@since('2.0.0')
def numIterations(self):
'\n Number of training iterations.\n '
return self._call_java('numIterations') | 3,231,421,437,338,347,000 | Number of training iterations. | python/pyspark/ml/regression.py | numIterations | AjithShetty2489/spark | python | @property
@since('2.0.0')
def numIterations(self):
'\n \n '
return self._call_java('numIterations') |
@property
@since('2.0.0')
def solver(self):
'\n The numeric solver used for training.\n '
return self._call_java('solver') | 7,895,560,103,752,479,000 | The numeric solver used for training. | python/pyspark/ml/regression.py | solver | AjithShetty2489/spark | python | @property
@since('2.0.0')
def solver(self):
'\n \n '
return self._call_java('solver') |
@property
@since('2.0.0')
def coefficientStandardErrors(self):
'\n Standard error of estimated coefficients and intercept.\n\n If :py:attr:`GeneralizedLinearRegression.fitIntercept` is set to True,\n then the last element returned corresponds to the intercept.\n '
return self._call_java('coefficientStandardErrors') | -3,088,971,962,521,040,400 | Standard error of estimated coefficients and intercept.
If :py:attr:`GeneralizedLinearRegression.fitIntercept` is set to True,
then the last element returned corresponds to the intercept. | python/pyspark/ml/regression.py | coefficientStandardErrors | AjithShetty2489/spark | python | @property
@since('2.0.0')
def coefficientStandardErrors(self):
'\n Standard error of estimated coefficients and intercept.\n\n If :py:attr:`GeneralizedLinearRegression.fitIntercept` is set to True,\n then the last element returned corresponds to the intercept.\n '
return self._call_java('coefficientStandardErrors') |
@property
@since('2.0.0')
def tValues(self):
'\n T-statistic of estimated coefficients and intercept.\n\n If :py:attr:`GeneralizedLinearRegression.fitIntercept` is set to True,\n then the last element returned corresponds to the intercept.\n '
return self._call_java('tValues') | 1,458,171,731,345,339,000 | T-statistic of estimated coefficients and intercept.
If :py:attr:`GeneralizedLinearRegression.fitIntercept` is set to True,
then the last element returned corresponds to the intercept. | python/pyspark/ml/regression.py | tValues | AjithShetty2489/spark | python | @property
@since('2.0.0')
def tValues(self):
'\n T-statistic of estimated coefficients and intercept.\n\n If :py:attr:`GeneralizedLinearRegression.fitIntercept` is set to True,\n then the last element returned corresponds to the intercept.\n '
return self._call_java('tValues') |
@property
@since('2.0.0')
def pValues(self):
'\n Two-sided p-value of estimated coefficients and intercept.\n\n If :py:attr:`GeneralizedLinearRegression.fitIntercept` is set to True,\n then the last element returned corresponds to the intercept.\n '
return self._call_java('pValues') | -4,774,701,551,750,324,000 | Two-sided p-value of estimated coefficients and intercept.
If :py:attr:`GeneralizedLinearRegression.fitIntercept` is set to True,
then the last element returned corresponds to the intercept. | python/pyspark/ml/regression.py | pValues | AjithShetty2489/spark | python | @property
@since('2.0.0')
def pValues(self):
'\n Two-sided p-value of estimated coefficients and intercept.\n\n If :py:attr:`GeneralizedLinearRegression.fitIntercept` is set to True,\n then the last element returned corresponds to the intercept.\n '
return self._call_java('pValues') |
@since('3.0.0')
def getFactorSize(self):
'\n Gets the value of factorSize or its default value.\n '
return self.getOrDefault(self.factorSize) | 1,791,135,980,955,474,200 | Gets the value of factorSize or its default value. | python/pyspark/ml/regression.py | getFactorSize | AjithShetty2489/spark | python | @since('3.0.0')
def getFactorSize(self):
'\n \n '
return self.getOrDefault(self.factorSize) |
@since('3.0.0')
def getFitLinear(self):
'\n Gets the value of fitLinear or its default value.\n '
return self.getOrDefault(self.fitLinear) | -4,194,490,835,834,387,000 | Gets the value of fitLinear or its default value. | python/pyspark/ml/regression.py | getFitLinear | AjithShetty2489/spark | python | @since('3.0.0')
def getFitLinear(self):
'\n \n '
return self.getOrDefault(self.fitLinear) |
@since('3.0.0')
def getMiniBatchFraction(self):
'\n Gets the value of miniBatchFraction or its default value.\n '
return self.getOrDefault(self.miniBatchFraction) | 3,609,176,603,815,900,000 | Gets the value of miniBatchFraction or its default value. | python/pyspark/ml/regression.py | getMiniBatchFraction | AjithShetty2489/spark | python | @since('3.0.0')
def getMiniBatchFraction(self):
'\n \n '
return self.getOrDefault(self.miniBatchFraction) |
@since('3.0.0')
def getInitStd(self):
'\n Gets the value of initStd or its default value.\n '
return self.getOrDefault(self.initStd) | 3,816,975,538,956,782,600 | Gets the value of initStd or its default value. | python/pyspark/ml/regression.py | getInitStd | AjithShetty2489/spark | python | @since('3.0.0')
def getInitStd(self):
'\n \n '
return self.getOrDefault(self.initStd) |
@keyword_only
def __init__(self, featuresCol='features', labelCol='label', predictionCol='prediction', factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-06, solver='adamW', seed=None):
'\n __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-6, solver="adamW", seed=None)\n '
super(FMRegressor, self).__init__()
self._java_obj = self._new_java_obj('org.apache.spark.ml.regression.FMRegressor', self.uid)
self._setDefault(factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-06, solver='adamW')
kwargs = self._input_kwargs
self.setParams(**kwargs) | 3,828,870,183,181,281,300 | __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-6, solver="adamW", seed=None) | python/pyspark/ml/regression.py | __init__ | AjithShetty2489/spark | python | @keyword_only
def __init__(self, featuresCol='features', labelCol='label', predictionCol='prediction', factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-06, solver='adamW', seed=None):
'\n \n '
super(FMRegressor, self).__init__()
self._java_obj = self._new_java_obj('org.apache.spark.ml.regression.FMRegressor', self.uid)
self._setDefault(factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-06, solver='adamW')
kwargs = self._input_kwargs
self.setParams(**kwargs) |
@keyword_only
@since('3.0.0')
def setParams(self, featuresCol='features', labelCol='label', predictionCol='prediction', factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-06, solver='adamW', seed=None):
'\n setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-6, solver="adamW", seed=None)\n Sets Params for FMRegressor.\n '
kwargs = self._input_kwargs
return self._set(**kwargs) | -4,552,423,437,633,609,700 | setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-6, solver="adamW", seed=None)
Sets Params for FMRegressor. | python/pyspark/ml/regression.py | setParams | AjithShetty2489/spark | python | @keyword_only
@since('3.0.0')
def setParams(self, featuresCol='features', labelCol='label', predictionCol='prediction', factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-06, solver='adamW', seed=None):
'\n setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-6, solver="adamW", seed=None)\n Sets Params for FMRegressor.\n '
kwargs = self._input_kwargs
return self._set(**kwargs) |
@since('3.0.0')
def setFactorSize(self, value):
'\n Sets the value of :py:attr:`factorSize`.\n '
return self._set(factorSize=value) | -8,753,162,586,989,364,000 | Sets the value of :py:attr:`factorSize`. | python/pyspark/ml/regression.py | setFactorSize | AjithShetty2489/spark | python | @since('3.0.0')
def setFactorSize(self, value):
'\n \n '
return self._set(factorSize=value) |
@since('3.0.0')
def setFitLinear(self, value):
'\n Sets the value of :py:attr:`fitLinear`.\n '
return self._set(fitLinear=value) | 4,134,348,066,261,796,000 | Sets the value of :py:attr:`fitLinear`. | python/pyspark/ml/regression.py | setFitLinear | AjithShetty2489/spark | python | @since('3.0.0')
def setFitLinear(self, value):
'\n \n '
return self._set(fitLinear=value) |
@since('3.0.0')
def setMiniBatchFraction(self, value):
'\n Sets the value of :py:attr:`miniBatchFraction`.\n '
return self._set(miniBatchFraction=value) | 5,309,665,991,897,720,000 | Sets the value of :py:attr:`miniBatchFraction`. | python/pyspark/ml/regression.py | setMiniBatchFraction | AjithShetty2489/spark | python | @since('3.0.0')
def setMiniBatchFraction(self, value):
'\n \n '
return self._set(miniBatchFraction=value) |
@since('3.0.0')
def setInitStd(self, value):
'\n Sets the value of :py:attr:`initStd`.\n '
return self._set(initStd=value) | 7,314,427,056,946,567,000 | Sets the value of :py:attr:`initStd`. | python/pyspark/ml/regression.py | setInitStd | AjithShetty2489/spark | python | @since('3.0.0')
def setInitStd(self, value):
'\n \n '
return self._set(initStd=value) |
@since('3.0.0')
def setMaxIter(self, value):
'\n Sets the value of :py:attr:`maxIter`.\n '
return self._set(maxIter=value) | 8,691,892,694,452,766,000 | Sets the value of :py:attr:`maxIter`. | python/pyspark/ml/regression.py | setMaxIter | AjithShetty2489/spark | python | @since('3.0.0')
def setMaxIter(self, value):
'\n \n '
return self._set(maxIter=value) |
@since('3.0.0')
def setStepSize(self, value):
'\n Sets the value of :py:attr:`stepSize`.\n '
return self._set(stepSize=value) | -6,862,698,385,601,250,000 | Sets the value of :py:attr:`stepSize`. | python/pyspark/ml/regression.py | setStepSize | AjithShetty2489/spark | python | @since('3.0.0')
def setStepSize(self, value):
'\n \n '
return self._set(stepSize=value) |
@since('3.0.0')
def setTol(self, value):
'\n Sets the value of :py:attr:`tol`.\n '
return self._set(tol=value) | 3,581,312,399,990,777,300 | Sets the value of :py:attr:`tol`. | python/pyspark/ml/regression.py | setTol | AjithShetty2489/spark | python | @since('3.0.0')
def setTol(self, value):
'\n \n '
return self._set(tol=value) |
@since('3.0.0')
def setSolver(self, value):
'\n Sets the value of :py:attr:`solver`.\n '
return self._set(solver=value) | 5,689,872,772,768,317,000 | Sets the value of :py:attr:`solver`. | python/pyspark/ml/regression.py | setSolver | AjithShetty2489/spark | python | @since('3.0.0')
def setSolver(self, value):
'\n \n '
return self._set(solver=value) |
@since('3.0.0')
def setSeed(self, value):
'\n Sets the value of :py:attr:`seed`.\n '
return self._set(seed=value) | 1,987,893,307,764,387,800 | Sets the value of :py:attr:`seed`. | python/pyspark/ml/regression.py | setSeed | AjithShetty2489/spark | python | @since('3.0.0')
def setSeed(self, value):
'\n \n '
return self._set(seed=value) |
@since('3.0.0')
def setFitIntercept(self, value):
'\n Sets the value of :py:attr:`fitIntercept`.\n '
return self._set(fitIntercept=value) | 4,746,861,393,854,669,000 | Sets the value of :py:attr:`fitIntercept`. | python/pyspark/ml/regression.py | setFitIntercept | AjithShetty2489/spark | python | @since('3.0.0')
def setFitIntercept(self, value):
'\n \n '
return self._set(fitIntercept=value) |
@since('3.0.0')
def setRegParam(self, value):
'\n Sets the value of :py:attr:`regParam`.\n '
return self._set(regParam=value) | 4,120,470,953,683,944,400 | Sets the value of :py:attr:`regParam`. | python/pyspark/ml/regression.py | setRegParam | AjithShetty2489/spark | python | @since('3.0.0')
def setRegParam(self, value):
'\n \n '
return self._set(regParam=value) |
@property
@since('3.0.0')
def intercept(self):
'\n Model intercept.\n '
return self._call_java('intercept') | -378,010,395,860,784,450 | Model intercept. | python/pyspark/ml/regression.py | intercept | AjithShetty2489/spark | python | @property
@since('3.0.0')
def intercept(self):
'\n \n '
return self._call_java('intercept') |
@property
@since('3.0.0')
def linear(self):
'\n Model linear term.\n '
return self._call_java('linear') | 8,724,079,305,889,703,000 | Model linear term. | python/pyspark/ml/regression.py | linear | AjithShetty2489/spark | python | @property
@since('3.0.0')
def linear(self):
'\n \n '
return self._call_java('linear') |
@property
@since('3.0.0')
def factors(self):
'\n Model factor term.\n '
return self._call_java('factors') | -1,686,756,612,127,754,800 | Model factor term. | python/pyspark/ml/regression.py | factors | AjithShetty2489/spark | python | @property
@since('3.0.0')
def factors(self):
'\n \n '
return self._call_java('factors') |
def min_time(x):
'my lib'
graph = GeometryTopology.Graph()
for i in range(h):
for j in range(w):
graph.add_node((i, j))
for i in range(h):
for j in range(w):
if (i > 0):
graph.add_edge((i, j), ((i - 1), j), weight=(1 if (s[(i - 1)][j] == '.') else x))
if (i < (h - 1)):
graph.add_edge((i, j), ((i + 1), j), weight=(1 if (s[(i + 1)][j] == '.') else x))
if (j > 0):
graph.add_edge((i, j), (i, (j - 1)), weight=(1 if (s[i][(j - 1)] == '.') else x))
if (j < (w - 1)):
graph.add_edge((i, j), (i, (j + 1)), weight=(1 if (s[i][(j + 1)] == '.') else x))
return graph.dijkstra(source)[target]
'networkx'
graph = nx.DiGraph()
for i in range(h):
for j in range(w):
if (i > 0):
graph.add_edge((i, j), ((i - 1), j), weight=(1 if (s[(i - 1)][j] == '.') else x))
if (i < (h - 1)):
graph.add_edge((i, j), ((i + 1), j), weight=(1 if (s[(i + 1)][j] == '.') else x))
if (j > 0):
graph.add_edge((i, j), (i, (j - 1)), weight=(1 if (s[i][(j - 1)] == '.') else x))
if (j < (w - 1)):
graph.add_edge((i, j), (i, (j + 1)), weight=(1 if (s[i][(j + 1)] == '.') else x))
return nx.dijkstra_path_length(graph, source, target)
return nx.astar_path_length(graph, source, target, heuristic_function) | -785,816,328,218,200,000 | my lib | jp.atcoder/abc009/abc009_4/17183548.py | min_time | kagemeka/atcoder-submissions | python | def min_time(x):
graph = GeometryTopology.Graph()
for i in range(h):
for j in range(w):
graph.add_node((i, j))
for i in range(h):
for j in range(w):
if (i > 0):
graph.add_edge((i, j), ((i - 1), j), weight=(1 if (s[(i - 1)][j] == '.') else x))
if (i < (h - 1)):
graph.add_edge((i, j), ((i + 1), j), weight=(1 if (s[(i + 1)][j] == '.') else x))
if (j > 0):
graph.add_edge((i, j), (i, (j - 1)), weight=(1 if (s[i][(j - 1)] == '.') else x))
if (j < (w - 1)):
graph.add_edge((i, j), (i, (j + 1)), weight=(1 if (s[i][(j + 1)] == '.') else x))
return graph.dijkstra(source)[target]
'networkx'
graph = nx.DiGraph()
for i in range(h):
for j in range(w):
if (i > 0):
graph.add_edge((i, j), ((i - 1), j), weight=(1 if (s[(i - 1)][j] == '.') else x))
if (i < (h - 1)):
graph.add_edge((i, j), ((i + 1), j), weight=(1 if (s[(i + 1)][j] == '.') else x))
if (j > 0):
graph.add_edge((i, j), (i, (j - 1)), weight=(1 if (s[i][(j - 1)] == '.') else x))
if (j < (w - 1)):
graph.add_edge((i, j), (i, (j + 1)), weight=(1 if (s[i][(j + 1)] == '.') else x))
return nx.dijkstra_path_length(graph, source, target)
return nx.astar_path_length(graph, source, target, heuristic_function) |
def group_by(keys, values=None, reduction=None, axis=0):
'construct a grouping object on the given keys, optionally performing the given reduction on the given values\n\n Parameters\n ----------\n keys : indexable object\n keys to group by\n values : array_like, optional\n sequence of values, of the same length as keys\n if a reduction function is provided, the given values are reduced by key\n if no reduction is provided, the given values are grouped and split by key\n reduction : lambda, optional\n reduction function to apply to the values in each group\n axis : int, optional\n axis to regard as the key-sequence, in case keys is multi-dimensional\n\n Returns\n -------\n iterable\n if values is None, a GroupBy object of the given keys object\n if reduction is None, an tuple of a sequence of unique keys and a sequence of grouped values\n else, a sequence of tuples of unique keys and reductions of values over that key-group\n\n See Also\n --------\n numpy_indexed.as_index : for information regarding the casting rules to a valid Index object\n '
g = GroupBy(keys, axis)
if (values is None):
return g
groups = g.split(values)
if (reduction is None):
return (g.unique, groups)
return [(key, reduction(group)) for (key, group) in zip(g.unique, groups)] | 6,910,904,156,956,246,000 | construct a grouping object on the given keys, optionally performing the given reduction on the given values
Parameters
----------
keys : indexable object
keys to group by
values : array_like, optional
sequence of values, of the same length as keys
if a reduction function is provided, the given values are reduced by key
if no reduction is provided, the given values are grouped and split by key
reduction : lambda, optional
reduction function to apply to the values in each group
axis : int, optional
axis to regard as the key-sequence, in case keys is multi-dimensional
Returns
-------
iterable
if values is None, a GroupBy object of the given keys object
if reduction is None, an tuple of a sequence of unique keys and a sequence of grouped values
else, a sequence of tuples of unique keys and reductions of values over that key-group
See Also
--------
numpy_indexed.as_index : for information regarding the casting rules to a valid Index object | numpy_indexed/grouping.py | group_by | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def group_by(keys, values=None, reduction=None, axis=0):
'construct a grouping object on the given keys, optionally performing the given reduction on the given values\n\n Parameters\n ----------\n keys : indexable object\n keys to group by\n values : array_like, optional\n sequence of values, of the same length as keys\n if a reduction function is provided, the given values are reduced by key\n if no reduction is provided, the given values are grouped and split by key\n reduction : lambda, optional\n reduction function to apply to the values in each group\n axis : int, optional\n axis to regard as the key-sequence, in case keys is multi-dimensional\n\n Returns\n -------\n iterable\n if values is None, a GroupBy object of the given keys object\n if reduction is None, an tuple of a sequence of unique keys and a sequence of grouped values\n else, a sequence of tuples of unique keys and reductions of values over that key-group\n\n See Also\n --------\n numpy_indexed.as_index : for information regarding the casting rules to a valid Index object\n '
g = GroupBy(keys, axis)
if (values is None):
return g
groups = g.split(values)
if (reduction is None):
return (g.unique, groups)
return [(key, reduction(group)) for (key, group) in zip(g.unique, groups)] |
def __init__(self, keys, axis=0):
'\n Parameters\n ----------\n keys : indexable object\n sequence of keys to group by\n axis : int, optional\n axis to regard as the key-sequence, in case keys is multi-dimensional\n\n See Also\n --------\n numpy_indexed.as_index : for information regarding the casting rules to a valid Index object\n '
self.index = as_index(keys, axis) | 6,020,760,228,939,865,000 | Parameters
----------
keys : indexable object
sequence of keys to group by
axis : int, optional
axis to regard as the key-sequence, in case keys is multi-dimensional
See Also
--------
numpy_indexed.as_index : for information regarding the casting rules to a valid Index object | numpy_indexed/grouping.py | __init__ | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def __init__(self, keys, axis=0):
'\n Parameters\n ----------\n keys : indexable object\n sequence of keys to group by\n axis : int, optional\n axis to regard as the key-sequence, in case keys is multi-dimensional\n\n See Also\n --------\n numpy_indexed.as_index : for information regarding the casting rules to a valid Index object\n '
self.index = as_index(keys, axis) |
@property
def unique(self):
'unique keys'
return self.index.unique | -930,526,704,603,093,000 | unique keys | numpy_indexed/grouping.py | unique | EelcoHoogendoorn/Numpy_arraysetops_EP | python | @property
def unique(self):
return self.index.unique |
@property
def count(self):
'count of each unique key'
return self.index.count | 8,502,613,712,486,878,000 | count of each unique key | numpy_indexed/grouping.py | count | EelcoHoogendoorn/Numpy_arraysetops_EP | python | @property
def count(self):
return self.index.count |
@property
def inverse(self):
'mapping such that unique[inverse]==keys'
return self.index.inverse | 5,544,252,425,276,580,000 | mapping such that unique[inverse]==keys | numpy_indexed/grouping.py | inverse | EelcoHoogendoorn/Numpy_arraysetops_EP | python | @property
def inverse(self):
return self.index.inverse |
@property
def groups(self):
'int, number of groups formed by the keys'
return self.index.groups | 8,731,109,496,834,587,000 | int, number of groups formed by the keys | numpy_indexed/grouping.py | groups | EelcoHoogendoorn/Numpy_arraysetops_EP | python | @property
def groups(self):
return self.index.groups |
def split_iterable_as_iterable(self, values):
'Group iterable into iterables, in the order of the keys\n\n Parameters\n ----------\n values : iterable of length equal to keys\n iterable of values to be grouped\n\n Yields\n ------\n iterable of items in values\n\n Notes\n -----\n Memory consumption depends on the amount of sorting required\n Worst case, if index.sorter[-1] = 0, we need to consume the entire value iterable,\n before we can start yielding any output\n But to the extent that the keys are already sorted, the grouping is lazy\n '
values = iter(enumerate(values))
cache = dict()
def get_value(ti):
try:
return cache.pop(ti)
except:
while True:
(i, v) = next(values)
if (i == ti):
return v
cache[i] = v
s = iter(self.index.sorter)
for c in self.count:
(yield (get_value(i) for i in itertools.islice(s, int(c)))) | -1,514,213,191,221,959,200 | Group iterable into iterables, in the order of the keys
Parameters
----------
values : iterable of length equal to keys
iterable of values to be grouped
Yields
------
iterable of items in values
Notes
-----
Memory consumption depends on the amount of sorting required
Worst case, if index.sorter[-1] = 0, we need to consume the entire value iterable,
before we can start yielding any output
But to the extent that the keys are already sorted, the grouping is lazy | numpy_indexed/grouping.py | split_iterable_as_iterable | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def split_iterable_as_iterable(self, values):
'Group iterable into iterables, in the order of the keys\n\n Parameters\n ----------\n values : iterable of length equal to keys\n iterable of values to be grouped\n\n Yields\n ------\n iterable of items in values\n\n Notes\n -----\n Memory consumption depends on the amount of sorting required\n Worst case, if index.sorter[-1] = 0, we need to consume the entire value iterable,\n before we can start yielding any output\n But to the extent that the keys are already sorted, the grouping is lazy\n '
values = iter(enumerate(values))
cache = dict()
def get_value(ti):
try:
return cache.pop(ti)
except:
while True:
(i, v) = next(values)
if (i == ti):
return v
cache[i] = v
s = iter(self.index.sorter)
for c in self.count:
(yield (get_value(i) for i in itertools.islice(s, int(c)))) |
def split_iterable_as_unordered_iterable(self, values):
'Group iterable into iterables, without regard for the ordering of self.index.unique\n key-group tuples are yielded as soon as they are complete\n\n Parameters\n ----------\n values : iterable of length equal to keys\n iterable of values to be grouped\n\n Yields\n ------\n tuple of key, and a list of corresponding items in values\n\n Notes\n -----\n This approach is lazy, insofar as grouped values are close in their iterable\n '
from collections import defaultdict
cache = defaultdict(list)
count = self.count
unique = self.unique
key = ((lambda i: unique[i]) if isinstance(unique, np.ndarray) else (lambda i: tuple((c[i] for c in unique))))
for (i, v) in zip(self.inverse, values):
cache[i].append(v)
if (len(cache[i]) == count[i]):
(yield (key(i), cache.pop(i))) | 7,352,415,599,736,740,000 | Group iterable into iterables, without regard for the ordering of self.index.unique
key-group tuples are yielded as soon as they are complete
Parameters
----------
values : iterable of length equal to keys
iterable of values to be grouped
Yields
------
tuple of key, and a list of corresponding items in values
Notes
-----
This approach is lazy, insofar as grouped values are close in their iterable | numpy_indexed/grouping.py | split_iterable_as_unordered_iterable | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def split_iterable_as_unordered_iterable(self, values):
'Group iterable into iterables, without regard for the ordering of self.index.unique\n key-group tuples are yielded as soon as they are complete\n\n Parameters\n ----------\n values : iterable of length equal to keys\n iterable of values to be grouped\n\n Yields\n ------\n tuple of key, and a list of corresponding items in values\n\n Notes\n -----\n This approach is lazy, insofar as grouped values are close in their iterable\n '
from collections import defaultdict
cache = defaultdict(list)
count = self.count
unique = self.unique
key = ((lambda i: unique[i]) if isinstance(unique, np.ndarray) else (lambda i: tuple((c[i] for c in unique))))
for (i, v) in zip(self.inverse, values):
cache[i].append(v)
if (len(cache[i]) == count[i]):
(yield (key(i), cache.pop(i))) |
def split_sequence_as_iterable(self, values):
'Group sequence into iterables\n\n Parameters\n ----------\n values : iterable of length equal to keys\n iterable of values to be grouped\n\n Yields\n ------\n iterable of items in values\n\n Notes\n -----\n This is the preferred method if values has random access, but we dont want it completely in memory.\n Like a big memory mapped file, for instance\n '
print(self.count)
s = iter(self.index.sorter)
for c in self.count:
(yield (values[i] for i in itertools.islice(s, int(c)))) | -1,918,695,829,166,377,500 | Group sequence into iterables
Parameters
----------
values : iterable of length equal to keys
iterable of values to be grouped
Yields
------
iterable of items in values
Notes
-----
This is the preferred method if values has random access, but we dont want it completely in memory.
Like a big memory mapped file, for instance | numpy_indexed/grouping.py | split_sequence_as_iterable | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def split_sequence_as_iterable(self, values):
'Group sequence into iterables\n\n Parameters\n ----------\n values : iterable of length equal to keys\n iterable of values to be grouped\n\n Yields\n ------\n iterable of items in values\n\n Notes\n -----\n This is the preferred method if values has random access, but we dont want it completely in memory.\n Like a big memory mapped file, for instance\n '
print(self.count)
s = iter(self.index.sorter)
for c in self.count:
(yield (values[i] for i in itertools.islice(s, int(c)))) |
def split_array_as_array(self, values):
'Group ndarray into ndarray by means of reshaping\n\n Parameters\n ----------\n values : ndarray_like, [index.size, ...]\n\n Returns\n -------\n ndarray, [groups, group_size, ...]\n values grouped by key\n\n Raises\n ------\n AssertionError\n This operation is only possible if index.uniform==True\n '
if (not self.index.uniform):
raise ValueError('Array can only be split as array if all groups have the same size')
values = np.asarray(values)
values = values[self.index.sorter]
return values.reshape(self.groups, (- 1), *values.shape[1:]) | 4,391,596,414,982,254,000 | Group ndarray into ndarray by means of reshaping
Parameters
----------
values : ndarray_like, [index.size, ...]
Returns
-------
ndarray, [groups, group_size, ...]
values grouped by key
Raises
------
AssertionError
This operation is only possible if index.uniform==True | numpy_indexed/grouping.py | split_array_as_array | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def split_array_as_array(self, values):
'Group ndarray into ndarray by means of reshaping\n\n Parameters\n ----------\n values : ndarray_like, [index.size, ...]\n\n Returns\n -------\n ndarray, [groups, group_size, ...]\n values grouped by key\n\n Raises\n ------\n AssertionError\n This operation is only possible if index.uniform==True\n '
if (not self.index.uniform):
raise ValueError('Array can only be split as array if all groups have the same size')
values = np.asarray(values)
values = values[self.index.sorter]
return values.reshape(self.groups, (- 1), *values.shape[1:]) |
def split_array_as_list(self, values):
'Group values as a list of arrays, or a jagged-array\n\n Parameters\n ----------\n values : ndarray, [keys, ...]\n\n Returns\n -------\n list of length self.groups of ndarray, [key_count, ...]\n '
values = np.asarray(values)
values = values[self.index.sorter]
return np.split(values, self.index.slices[1:(- 1)], axis=0) | -2,253,695,053,208,438,500 | Group values as a list of arrays, or a jagged-array
Parameters
----------
values : ndarray, [keys, ...]
Returns
-------
list of length self.groups of ndarray, [key_count, ...] | numpy_indexed/grouping.py | split_array_as_list | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def split_array_as_list(self, values):
'Group values as a list of arrays, or a jagged-array\n\n Parameters\n ----------\n values : ndarray, [keys, ...]\n\n Returns\n -------\n list of length self.groups of ndarray, [key_count, ...]\n '
values = np.asarray(values)
values = values[self.index.sorter]
return np.split(values, self.index.slices[1:(- 1)], axis=0) |
def split(self, values):
'some sensible defaults'
try:
return self.split_array_as_array(values)
except:
return self.split_array_as_list(values) | 8,993,428,587,264,464,000 | some sensible defaults | numpy_indexed/grouping.py | split | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def split(self, values):
try:
return self.split_array_as_array(values)
except:
return self.split_array_as_list(values) |
def __call__(self, values):
'not sure how i feel about this. explicit is better than implict?'
return (self.unique, self.split(values)) | 6,196,984,892,751,246,000 | not sure how i feel about this. explicit is better than implict? | numpy_indexed/grouping.py | __call__ | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def __call__(self, values):
return (self.unique, self.split(values)) |
def reduce(self, values, operator=np.add, axis=0, dtype=None):
'Reduce the values over identical key groups, using the given ufunc\n reduction is over the first axis, which should have elements corresponding to the keys\n all other axes are treated indepenently for the sake of this reduction\n\n Parameters\n ----------\n values : ndarray, [keys, ...]\n values to perform reduction over\n operator : numpy.ufunc\n a numpy ufunc, such as np.add or np.sum\n axis : int, optional\n the axis to reduce over\n dtype : output dtype\n\n Returns\n -------\n ndarray, [groups, ...]\n values reduced by operator over the key-groups\n '
values = np.take(values, self.index.sorter, axis=axis)
return operator.reduceat(values, self.index.start, axis=axis, dtype=dtype) | -1,888,145,151,123,266,800 | Reduce the values over identical key groups, using the given ufunc
reduction is over the first axis, which should have elements corresponding to the keys
all other axes are treated indepenently for the sake of this reduction
Parameters
----------
values : ndarray, [keys, ...]
values to perform reduction over
operator : numpy.ufunc
a numpy ufunc, such as np.add or np.sum
axis : int, optional
the axis to reduce over
dtype : output dtype
Returns
-------
ndarray, [groups, ...]
values reduced by operator over the key-groups | numpy_indexed/grouping.py | reduce | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def reduce(self, values, operator=np.add, axis=0, dtype=None):
'Reduce the values over identical key groups, using the given ufunc\n reduction is over the first axis, which should have elements corresponding to the keys\n all other axes are treated indepenently for the sake of this reduction\n\n Parameters\n ----------\n values : ndarray, [keys, ...]\n values to perform reduction over\n operator : numpy.ufunc\n a numpy ufunc, such as np.add or np.sum\n axis : int, optional\n the axis to reduce over\n dtype : output dtype\n\n Returns\n -------\n ndarray, [groups, ...]\n values reduced by operator over the key-groups\n '
values = np.take(values, self.index.sorter, axis=axis)
return operator.reduceat(values, self.index.start, axis=axis, dtype=dtype) |
def sum(self, values, axis=0, dtype=None):
'compute the sum over each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to sum per group\n axis : int, optional\n alternative reduction axis for values\n dtype : output dtype\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
values = np.asarray(values)
return (self.unique, self.reduce(values, axis=axis, dtype=dtype)) | -1,945,319,861,920,158,000 | compute the sum over each group
Parameters
----------
values : array_like, [keys, ...]
values to sum per group
axis : int, optional
alternative reduction axis for values
dtype : output dtype
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...]
value array, reduced over groups | numpy_indexed/grouping.py | sum | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def sum(self, values, axis=0, dtype=None):
'compute the sum over each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to sum per group\n axis : int, optional\n alternative reduction axis for values\n dtype : output dtype\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
values = np.asarray(values)
return (self.unique, self.reduce(values, axis=axis, dtype=dtype)) |
def prod(self, values, axis=0, dtype=None):
'compute the product over each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to multiply per group\n axis : int, optional\n alternative reduction axis for values\n dtype : output dtype\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
values = np.asarray(values)
return (self.unique, self.reduce(values, axis=axis, dtype=dtype, operator=np.multiply)) | 4,575,069,133,977,734,000 | compute the product over each group
Parameters
----------
values : array_like, [keys, ...]
values to multiply per group
axis : int, optional
alternative reduction axis for values
dtype : output dtype
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...]
value array, reduced over groups | numpy_indexed/grouping.py | prod | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def prod(self, values, axis=0, dtype=None):
'compute the product over each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to multiply per group\n axis : int, optional\n alternative reduction axis for values\n dtype : output dtype\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
values = np.asarray(values)
return (self.unique, self.reduce(values, axis=axis, dtype=dtype, operator=np.multiply)) |
def mean(self, values, axis=0, weights=None, dtype=None):
'compute the mean over each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take average of per group\n axis : int, optional\n alternative reduction axis for values\n weights : ndarray, [keys, ...], optional\n weight to use for each value\n dtype : output dtype\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
values = np.asarray(values)
if (weights is None):
result = self.reduce(values, axis=axis, dtype=dtype)
shape = ([1] * values.ndim)
shape[axis] = self.groups
weights = self.count.reshape(shape)
else:
weights = np.asarray(weights)
result = self.reduce((values * weights), axis=axis, dtype=dtype)
weights = self.reduce(weights, axis=axis, dtype=dtype)
return (self.unique, (result / weights)) | 8,492,916,686,960,966,000 | compute the mean over each group
Parameters
----------
values : array_like, [keys, ...]
values to take average of per group
axis : int, optional
alternative reduction axis for values
weights : ndarray, [keys, ...], optional
weight to use for each value
dtype : output dtype
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...]
value array, reduced over groups | numpy_indexed/grouping.py | mean | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def mean(self, values, axis=0, weights=None, dtype=None):
'compute the mean over each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take average of per group\n axis : int, optional\n alternative reduction axis for values\n weights : ndarray, [keys, ...], optional\n weight to use for each value\n dtype : output dtype\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
values = np.asarray(values)
if (weights is None):
result = self.reduce(values, axis=axis, dtype=dtype)
shape = ([1] * values.ndim)
shape[axis] = self.groups
weights = self.count.reshape(shape)
else:
weights = np.asarray(weights)
result = self.reduce((values * weights), axis=axis, dtype=dtype)
weights = self.reduce(weights, axis=axis, dtype=dtype)
return (self.unique, (result / weights)) |
def var(self, values, axis=0, weights=None, dtype=None):
'compute the variance over each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take variance of per group\n axis : int, optional\n alternative reduction axis for values\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
values = np.asarray(values)
(unique, mean) = self.mean(values, axis, weights, dtype)
err = (values - mean.take(self.inverse, axis))
if (weights is None):
shape = ([1] * values.ndim)
shape[axis] = self.groups
group_weights = self.count.reshape(shape)
var = self.reduce((err ** 2), axis=axis, dtype=dtype)
else:
weights = np.asarray(weights)
group_weights = self.reduce(weights, axis=axis, dtype=dtype)
var = self.reduce((weights * (err ** 2)), axis=axis, dtype=dtype)
return (unique, (var / group_weights)) | -4,991,949,059,122,910,000 | compute the variance over each group
Parameters
----------
values : array_like, [keys, ...]
values to take variance of per group
axis : int, optional
alternative reduction axis for values
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...]
value array, reduced over groups | numpy_indexed/grouping.py | var | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def var(self, values, axis=0, weights=None, dtype=None):
'compute the variance over each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take variance of per group\n axis : int, optional\n alternative reduction axis for values\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
values = np.asarray(values)
(unique, mean) = self.mean(values, axis, weights, dtype)
err = (values - mean.take(self.inverse, axis))
if (weights is None):
shape = ([1] * values.ndim)
shape[axis] = self.groups
group_weights = self.count.reshape(shape)
var = self.reduce((err ** 2), axis=axis, dtype=dtype)
else:
weights = np.asarray(weights)
group_weights = self.reduce(weights, axis=axis, dtype=dtype)
var = self.reduce((weights * (err ** 2)), axis=axis, dtype=dtype)
return (unique, (var / group_weights)) |
def std(self, values, axis=0, weights=None, dtype=None):
'standard deviation over each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take standard deviation of per group\n axis : int, optional\n alternative reduction axis for values\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
(unique, var) = self.var(values, axis, weights, dtype)
return (unique, np.sqrt(var)) | -5,936,213,147,425,417,000 | standard deviation over each group
Parameters
----------
values : array_like, [keys, ...]
values to take standard deviation of per group
axis : int, optional
alternative reduction axis for values
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...]
value array, reduced over groups | numpy_indexed/grouping.py | std | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def std(self, values, axis=0, weights=None, dtype=None):
'standard deviation over each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take standard deviation of per group\n axis : int, optional\n alternative reduction axis for values\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
(unique, var) = self.var(values, axis, weights, dtype)
return (unique, np.sqrt(var)) |
def median(self, values, axis=0, average=True):
'compute the median value over each group.\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to compute the median of per group\n axis : int, optional\n alternative reduction axis for values\n average : bool, optional\n when average is true, the average of the two central values is taken for groups with an even key-count\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
mid_2 = (self.index.start + self.index.stop)
hi = (mid_2 // 2)
lo = ((mid_2 - 1) // 2)
sorted_group_rank_per_key = self.index.sorted_group_rank_per_key
def median1d(slc):
slc = slc[self.index.sorter]
sorter = np.lexsort((slc, sorted_group_rank_per_key))
slc = slc[sorter]
return (((slc[lo] + slc[hi]) / 2) if average else slc[hi])
values = np.asarray(values)
if (values.ndim > 1):
values = np.apply_along_axis(median1d, axis, values)
else:
values = median1d(values)
return (self.unique, values) | -9,059,944,262,215,597,000 | compute the median value over each group.
Parameters
----------
values : array_like, [keys, ...]
values to compute the median of per group
axis : int, optional
alternative reduction axis for values
average : bool, optional
when average is true, the average of the two central values is taken for groups with an even key-count
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...]
value array, reduced over groups | numpy_indexed/grouping.py | median | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def median(self, values, axis=0, average=True):
'compute the median value over each group.\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to compute the median of per group\n axis : int, optional\n alternative reduction axis for values\n average : bool, optional\n when average is true, the average of the two central values is taken for groups with an even key-count\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
mid_2 = (self.index.start + self.index.stop)
hi = (mid_2 // 2)
lo = ((mid_2 - 1) // 2)
sorted_group_rank_per_key = self.index.sorted_group_rank_per_key
def median1d(slc):
slc = slc[self.index.sorter]
sorter = np.lexsort((slc, sorted_group_rank_per_key))
slc = slc[sorter]
return (((slc[lo] + slc[hi]) / 2) if average else slc[hi])
values = np.asarray(values)
if (values.ndim > 1):
values = np.apply_along_axis(median1d, axis, values)
else:
values = median1d(values)
return (self.unique, values) |
def mode(self, values, weights=None):
'compute the mode within each group.\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to compute the mode of per group\n weights : array_like, [keys], float, optional\n optional weight associated with each entry in values\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
if (weights is None):
(unique, weights) = npi.count((self.index.sorted_group_rank_per_key, values))
else:
(unique, weights) = npi.group_by((self.index.sorted_group_rank_per_key, values)).sum(weights)
(x, bin) = npi.group_by(unique[0]).argmax(weights)
return (x, unique[1][bin]) | 4,774,019,377,929,014,000 | compute the mode within each group.
Parameters
----------
values : array_like, [keys, ...]
values to compute the mode of per group
weights : array_like, [keys], float, optional
optional weight associated with each entry in values
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...]
value array, reduced over groups | numpy_indexed/grouping.py | mode | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def mode(self, values, weights=None):
'compute the mode within each group.\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to compute the mode of per group\n weights : array_like, [keys], float, optional\n optional weight associated with each entry in values\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
if (weights is None):
(unique, weights) = npi.count((self.index.sorted_group_rank_per_key, values))
else:
(unique, weights) = npi.group_by((self.index.sorted_group_rank_per_key, values)).sum(weights)
(x, bin) = npi.group_by(unique[0]).argmax(weights)
return (x, unique[1][bin]) |
def min(self, values, axis=0):
'return the minimum within each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take minimum of per group\n axis : int, optional\n alternative reduction axis for values\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
values = np.asarray(values)
return (self.unique, self.reduce(values, np.minimum, axis)) | -2,479,186,574,521,301,000 | return the minimum within each group
Parameters
----------
values : array_like, [keys, ...]
values to take minimum of per group
axis : int, optional
alternative reduction axis for values
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...]
value array, reduced over groups | numpy_indexed/grouping.py | min | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def min(self, values, axis=0):
'return the minimum within each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take minimum of per group\n axis : int, optional\n alternative reduction axis for values\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
values = np.asarray(values)
return (self.unique, self.reduce(values, np.minimum, axis)) |
def max(self, values, axis=0):
'return the maximum within each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take maximum of per group\n axis : int, optional\n alternative reduction axis for values\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
values = np.asarray(values)
return (self.unique, self.reduce(values, np.maximum, axis)) | -4,892,151,556,389,857,000 | return the maximum within each group
Parameters
----------
values : array_like, [keys, ...]
values to take maximum of per group
axis : int, optional
alternative reduction axis for values
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...]
value array, reduced over groups | numpy_indexed/grouping.py | max | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def max(self, values, axis=0):
'return the maximum within each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take maximum of per group\n axis : int, optional\n alternative reduction axis for values\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
values = np.asarray(values)
return (self.unique, self.reduce(values, np.maximum, axis)) |
def first(self, values, axis=0):
'return values at first occurance of its associated key\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to pick the first value of per group\n axis : int, optional\n alternative reduction axis for values\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
values = np.asarray(values)
return (self.unique, np.take(values, self.index.sorter[self.index.start], axis)) | 1,523,354,121,096,837,400 | return values at first occurance of its associated key
Parameters
----------
values : array_like, [keys, ...]
values to pick the first value of per group
axis : int, optional
alternative reduction axis for values
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...]
value array, reduced over groups | numpy_indexed/grouping.py | first | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def first(self, values, axis=0):
'return values at first occurance of its associated key\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to pick the first value of per group\n axis : int, optional\n alternative reduction axis for values\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
values = np.asarray(values)
return (self.unique, np.take(values, self.index.sorter[self.index.start], axis)) |
def last(self, values, axis=0):
'return values at last occurance of its associated key\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to pick the last value of per group\n axis : int, optional\n alternative reduction axis for values\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
values = np.asarray(values)
return (self.unique, np.take(values, self.index.sorter[(self.index.stop - 1)], axis)) | 6,716,917,442,927,277,000 | return values at last occurance of its associated key
Parameters
----------
values : array_like, [keys, ...]
values to pick the last value of per group
axis : int, optional
alternative reduction axis for values
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...]
value array, reduced over groups | numpy_indexed/grouping.py | last | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def last(self, values, axis=0):
'return values at last occurance of its associated key\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to pick the last value of per group\n axis : int, optional\n alternative reduction axis for values\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...]\n value array, reduced over groups\n '
values = np.asarray(values)
return (self.unique, np.take(values, self.index.sorter[(self.index.stop - 1)], axis)) |
def any(self, values, axis=0):
'compute if any item evaluates to true in each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take boolean predicate over per group\n axis : int, optional\n alternative reduction axis for values\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...], np.bool\n value array, reduced over groups\n '
values = np.asarray(values)
if (not (values.dtype == np.bool)):
values = (values != 0)
return (self.unique, (self.reduce(values, axis=axis) > 0)) | -577,262,749,674,790,800 | compute if any item evaluates to true in each group
Parameters
----------
values : array_like, [keys, ...]
values to take boolean predicate over per group
axis : int, optional
alternative reduction axis for values
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...], np.bool
value array, reduced over groups | numpy_indexed/grouping.py | any | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def any(self, values, axis=0):
'compute if any item evaluates to true in each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take boolean predicate over per group\n axis : int, optional\n alternative reduction axis for values\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...], np.bool\n value array, reduced over groups\n '
values = np.asarray(values)
if (not (values.dtype == np.bool)):
values = (values != 0)
return (self.unique, (self.reduce(values, axis=axis) > 0)) |
def all(self, values, axis=0):
'compute if all items evaluates to true in each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take boolean predicate over per group\n axis : int, optional\n alternative reduction axis for values\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...], np.bool\n value array, reduced over groups\n '
values = np.asarray(values)
return (self.unique, (self.reduce(values, axis=axis, operator=np.multiply) != 0)) | 1,479,049,310,855,061,500 | compute if all items evaluates to true in each group
Parameters
----------
values : array_like, [keys, ...]
values to take boolean predicate over per group
axis : int, optional
alternative reduction axis for values
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...], np.bool
value array, reduced over groups | numpy_indexed/grouping.py | all | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def all(self, values, axis=0):
'compute if all items evaluates to true in each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take boolean predicate over per group\n axis : int, optional\n alternative reduction axis for values\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n reduced : ndarray, [groups, ...], np.bool\n value array, reduced over groups\n '
values = np.asarray(values)
return (self.unique, (self.reduce(values, axis=axis, operator=np.multiply) != 0)) |
def argmin(self, values):
'return the index into values corresponding to the minimum value of the group\n\n Parameters\n ----------\n values : array_like, [keys]\n values to pick the argmin of per group\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n argmin : ndarray, [groups]\n index into value array, representing the argmin per group\n '
(keys, minima) = self.min(values)
minima = minima[self.inverse]
index = as_index((self.inverse, (values == minima)))
return (keys, index.sorter[index.start[(- self.groups):]]) | -7,292,802,029,241,178,000 | return the index into values corresponding to the minimum value of the group
Parameters
----------
values : array_like, [keys]
values to pick the argmin of per group
Returns
-------
unique: ndarray, [groups]
unique keys
argmin : ndarray, [groups]
index into value array, representing the argmin per group | numpy_indexed/grouping.py | argmin | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def argmin(self, values):
'return the index into values corresponding to the minimum value of the group\n\n Parameters\n ----------\n values : array_like, [keys]\n values to pick the argmin of per group\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n argmin : ndarray, [groups]\n index into value array, representing the argmin per group\n '
(keys, minima) = self.min(values)
minima = minima[self.inverse]
index = as_index((self.inverse, (values == minima)))
return (keys, index.sorter[index.start[(- self.groups):]]) |
def argmax(self, values):
'return the index into values corresponding to the maximum value of the group\n\n Parameters\n ----------\n values : array_like, [keys]\n values to pick the argmax of per group\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n argmax : ndarray, [groups]\n index into value array, representing the argmax per group\n '
(keys, maxima) = self.max(values)
maxima = maxima[self.inverse]
index = as_index((self.inverse, (values == maxima)))
return (keys, index.sorter[index.start[(- self.groups):]]) | -2,912,817,621,899,028,000 | return the index into values corresponding to the maximum value of the group
Parameters
----------
values : array_like, [keys]
values to pick the argmax of per group
Returns
-------
unique: ndarray, [groups]
unique keys
argmax : ndarray, [groups]
index into value array, representing the argmax per group | numpy_indexed/grouping.py | argmax | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def argmax(self, values):
'return the index into values corresponding to the maximum value of the group\n\n Parameters\n ----------\n values : array_like, [keys]\n values to pick the argmax of per group\n\n Returns\n -------\n unique: ndarray, [groups]\n unique keys\n argmax : ndarray, [groups]\n index into value array, representing the argmax per group\n '
(keys, maxima) = self.max(values)
maxima = maxima[self.inverse]
index = as_index((self.inverse, (values == maxima)))
return (keys, index.sorter[index.start[(- self.groups):]]) |
def __init__(self, raw_gadget):
'\n Gadget constructor\n :param str raw_gadget: raw line output from ROPgadget\n '
self.offset = raw_gadget[:raw_gadget.find(':')]
self.instruction_string = raw_gadget[(raw_gadget.find(':') + 2):]
self.instructions = []
for instr in self.instruction_string.split(' ; '):
self.instructions.append(Instruction(instr))
self.score = 0.0 | 8,029,491,231,991,800,000 | Gadget constructor
:param str raw_gadget: raw line output from ROPgadget | src/static_analyzer/Gadget.py | __init__ | michaelbrownuc/GadgetSetAnalyzer | python | def __init__(self, raw_gadget):
'\n Gadget constructor\n :param str raw_gadget: raw line output from ROPgadget\n '
self.offset = raw_gadget[:raw_gadget.find(':')]
self.instruction_string = raw_gadget[(raw_gadget.find(':') + 2):]
self.instructions = []
for instr in self.instruction_string.split(' ; '):
self.instructions.append(Instruction(instr))
self.score = 0.0 |
def is_useless_op(self):
'\n :return boolean: Returns True if the first instruction opcode is in the "useless" list, False otherwise\n Default behavior is to consider opcodes useful unless otherwise observed.\n '
first_opcode = self.instructions[0].opcode
if first_opcode.startswith('j'):
return True
if first_opcode.startswith('bnd'):
return True
if first_opcode.startswith('ret'):
return True
if first_opcode.startswith('iret'):
return True
if first_opcode.startswith('call'):
return True
useless = ['nop', 'fnop', 'ljmp']
return (first_opcode in useless) | -8,508,034,393,575,901,000 | :return boolean: Returns True if the first instruction opcode is in the "useless" list, False otherwise
Default behavior is to consider opcodes useful unless otherwise observed. | src/static_analyzer/Gadget.py | is_useless_op | michaelbrownuc/GadgetSetAnalyzer | python | def is_useless_op(self):
'\n :return boolean: Returns True if the first instruction opcode is in the "useless" list, False otherwise\n Default behavior is to consider opcodes useful unless otherwise observed.\n '
first_opcode = self.instructions[0].opcode
if first_opcode.startswith('j'):
return True
if first_opcode.startswith('bnd'):
return True
if first_opcode.startswith('ret'):
return True
if first_opcode.startswith('iret'):
return True
if first_opcode.startswith('call'):
return True
useless = ['nop', 'fnop', 'ljmp']
return (first_opcode in useless) |
def contains_unusable_op(self):
'\n :return boolean: Returns True if any instruction opcode is unusable. False otherwise\n unusable instructions are Ring-0 opcodes that trap in user mode and some other exceptional ops.\n '
for instr in self.instructions:
if instr.opcode.startswith('inv'):
return True
if (instr.opcode.startswith('vm') and (instr.opcode != 'vminsd') and (instr.opcode != 'vminpd')):
return True
if instr.opcode.startswith('ud'):
return True
unusable = ['clts', 'hlt', 'lgdt', 'lidt', 'lldt', 'lmsw', 'ltr', 'monitor', 'mwait', 'swapgs', 'sysexit', 'sysreturn', 'wbinvd', 'wrmsr', 'xsetbv', 'rsm', 'lock']
if (instr.opcode in unusable):
return True
if (instr.op1 is not None):
if (instr.op1.startswith('cr') or instr.op1.startswith('tr') or instr.op1.startswith('db')):
return True
if (instr.op2 is not None):
if (instr.op2.startswith('cr') or instr.op2.startswith('tr') or instr.op2.startswith('db')):
return True
return False | 5,801,953,904,756,299,000 | :return boolean: Returns True if any instruction opcode is unusable. False otherwise
unusable instructions are Ring-0 opcodes that trap in user mode and some other exceptional ops. | src/static_analyzer/Gadget.py | contains_unusable_op | michaelbrownuc/GadgetSetAnalyzer | python | def contains_unusable_op(self):
'\n :return boolean: Returns True if any instruction opcode is unusable. False otherwise\n unusable instructions are Ring-0 opcodes that trap in user mode and some other exceptional ops.\n '
for instr in self.instructions:
if instr.opcode.startswith('inv'):
return True
if (instr.opcode.startswith('vm') and (instr.opcode != 'vminsd') and (instr.opcode != 'vminpd')):
return True
if instr.opcode.startswith('ud'):
return True
unusable = ['clts', 'hlt', 'lgdt', 'lidt', 'lldt', 'lmsw', 'ltr', 'monitor', 'mwait', 'swapgs', 'sysexit', 'sysreturn', 'wbinvd', 'wrmsr', 'xsetbv', 'rsm', 'lock']
if (instr.opcode in unusable):
return True
if (instr.op1 is not None):
if (instr.op1.startswith('cr') or instr.op1.startswith('tr') or instr.op1.startswith('db')):
return True
if (instr.op2 is not None):
if (instr.op2.startswith('cr') or instr.op2.startswith('tr') or instr.op2.startswith('db')):
return True
return False |
def is_gpi_only(self):
"\n :return boolean: Returns True if the gadget is a single instruction and starts with 'ret', 'jmp', or 'call',\n False otherwise\n "
if (len(self.instructions) == 1):
opcode = self.instructions[0].opcode
if (opcode.startswith('ret') or opcode.startswith('jmp') or opcode.startswith('call')):
return True
return False | 4,594,664,549,577,642,500 | :return boolean: Returns True if the gadget is a single instruction and starts with 'ret', 'jmp', or 'call',
False otherwise | src/static_analyzer/Gadget.py | is_gpi_only | michaelbrownuc/GadgetSetAnalyzer | python | def is_gpi_only(self):
"\n :return boolean: Returns True if the gadget is a single instruction and starts with 'ret', 'jmp', or 'call',\n False otherwise\n "
if (len(self.instructions) == 1):
opcode = self.instructions[0].opcode
if (opcode.startswith('ret') or opcode.startswith('jmp') or opcode.startswith('call')):
return True
return False |
def is_invalid_branch(self):
"\n :return boolean: Returns True if the gadget is 'jmp' or 'call' ending and the call target is a constant offset\n or does not target a recognized register family. False otherwise\n "
last_instr = self.instructions[(len(self.instructions) - 1)]
if (last_instr.opcode.startswith('call') or last_instr.opcode.startswith('jmp')):
if (Instruction.get_operand_register_family(last_instr.op1) is None):
return True
return False | 5,528,830,466,194,106,000 | :return boolean: Returns True if the gadget is 'jmp' or 'call' ending and the call target is a constant offset
or does not target a recognized register family. False otherwise | src/static_analyzer/Gadget.py | is_invalid_branch | michaelbrownuc/GadgetSetAnalyzer | python | def is_invalid_branch(self):
"\n :return boolean: Returns True if the gadget is 'jmp' or 'call' ending and the call target is a constant offset\n or does not target a recognized register family. False otherwise\n "
last_instr = self.instructions[(len(self.instructions) - 1)]
if (last_instr.opcode.startswith('call') or last_instr.opcode.startswith('jmp')):
if (Instruction.get_operand_register_family(last_instr.op1) is None):
return True
return False |
def has_invalid_ret_offset(self):
"\n :return boolean: Returns True if the gadget is 'ret' ending and contains a constant offset that is not byte\n aligned or is greater than 32 bytes, False otherwise\n "
last_instr = self.instructions[(len(self.instructions) - 1)]
if (last_instr.opcode.startswith('ret') and (last_instr.op1 is not None)):
offset = Instruction.get_operand_as_constant(last_instr.op1)
if (((offset % 2) != 0) or (offset > 32)):
return True
return False | -862,678,852,484,315,100 | :return boolean: Returns True if the gadget is 'ret' ending and contains a constant offset that is not byte
aligned or is greater than 32 bytes, False otherwise | src/static_analyzer/Gadget.py | has_invalid_ret_offset | michaelbrownuc/GadgetSetAnalyzer | python | def has_invalid_ret_offset(self):
"\n :return boolean: Returns True if the gadget is 'ret' ending and contains a constant offset that is not byte\n aligned or is greater than 32 bytes, False otherwise\n "
last_instr = self.instructions[(len(self.instructions) - 1)]
if (last_instr.opcode.startswith('ret') and (last_instr.op1 is not None)):
offset = Instruction.get_operand_as_constant(last_instr.op1)
if (((offset % 2) != 0) or (offset > 32)):
return True
return False |
def clobbers_created_value(self):
'\n :return boolean: Returns True if the gadget completely overwrites the value created in the first instruction,\n False otherwise.\n '
first_instr = self.instructions[0]
if ((not first_instr.creates_value()) or ('xchg' in first_instr.opcode)):
return False
first_family = Instruction.get_operand_register_family(first_instr.op1)
if (first_family is None):
return False
for i in range(1, (len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if ((not cur_instr.creates_value()) or ('xchg' in cur_instr.opcode)):
continue
if (first_family == Instruction.get_operand_register_family(cur_instr.op1)):
if (((cur_instr.op2 is None) and (cur_instr.opcode not in ['inc', 'dec', 'neg', 'not'])) or ((cur_instr.op2 is not None) and (not Instruction.is_constant(cur_instr.op2)))):
return True
return False | -1,797,705,343,943,502,000 | :return boolean: Returns True if the gadget completely overwrites the value created in the first instruction,
False otherwise. | src/static_analyzer/Gadget.py | clobbers_created_value | michaelbrownuc/GadgetSetAnalyzer | python | def clobbers_created_value(self):
'\n :return boolean: Returns True if the gadget completely overwrites the value created in the first instruction,\n False otherwise.\n '
first_instr = self.instructions[0]
if ((not first_instr.creates_value()) or ('xchg' in first_instr.opcode)):
return False
first_family = Instruction.get_operand_register_family(first_instr.op1)
if (first_family is None):
return False
for i in range(1, (len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if ((not cur_instr.creates_value()) or ('xchg' in cur_instr.opcode)):
continue
if (first_family == Instruction.get_operand_register_family(cur_instr.op1)):
if (((cur_instr.op2 is None) and (cur_instr.opcode not in ['inc', 'dec', 'neg', 'not'])) or ((cur_instr.op2 is not None) and (not Instruction.is_constant(cur_instr.op2)))):
return True
return False |
def creates_unusable_value(self):
'\n :return boolean: Returns True if the gadget creates a value in segment or extension registers, or are\n RIP-relative, or are constant memory locations; False otherwise.\n '
first_instr = self.instructions[0]
if ((first_instr.opcode in ['cmp', 'test', 'push']) or (first_instr.op1 is None)):
return False
if ((not Instruction.is_constant(first_instr.op1)) and (Instruction.get_operand_register_family(first_instr.op1) is None)):
return True
return False | 2,964,850,469,619,353,600 | :return boolean: Returns True if the gadget creates a value in segment or extension registers, or are
RIP-relative, or are constant memory locations; False otherwise. | src/static_analyzer/Gadget.py | creates_unusable_value | michaelbrownuc/GadgetSetAnalyzer | python | def creates_unusable_value(self):
'\n :return boolean: Returns True if the gadget creates a value in segment or extension registers, or are\n RIP-relative, or are constant memory locations; False otherwise.\n '
first_instr = self.instructions[0]
if ((first_instr.opcode in ['cmp', 'test', 'push']) or (first_instr.op1 is None)):
return False
if ((not Instruction.is_constant(first_instr.op1)) and (Instruction.get_operand_register_family(first_instr.op1) is None)):
return True
return False |
def contains_intermediate_GPI(self):
"\n :return boolean: Returns True if the gadget's intermediate instructions contain a GPI (or a generic interrupt),\n False otherwise.\n "
for i in range((len(self.instructions) - 1)):
cur_opcode = self.instructions[i].opcode
cur_target = self.instructions[i].op1
if (cur_opcode.startswith('ret') or (cur_opcode == 'syscall') or (cur_opcode == 'sysenter') or cur_opcode.startswith('int') or (('jmp' in cur_opcode) and (not Instruction.is_constant(cur_target))) or (('call' in cur_opcode) and (not Instruction.is_constant(cur_target)))):
return True
return False | 1,243,145,747,006,903,300 | :return boolean: Returns True if the gadget's intermediate instructions contain a GPI (or a generic interrupt),
False otherwise. | src/static_analyzer/Gadget.py | contains_intermediate_GPI | michaelbrownuc/GadgetSetAnalyzer | python | def contains_intermediate_GPI(self):
"\n :return boolean: Returns True if the gadget's intermediate instructions contain a GPI (or a generic interrupt),\n False otherwise.\n "
for i in range((len(self.instructions) - 1)):
cur_opcode = self.instructions[i].opcode
cur_target = self.instructions[i].op1
if (cur_opcode.startswith('ret') or (cur_opcode == 'syscall') or (cur_opcode == 'sysenter') or cur_opcode.startswith('int') or (('jmp' in cur_opcode) and (not Instruction.is_constant(cur_target))) or (('call' in cur_opcode) and (not Instruction.is_constant(cur_target)))):
return True
return False |
def clobbers_stack_pointer(self):
"\n :return boolean: Returns True if the ROP gadget's instructions assign a non-static value to the stack pointer\n register, False otherwise.\n "
last_instr = self.instructions[(len(self.instructions) - 1)]
if last_instr.opcode.startswith('ret'):
for i in range((len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if (not cur_instr.creates_value()):
continue
if (Instruction.get_operand_register_family(cur_instr.op1) == 7):
if (((cur_instr.op2 is None) and (cur_instr.opcode not in ['inc', 'dec', 'pop'])) or ((cur_instr.op2 is not None) and (not Instruction.is_constant(cur_instr.op2)))):
return True
return False | -2,445,516,483,870,566,400 | :return boolean: Returns True if the ROP gadget's instructions assign a non-static value to the stack pointer
register, False otherwise. | src/static_analyzer/Gadget.py | clobbers_stack_pointer | michaelbrownuc/GadgetSetAnalyzer | python | def clobbers_stack_pointer(self):
"\n :return boolean: Returns True if the ROP gadget's instructions assign a non-static value to the stack pointer\n register, False otherwise.\n "
last_instr = self.instructions[(len(self.instructions) - 1)]
if last_instr.opcode.startswith('ret'):
for i in range((len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if (not cur_instr.creates_value()):
continue
if (Instruction.get_operand_register_family(cur_instr.op1) == 7):
if (((cur_instr.op2 is None) and (cur_instr.opcode not in ['inc', 'dec', 'pop'])) or ((cur_instr.op2 is not None) and (not Instruction.is_constant(cur_instr.op2)))):
return True
return False |
def clobbers_indirect_target(self):
"\n :return boolean: Returns True if the JOP/COP gadget's instructions modify the indirect branch register in\n certain ways, False otherwise.\n "
last_instr = self.instructions[(len(self.instructions) - 1)]
if (last_instr.opcode.startswith('jmp') or last_instr.opcode.startswith('call')):
family = Instruction.get_operand_register_family(last_instr.op1)
for i in range((len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if (cur_instr.op1 in Instruction.register_families[family]):
if ((cur_instr.opcode == 'xor') and (cur_instr.op1 == cur_instr.op2)):
return True
if ((cur_instr.opcode == 'lea') and (('rip' in cur_instr.op2) or ('eip' in cur_instr.op2))):
return True
if (cur_instr.opcode.startswith('lods') or (cur_instr.opcode == 'in')):
return True
if (('mov' in cur_instr.opcode) and (Instruction.is_constant(cur_instr.op2) or (Instruction.get_operand_register_family(cur_instr.op2) is None))):
return True
return False | 5,501,789,693,138,077,000 | :return boolean: Returns True if the JOP/COP gadget's instructions modify the indirect branch register in
certain ways, False otherwise. | src/static_analyzer/Gadget.py | clobbers_indirect_target | michaelbrownuc/GadgetSetAnalyzer | python | def clobbers_indirect_target(self):
"\n :return boolean: Returns True if the JOP/COP gadget's instructions modify the indirect branch register in\n certain ways, False otherwise.\n "
last_instr = self.instructions[(len(self.instructions) - 1)]
if (last_instr.opcode.startswith('jmp') or last_instr.opcode.startswith('call')):
family = Instruction.get_operand_register_family(last_instr.op1)
for i in range((len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if (cur_instr.op1 in Instruction.register_families[family]):
if ((cur_instr.opcode == 'xor') and (cur_instr.op1 == cur_instr.op2)):
return True
if ((cur_instr.opcode == 'lea') and (('rip' in cur_instr.op2) or ('eip' in cur_instr.op2))):
return True
if (cur_instr.opcode.startswith('lods') or (cur_instr.opcode == 'in')):
return True
if (('mov' in cur_instr.opcode) and (Instruction.is_constant(cur_instr.op2) or (Instruction.get_operand_register_family(cur_instr.op2) is None))):
return True
return False |
def has_invalid_int_handler(self):
"\n :return boolean: Returns True if the gadget's instructions assign a non-static value to the stack pointer\n register, False otherwise.\n "
last_instr = self.instructions[(len(self.instructions) - 1)]
if (last_instr.opcode.startswith('int') and (last_instr.op1 != '0x80')):
return True
return False | 6,179,865,065,802,890,000 | :return boolean: Returns True if the gadget's instructions assign a non-static value to the stack pointer
register, False otherwise. | src/static_analyzer/Gadget.py | has_invalid_int_handler | michaelbrownuc/GadgetSetAnalyzer | python | def has_invalid_int_handler(self):
"\n :return boolean: Returns True if the gadget's instructions assign a non-static value to the stack pointer\n register, False otherwise.\n "
last_instr = self.instructions[(len(self.instructions) - 1)]
if (last_instr.opcode.startswith('int') and (last_instr.op1 != '0x80')):
return True
return False |
def is_rip_relative_indirect_branch(self):
'\n :return boolean: Returns True if the gadget is a JOP/COP gadget relying on a RIP relative indirect branch,\n False otherwise.\n '
last_instr = self.instructions[(len(self.instructions) - 1)]
if (last_instr.opcode.startswith('jmp') or last_instr.opcode.startswith('call')):
if (('rip' in last_instr.op1) or ('eip' in last_instr.op1)):
return True
return False | -610,727,062,618,971,600 | :return boolean: Returns True if the gadget is a JOP/COP gadget relying on a RIP relative indirect branch,
False otherwise. | src/static_analyzer/Gadget.py | is_rip_relative_indirect_branch | michaelbrownuc/GadgetSetAnalyzer | python | def is_rip_relative_indirect_branch(self):
'\n :return boolean: Returns True if the gadget is a JOP/COP gadget relying on a RIP relative indirect branch,\n False otherwise.\n '
last_instr = self.instructions[(len(self.instructions) - 1)]
if (last_instr.opcode.startswith('jmp') or last_instr.opcode.startswith('call')):
if (('rip' in last_instr.op1) or ('eip' in last_instr.op1)):
return True
return False |
def is_equal(self, rhs):
'\n :return boolean: Returns True if the gadgets are an exact match, including offset. Used for gadget locality.\n '
return ((self.offset == rhs.offset) and (self.instruction_string == rhs.instruction_string)) | 2,534,057,557,342,863,000 | :return boolean: Returns True if the gadgets are an exact match, including offset. Used for gadget locality. | src/static_analyzer/Gadget.py | is_equal | michaelbrownuc/GadgetSetAnalyzer | python | def is_equal(self, rhs):
'\n \n '
return ((self.offset == rhs.offset) and (self.instruction_string == rhs.instruction_string)) |
def is_duplicate(self, rhs):
'\n :return boolean: Returns True if the gadgets are a semantic match. Used for non-locality gadget metrics.\n Semantic match is defined as the exact same sequence of equivalent instructions.\n '
if (len(self.instructions) != len(rhs.instructions)):
return False
for i in range(len(self.instructions)):
if (not self.instructions[i].is_equivalent(rhs.instructions[i])):
return False
return True | -8,467,245,155,612,059,000 | :return boolean: Returns True if the gadgets are a semantic match. Used for non-locality gadget metrics.
Semantic match is defined as the exact same sequence of equivalent instructions. | src/static_analyzer/Gadget.py | is_duplicate | michaelbrownuc/GadgetSetAnalyzer | python | def is_duplicate(self, rhs):
'\n :return boolean: Returns True if the gadgets are a semantic match. Used for non-locality gadget metrics.\n Semantic match is defined as the exact same sequence of equivalent instructions.\n '
if (len(self.instructions) != len(rhs.instructions)):
return False
for i in range(len(self.instructions)):
if (not self.instructions[i].is_equivalent(rhs.instructions[i])):
return False
return True |
def is_JOP_COP_dispatcher(self):
"\n :return boolean: Returns True if the gadget is a JOP or COP dispatcher. Defined as a gadget that begins with a\n arithmetic operation on a register and ends with a branch to a deference of that register. Used\n to iterate through instructions in payload. Only restrictions on the arithmetic operation is\n that it doesn't use the same register as both operands.\n "
first_instr = self.instructions[0]
last_instr = self.instructions[(len(self.instructions) - 1)]
if (('[' in last_instr.op1) and (first_instr.opcode in ['inc', 'dec', 'add', 'adc', 'sub', 'sbb']) and ('[' not in first_instr.op1)):
gpi_target = Instruction.get_operand_register_family(last_instr.op1)
arith_target_1 = Instruction.get_operand_register_family(first_instr.op1)
if Instruction.is_constant(first_instr.op2):
additive_value = Instruction.get_operand_as_constant(first_instr.op2)
if ((additive_value < 1) or (additive_value > 32)):
return False
arith_target_2 = Instruction.get_operand_register_family(first_instr.op2)
return ((gpi_target == arith_target_1) and (arith_target_1 != arith_target_2))
return False | 5,951,576,296,575,257,000 | :return boolean: Returns True if the gadget is a JOP or COP dispatcher. Defined as a gadget that begins with a
arithmetic operation on a register and ends with a branch to a deference of that register. Used
to iterate through instructions in payload. Only restrictions on the arithmetic operation is
that it doesn't use the same register as both operands. | src/static_analyzer/Gadget.py | is_JOP_COP_dispatcher | michaelbrownuc/GadgetSetAnalyzer | python | def is_JOP_COP_dispatcher(self):
"\n :return boolean: Returns True if the gadget is a JOP or COP dispatcher. Defined as a gadget that begins with a\n arithmetic operation on a register and ends with a branch to a deference of that register. Used\n to iterate through instructions in payload. Only restrictions on the arithmetic operation is\n that it doesn't use the same register as both operands.\n "
first_instr = self.instructions[0]
last_instr = self.instructions[(len(self.instructions) - 1)]
if (('[' in last_instr.op1) and (first_instr.opcode in ['inc', 'dec', 'add', 'adc', 'sub', 'sbb']) and ('[' not in first_instr.op1)):
gpi_target = Instruction.get_operand_register_family(last_instr.op1)
arith_target_1 = Instruction.get_operand_register_family(first_instr.op1)
if Instruction.is_constant(first_instr.op2):
additive_value = Instruction.get_operand_as_constant(first_instr.op2)
if ((additive_value < 1) or (additive_value > 32)):
return False
arith_target_2 = Instruction.get_operand_register_family(first_instr.op2)
return ((gpi_target == arith_target_1) and (arith_target_1 != arith_target_2))
return False |
def is_JOP_COP_dataloader(self):
'\n :return boolean: Returns True if the gadget is a JOP or COP data loader. Defined as a gadget that begins with a\n pop opcode to a non-memory location, that is also not the target of the GPI. Used to pop a\n necessary value off stack en masse before redirecting to the dispatcher.\n '
first_instr = self.instructions[0]
if ((first_instr.opcode == 'pop') and ('[' not in first_instr.op1)):
gpi_target = Instruction.get_operand_register_family(self.instructions[(len(self.instructions) - 1)].op1)
pop_target = Instruction.get_operand_register_family(first_instr.op1)
return (gpi_target != pop_target)
return False | 5,617,497,105,708,245,000 | :return boolean: Returns True if the gadget is a JOP or COP data loader. Defined as a gadget that begins with a
pop opcode to a non-memory location, that is also not the target of the GPI. Used to pop a
necessary value off stack en masse before redirecting to the dispatcher. | src/static_analyzer/Gadget.py | is_JOP_COP_dataloader | michaelbrownuc/GadgetSetAnalyzer | python | def is_JOP_COP_dataloader(self):
'\n :return boolean: Returns True if the gadget is a JOP or COP data loader. Defined as a gadget that begins with a\n pop opcode to a non-memory location, that is also not the target of the GPI. Used to pop a\n necessary value off stack en masse before redirecting to the dispatcher.\n '
first_instr = self.instructions[0]
if ((first_instr.opcode == 'pop') and ('[' not in first_instr.op1)):
gpi_target = Instruction.get_operand_register_family(self.instructions[(len(self.instructions) - 1)].op1)
pop_target = Instruction.get_operand_register_family(first_instr.op1)
return (gpi_target != pop_target)
return False |
def is_JOP_initializer(self):
'\n :return boolean: Returns True if the gadget is a JOP Initializer. Defined as a gadget that begins with a\n "pop all" opcode, used to pop necessary values off stack en masse before redirecting to the\n dispatcher.\n '
return self.instructions[0].opcode.startswith('popa') | 405,727,441,158,540,800 | :return boolean: Returns True if the gadget is a JOP Initializer. Defined as a gadget that begins with a
"pop all" opcode, used to pop necessary values off stack en masse before redirecting to the
dispatcher. | src/static_analyzer/Gadget.py | is_JOP_initializer | michaelbrownuc/GadgetSetAnalyzer | python | def is_JOP_initializer(self):
'\n :return boolean: Returns True if the gadget is a JOP Initializer. Defined as a gadget that begins with a\n "pop all" opcode, used to pop necessary values off stack en masse before redirecting to the\n dispatcher.\n '
return self.instructions[0].opcode.startswith('popa') |
def is_JOP_trampoline(self):
'\n :return boolean: Returns True if the gadget is a JOP trampoline. Defined as a gadget that begins with a\n pop opcode to a non-memory location, and that ends in a dereference of that value. Used to\n redirect execution to value stored in memory.\n '
first_instr = self.instructions[0]
gpi_target_op = self.instructions[(len(self.instructions) - 1)].op1
if ((first_instr.opcode == 'pop') and ('[' not in first_instr.op1)):
gpi_target = Instruction.get_operand_register_family(gpi_target_op)
pop_target = Instruction.get_operand_register_family(first_instr.op1)
return ((gpi_target == pop_target) and ('[' in gpi_target_op))
return False | -3,181,699,853,611,830,300 | :return boolean: Returns True if the gadget is a JOP trampoline. Defined as a gadget that begins with a
pop opcode to a non-memory location, and that ends in a dereference of that value. Used to
redirect execution to value stored in memory. | src/static_analyzer/Gadget.py | is_JOP_trampoline | michaelbrownuc/GadgetSetAnalyzer | python | def is_JOP_trampoline(self):
'\n :return boolean: Returns True if the gadget is a JOP trampoline. Defined as a gadget that begins with a\n pop opcode to a non-memory location, and that ends in a dereference of that value. Used to\n redirect execution to value stored in memory.\n '
first_instr = self.instructions[0]
gpi_target_op = self.instructions[(len(self.instructions) - 1)].op1
if ((first_instr.opcode == 'pop') and ('[' not in first_instr.op1)):
gpi_target = Instruction.get_operand_register_family(gpi_target_op)
pop_target = Instruction.get_operand_register_family(first_instr.op1)
return ((gpi_target == pop_target) and ('[' in gpi_target_op))
return False |
def is_COP_initializer(self):
'\n :return boolean: Returns True if the gadget is a COP initializer. Defined as a gadget that begins with a\n "pop all" opcode, does not use register bx/cx/dx/di as the call target, and does not clobber\n bx/cx/dx or the call target in an intermediate instruction\n '
first_instr = self.instructions[0]
last_instr = self.instructions[(len(self.instructions) - 1)]
call_target = Instruction.get_operand_register_family(last_instr.op1)
if (first_instr.opcode.startswith('popa') and (call_target not in [1, 2, 3, 5])):
protected_families = [1, 2, 3, call_target]
protected_registers = []
for family in protected_families:
for register in Instruction.register_families[family]:
protected_registers.append(register)
for i in range(1, (len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if (not cur_instr.creates_value()):
continue
if (cur_instr.op1 in protected_registers):
if (((cur_instr.op2 is None) and (cur_instr.opcode not in ['inc', 'dec', 'neg', 'not'])) or ((cur_instr.op2 is not None) and (not Instruction.is_constant(cur_instr.op2)))):
return False
return True
return False | -943,675,825,263,414,400 | :return boolean: Returns True if the gadget is a COP initializer. Defined as a gadget that begins with a
"pop all" opcode, does not use register bx/cx/dx/di as the call target, and does not clobber
bx/cx/dx or the call target in an intermediate instruction | src/static_analyzer/Gadget.py | is_COP_initializer | michaelbrownuc/GadgetSetAnalyzer | python | def is_COP_initializer(self):
'\n :return boolean: Returns True if the gadget is a COP initializer. Defined as a gadget that begins with a\n "pop all" opcode, does not use register bx/cx/dx/di as the call target, and does not clobber\n bx/cx/dx or the call target in an intermediate instruction\n '
first_instr = self.instructions[0]
last_instr = self.instructions[(len(self.instructions) - 1)]
call_target = Instruction.get_operand_register_family(last_instr.op1)
if (first_instr.opcode.startswith('popa') and (call_target not in [1, 2, 3, 5])):
protected_families = [1, 2, 3, call_target]
protected_registers = []
for family in protected_families:
for register in Instruction.register_families[family]:
protected_registers.append(register)
for i in range(1, (len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if (not cur_instr.creates_value()):
continue
if (cur_instr.op1 in protected_registers):
if (((cur_instr.op2 is None) and (cur_instr.opcode not in ['inc', 'dec', 'neg', 'not'])) or ((cur_instr.op2 is not None) and (not Instruction.is_constant(cur_instr.op2)))):
return False
return True
return False |
def is_COP_strong_trampoline(self):
'\n :return boolean: Returns True if the gadget is a COP strong trampoline. Defined as a gadget that begins with a\n pop opcode, and contains at least one other pop operation. The last non-pop all operation must\n target the call target.\n '
first_instr = self.instructions[0]
last_instr = self.instructions[(len(self.instructions) - 1)]
call_target = Instruction.get_operand_register_family(last_instr.op1)
if ((first_instr.opcode == 'pop') and ('[' not in first_instr.op1)):
cnt_pops = 1
last_pop_target = first_instr.op1
for i in range(1, (len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if cur_instr.opcode.startswith('popa'):
cnt_pops += 1
if ((cur_instr.opcode == 'pop') and ('[' not in cur_instr.op1)):
cnt_pops += 1
last_pop_target = cur_instr.op1
if ((cnt_pops > 1) and (last_pop_target in Instruction.register_families[call_target])):
return True
return False | -7,207,612,691,470,076,000 | :return boolean: Returns True if the gadget is a COP strong trampoline. Defined as a gadget that begins with a
pop opcode, and contains at least one other pop operation. The last non-pop all operation must
target the call target. | src/static_analyzer/Gadget.py | is_COP_strong_trampoline | michaelbrownuc/GadgetSetAnalyzer | python | def is_COP_strong_trampoline(self):
'\n :return boolean: Returns True if the gadget is a COP strong trampoline. Defined as a gadget that begins with a\n pop opcode, and contains at least one other pop operation. The last non-pop all operation must\n target the call target.\n '
first_instr = self.instructions[0]
last_instr = self.instructions[(len(self.instructions) - 1)]
call_target = Instruction.get_operand_register_family(last_instr.op1)
if ((first_instr.opcode == 'pop') and ('[' not in first_instr.op1)):
cnt_pops = 1
last_pop_target = first_instr.op1
for i in range(1, (len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if cur_instr.opcode.startswith('popa'):
cnt_pops += 1
if ((cur_instr.opcode == 'pop') and ('[' not in cur_instr.op1)):
cnt_pops += 1
last_pop_target = cur_instr.op1
if ((cnt_pops > 1) and (last_pop_target in Instruction.register_families[call_target])):
return True
return False |
def is_COP_intrastack_pivot(self):
'\n :return boolean: Returns True if the gadget is a COP Intra-stack pivot gadget. Defined as a gadget that begins\n with an additive operation on the stack pointer register. Used to move around in shellcode\n during COP exploits. Only restriction on the arithmetic operation is that the second operand\n is not a pointer.\n '
first_instr = self.instructions[0]
if ((first_instr.opcode in ['inc', 'add', 'adc', 'sub', 'sbb']) and ('[' not in first_instr.op1)):
arith_target = Instruction.get_operand_register_family(first_instr.op1)
if (arith_target == 7):
if ((first_instr.op2 is None) or ('[' not in first_instr.op2)):
return True
return False | 9,165,429,799,068,683,000 | :return boolean: Returns True if the gadget is a COP Intra-stack pivot gadget. Defined as a gadget that begins
with an additive operation on the stack pointer register. Used to move around in shellcode
during COP exploits. Only restriction on the arithmetic operation is that the second operand
is not a pointer. | src/static_analyzer/Gadget.py | is_COP_intrastack_pivot | michaelbrownuc/GadgetSetAnalyzer | python | def is_COP_intrastack_pivot(self):
'\n :return boolean: Returns True if the gadget is a COP Intra-stack pivot gadget. Defined as a gadget that begins\n with an additive operation on the stack pointer register. Used to move around in shellcode\n during COP exploits. Only restriction on the arithmetic operation is that the second operand\n is not a pointer.\n '
first_instr = self.instructions[0]
if ((first_instr.opcode in ['inc', 'add', 'adc', 'sub', 'sbb']) and ('[' not in first_instr.op1)):
arith_target = Instruction.get_operand_register_family(first_instr.op1)
if (arith_target == 7):
if ((first_instr.op2 is None) or ('[' not in first_instr.op2)):
return True
return False |
def check_contains_leave(self):
'\n :return void: Increases gadget\'s score if the gadget has an intermediate "leave" instruction.\n '
for i in range(1, (len(self.instructions) - 1)):
if (self.instructions[i].opcode == 'leave'):
self.score += 2.0
return | 7,210,409,693,577,871,000 | :return void: Increases gadget's score if the gadget has an intermediate "leave" instruction. | src/static_analyzer/Gadget.py | check_contains_leave | michaelbrownuc/GadgetSetAnalyzer | python | def check_contains_leave(self):
'\n :return void: Increases gadget\'s score if the gadget has an intermediate "leave" instruction.\n '
for i in range(1, (len(self.instructions) - 1)):
if (self.instructions[i].opcode == 'leave'):
self.score += 2.0
return |
def check_sp_target_of_operation(self):
"\n :return void: Increases gadget's score if the gadget has an intermediate instruction that performs certain\n operations on the stack pointer register family.\n "
for i in range((len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if (not cur_instr.creates_value()):
continue
if (Instruction.get_operand_register_family(cur_instr.op1) == 7):
if (('xchg' in cur_instr.opcode) or ('mov' in cur_instr.opcode) or (cur_instr.opcode in ['lea'])):
self.score += 4.0
elif (cur_instr.opcode in ['shl', 'shr', 'sar', 'sal', 'ror', 'rol', 'rcr', 'rcl']):
self.score += 3.0
elif (cur_instr.opcode == 'pop'):
self.score += 1.0
else:
self.score += 2.0 | -7,326,613,457,057,683,000 | :return void: Increases gadget's score if the gadget has an intermediate instruction that performs certain
operations on the stack pointer register family. | src/static_analyzer/Gadget.py | check_sp_target_of_operation | michaelbrownuc/GadgetSetAnalyzer | python | def check_sp_target_of_operation(self):
"\n :return void: Increases gadget's score if the gadget has an intermediate instruction that performs certain\n operations on the stack pointer register family.\n "
for i in range((len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if (not cur_instr.creates_value()):
continue
if (Instruction.get_operand_register_family(cur_instr.op1) == 7):
if (('xchg' in cur_instr.opcode) or ('mov' in cur_instr.opcode) or (cur_instr.opcode in ['lea'])):
self.score += 4.0
elif (cur_instr.opcode in ['shl', 'shr', 'sar', 'sal', 'ror', 'rol', 'rcr', 'rcl']):
self.score += 3.0
elif (cur_instr.opcode == 'pop'):
self.score += 1.0
else:
self.score += 2.0 |
def check_negative_sp_offsets(self):
"\n :return void: Increases gadget's score if its cumulative register offsets are negative.\n "
sp_offset = 0
for i in range(len(self.instructions)):
cur_instr = self.instructions[i]
if (cur_instr.opcode == 'push'):
sp_offset -= 8
elif ((cur_instr.opcode == 'pop') and (cur_instr.op1 not in Instruction.register_families[7])):
sp_offset += 8
elif ((cur_instr.opcode in ['add', 'adc']) and (cur_instr.op1 in Instruction.register_families[7]) and Instruction.is_constant(cur_instr.op2)):
sp_offset += Instruction.get_operand_as_constant(cur_instr.op2)
elif ((cur_instr.opcode in ['sub', 'sbb']) and (cur_instr.op1 in Instruction.register_families[7]) and Instruction.is_constant(cur_instr.op2)):
sp_offset -= Instruction.get_operand_as_constant(cur_instr.op2)
elif ((cur_instr.opcode == 'inc') and (cur_instr.op1 in Instruction.register_families[7])):
sp_offset += 1
elif ((cur_instr.opcode == 'dec') and (cur_instr.op1 in Instruction.register_families[7])):
sp_offset -= 1
elif (cur_instr.opcode.startswith('ret') and (cur_instr.op1 is not None)):
sp_offset += Instruction.get_operand_as_constant(cur_instr.op1)
if (sp_offset < 0):
self.score += 2.0 | -8,229,238,631,788,484,000 | :return void: Increases gadget's score if its cumulative register offsets are negative. | src/static_analyzer/Gadget.py | check_negative_sp_offsets | michaelbrownuc/GadgetSetAnalyzer | python | def check_negative_sp_offsets(self):
"\n \n "
sp_offset = 0
for i in range(len(self.instructions)):
cur_instr = self.instructions[i]
if (cur_instr.opcode == 'push'):
sp_offset -= 8
elif ((cur_instr.opcode == 'pop') and (cur_instr.op1 not in Instruction.register_families[7])):
sp_offset += 8
elif ((cur_instr.opcode in ['add', 'adc']) and (cur_instr.op1 in Instruction.register_families[7]) and Instruction.is_constant(cur_instr.op2)):
sp_offset += Instruction.get_operand_as_constant(cur_instr.op2)
elif ((cur_instr.opcode in ['sub', 'sbb']) and (cur_instr.op1 in Instruction.register_families[7]) and Instruction.is_constant(cur_instr.op2)):
sp_offset -= Instruction.get_operand_as_constant(cur_instr.op2)
elif ((cur_instr.opcode == 'inc') and (cur_instr.op1 in Instruction.register_families[7])):
sp_offset += 1
elif ((cur_instr.opcode == 'dec') and (cur_instr.op1 in Instruction.register_families[7])):
sp_offset -= 1
elif (cur_instr.opcode.startswith('ret') and (cur_instr.op1 is not None)):
sp_offset += Instruction.get_operand_as_constant(cur_instr.op1)
if (sp_offset < 0):
self.score += 2.0 |
def check_contains_conditional_op(self):
"\n :return void: Increases gadget's score if it contains conditional instructions like jumps, sets, and moves.\n "
for i in range((len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if (cur_instr.opcode.startswith('j') and (cur_instr.opcode != 'jmp')):
self.score += 3.0
elif (('cmov' in cur_instr.opcode) or ('cmpxchg' in cur_instr.opcode)):
self.score += 2.0
elif ('set' in cur_instr.opcode):
self.score += 1.0 | -501,580,019,472,423,000 | :return void: Increases gadget's score if it contains conditional instructions like jumps, sets, and moves. | src/static_analyzer/Gadget.py | check_contains_conditional_op | michaelbrownuc/GadgetSetAnalyzer | python | def check_contains_conditional_op(self):
"\n \n "
for i in range((len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if (cur_instr.opcode.startswith('j') and (cur_instr.opcode != 'jmp')):
self.score += 3.0
elif (('cmov' in cur_instr.opcode) or ('cmpxchg' in cur_instr.opcode)):
self.score += 2.0
elif ('set' in cur_instr.opcode):
self.score += 1.0 |
def check_register_ops(self):
"\n :return void: Increases gadget's score if it contains operations on a value carrying or a bystander register\n "
first_instr = self.instructions[0]
if ((not first_instr.creates_value()) or ('xchg' in first_instr.opcode)):
first_family = None
else:
first_family = Instruction.get_operand_register_family(first_instr.op1)
for i in range(1, (len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if (not cur_instr.creates_value()):
continue
if ((first_family is not None) and (first_family == Instruction.get_operand_register_family(cur_instr.op1))):
if (cur_instr.opcode in ['shl', 'shr', 'sar', 'sal', 'ror', 'rol', 'rcr', 'rcl']):
self.score += 1.5
else:
self.score += 1.0
elif (('xchg' not in cur_instr.opcode) and (cur_instr.opcode != 'pop')):
if ((cur_instr.op2 is not None) and (Instruction.get_operand_register_family(cur_instr.op2) is not None)):
self.score += 1.0
else:
self.score += 0.5 | 409,782,009,336,981,060 | :return void: Increases gadget's score if it contains operations on a value carrying or a bystander register | src/static_analyzer/Gadget.py | check_register_ops | michaelbrownuc/GadgetSetAnalyzer | python | def check_register_ops(self):
"\n \n "
first_instr = self.instructions[0]
if ((not first_instr.creates_value()) or ('xchg' in first_instr.opcode)):
first_family = None
else:
first_family = Instruction.get_operand_register_family(first_instr.op1)
for i in range(1, (len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if (not cur_instr.creates_value()):
continue
if ((first_family is not None) and (first_family == Instruction.get_operand_register_family(cur_instr.op1))):
if (cur_instr.opcode in ['shl', 'shr', 'sar', 'sal', 'ror', 'rol', 'rcr', 'rcl']):
self.score += 1.5
else:
self.score += 1.0
elif (('xchg' not in cur_instr.opcode) and (cur_instr.opcode != 'pop')):
if ((cur_instr.op2 is not None) and (Instruction.get_operand_register_family(cur_instr.op2) is not None)):
self.score += 1.0
else:
self.score += 0.5 |
def check_branch_target_of_operation(self):
"\n :return void: Increases gadget's score if the gadget has an intermediate instruction that performs certain\n operations on the indirect branch target register family.\n "
last_instr = self.instructions[(len(self.instructions) - 1)]
target_family = Instruction.get_operand_register_family(last_instr.op1)
for i in range((len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if (not cur_instr.creates_value()):
continue
if (Instruction.get_operand_register_family(cur_instr.op1) == target_family):
if (cur_instr.opcode in ['shl', 'shr', 'sar', 'sal', 'ror', 'rol', 'rcr', 'rcl']):
self.score += 3.0
else:
self.score += 2.0 | -2,168,993,783,908,520,400 | :return void: Increases gadget's score if the gadget has an intermediate instruction that performs certain
operations on the indirect branch target register family. | src/static_analyzer/Gadget.py | check_branch_target_of_operation | michaelbrownuc/GadgetSetAnalyzer | python | def check_branch_target_of_operation(self):
"\n :return void: Increases gadget's score if the gadget has an intermediate instruction that performs certain\n operations on the indirect branch target register family.\n "
last_instr = self.instructions[(len(self.instructions) - 1)]
target_family = Instruction.get_operand_register_family(last_instr.op1)
for i in range((len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if (not cur_instr.creates_value()):
continue
if (Instruction.get_operand_register_family(cur_instr.op1) == target_family):
if (cur_instr.opcode in ['shl', 'shr', 'sar', 'sal', 'ror', 'rol', 'rcr', 'rcl']):
self.score += 3.0
else:
self.score += 2.0 |
def check_memory_writes(self):
"\n :return void: Increases gadget's score if the gadget has an instruction that writes to memory.\n "
for i in range((len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if (not cur_instr.creates_value()):
continue
if (('xchg' in cur_instr.opcode) and (('[' in cur_instr.op1) or ('[' in cur_instr.op2))):
self.score += 1.0
elif ((cur_instr.op1 is not None) and ('[' in cur_instr.op1)):
self.score += 1.0 | 1,998,233,147,490,157,800 | :return void: Increases gadget's score if the gadget has an instruction that writes to memory. | src/static_analyzer/Gadget.py | check_memory_writes | michaelbrownuc/GadgetSetAnalyzer | python | def check_memory_writes(self):
"\n \n "
for i in range((len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if (not cur_instr.creates_value()):
continue
if (('xchg' in cur_instr.opcode) and (('[' in cur_instr.op1) or ('[' in cur_instr.op2))):
self.score += 1.0
elif ((cur_instr.op1 is not None) and ('[' in cur_instr.op1)):
self.score += 1.0 |
def proof_of_work(last_proof):
"\n Simple Proof of Work Algorithm\n - Find a number p' such that hash(pp') contains 6 leading\n zeroes, where p is the previous p'\n - p is the previous proof, and p' is the new proof\n "
print(f'''
Search for proof initialized.
''')
proof = 0
while (valid_proof(last_proof, proof) is False):
proof += 1
print(f'''
Search for proof complete, proof is {proof}
''')
return proof | 9,213,363,334,812,784,000 | Simple Proof of Work Algorithm
- Find a number p' such that hash(pp') contains 6 leading
zeroes, where p is the previous p'
- p is the previous proof, and p' is the new proof | client_mining_p/miner.py | proof_of_work | lambda-projects-lafriedel/Blockchain | python | def proof_of_work(last_proof):
"\n Simple Proof of Work Algorithm\n - Find a number p' such that hash(pp') contains 6 leading\n zeroes, where p is the previous p'\n - p is the previous proof, and p' is the new proof\n "
print(f'
Search for proof initialized.
')
proof = 0
while (valid_proof(last_proof, proof) is False):
proof += 1
print(f'
Search for proof complete, proof is {proof}
')
return proof |
def equal_up_to_global_phase(val: Any, other: Any, *, atol: Union[(int, float)]=1e-08) -> bool:
'Determine whether two objects are equal up to global phase.\n\n If `val` implements a `_equal_up_to_global_phase_` method then it is\n invoked and takes precedence over all other checks:\n - For complex primitive type the magnitudes of the values are compared.\n - For `val` and `other` both iterable of the same length, consecutive\n elements are compared recursively. Types of `val` and `other` does not\n necessarily needs to match each other. They just need to be iterable and\n have the same structure.\n - For all other types, fall back to `_approx_eq_`\n\n Args:\n val: Source object for approximate comparison.\n other: Target object for approximate comparison.\n atol: The minimum absolute tolerance. This places an upper bound on\n the differences in *magnitudes* of two compared complex numbers.\n\n Returns:\n True if objects are approximately equal up to phase, False otherwise.\n '
eq_up_to_phase_getter = getattr(val, '_equal_up_to_global_phase_', None)
if (eq_up_to_phase_getter is not None):
result = eq_up_to_phase_getter(other, atol)
if (result is not NotImplemented):
return result
other_eq_up_to_phase_getter = getattr(other, '_equal_up_to_global_phase_', None)
if (other_eq_up_to_phase_getter is not None):
result = other_eq_up_to_phase_getter(val, atol)
if (result is not NotImplemented):
return result
if (isinstance(val, Iterable) and isinstance(other, Iterable)):
a = np.asarray(val)
b = np.asarray(other)
if ((a.dtype.kind in 'uifc') and (b.dtype.kind in 'uifc')):
return linalg.allclose_up_to_global_phase(a, b, atol=atol)
if (isinstance(val, numbers.Number) and isinstance(other, numbers.Number)):
result = approx_eq(abs(val), abs(other), atol=atol)
if (result is not NotImplemented):
return result
return approx_eq(val, other, atol=atol) | -9,119,513,904,675,405,000 | Determine whether two objects are equal up to global phase.
If `val` implements a `_equal_up_to_global_phase_` method then it is
invoked and takes precedence over all other checks:
- For complex primitive type the magnitudes of the values are compared.
- For `val` and `other` both iterable of the same length, consecutive
elements are compared recursively. Types of `val` and `other` does not
necessarily needs to match each other. They just need to be iterable and
have the same structure.
- For all other types, fall back to `_approx_eq_`
Args:
val: Source object for approximate comparison.
other: Target object for approximate comparison.
atol: The minimum absolute tolerance. This places an upper bound on
the differences in *magnitudes* of two compared complex numbers.
Returns:
True if objects are approximately equal up to phase, False otherwise. | cirq-core/cirq/protocols/equal_up_to_global_phase_protocol.py | equal_up_to_global_phase | 95-martin-orion/Cirq | python | def equal_up_to_global_phase(val: Any, other: Any, *, atol: Union[(int, float)]=1e-08) -> bool:
'Determine whether two objects are equal up to global phase.\n\n If `val` implements a `_equal_up_to_global_phase_` method then it is\n invoked and takes precedence over all other checks:\n - For complex primitive type the magnitudes of the values are compared.\n - For `val` and `other` both iterable of the same length, consecutive\n elements are compared recursively. Types of `val` and `other` does not\n necessarily needs to match each other. They just need to be iterable and\n have the same structure.\n - For all other types, fall back to `_approx_eq_`\n\n Args:\n val: Source object for approximate comparison.\n other: Target object for approximate comparison.\n atol: The minimum absolute tolerance. This places an upper bound on\n the differences in *magnitudes* of two compared complex numbers.\n\n Returns:\n True if objects are approximately equal up to phase, False otherwise.\n '
eq_up_to_phase_getter = getattr(val, '_equal_up_to_global_phase_', None)
if (eq_up_to_phase_getter is not None):
result = eq_up_to_phase_getter(other, atol)
if (result is not NotImplemented):
return result
other_eq_up_to_phase_getter = getattr(other, '_equal_up_to_global_phase_', None)
if (other_eq_up_to_phase_getter is not None):
result = other_eq_up_to_phase_getter(val, atol)
if (result is not NotImplemented):
return result
if (isinstance(val, Iterable) and isinstance(other, Iterable)):
a = np.asarray(val)
b = np.asarray(other)
if ((a.dtype.kind in 'uifc') and (b.dtype.kind in 'uifc')):
return linalg.allclose_up_to_global_phase(a, b, atol=atol)
if (isinstance(val, numbers.Number) and isinstance(other, numbers.Number)):
result = approx_eq(abs(val), abs(other), atol=atol)
if (result is not NotImplemented):
return result
return approx_eq(val, other, atol=atol) |
@doc_private
def _equal_up_to_global_phase_(self, other: Any, *, atol: Union[(int, float)]) -> bool:
'Approximate comparator.\n\n Types implementing this protocol define their own logic for comparison\n with other types.\n\n Args:\n other: Target object for comparison of equality up to global phase.\n atol: The minimum absolute tolerance. See `np.isclose()`\n documentation for details.\n\n Returns:\n True if objects are equal up to a global phase, False otherwise.\n Returns NotImplemented when checking equality up to a global phase\n is not implemented for given types.\n ' | 556,117,564,477,804,200 | Approximate comparator.
Types implementing this protocol define their own logic for comparison
with other types.
Args:
other: Target object for comparison of equality up to global phase.
atol: The minimum absolute tolerance. See `np.isclose()`
documentation for details.
Returns:
True if objects are equal up to a global phase, False otherwise.
Returns NotImplemented when checking equality up to a global phase
is not implemented for given types. | cirq-core/cirq/protocols/equal_up_to_global_phase_protocol.py | _equal_up_to_global_phase_ | 95-martin-orion/Cirq | python | @doc_private
def _equal_up_to_global_phase_(self, other: Any, *, atol: Union[(int, float)]) -> bool:
'Approximate comparator.\n\n Types implementing this protocol define their own logic for comparison\n with other types.\n\n Args:\n other: Target object for comparison of equality up to global phase.\n atol: The minimum absolute tolerance. See `np.isclose()`\n documentation for details.\n\n Returns:\n True if objects are equal up to a global phase, False otherwise.\n Returns NotImplemented when checking equality up to a global phase\n is not implemented for given types.\n ' |
@tf_export('train.load_checkpoint')
def load_checkpoint(ckpt_dir_or_file):
'Returns `CheckpointReader` for checkpoint found in `ckpt_dir_or_file`.\n\n If `ckpt_dir_or_file` resolves to a directory with multiple checkpoints,\n reader for the latest checkpoint is returned.\n\n Args:\n ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint\n file.\n\n Returns:\n `CheckpointReader` object.\n\n Raises:\n ValueError: If `ckpt_dir_or_file` resolves to a directory with no\n checkpoints.\n '
filename = _get_checkpoint_filename(ckpt_dir_or_file)
if (filename is None):
raise ValueError(("Couldn't find 'checkpoint' file or checkpoints in given directory %s" % ckpt_dir_or_file))
return py_checkpoint_reader.NewCheckpointReader(filename) | 3,253,334,963,162,104,000 | Returns `CheckpointReader` for checkpoint found in `ckpt_dir_or_file`.
If `ckpt_dir_or_file` resolves to a directory with multiple checkpoints,
reader for the latest checkpoint is returned.
Args:
ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint
file.
Returns:
`CheckpointReader` object.
Raises:
ValueError: If `ckpt_dir_or_file` resolves to a directory with no
checkpoints. | tensorflow/python/training/checkpoint_utils.py | load_checkpoint | KodeWorker/tensorflow | python | @tf_export('train.load_checkpoint')
def load_checkpoint(ckpt_dir_or_file):
'Returns `CheckpointReader` for checkpoint found in `ckpt_dir_or_file`.\n\n If `ckpt_dir_or_file` resolves to a directory with multiple checkpoints,\n reader for the latest checkpoint is returned.\n\n Args:\n ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint\n file.\n\n Returns:\n `CheckpointReader` object.\n\n Raises:\n ValueError: If `ckpt_dir_or_file` resolves to a directory with no\n checkpoints.\n '
filename = _get_checkpoint_filename(ckpt_dir_or_file)
if (filename is None):
raise ValueError(("Couldn't find 'checkpoint' file or checkpoints in given directory %s" % ckpt_dir_or_file))
return py_checkpoint_reader.NewCheckpointReader(filename) |
@tf_export('train.load_variable')
def load_variable(ckpt_dir_or_file, name):
'Returns the tensor value of the given variable in the checkpoint.\n\n Args:\n ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.\n name: Name of the variable to return.\n\n Returns:\n A numpy `ndarray` with a copy of the value of this variable.\n '
if name.endswith(':0'):
name = name[:(- 2)]
reader = load_checkpoint(ckpt_dir_or_file)
return reader.get_tensor(name) | -7,616,513,250,938,454,000 | Returns the tensor value of the given variable in the checkpoint.
Args:
ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.
name: Name of the variable to return.
Returns:
A numpy `ndarray` with a copy of the value of this variable. | tensorflow/python/training/checkpoint_utils.py | load_variable | KodeWorker/tensorflow | python | @tf_export('train.load_variable')
def load_variable(ckpt_dir_or_file, name):
'Returns the tensor value of the given variable in the checkpoint.\n\n Args:\n ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.\n name: Name of the variable to return.\n\n Returns:\n A numpy `ndarray` with a copy of the value of this variable.\n '
if name.endswith(':0'):
name = name[:(- 2)]
reader = load_checkpoint(ckpt_dir_or_file)
return reader.get_tensor(name) |
@tf_export('train.list_variables')
def list_variables(ckpt_dir_or_file):
'Returns list of all variables in the checkpoint.\n\n Args:\n ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.\n\n Returns:\n List of tuples `(name, shape)`.\n '
reader = load_checkpoint(ckpt_dir_or_file)
variable_map = reader.get_variable_to_shape_map()
names = sorted(variable_map.keys())
result = []
for name in names:
result.append((name, variable_map[name]))
return result | 1,467,950,224,971,931,600 | Returns list of all variables in the checkpoint.
Args:
ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.
Returns:
List of tuples `(name, shape)`. | tensorflow/python/training/checkpoint_utils.py | list_variables | KodeWorker/tensorflow | python | @tf_export('train.list_variables')
def list_variables(ckpt_dir_or_file):
'Returns list of all variables in the checkpoint.\n\n Args:\n ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.\n\n Returns:\n List of tuples `(name, shape)`.\n '
reader = load_checkpoint(ckpt_dir_or_file)
variable_map = reader.get_variable_to_shape_map()
names = sorted(variable_map.keys())
result = []
for name in names:
result.append((name, variable_map[name]))
return result |
def wait_for_new_checkpoint(checkpoint_dir, last_checkpoint=None, seconds_to_sleep=1, timeout=None):
"Waits until a new checkpoint file is found.\n\n Args:\n checkpoint_dir: The directory in which checkpoints are saved.\n last_checkpoint: The last checkpoint path used or `None` if we're expecting\n a checkpoint for the first time.\n seconds_to_sleep: The number of seconds to sleep for before looking for a\n new checkpoint.\n timeout: The maximum number of seconds to wait. If left as `None`, then the\n process will wait indefinitely.\n\n Returns:\n a new checkpoint path, or None if the timeout was reached.\n "
logging.info('Waiting for new checkpoint at %s', checkpoint_dir)
stop_time = ((time.time() + timeout) if (timeout is not None) else None)
while True:
checkpoint_path = checkpoint_management.latest_checkpoint(checkpoint_dir)
if ((checkpoint_path is None) or (checkpoint_path == last_checkpoint)):
if ((stop_time is not None) and ((time.time() + seconds_to_sleep) > stop_time)):
return None
time.sleep(seconds_to_sleep)
else:
logging.info('Found new checkpoint at %s', checkpoint_path)
return checkpoint_path | -1,605,284,766,611,941,000 | Waits until a new checkpoint file is found.
Args:
checkpoint_dir: The directory in which checkpoints are saved.
last_checkpoint: The last checkpoint path used or `None` if we're expecting
a checkpoint for the first time.
seconds_to_sleep: The number of seconds to sleep for before looking for a
new checkpoint.
timeout: The maximum number of seconds to wait. If left as `None`, then the
process will wait indefinitely.
Returns:
a new checkpoint path, or None if the timeout was reached. | tensorflow/python/training/checkpoint_utils.py | wait_for_new_checkpoint | KodeWorker/tensorflow | python | def wait_for_new_checkpoint(checkpoint_dir, last_checkpoint=None, seconds_to_sleep=1, timeout=None):
"Waits until a new checkpoint file is found.\n\n Args:\n checkpoint_dir: The directory in which checkpoints are saved.\n last_checkpoint: The last checkpoint path used or `None` if we're expecting\n a checkpoint for the first time.\n seconds_to_sleep: The number of seconds to sleep for before looking for a\n new checkpoint.\n timeout: The maximum number of seconds to wait. If left as `None`, then the\n process will wait indefinitely.\n\n Returns:\n a new checkpoint path, or None if the timeout was reached.\n "
logging.info('Waiting for new checkpoint at %s', checkpoint_dir)
stop_time = ((time.time() + timeout) if (timeout is not None) else None)
while True:
checkpoint_path = checkpoint_management.latest_checkpoint(checkpoint_dir)
if ((checkpoint_path is None) or (checkpoint_path == last_checkpoint)):
if ((stop_time is not None) and ((time.time() + seconds_to_sleep) > stop_time)):
return None
time.sleep(seconds_to_sleep)
else:
logging.info('Found new checkpoint at %s', checkpoint_path)
return checkpoint_path |
@tf_export('train.checkpoints_iterator')
def checkpoints_iterator(checkpoint_dir, min_interval_secs=0, timeout=None, timeout_fn=None):
'Continuously yield new checkpoint files as they appear.\n\n The iterator only checks for new checkpoints when control flow has been\n reverted to it. This means it can miss checkpoints if your code takes longer\n to run between iterations than `min_interval_secs` or the interval at which\n new checkpoints are written.\n\n The `timeout` argument is the maximum number of seconds to block waiting for\n a new checkpoint. It is used in combination with the `timeout_fn` as\n follows:\n\n * If the timeout expires and no `timeout_fn` was specified, the iterator\n stops yielding.\n * If a `timeout_fn` was specified, that function is called and if it returns\n a true boolean value the iterator stops yielding.\n * If the function returns a false boolean value then the iterator resumes the\n wait for new checkpoints. At this point the timeout logic applies again.\n\n This behavior gives control to callers on what to do if checkpoints do not\n come fast enough or stop being generated. For example, if callers have a way\n to detect that the training has stopped and know that no new checkpoints\n will be generated, they can provide a `timeout_fn` that returns `True` when\n the training has stopped. If they know that the training is still going on\n they return `False` instead.\n\n Args:\n checkpoint_dir: The directory in which checkpoints are saved.\n min_interval_secs: The minimum number of seconds between yielding\n checkpoints.\n timeout: The maximum number of seconds to wait between checkpoints. If left\n as `None`, then the process will wait indefinitely.\n timeout_fn: Optional function to call after a timeout. If the function\n returns True, then it means that no new checkpoints will be generated and\n the iterator will exit. The function is called with no arguments.\n\n Yields:\n String paths to latest checkpoint files as they arrive.\n '
checkpoint_path = None
while True:
new_checkpoint_path = wait_for_new_checkpoint(checkpoint_dir, checkpoint_path, timeout=timeout)
if (new_checkpoint_path is None):
if (not timeout_fn):
logging.info('Timed-out waiting for a checkpoint.')
return
if timeout_fn():
return
else:
continue
start = time.time()
checkpoint_path = new_checkpoint_path
(yield checkpoint_path)
time_to_next_eval = ((start + min_interval_secs) - time.time())
if (time_to_next_eval > 0):
time.sleep(time_to_next_eval) | 8,677,676,642,965,667,000 | Continuously yield new checkpoint files as they appear.
The iterator only checks for new checkpoints when control flow has been
reverted to it. This means it can miss checkpoints if your code takes longer
to run between iterations than `min_interval_secs` or the interval at which
new checkpoints are written.
The `timeout` argument is the maximum number of seconds to block waiting for
a new checkpoint. It is used in combination with the `timeout_fn` as
follows:
* If the timeout expires and no `timeout_fn` was specified, the iterator
stops yielding.
* If a `timeout_fn` was specified, that function is called and if it returns
a true boolean value the iterator stops yielding.
* If the function returns a false boolean value then the iterator resumes the
wait for new checkpoints. At this point the timeout logic applies again.
This behavior gives control to callers on what to do if checkpoints do not
come fast enough or stop being generated. For example, if callers have a way
to detect that the training has stopped and know that no new checkpoints
will be generated, they can provide a `timeout_fn` that returns `True` when
the training has stopped. If they know that the training is still going on
they return `False` instead.
Args:
checkpoint_dir: The directory in which checkpoints are saved.
min_interval_secs: The minimum number of seconds between yielding
checkpoints.
timeout: The maximum number of seconds to wait between checkpoints. If left
as `None`, then the process will wait indefinitely.
timeout_fn: Optional function to call after a timeout. If the function
returns True, then it means that no new checkpoints will be generated and
the iterator will exit. The function is called with no arguments.
Yields:
String paths to latest checkpoint files as they arrive. | tensorflow/python/training/checkpoint_utils.py | checkpoints_iterator | KodeWorker/tensorflow | python | @tf_export('train.checkpoints_iterator')
def checkpoints_iterator(checkpoint_dir, min_interval_secs=0, timeout=None, timeout_fn=None):
'Continuously yield new checkpoint files as they appear.\n\n The iterator only checks for new checkpoints when control flow has been\n reverted to it. This means it can miss checkpoints if your code takes longer\n to run between iterations than `min_interval_secs` or the interval at which\n new checkpoints are written.\n\n The `timeout` argument is the maximum number of seconds to block waiting for\n a new checkpoint. It is used in combination with the `timeout_fn` as\n follows:\n\n * If the timeout expires and no `timeout_fn` was specified, the iterator\n stops yielding.\n * If a `timeout_fn` was specified, that function is called and if it returns\n a true boolean value the iterator stops yielding.\n * If the function returns a false boolean value then the iterator resumes the\n wait for new checkpoints. At this point the timeout logic applies again.\n\n This behavior gives control to callers on what to do if checkpoints do not\n come fast enough or stop being generated. For example, if callers have a way\n to detect that the training has stopped and know that no new checkpoints\n will be generated, they can provide a `timeout_fn` that returns `True` when\n the training has stopped. If they know that the training is still going on\n they return `False` instead.\n\n Args:\n checkpoint_dir: The directory in which checkpoints are saved.\n min_interval_secs: The minimum number of seconds between yielding\n checkpoints.\n timeout: The maximum number of seconds to wait between checkpoints. If left\n as `None`, then the process will wait indefinitely.\n timeout_fn: Optional function to call after a timeout. If the function\n returns True, then it means that no new checkpoints will be generated and\n the iterator will exit. The function is called with no arguments.\n\n Yields:\n String paths to latest checkpoint files as they arrive.\n '
checkpoint_path = None
while True:
new_checkpoint_path = wait_for_new_checkpoint(checkpoint_dir, checkpoint_path, timeout=timeout)
if (new_checkpoint_path is None):
if (not timeout_fn):
logging.info('Timed-out waiting for a checkpoint.')
return
if timeout_fn():
return
else:
continue
start = time.time()
checkpoint_path = new_checkpoint_path
(yield checkpoint_path)
time_to_next_eval = ((start + min_interval_secs) - time.time())
if (time_to_next_eval > 0):
time.sleep(time_to_next_eval) |
@tf_export(v1=['train.init_from_checkpoint'])
def init_from_checkpoint(ckpt_dir_or_file, assignment_map):
"Replaces `tf.Variable` initializers so they load from a checkpoint file.\n\n Values are not loaded immediately, but when the initializer is run\n (typically by running a `tf.compat.v1.global_variables_initializer` op).\n\n Note: This overrides default initialization ops of specified variables and\n redefines dtype.\n\n Assignment map supports following syntax:\n\n * `'checkpoint_scope_name/': 'scope_name/'` - will load all variables in\n current `scope_name` from `checkpoint_scope_name` with matching tensor\n names.\n * `'checkpoint_scope_name/some_other_variable': 'scope_name/variable_name'` -\n will initialize `scope_name/variable_name` variable\n from `checkpoint_scope_name/some_other_variable`.\n * `'scope_variable_name': variable` - will initialize given `tf.Variable`\n object with tensor 'scope_variable_name' from the checkpoint.\n * `'scope_variable_name': list(variable)` - will initialize list of\n partitioned variables with tensor 'scope_variable_name' from the checkpoint.\n * `'/': 'scope_name/'` - will load all variables in current `scope_name` from\n checkpoint's root (e.g. no scope).\n\n Supports loading into partitioned variables, which are represented as\n `'<variable>/part_<part #>'`.\n\n Example:\n\n ```python\n\n # Say, '/tmp/model.ckpt' has the following tensors:\n # -- name='old_scope_1/var1', shape=[20, 2]\n # -- name='old_scope_1/var2', shape=[50, 4]\n # -- name='old_scope_2/var3', shape=[100, 100]\n\n # Create new model's variables\n with tf.compat.v1.variable_scope('new_scope_1'):\n var1 = tf.compat.v1.get_variable('var1', shape=[20, 2],\n initializer=tf.compat.v1.zeros_initializer())\n with tf.compat.v1.variable_scope('new_scope_2'):\n var2 = tf.compat.v1.get_variable('var2', shape=[50, 4],\n initializer=tf.compat.v1.zeros_initializer())\n # Partition into 5 variables along the first axis.\n var3 = tf.compat.v1.get_variable(name='var3', shape=[100, 100],\n initializer=tf.compat.v1.zeros_initializer(),\n partitioner=lambda shape, dtype: [5, 1])\n\n # Initialize all variables in `new_scope_1` from `old_scope_1`.\n init_from_checkpoint('/tmp/model.ckpt', {'old_scope_1/': 'new_scope_1'})\n\n # Use names to specify which variables to initialize from checkpoint.\n init_from_checkpoint('/tmp/model.ckpt',\n {'old_scope_1/var1': 'new_scope_1/var1',\n 'old_scope_1/var2': 'new_scope_2/var2'})\n\n # Or use tf.Variable objects to identify what to initialize.\n init_from_checkpoint('/tmp/model.ckpt',\n {'old_scope_1/var1': var1,\n 'old_scope_1/var2': var2})\n\n # Initialize partitioned variables using variable's name\n init_from_checkpoint('/tmp/model.ckpt',\n {'old_scope_2/var3': 'new_scope_2/var3'})\n\n # Or specify the list of tf.Variable objects.\n init_from_checkpoint('/tmp/model.ckpt',\n {'old_scope_2/var3': var3._get_variable_list()})\n\n ```\n\n Args:\n ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.\n assignment_map: Dict, where keys are names of the variables in the\n checkpoint and values are current variables or names of current variables\n (in default graph).\n\n Raises:\n ValueError: If missing variables in current graph, or if missing\n checkpoints or tensors in checkpoints.\n "
init_from_checkpoint_fn = (lambda _: _init_from_checkpoint(ckpt_dir_or_file, assignment_map))
if distribution_strategy_context.get_cross_replica_context():
init_from_checkpoint_fn(None)
else:
distribution_strategy_context.get_replica_context().merge_call(init_from_checkpoint_fn) | -8,538,280,477,431,354,000 | Replaces `tf.Variable` initializers so they load from a checkpoint file.
Values are not loaded immediately, but when the initializer is run
(typically by running a `tf.compat.v1.global_variables_initializer` op).
Note: This overrides default initialization ops of specified variables and
redefines dtype.
Assignment map supports following syntax:
* `'checkpoint_scope_name/': 'scope_name/'` - will load all variables in
current `scope_name` from `checkpoint_scope_name` with matching tensor
names.
* `'checkpoint_scope_name/some_other_variable': 'scope_name/variable_name'` -
will initialize `scope_name/variable_name` variable
from `checkpoint_scope_name/some_other_variable`.
* `'scope_variable_name': variable` - will initialize given `tf.Variable`
object with tensor 'scope_variable_name' from the checkpoint.
* `'scope_variable_name': list(variable)` - will initialize list of
partitioned variables with tensor 'scope_variable_name' from the checkpoint.
* `'/': 'scope_name/'` - will load all variables in current `scope_name` from
checkpoint's root (e.g. no scope).
Supports loading into partitioned variables, which are represented as
`'<variable>/part_<part #>'`.
Example:
```python
# Say, '/tmp/model.ckpt' has the following tensors:
# -- name='old_scope_1/var1', shape=[20, 2]
# -- name='old_scope_1/var2', shape=[50, 4]
# -- name='old_scope_2/var3', shape=[100, 100]
# Create new model's variables
with tf.compat.v1.variable_scope('new_scope_1'):
var1 = tf.compat.v1.get_variable('var1', shape=[20, 2],
initializer=tf.compat.v1.zeros_initializer())
with tf.compat.v1.variable_scope('new_scope_2'):
var2 = tf.compat.v1.get_variable('var2', shape=[50, 4],
initializer=tf.compat.v1.zeros_initializer())
# Partition into 5 variables along the first axis.
var3 = tf.compat.v1.get_variable(name='var3', shape=[100, 100],
initializer=tf.compat.v1.zeros_initializer(),
partitioner=lambda shape, dtype: [5, 1])
# Initialize all variables in `new_scope_1` from `old_scope_1`.
init_from_checkpoint('/tmp/model.ckpt', {'old_scope_1/': 'new_scope_1'})
# Use names to specify which variables to initialize from checkpoint.
init_from_checkpoint('/tmp/model.ckpt',
{'old_scope_1/var1': 'new_scope_1/var1',
'old_scope_1/var2': 'new_scope_2/var2'})
# Or use tf.Variable objects to identify what to initialize.
init_from_checkpoint('/tmp/model.ckpt',
{'old_scope_1/var1': var1,
'old_scope_1/var2': var2})
# Initialize partitioned variables using variable's name
init_from_checkpoint('/tmp/model.ckpt',
{'old_scope_2/var3': 'new_scope_2/var3'})
# Or specify the list of tf.Variable objects.
init_from_checkpoint('/tmp/model.ckpt',
{'old_scope_2/var3': var3._get_variable_list()})
```
Args:
ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.
assignment_map: Dict, where keys are names of the variables in the
checkpoint and values are current variables or names of current variables
(in default graph).
Raises:
ValueError: If missing variables in current graph, or if missing
checkpoints or tensors in checkpoints. | tensorflow/python/training/checkpoint_utils.py | init_from_checkpoint | KodeWorker/tensorflow | python | @tf_export(v1=['train.init_from_checkpoint'])
def init_from_checkpoint(ckpt_dir_or_file, assignment_map):
"Replaces `tf.Variable` initializers so they load from a checkpoint file.\n\n Values are not loaded immediately, but when the initializer is run\n (typically by running a `tf.compat.v1.global_variables_initializer` op).\n\n Note: This overrides default initialization ops of specified variables and\n redefines dtype.\n\n Assignment map supports following syntax:\n\n * `'checkpoint_scope_name/': 'scope_name/'` - will load all variables in\n current `scope_name` from `checkpoint_scope_name` with matching tensor\n names.\n * `'checkpoint_scope_name/some_other_variable': 'scope_name/variable_name'` -\n will initialize `scope_name/variable_name` variable\n from `checkpoint_scope_name/some_other_variable`.\n * `'scope_variable_name': variable` - will initialize given `tf.Variable`\n object with tensor 'scope_variable_name' from the checkpoint.\n * `'scope_variable_name': list(variable)` - will initialize list of\n partitioned variables with tensor 'scope_variable_name' from the checkpoint.\n * `'/': 'scope_name/'` - will load all variables in current `scope_name` from\n checkpoint's root (e.g. no scope).\n\n Supports loading into partitioned variables, which are represented as\n `'<variable>/part_<part #>'`.\n\n Example:\n\n ```python\n\n # Say, '/tmp/model.ckpt' has the following tensors:\n # -- name='old_scope_1/var1', shape=[20, 2]\n # -- name='old_scope_1/var2', shape=[50, 4]\n # -- name='old_scope_2/var3', shape=[100, 100]\n\n # Create new model's variables\n with tf.compat.v1.variable_scope('new_scope_1'):\n var1 = tf.compat.v1.get_variable('var1', shape=[20, 2],\n initializer=tf.compat.v1.zeros_initializer())\n with tf.compat.v1.variable_scope('new_scope_2'):\n var2 = tf.compat.v1.get_variable('var2', shape=[50, 4],\n initializer=tf.compat.v1.zeros_initializer())\n # Partition into 5 variables along the first axis.\n var3 = tf.compat.v1.get_variable(name='var3', shape=[100, 100],\n initializer=tf.compat.v1.zeros_initializer(),\n partitioner=lambda shape, dtype: [5, 1])\n\n # Initialize all variables in `new_scope_1` from `old_scope_1`.\n init_from_checkpoint('/tmp/model.ckpt', {'old_scope_1/': 'new_scope_1'})\n\n # Use names to specify which variables to initialize from checkpoint.\n init_from_checkpoint('/tmp/model.ckpt',\n {'old_scope_1/var1': 'new_scope_1/var1',\n 'old_scope_1/var2': 'new_scope_2/var2'})\n\n # Or use tf.Variable objects to identify what to initialize.\n init_from_checkpoint('/tmp/model.ckpt',\n {'old_scope_1/var1': var1,\n 'old_scope_1/var2': var2})\n\n # Initialize partitioned variables using variable's name\n init_from_checkpoint('/tmp/model.ckpt',\n {'old_scope_2/var3': 'new_scope_2/var3'})\n\n # Or specify the list of tf.Variable objects.\n init_from_checkpoint('/tmp/model.ckpt',\n {'old_scope_2/var3': var3._get_variable_list()})\n\n ```\n\n Args:\n ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.\n assignment_map: Dict, where keys are names of the variables in the\n checkpoint and values are current variables or names of current variables\n (in default graph).\n\n Raises:\n ValueError: If missing variables in current graph, or if missing\n checkpoints or tensors in checkpoints.\n "
init_from_checkpoint_fn = (lambda _: _init_from_checkpoint(ckpt_dir_or_file, assignment_map))
if distribution_strategy_context.get_cross_replica_context():
init_from_checkpoint_fn(None)
else:
distribution_strategy_context.get_replica_context().merge_call(init_from_checkpoint_fn) |
def _init_from_checkpoint(ckpt_dir_or_file, assignment_map):
'See `init_from_checkpoint` for documentation.'
ckpt_file = _get_checkpoint_filename(ckpt_dir_or_file)
reader = load_checkpoint(ckpt_dir_or_file)
variable_map = reader.get_variable_to_shape_map()
for (tensor_name_in_ckpt, current_var_or_name) in sorted(six.iteritems(assignment_map)):
var = None
if (_is_variable(current_var_or_name) or (isinstance(current_var_or_name, list) and all((_is_variable(v) for v in current_var_or_name)))):
var = current_var_or_name
else:
store_vars = vs._get_default_variable_store()._vars
var = store_vars.get(current_var_or_name, None)
if (var is None):
var = _collect_partitioned_variable(current_var_or_name, store_vars)
if (var is not None):
if (tensor_name_in_ckpt not in variable_map):
raise ValueError(('Tensor %s is not found in %s checkpoint %s' % (tensor_name_in_ckpt, ckpt_dir_or_file, variable_map)))
if _is_variable(var):
if (not var.get_shape().is_compatible_with(variable_map[tensor_name_in_ckpt])):
raise ValueError(("Shape of variable %s (%s) doesn't match with shape of tensor %s (%s) from checkpoint reader." % (var.name, str(var.get_shape()), tensor_name_in_ckpt, str(variable_map[tensor_name_in_ckpt]))))
var_name = var.name
else:
var_name = ','.join([v.name for v in var])
_set_variable_or_list_initializer(var, ckpt_file, tensor_name_in_ckpt)
logging.debug('Initialize variable %s from checkpoint %s with %s', var_name, ckpt_dir_or_file, tensor_name_in_ckpt)
else:
scopes = ''
if ('/' in current_var_or_name):
scopes = current_var_or_name[:current_var_or_name.rindex('/')]
if (not tensor_name_in_ckpt.endswith('/')):
raise ValueError("Assignment map with scope only name {} should map to scope only {}. Should be 'scope/': 'other_scope/'.".format(scopes, tensor_name_in_ckpt))
scope_variables = set()
for var_name in store_vars:
if ((not scopes) or var_name.startswith((scopes + '/'))):
if ('/part_' in var_name):
var_name = var_name[:var_name.index('/part_')]
scope_variables.add(var_name)
for var_name in sorted(scope_variables):
full_tensor_name = var_name[len(scopes):]
if (current_var_or_name != '/'):
full_tensor_name = full_tensor_name[1:]
if (tensor_name_in_ckpt != '/'):
full_tensor_name = (tensor_name_in_ckpt + full_tensor_name)
if full_tensor_name.endswith('/'):
full_tensor_name = full_tensor_name[:(- 1)]
if (full_tensor_name not in variable_map):
raise ValueError(('Tensor %s (%s in %s) is not found in %s checkpoint' % (full_tensor_name, var_name[(len(scopes) + 1):], tensor_name_in_ckpt, ckpt_dir_or_file)))
var = store_vars.get(var_name, None)
if (var is None):
var = _collect_partitioned_variable(var_name, store_vars)
_set_variable_or_list_initializer(var, ckpt_file, full_tensor_name)
logging.debug('Initialize variable %s from checkpoint %s with %s', var_name, ckpt_dir_or_file, full_tensor_name) | -3,119,381,913,987,592,700 | See `init_from_checkpoint` for documentation. | tensorflow/python/training/checkpoint_utils.py | _init_from_checkpoint | KodeWorker/tensorflow | python | def _init_from_checkpoint(ckpt_dir_or_file, assignment_map):
ckpt_file = _get_checkpoint_filename(ckpt_dir_or_file)
reader = load_checkpoint(ckpt_dir_or_file)
variable_map = reader.get_variable_to_shape_map()
for (tensor_name_in_ckpt, current_var_or_name) in sorted(six.iteritems(assignment_map)):
var = None
if (_is_variable(current_var_or_name) or (isinstance(current_var_or_name, list) and all((_is_variable(v) for v in current_var_or_name)))):
var = current_var_or_name
else:
store_vars = vs._get_default_variable_store()._vars
var = store_vars.get(current_var_or_name, None)
if (var is None):
var = _collect_partitioned_variable(current_var_or_name, store_vars)
if (var is not None):
if (tensor_name_in_ckpt not in variable_map):
raise ValueError(('Tensor %s is not found in %s checkpoint %s' % (tensor_name_in_ckpt, ckpt_dir_or_file, variable_map)))
if _is_variable(var):
if (not var.get_shape().is_compatible_with(variable_map[tensor_name_in_ckpt])):
raise ValueError(("Shape of variable %s (%s) doesn't match with shape of tensor %s (%s) from checkpoint reader." % (var.name, str(var.get_shape()), tensor_name_in_ckpt, str(variable_map[tensor_name_in_ckpt]))))
var_name = var.name
else:
var_name = ','.join([v.name for v in var])
_set_variable_or_list_initializer(var, ckpt_file, tensor_name_in_ckpt)
logging.debug('Initialize variable %s from checkpoint %s with %s', var_name, ckpt_dir_or_file, tensor_name_in_ckpt)
else:
scopes =
if ('/' in current_var_or_name):
scopes = current_var_or_name[:current_var_or_name.rindex('/')]
if (not tensor_name_in_ckpt.endswith('/')):
raise ValueError("Assignment map with scope only name {} should map to scope only {}. Should be 'scope/': 'other_scope/'.".format(scopes, tensor_name_in_ckpt))
scope_variables = set()
for var_name in store_vars:
if ((not scopes) or var_name.startswith((scopes + '/'))):
if ('/part_' in var_name):
var_name = var_name[:var_name.index('/part_')]
scope_variables.add(var_name)
for var_name in sorted(scope_variables):
full_tensor_name = var_name[len(scopes):]
if (current_var_or_name != '/'):
full_tensor_name = full_tensor_name[1:]
if (tensor_name_in_ckpt != '/'):
full_tensor_name = (tensor_name_in_ckpt + full_tensor_name)
if full_tensor_name.endswith('/'):
full_tensor_name = full_tensor_name[:(- 1)]
if (full_tensor_name not in variable_map):
raise ValueError(('Tensor %s (%s in %s) is not found in %s checkpoint' % (full_tensor_name, var_name[(len(scopes) + 1):], tensor_name_in_ckpt, ckpt_dir_or_file)))
var = store_vars.get(var_name, None)
if (var is None):
var = _collect_partitioned_variable(var_name, store_vars)
_set_variable_or_list_initializer(var, ckpt_file, full_tensor_name)
logging.debug('Initialize variable %s from checkpoint %s with %s', var_name, ckpt_dir_or_file, full_tensor_name) |
def _get_checkpoint_filename(ckpt_dir_or_file):
'Returns checkpoint filename given directory or specific checkpoint file.'
if gfile.IsDirectory(ckpt_dir_or_file):
return checkpoint_management.latest_checkpoint(ckpt_dir_or_file)
return ckpt_dir_or_file | -5,948,685,012,336,749,000 | Returns checkpoint filename given directory or specific checkpoint file. | tensorflow/python/training/checkpoint_utils.py | _get_checkpoint_filename | KodeWorker/tensorflow | python | def _get_checkpoint_filename(ckpt_dir_or_file):
if gfile.IsDirectory(ckpt_dir_or_file):
return checkpoint_management.latest_checkpoint(ckpt_dir_or_file)
return ckpt_dir_or_file |
def _set_checkpoint_initializer(variable, ckpt_file, tensor_name, slice_spec, name='checkpoint_initializer', write_version=saver_pb2.SaverDef.DIT):
"Overrides given variable's initialization op.\n\n Sets variable initializer to assign op that initializes variable from tensor's\n value in the checkpoint.\n\n Args:\n variable: `tf.Variable` object.\n ckpt_file: string, full path of the checkpoint.\n tensor_name: Name of the tensor to load from the checkpoint.\n slice_spec: Slice specification for loading partitioned tensors.\n name: Name of the operation.\n "
base_type = variable.dtype.base_dtype
with ops.device(variable.device), ops.device('/cpu:0'):
if ((self._write_version == saver_pb2.SaverDef.V1) or (self._write_version == saver_pb2.SaverDef.V2)):
restore_op = io_ops.restore_v2(ckpt_file, [tensor_name], [slice_spec], [base_type], name=name)[0]
elif (self._write_version == saver_pb2.SaverDef.DIT):
restore_op = io_ops.restore_dit(ckpt_file, [tensor_name], [slice_spec], [base_type], name=name)[0]
else:
raise RuntimeError(('Unexpected write_version: ' + self._write_version))
names_to_saveables = saveable_object_util.op_list_to_dict([variable])
saveable_objects = []
for (name, op) in names_to_saveables.items():
for s in saveable_object_util.saveable_objects_for_op(op, name):
saveable_objects.append(s)
assert (len(saveable_objects) == 1)
init_op = saveable_objects[0].restore([restore_op], restored_shapes=None)
variable._initializer_op = init_op
restore_op.set_shape(variable.shape)
variable._initial_value = restore_op | 7,078,638,621,091,424,000 | Overrides given variable's initialization op.
Sets variable initializer to assign op that initializes variable from tensor's
value in the checkpoint.
Args:
variable: `tf.Variable` object.
ckpt_file: string, full path of the checkpoint.
tensor_name: Name of the tensor to load from the checkpoint.
slice_spec: Slice specification for loading partitioned tensors.
name: Name of the operation. | tensorflow/python/training/checkpoint_utils.py | _set_checkpoint_initializer | KodeWorker/tensorflow | python | def _set_checkpoint_initializer(variable, ckpt_file, tensor_name, slice_spec, name='checkpoint_initializer', write_version=saver_pb2.SaverDef.DIT):
"Overrides given variable's initialization op.\n\n Sets variable initializer to assign op that initializes variable from tensor's\n value in the checkpoint.\n\n Args:\n variable: `tf.Variable` object.\n ckpt_file: string, full path of the checkpoint.\n tensor_name: Name of the tensor to load from the checkpoint.\n slice_spec: Slice specification for loading partitioned tensors.\n name: Name of the operation.\n "
base_type = variable.dtype.base_dtype
with ops.device(variable.device), ops.device('/cpu:0'):
if ((self._write_version == saver_pb2.SaverDef.V1) or (self._write_version == saver_pb2.SaverDef.V2)):
restore_op = io_ops.restore_v2(ckpt_file, [tensor_name], [slice_spec], [base_type], name=name)[0]
elif (self._write_version == saver_pb2.SaverDef.DIT):
restore_op = io_ops.restore_dit(ckpt_file, [tensor_name], [slice_spec], [base_type], name=name)[0]
else:
raise RuntimeError(('Unexpected write_version: ' + self._write_version))
names_to_saveables = saveable_object_util.op_list_to_dict([variable])
saveable_objects = []
for (name, op) in names_to_saveables.items():
for s in saveable_object_util.saveable_objects_for_op(op, name):
saveable_objects.append(s)
assert (len(saveable_objects) == 1)
init_op = saveable_objects[0].restore([restore_op], restored_shapes=None)
variable._initializer_op = init_op
restore_op.set_shape(variable.shape)
variable._initial_value = restore_op |
def _set_variable_or_list_initializer(variable_or_list, ckpt_file, tensor_name):
'Overrides initialization op of given variable or list of variables.\n\n Calls `_set_checkpoint_initializer` for each variable in the given list of\n variables.\n\n Args:\n variable_or_list: `tf.Variable` object or a list of `tf.Variable` objects.\n ckpt_file: string, full path of the checkpoint.\n tensor_name: Name of the tensor to load from the checkpoint.\n\n Raises:\n ValueError: if all objects in `variable_or_list` are not partitions of the\n same large variable.\n '
if isinstance(variable_or_list, (list, tuple)):
slice_name = None
for v in variable_or_list:
slice_info = v._save_slice_info
if (slice_name is None):
slice_name = slice_info.full_name
elif (slice_name != slice_info.full_name):
raise ValueError(('Slices must all be from the same tensor: %s != %s' % (slice_name, slice_info.full_name)))
_set_checkpoint_initializer(v, ckpt_file, tensor_name, slice_info.spec)
else:
_set_checkpoint_initializer(variable_or_list, ckpt_file, tensor_name, '') | 4,867,478,037,457,606,000 | Overrides initialization op of given variable or list of variables.
Calls `_set_checkpoint_initializer` for each variable in the given list of
variables.
Args:
variable_or_list: `tf.Variable` object or a list of `tf.Variable` objects.
ckpt_file: string, full path of the checkpoint.
tensor_name: Name of the tensor to load from the checkpoint.
Raises:
ValueError: if all objects in `variable_or_list` are not partitions of the
same large variable. | tensorflow/python/training/checkpoint_utils.py | _set_variable_or_list_initializer | KodeWorker/tensorflow | python | def _set_variable_or_list_initializer(variable_or_list, ckpt_file, tensor_name):
'Overrides initialization op of given variable or list of variables.\n\n Calls `_set_checkpoint_initializer` for each variable in the given list of\n variables.\n\n Args:\n variable_or_list: `tf.Variable` object or a list of `tf.Variable` objects.\n ckpt_file: string, full path of the checkpoint.\n tensor_name: Name of the tensor to load from the checkpoint.\n\n Raises:\n ValueError: if all objects in `variable_or_list` are not partitions of the\n same large variable.\n '
if isinstance(variable_or_list, (list, tuple)):
slice_name = None
for v in variable_or_list:
slice_info = v._save_slice_info
if (slice_name is None):
slice_name = slice_info.full_name
elif (slice_name != slice_info.full_name):
raise ValueError(('Slices must all be from the same tensor: %s != %s' % (slice_name, slice_info.full_name)))
_set_checkpoint_initializer(v, ckpt_file, tensor_name, slice_info.spec)
else:
_set_checkpoint_initializer(variable_or_list, ckpt_file, tensor_name, ) |
def _collect_partitioned_variable(name, all_vars):
'Returns list of `tf.Variable` that comprise the partitioned variable.'
if ((name + '/part_0') in all_vars):
var = []
i = 0
while ((name + ('/part_%d' % i)) in all_vars):
var.append(all_vars[(name + ('/part_%d' % i))])
i += 1
return var
return None | -3,062,386,609,698,979,300 | Returns list of `tf.Variable` that comprise the partitioned variable. | tensorflow/python/training/checkpoint_utils.py | _collect_partitioned_variable | KodeWorker/tensorflow | python | def _collect_partitioned_variable(name, all_vars):
if ((name + '/part_0') in all_vars):
var = []
i = 0
while ((name + ('/part_%d' % i)) in all_vars):
var.append(all_vars[(name + ('/part_%d' % i))])
i += 1
return var
return None |
def add_const(self, const):
'\n Add a constant to the environment, return its index.\n '
if isinstance(const, str):
const = utils.intern(const)
for (index, val) in enumerate(self.env.consts):
if (val is const):
break
else:
index = len(self.env.consts)
self.env.consts.append(const)
return index | 3,270,935,112,231,871,000 | Add a constant to the environment, return its index. | numba/core/pythonapi.py | add_const | DrTodd13/numba | python | def add_const(self, const):
'\n \n '
if isinstance(const, str):
const = utils.intern(const)
for (index, val) in enumerate(self.env.consts):
if (val is const):
break
else:
index = len(self.env.consts)
self.env.consts.append(const)
return index |
def read_const(self, index):
'\n Look up constant number *index* inside the environment body.\n A borrowed reference is returned.\n\n The returned LLVM value may have NULL value at runtime which indicates\n an error at runtime.\n '
assert (index < len(self.env.consts))
builder = self.pyapi.builder
consts = self.env_body.consts
ret = cgutils.alloca_once(builder, self.pyapi.pyobj, zfill=True)
with builder.if_else(cgutils.is_not_null(builder, consts)) as (br_not_null, br_null):
with br_not_null:
getitem = self.pyapi.list_getitem(consts, index)
builder.store(getitem, ret)
with br_null:
self.pyapi.err_set_string('PyExc_RuntimeError', '`env.consts` is NULL in `read_const`')
return builder.load(ret) | 7,686,056,002,697,819,000 | Look up constant number *index* inside the environment body.
A borrowed reference is returned.
The returned LLVM value may have NULL value at runtime which indicates
an error at runtime. | numba/core/pythonapi.py | read_const | DrTodd13/numba | python | def read_const(self, index):
'\n Look up constant number *index* inside the environment body.\n A borrowed reference is returned.\n\n The returned LLVM value may have NULL value at runtime which indicates\n an error at runtime.\n '
assert (index < len(self.env.consts))
builder = self.pyapi.builder
consts = self.env_body.consts
ret = cgutils.alloca_once(builder, self.pyapi.pyobj, zfill=True)
with builder.if_else(cgutils.is_not_null(builder, consts)) as (br_not_null, br_null):
with br_not_null:
getitem = self.pyapi.list_getitem(consts, index)
builder.store(getitem, ret)
with br_null:
self.pyapi.err_set_string('PyExc_RuntimeError', '`env.consts` is NULL in `read_const`')
return builder.load(ret) |
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