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stringlengths 7
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stringlengths 0
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|---|---|---|
tests/tensor_core/ravel_index.cairo | // ===== 1D ===== //
#[cfg(test)]
mod tensor_1D {
use core::array::ArrayTrait;
use orion::operators::tensor::core::{ravel_index};
#[test]
#[available_gas(2000000)]
fn tensor_ravel_index() {
let mut shape = ArrayTrait::new();
shape.append(5);
let mut indices = ArrayTrait::new();
indices.append(2);
let result = ravel_index(shape.span(), indices.span());
assert(result == 2, 'result = 2');
}
}
// ===== 2D ===== //
#[cfg(test)]
mod tensor_2D {
use core::array::ArrayTrait;
use orion::operators::tensor::core::{ravel_index};
#[test]
#[available_gas(2000000)]
fn tensor_ravel_index() {
let mut shape = ArrayTrait::new();
shape.append(2);
shape.append(4);
let mut indices = ArrayTrait::new();
indices.append(1);
indices.append(2);
let result = ravel_index(shape.span(), indices.span());
assert(result == 6, 'result = 6');
}
}
// ===== 3D ===== //
#[cfg(test)]
mod tensor_3D {
use core::array::ArrayTrait;
use orion::operators::tensor::core::{ravel_index};
#[test]
#[available_gas(2000000)]
fn tensor_ravel_index() {
let mut shape = ArrayTrait::new();
shape.append(2);
shape.append(4);
shape.append(6);
let mut indices = ArrayTrait::new();
indices.append(1);
indices.append(3);
indices.append(0);
let result = ravel_index(shape.span(), indices.span());
assert(result == 42, 'result = 42');
}
}
| https://github.com/gizatechxyz/orion |
tests/tensor_core/stride.cairo | mod stride_u32_test;
mod stride_i32_test;
mod stride_bool_test;
mod stride_fp_test;
| https://github.com/gizatechxyz/orion |
tests/tensor_core/stride/stride_bool_test.cairo | // ===== 1D ===== //
#[cfg(test)]
mod tensor_1D {
use core::array::ArrayTrait;
use orion::operators::tensor::{BoolTensor};
use orion::operators::tensor::core::{TensorTrait};
#[test]
#[available_gas(2000000)]
fn tensor_at() {
let mut sizes = ArrayTrait::new();
sizes.append(3);
let mut data = ArrayTrait::new();
data.append(false);
data.append(true);
data.append(false);
let tensor = TensorTrait::<bool>::new(sizes.span(), data.span());
let result = tensor.stride();
assert(*result[0] == 1, 'stride x = 1');
assert(result.len() == 1, 'len = 1');
}
}
// ===== 2D ===== //
#[cfg(test)]
mod tensor_2D {
use core::array::ArrayTrait;
use orion::operators::tensor::{BoolTensor};
use orion::operators::tensor::core::{TensorTrait};
#[test]
#[available_gas(2000000)]
fn tensor_at() {
let mut sizes = ArrayTrait::new();
sizes.append(2);
sizes.append(2);
let mut data = ArrayTrait::new();
data.append(false);
data.append(false);
data.append(false);
data.append(true);
let tensor = TensorTrait::<bool>::new(sizes.span(), data.span());
let result = tensor.stride();
assert(*result[0] == 2, 'stride x = 2');
assert(*result[1] == 1, 'stride y = 1');
assert(result.len() == 2, 'len = 2');
}
}
// ===== 3D ===== //
#[cfg(test)]
mod tensor_3D {
use core::array::ArrayTrait;
use orion::operators::tensor::{BoolTensor};
use orion::operators::tensor::core::{TensorTrait};
#[test]
#[available_gas(2000000)]
fn tensor_at() {
let mut sizes = ArrayTrait::new();
sizes.append(2);
sizes.append(2);
sizes.append(2);
let mut data = ArrayTrait::new();
data.append(false);
data.append(false);
data.append(false);
data.append(true);
data.append(false);
data.append(false);
data.append(false);
data.append(false);
let tensor = TensorTrait::<bool>::new(sizes.span(), data.span());
let result = tensor.stride();
assert(*result[0] == 4, 'stride x = 4');
assert(*result[1] == 2, 'stride y = 2');
assert(*result[2] == 1, 'stride z = 1');
assert(result.len() == 3, 'len = 3');
}
}
| https://github.com/gizatechxyz/orion |
tests/tensor_core/stride/stride_fp_test.cairo | mod stride_fp8x23_test;
mod stride_fp16x16_test;
| https://github.com/gizatechxyz/orion |
tests/tensor_core/stride/stride_fp_test/stride_fp16x16_test.cairo | // ===== 1D ===== //
#[cfg(test)]
mod tensor_1D {
use core::array::ArrayTrait;
use core::array::SpanTrait;
use orion::operators::tensor::implementations::tensor_fp16x16::FP16x16Tensor;
use orion::operators::tensor::core::{TensorTrait};
use orion::test_helper::tensor::fixed_point::fp16x16::fp_tensor_1x3_helper;
#[test]
#[available_gas(2000000)]
fn tensor_stride() {
let tensor = fp_tensor_1x3_helper();
let result = tensor.stride();
assert(*result[0] == 1, 'stride x = 1');
assert(result.len() == 1, 'len = 1');
}
}
// ===== 2D ===== //
#[cfg(test)]
mod tensor_2D {
use core::array::ArrayTrait;
use core::array::SpanTrait;
use orion::operators::tensor::implementations::tensor_fp16x16::FP16x16Tensor;
use orion::operators::tensor::core::{TensorTrait};
use orion::test_helper::tensor::fixed_point::fp16x16::fp_tensor_2x2_helper;
#[test]
#[available_gas(2000000)]
fn tensor_stride() {
let tensor = fp_tensor_2x2_helper();
let result = tensor.stride();
assert(*result[0] == 2, 'stride x = 2');
assert(*result[1] == 1, 'stride y = 1');
assert(result.len() == 2, 'len = 2');
}
}
// ===== 3D ===== //
#[cfg(test)]
mod tensor_3D {
use core::array::ArrayTrait;
use core::array::SpanTrait;
use orion::operators::tensor::implementations::tensor_fp16x16::FP16x16Tensor;
use orion::operators::tensor::core::{TensorTrait};
use orion::test_helper::tensor::fixed_point::fp16x16::fp_tensor_2x2x2_helper;
#[test]
#[available_gas(2000000)]
fn tensor_stride() {
let tensor = fp_tensor_2x2x2_helper();
let result = tensor.stride();
assert(*result[0] == 4, 'stride x = 4');
assert(*result[1] == 2, 'stride y = 2');
assert(*result[2] == 1, 'stride z = 1');
assert(result.len() == 3, 'len = 3');
}
}
| https://github.com/gizatechxyz/orion |
tests/tensor_core/stride/stride_fp_test/stride_fp8x23_test.cairo | // ===== 1D ===== //
#[cfg(test)]
mod tensor_1D {
use core::array::ArrayTrait;
use core::array::SpanTrait;
use orion::operators::tensor::implementations::tensor_fp8x23::FP8x23Tensor;
use orion::operators::tensor::core::{TensorTrait};
use orion::test_helper::tensor::fixed_point::fp8x23::fp_tensor_1x3_helper;
#[test]
#[available_gas(2000000)]
fn tensor_stride() {
let tensor = fp_tensor_1x3_helper();
let result = tensor.stride();
assert(*result[0] == 1, 'stride x = 1');
assert(result.len() == 1, 'len = 1');
}
}
// ===== 2D ===== //
#[cfg(test)]
mod tensor_2D {
use core::array::ArrayTrait;
use core::array::SpanTrait;
use orion::operators::tensor::implementations::tensor_fp8x23::FP8x23Tensor;
use orion::operators::tensor::core::{TensorTrait};
use orion::test_helper::tensor::fixed_point::fp8x23::fp_tensor_2x2_helper;
#[test]
#[available_gas(2000000)]
fn tensor_stride() {
let tensor = fp_tensor_2x2_helper();
let result = tensor.stride();
assert(*result[0] == 2, 'stride x = 2');
assert(*result[1] == 1, 'stride y = 1');
assert(result.len() == 2, 'len = 2');
}
}
// ===== 3D ===== //
#[cfg(test)]
mod tensor_3D {
use core::array::ArrayTrait;
use core::array::SpanTrait;
use orion::operators::tensor::implementations::tensor_fp8x23::FP8x23Tensor;
use orion::operators::tensor::core::{TensorTrait};
use orion::test_helper::tensor::fixed_point::fp8x23::fp_tensor_2x2x2_helper;
#[test]
#[available_gas(2000000)]
fn tensor_stride() {
let tensor = fp_tensor_2x2x2_helper();
let result = tensor.stride();
assert(*result[0] == 4, 'stride x = 4');
assert(*result[1] == 2, 'stride y = 2');
assert(*result[2] == 1, 'stride z = 1');
assert(result.len() == 3, 'len = 3');
}
}
| https://github.com/gizatechxyz/orion |
tests/tensor_core/stride/stride_i32_test.cairo | // ===== 1D ===== //
#[cfg(test)]
mod tensor_1D {
use core::array::ArrayTrait;
use core::array::SpanTrait;
use orion::operators::tensor::I32Tensor;
use orion::operators::tensor::core::{TensorTrait};
use orion::test_helper::tensor::i32::i32_tensor_1x3_helper;
#[test]
#[available_gas(2000000)]
fn tensor_stride() {
let tensor = i32_tensor_1x3_helper();
let result = tensor.stride();
assert(*result[0] == 1, 'stride x = 1');
assert(result.len() == 1, 'len = 1');
}
}
// ===== 2D ===== //
#[cfg(test)]
mod tensor_2D {
use core::array::ArrayTrait;
use core::array::SpanTrait;
use orion::operators::tensor::I32Tensor;
use orion::operators::tensor::core::{TensorTrait};
use orion::test_helper::tensor::i32::i32_tensor_2x2_helper;
#[test]
#[available_gas(2000000)]
fn tensor_at() {
let tensor = i32_tensor_2x2_helper();
let result = tensor.stride();
assert(*result[0] == 2, 'stride x = 2');
assert(*result[1] == 1, 'stride y = 1');
assert(result.len() == 2, 'len = 2');
}
}
// ===== 3D ===== //
#[cfg(test)]
mod tensor_3D {
use core::array::ArrayTrait;
use core::array::SpanTrait;
use orion::operators::tensor::I32Tensor;
use orion::operators::tensor::core::{TensorTrait};
use orion::test_helper::tensor::i32::i32_tensor_2x2x2_helper;
#[test]
#[available_gas(2000000)]
fn tensor_at() {
let tensor = i32_tensor_2x2x2_helper();
let result = tensor.stride();
assert(*result[0] == 4, 'stride x = 4');
assert(*result[1] == 2, 'stride y = 2');
assert(*result[2] == 1, 'stride z = 1');
assert(result.len() == 3, 'len = 3');
}
}
| https://github.com/gizatechxyz/orion |
tests/tensor_core/stride/stride_u32_test.cairo | // ===== 1D ===== //
#[cfg(test)]
mod tensor_1D {
use core::array::ArrayTrait;
use core::array::SpanTrait;
use orion::operators::tensor::U32Tensor;
use orion::operators::tensor::core::{TensorTrait};
use orion::test_helper::tensor::u32::u32_tensor_1x3_helper;
#[test]
#[available_gas(2000000)]
fn tensor_stride() {
let tensor = u32_tensor_1x3_helper();
let result = tensor.stride();
assert(*result[0] == 1, 'stride x = 1');
assert(result.len() == 1, 'len = 1');
}
}
// ===== 2D ===== //
#[cfg(test)]
mod tensor_2D {
use core::array::ArrayTrait;
use core::array::SpanTrait;
use orion::operators::tensor::U32Tensor;
use orion::operators::tensor::core::{TensorTrait};
use orion::test_helper::tensor::u32::u32_tensor_2x2_helper;
#[test]
#[available_gas(2000000)]
fn tensor_at() {
let tensor = u32_tensor_2x2_helper();
let result = tensor.stride();
assert(*result[0] == 2, 'stride x = 2');
assert(*result[1] == 1, 'stride y = 1');
assert(result.len() == 2, 'len = 2');
}
}
// ===== 3D ===== //
#[cfg(test)]
mod tensor_3D {
use core::array::ArrayTrait;
use core::array::SpanTrait;
use orion::operators::tensor::U32Tensor;
use orion::operators::tensor::core::{TensorTrait};
use orion::test_helper::tensor::u32::u32_tensor_2x2x2_helper;
#[test]
#[available_gas(2000000)]
fn tensor_at() {
let tensor = u32_tensor_2x2x2_helper();
let result = tensor.stride();
assert(*result[0] == 4, 'stride x = 4');
assert(*result[1] == 2, 'stride y = 2');
assert(*result[2] == 1, 'stride z = 1');
assert(result.len() == 3, 'len = 3');
}
}
| https://github.com/gizatechxyz/orion |
tests/tensor_core/unravel_index.cairo | // ===== 1D ===== //
#[cfg(test)]
mod tensor_1D {
use core::array::ArrayTrait;
use core::array::SpanTrait;
use orion::operators::tensor::core::{unravel_index};
#[test]
#[available_gas(2000000)]
fn tensor_unravel_index() {
let mut shape = ArrayTrait::new();
shape.append(5);
let result = unravel_index(2, shape.span());
assert(*result[0] == 2, 'result[0] = 2');
}
}
// ===== 2D ===== //
#[cfg(test)]
mod tensor_2D {
use core::array::ArrayTrait;
use core::array::SpanTrait;
use orion::operators::tensor::core::{unravel_index};
#[test]
#[available_gas(2000000)]
fn tensor_unravel_index() {
let mut shape = ArrayTrait::new();
shape.append(2);
shape.append(4);
let result = unravel_index(6, shape.span());
assert(*result[0] == 1, 'result[0] = 1');
assert(*result[1] == 2, 'result[1] = 2');
}
}
// ===== 3D ===== //
#[cfg(test)]
mod tensor_3D {
use core::array::ArrayTrait;
use core::array::SpanTrait;
use orion::operators::tensor::core::{unravel_index};
#[test]
#[available_gas(2000000)]
fn tensor_unravel_index() {
let mut shape = ArrayTrait::new();
shape.append(2);
shape.append(4);
shape.append(6);
let result = unravel_index(42, shape.span());
assert(*result[0] == 1, 'result[0] = 1');
assert(*result[1] == 3, 'result[1] = 3');
assert(*result[2] == 0, 'result[2] = 0');
}
}
| https://github.com/gizatechxyz/orion |
awesome-giza-agents/uni-v3-lp-agent/action_agent.py | import argparse
import os
import pprint
import numpy as np
from addresses import ADDRESSES
from dotenv import find_dotenv, load_dotenv
from giza_actions.action import action
from giza_actions.agent import AgentResult, GizaAgent
from giza_actions.task import task
from lp_tools import get_tick_range
from prefect import get_run_logger
from uni_helpers import (
approve_token,
check_allowance,
close_position,
get_all_user_positions,
get_mint_params,
)
load_dotenv(find_dotenv())
os.environ["DEV_PASSPHRASE"] = os.environ.get("DEV_PASSPHRASE")
sepolia_rpc_url = os.environ.get("SEPOLIA_RPC_URL")
@task(name="Data processing")
def process_data(realized_vol: float, dec_price_change: float):
pct_change_sq = (100 * dec_price_change) ** 2
X = np.array([[realized_vol, pct_change_sq]])
return X
@task(name="Get volatility and price change data")
def get_data():
# TODO: implement fetching onchain or from some other source
# hardcoding the values for now
realized_vol = 4.20
dec_price_change = 0.1
return realized_vol, dec_price_change
@task(name="Create a Giza agent for the Volatility prediction model")
def create_agent(
model_id: int, version_id: int, chain: str, contracts: dict, account: str
):
"""
Create a Giza agent for the volatility prediction model
"""
agent = GizaAgent(
contracts=contracts,
id=model_id,
version_id=version_id,
chain=chain,
account=account,
)
return agent
@task(name="Run the volatility prediction model")
def predict(agent: GizaAgent, X: np.ndarray):
"""
Predict the next day volatility.
Args:
X (np.ndarray): Input to the model.
Returns:
int: Predicted value.
"""
prediction = agent.predict(input_feed={"val": X}, verifiable=True, job_size="XL")
return prediction
@task(name="Verify the inference proof and return the predicted value")
def get_pred_val(prediction: AgentResult):
"""
Get the value from the prediction.
Args:
prediction (dict): Prediction from the model.
Returns:
int: Predicted value.
"""
# This will block the executon until the prediction has generated the proof
# and the proof has been verified
return prediction.value[0][0]
# Create Action
@action(log_prints=True)
def rebalance_lp(
tokenA_amount: int,
tokenB_amount: int,
pred_model_id: int,
pred_version_id: int,
account="dev",
chain=f"ethereum:sepolia:{sepolia_rpc_url}",
nft_id=None,
):
logger = get_run_logger()
nft_manager_address = ADDRESSES["NonfungiblePositionManager"][11155111]
tokenA_address = ADDRESSES["UNI"][11155111]
tokenB_address = ADDRESSES["WETH"][11155111]
pool_address = "0x287B0e934ed0439E2a7b1d5F0FC25eA2c24b64f7"
user_address = "0xCBB090699E0664f0F6A4EFbC616f402233718152"
pool_fee = 3000
logger.info("Fetching input data")
realized_vol, dec_price_change = get_data()
logger.info(f"Input data: {realized_vol}, {dec_price_change}")
X = process_data(realized_vol, dec_price_change)
contracts = {
"nft_manager": nft_manager_address,
"tokenA": tokenA_address,
"tokenB": tokenB_address,
"pool": pool_address,
}
agent = create_agent(
model_id=pred_model_id,
version_id=pred_version_id,
chain=chain,
contracts=contracts,
account=account,
)
result = predict(agent, X)
predicted_value = get_pred_val(result)
logger.info(f"Result: {result}")
with agent.execute() as contracts:
logger.info("Executing contract")
if nft_id is None:
positions = [
max(get_all_user_positions(contracts.nft_manager, user_address))
]
else:
positions = [nft_id]
logger.info(f"Closing the following positions {positions}")
for nft_id in positions:
close_position(user_address, contracts.nft_manager, nft_id)
logger.info("Calculating mint params...")
_, curr_tick, _, _, _, _, _ = contracts.pool.slot0()
if not check_allowance(
contracts.tokenA, nft_manager_address, account, tokenA_amount
):
approve_token(contracts.tokenA, nft_manager_address, tokenA_amount)
if not check_allowance(
contracts.tokenB, nft_manager_address, account, tokenB_amount
):
approve_token(contracts.tokenB, nft_manager_address, tokenB_amount)
tokenA_decimals = contracts.tokenA.decimals()
tokenB_decimals = contracts.tokenB.decimals()
predicted_value = predicted_value / 100 * 1.96 # convert to decimal %
lower_tick, upper_tick = get_tick_range(
curr_tick, predicted_value, tokenA_decimals, tokenB_decimals, pool_fee
)
mint_params = get_mint_params(
user_address,
contracts.tokenA.address,
contracts.tokenB.address,
tokenA_amount,
tokenB_amount,
pool_fee,
lower_tick,
upper_tick,
)
# step 5: mint new position
logger.info("Minting new position...")
contract_result = contracts.nft_manager.mint(mint_params)
logger.info("SUCCESSFULLY MINTED A POSITION")
logger.info("Contract executed")
logger.info(f"Contract result: {contract_result}")
pprint.pprint(contract_result.__dict__)
logger.info("Finished")
if __name__ == "__main__":
# Create the parser
parser = argparse.ArgumentParser()
# Add arguments
parser.add_argument("--model-id", metavar="M", type=int, help="model-id")
parser.add_argument("--version-id", metavar="V", type=int, help="version-id")
parser.add_argument("--tokenA-amount", metavar="A", type=int, help="tokenA-amount")
parser.add_argument("--tokenB-amount", metavar="B", type=int, help="tokenB-amount")
# Parse arguments
args = parser.parse_args()
MODEL_ID = args.model_id
VERSION_ID = args.version_id
tokenA_amount = args.tokenA_amount
tokenB_amount = args.tokenB_amount
rebalance_lp(tokenA_amount, tokenB_amount, MODEL_ID, VERSION_ID)
| https://github.com/gizatechxyz/Giza-Hub |
awesome-giza-agents/uni-v3-lp-agent/addresses.py | # source: https://docs.uniswap.org/contracts/v3/reference/deployments
ADDRESSES = {
"WETH": {
1: "0xc02aaa39b223fe8d0a0e5c4f27ead9083c756cc2",
11155111: "0xfFf9976782d46CC05630D1f6eBAb18b2324d6B14",
5: "0xB4FBF271143F4FBf7B91A5ded31805e42b2208d6",
42161: "0x82aF49447D8a07e3bd95BD0d56f35241523fBab1",
},
"UNI": {
1: "0x1f9840a85d5aF5bf1D1762F925BDADdC4201F984",
11155111: "0x1f9840a85d5aF5bf1D1762F925BDADdC4201F984",
5: "0x1f9840a85d5aF5bf1D1762F925BDADdC4201F984",
42161: "0xFa7F8980b0f1E64A2062791cc3b0871572f1F7f0",
},
"USDC": {
1: "0xa0b86991c6218b36c1d19d4a2e9eb0ce3606eb48",
11155111: "0x1c7D4B196Cb0C7B01d743Fbc6116a902379C7238",
5: "",
42161: "0xaf88d065e77c8cC2239327C5EDb3A432268e5831",
},
"NonfungiblePositionManager": {
1: "0xC36442b4a4522E871399CD717aBDD847Ab11FE88",
11155111: "0x1238536071E1c677A632429e3655c799b22cDA52",
5: "0xC36442b4a4522E871399CD717aBDD847Ab11FE88",
42161: "0xC36442b4a4522E871399CD717aBDD847Ab11FE88",
},
"PoolFactory": {
1: "0x1F98431c8aD98523631AE4a59f267346ea31F984",
11155111: "0x0227628f3F023bb0B980b67D528571c95c6DaC1c",
5: "0x1F98431c8aD98523631AE4a59f267346ea31F984",
42161: "0x1F98431c8aD98523631AE4a59f267346ea31F984",
},
"Router": {
1: "0x68b3465833fb72A70ecDF485E0e4C7bD8665Fc45",
11155111: "0x3bFA4769FB09eefC5a80d6E87c3B9C650f7Ae48E",
5: "0x68b3465833fb72A70ecDF485E0e4C7bD8665Fc45",
42161: "0x68b3465833fb72A70ecDF485E0e4C7bD8665Fc45",
},
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-giza-agents/uni-v3-lp-agent/get_tokens.py | import os
from addresses import ADDRESSES
from ape import Contract, accounts, chain, networks
from dotenv import find_dotenv, load_dotenv
load_dotenv(find_dotenv())
dev_passphrase = os.environ.get("DEV_PASSPHRASE")
sepolia_rpc_url = os.environ.get("SEPOLIA_RPC_URL")
if __name__ == "__main__":
networks.parse_network_choice(f"ethereum:sepolia:{sepolia_rpc_url}").__enter__()
chain_id = chain.chain_id
weth_mint_amount = 0.0001
pool_fee = 3000
uni = Contract(ADDRESSES["UNI"][chain_id])
weth = Contract(ADDRESSES["WETH"][chain_id])
weth_decimals = weth.decimals()
uni_decimals = uni.decimals()
weth_mint_amount = int(weth_mint_amount * 10**weth_decimals)
uni_mint_amount = int(0.5 * weth_mint_amount)
pool_factory = Contract(ADDRESSES["PoolFactory"][chain_id])
pool_address = "0x287B0e934ed0439E2a7b1d5F0FC25eA2c24b64f7"
pool = Contract(pool_address)
swap_router = Contract(ADDRESSES["Router"][chain_id])
wallet = accounts.load("dev")
wallet.set_autosign(True, passphrase=dev_passphrase)
with accounts.use_sender("dev"):
print(f"Minting {weth_mint_amount/10**weth_decimals} WETH")
weth.deposit(value=weth_mint_amount)
print("Approving WETH for swap")
weth.approve(swap_router.address, weth_mint_amount)
swap_params = {
"tokenIn": weth.address,
"tokenOut": uni.address,
"fee": pool_fee,
"recipient": wallet.address,
"amountIn": weth_mint_amount,
"amountOutMinimum": 0,
"sqrtPriceLimitX96": 0,
}
swap_params = tuple(swap_params.values())
print("Swapping WETH for UNI")
amountOut = swap_router.exactInputSingle(swap_params)
print(f"Successfully minted {uni_mint_amount/10**uni_decimals} UNI")
print(f"Your WETH balance: {weth.balanceOf(wallet.address)/10**weth_decimals}")
print(f"Your UNI balance: {uni.balanceOf(wallet.address)/10**uni_decimals}")
| https://github.com/gizatechxyz/Giza-Hub |
awesome-giza-agents/uni-v3-lp-agent/lp_tools.py | import math
MIN_TICK = -887272
MAX_TICK = -MIN_TICK
TICKS_Q = 1.0001
Q96 = 2**96
MAX_UINT_128 = 2 ** (128) - 1
_tick_spacing = {100: 1, 500: 10, 3_000: 60, 10_000: 200}
# https://ethereum.stackexchange.com/questions/150280/calculate-amount-of-eth-and-usdc-after-minting-a-position-in-uniswap-v3
def price_to_tick(price):
sqrtPriceX96 = int(price * 2**96)
tick = math.floor(math.log((sqrtPriceX96 / Q96) ** 2) / math.log(TICKS_Q))
return tick
def tick_to_price(tick, decimals0, decimals1, invert=False):
if invert:
return 1 / (TICKS_Q**tick / 10 ** (decimals1 - decimals0))
else:
return TICKS_Q**tick / 10 ** (decimals1 - decimals0)
def get_min_tick(fee: int):
min_tick_spacing: int = _tick_spacing[fee]
return -(MIN_TICK // -min_tick_spacing) * min_tick_spacing
def get_max_tick(fee: int):
max_tick_spacing: int = _tick_spacing[fee]
return (MAX_TICK // max_tick_spacing) * max_tick_spacing
def default_tick_range(fee: int):
min_tick = get_min_tick(fee)
max_tick = get_max_tick(fee)
return min_tick, max_tick
def nearest_tick(tick: int, fee: int):
min_tick, max_tick = default_tick_range(fee)
assert (
min_tick <= tick <= max_tick
), f"Provided tick is out of bounds: {(min_tick, max_tick)}"
tick_spacing = _tick_spacing[fee]
rounded_tick_spacing = round(tick / tick_spacing) * tick_spacing
if rounded_tick_spacing < min_tick:
return rounded_tick_spacing + tick_spacing
elif rounded_tick_spacing > max_tick:
return rounded_tick_spacing - tick_spacing
else:
return rounded_tick_spacing
def get_tick_range(curr_tick, pct_dev, tokenA_decimals, tokenB_decimals, fee):
curr_price = tick_to_price(curr_tick, tokenA_decimals, tokenB_decimals)
upper_price = curr_price * (1 + pct_dev)
lower_price = curr_price * (1 - pct_dev)
lower_tick = price_to_tick(lower_price)
upper_tick = price_to_tick(upper_price)
lower_tick = nearest_tick(lower_tick, fee)
upper_tick = nearest_tick(upper_tick, fee)
return lower_tick, upper_tick
| https://github.com/gizatechxyz/Giza-Hub |
awesome-giza-agents/uni-v3-lp-agent/model_training.py | import datetime
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
import yfinance as yf
from sklearn.metrics import mean_squared_error as mse
def download_data():
uni_ticker = "UNI-USD"
eth_ticker = "ETH-USD"
start = datetime.datetime(2019, 1, 1)
end = datetime.datetime(2024, 4, 1)
uni = yf.download(uni_ticker, start=start, end=end, interval="1d")
eth = yf.download(eth_ticker, start=start, end=end, interval="1d")
uni = uni.reset_index()
uni.to_csv("uni.csv", index=False)
eth = eth.reset_index()
eth.to_csv("eth.csv", index=False)
return uni, eth
def process_data(uni: pd.DataFrame, eth: pd.DataFrame):
uni = uni[uni["Open"] < 0.30]
uni = uni[["Date", "Open"]]
eth = eth[["Date", "Open"]]
uni.rename(columns={"Open": "UNI"}, inplace=True)
eth.rename(columns={"Open": "ETH"}, inplace=True)
df = pd.merge(uni, eth, on="Date")
df.dropna(inplace=True)
df["price"] = df["ETH"] / df["UNI"]
ret = 100 * (df["price"].pct_change()[1:])
realized_vol = ret.rolling(5).std()
realized_vol = pd.DataFrame(realized_vol)
realized_vol.reset_index(drop=True, inplace=True)
returns_svm = ret**2 # returns squared
returns_svm = returns_svm.reset_index()
X = pd.concat([realized_vol, returns_svm], axis=1, ignore_index=True)
X = X[4:].copy()
X = X.reset_index()
X.drop("index", axis=1, inplace=True)
X.drop(1, axis=1, inplace=True)
X.rename(columns={0: "realized_vol", 2: "returns_squared"}, inplace=True)
X["target"] = X["realized_vol"].shift(-5)
X.dropna(inplace=True)
Y = X["target"]
X.drop("target", axis=1, inplace=True)
n = 252
X_train = X.iloc[:-n]
X_test = X.iloc[-n:]
Y_train = Y.iloc[:-n]
Y_test = Y.iloc[-n:]
return X_train, X_test, Y_train, Y_test
def train_model(
X_train: pd.DataFrame,
X_test: pd.DataFrame,
Y_train: pd.DataFrame,
Y_test: pd.DataFrame,
):
model = nn.Sequential(
nn.Linear(X_train.shape[1], 128),
nn.ReLU(),
nn.Linear(128, 64),
nn.ReLU(),
nn.Linear(64, 1),
)
# Loss and optimizer
criterion = nn.MSELoss()
optimizer = optim.RMSprop(model.parameters())
# Convert data to PyTorch tensors
X_tensor = torch.tensor(X_train.values, dtype=torch.float32)
y_tensor = torch.tensor(Y_train.values.reshape(-1, 1), dtype=torch.float32)
X_test_tensor = torch.tensor(X_test.values, dtype=torch.float32)
# Training loop
epochs_trial = np.arange(100, 400, 4)
batch_trial = np.arange(100, 400, 4)
DL_pred = []
DL_RMSE = []
for i, j, k in zip(range(4), epochs_trial, batch_trial):
for epoch in range(j):
optimizer.zero_grad()
outputs = model(X_tensor)
loss = criterion(outputs, y_tensor)
loss.backward()
optimizer.step()
with torch.no_grad():
DL_predict = model(X_test_tensor).numpy()
DL_RMSE.append(
np.sqrt(mse(Y_test.values / 100, DL_predict.flatten() / 100))
)
DL_pred.append(DL_predict)
print("DL_RMSE_{}:{:.6f}".format(i + 1, DL_RMSE[i]))
return model
def serialize_to_onnx(
model: nn.Module, X_train: pd.DataFrame, save_path="torch_vol_model"
):
# Ensure the model is in evaluation mode
model.eval()
# Dummy input matching the input size
sample_input = torch.randn(
1, X_train.shape[1]
) # Replace 1 with the batch size you'd like to use
# Specify the path to save the ONNX model
onnx_file_path = save_path + ".onnx"
torch.onnx.export(
model,
sample_input,
onnx_file_path,
export_params=True,
opset_version=10,
do_constant_folding=True,
input_names=["input"],
output_names=["output"],
dynamic_axes={
"input": {0: "batch_size"},
"output": {0: "batch_size"},
},
)
print(f"Saved serialized ONNX model to {onnx_file_path}.")
def main():
uni, eth = download_data()
X_train, X_test, Y_train, Y_test = process_data(uni, eth)
model = train_model(X_train, X_test, Y_train, Y_test)
serialize_to_onnx(model, X_train)
if __name__ == "__main__":
main()
| https://github.com/gizatechxyz/Giza-Hub |
awesome-giza-agents/uni-v3-lp-agent/uni_helpers.py | import os
import time
from ape.contracts.base import ContractInstance
from dotenv import find_dotenv, load_dotenv
from giza_actions.task import task
from lp_tools import MAX_UINT_128
load_dotenv(find_dotenv())
dev_passphrase = os.environ.get("DEV_PASSPHRASE")
sepolia_rpc_url = os.environ.get("SEPOLIA_RPC_URL")
@task(name="Check allowance")
def check_allowance(token: ContractInstance, spender: str, account: str, amount: int):
return token.allowance(account, spender) >= amount
@task(name="Approve token spend")
def approve_token(token: ContractInstance, spender: str, amount: int):
return token.approve(spender, amount)
@task(name="Get mint parameters")
def get_mint_params(
user_address: str,
tokenA_address: str,
tokenB_address: str,
amount0: int,
amount1: int,
pool_fee: int,
lower_tick: int,
upper_tick: int,
deadline=None,
slippage_tolerance=0.01,
):
if deadline is None:
deadline = int(time.time()) + 60
mint_params = {
"token0": tokenA_address,
"token1": tokenB_address,
"fee": pool_fee,
"tickLower": lower_tick,
"tickUpper": upper_tick,
"amount0Desired": amount0,
"amount1Desired": amount1,
"amount0Min": 0, # int(amount0 * (1 - slippage_tolerance)),
"amount1Min": 0, # int(amount1 * (1 - slippage_tolerance)),
"recipient": user_address,
"deadline": deadline,
}
return tuple(mint_params.values())
@task(name="Get all user LP positions")
def get_all_user_positions(nft_manager: ContractInstance, user_address: str):
n_positions = nft_manager.balanceOf(user_address)
positions = []
for n in range(n_positions):
position = nft_manager.tokenOfOwnerByIndex(user_address, n)
positions.append(position)
return positions
def get_pos_liquidity(nft_manager: ContractInstance, nft_id: int):
(
nonce,
operator,
token0,
token1,
fee,
tickLower,
tickUpper,
liquidity,
feeGrowthInside0LastX128,
feeGrowthInside1LastX128,
tokensOwed0,
tokensOwed1,
) = nft_manager.positions(nft_id)
return liquidity
@task(name="Close the position")
def close_position(user_address: str, nft_manager: ContractInstance, nft_id: int):
liq = get_pos_liquidity(nft_manager, nft_id)
if liq > 0:
nft_manager.decreaseLiquidity((nft_id, liq, 0, 0, int(time.time() + 60)))
nft_manager.collect((nft_id, user_address, MAX_UINT_128, MAX_UINT_128))
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/mnist_nn/QAT_MNIST_MLP.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "04w3OowXkaQh"
},
"source": [
"# Build Your Neural Network In Cairo 1.0 with Orion\n",
"\n",
"Orion is a dedicated Cairo-based library designed specifically to build machine learning models for ValidityML. Its purpose is to facilitate verifiable inference. For better performance we will operate with an 8-bit quantized model. In this tutorial, you will be guided on how to train your model using Quantized Aware Training using MNIST dataset, how to convert your pre-trained model to Cairo 1, and how to perform inference with Orion."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Install Dependecies, Rust, Cairo and Scarb\n",
"Let's start by installing all dependecies."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "1Sfci39Llvii"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"import sys\n",
"!{sys.executable} -m pip install numpy tensorflow tensorflow_model_optimization matplotlib scipy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GwTsquVDEe4p"
},
"outputs": [],
"source": [
"!curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "uBsZQCRBExEc",
"outputId": "54cbbc65-854e-4afa-b809-d2fc838f6ecf"
},
"outputs": [],
"source": [
"# Create .cairo folder if it doesn't exist yet\n",
"! mkdir $HOME/.cairo\n",
"\n",
"! source \"$HOME/.cargo/env\"\n",
"\n",
"# Clone the Cairo compiler in $CAIRO_ROOT (default root)\n",
"! cd $HOME/.cairo && git clone https://github.com/starkware-libs/cairo.git .\n",
"\n",
"# OPTIONAL/RECOMMENDED: If you want to install a specific version of the compiler\n",
"# Fetch all tags (versions)\n",
"! git fetch --all --tags\n",
"# View tags (you can also do this in the cairo compiler repository)\n",
"! git describe --tags `git rev-list --tags`\n",
"# Checkout the version you want\n",
"! git checkout tags/v1.1.0\n",
"\n",
"# Generate release binaries\n",
"! cd $HOME/.cairo && $HOME/.cargo/bin/cargo build --all --release"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Install Scarb\n",
"! curl --proto '=https' --tlsv1.2 -sSf https://docs.swmansion.com/scarb/install.sh | sh"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train and Test your Neural Network with Tensorflow\n",
"In this section we will use Tensorflow to train and test a feedforward neural network with MNIST dataset."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "nKiq8oKxklon"
},
"source": [
"### Dataset Preparation\n",
"Import the required libraries and load the dataset."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "4po2PWTAkWOR"
},
"outputs": [],
"source": [
"from tensorflow import keras\n",
"from keras.datasets import mnist\n",
"from scipy.ndimage import zoom\n",
"import numpy as np\n",
"\n",
"(x_train, y_train), (x_test, y_test) = mnist.load_data()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NXigQHM_k0ux"
},
"source": [
"We have a total of 70,000 grayscale images, each with a dimension of 28 x 28 pixels. 60,000 images are for training and the remaining 10,000 are for testing. \n",
"\n",
"We now need to pre-process our data. For the purposes of this tutorial and performance, we'll resize the images to 14 x 14 pixels. You'll see later that the neural network's input layer supports a 1D tensor. We, therefore, need to flatten and normalize our data."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "6AwxawnnkaA5"
},
"outputs": [],
"source": [
"from scipy.ndimage import zoom\n",
"\n",
"# Resizing function\n",
"def resize_images(images):\n",
" return np.array([zoom(image, 0.5) for image in images])\n",
"\n",
"# Resize\n",
"x_train = resize_images(x_train)\n",
"x_test = resize_images(x_test)\n",
"\n",
"# Then reshape\n",
"x_train = x_train.reshape(60000, 14*14)\n",
"x_test = x_test.reshape(10000, 14*14)\n",
"x_train = x_train.astype('float32')\n",
"x_test = x_test.astype('float32')\n",
"\n",
"# normalize to range [0, 1]\n",
"x_train /= 255\n",
"x_test /= 255"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "ix3qnUgElDlB"
},
"source": [
"### Model Definition and Training\n",
"We will design a straightforward feedforward neural network."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "7gilDCS6k9-u"
},
"outputs": [],
"source": [
"from tensorflow.keras import layers\n",
"\n",
"num_classes = 10\n",
"\n",
"model = keras.Sequential([\n",
" keras.layers.InputLayer(input_shape=(14*14,)),\n",
" keras.layers.Dense(10, activation='relu'), \n",
" keras.layers.Dense(10, activation='softmax')\n",
"])\n",
"\n",
"model.compile(optimizer='adam', \n",
" loss='sparse_categorical_crossentropy', \n",
" metrics=['accuracy'])\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BFUZZOudlQmP"
},
"source": [
"Now let's train this model on our training data."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "jjkH102GlOd2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"1500/1500 [==============================] - 1s 449us/step - loss: 0.8094 - accuracy: 0.7795 - val_loss: 0.3885 - val_accuracy: 0.8953\n",
"Epoch 2/10\n",
"1500/1500 [==============================] - 1s 399us/step - loss: 0.3693 - accuracy: 0.8961 - val_loss: 0.3152 - val_accuracy: 0.9122\n",
"Epoch 3/10\n",
"1500/1500 [==============================] - 1s 402us/step - loss: 0.3239 - accuracy: 0.9072 - val_loss: 0.2905 - val_accuracy: 0.9185\n",
"Epoch 4/10\n",
"1500/1500 [==============================] - 1s 403us/step - loss: 0.3041 - accuracy: 0.9130 - val_loss: 0.2842 - val_accuracy: 0.9192\n",
"Epoch 5/10\n",
"1500/1500 [==============================] - 1s 428us/step - loss: 0.2931 - accuracy: 0.9162 - val_loss: 0.2716 - val_accuracy: 0.9237\n",
"Epoch 6/10\n",
"1500/1500 [==============================] - 1s 406us/step - loss: 0.2849 - accuracy: 0.9193 - val_loss: 0.2689 - val_accuracy: 0.9228\n",
"Epoch 7/10\n",
"1500/1500 [==============================] - 1s 407us/step - loss: 0.2791 - accuracy: 0.9202 - val_loss: 0.2645 - val_accuracy: 0.9259\n",
"Epoch 8/10\n",
"1500/1500 [==============================] - 1s 401us/step - loss: 0.2747 - accuracy: 0.9215 - val_loss: 0.2608 - val_accuracy: 0.9269\n",
"Epoch 9/10\n",
"1500/1500 [==============================] - 1s 401us/step - loss: 0.2705 - accuracy: 0.9226 - val_loss: 0.2585 - val_accuracy: 0.9283\n",
"Epoch 10/10\n",
"1500/1500 [==============================] - 1s 401us/step - loss: 0.2674 - accuracy: 0.9236 - val_loss: 0.2582 - val_accuracy: 0.9283\n"
]
}
],
"source": [
"batch_size = 256\n",
"epochs = 10\n",
"history = model.fit(x_train, y_train,\n",
" epochs=epochs,\n",
" validation_split=0.2)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uNRdKGpflar4"
},
"source": [
"At this point, we have trained a regular model.\n",
"\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "7Bu55FCqlbj4"
},
"source": [
"### Making the Model Quantization Aware\n",
"Now, let's transform our model into a quantization aware model. We use the TensorFlow Model Optimization Toolkit for this.\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "iAZYo9vKlTHK"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" quantize_layer (QuantizeLa (None, 196) 3 \n",
" yer) \n",
" \n",
" quant_dense (QuantizeWrapp (None, 10) 1975 \n",
" erV2) \n",
" \n",
" quant_dense_1 (QuantizeWra (None, 10) 115 \n",
" pperV2) \n",
" \n",
"=================================================================\n",
"Total params: 2093 (8.18 KB)\n",
"Trainable params: 2080 (8.12 KB)\n",
"Non-trainable params: 13 (52.00 Byte)\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"import tensorflow_model_optimization as tfmot\n",
"\n",
"# Apply quantization to the layers\n",
"quantize_model = tfmot.quantization.keras.quantize_model\n",
"\n",
"# q_aware stands for 'quantization aware'\n",
"q_aware_model = quantize_model(model)\n",
"\n",
"# 'quantize_model' requires a recompile\n",
"q_aware_model.compile(optimizer='adam',\n",
" loss='sparse_categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"\n",
"q_aware_model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eYPUWWwTl_np"
},
"source": [
"We have now created a new model, q_aware_model, which is a quantization aware version of our original model. Now we can train this model exactly like our original model."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "U-t5MPhGlqoI"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"1500/1500 [==============================] - 1s 526us/step - loss: 0.2671 - accuracy: 0.9236 - val_loss: 0.2581 - val_accuracy: 0.9283\n",
"Epoch 2/10\n",
"1500/1500 [==============================] - 1s 478us/step - loss: 0.2625 - accuracy: 0.9235 - val_loss: 0.2538 - val_accuracy: 0.9293\n",
"Epoch 3/10\n",
"1500/1500 [==============================] - 1s 491us/step - loss: 0.2602 - accuracy: 0.9246 - val_loss: 0.2521 - val_accuracy: 0.9308\n",
"Epoch 4/10\n",
"1500/1500 [==============================] - 1s 481us/step - loss: 0.2579 - accuracy: 0.9265 - val_loss: 0.2541 - val_accuracy: 0.9296\n",
"Epoch 5/10\n",
"1500/1500 [==============================] - 1s 481us/step - loss: 0.2557 - accuracy: 0.9271 - val_loss: 0.2490 - val_accuracy: 0.9317\n",
"Epoch 6/10\n",
"1500/1500 [==============================] - 1s 482us/step - loss: 0.2540 - accuracy: 0.9274 - val_loss: 0.2492 - val_accuracy: 0.9313\n",
"Epoch 7/10\n",
"1500/1500 [==============================] - 1s 497us/step - loss: 0.2522 - accuracy: 0.9277 - val_loss: 0.2495 - val_accuracy: 0.9296\n",
"Epoch 8/10\n",
"1500/1500 [==============================] - 1s 491us/step - loss: 0.2505 - accuracy: 0.9277 - val_loss: 0.2472 - val_accuracy: 0.9306\n",
"Epoch 9/10\n",
"1500/1500 [==============================] - 1s 479us/step - loss: 0.2491 - accuracy: 0.9288 - val_loss: 0.2461 - val_accuracy: 0.9337\n",
"Epoch 10/10\n",
"1500/1500 [==============================] - 1s 483us/step - loss: 0.2477 - accuracy: 0.9285 - val_loss: 0.2444 - val_accuracy: 0.9325\n",
"Test loss: 0.25082069635391235\n",
"Test accuracy: 0.9277999997138977\n"
]
}
],
"source": [
"batch_size = 256\n",
"epochs = 10\n",
"history = q_aware_model.fit(x_train, y_train,\n",
" epochs=epochs,\n",
" validation_split=0.2)\n",
"\n",
"scores, acc = q_aware_model.evaluate(x_test, y_test, verbose=0)\n",
"print('Test loss:', scores)\n",
"print('Test accuracy:', acc)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "XhbweTQEmWN-"
},
"source": [
"### Converting to TFLite Format\n",
"Now, we will convert our model to TFLite format, which is a format optimized for on-device machine learning."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "uOwZiCRWmHDT"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: /var/folders/s3/6c0gmns50x36dt6vvfhv6jhc0000gn/T/tmpwwestso3/assets\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: /var/folders/s3/6c0gmns50x36dt6vvfhv6jhc0000gn/T/tmpwwestso3/assets\n",
"/Users/raphaeldoukhan/Desktop/Orion-Giza/Academy/Tutorials/orion_tutorials/.conda/lib/python3.10/site-packages/tensorflow/lite/python/convert.py:887: UserWarning: Statistics for quantized inputs were expected, but not specified; continuing anyway.\n",
" warnings.warn(\n",
"2023-09-08 08:57:11.760815: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:364] Ignored output_format.\n",
"2023-09-08 08:57:11.760826: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:367] Ignored drop_control_dependency.\n",
"2023-09-08 08:57:11.761006: I tensorflow/cc/saved_model/reader.cc:45] Reading SavedModel from: /var/folders/s3/6c0gmns50x36dt6vvfhv6jhc0000gn/T/tmpwwestso3\n",
"2023-09-08 08:57:11.761945: I tensorflow/cc/saved_model/reader.cc:91] Reading meta graph with tags { serve }\n",
"2023-09-08 08:57:11.761951: I tensorflow/cc/saved_model/reader.cc:132] Reading SavedModel debug info (if present) from: /var/folders/s3/6c0gmns50x36dt6vvfhv6jhc0000gn/T/tmpwwestso3\n",
"2023-09-08 08:57:11.764080: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:375] MLIR V1 optimization pass is not enabled\n",
"2023-09-08 08:57:11.765054: I tensorflow/cc/saved_model/loader.cc:231] Restoring SavedModel bundle.\n",
"2023-09-08 08:57:11.795103: I tensorflow/cc/saved_model/loader.cc:215] Running initialization op on SavedModel bundle at path: /var/folders/s3/6c0gmns50x36dt6vvfhv6jhc0000gn/T/tmpwwestso3\n",
"2023-09-08 08:57:11.804542: I tensorflow/cc/saved_model/loader.cc:314] SavedModel load for tags { serve }; Status: success: OK. Took 43536 microseconds.\n",
"2023-09-08 08:57:11.814757: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:255] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
"fully_quantize: 0, inference_type: 6, input_inference_type: INT8, output_inference_type: INT8\n"
]
},
{
"data": {
"text/plain": [
"4312"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import tensorflow as tf\n",
"\n",
"# Create a converter\n",
"converter = tf.lite.TFLiteConverter.from_keras_model(q_aware_model)\n",
"\n",
"# Indicate that you want to perform default optimizations,\n",
"# which include quantization\n",
"converter.optimizations = [tf.lite.Optimize.DEFAULT]\n",
"\n",
"# Define a generator function that provides your test data's numpy arrays\n",
"def representative_data_gen():\n",
" for i in range(500):\n",
" yield [x_test[i:i+1]]\n",
"\n",
"# Use the generator function to guide the quantization process\n",
"converter.representative_dataset = representative_data_gen\n",
"\n",
"# Ensure that if any ops can't be quantized, the converter throws an error\n",
"converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\n",
"\n",
"# Set the input and output tensors to int8\n",
"converter.inference_input_type = tf.int8\n",
"converter.inference_output_type = tf.int8\n",
"\n",
"# Convert the model\n",
"tflite_model = converter.convert()\n",
"\n",
"# Save the model to disk\n",
"open(\"q_aware_model.tflite\", \"wb\").write(tflite_model)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "zxGWitSs3MAH"
},
"source": [
"### Testing the Quantized Model\n",
"Now that we have trained a quantization-aware model and converted it to the TFLite format, we can now perform inference using the TensorFlow Lite interpreter.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8qbfjJFa3Zy7"
},
"source": [
"We first load the TFLite model and allocate the required tensors. The Interpreter class provides methods for loading a model and running inferences.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "sXEM3WEv3SSt"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO: Created TensorFlow Lite XNNPACK delegate for CPU.\n"
]
}
],
"source": [
"# Load the TFLite model and allocate tensors.\n",
"interpreter = tf.lite.Interpreter(model_path=\"q_aware_model.tflite\")\n",
"interpreter.allocate_tensors()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mP6WRo1L3dZp"
},
"source": [
"Next, we get the details of the input and output tensors. Each tensor in a TensorFlow Lite model has a name, index, shape, data type, and quantization parameters. These can be accessed via the input_details and output_details methods."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "T7r8JFTC3Xxe"
},
"outputs": [],
"source": [
"# Get input and output tensors.\n",
"input_details = interpreter.get_input_details()\n",
"output_details = interpreter.get_output_details()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ofFMDXq13ix7"
},
"source": [
"Before performing the inference, we need to normalize the input to match the data type of our model's input tensor, which in our case is int8. Then, we use the set_tensor method to provide the input data to the model. We perform the inference using the invoke method."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "-1yOJjl83b9i"
},
"outputs": [],
"source": [
"# Normalize the input value to int8\n",
"input_shape = input_details[0]['shape']\n",
"input_data = np.array(x_test[0:1], dtype=np.int8)\n",
"interpreter.set_tensor(input_details[0]['index'], input_data)\n",
"\n",
"# Perform the inference\n",
"interpreter.invoke()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ge-Y3K1J3oWs"
},
"source": [
"After the inference, we get the output data from the model's output tensor.\n",
"\n",
"Now, we are going to run the inference for the entire test set:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "MtfTBPCl3iKm"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[-122 -128 -22 -67 -128 -47 -128 -127 -128 -128]]\n"
]
}
],
"source": [
"# Get the result\n",
"output_data = interpreter.get_tensor(output_details[0]['index'])\n",
"print(output_data)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JO9fwtAF3sRP"
},
"source": [
"We normalize the entire test set and initialize an array to store the predictions.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"id": "B5fDA1Vt3num"
},
"outputs": [],
"source": [
"(_, _), (x_test_image, y_test_label) = mnist.load_data()\n",
"\n",
"# Resize and Normalize x_test_image to int8\n",
"x_test_image = resize_images(x_test_image)\n",
"x_test_image_norm = (x_test_image / 255.0 * 255 - 128).astype(np.int8)\n",
"\n",
"# Initialize an array to store the predictions\n",
"predictions = []\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "l1PzXSuj3uy9"
},
"source": [
"We then iterate over the test set, making predictions for each image. For each image, we flatten the image, normalize it, and then expand its dimensions to match the shape of our model's input tensor.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"id": "z1Clh4Kt3fuF"
},
"outputs": [],
"source": [
"# Iterate over the test data and make predictions\n",
"for i in range(len(x_test_image_norm)):\n",
" test_image = np.expand_dims(x_test_image_norm[i].flatten(), axis=0)\n",
" \n",
" # Set the value for the input tensor\n",
" interpreter.set_tensor(input_details[0]['index'], test_image)\n",
" \n",
" # Run the inference\n",
" interpreter.invoke()\n",
"\n",
" output = interpreter.get_tensor(output_details[0]['index'])\n",
" predictions.append(output)\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we use a function to plot the test images along with their predicted labels. This will give us a visual representation of how well our model is performing."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"id": "5wWyve_E3uL8"
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1200x1400 with 25 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"def plot_images_labels_prediction(images,labels,idx,num=10):\n",
" fig=plt.gcf()\n",
" fig.set_size_inches(12, 14)\n",
" if num > 25: num=25\n",
" for i in range(0, num):\n",
" ax=plt.subplot(5, 5, i+1)\n",
" ax.imshow(images[idx], cmap='binary')\n",
" title=\"label=\" + str(labels[idx])\n",
" ax.set_title(title, fontsize=10)\n",
" ax.set_xticks([]);\n",
" ax.set_yticks([]);\n",
" idx += 1\n",
" plt.show()\n",
"\n",
"plot_images_labels_prediction(x_test_image, y_test_label, 0, 25)\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "VOHKHUMp30j8"
},
"source": [
"That's it! We have successfully trained a quantization-aware model, converted it to the TFLite format, and performed inference using the TensorFlow Lite interpreter."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "ywhXM1mA-a7F"
},
"source": [
"## Convert your model to Orion's Cairo code\n",
"In this section you will generate Cairo files for each bias and weight of the model. "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"id": "wP_kVSuEKA1U"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"import os\n",
"\n",
"\n",
"# Load the TFLite model and allocate tensors.\n",
"interpreter = tf.lite.Interpreter(model_path=\"q_aware_model.tflite\")\n",
"interpreter.allocate_tensors()\n",
"\n",
"# Create an object with all tensors (an input + all weights and biases)\n",
"tensors = {\n",
" \"input\": x_test_image[0].flatten(), #7\n",
" \"fc1_weights\": interpreter.get_tensor(1), \n",
" \"fc1_bias\": interpreter.get_tensor(2), \n",
" \"fc2_weights\": interpreter.get_tensor(4), \n",
" \"fc2_bias\": interpreter.get_tensor(5)\n",
"}\n",
"\n",
"# Create the directory if it doesn't exist\n",
"os.makedirs('src/generated', exist_ok=True)\n",
"\n",
"for tensor_name, tensor in tensors.items():\n",
" with open(os.path.join('src', 'generated', f\"{tensor_name}.cairo\"), \"w\") as f:\n",
" f.write(\n",
" \"use array::ArrayTrait;\\n\" +\n",
" \"use orion::operators::tensor::{TensorTrait, Tensor, I32Tensor};\\n\" +\n",
" \"use orion::numbers::i32;\\n\\n\" +\n",
" \"\\nfn {0}() -> Tensor<i32> \".format(tensor_name) + \"{\\n\" +\n",
" \" let mut shape = ArrayTrait::<usize>::new();\\n\"\n",
" )\n",
" for dim in tensor.shape:\n",
" f.write(\" shape.append({0});\\n\".format(dim))\n",
" f.write(\n",
" \" let mut data = ArrayTrait::<i32>::new();\\n\"\n",
" )\n",
" for val in np.nditer(tensor.flatten()):\n",
" f.write(\" data.append(i32 {{ mag: {0}, sign: {1} }});\\n\".format(abs(int(val)), str(val < 0).lower()))\n",
" f.write(\n",
" \" TensorTrait::new(shape.span(), data.span())\\n\" +\n",
" \"}\\n\"\n",
" )\n",
" \n",
"with open(os.path.join('src', 'generated.cairo'), 'w') as f:\n",
" for param_name in tensors.keys():\n",
" f.write(f\"mod {param_name};\\n\")\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"id": "iobaWNzdW4Jq"
},
"source": [
"## Build your NN with Cairo and Orion\n",
"In this section you will perform inference with Cairo and Orion.\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Create the `nn.cairo` file in which we'll build the neural network functions."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"id": "x2slaqnnUWlB"
},
"outputs": [],
"source": [
"! touch src/nn.cairo"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's create the two dense layer functions of the neural network: `fc1` and `fc2`."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"id": "UMF0u2gcUko9"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting src/nn.cairo\n"
]
}
],
"source": [
"%%writefile src/nn.cairo\n",
"use orion::operators::tensor::core::Tensor;\n",
"use orion::numbers::signed_integer::{integer_trait::IntegerTrait, i32::i32};\n",
"use orion::operators::nn::{NNTrait, I32NN};\n",
"\n",
"fn fc1(i: Tensor<i32>, w: Tensor<i32>, b: Tensor<i32>) -> Tensor<i32> {\n",
" let x = NNTrait::linear(i, w, b);\n",
" NNTrait::relu(@x)\n",
"}\n",
"\n",
"fn fc2(i: Tensor<i32>, w: Tensor<i32>, b: Tensor<i32>) -> Tensor<i32> {\n",
" NNTrait::linear(i, w, b)\n",
"}\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"We will make predictions in a test. First, create the testing file."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"id": "OF8ANN0IU0BL"
},
"outputs": [],
"source": [
"! touch src/test.cairo"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's now define the input data and parameters generated earlier, and set the neural network.\n",
"The input data represents the number 7. The probability at index 7 must therefore be close to 1."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"id": "FsWjlfyeWJF8"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting src/test.cairo\n"
]
}
],
"source": [
"%%writefile src/test.cairo\n",
"use core::array::SpanTrait;\n",
"\n",
"use mnist_nn::nn::fc1;\n",
"use mnist_nn::nn::fc2;\n",
"use mnist_nn::generated::input::input;\n",
"use mnist_nn::generated::fc1_bias::fc1_bias;\n",
"use mnist_nn::generated::fc1_weights::fc1_weights;\n",
"use mnist_nn::generated::fc2_bias::fc2_bias;\n",
"use mnist_nn::generated::fc2_weights::fc2_weights;\n",
"\n",
"use orion::operators::tensor::I32Tensor;\n",
"\n",
"#[test]\n",
"#[available_gas(99999999999999999)]\n",
"fn mnist_nn_test() {\n",
" let input = input();\n",
" let fc1_bias = fc1_bias();\n",
" let fc1_weights = fc1_weights();\n",
" let fc2_bias = fc2_bias();\n",
" let fc2_weights = fc2_weights();\n",
"\n",
" let x = fc1(input, fc1_weights, fc1_bias);\n",
" let x = fc2(x, fc2_weights, fc2_bias);\n",
"\n",
" let x = *x.argmax(0, Option::None(()), Option::None(())).data.at(0);\n",
"\n",
" assert(x == 7, 'should predict 7');\n",
"}\n",
"\n"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Run the following cell to test your file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lkep4mVkWMtS"
},
"outputs": [],
"source": [
"! scarb run test"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/mnist_nn/src/generated.cairo | mod input;
mod fc1_weights;
mod fc1_bias;
mod fc2_weights;
mod fc2_bias;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/mnist_nn/src/generated/fc1_bias.cairo | use array::ArrayTrait;
use orion::operators::tensor::{TensorTrait, Tensor, I32Tensor};
use orion::numbers::i32;
fn fc1_bias() -> Tensor<i32> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(10);
let mut data = ArrayTrait::<i32>::new();
data.append(i32 { mag: 1287, sign: false });
data.append(i32 { mag: 3667, sign: true });
data.append(i32 { mag: 2954, sign: false });
data.append(i32 { mag: 7938, sign: false });
data.append(i32 { mag: 3959, sign: false });
data.append(i32 { mag: 5862, sign: true });
data.append(i32 { mag: 4886, sign: false });
data.append(i32 { mag: 4992, sign: false });
data.append(i32 { mag: 10126, sign: false });
data.append(i32 { mag: 2237, sign: true });
TensorTrait::new(shape.span(), data.span())
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/mnist_nn/src/generated/fc1_weights.cairo | use array::ArrayTrait;
use orion::operators::tensor::{TensorTrait, Tensor, I32Tensor};
use orion::numbers::i32;
fn fc1_weights() -> Tensor<i32> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(10);
shape.append(196);
let mut data = ArrayTrait::<i32>::new();
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 23, sign: false });
data.append(i32 { mag: 11, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 20, sign: true });
data.append(i32 { mag: 25, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 30, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 23, sign: false });
data.append(i32 { mag: 55, sign: false });
data.append(i32 { mag: 46, sign: false });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 42, sign: true });
data.append(i32 { mag: 14, sign: true });
data.append(i32 { mag: 11, sign: true });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 31, sign: false });
data.append(i32 { mag: 31, sign: false });
data.append(i32 { mag: 53, sign: false });
data.append(i32 { mag: 51, sign: false });
data.append(i32 { mag: 27, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 33, sign: true });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 28, sign: true });
data.append(i32 { mag: 25, sign: true });
data.append(i32 { mag: 21, sign: true });
data.append(i32 { mag: 20, sign: true });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 35, sign: false });
data.append(i32 { mag: 75, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 28, sign: true });
data.append(i32 { mag: 57, sign: true });
data.append(i32 { mag: 44, sign: true });
data.append(i32 { mag: 28, sign: true });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 35, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 45, sign: true });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 53, sign: true });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 12, sign: true });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 10, sign: true });
data.append(i32 { mag: 10, sign: true });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 18, sign: true });
data.append(i32 { mag: 32, sign: true });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 26, sign: false });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 16, sign: false });
data.append(i32 { mag: 46, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 21, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 31, sign: false });
data.append(i32 { mag: 32, sign: false });
data.append(i32 { mag: 23, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 23, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 35, sign: true });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 27, sign: false });
data.append(i32 { mag: 50, sign: false });
data.append(i32 { mag: 45, sign: false });
data.append(i32 { mag: 23, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 37, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 19, sign: true });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 41, sign: false });
data.append(i32 { mag: 25, sign: false });
data.append(i32 { mag: 16, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 33, sign: true });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 22, sign: true });
data.append(i32 { mag: 23, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 23, sign: false });
data.append(i32 { mag: 21, sign: false });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 17, sign: false });
data.append(i32 { mag: 34, sign: true });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 61, sign: true });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 13, sign: false });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 32, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 71, sign: true });
data.append(i32 { mag: 78, sign: true });
data.append(i32 { mag: 63, sign: true });
data.append(i32 { mag: 74, sign: true });
data.append(i32 { mag: 49, sign: true });
data.append(i32 { mag: 54, sign: true });
data.append(i32 { mag: 29, sign: true });
data.append(i32 { mag: 29, sign: true });
data.append(i32 { mag: 36, sign: true });
data.append(i32 { mag: 37, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 17, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 17, sign: false });
data.append(i32 { mag: 19, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 32, sign: false });
data.append(i32 { mag: 36, sign: false });
data.append(i32 { mag: 38, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 19, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 35, sign: false });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 37, sign: false });
data.append(i32 { mag: 25, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 33, sign: true });
data.append(i32 { mag: 31, sign: true });
data.append(i32 { mag: 17, sign: true });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 17, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 28, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 11, sign: true });
data.append(i32 { mag: 25, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 13, sign: false });
data.append(i32 { mag: 19, sign: false });
data.append(i32 { mag: 16, sign: false });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 16, sign: false });
data.append(i32 { mag: 36, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 29, sign: true });
data.append(i32 { mag: 12, sign: true });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 26, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 13, sign: false });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 42, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 16, sign: false });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 34, sign: false });
data.append(i32 { mag: 45, sign: false });
data.append(i32 { mag: 31, sign: false });
data.append(i32 { mag: 35, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 17, sign: true });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 25, sign: false });
data.append(i32 { mag: 39, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 32, sign: true });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 40, sign: false });
data.append(i32 { mag: 49, sign: false });
data.append(i32 { mag: 27, sign: false });
data.append(i32 { mag: 13, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 65, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 23, sign: false });
data.append(i32 { mag: 41, sign: true });
data.append(i32 { mag: 25, sign: true });
data.append(i32 { mag: 27, sign: true });
data.append(i32 { mag: 18, sign: true });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 12, sign: true });
data.append(i32 { mag: 14, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 11, sign: true });
data.append(i32 { mag: 12, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 23, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 17, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 33, sign: true });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 19, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 16, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 12, sign: true });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 53, sign: true });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 21, sign: true });
data.append(i32 { mag: 41, sign: true });
data.append(i32 { mag: 17, sign: true });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 16, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 11, sign: true });
data.append(i32 { mag: 44, sign: true });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 37, sign: true });
data.append(i32 { mag: 32, sign: true });
data.append(i32 { mag: 14, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 31, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 10, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 21, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 30, sign: false });
data.append(i32 { mag: 16, sign: true });
data.append(i32 { mag: 11, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 19, sign: false });
data.append(i32 { mag: 16, sign: false });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 23, sign: false });
data.append(i32 { mag: 26, sign: false });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 14, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 21, sign: false });
data.append(i32 { mag: 42, sign: false });
data.append(i32 { mag: 55, sign: false });
data.append(i32 { mag: 97, sign: false });
data.append(i32 { mag: 125, sign: false });
data.append(i32 { mag: 68, sign: false });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 21, sign: false });
data.append(i32 { mag: 47, sign: false });
data.append(i32 { mag: 42, sign: false });
data.append(i32 { mag: 46, sign: false });
data.append(i32 { mag: 27, sign: false });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 28, sign: false });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 21, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 26, sign: false });
data.append(i32 { mag: 23, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 31, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 25, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 44, sign: false });
data.append(i32 { mag: 26, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 33, sign: true });
data.append(i32 { mag: 40, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 18, sign: true });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 14, sign: true });
data.append(i32 { mag: 19, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 10, sign: true });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 12, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 21, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 21, sign: true });
data.append(i32 { mag: 22, sign: true });
data.append(i32 { mag: 12, sign: true });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 10, sign: true });
data.append(i32 { mag: 28, sign: true });
data.append(i32 { mag: 32, sign: true });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 38, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 22, sign: true });
data.append(i32 { mag: 27, sign: true });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 14, sign: true });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 17, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 50, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 39, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 10, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 54, sign: false });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 23, sign: true });
data.append(i32 { mag: 43, sign: false });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 17, sign: true });
data.append(i32 { mag: 23, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 16, sign: true });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 56, sign: false });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 19, sign: false });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 44, sign: false });
data.append(i32 { mag: 21, sign: false });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 40, sign: false });
data.append(i32 { mag: 34, sign: false });
data.append(i32 { mag: 19, sign: false });
data.append(i32 { mag: 42, sign: false });
data.append(i32 { mag: 37, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 22, sign: true });
data.append(i32 { mag: 35, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 27, sign: false });
data.append(i32 { mag: 32, sign: false });
data.append(i32 { mag: 27, sign: false });
data.append(i32 { mag: 26, sign: false });
data.append(i32 { mag: 30, sign: false });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 27, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 80, sign: false });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 27, sign: true });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 26, sign: false });
data.append(i32 { mag: 44, sign: false });
data.append(i32 { mag: 68, sign: false });
data.append(i32 { mag: 32, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 13, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 19, sign: false });
data.append(i32 { mag: 31, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 26, sign: true });
data.append(i32 { mag: 46, sign: true });
data.append(i32 { mag: 60, sign: true });
data.append(i32 { mag: 84, sign: true });
data.append(i32 { mag: 84, sign: true });
data.append(i32 { mag: 95, sign: true });
data.append(i32 { mag: 70, sign: true });
data.append(i32 { mag: 36, sign: true });
data.append(i32 { mag: 26, sign: true });
data.append(i32 { mag: 38, sign: true });
data.append(i32 { mag: 39, sign: true });
data.append(i32 { mag: 27, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 49, sign: true });
data.append(i32 { mag: 63, sign: true });
data.append(i32 { mag: 23, sign: true });
data.append(i32 { mag: 35, sign: true });
data.append(i32 { mag: 18, sign: true });
data.append(i32 { mag: 14, sign: true });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 17, sign: true });
data.append(i32 { mag: 16, sign: true });
data.append(i32 { mag: 18, sign: true });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 27, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 21, sign: false });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 30, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 23, sign: false });
data.append(i32 { mag: 34, sign: false });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 13, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 34, sign: true });
data.append(i32 { mag: 16, sign: false });
data.append(i32 { mag: 40, sign: false });
data.append(i32 { mag: 34, sign: false });
data.append(i32 { mag: 33, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 13, sign: false });
data.append(i32 { mag: 40, sign: false });
data.append(i32 { mag: 30, sign: false });
data.append(i32 { mag: 66, sign: false });
data.append(i32 { mag: 121, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 36, sign: false });
data.append(i32 { mag: 41, sign: false });
data.append(i32 { mag: 39, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 16, sign: true });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 23, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 13, sign: false });
data.append(i32 { mag: 43, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 42, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 16, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 32, sign: false });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 29, sign: true });
data.append(i32 { mag: 19, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 33, sign: false });
data.append(i32 { mag: 33, sign: true });
data.append(i32 { mag: 17, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 10, sign: true });
data.append(i32 { mag: 27, sign: false });
data.append(i32 { mag: 21, sign: false });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 12, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 22, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 10, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 38, sign: false });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 14, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 17, sign: true });
data.append(i32 { mag: 18, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 17, sign: true });
data.append(i32 { mag: 10, sign: true });
data.append(i32 { mag: 11, sign: true });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 22, sign: true });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 12, sign: true });
data.append(i32 { mag: 24, sign: true });
data.append(i32 { mag: 23, sign: true });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 24, sign: true });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 28, sign: true });
data.append(i32 { mag: 16, sign: true });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 30, sign: true });
data.append(i32 { mag: 19, sign: true });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 10, sign: true });
data.append(i32 { mag: 17, sign: false });
data.append(i32 { mag: 127, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 13, sign: false });
data.append(i32 { mag: 54, sign: false });
data.append(i32 { mag: 47, sign: false });
data.append(i32 { mag: 55, sign: false });
data.append(i32 { mag: 62, sign: false });
data.append(i32 { mag: 25, sign: false });
data.append(i32 { mag: 51, sign: false });
data.append(i32 { mag: 67, sign: false });
data.append(i32 { mag: 23, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 41, sign: true });
data.append(i32 { mag: 24, sign: true });
data.append(i32 { mag: 83, sign: true });
data.append(i32 { mag: 71, sign: true });
data.append(i32 { mag: 27, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 20, sign: true });
data.append(i32 { mag: 16, sign: false });
data.append(i32 { mag: 33, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 33, sign: true });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 14, sign: true });
data.append(i32 { mag: 60, sign: true });
data.append(i32 { mag: 65, sign: true });
data.append(i32 { mag: 29, sign: true });
data.append(i32 { mag: 46, sign: true });
data.append(i32 { mag: 17, sign: true });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 28, sign: false });
data.append(i32 { mag: 29, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 42, sign: true });
data.append(i32 { mag: 43, sign: true });
data.append(i32 { mag: 55, sign: true });
data.append(i32 { mag: 44, sign: true });
data.append(i32 { mag: 26, sign: true });
data.append(i32 { mag: 17, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 21, sign: false });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 41, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 52, sign: true });
data.append(i32 { mag: 55, sign: true });
data.append(i32 { mag: 21, sign: true });
data.append(i32 { mag: 22, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 14, sign: true });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 27, sign: false });
data.append(i32 { mag: 53, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 66, sign: true });
data.append(i32 { mag: 70, sign: true });
data.append(i32 { mag: 58, sign: true });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 37, sign: false });
data.append(i32 { mag: 36, sign: false });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 30, sign: true });
data.append(i32 { mag: 34, sign: true });
data.append(i32 { mag: 27, sign: true });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 28, sign: true });
data.append(i32 { mag: 45, sign: true });
data.append(i32 { mag: 18, sign: true });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 37, sign: false });
data.append(i32 { mag: 45, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 10, sign: true });
data.append(i32 { mag: 89, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 28, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 50, sign: true });
data.append(i32 { mag: 27, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 18, sign: true });
data.append(i32 { mag: 33, sign: false });
data.append(i32 { mag: 37, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 21, sign: true });
data.append(i32 { mag: 45, sign: true });
data.append(i32 { mag: 84, sign: true });
data.append(i32 { mag: 71, sign: true });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 27, sign: true });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 25, sign: true });
data.append(i32 { mag: 33, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 24, sign: true });
data.append(i32 { mag: 37, sign: true });
data.append(i32 { mag: 34, sign: true });
data.append(i32 { mag: 32, sign: true });
data.append(i32 { mag: 53, sign: true });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 28, sign: false });
data.append(i32 { mag: 78, sign: true });
data.append(i32 { mag: 39, sign: true });
data.append(i32 { mag: 12, sign: true });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 33, sign: true });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 36, sign: false });
data.append(i32 { mag: 33, sign: true });
data.append(i32 { mag: 26, sign: true });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 24, sign: true });
data.append(i32 { mag: 30, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 10, sign: true });
data.append(i32 { mag: 16, sign: true });
data.append(i32 { mag: 22, sign: true });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 27, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 28, sign: false });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 49, sign: true });
data.append(i32 { mag: 37, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 17, sign: true });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 14, sign: true });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 19, sign: true });
data.append(i32 { mag: 30, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 31, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 46, sign: true });
data.append(i32 { mag: 16, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 13, sign: false });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 29, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 23, sign: true });
data.append(i32 { mag: 24, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 29, sign: true });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 19, sign: false });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 73, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 14, sign: true });
data.append(i32 { mag: 10, sign: true });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 19, sign: true });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 23, sign: true });
data.append(i32 { mag: 32, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 13, sign: false });
data.append(i32 { mag: 80, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 22, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 49, sign: true });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 26, sign: false });
data.append(i32 { mag: 25, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 84, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 23, sign: true });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 47, sign: true });
data.append(i32 { mag: 12, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 34, sign: false });
data.append(i32 { mag: 25, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 20, sign: true });
data.append(i32 { mag: 23, sign: false });
data.append(i32 { mag: 38, sign: false });
data.append(i32 { mag: 28, sign: false });
data.append(i32 { mag: 17, sign: false });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 39, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 35, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 16, sign: false });
data.append(i32 { mag: 40, sign: false });
data.append(i32 { mag: 45, sign: false });
data.append(i32 { mag: 17, sign: true });
data.append(i32 { mag: 20, sign: true });
data.append(i32 { mag: 16, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 25, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 30, sign: false });
data.append(i32 { mag: 32, sign: false });
data.append(i32 { mag: 36, sign: true });
data.append(i32 { mag: 16, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 21, sign: false });
data.append(i32 { mag: 17, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 17, sign: false });
data.append(i32 { mag: 10, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 25, sign: true });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 30, sign: false });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 10, sign: true });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 30, sign: true });
data.append(i32 { mag: 74, sign: true });
data.append(i32 { mag: 48, sign: true });
data.append(i32 { mag: 36, sign: true });
data.append(i32 { mag: 37, sign: true });
data.append(i32 { mag: 22, sign: true });
data.append(i32 { mag: 38, sign: true });
data.append(i32 { mag: 23, sign: true });
data.append(i32 { mag: 36, sign: true });
data.append(i32 { mag: 40, sign: true });
data.append(i32 { mag: 20, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 23, sign: false });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 30, sign: true });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 21, sign: false });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 36, sign: false });
data.append(i32 { mag: 40, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 65, sign: false });
data.append(i32 { mag: 104, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 16, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 11, sign: true });
data.append(i32 { mag: 26, sign: true });
data.append(i32 { mag: 14, sign: true });
data.append(i32 { mag: 29, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 16, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 25, sign: false });
data.append(i32 { mag: 21, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 16, sign: false });
data.append(i32 { mag: 13, sign: false });
data.append(i32 { mag: 19, sign: true });
data.append(i32 { mag: 28, sign: true });
data.append(i32 { mag: 21, sign: true });
data.append(i32 { mag: 11, sign: true });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 21, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 40, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 32, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 55, sign: true });
data.append(i32 { mag: 42, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 13, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 43, sign: true });
data.append(i32 { mag: 85, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 36, sign: false });
data.append(i32 { mag: 23, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 14, sign: true });
data.append(i32 { mag: 25, sign: false });
data.append(i32 { mag: 53, sign: false });
data.append(i32 { mag: 35, sign: false });
data.append(i32 { mag: 41, sign: false });
data.append(i32 { mag: 21, sign: false });
data.append(i32 { mag: 71, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 34, sign: true });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 16, sign: true });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 44, sign: false });
data.append(i32 { mag: 48, sign: false });
data.append(i32 { mag: 25, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 16, sign: true });
data.append(i32 { mag: 44, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 19, sign: false });
data.append(i32 { mag: 28, sign: true });
data.append(i32 { mag: 20, sign: true });
data.append(i32 { mag: 27, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 35, sign: false });
data.append(i32 { mag: 44, sign: false });
data.append(i32 { mag: 27, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 11, sign: true });
data.append(i32 { mag: 21, sign: true });
data.append(i32 { mag: 28, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 12, sign: true });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 52, sign: true });
data.append(i32 { mag: 37, sign: true });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 10, sign: true });
data.append(i32 { mag: 32, sign: true });
data.append(i32 { mag: 14, sign: true });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 30, sign: false });
data.append(i32 { mag: 35, sign: true });
data.append(i32 { mag: 30, sign: true });
data.append(i32 { mag: 36, sign: true });
data.append(i32 { mag: 20, sign: true });
data.append(i32 { mag: 16, sign: true });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 34, sign: true });
data.append(i32 { mag: 40, sign: true });
data.append(i32 { mag: 19, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 43, sign: true });
data.append(i32 { mag: 21, sign: true });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 22, sign: true });
data.append(i32 { mag: 20, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 24, sign: true });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 38, sign: false });
data.append(i32 { mag: 48, sign: false });
data.append(i32 { mag: 35, sign: false });
data.append(i32 { mag: 19, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 30, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 13, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 35, sign: true });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 32, sign: true });
data.append(i32 { mag: 10, sign: true });
data.append(i32 { mag: 50, sign: true });
data.append(i32 { mag: 95, sign: true });
data.append(i32 { mag: 60, sign: true });
data.append(i32 { mag: 17, sign: true });
data.append(i32 { mag: 23, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 11, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 45, sign: false });
data.append(i32 { mag: 45, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 25, sign: false });
data.append(i32 { mag: 28, sign: false });
data.append(i32 { mag: 16, sign: false });
data.append(i32 { mag: 13, sign: false });
data.append(i32 { mag: 21, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 31, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 28, sign: false });
data.append(i32 { mag: 17, sign: false });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 17, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 16, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 17, sign: false });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 78, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 19, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 18, sign: true });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 21, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 53, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 33, sign: true });
data.append(i32 { mag: 39, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 21, sign: false });
data.append(i32 { mag: 32, sign: false });
data.append(i32 { mag: 49, sign: false });
data.append(i32 { mag: 30, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 19, sign: true });
data.append(i32 { mag: 65, sign: true });
data.append(i32 { mag: 56, sign: true });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 28, sign: false });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 17, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 57, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 29, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 35, sign: true });
data.append(i32 { mag: 11, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 79, sign: true });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 30, sign: true });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 17, sign: false });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 39, sign: true });
data.append(i32 { mag: 34, sign: true });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 21, sign: false });
data.append(i32 { mag: 16, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 93, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 23, sign: true });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 13, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 22, sign: true });
data.append(i32 { mag: 20, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 13, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 14, sign: true });
data.append(i32 { mag: 109, sign: true });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 25, sign: false });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 16, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 13, sign: false });
data.append(i32 { mag: 17, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 21, sign: true });
data.append(i32 { mag: 48, sign: true });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 40, sign: false });
data.append(i32 { mag: 52, sign: false });
data.append(i32 { mag: 52, sign: false });
data.append(i32 { mag: 40, sign: false });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 41, sign: false });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 27, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 40, sign: false });
data.append(i32 { mag: 17, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 39, sign: false });
data.append(i32 { mag: 47, sign: false });
data.append(i32 { mag: 63, sign: false });
data.append(i32 { mag: 37, sign: false });
data.append(i32 { mag: 54, sign: false });
data.append(i32 { mag: 10, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 61, sign: true });
data.append(i32 { mag: 25, sign: true });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 14, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 36, sign: false });
data.append(i32 { mag: 11, sign: true });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 14, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 27, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 31, sign: false });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 17, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 11, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 26, sign: false });
data.append(i32 { mag: 38, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 45, sign: false });
data.append(i32 { mag: 12, sign: true });
data.append(i32 { mag: 26, sign: true });
data.append(i32 { mag: 11, sign: true });
data.append(i32 { mag: 14, sign: true });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 30, sign: false });
data.append(i32 { mag: 36, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 27, sign: false });
data.append(i32 { mag: 21, sign: false });
data.append(i32 { mag: 28, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 46, sign: true });
data.append(i32 { mag: 17, sign: true });
data.append(i32 { mag: 23, sign: true });
data.append(i32 { mag: 32, sign: true });
data.append(i32 { mag: 21, sign: true });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 50, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 37, sign: true });
data.append(i32 { mag: 35, sign: true });
data.append(i32 { mag: 18, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 16, sign: false });
data.append(i32 { mag: 49, sign: false });
data.append(i32 { mag: 10, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 38, sign: true });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 14, sign: true });
data.append(i32 { mag: 11, sign: true });
data.append(i32 { mag: 54, sign: true });
data.append(i32 { mag: 18, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 16, sign: false });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 23, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 23, sign: true });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 10, sign: true });
data.append(i32 { mag: 16, sign: false });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 13, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 23, sign: true });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 28, sign: false });
data.append(i32 { mag: 30, sign: false });
data.append(i32 { mag: 28, sign: false });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 17, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 14, sign: true });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 26, sign: true });
data.append(i32 { mag: 29, sign: true });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 35, sign: false });
data.append(i32 { mag: 11, sign: true });
data.append(i32 { mag: 23, sign: false });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 39, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 23, sign: true });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 41, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 45, sign: false });
data.append(i32 { mag: 30, sign: false });
data.append(i32 { mag: 32, sign: false });
data.append(i32 { mag: 11, sign: true });
data.append(i32 { mag: 23, sign: true });
data.append(i32 { mag: 20, sign: true });
data.append(i32 { mag: 32, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 68, sign: false });
data.append(i32 { mag: 23, sign: false });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 38, sign: false });
data.append(i32 { mag: 44, sign: false });
data.append(i32 { mag: 35, sign: false });
data.append(i32 { mag: 40, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 57, sign: true });
data.append(i32 { mag: 16, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 35, sign: false });
data.append(i32 { mag: 41, sign: false });
data.append(i32 { mag: 31, sign: false });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 16, sign: true });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 44, sign: false });
data.append(i32 { mag: 69, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 19, sign: false });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 19, sign: false });
data.append(i32 { mag: 27, sign: false });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 19, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 73, sign: false });
data.append(i32 { mag: 37, sign: false });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 18, sign: true });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 7, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 13, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 34, sign: false });
data.append(i32 { mag: 20, sign: true });
data.append(i32 { mag: 74, sign: true });
data.append(i32 { mag: 89, sign: true });
data.append(i32 { mag: 60, sign: true });
data.append(i32 { mag: 68, sign: true });
data.append(i32 { mag: 21, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 15, sign: true });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 16, sign: true });
data.append(i32 { mag: 49, sign: true });
data.append(i32 { mag: 41, sign: false });
data.append(i32 { mag: 8, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 29, sign: true });
data.append(i32 { mag: 45, sign: true });
data.append(i32 { mag: 53, sign: true });
data.append(i32 { mag: 42, sign: true });
data.append(i32 { mag: 36, sign: true });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 39, sign: false });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 23, sign: true });
data.append(i32 { mag: 20, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 74, sign: false });
data.append(i32 { mag: 37, sign: false });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 18, sign: true });
data.append(i32 { mag: 11, sign: true });
data.append(i32 { mag: 38, sign: false });
data.append(i32 { mag: 17, sign: false });
data.append(i32 { mag: 6, sign: true });
data.append(i32 { mag: 11, sign: true });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 25, sign: false });
data.append(i32 { mag: 39, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 30, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 27, sign: false });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 28, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 49, sign: false });
data.append(i32 { mag: 56, sign: false });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 58, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 39, sign: false });
data.append(i32 { mag: 27, sign: false });
data.append(i32 { mag: 25, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 10, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 33, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 36, sign: false });
data.append(i32 { mag: 38, sign: false });
data.append(i32 { mag: 26, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 17, sign: false });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 18, sign: false });
data.append(i32 { mag: 12, sign: true });
data.append(i32 { mag: 12, sign: true });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 36, sign: true });
data.append(i32 { mag: 38, sign: true });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 23, sign: false });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 8, sign: false });
data.append(i32 { mag: 17, sign: true });
data.append(i32 { mag: 40, sign: true });
data.append(i32 { mag: 59, sign: true });
data.append(i32 { mag: 16, sign: true });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 4, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 7, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 4, sign: false });
data.append(i32 { mag: 9, sign: true });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 7, sign: true });
TensorTrait::new(shape.span(), data.span())
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/mnist_nn/src/generated/fc2_bias.cairo | use array::ArrayTrait;
use orion::operators::tensor::{TensorTrait, Tensor, I32Tensor};
use orion::numbers::i32;
fn fc2_bias() -> Tensor<i32> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(10);
let mut data = ArrayTrait::<i32>::new();
data.append(i32 { mag: 313, sign: true });
data.append(i32 { mag: 1064, sign: false });
data.append(i32 { mag: 28, sign: true });
data.append(i32 { mag: 184, sign: true });
data.append(i32 { mag: 1012, sign: true });
data.append(i32 { mag: 1885, sign: false });
data.append(i32 { mag: 787, sign: true });
data.append(i32 { mag: 835, sign: false });
data.append(i32 { mag: 1819, sign: true });
data.append(i32 { mag: 208, sign: false });
TensorTrait::new(shape.span(), data.span())
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/mnist_nn/src/generated/fc2_weights.cairo | use array::ArrayTrait;
use orion::operators::tensor::{TensorTrait, Tensor, I32Tensor};
use orion::numbers::i32;
fn fc2_weights() -> Tensor<i32> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(10);
shape.append(10);
let mut data = ArrayTrait::<i32>::new();
data.append(i32 { mag: 42, sign: true });
data.append(i32 { mag: 41, sign: false });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 27, sign: true });
data.append(i32 { mag: 58, sign: true });
data.append(i32 { mag: 71, sign: false });
data.append(i32 { mag: 21, sign: true });
data.append(i32 { mag: 50, sign: true });
data.append(i32 { mag: 32, sign: false });
data.append(i32 { mag: 21, sign: true });
data.append(i32 { mag: 38, sign: true });
data.append(i32 { mag: 67, sign: true });
data.append(i32 { mag: 35, sign: true });
data.append(i32 { mag: 112, sign: true });
data.append(i32 { mag: 95, sign: false });
data.append(i32 { mag: 78, sign: false });
data.append(i32 { mag: 15, sign: false });
data.append(i32 { mag: 28, sign: true });
data.append(i32 { mag: 64, sign: false });
data.append(i32 { mag: 49, sign: false });
data.append(i32 { mag: 19, sign: false });
data.append(i32 { mag: 69, sign: true });
data.append(i32 { mag: 53, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 62, sign: true });
data.append(i32 { mag: 47, sign: false });
data.append(i32 { mag: 30, sign: true });
data.append(i32 { mag: 70, sign: true });
data.append(i32 { mag: 28, sign: true });
data.append(i32 { mag: 48, sign: false });
data.append(i32 { mag: 69, sign: true });
data.append(i32 { mag: 21, sign: true });
data.append(i32 { mag: 35, sign: false });
data.append(i32 { mag: 38, sign: true });
data.append(i32 { mag: 100, sign: true });
data.append(i32 { mag: 41, sign: true });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 78, sign: false });
data.append(i32 { mag: 12, sign: true });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 59, sign: false });
data.append(i32 { mag: 49, sign: true });
data.append(i32 { mag: 36, sign: true });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 31, sign: false });
data.append(i32 { mag: 19, sign: true });
data.append(i32 { mag: 99, sign: true });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 11, sign: false });
data.append(i32 { mag: 29, sign: false });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 2, sign: true });
data.append(i32 { mag: 127, sign: true });
data.append(i32 { mag: 117, sign: true });
data.append(i32 { mag: 31, sign: false });
data.append(i32 { mag: 39, sign: false });
data.append(i32 { mag: 17, sign: true });
data.append(i32 { mag: 67, sign: false });
data.append(i32 { mag: 9, sign: false });
data.append(i32 { mag: 42, sign: false });
data.append(i32 { mag: 112, sign: true });
data.append(i32 { mag: 26, sign: false });
data.append(i32 { mag: 10, sign: true });
data.append(i32 { mag: 1, sign: true });
data.append(i32 { mag: 73, sign: true });
data.append(i32 { mag: 21, sign: false });
data.append(i32 { mag: 65, sign: true });
data.append(i32 { mag: 76, sign: true });
data.append(i32 { mag: 5, sign: true });
data.append(i32 { mag: 90, sign: true });
data.append(i32 { mag: 19, sign: false });
data.append(i32 { mag: 75, sign: true });
data.append(i32 { mag: 36, sign: true });
data.append(i32 { mag: 71, sign: false });
data.append(i32 { mag: 45, sign: true });
data.append(i32 { mag: 82, sign: false });
data.append(i32 { mag: 13, sign: false });
data.append(i32 { mag: 5, sign: false });
data.append(i32 { mag: 81, sign: false });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 13, sign: true });
data.append(i32 { mag: 22, sign: false });
data.append(i32 { mag: 28, sign: true });
data.append(i32 { mag: 46, sign: true });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 110, sign: true });
data.append(i32 { mag: 3, sign: true });
data.append(i32 { mag: 82, sign: true });
data.append(i32 { mag: 16, sign: false });
data.append(i32 { mag: 32, sign: false });
data.append(i32 { mag: 12, sign: false });
data.append(i32 { mag: 31, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 45, sign: false });
data.append(i32 { mag: 30, sign: true });
data.append(i32 { mag: 87, sign: true });
data.append(i32 { mag: 125, sign: true });
TensorTrait::new(shape.span(), data.span())
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/mnist_nn/src/generated/input.cairo | use array::ArrayTrait;
use orion::operators::tensor::{TensorTrait, Tensor, I32Tensor};
use orion::numbers::i32;
fn input() -> Tensor<i32> {
let mut shape = ArrayTrait::<usize>::new();
shape.append(196);
let mut data = ArrayTrait::<i32>::new();
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 14, sign: false });
data.append(i32 { mag: 24, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 220, sign: false });
data.append(i32 { mag: 216, sign: false });
data.append(i32 { mag: 255, sign: false });
data.append(i32 { mag: 240, sign: false });
data.append(i32 { mag: 246, sign: false });
data.append(i32 { mag: 245, sign: false });
data.append(i32 { mag: 237, sign: false });
data.append(i32 { mag: 114, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 27, sign: false });
data.append(i32 { mag: 26, sign: false });
data.append(i32 { mag: 171, sign: false });
data.append(i32 { mag: 108, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 19, sign: false });
data.append(i32 { mag: 255, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 207, sign: false });
data.append(i32 { mag: 72, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 1, sign: false });
data.append(i32 { mag: 20, sign: false });
data.append(i32 { mag: 255, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 244, sign: false });
data.append(i32 { mag: 73, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 3, sign: false });
data.append(i32 { mag: 120, sign: false });
data.append(i32 { mag: 187, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 28, sign: false });
data.append(i32 { mag: 255, sign: false });
data.append(i32 { mag: 6, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 179, sign: false });
data.append(i32 { mag: 255, sign: false });
data.append(i32 { mag: 2, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
data.append(i32 { mag: 0, sign: false });
TensorTrait::new(shape.span(), data.span())
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/mnist_nn/src/inference.cairo | // use orion::operators::tensor::core::TensorTrait;
// use core::array::{SpanTrait, ArrayTrait};
// use mnist_nn::nn::fc1;
// use mnist_nn::nn::fc2;
// use mnist_nn::generated::input::input;
// use mnist_nn::generated::fc1_bias::fc1_bias;
// use mnist_nn::generated::fc1_weights::fc1_weights;
// use mnist_nn::generated::fc2_bias::fc2_bias;
// use mnist_nn::generated::fc2_weights::fc2_weights;
// use orion::operators::tensor::implementations::impl_tensor_fp::Tensor_fp;
// fn main() -> u32 {
// let input = input();
// let fc1_bias = fc1_bias();
// let fc1_weights = fc1_weights();
// let fc2_bias = fc2_bias();
// let fc2_weights = fc2_weights();
// let x = fc1(input, fc1_weights, fc1_bias);
// let x = fc2(x, fc2_weights, fc2_bias);
// let x = *x.argmax(0, Option::None(()), Option::None(())).data.at(0);
// x
// } | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/mnist_nn/src/lib.cairo | mod generated;
mod nn;
mod test;
mod inference; | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/mnist_nn/src/nn.cairo | use orion::operators::tensor::core::Tensor;
use orion::numbers::signed_integer::{integer_trait::IntegerTrait, i32::i32};
use orion::operators::nn::{NNTrait, I32NN};
fn fc1(i: Tensor<i32>, w: Tensor<i32>, b: Tensor<i32>) -> Tensor<i32> {
let x = NNTrait::linear(i, w, b);
NNTrait::relu(@x)
}
fn fc2(i: Tensor<i32>, w: Tensor<i32>, b: Tensor<i32>) -> Tensor<i32> {
NNTrait::linear(i, w, b)
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/mnist_nn/src/test.cairo | use core::array::SpanTrait;
use mnist_nn::nn::fc1;
use mnist_nn::nn::fc2;
use mnist_nn::generated::input::input;
use mnist_nn::generated::fc1_bias::fc1_bias;
use mnist_nn::generated::fc1_weights::fc1_weights;
use mnist_nn::generated::fc2_bias::fc2_bias;
use mnist_nn::generated::fc2_weights::fc2_weights;
use orion::operators::tensor::{I32Tensor, Tensor};
use orion::numbers::i32;
#[test]
#[available_gas(99999999999999999)]
fn mnist_nn_test() {
let input = input();
let fc1_bias = fc1_bias();
let fc1_weights = fc1_weights();
let fc2_bias = fc2_bias();
let fc2_weights = fc2_weights();
let x = fc1(input, fc1_weights, fc1_bias);
let x = fc2(x, fc2_weights, fc2_bias);
let x = *x.argmax(0, Option::None(()), Option::None(())).data.at(0);
assert(x == 7, 'should predict 7');
} | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_linear_regression_model/notebooks/fp16x16 to decimal converter.ipynb | {
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"id": "c7d8c871",
"metadata": {},
"outputs": [],
"source": [
"def fp16x16_to_decimal(fp_number):\n",
" whole_number = (fp_number >> 16) & 0xFFFF\n",
" fractional_part = fp_number & 0xFFFF\n",
" decimal_value = whole_number + (fractional_part / 65536) # Divide by 2^16\n",
" return decimal_value\n",
"\n",
"def decimal_to_fp16x16(decimal_number):\n",
" whole_number = int(decimal_number)\n",
" fractional_part = int((decimal_number - whole_number) * 65536) # Multiply by 2^16\n",
" fp_number = (whole_number << 16) + fractional_part\n",
" return fp_number\n",
"\n",
"def hex_to_decimal(hex_string):\n",
" try:\n",
" decimal_number = int(hex_string, 16)\n",
" return decimal_number\n",
" except ValueError:\n",
" return \"Invalid hexadecimal input\"\n",
" \n",
"def fp16x16_hex_to_decimal(hex_string):\n",
" hex_to_decimal_variable = hex_to_decimal(hex_string)\n",
" decimal_value = fp16x16_to_decimal(hex_to_decimal_variable)\n",
" return decimal_value\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "b0d907a8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"38.45289611816406"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"felt_to_number(\"0x2673f1\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9fe1872d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_linear_regression_model/notebooks/verifiable_simple_linear_regression_model.ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "4a28cde5",
"metadata": {},
"source": [
"# Verifiable Simple Linear Regression model\n",
"\n",
"Simple Linear Regression model is a foundational technique used to determine the linear relationship between an independent variables (predictors) and a dependent variable (outcome) . By identifying the line of best fit, we can make informed predictions based on new data points or decipher how changes in one variable may lead to changes in another. \n",
"\n",
"The following is a small run through of the implementation of a Simple Linear Regression model using <b>Ordinary Least Squares</b> (OLS) in python, which we will in later stages convert it to Cairo to turn it into a <b>\"Verifiable Linear Regression model\" </b>. By utilysing the <b>Orion's</b> library we will be able to add an extra layer of transparency and robustness enabling us to make <b>verifiable inferences</b> which can be easily <b>proved</b> using the LambdaClass STARK Prover.\n",
"\n",
"In this particular exercise, we'll replicate the entire linear regression model in Cairo. This approach will not only enable us to validate each individual inference made to the model but also allow us to verify all the steps executed during the model's construction phase also. This is to provide an opportunity to get familiar with Orion's main functions and operators along the process. \n"
]
},
{
"cell_type": "markdown",
"id": "2d479a73",
"metadata": {},
"source": [
"# Generate a synthetic dataset"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "f60750c4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"X values = [-0.5 -0.49328859 -0.48657718 -0.47986577 -0.47315436 -0.46644295\n",
" -0.45973154 -0.45302013 -0.44630872 -0.43959732 -0.43288591 -0.4261745\n",
" -0.41946309 -0.41275168 -0.40604027 -0.39932886 -0.39261745 -0.38590604\n",
" -0.37919463 -0.37248322 -0.36577181 -0.3590604 -0.35234899 -0.34563758\n",
" -0.33892617 -0.33221477 -0.32550336 -0.31879195 -0.31208054 -0.30536913\n",
" -0.29865772 -0.29194631 -0.2852349 -0.27852349 -0.27181208 -0.26510067\n",
" -0.25838926 -0.25167785 -0.24496644 -0.23825503 -0.23154362 -0.22483221\n",
" -0.21812081 -0.2114094 -0.20469799 -0.19798658 -0.19127517 -0.18456376\n",
" -0.17785235 -0.17114094 -0.16442953 -0.15771812 -0.15100671 -0.1442953\n",
" -0.13758389 -0.13087248 -0.12416107 -0.11744966 -0.11073826 -0.10402685\n",
" -0.09731544 -0.09060403 -0.08389262 -0.07718121 -0.0704698 -0.06375839\n",
" -0.05704698 -0.05033557 -0.04362416 -0.03691275 -0.03020134 -0.02348993\n",
" -0.01677852 -0.01006711 -0.0033557 0.0033557 0.01006711 0.01677852\n",
" 0.02348993 0.03020134 0.03691275 0.04362416 0.05033557 0.05704698\n",
" 0.06375839 0.0704698 0.07718121 0.08389262 0.09060403 0.09731544\n",
" 0.10402685 0.11073826 0.11744966 0.12416107 0.13087248 0.13758389\n",
" 0.1442953 0.15100671 0.15771812 0.16442953 0.17114094 0.17785235\n",
" 0.18456376 0.19127517 0.19798658 0.20469799 0.2114094 0.21812081\n",
" 0.22483221 0.23154362 0.23825503 0.24496644 0.25167785 0.25838926\n",
" 0.26510067 0.27181208 0.27852349 0.2852349 0.29194631 0.29865772\n",
" 0.30536913 0.31208054 0.31879195 0.32550336 0.33221477 0.33892617\n",
" 0.34563758 0.35234899 0.3590604 0.36577181 0.37248322 0.37919463\n",
" 0.38590604 0.39261745 0.39932886 0.40604027 0.41275168 0.41946309\n",
" 0.4261745 0.43288591 0.43959732 0.44630872 0.45302013 0.45973154\n",
" 0.46644295 0.47315436 0.47986577 0.48657718 0.49328859 0.5 ]\n",
"y values = [4.04967142 3.99959639 4.09161449 4.19257144 4.03027594 4.0437004\n",
" 4.23845819 4.1707032 4.06043511 4.17506137 4.08788642 4.10107803\n",
" 4.18527005 3.98316862 4.01542768 4.14511353 4.11348199 4.25961265\n",
" 4.15080833 4.11380319 4.41502125 4.25930156 4.30205483 4.16625001\n",
" 4.26770938 4.34666273 4.23389393 4.39998591 4.31577506 4.36009237\n",
" 4.3425139 4.6013352 4.42818048 4.33718193 4.53863033 4.34771429\n",
" 4.50410784 4.30067728 4.37724851 4.54317606 4.61075941 4.5674724\n",
" 4.55219356 4.54707084 4.44275183 4.53204242 4.57138579 4.73658471\n",
" 4.67865713 4.48141411 4.70354934 4.64605553 4.63029438 4.77257702\n",
" 4.82793217 4.83138305 4.6677561 4.73417943 4.81164983 4.88950082\n",
" 4.7574517 4.80022605 4.72158127 4.72601692 4.94031298 5.00810722\n",
" 4.87870503 4.99968215 4.94891528 4.86166252 4.97573688 5.10682379\n",
" 4.96286035 5.13633014 4.73131408 5.08890166 5.02883894 5.00365631\n",
" 5.05615594 4.86164579 5.05185831 5.12295958 5.24846055 5.06226694\n",
" 5.04666742 5.09076389 5.24590263 5.20066035 5.12823203 5.24595762\n",
" 5.21776145 5.31834101 5.16469402 5.21555593 5.22253415 5.12881629\n",
" 5.31820263 5.32811895 5.31594759 5.30540035 5.2007448 5.31364017\n",
" 5.33485607 5.30232261 5.37984458 5.44980106 5.61143738 5.45369939\n",
" 5.47541947 5.45564266 5.28463295 5.4872815 5.50937873 5.76310273\n",
" 5.51096525 5.5737789 5.5535758 5.45360199 5.6981749 5.67250874\n",
" 5.68984145 5.53322233 5.77786332 5.51082161 5.72311524 5.89689791\n",
" 5.59222154 5.64806821 5.72808594 5.68119606 5.5899001 5.76524556\n",
" 5.66558171 5.83259414 5.70671529 5.96707398 5.74717803 5.80672002\n",
" 5.93370072 5.74268538 5.90194062 6.02333173 5.74529195 5.93792647\n",
" 5.95887419 6.02449101 5.83603647 5.8411087 6.03877134 6.02969847]\n"
]
}
],
"source": [
"import numpy as np\n",
"import os\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Constants for reproducibility\n",
"SEED = 42\n",
"np.random.seed(SEED)\n",
"\n",
"\n",
"X = np.linspace(-0.5, 0.5, 150).astype('float64')\n",
"noise = np.random.normal(0, 0.1, len(X)).astype('float64')\n",
"y = 2 * X + 5 + noise # y=2x+5 + error\n",
"\n",
"print('X values = ',X)\n",
"print('y values = ', y)\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "fa6fc5c0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'y values')"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X,y)\n",
"plt.xlabel('X values')\n",
"plt.ylabel('y values')"
]
},
{
"cell_type": "markdown",
"id": "68a91d93",
"metadata": {},
"source": [
"# Equation of a straight line \n",
"\n",
"<br>\n",
"\n",
"<img src=\"line_equation.gif\" width=\"200px\" height=\"200px\" align=\"left\"> <br><br>\n",
"\n",
"**y:** y values \n",
"**x:** x values \n",
"**b:** gradient \n",
"**a:** y intercept \n"
]
},
{
"cell_type": "markdown",
"id": "4999fbae",
"metadata": {},
"source": [
"# Calculating the gradient of the line of best fit\n",
"<br>\n",
"\n",
"<img src=\"gradient.png\" width=\"230px\" height=\"230px\" align=\"left\">"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "7fe2a45b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The slope of regression line: 2.0133337976122685\n"
]
}
],
"source": [
"numerator = sum((X - X.mean()) * (y - y.mean()))\n",
"denominator = sum((X - X.mean())**2)\n",
"\n",
"beta = numerator / denominator\n",
"print('The slope of regression line:', beta)"
]
},
{
"cell_type": "markdown",
"id": "5d126c20",
"metadata": {},
"source": [
"# Calculating the y intercept\n",
"\n",
"<br>\n",
"\n",
"<img src=\"intercept.png\" width=\"150px\" height=\"150px\" align=\"left\">"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "4f3979d7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The y intercept of our regression line: 4.991767313284746\n"
]
}
],
"source": [
"intercept = y.mean() - beta * X.mean()\n",
"print('The y intercept of our regression line:', intercept)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "88c5f347",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Given x=0.17 , our model predicts the corresponing y value shoud be 5.3340340588788315\n"
]
}
],
"source": [
"# making new predictions using our model\n",
"predicted_y_value = beta * 0.17 + intercept \n",
"print(f'Given x=0.17 , our model predicts the corresponing y value shoud be {predicted_y_value}')"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "d73113cf",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Calculated beta: 2.0133337976122685\n",
"Calculated intercept: 4.991767313284746\n"
]
}
],
"source": [
"plt.scatter(X, y, label='Data Points')\n",
"plt.plot(X, beta * X + intercept, color='red', label='Regression Line')\n",
"plt.scatter(0.17,predicted_y_value, color='green', label='pred for x = 0.17 ')\n",
"plt.xlabel('X')\n",
"plt.ylabel('y')\n",
"plt.title('Linear Regression')\n",
"plt.legend()\n",
"plt.grid(True)\n",
"plt.show()\n",
"\n",
"\n",
"print(f\"Calculated beta: {beta}\")\n",
"print(f\"Calculated intercept: {intercept}\")\n"
]
},
{
"cell_type": "markdown",
"id": "07ce1abf",
"metadata": {},
"source": [
"# Calculating the accuracy of our linear regression model\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 64,
"id": "e4a526a3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean Squared Error (MSE): 0.008805873341370826\n",
"R-squared (R^2): 0.974921526753728\n"
]
}
],
"source": [
"y_pred = beta * X + intercept\n",
"\n",
"mse = np.mean((y - y_pred)**2)\n",
"y_mean = np.mean(y)\n",
"r_squared = 1 - np.sum((y - y_pred)**2) / np.sum((y - y_mean)**2)\n",
"\n",
"\n",
"print(\"Mean Squared Error (MSE):\", mse)\n",
"print(\"R-squared (R^2):\", r_squared)\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "f0e27741",
"metadata": {},
"source": [
"# Replicating the linear regression model using the Orion library\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "da10af23",
"metadata": {},
"source": [
"### Create a scarb project \n",
"Scarb is the Cairo package manager specifically created to streamline our Cairo and Starknet development process. You can find all information about Scarb and Cairo installation <a src='https://github.com/gizatechxyz/orion/blob/develop/docs/framework/get-started.md#installations'>here</a>"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "a07f433b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[31merror\u001b[0m: destination `verifiable_linear_regression` already exists\n",
"help: use `scarb init` to initialize the directory\n"
]
}
],
"source": [
"! scarb new verifiable_linear_regression"
]
},
{
"cell_type": "markdown",
"id": "b27b16d7",
"metadata": {},
"source": [
"A new project folder will be created for you and make sure to replace the content in Scarb.toml file with the following code:"
]
},
{
"cell_type": "markdown",
"id": "06745de8",
"metadata": {},
"source": [
"```tom\n",
"[package]\n",
"name = \"verifiable_linear_regression\"\n",
"version = \"0.1.0\"\n",
"\n",
"[dependencies]\n",
"orion = { git = \"https://github.com/gizatechxyz/orion.git\", branch = \"develop\" }\n",
"\n",
"[scripts]\n",
"test = \"scarb cairo-test -f linear_regression_test\"\n",
"\n",
"```\n"
]
},
{
"cell_type": "markdown",
"id": "1d2536ce",
"metadata": {},
"source": [
"### Generate the x and y values in cairo and importing the neccessary libs\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 68,
"id": "c1b1057f",
"metadata": {},
"outputs": [],
"source": [
"tensor_name =['X_values', 'Y_values']\n",
"\n",
"base_path = os.path.expanduser(\"~/verifiable_linear_regression/src\")\n",
"\n",
"def generate_cairo_files(data, name):\n",
" generated_path = os.path.join(base_path, 'generated')\n",
" os.makedirs(generated_path, exist_ok=True)\n",
"\n",
" with open(os.path.join(generated_path, f\"{name}.cairo\"), \"w\") as f:\n",
" f.write(\n",
" \"use array::ArrayTrait;\\n\" +\n",
" \"use orion::operators::tensor::{{FP16x16Tensor, TensorTrait, Tensor}};\\n\" +\n",
" \"use orion::numbers::{{FixedTrait, FP16x16, FP16x16Impl}};\\n\"\n",
" \"\\nfn {0}() -> Tensor<FixedType> \".format(name) + \"{{\\n\" +\n",
" \" let mut shape = ArrayTrait::new();\\n\"\n",
" )\n",
" for dim in data.shape:\n",
" f.write(\" shape.append({0});\\n\".format(dim))\n",
" f.write(\n",
" \" let mut data = ArrayTrait::new();\\n\"\n",
" )\n",
" for val in np.nditer(data.flatten()):\n",
" f.write(\" data.append(FixedTrait::new({0}, {1} ));\\n\".format(abs(int(val * 2**16)), str(val < 0).lower()))\n",
" f.write(\n",
" \"let tensor = TensorTrait::<FixedType>::new(shape.span(), data.span()); \\n \\n\" +\n",
" \"return tensor;\\n\\n\"+\n",
" \"}\\n\"\n",
" )\n",
" with open(os.path.join(base_path, 'generated.cairo'), 'w') as f:\n",
" for param_name in tensor_name:\n",
" f.write(f\"mod {param_name};\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 69,
"id": "e1f168e7",
"metadata": {},
"outputs": [],
"source": [
"generate_cairo_files(X, 'X_values')\n",
"generate_cairo_files(y, 'Y_values')"
]
},
{
"cell_type": "markdown",
"id": "31671139",
"metadata": {},
"source": [
"## Building our OLS functions in cairo using Orion lib"
]
},
{
"cell_type": "code",
"execution_count": 75,
"id": "c7204a1b",
"metadata": {},
"outputs": [],
"source": [
"! touch verifiable_linear_regression/src/lin_reg_func.cairo"
]
},
{
"cell_type": "code",
"execution_count": 76,
"id": "4c0b8e16",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting verifiable_linear_regression/src/lin_reg_func.cairo\n"
]
}
],
"source": [
"%%writefile verifiable_linear_regression/src/lin_reg_func.cairo\n",
"\n",
"use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};\n",
"use orion::numbers::{FP16x16, FixedTrait};\n",
"\n",
"/// Calculates the mean of a given 1D tensor.\n",
"fn calculate_mean(tensor_data: Tensor<FP16x16>) -> FP16x16 {\n",
" let tensor_size = FixedTrait::<FP16x16>::new_unscaled(tensor_data.data.len(), false);\n",
"\n",
" let cumulated_sum = tensor_data.cumsum(0, Option::None(()), Option::None(()));\n",
" let sum_result = cumulated_sum.data[tensor_data.data.len() - 1];\n",
" let mean = *sum_result / tensor_size;\n",
"\n",
" return mean;\n",
"}\n",
"\n",
"/// Calculates the deviation of each element from the mean of the provided 1D tensor.\n",
"fn deviation_from_mean(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {\n",
" let mean_value = calculate_mean(tensor_data);\n",
"\n",
" let mut tensor_shape = array::ArrayTrait::new();\n",
" tensor_shape.append(tensor_data.data.len());\n",
"\n",
" let mut deviation_values = array::ArrayTrait::new();\n",
"\n",
" let mut i: u32 = 0;\n",
" loop {\n",
" if i >= tensor_data.data.len() {\n",
" break ();\n",
" }\n",
" let distance_from_mean = *tensor_data.data.at(i) - mean_value;\n",
" deviation_values.append(distance_from_mean);\n",
" i += 1;\n",
" };\n",
"\n",
" let distance_from_mean_tensor = TensorTrait::<FP16x16>::new(\n",
" tensor_shape.span(), deviation_values.span()\n",
" );\n",
"\n",
" return distance_from_mean_tensor;\n",
"}\n",
"\n",
"\n",
"/// Calculates the beta value for linear regression.\n",
"fn compute_beta(x_values: Tensor<FP16x16>, y_values: Tensor<FP16x16>) -> FP16x16 {\n",
" let x_deviation = deviation_from_mean(x_values);\n",
" let y_deviation = deviation_from_mean(y_values);\n",
"\n",
" let x_y_covariance = x_deviation.matmul(@y_deviation);\n",
" let x_variance = x_deviation.matmul(@x_deviation);\n",
"\n",
" let beta_value = *x_y_covariance.data.at(0) / *x_variance.data.at(0);\n",
"\n",
" return beta_value;\n",
"}\n",
"\n",
"/// Calculates the intercept for linear regression.\n",
"fn compute_intercept(\n",
" beta_value: FP16x16, x_values: Tensor<FP16x16>, y_values: Tensor<FP16x16>\n",
") -> FP16x16 {\n",
" let x_mean = calculate_mean(x_values);\n",
" let y_mean = calculate_mean(y_values);\n",
"\n",
" let mx = beta_value * x_mean;\n",
" let intercept = y_mean - mx;\n",
"\n",
" return intercept;\n",
"}\n",
"\n",
"/// Predicts the y values using the provided x values and computed beta and intercept.\n",
"fn predict_y_values(\n",
" beta_value: FP16x16, x_values: Tensor<FP16x16>, y_values: Tensor<FP16x16>\n",
") -> Tensor<FP16x16> {\n",
" let beta = compute_beta(x_values, y_values);\n",
" let intercept = compute_intercept(beta_value, x_values, y_values);\n",
"\n",
" //create a tensor to hold all the y_pred values\n",
" let mut y_pred_shape = array::ArrayTrait::new();\n",
" y_pred_shape.append(y_values.data.len());\n",
"\n",
" let mut y_pred_vals = array::ArrayTrait::new();\n",
"\n",
" let mut i: u32 = 0;\n",
" loop {\n",
" if i >= y_values.data.len() {\n",
" break ();\n",
" }\n",
" // (*x_values.data.at(i)).print();\n",
" let predicted_value = beta * *x_values.data.at(i) + intercept;\n",
" y_pred_vals.append(predicted_value);\n",
" i += 1;\n",
" };\n",
"\n",
" let y_pred_tensor = TensorTrait::<FP16x16>::new(y_pred_shape.span(), y_pred_vals.span());\n",
"\n",
" return y_pred_tensor;\n",
"}\n",
"\n",
"\n",
"/// Calculates the mean squared error between the true y values and the predicted y values.\n",
"fn compute_mse(y_values: Tensor<FP16x16>, y_pred_values: Tensor<FP16x16>) -> FP16x16 {\n",
" let mut squared_diff_shape = array::ArrayTrait::new();\n",
" squared_diff_shape.append(y_values.data.len());\n",
"\n",
" let mut squared_diff_vals = array::ArrayTrait::new();\n",
"\n",
" let mut i: u32 = 0;\n",
" loop {\n",
" if i >= y_values.data.len() {\n",
" break ();\n",
" }\n",
" let diff = *y_values.data.at(i) - *y_pred_values.data.at(i);\n",
" let squared_diff = diff * diff;\n",
" squared_diff_vals.append(squared_diff);\n",
" i += 1;\n",
" };\n",
"\n",
" let squared_diff_tensor = TensorTrait::<FP16x16>::new(\n",
" squared_diff_shape.span(), squared_diff_vals.span()\n",
" );\n",
"\n",
" let mse = calculate_mean(squared_diff_tensor);\n",
"\n",
" return mse;\n",
"}\n",
"\n",
"/// Calculates the R squared score.\n",
"fn calculate_r_score(y_values: Tensor<FP16x16>, y_pred_values: Tensor<FP16x16>) -> FP16x16 {\n",
" let mean_y_value = calculate_mean(y_values);\n",
"\n",
" // creating the appropriate tensor shapes and empty arrays to populate values into\n",
" let mut squared_diff_shape = array::ArrayTrait::new();\n",
" squared_diff_shape.append(y_values.data.len());\n",
" let mut squared_diff_vals = array::ArrayTrait::new();\n",
"\n",
" let mut squared_mean_diff_shape = array::ArrayTrait::new();\n",
" squared_mean_diff_shape.append(y_values.data.len());\n",
" let mut squared_mean_diff_vals = array::ArrayTrait::new();\n",
"\n",
" let mut i: u32 = 0;\n",
" loop {\n",
" if i >= y_values.data.len() {\n",
" break ();\n",
" }\n",
" let diff_pred = *y_values.data.at(i) - *y_pred_values.data.at(i);\n",
" let squared_diff = diff_pred * diff_pred;\n",
" squared_diff_vals.append(squared_diff);\n",
"\n",
" let diff_mean = *y_values.data.at(i) - mean_y_value;\n",
" let squared_mean_diff = diff_mean * diff_mean;\n",
" squared_mean_diff_vals.append(squared_mean_diff);\n",
" i += 1;\n",
" };\n",
"\n",
" let squared_diff_tensor = TensorTrait::<FP16x16>::new(\n",
" squared_diff_shape.span(), squared_diff_vals.span()\n",
" );\n",
" let squared_mean_diff_tensor = TensorTrait::<FP16x16>::new(\n",
" squared_mean_diff_shape.span(), squared_mean_diff_vals.span()\n",
" );\n",
"\n",
" let sum_squared_diff = squared_diff_tensor.cumsum(0, Option::None(()), Option::None(()));\n",
" let sum_squared_mean_diff = squared_mean_diff_tensor\n",
" .cumsum(0, Option::None(()), Option::None(()));\n",
"\n",
" let r_score = FixedTrait::new_unscaled(1, false)\n",
" - *sum_squared_diff.data.at(y_values.data.len() - 1)\n",
" / *sum_squared_mean_diff.data.at(y_values.data.len() - 1);\n",
"\n",
" return r_score;\n",
"}\n"
]
},
{
"cell_type": "markdown",
"id": "cdc968d1",
"metadata": {},
"source": [
"## Running test on our model"
]
},
{
"cell_type": "code",
"execution_count": 77,
"id": "cd53bcfb",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Writing verifiable_linear_regression/src/test.cairo\n"
]
}
],
"source": [
"%%writefile verifiable_linear_regression/src/test.cairo\n",
"use debug::PrintTrait;\n",
"\n",
"use verifiable_linear_regression::generated::X_values::X_values;\n",
"use verifiable_linear_regression::generated::Y_values::Y_values;\n",
"use verifiable_linear_regression::lin_reg_func::{\n",
" calculate_mean, deviation_from_mean, compute_beta, compute_intercept, predict_y_values,\n",
" compute_mse, calculate_r_score\n",
"};\n",
"\n",
"\n",
"#[test]\n",
"#[available_gas(99999999999999999)]\n",
"fn linear_regression_test() {\n",
" // Fetching the x and y values\n",
" let y_values = Y_values();\n",
" let x_values = X_values();\n",
"\n",
" // (*x_values.data.at(18)).print();\n",
"\n",
" let beta_value = compute_beta(x_values, y_values);\n",
" // beta_value.print(); // calculated gradient value\n",
"\n",
" let intercept_value = compute_intercept(beta_value, x_values, y_values);\n",
" // intercept_value.print(); // calculated intercept value\n",
"\n",
" let y_pred = predict_y_values(beta_value, x_values, y_values);\n",
"\n",
" let mse = compute_mse(y_values, y_pred);\n",
" // mse.print(); // mean squared error ouput\n",
"\n",
" let r_score = calculate_r_score(y_values, y_pred);\n",
" r_score.print(); // accuracy of model around 0.97494506835\n",
"\n",
" assert(beta_value.mag > 0, 'x & y not positively correlated');\n",
" assert(r_score.mag > 0, 'R-Squared needs to be above 0');\n",
" assert(\n",
" r_score.mag < 65536, 'R-Squared has to be below 65536'\n",
" ); // 65536 represents ONE in fp16x16.\n",
" assert(r_score.mag > 32768, 'Accuracy below 50% ');\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting verifiable_linear_regression/src/lib.cairo\n"
]
}
],
"source": [
"%%writefile verifiable_linear_regression/src/lib.cairo\n",
"\n",
"mod generated;\n",
"mod lin_reg_func;\n",
"mod test;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Run test"
]
},
{
"cell_type": "code",
"execution_count": 81,
"id": "ae8a18aa",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2K\u001b[32m\u001b[1m Compiling\u001b[0m test(verifiable_linear_regression_unittest) verifiable_linear_regression v0.1.0 (/Users/raphaeldoukhan/Desktop/Orion-Giza/Academy/Tutorials/orion_tutorials/basic/verifiable_linear_regression_model/Scarb.toml)e --recurse-submodules /Users/ 0s\n",
"\u001b[32m\u001b[1m Finished\u001b[0m release target(s) in 7 seconds\n",
"testing verifiable_linear_regression ...\n",
"running 1 tests\n",
"[DEBUG]\tfalse \t(raw: 0x66616c7365\n",
"\n",
"[DEBUG]\t \t(raw: 0xf996 \n",
"\n",
"test verifiable_linear_regression::test::linear_regression_test ... \u001b[92mok\u001b[0m (gas usage est.: 273795540)\n",
"test result: \u001b[92mok\u001b[0m. 1 passed; 0 failed; 0 ignored; 0 filtered out;\n",
"\n"
]
}
],
"source": [
"! cd verifiable_linear_regression\n",
"! scarb cairo-test -f linear_regression_test"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cfbcc71c",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_linear_regression_model/src/generated.cairo | mod X_values;
mod Y_values;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_linear_regression_model/src/generated/X_values.cairo | use array::ArrayTrait;
use orion::operators::tensor::{FP16x16Tensor, TensorTrait, Tensor};
use orion::numbers::{FixedTrait, FP16x16, FP16x16Impl};
fn X_values() -> Tensor<FP16x16> {
let mut shape = ArrayTrait::new();
shape.append(150);
let mut data = ArrayTrait::new();
data.append(FixedTrait::new(32768, true));
data.append(FixedTrait::new(32328, true));
data.append(FixedTrait::new(31888, true));
data.append(FixedTrait::new(31448, true));
data.append(FixedTrait::new(31008, true));
data.append(FixedTrait::new(30568, true));
data.append(FixedTrait::new(30128, true));
data.append(FixedTrait::new(29689, true));
data.append(FixedTrait::new(29249, true));
data.append(FixedTrait::new(28809, true));
data.append(FixedTrait::new(28369, true));
data.append(FixedTrait::new(27929, true));
data.append(FixedTrait::new(27489, true));
data.append(FixedTrait::new(27050, true));
data.append(FixedTrait::new(26610, true));
data.append(FixedTrait::new(26170, true));
data.append(FixedTrait::new(25730, true));
data.append(FixedTrait::new(25290, true));
data.append(FixedTrait::new(24850, true));
data.append(FixedTrait::new(24411, true));
data.append(FixedTrait::new(23971, true));
data.append(FixedTrait::new(23531, true));
data.append(FixedTrait::new(23091, true));
data.append(FixedTrait::new(22651, true));
data.append(FixedTrait::new(22211, true));
data.append(FixedTrait::new(21772, true));
data.append(FixedTrait::new(21332, true));
data.append(FixedTrait::new(20892, true));
data.append(FixedTrait::new(20452, true));
data.append(FixedTrait::new(20012, true));
data.append(FixedTrait::new(19572, true));
data.append(FixedTrait::new(19132, true));
data.append(FixedTrait::new(18693, true));
data.append(FixedTrait::new(18253, true));
data.append(FixedTrait::new(17813, true));
data.append(FixedTrait::new(17373, true));
data.append(FixedTrait::new(16933, true));
data.append(FixedTrait::new(16493, true));
data.append(FixedTrait::new(16054, true));
data.append(FixedTrait::new(15614, true));
data.append(FixedTrait::new(15174, true));
data.append(FixedTrait::new(14734, true));
data.append(FixedTrait::new(14294, true));
data.append(FixedTrait::new(13854, true));
data.append(FixedTrait::new(13415, true));
data.append(FixedTrait::new(12975, true));
data.append(FixedTrait::new(12535, true));
data.append(FixedTrait::new(12095, true));
data.append(FixedTrait::new(11655, true));
data.append(FixedTrait::new(11215, true));
data.append(FixedTrait::new(10776, true));
data.append(FixedTrait::new(10336, true));
data.append(FixedTrait::new(9896, true));
data.append(FixedTrait::new(9456, true));
data.append(FixedTrait::new(9016, true));
data.append(FixedTrait::new(8576, true));
data.append(FixedTrait::new(8137, true));
data.append(FixedTrait::new(7697, true));
data.append(FixedTrait::new(7257, true));
data.append(FixedTrait::new(6817, true));
data.append(FixedTrait::new(6377, true));
data.append(FixedTrait::new(5937, true));
data.append(FixedTrait::new(5497, true));
data.append(FixedTrait::new(5058, true));
data.append(FixedTrait::new(4618, true));
data.append(FixedTrait::new(4178, true));
data.append(FixedTrait::new(3738, true));
data.append(FixedTrait::new(3298, true));
data.append(FixedTrait::new(2858, true));
data.append(FixedTrait::new(2419, true));
data.append(FixedTrait::new(1979, true));
data.append(FixedTrait::new(1539, true));
data.append(FixedTrait::new(1099, true));
data.append(FixedTrait::new(659, true));
data.append(FixedTrait::new(219, true));
data.append(FixedTrait::new(219, false));
data.append(FixedTrait::new(659, false));
data.append(FixedTrait::new(1099, false));
data.append(FixedTrait::new(1539, false));
data.append(FixedTrait::new(1979, false));
data.append(FixedTrait::new(2419, false));
data.append(FixedTrait::new(2858, false));
data.append(FixedTrait::new(3298, false));
data.append(FixedTrait::new(3738, false));
data.append(FixedTrait::new(4178, false));
data.append(FixedTrait::new(4618, false));
data.append(FixedTrait::new(5058, false));
data.append(FixedTrait::new(5497, false));
data.append(FixedTrait::new(5937, false));
data.append(FixedTrait::new(6377, false));
data.append(FixedTrait::new(6817, false));
data.append(FixedTrait::new(7257, false));
data.append(FixedTrait::new(7697, false));
data.append(FixedTrait::new(8137, false));
data.append(FixedTrait::new(8576, false));
data.append(FixedTrait::new(9016, false));
data.append(FixedTrait::new(9456, false));
data.append(FixedTrait::new(9896, false));
data.append(FixedTrait::new(10336, false));
data.append(FixedTrait::new(10776, false));
data.append(FixedTrait::new(11215, false));
data.append(FixedTrait::new(11655, false));
data.append(FixedTrait::new(12095, false));
data.append(FixedTrait::new(12535, false));
data.append(FixedTrait::new(12975, false));
data.append(FixedTrait::new(13415, false));
data.append(FixedTrait::new(13854, false));
data.append(FixedTrait::new(14294, false));
data.append(FixedTrait::new(14734, false));
data.append(FixedTrait::new(15174, false));
data.append(FixedTrait::new(15614, false));
data.append(FixedTrait::new(16054, false));
data.append(FixedTrait::new(16493, false));
data.append(FixedTrait::new(16933, false));
data.append(FixedTrait::new(17373, false));
data.append(FixedTrait::new(17813, false));
data.append(FixedTrait::new(18253, false));
data.append(FixedTrait::new(18693, false));
data.append(FixedTrait::new(19132, false));
data.append(FixedTrait::new(19572, false));
data.append(FixedTrait::new(20012, false));
data.append(FixedTrait::new(20452, false));
data.append(FixedTrait::new(20892, false));
data.append(FixedTrait::new(21332, false));
data.append(FixedTrait::new(21772, false));
data.append(FixedTrait::new(22211, false));
data.append(FixedTrait::new(22651, false));
data.append(FixedTrait::new(23091, false));
data.append(FixedTrait::new(23531, false));
data.append(FixedTrait::new(23971, false));
data.append(FixedTrait::new(24411, false));
data.append(FixedTrait::new(24850, false));
data.append(FixedTrait::new(25290, false));
data.append(FixedTrait::new(25730, false));
data.append(FixedTrait::new(26170, false));
data.append(FixedTrait::new(26610, false));
data.append(FixedTrait::new(27050, false));
data.append(FixedTrait::new(27489, false));
data.append(FixedTrait::new(27929, false));
data.append(FixedTrait::new(28369, false));
data.append(FixedTrait::new(28809, false));
data.append(FixedTrait::new(29249, false));
data.append(FixedTrait::new(29689, false));
data.append(FixedTrait::new(30128, false));
data.append(FixedTrait::new(30568, false));
data.append(FixedTrait::new(31008, false));
data.append(FixedTrait::new(31448, false));
data.append(FixedTrait::new(31888, false));
data.append(FixedTrait::new(32328, false));
data.append(FixedTrait::new(32768, false));
let tensor = TensorTrait::<FP16x16>::new(shape.span(), data.span());
return tensor;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_linear_regression_model/src/generated/Y_values.cairo | use array::ArrayTrait;
use orion::operators::tensor::{FP16x16Tensor, TensorTrait, Tensor};
use orion::numbers::{FixedTrait, FP16x16, FP16x16Impl};
fn Y_values() -> Tensor<FP16x16> {
let mut shape = ArrayTrait::new();
shape.append(150);
let mut data = ArrayTrait::new();
data.append(FixedTrait::new(265399, false));
data.append(FixedTrait::new(262117, false));
data.append(FixedTrait::new(268148, false));
data.append(FixedTrait::new(274764, false));
data.append(FixedTrait::new(264128, false));
data.append(FixedTrait::new(265007, false));
data.append(FixedTrait::new(277771, false));
data.append(FixedTrait::new(273331, false));
data.append(FixedTrait::new(266104, false));
data.append(FixedTrait::new(273616, false));
data.append(FixedTrait::new(267903, false));
data.append(FixedTrait::new(268768, false));
data.append(FixedTrait::new(274285, false));
data.append(FixedTrait::new(261040, false));
data.append(FixedTrait::new(263155, false));
data.append(FixedTrait::new(271654, false));
data.append(FixedTrait::new(269581, false));
data.append(FixedTrait::new(279157, false));
data.append(FixedTrait::new(272027, false));
data.append(FixedTrait::new(269602, false));
data.append(FixedTrait::new(289342, false));
data.append(FixedTrait::new(279137, false));
data.append(FixedTrait::new(281939, false));
data.append(FixedTrait::new(273039, false));
data.append(FixedTrait::new(279688, false));
data.append(FixedTrait::new(284862, false));
data.append(FixedTrait::new(277472, false));
data.append(FixedTrait::new(288357, false));
data.append(FixedTrait::new(282838, false));
data.append(FixedTrait::new(285743, false));
data.append(FixedTrait::new(284590, false));
data.append(FixedTrait::new(301553, false));
data.append(FixedTrait::new(290205, false));
data.append(FixedTrait::new(284241, false));
data.append(FixedTrait::new(297443, false));
data.append(FixedTrait::new(284931, false));
data.append(FixedTrait::new(295181, false));
data.append(FixedTrait::new(281849, false));
data.append(FixedTrait::new(286867, false));
data.append(FixedTrait::new(297741, false));
data.append(FixedTrait::new(302170, false));
data.append(FixedTrait::new(299333, false));
data.append(FixedTrait::new(298332, false));
data.append(FixedTrait::new(297996, false));
data.append(FixedTrait::new(291160, false));
data.append(FixedTrait::new(297011, false));
data.append(FixedTrait::new(299590, false));
data.append(FixedTrait::new(310416, false));
data.append(FixedTrait::new(306620, false));
data.append(FixedTrait::new(293693, false));
data.append(FixedTrait::new(308251, false));
data.append(FixedTrait::new(304483, false));
data.append(FixedTrait::new(303450, false));
data.append(FixedTrait::new(312775, false));
data.append(FixedTrait::new(316403, false));
data.append(FixedTrait::new(316629, false));
data.append(FixedTrait::new(305906, false));
data.append(FixedTrait::new(310259, false));
data.append(FixedTrait::new(315336, false));
data.append(FixedTrait::new(320438, false));
data.append(FixedTrait::new(311784, false));
data.append(FixedTrait::new(314587, false));
data.append(FixedTrait::new(309433, false));
data.append(FixedTrait::new(309724, false));
data.append(FixedTrait::new(323768, false));
data.append(FixedTrait::new(328211, false));
data.append(FixedTrait::new(319730, false));
data.append(FixedTrait::new(327659, false));
data.append(FixedTrait::new(324332, false));
data.append(FixedTrait::new(318613, false));
data.append(FixedTrait::new(326089, false));
data.append(FixedTrait::new(334680, false));
data.append(FixedTrait::new(325246, false));
data.append(FixedTrait::new(336614, false));
data.append(FixedTrait::new(310071, false));
data.append(FixedTrait::new(333506, false));
data.append(FixedTrait::new(329569, false));
data.append(FixedTrait::new(327919, false));
data.append(FixedTrait::new(331360, false));
data.append(FixedTrait::new(318612, false));
data.append(FixedTrait::new(331078, false));
data.append(FixedTrait::new(335738, false));
data.append(FixedTrait::new(343963, false));
data.append(FixedTrait::new(331760, false));
data.append(FixedTrait::new(330738, false));
data.append(FixedTrait::new(333628, false));
data.append(FixedTrait::new(343795, false));
data.append(FixedTrait::new(340830, false));
data.append(FixedTrait::new(336083, false));
data.append(FixedTrait::new(343799, false));
data.append(FixedTrait::new(341951, false));
data.append(FixedTrait::new(348542, false));
data.append(FixedTrait::new(338473, false));
data.append(FixedTrait::new(341806, false));
data.append(FixedTrait::new(342263, false));
data.append(FixedTrait::new(336122, false));
data.append(FixedTrait::new(348533, false));
data.append(FixedTrait::new(349183, false));
data.append(FixedTrait::new(348385, false));
data.append(FixedTrait::new(347694, false));
data.append(FixedTrait::new(340836, false));
data.append(FixedTrait::new(348234, false));
data.append(FixedTrait::new(349625, false));
data.append(FixedTrait::new(347493, false));
data.append(FixedTrait::new(352573, false));
data.append(FixedTrait::new(357158, false));
data.append(FixedTrait::new(367751, false));
data.append(FixedTrait::new(357413, false));
data.append(FixedTrait::new(358837, false));
data.append(FixedTrait::new(357540, false));
data.append(FixedTrait::new(346333, false));
data.append(FixedTrait::new(359614, false));
data.append(FixedTrait::new(361062, false));
data.append(FixedTrait::new(377690, false));
data.append(FixedTrait::new(361166, false));
data.append(FixedTrait::new(365283, false));
data.append(FixedTrait::new(363959, false));
data.append(FixedTrait::new(357407, false));
data.append(FixedTrait::new(373435, false));
data.append(FixedTrait::new(371753, false));
data.append(FixedTrait::new(372889, false));
data.append(FixedTrait::new(362625, false));
data.append(FixedTrait::new(378658, false));
data.append(FixedTrait::new(361157, false));
data.append(FixedTrait::new(375070, false));
data.append(FixedTrait::new(386459, false));
data.append(FixedTrait::new(366491, false));
data.append(FixedTrait::new(370151, false));
data.append(FixedTrait::new(375395, false));
data.append(FixedTrait::new(372322, false));
data.append(FixedTrait::new(366339, false));
data.append(FixedTrait::new(377831, false));
data.append(FixedTrait::new(371299, false));
data.append(FixedTrait::new(382244, false));
data.append(FixedTrait::new(373995, false));
data.append(FixedTrait::new(391058, false));
data.append(FixedTrait::new(376647, false));
data.append(FixedTrait::new(380549, false));
data.append(FixedTrait::new(388871, false));
data.append(FixedTrait::new(376352, false));
data.append(FixedTrait::new(386789, false));
data.append(FixedTrait::new(394745, false));
data.append(FixedTrait::new(376523, false));
data.append(FixedTrait::new(389147, false));
data.append(FixedTrait::new(390520, false));
data.append(FixedTrait::new(394821, false));
data.append(FixedTrait::new(382470, false));
data.append(FixedTrait::new(382802, false));
data.append(FixedTrait::new(395756, false));
data.append(FixedTrait::new(395162, false));
let tensor = TensorTrait::<FP16x16>::new(shape.span(), data.span());
return tensor;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_linear_regression_model/src/lib.cairo | mod generated;
mod test;
mod lin_reg_func; | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_linear_regression_model/src/lin_reg_func.cairo | use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};
use orion::numbers::{FP16x16, FixedTrait};
/// Calculates the mean of a given 1D tensor.
fn calculate_mean(tensor_data: Tensor<FP16x16>) -> FP16x16 {
let tensor_size = FixedTrait::<FP16x16>::new_unscaled(tensor_data.data.len(), false);
let cumulated_sum = tensor_data.cumsum(0, Option::None(()), Option::None(()));
let sum_result = cumulated_sum.data[tensor_data.data.len() - 1];
let mean = *sum_result / tensor_size;
return mean;
}
/// Calculates the deviation of each element from the mean of the provided 1D tensor.
fn deviation_from_mean(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {
let mean_value = calculate_mean(tensor_data);
let mut tensor_shape = array::ArrayTrait::new();
tensor_shape.append(tensor_data.data.len());
let mut deviation_values = array::ArrayTrait::new();
let mut i: u32 = 0;
loop {
if i >= tensor_data.data.len() {
break ();
}
let distance_from_mean = *tensor_data.data.at(i) - mean_value;
deviation_values.append(distance_from_mean);
i += 1;
};
let distance_from_mean_tensor = TensorTrait::<FP16x16>::new(
tensor_shape.span(), deviation_values.span()
);
return distance_from_mean_tensor;
}
/// Calculates the beta value for linear regression.
fn compute_beta(x_values: Tensor<FP16x16>, y_values: Tensor<FP16x16>) -> FP16x16 {
let x_deviation = deviation_from_mean(x_values);
let y_deviation = deviation_from_mean(y_values);
let x_y_covariance = x_deviation.matmul(@y_deviation);
let x_variance = x_deviation.matmul(@x_deviation);
let beta_value = *x_y_covariance.data.at(0) / *x_variance.data.at(0);
return beta_value;
}
/// Calculates the intercept for linear regression.
fn compute_intercept(
beta_value: FP16x16, x_values: Tensor<FP16x16>, y_values: Tensor<FP16x16>
) -> FP16x16 {
let x_mean = calculate_mean(x_values);
let y_mean = calculate_mean(y_values);
let mx = beta_value * x_mean;
let intercept = y_mean - mx;
return intercept;
}
/// Predicts the y values using the provided x values and computed beta and intercept.
fn predict_y_values(
beta_value: FP16x16, x_values: Tensor<FP16x16>, y_values: Tensor<FP16x16>
) -> Tensor<FP16x16> {
let beta = compute_beta(x_values, y_values);
let intercept = compute_intercept(beta_value, x_values, y_values);
//create a tensor to hold all the y_pred values
let mut y_pred_shape = array::ArrayTrait::new();
y_pred_shape.append(y_values.data.len());
let mut y_pred_vals = array::ArrayTrait::new();
let mut i: u32 = 0;
loop {
if i >= y_values.data.len() {
break ();
}
// (*x_values.data.at(i)).print();
let predicted_value = beta * *x_values.data.at(i) + intercept;
y_pred_vals.append(predicted_value);
i += 1;
};
let y_pred_tensor = TensorTrait::<FP16x16>::new(y_pred_shape.span(), y_pred_vals.span());
return y_pred_tensor;
}
/// Calculates the mean squared error between the true y values and the predicted y values.
fn compute_mse(y_values: Tensor<FP16x16>, y_pred_values: Tensor<FP16x16>) -> FP16x16 {
let mut squared_diff_shape = array::ArrayTrait::new();
squared_diff_shape.append(y_values.data.len());
let mut squared_diff_vals = array::ArrayTrait::new();
let mut i: u32 = 0;
loop {
if i >= y_values.data.len() {
break ();
}
let diff = *y_values.data.at(i) - *y_pred_values.data.at(i);
let squared_diff = diff * diff;
squared_diff_vals.append(squared_diff);
i += 1;
};
let squared_diff_tensor = TensorTrait::<FP16x16>::new(
squared_diff_shape.span(), squared_diff_vals.span()
);
let mse = calculate_mean(squared_diff_tensor);
return mse;
}
/// Calculates the R squared score.
fn calculate_r_score(y_values: Tensor<FP16x16>, y_pred_values: Tensor<FP16x16>) -> FP16x16 {
let mean_y_value = calculate_mean(y_values);
// creating the appropriate tensor shapes and empty arrays to populate values into
let mut squared_diff_shape = array::ArrayTrait::new();
squared_diff_shape.append(y_values.data.len());
let mut squared_diff_vals = array::ArrayTrait::new();
let mut squared_mean_diff_shape = array::ArrayTrait::new();
squared_mean_diff_shape.append(y_values.data.len());
let mut squared_mean_diff_vals = array::ArrayTrait::new();
let mut i: u32 = 0;
loop {
if i >= y_values.data.len() {
break ();
}
let diff_pred = *y_values.data.at(i) - *y_pred_values.data.at(i);
let squared_diff = diff_pred * diff_pred;
squared_diff_vals.append(squared_diff);
let diff_mean = *y_values.data.at(i) - mean_y_value;
let squared_mean_diff = diff_mean * diff_mean;
squared_mean_diff_vals.append(squared_mean_diff);
i += 1;
};
let squared_diff_tensor = TensorTrait::<FP16x16>::new(
squared_diff_shape.span(), squared_diff_vals.span()
);
let squared_mean_diff_tensor = TensorTrait::<FP16x16>::new(
squared_mean_diff_shape.span(), squared_mean_diff_vals.span()
);
let sum_squared_diff = squared_diff_tensor.cumsum(0, Option::None(()), Option::None(()));
let sum_squared_mean_diff = squared_mean_diff_tensor
.cumsum(0, Option::None(()), Option::None(()));
let r_score = FixedTrait::new_unscaled(1, false)
- *sum_squared_diff.data.at(y_values.data.len() - 1)
/ *sum_squared_mean_diff.data.at(y_values.data.len() - 1);
return r_score;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_linear_regression_model/src/test.cairo | use debug::PrintTrait;
use verifiable_linear_regression::generated::X_values::X_values;
use verifiable_linear_regression::generated::Y_values::Y_values;
use verifiable_linear_regression::lin_reg_func::{
calculate_mean, deviation_from_mean, compute_beta, compute_intercept, predict_y_values,
compute_mse, calculate_r_score
};
#[test]
#[available_gas(99999999999999999)]
fn linear_regression_test() {
// Fetching the x and y values
let y_values = Y_values();
let x_values = X_values();
// (*x_values.data.at(18)).print();
let beta_value = compute_beta(x_values, y_values);
// beta_value.print(); // calculated gradient value
let intercept_value = compute_intercept(beta_value, x_values, y_values);
// intercept_value.print(); // calculated intercept value
let y_pred = predict_y_values(beta_value, x_values, y_values);
let mse = compute_mse(y_values, y_pred);
// mse.print(); // mean squared error ouput
let r_score = calculate_r_score(y_values, y_pred);
r_score.print(); // accuracy of model around 0.97494506835
assert(beta_value.mag > 0, 'x & y not positively correlated');
assert(r_score.mag > 0, 'R-Squared needs to be above 0');
assert(
r_score.mag < 65536, 'R-Squared has to be below 65536'
); // 65536 represents ONE in fp16x16.
assert(r_score.mag > 32768, 'Accuracy below 50% ');
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_principal_component_analysis/notebooks/pca_iris.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style='color:Black'> **Verifiable Principal Components Analysis** </span> "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"The Principal Component Analysis (PCA) method is an unsupervised learning algorithm that aims to reduce the dimensionality of a dataset consisting of a large number of interrelated variables, while at the same time preserving as much of the variation present in the original dataset as possible. This is achieved by transforming to a new set of variables, the principal components (PC), which are uncorrelated and are ordered in such a way that the first ones retain most of the variation present in all the original variables. More formally, with PCA, given \n",
"$n$ observations of $p$ variables, it seeks the possibility of adequately representing this information with a smaller number of variables, constructed as linear combinations of the original variables."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below, we provide a brief review of the implementation of a Principal Component Analysis (PCA) in Python, which we will then convert to Cairo to transform it into a verifiable ZKML (Principal Component Analysis), using the Orion library. This provides an opportunity to become familiar with the main functions and operators that the framework offers for the implementation of PCA."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style='color:Black'> **Used DataSet** </span>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the purposes of this tutorial, we will use the iris dataset from sklearn.datasets."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import math\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from sklearn.datasets import load_iris"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Cargar datos de Iris\n",
"data = load_iris()\n",
"X = data['data']\n",
"\n",
"y = data['target']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the purposes of this tutorial, we will not take into account the total number of records in the original dataset. In this sense, we will only focus on the first 105 individuals and the first 3 variables, in order to have comparable results between the python and cairo implementations, taking into consideration the same number of iterations in both programs to achieve orthogonality between the components at the computational level. Therefore, we will have the total number of individuals of the species versicolor and virginica, partially the individuals of the species setosa and with the exclusion of the variable petal width."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"X, y = X[:105,0:3], y[:105]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before applying PCA, it is important to standardize the data. This ensures that each feature has an equal weight in the calculation of principal components."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"mu = np.mean(X, axis=0)\n",
"sigma = np.std(X, axis=0)\n",
"X_std = (X - mu)/sigma"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We print the first 20 rows"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[-0.62902748, 0.86993347, -1.04142584],\n",
" [-0.93199944, -0.19965686, -1.04142584],\n",
" [-1.23497139, 0.22817927, -1.10670925],\n",
" [-1.38645737, 0.0142612 , -0.97614243],\n",
" [-0.78051346, 1.08385154, -1.04142584],\n",
" [-0.17456955, 1.72560573, -0.84557561],\n",
" [-1.38645737, 0.6560154 , -1.04142584],\n",
" [-0.78051346, 0.6560154 , -0.97614243],\n",
" [-1.68942932, -0.41357493, -1.04142584],\n",
" [-0.93199944, 0.0142612 , -0.97614243],\n",
" [-0.17456955, 1.2977696 , -0.97614243],\n",
" [-1.08348542, 0.6560154 , -0.91085902],\n",
" [-1.08348542, -0.19965686, -1.04142584],\n",
" [-1.8409153 , -0.19965686, -1.23727607],\n",
" [ 0.43137435, 1.9395238 , -1.17199266],\n",
" [ 0.27988838, 2.79519606, -0.97614243],\n",
" [-0.17456955, 1.72560573, -1.10670925],\n",
" [-0.62902748, 0.86993347, -1.04142584],\n",
" [ 0.27988838, 1.51168767, -0.84557561],\n",
" [-0.62902748, 1.51168767, -0.97614243]])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_std[0:20,:]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style='color:Black'> **Implementation of the Jacobi algorithm** </span>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Jacobi algorithm is an iterative method for finding the eigenvalues and eigenvectors of a symmetric matrix, which in our case is the correlation matrix over $\\mathbf{X_{std}}$. With this method, the aim is to identify pairs of elements off the main diagonal of the matrix and \"rotate\" them to zero using orthogonal transformations. The idea is that, after enough rotations, the matrix will converge to a diagonal matrix whose diagonal elements will be the eigenvalues of the original matrix. The eigenvectors, on the other hand, are constructed from the rotation matrices applied during the process."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def extract_diagonal(matrix):\n",
" return [matrix[i][i] for i in range(len(matrix))]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def find_max_off_diag(A):\n",
" n = A.shape[0]\n",
" p, q = 0, 1\n",
" max_val = abs(A[p, q])\n",
" for i in range(n-1):\n",
" for j in range(i+1, n):\n",
" if abs(A[i, j]) > max_val:\n",
" max_val = abs(A[i, j])\n",
" p, q = i, j\n",
" return p, q\n",
"\n",
"def jacobi_eigensystem(A, tol=1e-2, max_iter=500): \n",
" if len(A.shape) != 2 or A.shape[0] != A.shape[1]:\n",
" raise ValueError(\"A must be a square matrix\")\n",
" \n",
" n = A.shape[0]\n",
" V = np.eye(n)\n",
"\n",
" for _ in range(max_iter):\n",
" p, q = find_max_off_diag(A)\n",
" \n",
" if abs(A[p, q]) < tol:\n",
" break\n",
" \n",
" if A[p, p] == A[q, q]:\n",
" theta = math.pi/4\n",
" else:\n",
" theta = 0.5 * math.atan(2 * A[p, q] / (A[p, p] - A[q, q]))\n",
" \n",
" J = np.eye(n)\n",
" J[p, p], J[q, q] = math.cos(theta), math.cos(theta)\n",
" J[p, q], J[q, p] = math.sin(theta), -math.sin(theta)\n",
" \n",
" A = np.matmul(np.matmul(J.T,A),J)\n",
" V = np.matmul(V,J)\n",
"\n",
" return extract_diagonal(A), V"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style='color:Black'> **Correlation Matrix** </span>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At this point, we determine the correlation matrix, based on the calculations of the variance and covariance matrix for **$\\mathbf{X_{std}}$** :\n",
"\n",
"We compute the covariance matrix\n",
"\n",
" $\\mathbf{S}$ = $\\mathbf{X}^{T}$ $\\mathbf{X}$ $/\\mathbf{n-1}$ \n",
"\n",
"Then, we determine the correlation matrix :\n",
"\n",
"$\\mathbf{r}$ = $\\mathbf{Cov(X,Y)}$ $/\\mathbf{S_{X} S_{Y}}$,\n",
"\n",
"where $\\mathbf{S_{X} S_{Y}}$ are the standard deviations of X and Y respectively. "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"n = (X_std.shape[0]-1) \n",
"cov_matrix = np.dot(X_std.T,X_std)/n \n",
"stddevs = np.sqrt(extract_diagonal(cov_matrix))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1.00961538, -0.20656485, 0.83469099],\n",
" [-0.20656485, 1.00961538, -0.57369635],\n",
" [ 0.83469099, -0.57369635, 1.00961538]])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cov_matrix"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"corr_matrix = cov_matrix / np.matmul(stddevs.reshape(-1, 1),stddevs.reshape(1, -1))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1. , -0.20459756, 0.82674155],\n",
" [-0.20459756, 1. , -0.56823258],\n",
" [ 0.82674155, -0.56823258, 1. ]])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"corr_matrix"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"evalu, evec = jacobi_eigensystem(corr_matrix)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At this point, we have the eigenvalues and eigenvectors associated with the correlation matrix. Now, we sort the eigenvalues in decreasing order, as the largest of these will be associated with the component that explains the most variability in the data. Consequently, the principal components will be sorted in the same order as the eigenvalues and eigenvectors."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"idx = np.argsort(evalu)[::-1]\n",
"evec = evec[:,idx]\n",
"evalu = np.sort(evalu)[::-1]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([2.10545934, 0.81005256, 0.0844881 ]),\n",
" array([[-0.58699831, 0.55819468, -0.58638867],\n",
" [ 0.45714577, 0.82631669, 0.32896575],\n",
" [-0.66816968, 0.07496276, 0.74022284]]))"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"evalu, evec"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style='color:Black'> **Loadings PCA** </span>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once the aforementioned order is established, we find the loadings which are represented by the discovered eigenvectors (evec). These loadings represent the coefficients of each variable in each of the principal components."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PC1</th>\n",
" <th>PC2</th>\n",
" <th>PC3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>sepal length (cm)</th>\n",
" <td>-0.586998</td>\n",
" <td>0.558195</td>\n",
" <td>-0.586389</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sepal width (cm)</th>\n",
" <td>0.457146</td>\n",
" <td>0.826317</td>\n",
" <td>0.328966</td>\n",
" </tr>\n",
" <tr>\n",
" <th>petal length (cm)</th>\n",
" <td>-0.668170</td>\n",
" <td>0.074963</td>\n",
" <td>0.740223</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PC1 PC2 PC3\n",
"sepal length (cm) -0.586998 0.558195 -0.586389\n",
"sepal width (cm) 0.457146 0.826317 0.328966\n",
"petal length (cm) -0.668170 0.074963 0.740223"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loadings = pd.DataFrame(evec,columns=['PC1','PC2','PC3'], index = data['feature_names'][:3])\n",
"loadings"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style='color:Black'> **New axis, Principal Components** </span>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we identify the new axes or principal components, which are obtained as a linear combination of the standardized original variables."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$\\mathbf{PC_{i}}$ = $\\mathbf{a_{i1}X_1} + \\mathbf{a_{i2}X_2} + \\mathbf{...} + \\mathbf{a_{in}X_n}$,\n",
"\n",
"Where,\n",
"\n",
"$\\mathbf{X_1}, \\mathbf{X_2}, \\mathbf{...} + \\mathbf{X_n}$ are the standardized original variables.\n",
"\n",
"$\\mathbf{a_{i1}}, \\mathbf{a_{i2}}, \\mathbf{...} + \\mathbf{a_{in}}$ are the coefficients or loadings of the $\\mathbf{i-th}$ eigenvector."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>principal component 1</th>\n",
" <th>principal component 2</th>\n",
" <th>principal component 3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.462774</td>\n",
" <td>0.289653</td>\n",
" <td>-0.115854</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.151659</td>\n",
" <td>-0.763285</td>\n",
" <td>-0.290054</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.568707</td>\n",
" <td>-0.583768</td>\n",
" <td>-0.019975</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.472596</td>\n",
" <td>-0.835303</td>\n",
" <td>0.095131</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.649487</td>\n",
" <td>0.381858</td>\n",
" <td>0.043347</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>-1.806412</td>\n",
" <td>1.175944</td>\n",
" <td>0.900363</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101</th>\n",
" <td>-1.555969</td>\n",
" <td>-0.351478</td>\n",
" <td>0.487362</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td>-2.767543</td>\n",
" <td>1.317228</td>\n",
" <td>-0.069714</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td>-2.023098</td>\n",
" <td>0.449313</td>\n",
" <td>0.425579</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td>-2.190391</td>\n",
" <td>0.804982</td>\n",
" <td>0.414940</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>105 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" principal component 1 principal component 2 principal component 3\n",
"0 1.462774 0.289653 -0.115854\n",
"1 1.151659 -0.763285 -0.290054\n",
"2 1.568707 -0.583768 -0.019975\n",
"3 1.472596 -0.835303 0.095131\n",
"4 1.649487 0.381858 0.043347\n",
".. ... ... ...\n",
"100 -1.806412 1.175944 0.900363\n",
"101 -1.555969 -0.351478 0.487362\n",
"102 -2.767543 1.317228 -0.069714\n",
"103 -2.023098 0.449313 0.425579\n",
"104 -2.190391 0.804982 0.414940\n",
"\n",
"[105 rows x 3 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"principalDf = pd.DataFrame(np.dot(X_std,loadings))\n",
"principalDf.columns = [\"principal component {}\".format(i+1) for i in range(principalDf.shape[1])]\n",
"principalDf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we validate the orthogonality between the principal components, as we observe the lack of correlation between these new variables (Principal Components)."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"n = principalDf.shape[0] - 1\n",
"cov_new = np.dot(np.array(principalDf).T,np.array(principalDf))/n \n",
"stddevs = np.sqrt(extract_diagonal(cov_new))\n",
"corr_new = cov_new / np.matmul(stddevs.reshape(-1, 1),stddevs.reshape(1, -1))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1. , -0.002632 , -0.01975951],\n",
" [-0.002632 , 1. , 0.0200618 ],\n",
" [-0.01975951, 0.0200618 , 1. ]])"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"corr_new"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2\n",
"0 1.0 0.0 0.0\n",
"1 0.0 1.0 0.0\n",
"2 0.0 0.0 1.0"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"new_corr = round(abs(pd.DataFrame(corr_new)))\n",
"new_corr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The implementation of Jacobi in Python is carried out considering 500 iterations, in order to optimize its implementation at the Cairo level. That is why rounding is applied when checking for orthogonality."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style='color:Black'> **Selection of the number of components to be retained** </span>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each principal component accounts for a proportion of the total variance, and such proportion can be determined by the ratio of each eigenvalue to the total sum of all eigenvalues. Thus, the percentage of variance explained by the i-th component is given by:\n",
"\n",
"$\\frac{\\lambda_i}{\\sum_{j=1}^{p} \\lambda_j}$"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(np.cumsum(evalu)/np.sum(evalu))\n",
"plt.xlabel('number of components')\n",
"plt.ylabel('cumulative explained variance');"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"97.0"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"select_pc = round(((evalu)/np.sum(evalu))[:2].sum(),2)*100\n",
"select_pc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As observed in the previous graph, we decided to keep the **first 2 components**, which explain **97%** of the total variability of the data."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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vWz5u3LhxvW5bV3V1Nf773/8CAIYMGeLwrOzJkydbblP3xKKaHnzwQbvX9erVy6V15s4PR48etbsGEL/PjRo1snldVFSU5eS5n376qda0p61bt8JoNAIQWzLZ07lzZwwePNjqNnU1a9YMI0eOtHs/5hOCbH09q1evBgD89a9/RUREhN37UCqV6N+/PwBg27ZtdteNGTPG7v3ExcUhKirKbiyuatu2LQDx57ewsNDt+7HFGz+TnnT33XfXmlDWsmVLPPDAA5aTZu+44w7k5+fXuo35d7ahv681XbhwwXKC5rhx46xOwr355pvRu3dvANe7KZD0MbklSZkzZw4E8RUJm2/PP/98ve+ze/fuiIyMtHt9RESEpfXNypUr0a1bN8yYMQPr1q3DH3/84eZX4n1t2rRBjx49ANROXM0f33TTTWjVqpXN5Hb37t3Q6XQAaifJALB//37Lx3feeafDGGpeX/N2rqiZNF+5cqVet63r6NGjuHr1qlVMtrgas6Oeos2aNavXupqJvC3mZvr21Dx7fd++fZaP3TlWV69etZsQdu/eHXK5/X8r0dHRAKy/HqPRiL179wIA3nvvPacjtFeuXAkADseymn+27WnevLnNWOrjvvvusxyjlJQU3HPPPcjJycGuXbvsPgFwlTd+Jr1NoVDgtttuw5tvvomSkhJLKy4z8+9sQ39fa/r0008tXVcefvhhm2vMl3/33Xc4cuSIxx6bAheTWyInzP8EHXnnnXcwatQoAGLrm//85z8YOXIkWrRogTvuuAP/+c9/cOnSJW+HWm/mxNRcIar5sTmpNb//3//+hwsXLtRao1AokJiYWOs+zWsAOG3NFRMTY/N2rmjRooXl4/Ly8nrdti5vxGyvkgqgVgLoyjpniZKzmNu0aWP5uGbMnv66HX0twPWvx9y+reb9ufOKhzn5a0gsDUlCW7RogdWrV6Ndu3YQBAFbtmxBVlaWpYVZamqq0zZq9vjq98hdCxcuxL59+7Bv3z7s378fx44dw+XLl7Fr1y5MmzbNZk9d8+/spUuXGtwazczcw/bWW29FfHy8zTXjxo2DQqGotZ6kjcktkRPmP4qOqNVqrF69Gt9//z2mT5+O22+/HQqFAiaTCTt37sSMGTNw4403OnwZ1R/MievZs2ctPSLrJrcdO3ZE586dIQgCvv3221prevfuDbVabff+69Ont75qvrRf3y0NjngzZm/xRMz+/LprJpj/+Mc/LEmTs7evvvrKbzGbJSYm4tdff8WSJUswbtw4y3hvnU6HgoICjBo1CsOGDXOYiDsTiD+T5gllt9xyC26++WZ07twZKpXK4W3Mv7Mmk8lSqW+IQ4cO4YcffgAgPvm2V+mPjY21/IwtWbIEgiA0+LEpsDG5JfKgvn37Yt68edi5cycuXryI1atXIzU1FQDw22+/4W9/+xv0er2fo7yu7r7b06dP49dff7XstzWruTXBZDKhuLi41uU1mV96BpxXVGu+rFzzdq4YMGCA5YnH+vXrraqB9eGrmL3FWcw1r68Zc6B83TXvTxAES9Lk7M2T42QbIjIyEuPHj8fSpUtx8uRJHD16FG+//bYlvo0bN7o0PramQDk2nlTzb0rdQSbucKcKe/ToUWi12gY/NgU2JrdEXtKkSROMGjUKX3zxBaZNmwYAKCsrs/rD6umqTH3ur23btpZ/wFu3brVUZM37bc1qJrd79+61bLGou98WQK15899//73DxzdXXerezhXNmjXD6NGjAYhbQexNUXNF165dLS9jezNmb9mxY4fL19eM2Z1j1ahRI3Tt2tWdMO0KDw/HzTffDACWExX9qaG/k126dMHUqVOxY8cOSyX3s88+q9d9BPvPpC0PPvigpbq7YMGCBu29FQQBS5YsASBuSfj0008dvi1btsxy7gS3Jkgfk1siH7j33nstH//++++1rjP/wfXUHrT63l/Nfbfmk8bqVmRr7rv98ssvAYh7FuvutwWA2NhY9OzZE4D4D93euFqj0WgZkdq8eXPcdtttLsVb06xZsyx7Jx9//HH89ttvLt2usrKyVrKhVCot34dNmzbZHM1qtmDBAsttvDW+tL4+//xzu68IXLlyxfK13nTTTZYz/AHxuJqr3wsXLrR7/6Wlpdi0aZPVbTzpvvvuAwAcPHgQGzdu9Pj910fNE0gb8nupVqstJ/vV/b13Jth/Jm1p1aqVpbNDWVkZnnzySZdve+DAAezatcvy+datW1FaWgoAeOSRR/Dggw86fHvooYcwdOhQAOLviysjqSl4MbklaqCjR4/WOiHLlpp7A7t06VLrOnOy8dtvvzXozO269+fqWcE1992ak6C6/yA7deqETp06QRAEvPPOOwDE/XM1z/qvKSMjA4A4S95cta7rhRdewIEDBwCIrYwctX+y5/bbb8ezzz4LQGxBNmDAgFrdAGzZtm0bNBoNli1bZjPmqqoqTJo0yeYs+oULF1qOZWpqaq1E0Z/Onj2L6dOn27wuKyvLkvRPmTKl1nWxsbFISUkBIG7t+Oijj6xuX1VVhYkTJ1q+H1OnTvVk6BZPPPGEpT3XhAkT8NNPPzlcv3btWvzvf//zSiw1j6uj36ONGzeirKzM7vWXLl2yVFXr/t67Iph/Ju155ZVXLE9+FyxYgMmTJ9t9AgwABoMB77zzDu68806cPHnScnnN6uvf/vY3lx57zJgxAMTj4unWbRRYlP4OgCjYlZaW4u6778ZNN92ElJQUJCQkoF27dgDEnrErVqywJI29e/e2autz1113ARBPskhLS8Pjjz+Oli1bWq7v1q1bveK56667sGXLFuzYsQOvvvoqhg8fbukrqVKpLLGZ1dxacOnSJav9tmaDBg3CRx99ZNmS4KhClJaWhqVLl2Lbtm1YtGgRTpw4gfT0dHTp0gVlZWVYuHChpQJ8ww034LnnnqvX11jTnDlzcPbsWbz//vv45Zdf0Lt3b4wePRojR45Et27d0KRJE5w7dw779+/H6tWrLU9EzC8Xm40cORL3338/Pv/8c3z11Vfo168fsrKy0KNHD1y8eBHLly+3VDejo6ORnZ3tdsyelpCQgLy8PBw7dgxpaWno0KEDTp48iby8PEsVtE+fPkhLS7O6bU5ODjZv3oyLFy9i4sSJ0Gq1eOCBB9C8eXMcPHgQ8+bNs5z8M3bsWAwfPtwrX0ObNm3w0UcfYcyYMSgrK0NCQgIee+wxDB8+HO3bt4fBYMCpU6fwww8/YOXKlTh69CiKioosvYA9qU+fPoiMjMS1a9fw3HPPISwsDJ06dbK8StCuXTuoVCp8+umnGDVqFAYPHowhQ4bglltuQXR0NC5fvoz9+/fjnXfewenTpwHA5vfemWD+mbSncePGWLNmDYYPH47Dhw9jwYIFWL16NcaPH4+BAweibdu2EAQBZWVl+Pbbb/HFF19YKrRmV69exRdffAFAfILbuXNnlx571KhRCA8PR1VVFT7++GNLC0eSIH9MjiDypJqTmmpOIHPG1QllAwcOdPnxHb316NHD5ix3o9Fomdxk662+Tp06JURHR9u8L3tfi3ksJmxMsTKr+f0CIKxatcphHOfPnxc0Go3D70nPnj1rjedsiLy8PMskLmdvd911l/DDDz9Y3Yder7dMO7L3FhsbK+zZs8dmDK5Otqo56ckRR9Prav7cbdy4URgyZIjDn73Tp0/bfZzdu3cLsbGxDr/u1NRUQa/X27y9q78rrnzdq1evtvvzW/NNLpdbjVqtOaFs0aJFDmNxNrVrxowZdh/bPJ3LfHycvaWlpQlGo9FhPPY09GfSla/VVa6M33XV+fPnhb///e+WUdSO3sLCwoRp06YJf/zxhyAIgrBkyRLLdXPnzq3X444YMUIAxGluZ8+ebdDXQIGL2xKIGigxMRFbt27FrFmzcPfdd1uqhWFhYWjTpg2GDBmC/Px87N271+ZLk3K5HF999RWeffZZ9OrVC1FRUQ06oaVdu3b44YcfMGnSJHTr1s3hAAozW50R6qp5uVwuR1JSksP7jI6OxrfffouPP/4Yw4YNQ5s2bRAWFoYWLVpg0KBBeOedd7B371506tTJpa/LmbS0NBw9ehQffPABxowZgxtuuAFqtRphYWFo1aqVper1ww8/oKSkxObgg8jISHz55ZeWLhexsbEIDw9H8+bNceedd2Lu3Lk4dOiQZeJRoAgPD8e6deuQm5uLfv36oVmzZmjUqBHi4+Px0ksvYffu3YiNjbV7+z59+uDQoUOYO3cu7rzzTjRr1gzh4eGIjY1FamoqVq9ejS+++MKln6WGGjVqFI4dO4Z58+bhnnvusfzcqFQqdOnSBX/961+RnZ2N48eP4+677/ZaHK+++io++OADJCYmIjo62uY+45ycHCxZsgQTJ060vGITHh4OlUqFG2+8EY8++iiKi4uRl5fncLiFI8H6M+lMdHQ0Pv74Y+zfvx9z5szBgAED0K5dO0RERKBRo0bo2LEjRo0ahZycHJw6dQpvvvmmZSiEO1sS6q6vrq622ppE0iETBDZ8IyIKNlu3brUkd1u2bAnoE4mIiHyJlVsiIiIikgwmt0REREQkGUxuiYiIiEgymNwSERERkWQwuSUiIiIiyWC3BCIiIiKSDE4ogzgZ6syZM2jSpEmD+osSERERkXcIgoDLly8jNjbWYe9oJrcQZ9J36NDB32EQERERkRMnT560GqFeE5NbAE2aNAEALFiwAKNHj0ZYWJifIyJ3GQwGfPXVVxgyZAiPYxDjcZQGHkdp4HGUBikcR51Ohw4dOljyNnuY3AKWrQiNGjWyjOuk4GQwGHgcJYDHURp4HKWBx1EapHQcnW0hZbcEIiIiIpIMJrdEREREJBlMbomIiIhIMrjn1kWCIKC6uhpGo9HfoZADBoMBSqUS165ds3uswsLCoFAofBwZERER+QKTWxdUVVWhrKwMV69e9Xco5IQgCIiJicHJkyftbjiXyWRo3749oqKifBwdEREReRuTWydMJhOOHTsGhUKB2NhYhIeHc9BDADOZTKioqEBUVJTNBs+CIODcuXM4deoUunfvzgouERGRxDC5daKqqgomkwkdOnRAo0aN/B0OOWEymVBVVYXIyEi700tatWqF48ePw2AwMLklIiKSGJ5Q5iJHY94ouLDyTkREJF3M2IiIiIhIMpjcEhEREZFkMLklIiIiIslgcutLej1QXi6+D2DHjx+HTCbD3r17/R0KERERUb0wufUFrRZITQWiooCYGPF9aipQUuLvyIiIiIgkhcmtt+XlAUlJQFERYDKJl5lM4ueJiUB+vtceeuXKlYiPj4dKpUKLFi3wl7/8BVeuXAEALFiwAD179kRkZCR69OiB3Nxcy+26dOkCAOjTpw9kMhkGDRr0Z9gmvPjii2jfvj0iIiLQu3dvbNiwwXK7qqoqTJ06FW3btkVkZCQ6deqEuXPnWq7Pzs5GfHw8GjdujA4dOiA9PR0VFRVe+/qJiIgo9LDPrTdptUBGBiAIQHV17evMn6enA/HxgEbj0YcuKyvDQw89hNdffx0pKSm4fPkyiouLIQgCli5ditmzZ+Odd95Bnz59sGfPHkyePBmNGzfGo48+ih9++AF9+/bF119/jZtvvhnh4eEAgDfffBPz58/He++9hz59+mDhwoW477778NNPP6F79+546623sHr1anz22Wfo2LEjTp48iZMnT1piksvleOutt9ClSxccPXoU6enpmDFjRq3EmoiIiKghmNx6U3Y2oFBYJ7Y1KRRATo5Xktvq6mqkpqaiU6dOAID4+HgAwJw5czB//nykpqYCECu1Bw4cwHvvvYdHH30UrVq1AgC0aNECMTExlvucN28eZs6ciQcffBAA8Nprr2HLli1444038O6776K0tBTdu3fHgAEDIJPJLI9r9uSTT1o+7ty5M1566SWkpaUxuSUiIiKPYXLrLXo9UFh4fSuCPdXVQEGBuF6l8tjD9+rVC/feey/i4+MxdOhQDBkyBGPGjEF4eDiOHDmCSZMmYfLkyTXCqEbTpk3t3p9Op8OZM2egqZOEazQa/PjjjwCAxx57DIMHD0ZcXByGDRuGv/71rxgyZIhl7ddff425c+fi4MGD0Ol0qK6uxrVr13D16lVOfyMiIiKP4J5bb9HpnCe2ZiaTuN6DFAoFNm3ahPXr1+Omm27C22+/jbi4OOzfvx8A8MEHH2Dv3r2Wt/3792P79u0NeszbbrsNx44dw7///W/o9XqMHTsWY8aMASB2YPjrX/+KW2+9FV988QV27dqFd999F4C4V5eIiIjIE1i59Ra1GpDLXUtw5XJxvYfJZDJoNBpoNBrMnj0bnTp1QklJCWJjY3H06FGMHz/e5u3Me2yNRqPlMrVajdjYWJSUlGDgwIGWy0tKStC3b99a6x544AE88MADGDNmDIYNG4YLFy5g165dMJlMmD9/vmWU8Weffebxr5mIiIhCG5Nbb1GpgORksSuCoz23SqW4zoNbEgDg+++/x+bNmzFkyBC0bt0a33//Pc6dO4eePXvihRdewLRp09C0aVMMGzYMlZWV2LlzJy5evIisrCy0bt0aKpUKGzZsQPv27REZGYmmTZviX//6F+bMmYMbbrgBvXv3xqJFi7B3714sXboUgNgNoW3btujTpw/kcjk+//xzxMTEoFmzZujWrRsMBgPefvttjBo1CiUlJcj3YqcIIiKigKfXi6/cqtUezwNCGbcleFNWFlCj+mmT0QhkZnr8odVqNb799luMGDECN954I5599lnMnz8fw4cPxz/+8Q8sWLAAixYtQnx8PAYOHIjFixdbWoAplUq89dZbeO+99xAbG4vk5GQAwLRp05CVlYXp06cjPj4eGzZswOrVq9G9e3cAQJMmTfD6668jISEBd9xxB44fP45169ZBLpejV69eyM7OxmuvvYZbbrkFS5curdUmjIiIKGSw/71XyQRBEPwdhL/pdDo0bdoUy5Ytw5gxYxAWFma57tq1azh27Bi6dOmCyMjI+t95fr7Y7qtu1wSlUkxsc3OBtDQPfBUEiL14dTod1Gq1ZftDXQ0+puR1BoMB69atw4gRI2r9PlJw4XGUBh5HD8vLE9uE+jgvkMJxNOdrly5dgtrBdk5Wbr0tLQ0oLha3HpiTLblc/Ly4mIktERFRqHDW/14QxIIYK7gNwj23vqDRiG/cW0NERBS6/Nj/PpQwufUllYpJLRERUSjyc//7UMJtCURERETe5uf+96GEyS0RERGRt5n737vCS/3vQwWTWyIiIiJvM/e/VzrZEapUAikp3JLQAExuiYiIiHzBj/3vQwmTWyIiIiJfGDBA7GMrk1lXcJVK8fLcXHZKaCAmt0RERES+wv73Xsfkljzm+PHjkMlk2Lt3b0DeHxERUUDQaICVK4GKCuDsWfH9ypWs2HoI+9z6kN6gh65SB3WEGqow6W0U79ChA8rKytCyZUt/h0JERBT42P/eK1i59QFtqRapK1IRNTcKMfNjEDU3CqkrUlFSGlzj9QwGg8PrFQoFYmJioHR2JqgPVVVV+TsEIiIi8iEmt16WtyMPSYuSUHS4CCZBbN5sEkwoOlyExEWJyN+Z75XHff/99xEbGwtTnYbRycnJmDhxIgCgsLAQt912GyIjI9G1a1e88MILqK4xElAmkyEvLw/33XcfGjdujJdffhkXL17E+PHj0apVK6hUKnTv3h2LFi0CYHsbwU8//YS//vWvUKvVaNKkCRITE3HkyBHx+2Ay4cUXX0T79u0RERGB3r17Y8OGDQ6/rv/+97/o27cvIiIi0LZtWzz99NO1Yr7nnnvwr3/9C5mZmWjZsiWGDh3aoO8jERERBRcmt16kLdUiY10GBAioNtWeI11tqoYAAelr071Swb3//vtx/vx5bNmyxXLZhQsXsGHDBowfPx7FxcV45JFH8MQTT+DAgQN47733sHjxYrz88su17uf5559HSkoK9u3bh4kTJ+K5557DgQMHsH79evz888/Iy8uzuw3h9OnTSEpKQkREBL755hvs2rULEydOtCSjb775JubPn4958+bhf//7H4YOHYr77rsPv/zyi937GzFiBO644w78+OOPyMvLw4cffoiXXnqp1rrly5cjPDwcJSUlyM/3zpMHIiIiCkyB8/qxBGVvy4ZCrrBKbGtSyBXI2Z4DTUfPbiJv3rw5hg8fjmXLluHee+8FAKxcuRItW7bE3XffjSFDhuDpp5/Go48+CgDo2rUr/v3vf2PGjBmYM2eO5X7GjRuHCRMmWD4vLS1Fnz59kJCQAADo3Lmz3RjeffddNG3aFMuXL0dYWBgA4MYbb7RcP2/ePMycORMPPvggAOC1117Dli1b8MYbb+Ddd9+1ur/c3Fx06NAB77zzDmQyGXr06IEzZ85g5syZmD17NuR/nnXatWtXvPbaa5bPiYiIKHTwv7+X6A16FB4qdJjYAmIFt+BgAfQGvcdjGD9+PL744gtUVlYCAJYuXYoHH3wQcrkcP/74I1588UVERUVZ3iZPnoyysjJcvXrVch/mJNZsypQpWL58OXr37o0ZM2bgu+++s/v4e/fuRWJioiWxrUmn0+HMmTPQ1DkzVKPR4Oeff7Z5fz///DP69+8PmUxWa31FRQVOnTpluax37972vylEREQkaUxuvURXqbPssXXGJJigq9R5PIZRo0ZBEASsXbsWJ0+eRHFxMcaPHw8AqKiowAsvvIC9e/da3vbt24dffvkFkZGRlvto3LhxrfscPnw4Tpw4gczMTJw5cwb33nsvnnrqKZuPr/LTGaCNGjXyy+MSERGR/zG59RJ1hBpymWvfXrlMDnWE2uMxREZGIjU1FUuXLsWnn36KuLg43HbbbQCA2267DYcOHUK3bt2s3py9nN+qVSs8+uijWLJkCd544w28//77NtfdeuutKC4uttllQa1WIzY2FiUltfcbl5SU4KabbrJ5fz179sS2bdsgCEKt9U2aNEH79u0dxkxEREShgcmtl6jCVEiOS4ZS7nhbs1KuREqPFK/1vR0/fjzWrl2LhQsXWqq2ADB79mx8/PHHeOGFF/DTTz/h559/xvLly/Hss886vL/Zs2ejsLAQv/76K3766SesWbMGPXv2tLl26tSp0Ol0ePDBB7Fz50788ssv+OSTT3Do0CEAwL/+9S+89tprWLFiBQ4dOoSnn34ae/fuxRNPPGHz/tLT03Hy5Ek8/vjjOHjwIAoLCzFnzhxkZWVxfy0REREBYHLrVVn9s2A0GR2uMZqMyOyX6bUY7rnnHkRHR+PQoUMYN26c5fKhQ4dizZo1+Oqrr3DHHXegX79+yMnJQadOnRzeX3h4OGbNmoVbb70VSUlJUCgUWL58uc21LVq0wDfffIOKigoMHDgQt99+Oz744APLHtxp06YhKysL06dPR3x8PDZs2IDVq1eje/fuNu+vXbt2WLduHX744Qf06tULaWlpmDRpktOEnIiIiEIHuyV40YCOA5A7Mhfpa9OtuiYo5UoYTUbkjsz1eKeEmuRyOc6cOWPzuqFDhzrsA1vz5X+zZ5991m4y2blzZ6vb3Hrrrdi4caPd2ObMmVOrO4Oz+xs4cCB++OEHuzF/88030Ok8v3+ZiIiIggMrt16WlpCG4gnFSI5LtuzBlcvkSI5LRvGEYqQlpPk5QiIiIiLpYOXWBzQdNdB01EBv0ENXqYM6Qu21PbZEREREoYzJrQ+pwlRMaomIiIi8iNsSiIiIiEgymNwSERERkWQwuSUiIiIiyWByS0RERESSweSWiIiIiCSDyS0RERERSQaTWx/S64HycvG9vz3//PPo3bt3g+9n69atkMlk+OOPP1y+zWOPPYbRo0c3+LGJiIiI6mJy6wNaLZCaCkRFATEx4vvUVKCkxH8xPfXUU9i8eXOD7+euu+5CWVkZmjZt6vJt3nzzTSxevLjBj01ERERUF5NbL8vLA5KSgKIiwGQSLzOZxM8TE4H8fP/EFRUVhRYtWti9vqqqyqX7CQ8PR0xMDGQymcuP3bRpUzRr1szl9URERESuYnLrRVotkJEBCAJQXV37uupq8fL0dO9UcN9//33ExsbCZM6o/5ScnIyJEydabUswbxV4+eWXERsbi7i4OADAd999h969eyMyMhIJCQlYtWoVZDIZ9u7dC8B6W8LixYvRrFkzbNy4ET179kRUVBSGDRuGsrIyq8cyM5lMeP3119GtWzdERESgY8eOePnlly3Xz5w5EzfeeCMaNWqErl274rnnnoPBYPDsN4yIiIgkgcmtF2VnAwqF4zUKBZCT4/nHvv/++3H+/Hls2bLFctmFCxewYcMGjB8/3uZtNm/ejEOHDmHTpk1Ys2YNdDodRo0ahfj4eOzevRv//ve/MXPmTKePffXqVcybNw+ffPIJvv32W5SWluKpp56yu37WrFl49dVX8dxzz+HAgQNYtmwZ2rRpY7m+SZMmWLx4MQ4cOIA333wTH3zwAXK88U0jIiKioKf0dwBSpdcDhYXXtyLYU10NFBSI61Uqzz1+8+bNMXz4cCxbtgz33nsvAGDlypVo2bIl7r77bhQXF1vdpnHjxliwYAHCw8MBAPn5+ZDJZPjggw8QGRmJm266CadPn8bkyZMdPrbBYEB+fj5uuOEGAMDUqVPx4osv2lx7+fJlvPnmm3jnnXfw6KOPAgBuuOEGDBgwwLLm2WeftXzcuXNnPPXUU1i+fDlmzJhRj+8IERERhQJWbr1Ep3Oe2JqZTOJ6Txs/fjy++OILVFZWAgCWLl2KBx98EHK57cMeHx9vSWwB4NChQ7j11lsRGRlpuaxv375OH7dRo0aWxBYA2rZti99++83m2p9//hmVlZWWBNyWFStWQKPRICYmBlFRUXj22WdRWlrqNA4iIiIKPUxuvUStBuzkkFbkcnG9p40aNQqCIGDt2rU4efIkiouL7W5JAMTKrSeEhYXV+lwmk0EQBJtrVU7K1du2bcP48eMxYsQIrFmzBnv27MEzzzzj8glvREREFFqY3HqJSgUkJwNKJxs/lEogJcWzWxLMIiMjkZqaiqVLl+LTTz9FXFwcbrvtNpdvHxcXh3379lkqvwCwY8cOj8bYvXt3qFQqu23JvvvuO3Tq1AnPPPMMEhIS0L17d5w4ccKjMRAREZF0MLn1oqwswGh0vMZoBDIzvRfD+PHjsXbtWixcuNBh1daWcePGwWQy4f/9v/+Hn3/+GRs3bsS8efMAoF6tvxyJjIzEzJkzMWPGDHz88cc4cuQItm/fjg8//BCAmPyWlpZi+fLlOHLkCN566y0UFBR45LGJiIhIepjcetGAAUBuLiCTWVdwlUrx8txcQKPxXgz33HMPoqOjcejQIYwbN65et1Wr1SgqKsLevXvRu3dvPPPMM5g9ezYA1NqH21DPPfccpk+fjtmzZ6Nnz5544IEHLHt077vvPmRmZmLq1Kno3bs3vvvuOzz33HMee2wiIiKSFplgbzNkCNHpdGjatCmWLVuGMWPG1Nozeu3aNRw7dgxdunRxO6ErKRHbfRUUiCePyeXiVoTMTO8mtt6wdOlSTJgwAZcuXXK6X9YfTCYTdDod1Gq13RPnPHFMybsMBgPWrVuHESNGWO3hpuDB4ygNPI7SIIXjaM7XLl26BLWDk5XYCswHNBrxTa8XuyKo1d7ZY+sNH3/8Mbp27Yp27drhxx9/xMyZMzF27NiATGyJiIiImNz6kEoVPEmt2dmzZzF79mycPXsWbdu2xf33319rehgRERFRIGFySw7NmDGDwxKIiIgoaAT9CWVz587FHXfcgSZNmqB169YYPXo0Dh065O+wiIiIiMgPgj65/e9//4uMjAxs374dmzZtgsFgwJAhQ3DlyhWPPg7Pu5MOHksiIiLpCvptCRs2bKj1+eLFi9G6dWvs2rULSUlJDb5/8xmFV69e5UlUEmGebqZQKPwcCREREXla0Ce3dV26dAkAEB0dbXdNZWVlralbOp3O8rHBYLBa36RJE5SXl8NkMqFRo0YeG2BAnicIAqqqqqDX620eJ5PJhN9++w2RkZEQBMHm8Sb/Mx8XHp/gxuMoDTyO0iCF4+hq7JLqc2symXDffffhjz/+gFartbvu+eefxwsvvGB1+bJly9CoUSObt2nSpAmaNGlit3cqBQ+DwYBz587BZDL5OxQiIiJy0dWrVzFu3DinfW4lldxOmTIF69evh1arRfv27e2us1W57dChA5YtW4bk5GS7zY2NRiOqq6u5ZzOAVVdX47vvvsNdd90FZd2xcBDHBoeFhfFJSoAzGAzYtGkTBg8eHLTNxonHUSp4HKVBCsdRp9OhZcuWoTPEYerUqVizZg2+/fZbh4ktAERERCAiIsLmdWFhYXYPerD+MIQSg8GA6upqREVF8XhJgKPfRwoePI7SwOMoDcF8HF2NO+iTW0EQ8Pjjj6OgoABbt25Fly5d/B0SEREREflJ0Ce3GRkZWLZsGQoLC9GkSROcPXsWANC0aVN2NyAiIiIKMUG/8TAvLw+XLl3CoEGD0LZtW8vbihUr/B0aEREREflY0FdueXIXEREREZkFfeWWiIiIiMiMyS0RERERSQaTWyIiIiKSDCa3RERERP6m1wPl5eJ7ahAmt0RERET+otUCqalAVBQQEyO+T00FSkr8HVnQYnJLRERE5A95eUBSElBUBJhM4mUmk/h5YiKQn+/f+IIUk1siIiIiX9NqgYwMQBCA6ura11VXi5enp7OC6wYmt0RERES+lp0NKBSO1ygUQE6Ob+KRECa3RERERL6k1wOFhdYV27qqq4GCAp5kVk9MbomIiIh8Sae7vsfWGZNJXE8uY3JLRERE5EtqNSB3MQWTy8X15DImt0RERES+pFIBycmAUul4nVIJpKSI68llTG6JiIiIfC0rCzAaHa8xGoHMTN/EIyFMbomIiIh8bcAAIDcXkMmsK7hKpXh5bi6g0fgnviDG5JaIiIjIH9LSgOJicYuCeQ+uXC5+XlwsXk/15mSzBxERERF5jUYjvun1YlcEtZp7bBuIyS0RERFJV7AkjSpVYMcXRLgtgYiIiKRHqwVSU4GoKCAmRnyfmspxtiGAyS0RERFJS14ekJQEFBVdH5ZgMomfJyYC+fn+jY+8isktERERSYdWC2RkAIJgPd62ulq8PD2dFVwJY3JLRERE0pGdDSgUjtcoFEBOjm/iIZ9jcktERETSoNcDhYXWFdu6qquBggJxPUkOk1siIiKSBp3u+h5bZ0wmcT1JDpNbIiIikga1+vowBGfkcnE9SQ6TWyIiIpIGlUqc7lV3nG1dSiWQksK+shLF5JaIiIikIysLMBodrzEagcxM38RDPsfkloiIiKRjwAAgNxeQyawruEqleHlurjjyliSJyS0RERFJS1oaUFwsblEw78GVy8XPi4vF60mynGxKISIiIgpCGo34pteLXRHUau6xDRFMbomIiEi6VComtSGG2xKIiIiISDKY3BIRERGRZDC5JSIiIiLJYHJLRERERJLB5JaIiIiIJIPJLRERERFJBpNbIiIiIpIMJrdEREREJBlMbomIiIhIMpjcEhEREZFkMLklIiIiIslgcktEREREksHkloiIiIgkg8ktSZ7eoEd5RTn0Br2/QyEiIiIvY3JLkqUt1SJ1RSqi5kYhZn4MouZGIXVFKkpKS/wdGhEREXkJk1uSpA93f4ikRUkoOlwEk2ACAJgEE4oOFyFxUSLyd+b7OUIiIiLyBia3JEnTv5oOAQKqTdW1Lq82VUOAgPS16azgEhERSRCTW5IkhVzh9Pqc7Tk+ioaIiIh8hcktSco1wzUAsKrY1lVtqkbBwQKeZEZERCQxTG5JUnRVOpfXmgQTdJWuryciIqLAx+SWJEUdrnZ5rVwmhzrC9fVEREQU+JT+DoDIkyLDIgEASrkSMNlfp5QrkRyXDFWYykeRERERkS+wckuSZDQZnV6f2S/TR9EQERGRrzC5JUnKHpoNGWRiBbcGpVwJGWTIHZkLTUeNn6IjIiLyIL0eKC8X3xOTW5KmiX0monhCMZLjkiGXiT/mcpkcyXHJKJ5QjLSEND9HSERE1EBaLZCaCkRFATEx4vvUVKAktPu4c88tSZamowaajhroDXroKnVQR6i5x5aIiKQhLw/IyAAUCsD050kmJhNQVASsWgXk5gJpoVnIYeWWJE8VpkKbqDZMbImISBq0WjGxFQSguk5f9+pq8fL09JCt4DK5JSIiIgom2dlixdYRhQLICc1JnExuiYiIiIKFXg8UFlpXbOuqrgYKCkLyJDMmt0RERETBQqe7vsfWGZNJXB9imNwSERERBQu1GpC7mL7J5eL6EMPkloiIiChYqFRAcjKgdNLwSqkEUlLE9SGGyS0RERFRMMnKAoyOJ3HCaAQyQ3MSJ5NbIiIiomAyYIDYx1Yms67gKpXi5bm5gCY0J3EyuSUiIiIKNmlpQHGxuEXBvAdXLhc/Ly4O2QEOACeUERERkafo9eLZ+Wp1SO719DmNRnzj970WVm6JiIioYbRaIDUViIoCYmLE96mpITshy+dUKqBNGya2f2JyS0RERO7LywOSkoCiouv9V00m8fPERCA/37/xUchhcusHej1QXh6SQ0OIiEhKtFogIwMQBOuJWdXV4uXp6azgkk8xufUhvmpDRESSkp0NKBSO1ygUQE6Ob+IhApNbn+GrNkREJCl6PVBYaF2xrau6Gigo4MuV5DNMbn2Ar9oQEZHk6HTXqzXOmEzieiIfYHLrA3zVhoiIJEetvt5f1Rm5XFxP5ANMbr2Mr9p4h96gR3lFOfQGfsOIiPxCpRIHBtSdkFWXUgmkpLBNFfkMk1sv46s2nqUt1SJ1RSqi5kYhZn4MouZGIXVFKkpKuaeDiMjnsrIAo9HxGqMRyMz0TTyuYMsiyWNy62V81cZz8nbkIWlREooOF8EkiM8YTIIJRYeLkLgoEfk7eVYeEZFPDRgA5OYCMpl1BVepFC/PzRWnaPkbWxaFDCa3XsZXbTxDW6pFxroMCBBQbaq9x6PaVA0BAtLXpmP7qe1+ipCIKESlpQHFxeI/O3M1Ry4XPy8uFq/3N2+2LGIlOOAwufWBYHzVJtBkb8uGQu74rDyFXIHcHbk+ioiIiCw0GmDlSqCiAjh7Vny/cmXgVGy90bKIleCAxeTWB4LpVZtApDfoUXio0KpiW1e1qRpFh4t8FBUREVlRqYA2bQLrZUhvtCxi8/qAxuTWR4LhVZtApavUWfbYOuPqOiIiCgHeaFnE5vUBz8lOUPIkjUZ80+vFrghqdWA9uQ1U6gg15DK5S4mrXMbna0RE9Cd3WhY5+8dsrgQ7SpjNlWC+JOsXzAT8IBBftQlkqjAVkuOSoZQ7fi6mlCsx6sZRPoqKiIgCnqdbFrF5fVBgcktBIat/Fowmx2flGU1GpN+R7qOIiIgo4Hm6ZRGb1wcFJrcUFAZ0HIDckbmQQWZVwVXKlZBBhtyRuejXvp+fIiQiooDkyZZFbF4fFJjcUtBIS0hD8YRiJMclW/bWymVyJMclo3hCMdISeFYeERHV4cmWRWxeHxR4QhkFFU1HDTQdNdAb9NBV6qCOUEMV5v0/HjwJkIgoiKWlAfHx4kleBQXilgFzy6LMzPqd+JWVBaxa5XgNm9f7FSu3FJRUYSq0iWrj9cSWPbqJiCTCU4Mm2Lw+4DG5JbKDPbqJiCTIEy2L2Lw+oHFbApENznp0A2KP7vh4PjknIgpJbF4fsFi5JbLBG9MaiYhIgti8PuAwuSWqgz26iYiIgheTW6I62KObiIgoeDG5JaqDPbpt0xv0KK8oh97AUjUREQUuJrdEdbBHd23aUi1SV6Qiam4UYubHIGpuFFJXpKKklP3QiIgo8DC5JbLBk9Mag1nejjwkLUpC0eEimARxr4ZJMKHocBESFyUifyf7oRERUWBhcktkg6Me3ZAbAJjQa2I+0EG61UttqRYZ6zIgQEC1qfbZddWmaggQkL42nRVcIiIKKExuieww9+i+JfEIIPuzjCszAnGFwMRE7O/0eL2ql8G2ZzV7WzYUcsf90BRyBXK2sx8aEREFDg5xIHJA6KDFjwOTgLsigEo1EKEDwq4BAKr/7KiQvjYd8a3joeloe5qDtlSL7G3ZKDxUCJNgglwmR3JcMqb3n273Nv6mN+gt8TpSbapGwcEC6A16r49CJiIicgUrt0QOWKqXYdeAqN8siW1NjqqXwbpnVVepc5rYmpkEE3SV7IdGRESBgcktkR3m6mXd/aZ11axe1hTMe1bVEWrIZa79eZDL5FBHhEg/NCIiCnhMbonsaGj1Mpj3rKrCVEiOS4ZS7njnklKuREqPFG5JICKigMHklsiOhlQvG1r1DQRZ/bNgNDnuh2Y0GZHZT+L90IiIKKgwuSWyoyHVSynsWR3QcQByR+ZCBpnV90ApV0IGGXJH5gbsSXHOBFv3CiIicg2TWyIH3K1eSmXPalpCGoonFCM5Ltny9Zi7PRRPKEZaQpqfI6w/TlwjIpI2tgIjcsBcvUxfmw6FXFFrm4FSroTRZLRZvTRXfYsOFzncmqCUK5Ecl+y3Pat6gx66Sh3UEWq7MWg6aqDpqHFpbaDL25GHjHUZUMgVVt0rVh1chdyRuUGZsBMR0XWs3BI54W71MpD3rLpTvVSFqdAmqk3QJrbB3L2CiIhcx8otkQvcqV66W/X1tlCtXpq7VziqpJu7VwTrPmIiImLllqhe6lu9DLQ9q6FavZRC9woiInKNJCq33377Lf7zn/9g165dKCsrQ0FBAUaPHu3vsIgABNae1VCtXrrTvSJYt18QEYU6SVRur1y5gl69euHdd9/1dyhEdvl7z2ooVy+l0r2CiIick0Tldvjw4Rg+fLjL6ysrK1FZWWn5XKe73mPUYDB4NDbyLfPx43G0duHKBUTIIgCZ6+tbN27t3aDs8PRxVEKJMXFjsP7X9U67V4zsPhJKKPkz5AH8fZQGHkdpkMJxdDV2mSAIQn3uuLq6GufPn0d0dDTCwsIcrr1w4QIqKirQsWPH+jxEg8hkMqfbEp5//nm88MILVpcvW7YMjRo18mJ0REREROSOq1evYty4cbh06RLUavuvsLlcuf3999/x5JNP4ssvv0RlZSXCwsIwfPhwvPjii4iPj7d5m+nTp+OTTz5BdbXjl0F9bdasWcjKyrJ8rtPp0KFDBwDA4MGDnSbtFLgMBgM2bdrE42jHw18+7HL18uOUj30YWW3eOo4L9yxE1sYsu90rsodmY2KfiR57vFDH30dp4HGUBikcx5qvtDviUnJ75coVJCUl4dChQzAXequqqlBYWIj169dj3rx5mDp1qs3b1rMw7BMRERGIiIiweV1YWFjQHvRQZj5Zq5FcrLzzONr2eP/H8fnBzyHA/u+lzCTD1H5TA+L75+nj+M++/8QtMbcgZ3sOCg4WwCSYIJfJMaL7CGT2y5TUSXSBhL+P0sDjKA3BfBxdjdulMyyys7Nx8OBB9O7dG9999x2uXLmCffv2YdKkSTAYDHjiiScwY8aMBgVM5I66wwjaZrcFAGw/td3PkQUmc+9dGWRQyms/t1XKlZBB5pfeu76k6ajByrErUTGrAmenn0XFrAqsHLtS0l8zEVEocSm5/eKLL6BWq7Fu3Tr069cPKpUKN998Mz744AMUFRWhadOmmD9/PiZPnhyQlVqSprwdeUhalISiw0W1hhEAwLAlw5C/M9+f4QWsQOu96y/+7l5BRETe4dK2hF9//RVJSUlo06aN1XUjRozAd999h2HDhmHhwoW4dOkSli1bBqXSd40YKioq8Ouvv1o+P3bsGPbu3Yvo6GifnsxGvuNoGAEAyzCC+NbxrMjZEEi9d4mIiDzJpcqt0Wh0eFZajx49UFJSgh49euCLL75AcnIyrl275rEgndm5cyf69OmDPn36AACysrLQp08fzJ4922cxkG+ZhxE4Yh5GQPaxeklEQU+vB8rLxfdEcDG57dSpE/bv3+9wTbt27aDVapGQkIANGzZg2LBhLp/V1lCDBg2CIAhWb4sXL/bJ45NvhfIwAiIi+pNWC6SmAlFRQEyM+D41FSiR1vhwqj+XkluNRoOff/4Zhw8fdriuefPm+OabbzBo0CB8++23WLVqlSdiJKrFnVGqREQkIXl5QFISUFQEmP78f2AyiZ8nJgL5POcilLmU3N53330QBAE5Oc5f4m3cuDHWr1+P0aNH8+Qy8gqOUiUiCmFaLZCRAQgCULePfnW1eHl6Oiu4Icyls76GDBmCDz74wOX+YuHh4Vi5ciXeeecdXLx4sUEBEtWlClMhOS4ZRYeLnA4jSI5L5n5SIiIpyc4GFArrxLYmhQLIyQE0PKE4FLmU3KpUKkyaNKledyyXyzFt2jS3giJyJqt/FlYdXOVwjdFkRGa/TN8ERERE3qfXA4WF17ci2FNdDRQUiOtVLHCEGtde2yUKMI6GEQAIiWEEREQhR6dzntiamUziego5TG4paNkbRgAAGx7eEDLDCIiIQoZaDchdTF3kcnG9O9heLKgxuaWgVneUallWGQCgX/t+fo6MiIg8TqUCkpMBZ4OilEogJaX+WxLYXkwSmNySJJiHEUSGRfo7FCIi8qasLMBodLzGaAQy63nOhafai9mq+rIS7FNMbol8QG/Qo7yinAMliIgaasAAIDcXkMmsK7hKpXh5bq51pwRHCaYn2ovZqvoOHCi+sRLsU0xuyW9C4YmstlSL1BWpiJobhZj5MYiaG4XUFakoKeUfNiIit6WlAcXF4hYF8x5cuVz8vLhYvN7Mla0G5vZijpjbi9lir+r77bfiGwdN+BSTW/K5UNnSlLcjD0mLklB0uMgyUc0kmFB0uAiJixKRv5N/2IiI3KbRACtXAhUVwNmz4vuVK2tXbF3ZamBuL+aoby5Qu71YTY6qvvbuh4MmvMqt5LZr166YOXOm03WzZs3CDTfc4M5DkESFysREbakWGesyIECwGjRRbaqGAAHpa9NZwSUiaiiVCmjTxvrkMVe3Gnz9dcPai7lS9bXFUSWYGsSt5Pb48eM4d+6c03W///47jh8/7s5DkASF0sTE7G3ZUMgd/7FTyBXI2c4/bEREXuHqVoOFC91vL+Zq1dcWe5VgajCvbku4cuWKyyN7SfoauqUpWOgNehQeKnQ4GhgQK7gFBwt4khkRkafVZ6vB6tXAX//qXnux+gyVsIWDJrzCK8mtyWTCzz//jC1btqBjx47eeAgKMg3d0hRMdJU6yx5bZ0yCCbpK/mEjIvKo+k4ymzTJvfZi9RkqYUtDBk2QXS4fEYVCYXkDgI8++qjWZTXfwsLCcMstt6C8vBwPPfSQ14Kn4BFKExPVEWrLpDRn5DI51BH8w0ZE5FH1nWQ2eLB77cVcHSphi7uDJsgpl49Ghw4dIJPJAAClpaVo1KgRWrZsaXNteHg4YmNjcd9992HatGmeiZSCmvnvjCsJbrA/kVWFqZAcl4yiw0UOtyYo5UokxyVDFcY/bEREHmVOOouKHL9kqFSK61QqsX1YfLy4N66gQPyHZW4vlplpndiaZWUBq1bVP0Z3Bk2QS1xObmueGCaXy3H//fdj4cKF3oiJJMidvzPBLKt/FlYdXOVwjdFkRGY//mEjIvIKV5LOugmmRiO+6fXiS4hqtfN/SOahEunp4okjzvbfKZXi49qqBJNHuLVRZMuWLS61AiOqyVsTEwPRgI4DkDsyFzLIoJTXfg6plCshgwy5I3Oh6cg/bEREXuHuJDPAfnsxe+wNlTBPKHM2aII8yo1NIsDAgQM9HQeFAEdPbqX4RDYtIQ3xreORsz0HBQcLYBJMkMvkSI5LRma/TCa2RETe5u5WA3c4qvrWpxJMDeZWcmt27NgxFBcXo6ysDJWVlTbXyGQyPPfccw15GJIQX/6dCQSajhpoOmqgN+ihq9RBHaHmHlsiIl9yZ6tBQ6hU1vdv6zLyGreS26qqKvzjH//A0qVLAQCCINhdy+SW6vL135lAoApTMaklIvInJpghw63kdvbs2ViyZAmaNWuGhx9+GDfeeCOaNGni6dhI4vh3hoiIiDzNreR22bJlaNasGfbs2YNOnTp5OiYiIiIiIre41S3ht99+Q2JiIhNbIiIiIgoobiW3TGqJiIiIKBC5ldxOnDgRW7duxblz5zwdDxERERGR29xKbv/1r39h+PDhuPvuu7FlyxaH3RKIatIb9CivKIfeoPd3KHYFQ4xERERkm1snlHXr1g0AcOLECfzlL39BWFgYYmJiIJdb58oymQxHjhxpWJQU9LSlWmRvy0bhocJawwym958eMMMMgiFGIiIKEKHUzzLIuJXcHj9+vNbnVVVVKC0t9UQ8JEF5O/KQsS4DCrkCJsEEADAJJhQdLsKqg6uQOzIXaQn+HUMYDDESEVEA0GqB7GygsLD2JKLp06U3iShIubUtwWQy1euNQpe2VIuMdRkQIKDaVF3rumpTNQQISF+bjpLSEj9FGBwxEhFRAMjLA5KSgKIiMbEFxPdFRUBiIpCf79/4CICbyS2Rq7K3ZUMhVzhco5ArkLM9x0cRWQuGGImIgppeD5SXi++DlVYLZGQAggBU1y6EoLpavDw9HShhIcTfmNyS1+gNehQeKrSqhtZVbapGwcECv5zAFQwxEhEFLa0WSE0FoqKAmBjxfWpqcCaA2dmAwnEhBAoFkMNCiL81KLn96quvkJKSgnbt2iEiIgKTJk2yXLdx40ZkZWXhzJkzDQ6SgpOuUmfZv+qMSTBBV6nzckTWgiHGUMEuFUQSI6WX8PV6cY9t3YptXdXVQEFBcFeoJcDt5PaJJ57A8OHDUVhYiMuXL8NgMNRqCda2bVu88cYbWLFihUcCpeCjjlBDLnPtR0wuk0MdofZyRNaCIUap05ZqkboiFVFzoxAzPwZRc6Pw8JcP+zssImoIqb2Er9NdT9CdMZnE9eQ3biW3H3/8Md5++23cfvvt2L17N3Q2DuKtt96KDh06oKioqMFBUnBShamQHJcMpdxxUw6lXImUHilQhfm+lUowxChleTvykLQoCUWHi2p1qVj/63oAwMI9C/0ZHhG5S2ov4avVYlcEV8jl4nryG7eS27y8PDRr1gxr165F79697a679dZbcfToUXdjIwnI6p8Fo8nocI3RZERmv0wfRWQtGGKUImddKgAga2MWu1QQBRspvoSvUontvpROOqgqlUBKCvve+plbye3+/ftx1113oVWrVg7XNW3aFOXl5W4FRtIwoOMA5I7MhQwyq+qoUq6EDDLkjsz165CEYIhRitilgkiipPoSflYWYHRcCIHRCGSyEOJvbu+5lclkTtecOXMGKj57CXlpCWkonlCM5Lhky/5W8/Sv4gnFATEcIRhilBJ2qSCSMKm+hD9gAJCbC8hk1hVcpVK8PDeXgxwCgFsTyrp3747du3fDYDAgLCzM5prLly9j7969uPnmmxsUIEmDpqMGmo4a6A166Cp1UEeoA27/ajDEKBXudKngsSAKEuaX8IuKHG9NUCrFdcFUBEtLA+Ljxb3CBQW1J5RlZjKxDRBuVW7vv/9+lJWV4emnn7a7ZtasWbh06RIefPBBt4Mj6VGFqdAmqk1AJyrBEGOwY5cKIomT8kv4Gg2wciVQUQGcPSu+X7mSiW0AcSu5ffLJJxEfH4833ngD/fv3x6uvvgoAOHLkCHJycpCUlITc3Fz06dMHkydP9mjARBT82KWCSOJC4SV8lQpo0ya4Ks8hwq3kVqVS4euvv8awYcPw/fff45lnngEAFBcXY/r06dBqtRg8eDDWr1+P8PBwjwZMRNLALhVEEpeWBhQXiy/Zm/fgml/CLy4WryfyArf23AJAq1atsHbtWvz444/46quvcPz4cZhMJrRv3x6DBw9G3759PRknEUmMuUtF+tp0KOSKWieXmSu62UOz2aWCKJhpNOKbXi92RVCrWekkr3M7uTXr1asXevXq5YlYiCjEpCWkIb51PHK256DgYAFMgglymRwju48EAEzsM9HPERKRR6hUTGrJZxqc3BIRNYStLhVKKLFu3Tp/h0ZEREGoQcntsWPHUFxcjLKyMlRWVtpcI5PJ8NxzzzXkYYgoBKjCVJYTxwwGg5+jISKiYOVWcltVVYV//OMfWLp0KQBAEAS7a5ncEhEREZGvuJXczp49G0uWLEGzZs3w8MMP48Ybb0STJk08HRsRERERUb24ldwuW7YMzZo1w549e9CpUydPx0REf+K0NCIiovpxq8/tb7/9hsTERCa2RF6iLdUidUUqouZGIWZ+DKLmRiF1RSpKSkv8HRoREVFAcyu5ZVJLgUavB8rLgWvX/B1Jw+XtyEPSoiQUHS6CSTABAEyCCUWHi5C4KBH5O/P9HCEREVHgciu5nThxIrZu3Ypz5855Oh6ietFqgdRUICoKiIkB2rYVL9++3b9xuUtbqkXGugwIEGoNNQCAalM1BAhIX5vOCi4REZEdbiW3//rXvzB8+HDcfffd2LJli8NuCUTekpcHJCUBRUWASSxwWt4PGwbkB2GBM3tbNhRyhcM1CrkCOdtzfBQRERFRcHHrhLJu3boBAE6cOIG//OUvCAsLQ0xMDORy61xZJpPhyJEjDYuSqA6tFsjIAAQBqK62vl4QgPR0ID5enPwYDPQGPQoPFVq2IthTbapGwcEC6A16nmRGRERUh1vJ7fHjx2t9XlVVhdLSUk/EQ+SS7GxAobCd2JopFEBOTvAkt7pKndPE1swkmKCr1DG5JSIiqsOt5NZkcu0fMJE36PVAYeH1LQj2VFcDBQXi+mAYaa6OUEMuk7uU4Mplcqgj1B59fLYdIyIiKXBrzy2RP+l0zhNbM5NJXB8MVGEqJMclQyl3/JxTKVcipUeKxxJQth0jIiIpYXJLQUetBmxs77ZJLhfXu0pv0KO8ohx6g9694Booq38WjCajwzVGkxGZ/TI98nhsO0ZERFLToOT2f//7H/75z3/ipptuQtOmTdG0aVPcdNNNSEtLw//+9z9PxUhUi0oFJCcDSiebapRKICXFtS0JgVK9HNBxAHJH5kIGmVUFVylXQgYZckfmQtOx4RuJ2XaMiMiDzA3X9f4pjtB1bie3b775JhISErBgwQIcPHgQly9fxuXLl3Hw4EG8//77SEhIwJtvvunJWIkssrIAo+MCJ4xGINOFAmegVS/TEtJQPKEYyXHJkMvEX1G5TI7kuGQUTyhGWkKaRx6HbceIyKZgSdICJc66DdejosTPS1gY8Be3kttNmzYhMzMT4eHhyMzMxJ49e3Dx4kX88ccf2Lt3L6ZPn46IiAhkZWVh8+bNno6ZCAMGALm5gExmu4Irk4nXO+uUEKjVS01HDVaOXYmKWRU4O/0sKmZVYOXYlR6p2ALX247V/Zrrqtl2jIgkzl6SZm8qjr+Sy0BKJu01XC8qAhITg7PhugS4ldxmZ2dDqVTiq6++wrx589CrVy80bdoUarUat956K/7zn//gq6++glwux/z58z0dMxEAIC0NKC4WtyiY9+Ca32/YIF7vTKBXL1VhKrSJauPx7gXutB0jIglzlKQNG1Z7rT+Ty0BKJh01XK+uvt5wnRVcn3Mruf3hhx8wcOBA3HXXXXbX9O/fH4MGDcL333/vdnBEzmg0wMqVQEUFcPYsUFYmXt6vn/PbhnL10tx2zBXeaDtGRAHElSQNECu4/kwuAy2ZNDdcd8TccJ18yq3k9urVq2jVqpXTda1atcLVq1fdeQiielGpgDZtgMhI128TytVLf7UdI6IA5EqSBgD//rd/k8tASibNDdcdTRICajdcJ59xK7nt0KEDtm3bhmoHB7W6uhrbtm1Dhw4d3A6OyJtCvXrp67ZjRBSAXE3SALFy6q/kMtCSSak2XJcIt5Lb5ORknDhxAhMnTsQff/xhdb1Op8PkyZNRWlqK0aNHNzBEIu8I9eqlL9uOEVGAqk+SBvgvuQy0ZNKbDdepwdwavztr1ix8+eWXWLp0KQoLCzFs2DB07twZAHDixAls2LABOp0OXbt2xaxZszwZL5FHZfXPwqqDqxyukXL1Mi0hDfGt45GzPQcFBwtgEkyWtmOZ/TKZ2BJJnTlJq0+C64w5ufTk3PP6xOmLZNLccL2oyHHCr1SK64JhBryEuJXcRkdHo7i4GP/85z+xdu1afP7551ZrRo4ciffeew/NmzdvcJBE3mKuXqavTYdCrqh1cplSroTRZJR89VLTUQNNRw30Bj10lTqoI9SSq1ITkR2uJmn14Y3kMhCTyawsYNUqx2tcbbhOHuVWcgsAsbGxKCoqwrFjx6DVanHmzBnL5QMGDECXLl08FiSRN4Va9dJeEqsKUzGpJQpFriRpgNieZutW/yWXgZZMmhuup6eLe41rfl+USjEWWw3X9Xqxsq1Ws6LrJW4nt2ZdunRhIktBLxSql9pSLbK3ZaPwUGGtBH56/+mSS+CJqB6cJWkymfjxc88BzgYzeTO5dDeZtOXCBeDMGSA2FoiOdj+mtDQgPl48ia6gQNw2IZeLCX5mZu1YtFqx40NhYe1106e7FjO5zO3xuzWVl5djz5492LNnD8rLyz1xl0R+4a2hCf4WaCOGiSjA2JuKk5wsTsUBgP797Y+GNCfBriaX3oizuNj59J7cXDGhbdFCTEpbtBA/z8tzP6a6DdcrKsTPa34fAmn4RAhwO7kVBAFvvfUWbrzxRsTGxiIhIQEJCQmIjY1F9+7d8eabb8LkyQ3qROSWQB0xTEQBxl6SVnMqTkOTS2/G6SypfughsU+vedqPWVmZWA0eN65hcZkbrtfdahBowydCgFvbEiorKzFq1Chs3rwZgiCgefPm6NSpEwCgtLQUR44cQVZWFtasWYM1a9YgIiLCo0ETkevMI4YdTWIzjxjm9gQigkrleC+oRiO++XvvqLM4a8rNBZYvd7zm00/FKuqUKQ2PrSbz8AlHe5XN/YG5PcEj3KrcvvLKK/j6669x8803Y/369Th//jx2796N3bt34/fff8eGDRtwyy234JtvvsErr7zi6ZiJyEWhPGKYiLzMXqUyEL30kmfXuSrQhk+ECLeS2yVLlqBZs2bYsmULhg4danX9kCFDsHnzZjRt2hSffPJJg4MkIveE8ohhIiIA4sljdbci2HPmjLjeUwJt+ESIcCu5PXPmDO699160aNHC7pqWLVvinnvuQZmrP1BE5HGhPmKYiAh/tir12npHOMnML9xKbtu1a4eqqiqn6wwGA2JjY915CCKqJ71Bj/KK8lpbC0J9xDAREeqbh3gybzEPn6jbXaIupRJISQmOLR5BwK3kdvz48di8eTNOnDhhd82JEyewefNmjGvo2YdE5JC2VIvUFamImhuFmPkxiJobhdQVqZbuB1n9s2A0GR3eh5RHDBNRiIuOBtq2dW1tQ/ve2pKVJfbgdYSTzDzKreT22WefxT333IOkpCQsXLgQV65csVx35coVLFq0CAMHDsS9996L2bNneyxYIqrNlf615hHDMsisKrhKuRIyyIJixLCtyjQRkUuefdaz6+rDPHzCn/2BQ4xbrcDi4uIgCAJOnTqFyZMnY/LkyWjevDkA4OLFi5Z1MpkMcXFxtW4rk8lw5MiRBoRMRIDz/rUAkL42HfGt44N6xDAnqxFRg6Wni/1mP/3U/pqHHvJ8GzCz+kwyowZzK7k9fvy41WUXbJxd6GjbAhE1TH371wbjiOEPd3+I9A3pUMgVVpXpVQdXIXdkLtISfNA0noj8x1P9dBMTHSe3SUnejSdQ+gOHALe2JZhMpga9EVHDNKR/bTCNGJ7+1XROViMKVVotkJoKREUBMTHi+9RU9yZ5maeEOeJsSpin4gmm/sBByu3xu0TkP6HSv1YhVzi9Pmd7jo+iISKfycsTK6lFRdf7xJpM4ueJiUB+fv3uzzwlzBHzlDBfxENexeSWKAhJvX/tNcM1AOBkNaJQZK6yCoL1ZK/qavFyZ1XWmho6JczT8ZDXubXn1uzq1avYuXMnysrKUFlZaXfdI4880pCHIaI6zP1riw4XOUwAlXIlkuOSg2ILQk26KtcrzebKdLB9jURkh7nK6igZNVdZXTkRy50pYTW3DHg6HvI6t5JbQRAwe/ZsvPHGG7h69arDdTKZjMktkRdk9c/CqoOrHK4J1v616nDXK83BWJkmIjvMVVZnyWjNKquzvavmKWGuJLh1p4R5Ix7yOreS2xdffBEvv/wywsPDMXr0aHTt2hVRUVGejo2IHDD3r01fm27VNUEpV8JoMgZF/1pbIsMiAYhfBxz8TwnWyjQR2dHQKqst5ilhRUWOq69Kpbiu5v15Ix7yOreS2w8//BBqtRrbtm1Dz549PR0TEbnIm/1rA6FlGCerEYWYhlRZHcnKAlatcrzG1pQwb8VDXuXWCWW///47Bg4cyMSWKABoOmqwcuxKVMyqwNnpZ1ExqwIrx650O7F1Ns7Xl7KHZgf9ZDUiqgdzlbXuJK+6lEogJcX1Kqm7U8K8FQ95lVvJbffu3dmvlijAeKJ/rSvjfH1pYp+JKJ5QjOS4ZEt3CHNlunhCMQc4EElRVpZYRXXEVpXVmbQ0oLhYTFblf6Y/5ilhxcXi9b6Mh7zGrW0JU6ZMwVNPPYXjx4+jc+fOHg6JiPyhPuN8fVktDcbJakTUAOYqa3q6dZcCpVJMJN94A+jWrf4ncLkzJcyVeGxVfclv3KrcpqWlYdKkSUhMTMTixYtx+vRpT8dFRD5mHufriD+HJgTTZDUiaiB7VVaNRhyakJnp2ylh7lZ9yS/c7nP7z3/+E5s3b8akSZMcrpPJZKh21jiZiPzKPM7X2dSzmkMTmGQSkVfVrbIuXy4mtQqF9ZSwVavE6qk3k0x3qr7kF24lt9u2bcOQIUNw5coVyGQyREdHsxUYURBzZ5wvk1si8gmVCti1S0xs7U0JA8RtA/Hx3t8eoFIxqQ1wbiW3M2bMwJUrVzBnzhxkZmZCzdYXREHNPM7XlQSXQxOIyOc4JYzqwa09t3v27EG/fv0wZ84cJrZEEmAe51u35VZdSrkSKT1SWLUlIt8xTwlztsWx5pQwCmluJbdNmjRhlwQiicnqn8WhCUQUeNyZEkYhza3kdsSIEdi+fTuMzvq+EVGD6A16lFeUQ2/wfiXCPM6XQxOIKKCYp4S5glPCCG4mt6+++irkcjkmTZqES5cueTomopDnrylhaQlpITM0wZdPHIioATgljOrJrRPKZs6cifj4eHzyyScoLCxEQkIC2rVrB7mNZ1YymQwffvhhgwMlChV5O/KQsS4DCrnCakrYqoOrkDsy16tJptSHJmhLtcjelm1pfWZO3qf3n86qNFGgysoS2305YjQCjz5a/8EOJDluJbeLFy+2fHzp0iVs3rzZ7lomt0SuC6QpYaowlaSSWsD/TxyIyE2OpoQpFNfH49533/XhCtOnA7fdxp60Icit5HbLli2ejoOIcH1KWN3EtibzlDBfVhmlUMUNpCcOROSGtDSxj21OjtgVwWQCZDIxsa2Z4JpMwOrV4hqZTOyNWzPhZaswzwnQgRZuJbcDBw70dBxEIS8Qp4RJ6SX8QH3iQET1UHNK2KZNYsIKXE9szcyfC4L43peTzEKBViv2Hi4sFL+3Afbkwa0TyojI89yZEuZNeTvykLQoCUWHi6xewk9clIj8nflefXxPMj9xcJTYArWfOBBRAFOpgMWLnZ9kVlN1tZjspqcDJd49OVfS8vKApCTxyULdMciJiUC+//83uFW5NauqqsIXX3yB4uJinD59GgDQrl07JCYm4m9/+xvCw8M9EiRRKAikKWFSewmf44WJJMY82MHV/rc1cZKZ+7RaICMjMMYgO+B2cltSUoJx48bh1KlTEMxl/z+99957ePrpp/Hpp5/irrvuanCQRKHAPCWs6HCRwwqjUq5EclyyV5Mvqb2EH0hPHIjIA+oz2KGumpPMAmifaFAIkjHIbm1LOHz4MIYPH46TJ0/itttuQ05ODgoKCrBq1Sq88cYbuO2223Dy5EmMGDECv/zyi6djtundd99F586dERkZiTvvvBM//PCDTx6XyJMCYUqYVF/CH3bDMI4XJpKK+gx2sIWTzOoviMYgu/WT8fLLL6OiogI5OTnYsWMHnnjiCSQnJ+O+++7DtGnTsGPHDrzxxhvQ6XR4+eWXPR2zlRUrViArKwtz5szB7t270atXLwwdOhS//fab1x+byJMCYUpYoO39bYiawzDW/brOacLO8cJEQcLVwQ72cJJZ/QXRGGS3ktvNmzejT58+eOKJJ+yumTZtGvr06YOvv/7a7eBclZ2djcmTJ2PChAm46aabkJ+fj0aNGmHhwoVef2wiT/P3lDDzS/iuCOSX8G2dEGdPfZ84cLoZUQDIyrLukuAKTjJzTxCNQXbrKc+5c+dcagfWo0cP/PTTT+48hMuqqqqwa9cuzJo1y3KZXC7HX/7yF2zbts3mbSorK1FZWWn5XFfj2YXBYPBesOR15uMX7Mexb9u++DTlU1wzXIOuSgd1uBqRYZEAvP+1KaHEmLgxWP/reqd7f0d2HwkllB6PqaHHcdupbXhqw1OIlIvfszB5mN21cpkco24chfQ70tGvfT+Hj7nt1Da8+8O7WPvLWktrtJHdR2Jq36no176fW7FKmVR+H0Ndg4/jtWvXe6FGRnousDvvFM/cz8pyvg+0JpkMeOIJIMR+Lht8HJVKYMwYYP16x99rpRIYOVJ876X/Dc7IhLpng7kgNjYWsbGx2Llzp8N1d9xxB06fPo0zZ87U9yFcdubMGbRr1w7fffcd+vfvb7l8xowZ+O9//4vvv//e6jbPP/88XnjhBavLly1bhkaNGnktViIiIiJyz9WrVzFu3DhcunQJageVYbcqt3fffTeWL1+O/Px8pNlphPzBBx9g165dGDdunDsP4VWzZs1CVlaW5XOdTocOHToAAAYPHoywMPtVHgpsBoMBmzZt4nH0gIV7FiJrY5ZV1wSlXAmjyYjsodmY2GeiVx67IcfxmuEa2ma3dbkzQllWmaUqbs+2U9swfMlwCLBfC5BBhg0Pb2AFtwb+PkqDW8fxww/Fhv51K6pKpbiVIDsbmOjhvx81K8R794rDGsy9WOVyYNQosU1Vv9D8HfXY7+PChbar5d48tn/SubiP163k9tlnn8WqVauQkZGBpUuXYty4cejcuTMA4MSJE/j000+h1WrRqFEjPPPMM+48hMtatmwJhUKB8vLyWpeXl5cjJibG5m0iIiIQERFh87qwsDD+EZYAHseG+2fff+KWmFuQsz0HBQcLLC/Dj+g+Apn9Mn3SAsyd43ih8gKuGK+4vP6q6SqahDVxuOaNH95AFaqcbtN4c8ebSOyS6PJjhwr+PkqDy8dRqxWTSEcvDE+ZAtxyi2fbRYWFAU3+/F1OTBTfAnQ8rD81+Pfxn/8Uj13NMchyOTBiBJCZ6dUWYK7G7VZy27NnT6xevRrjx49HSUkJvvvuu1rXC4KANm3aYOnSpejZs6c7D+Gy8PBw3H777di8eTNGjx4NADCZTNi8eTOmTp3q1ccmkjpNRw00HTXQG/TQVeqgjlAHfJssT/e0DcSxyEQBLZB6oapUTGq9oeYY5AB88uD2EId7770XR48exWeffYbi4mLLvtrY2FgkJiZi7NixPtu/mpWVhUcffRQJCQno27cv3njjDVy5cgUTJkzwyeMTSZ0qTBU0CZunh2FwuhlRPbg6OYyDFKQhQJ88NGj8bqNGjfDYY4/hscce81A47nnggQdw7tw5zJ49G2fPnkXv3r2xYcMGtGnTxq9xEZF/ZPXPwqqDqxyucbWnLaebEdWDO71QAzA5ouDWgPEegWXq1Kk4ceIEKisr8f333+POO+/0d0hE5CeeHIZhrgRzuhmRC4KoFypJl0s/gQaDAd26dUNkZKTd3rE1bdu2DZGRkejZsyeM7jRYJiJqIE8OwwiEschEQcHVyWG+GqSg1wPl5X4dBUu+51Jyu3z5chw9ehRPPfVUrV6y9vTv3x8zZ87E4cOH8fnnnzc4SCIid2g6arBy7EpUzKrA2elnUTGrAivHrqx3p4dAGItMFDRcmRxmNIpn1nuLVgukpgJRUUBMjPg+NRUoKfHeY1LAcCm5XblyJcLDw/Gvf/3L5TuePn06wsLCsGLFCreDIyKqD3tjcVVhKrSJatOgLQP+HotMFDQGDBB7zMpk1hVcpVK8PDfXe50S8vKApKTrPW4B8X1RkdgeLD/ftfth1TdouXRC2a5du9C3b180bdrU5TtWq9W48847nU4xIyJqKG2pFtnbsi0tu8xJ5/T+0z1aTQ3G1mhEfpGWBsTHW/dCTU72bi9UrRbIyBB77NZtRWb+PD1djM1eDFqt2M7M3PXBHPf06d5vXUYe4VLl9vfff7dM8KqP9u3b49y5c/W+HRGRq/J25CFpURKKDhdZOhqYBBOKDhchcVEi8ne6WKWpB09UgokkT6MBVq4EKiqAs2fF9ytXeidBNFdZ//MfsYeuI+Yeu7Z4qupLfuVSchsZGQm9G2V5vV6PyEjHYy2JiNylLdUiY10GBAhWPW2rTdUQICB9bTpKSrnPjshvVCqgTRvvnDxWd2/t6tWOh0cAtXvs1r0vR1VfQRCrvty3G/BcSm47dOiAPXv21PvO9+zZg/bt29f7dkRErsjelg2F3HGVRiFXIGe7nSoNEQUvW1VWV5l77NZknqzmiKOqLwUMl5LbQYMGobS0FBs3bnT5jtevX48TJ07gnnvucTs4IiJ7zGNxHU0hA2qPxSUiiXBUZXVF3R675slq7lZ9KaC4lNymp6dDLpdj4sSJOHz4sNP1hw4dwqRJk6BQKDBlypQGB0kUDOydqU/e4c5YXCKSCFeqrPbY6rHrzmQ1ClguJbc9e/bE008/jbKyMtx222145plnsH//fgiCYFkjCAL279+P//u//8Ptt9+Os2fPYtasWejZs6fXgicKBNpSLVJXpCJqbhRi5scgam4UUlekcp+nl5nH4rrCG2Nx+WSGyE9crbLaY6vHLierSYrL43f//e9/Y+bMmdDr9Xj11VfRq1cvNG7cGO3atUO7du3QuHFj9OrVC6+99hr0ej1mzpyJF1980ZuxE/mdP87UJ5G/xuLyyQyRn9WnylqTox67gTZZjRrE5eQWAObOnYvt27djzJgxaNKkCa5du4aysjKUlZXh2rVraNKkCe6//35s27YNc+fO9VbMRAGBZ+r7n6/H4vLJDFEAqE+V1czcq7a4WOzBa0sgTFYjj6jnTwdwxx13YMWKFbh48SJ++eUXbN++Hdu3b8cvv/yCixcvYvny5ejbt683YiUKKDxT3/98ORaXT2aIAkR9qqyjR7veY9ffk9XIY+qd3JrJZDLccMMN6Nu3L/r27YsbbrgBMpnMk7ERBSyeqR84fDUWl09miAKIq1XWp56qX4/dtDSxupucfL067ErVlwKKS+N3iag2d87U5zQr7/H2WFzzkxlnx7zmkxkebyIvMldZ09PFrgk1Ty5TKsXE1t0qq0Yjvun14v5etZp7bIOM25VbolDm7zP1yTZvjcX1VNsxdlgg8iBvV1m9OVmNvIqVWyI3mM/ULzpc5HBrglKuRHJcMqt4Qc78ZMaVBNfWkxltqRbZ27It1V/ztonp/ad7ZD8wUchilZVsYOWWyE2+PlOf/KchbcfYYYHIB1hlpRqY3BK5yZdn6pP/ufNkhh0WiIh8j8ktUQP46kx98j93nsywwwIRke9xzy1RA3n7TH0KHGkJaYhvHY+c7TkoOFhQa/9sZr/MWoktOywQEfkHk1sKaMGUMKrCVAEfo78E03F0xtUnM2wXR0TkHy4ltwqF45fVHJHJZKiudtzonqgunl0uDVI+js6ezDS0wwIREbnHpeS2Q4cOnD5GPpO3Iw8Z6zKgkCuszi5fdXAVckfmci9rEPDmcQyGSjDbxRGRW9jWrMFcSm6PHz/u5TCIRM7OLgeA9LXpiG8dH/SVPynz1nEMtkpwVv8srDq4yuEatosjIgCAVgtkZwOFhYDJdH0gxfTp7k1aC2HslkABhWeXS4M3jmMw9otluzgickleHpCUBBQViYktIL4vKgISE4H8wPv7FsiY3FLAMJ9d7uglXKD22eUUeLxxHIO5XyzbxRF5mV4PlJeL74ORVgtkZACCANQ9R6m6Wrw8PR0oCby/b4Gqwd0SLl++jCNHjuDy5csQBMHmmqSkpIY+DIUAnl0uDd44juZKsKOE2VwJDsQqKNvFEXmBVF7Gz84GFArrxLYmhQLIyQmur8uP3E5u9+/fjyeffBJbt261m9SaGY2Op/oQATy7vK5gPafA08dRSv1i2S6OyEPy8sRqp0Jh/TL+qlVAbi6QFgSviuj115NzR6qrgYICcX0w/UPwE7e2Jfzyyy8YMGAAvvnmG/Tv3x9dunQBADz44IPo27cvlEoxZ77vvvvwyCOPeC5akjTz2eV19ybWpZQrkdIjRbJJglYLpKYCUVFATIz4PjU1eF6R8vRxdKcSTEQSJqWX8XU654mtmckkrien3EpuX3rpJVy+fBmLFi1CcXExEhMTAQBLly7Ftm3b8NNPP2HAgAE4cOAAsrOzPRowSVtW/ywYTY4r/VI+u1wq5xR48jiaK8GuCIWKPlHIM7+M74j5ZfxAp1aL2ylcIZeL68kpt5Lbb775Bj179sSjjz5q8/pu3bqhsLAQ586dw3PPPdegACm0hPLZ5VIqRnjyOLKiT0QW5pfxnQ2HqvkyfiBTqcR9wkonu0SVSiAlhVsSXORWcvvbb7/hpptusnweFhYGALh27ZrlsmbNmmHQoEFYs2ZNA0OkUBOqZ5dLqRgBePY4hnpFn4j+JMWX8bOyAGfnJhmNQCb/vrnKrRPKoqOjUVlZWetzADhx4gTi4uJqrf3tt98aEB6FqlA7u1yq5xR46jiaK8Hpa9OtuiYo5UoYTUbJVvSJqAbzy/iuJLjB8jL+gAHiCXDp6dZdE5RKMbHNzWWnhHpwq3LbpUsXnDhxwvJ57969IQgCVqxYYbns999/x9atW9GxY8eGR0khSxWmQpuoNpJObAFpFiPMPPUEJVQr+kRUg1Rfxk9LA4qLxa/NvAfX3NqsuDg4Oj8EELcqt0OGDMFLL72EEydOoFOnThg1ahRatmyJF198EQcOHEC7du3w5Zdf4tKlS5g6daqnYyaSHCkWI+o7KrdmEqy086cp1Cr6RGRDVpbY7suRYHwZX6MR34K1D2QAcSu5/fvf/47KykqUl5ejU6dOaNy4MZYvX46xY8fis88+s6wbPHgwnnnmGY8FSyRV5mJEUZHj8ySUSnFdoP+9y9uRh4x1GVDIFVajclcdXIXckbmWSqutJHhM3BiMixxn9/7ZL5YohEn9ZXyVKvD/yAc4t5LbG264AXPnzq112T333IMTJ06guLgYFy9exI033ojbb7/dI0EShQKpFCOcjcoFgPS16YhvHY//lf/PZhK8/tf1GHfLOCzcsxD/7PtPn38NRBTg0tKA+HjxDNuCgtoTyjIzgzexJY9o8Pjdmho3boxhw4Z58i6JQoZUihGujsp95ptn8O2Jbx0mwVkbs3BLzC08UYyIrPFlfLLDrRPK6iovL8eePXuwZ88elJeXe+IuiUJSsJ9TYB6V6yixBcTk9b8n/guF3HHvM4VcgZztQdL7jIj8Q6UC2rRhYksWblduBUHA22+/jXfeeQdHjhypdV3Xrl0xdepUPP7445C7OnmDiAAEdzGiPqNyAbiUBBccLIDeoOceWyIicolbyW1lZSVGjRqFzZs3QxAENG/eHJ06dQIAlJaW4siRI8jKysKaNWuwZs0aREREeDRoolAQjOcUmEfl1ifBdcYkmKCr1DG5JSIil7hVVn3llVfw9ddf4+abb8b69etx/vx57N69G7t378bvv/+ODRs24JZbbsE333yDV155xdMxE1GAcnVUrkLmZBRbDXKZHOqIIOh9RkREAcGt5HbJkiVo1qwZtmzZgqFDh1pdP2TIEGzevBlNmzbFJ5980uAgiSh4uDIq1ySYkNQpyWkSrJQrkdIjhVVbIiJymVvJ7ZkzZ3DvvfeiRYsWdte0bNkS99xzD8rKytwOjoiCj3lUrgwyq+RVKVdCBhlyR+bi5XtedpoEG01GZPYL8N5nREQUUNxKbtu1a4eqqiqn6wwGA2JjY915CCIKYq6MynWWBANA9tBstgEjIqJ6ceuEsvHjx2P+/PmW8bu2nDhxAps3b0ZmoHecJyKvcGVUblpCGuJbxyNnew4KDhZYJpSN7D4SADCxz0R/hE5EREHMreT22WefxZ49e5CUlIQ5c+bggQceQOPGjQEAV65cwWeffYYXXngB9957L2bPnu3RgIkouDgblWsrCVZCiXXr1vkwSiIikgq3ktu4uDgIgoBTp05h8uTJmDx5Mpo3bw4AuHjxomWdTCZDXFxcrdvKZDKrvrhERDWTYIPB4OdoiIgoWLmV3B4/ftzqsgsXLlhdduLECXfunoiIiIjILW4ltyaT5xq0ExERERF5CmfjEhEREZFkMLklIiIiIslwaVtCaWkpALG/rUKhsHzuqo4dO9Y/MiIKedcM13Ch8oLNNmJERES2uJTcdu7cGXK5HAcOHMCNN96Izp07QyaTufQAMpkM1dXVDQqSiELLtlPbAABts9viivGKZQDE9P7TOdSBiNyj1wM6HaBWAyo+WZYyl5LbpKQkyGQyNGrUqNbnRERmjoY11Efejjw8teEpLLt1GUyCePKqSTCh6HARVh1chdyRuUhLSPNU2ETkbxcuAGfOALGxQHS05+9fqwWys4HCQsBkAuRyIDkZmD4d0PDJshS5lNxu3brV4edEFLq0pVpkb8tG4aFCy4Qxd6us2lItMtZlIFIeaXVdtUl8BSh9bTriW8ezgksU7HJzgZdeAsrKrl/Wti3w3HPAlCmeeYy8PCAjA1AoxMQWEN8XFQGrVokxpLnwZJlV36DCE8qIyG15O/KQtCgJRYeLrKqsiYsSkb8zv173l70tGwq5wuEahVyBnO05bsdMRAHgoYfEpLNmYguIn6enA+PGNfwxtFrxMQQBqLs9srpavDw9HSgpcXwfqalAVBQQEyO+T011fBvyOya3ROQWc5VVgGCpqppVm6ohQED62nSUlLr2T0Bv0KPwUKHVfdVVbapGwcEC6A16t2MnIj/KzQWWL3e85tNPxaprQ2RnixVbRxQKIMfOk+W8PCApSazy1q36JiYC+fV78k6+41Zyu2DBAkRHR2PDhg1216xfvx7R0dFYvHixu7ERUQDzdJVVV6mzVH+dMQkm6Cp1Lq0logDz0kueXWeLXi/usXV2Qnt1NVBQIK6vyRNVX/Ibt5Lb5cuXIyIiAkOGDLG7ZsiQIQgPD8eyZcvcDo6IApM3qqzqCDXkMtf+JMllcqgj1C6tJaIAcuGC9VYEe86cEde7Q6e7Xm11xmQS19fU0Kov+ZVbye2BAwdw6623Qi63f3OFQoFevXrhwIEDbgdHRIHJG1VWVZgKyXHJUModn+eqlCuR0iOFfW+JgtGZM95db6ZWi10RXCGXi+vNGlr1Jb9zK7m9cOECWrZs6XRdy5Yt8fvvv7vzEEQUwLxVZc3qnwWjyehwjdFkRGa/TJfuj4gCTGysd9ebqVRiuy+lk6ZQSiWQklK7A0JDq77kd24lty1btsQvv/zidN0vv/yC5s2bu/MQRBTAvFVlHdBxAHJH5kIG6z7aSrkSMsiQOzKXbcCIglV0tNjuyxUN7XublQUYHT9ZhtEIZNZ5styQqi8FBLeS2wEDBmDXrl0O+91u3boVO3fuhIYNkokkyVtV1rSENGx4WDxZ1VwdNvfOLZ5QzAEORMHu2Wc9u86eAQPEzgwymXUFV6kUL8/NtR7k0JCqLwUEt5LbrKwsyGQyjB49GvPmzcOlS5cs1+l0OsybNw8pKSmQy+XIrPuMiIgkoWaVtW4Ft6FV1n7t+wEAyrLKcHb6WVTMqsDKsStZsSWSgvR0sc+tI/ffL/aTbeh+1rQ0oLhYTFbN1VjzhLLiYvsDHFyp+lZXA48+yj23Acit5LZv376YP38+Ll++jJkzZyI6OhqtWrVCq1at0Lx5c8ycORM6nQ6vv/46K7fkMXqDHuUV5exvGkDSEtJQPKEYyXHJXqmyRoZFok1UG548RuRrej1QXu69xG3ZMrFqWndPbcuWQK9ewBdfeG5ogkYDrFwJVFQAZ8+K71eudDx611HV19xFQSYD7ruPgx0CkNtDHJ544gls2bIFQ4cORWRkJM6fP4/z588jMjISw4YNw5YtW5CVleXJWClEaUu1SF2Riqi5UYiZH4OouVFIXZHq8nAA8i5NRw1Wjl2JilkVrLISBTtbE7keftg7jzVlCnD6NHD+PLBvH/D66+LHP/3knaEJKhXQpo3r2whsVX1lMrGiq1CIvW49HSN5RIMmlCUlJWHdunXQ6XQ4e/Yszp49i8uXL2Pt2rVISkryVIwUwjw93pW8RxWmCogqKyv8RG6yN5Fr/Xrx44ULvfO40dHAH38AM2cG3tCEmlXfwsLrl9fdssDBDgHFI+N3FQoFWrdujdatWzvsfUtUH54e70rSxgo/UQM4m8gFiPtQvZW4BfrQBJUKWLw4sGMkC2aiFLA8Pd6VpIsVfqIG8mdyGQxDE4IhRrJwO7k9cOAAHnvsMXTt2hUqlQoKhcLmm9JZKw0iG7wx3pWkiRV+ogbyd+IWDEMTgiFGsnAr89y2bRv+8pe/QP/nD3h0dDRiYmI8GhiFNnfGu/p7ryf5h7nC7+iJkLnCz5PciGxwJ3HzZG9X89AEV2Lw19CEYIiRLNxKbmfNmgW9Xo8nn3wSzz77LKIbMkGEyAbzeFdXEtz6jHclaTFX+J39nNSs8PNJEFEd/k7czEMTioocV4+VSnGdP4YmBEOMZOHWtoSdO3eid+/eyM7OZmJLXuGt8a4kLe5U+ImojkCYyOXuqFxfCoYYCYCbyW14eDh69Ojh6ViIavHWeFeSDnOF3xWs8BM54O/Ezd1Rub4UDDESADeT2wEDBuDAgQOejoWoFm+OdyVpYIWfyEOcJW6A2FHBm4mbu6NyfSkYYiT3kttXXnkFv/76K959911Px0NUi7fHu1LwY4WfyEPsJW4jR4ofT5zo/n27Os7XnVG5vhYMMYY4t04o2717NyZMmIBp06bhs88+w+DBg9G+fXu7AxweeeSRBgVJoU3TUQNNRw30Bj10lTqoI9SswJGFucKfvjbdqmuCUq6E0WRkhZ/IVRqN+KbXi10R1GqxcrtunXv3p9WKFd/CQvGENXOVc/p0x8mgShX4J2UFQ4whyq3k9rHHHoNMJoMgCCguLkZxcTFkMpnVOkEQIJPJmNySR6jCVExqyaa0hDTEt45HzvYcFBwsgEkwWSr8mf0ymdgS1VfNxM1gcO8+8vLEqWcKRe1xvkVFwKpV4jYIvoxPXuBWcjt79mybySwRkb+wwk8UQFwZ55ueDsTH8+V88ji3ktvnn3/ew2EQEXkGK/xEAcA8ztdRT1jzOF8mt+Rhbo/fJSIiIrLi73G+FPKY3BKRT+kNepRXlENv4D80IklyZ5wvkQe5tC1h4sSJkMlkeOWVV9CmTRtMrEc7EJlMhg8//NDtAIlIGrSlWmRvy7aMyzWf8DW9/3Se8EUkJf4e50shz6XkdvHixZDJZJg5cybatGmDxYsXu/wATG6JKG9HHjLWZUAhV1jG5ZoEE4oOF2HVwVXIHZnLnsVEUmEe51tU5HhrglIprvNXO62a7c7Y0ktSXEput2zZAgDo2LFjrc+JiJzRlmqRsS4DAoRaPWgBWD5PX5uO+NbxrOASSUVWltjuyxFvjvN1xN3eu/7ABNwtLiW3AwcOdPg5EZE92duyrYYr1KWQK5CzPYfJLZFUmMf5pqdbd01QKsXENjfXdjJ54QJw5gwQGwtER3s2rmDpvRtMCXgAcuuEstTUVGRkZHg6FiKSGL1Bj8JDhQ4TW0Cs4BYcLOBJZkRSYm+cb3KyeHndJDI3V0xoW7QQ+9+2aCF+npfnmXic9d4VBDEZLynxzOO5Ky8PSEoSE+66CXhiIpCf79/4goBbye26detw/vx5T8dCRBKjq9RZ9tg6YxJM0FXyrGkiSdFogJUrgYoK4OxZ8f3KldbVx4ceEhPPsrLal5eViQnnuHENj8Xce9cRc+9dfwmWBDzAuZXcdunSBVeuXPF0LEQkMeoINeQy1/7MyGVyqCN41jSRJKlUQJs2tveN5uYCy5c7vv2nnzasghssvXeDIQEPAm4ltw899BD++9//4uzZs56Oh4gkRBWmQnJcMpRyx9v7lXIlUnqkcLIYUSh66SXPrrMlGHrvBksCHgTcSm5nzZqFxMREDBw4EAUFBTAYDJ6Oi4gkIqt/Fowmo8M1RpMRmf38cNY0EfnXhQvWWxHsOXNGXO8Oc+9dV/ir924wJOBBwqVuCXXFxcXBZDLh5MmTGDNmDGQyGVq3bo3IyEirtTKZDEeOHGlwoEQUnAZ0HIDckblIX5tu1TVBKVfCaDIid2QuOyUQhaIzZ+q/3p0OCsHQe5fDLzzGreT2+PHjtT4XBIFbFIjIrrSENMS3jkfO9hwUHCyoNaEss18mE1uiUBUb6931NQVy710gOBLwIOFWcmtytWxORPQnTUcNNB010Bv00FXqoI5Qh/QeW34fiCBWYdu2dW1rQkP73jak966vBHoCHiTc2nNL5E16gx7lFeXseSpRqjAV2kS1CdmETluqReqKVETNjULM/BhEzY1C6opUlJSytQ+FqGef9ew6R+rbe9fXzAm4TCYm3DUpleLl/k7Ag0C9Krfr1q3DqlWrcPLkSURERODWW2/FhAkT0KVLF2/FRyFEW6pF9rZsFB4qrPWy9fT+0/mytYSFUgUzb0ceMtZlQCFXWPr/mgQTig4XYdXBVcgdmYu0hACYjkTkS+npYn/XTz+1v+ahh4ApUzzzeBqN+Baoo23T0sQhFjk5YleEmhPKMjOZ2LrA5eR2/PjxWP5nHzpBEAAARUVFmDdvHpYvX4777rvPOxFSSOA//dDj6MlM37Z9/R2ex2lLtchYlwEBgtXENvPn6WvTEd86nk/mKPQsWyZO33rppdonmcXGihVbTyW2NalUgZXU1hToCXiAcym5/fDDD/Hpp59CqVTi73//O/r06YPLly9jzZo12LZtGx555BGcOHECTZs29Xa8JEH8px96nD2ZyRuehxjE+DlKz8relm3VLaIuhVyBnO05/Dmn0DRlivh24YKY4DZ0j60UBHICHsBc2nP70UcfQS6XY/369fjwww8xdepUzJo1CyUlJXj00Udx+fJlfPnll96OlSTK/E/fEfM/fQp+zp7MCBCQtTHLT9F5h96gR+GhQoeJLSB+/QUHC7jfnEJbdDRwyy1MbMltLiW3+/btQ79+/XDvvfdaXfd///d/EAQB+/bt83hwJH38px96XH0yIyW6Sp2lQu2MSTBBV8nm7ERE7nIpudXpdLjhhhtsXme+XMdJGeQG/tMPLfV5MgMA1wzXfBGW16kj1JDLXGtOI5fJoY5gc3YiIne59NdWEAQoFLYrKfI/W2mw9y25g//0Q0t9nswAgK5KGk9mVGEqJMclQyl3fJqDUq5ESo8UyXeNICLyJva5Jb/iP/3QUp8nMwCgDpfOk5ms/lkwmowO1xhNRmT2Y3N2IqKGcPm/zEcffQSFQmHzTSaT2b1eWbcJMVEd/KcfOurzZAYAIsMifRGWTwzoOAC5I3Mhg8zq61fKlZBBhtyRueyUQETUQC4nt4IguPXG7QrkDP/phxZXnsxIbc+tWVpCGoonFCM5LtlSwTb39y2eUMxezkREHuBSWTWQE9SXX34Za9euxd69exEeHo4//vjD3yGRG9IS0hDfOh4523NQcLCgVlP/zH6ZTGwlxPxkJn1tulXfV4VMAaNghAwyAEDb7LYY0n2IpKbUaTpqoOmoCanJbEREvhT0ewaqqqpw//33o3///vjwww/9HQ41AP/phw5bT2ZkkMEoGKGQKSBAnIIo5Sl1qjAVf76JiLwg6JPbF154AQCwePFi/wZCHsN/+qGh5pOZTUc2YfSK0QAAo2DEn4VbAJxSR0RE9RP0ya07KisrUVlZafm8Zo9eg8Hgj5DIQ8zHj8cxeCihxNIflyJKGWVJZFVyVa33gLj/+p3t76Bv275+iZPqj7+P0sDjKA1SOI6uxi4TBEHwciw+sXjxYjz55JMu7bl9/vnnLRXfmpYtW4ZGjRp5IToiIiIiaoirV69i3LhxuHTpEtRq+60iA7Jy+/TTT+O1115zuObnn39Gjx493Lr/WbNmISvr+ux6nU6HDh06AAAGDx6MsLAwt+6X/M9gMGDTpk08jkHktyu/ofvb3WtdppKrsPCWhZi4fyL0ptojl395/Be0btzalyGSm/j7KA08jtIghePo6jTcgExup0+fjscee8zhmq5du7p9/xEREYiIiLB5XVhYWNAedLqOxzF4RDeORqVQaXNymd6kr5XcymVyRDeO5rENMvx9lAYeR2kI5uPoatwBmdy2atUKrVq18ncYROQD5sEORYeLarUFq0spVyI5LpknGxIRkUNBP363tLQUe/fuRWlpKYxGI/bu3Yu9e/eioqLC36ERkYs4pY6IiDwl6JPb2bNno0+fPpgzZw4qKirQp08f9OnTBzt37vR3aETkIk6pIyIiTwn65Hbx4sU2x/4OGjTI36ERUT1wNC0REXlCQO65JaLQZB7scPnqZXyz6RuUZZWhSaMm/g6LiIiCSNBXbokCgd6gR3lFOfQGvfPF5FRkWGSt90RERK5ickvUANpSLVJXpCJqbhRi5scgam4UUlekoqS0xN+hERERhSQmt0RuytuRh6RFSSg6XGTp0WoSTCg6XITERYnI35nv5wiJiIhCD5NbIjdoS7XIWJcBAYJVb9ZqUzUECEhfm84KLhERkY8xuSVyQ/a2bCjkCodrFHIFcrbn+CgiIiIiApjcEtWb3qBH4aFCh9O0ALGCW3CwgCeZERER+RCTW6J60lXqLHtsnTEJJugqdV6OiIiIiMyY3BLVkzpCbRky4IxcJoc6Qu3liIiIiMiMyS1RPanCVEiOS7YaE1uXUq5ESo8UqMJUPoqMiIj8Qq8HysvF9+R3TG6J3JDVPwtGk9HhGqPJiMx+mT6KSDr0Bj1+u/Kbv8MgInJOqwVSU4GoKCAmRnyfmgqUsFOOPzG5JXLDgI4DkDsyFzLIrCq4SrkSMsiQOzIXmo4aP0UYfGoOxOj+dncAwMNfPoyS0hJOgCOiwJOXByQlAUVFgOnP8zBMJvHzxEQgn73O/cXx66pEZFdaQhriW8cjZ3sOCg4WwCSYIJfJkRyXjMx+mUxs6yFvRx4y1mVAIVeIJ+vJxMvX/bIOnx38DDLIIECwfH+n95/O7y8R+Y9WC2RkAIIAVNfpnGP+PD0diI8HNPxb5WtMbokaQNNRA01HDfQGPXSVOqgj1NxjW0+OBmIYBXHrhwABwPUJcKsOrkLuyFykJaT5PF4iImRnAwqFdWJbk0IB5OQwufUDbksg8gBVmAptotowsXWDKwMxauIEOCLyK70eKCx0nNgC4vUFBTzJzA+Y3BKR37g6EMMWToAjIr/Q6a7vsXXGZBLXk08xuSUiv6nPQIy6OAGOiPxCrQbkLqZPcrm4nnyKyS0R+U19BmLYwglwRORzKhWQnAwonZy2pFQCKSnievIpJrdE5DeuDsSwhxPgiMgvsrIAo+Ne5zAagUz2OvcHJrdE5FeuDMSwhRPgiMhvBgwAcnMBmcy6gqtUipfn5rJTgp8wuSUiv3I0EMMRToAjIr9KSwOKi8UtCuY9uHK5+HlxsXg9+QX73BKR39UdiFGTQqaw9LsFxIqt0WTkBDgi8j+NRnzT68WuCGq1Z/bYevr+Qgwrt0QUEDQdNVg5diUqZlXgl8d/AQBsfHgjRvcYbTnpzDyhrHhCMQc4EFHgUKmANm0anohqtUBqKhAVBcTEiO9TU4ES9vSuD1ZuiSigqMJUaN24NQCgX/t+SOySyAlwRCR9eXniSF+F4nofXZMJKCoCVq0S9/Byq4NLWLklooDHCXBEJGlarZjYCoL15LPqavHy9HRWcF3E5JaIiIjIn7KzxYqtIwoFkMOpjK5gcktERETkL3o9UFhoXbGtq7oaKCgQ15NDTG6JiIiI/EWnu77H1hmTSVxPDjG5JSIiIvIXtfp6n1xn5HJxPTnE5JaIiIjIX1QqcfBD3UlndSmVQEoK+966gMktEblFb9CjvKIcegP3fxFRkNPrgfJy/+1nzcoCjE7GkBuNQCanMrqCyS0R1Yu2VIvUFamImhuFmPkxiJobhdQVqSgpZYsaIgoygTI0YcAAsY+tTGZdwVUqxctzc8VpaOQUk1siclnejjwkLUpC0eEimATxBAiTYELR4SIkLkpE/s58P0dIROSivDwgKUkcklB3aEJiIpDv479naWlAcbG4RcG8B1cuFz8vLuYAh3rghDIicom2VIuMdRkQIKDaVLtljfnz9LXpiG8dD01HVheIKIA5G5oAiEMT4uN9Wy3VaMQ3vV7siqBWc4+tG1i5JSKXZG/LhkLuuMm4Qq5AznY2GQ8W3DdNISvQhyaoVECbNkxs3cTkloic0hv0KDxUaFWxravaVI2CgwVMlgIc901TSOPQBMljcktETukqdZY9ts6YBBN0lWwyHqi4b5pCHocmSB6TWyJySh2hhlzm2p8LuUwOdQSbjAciZ/umBQhIX5vOCi5JG4cmSB6TWyJyShWmQnJcMpRyx+egKuVKpPRIgSqM+8QCEfdNE4FDE0IAk1sicklW/ywYTY6bjBtNRmT2Y5PxQMR900Q1cGiCpDG5JSKXDOg4ALkjcyGDzKqCq5QrIYMMuSNz2QYsQHHfNFENHJogaUxuichlaQlpKJ5QjOS4ZMseXLlMjuS4ZBRPKEZaApuMByrumyaqg0MTJItDHIioXjQdNdB01EBv0ENXqYM6Qs09tkHAvG+66HCRw60JSrkSyXHJPKYUGjg0QZJYuSUit6jCVGgT1YZJUBDhvmkiOzg0QVKY3BIRhQjumyaiUMDklogohHDfNBFJHffcEhGFGO6bJiIpY3JLRBSiVGEqJrVEJDnclkBEREREksHkloiIiIgkg8ktEREREUkGk1siIiIikgwmt0REREQkGUxuiYiIiEgymNwSERERkWQwuSUiIiIiyWByS0RERESSweSWiIiIiCSDyS0RERERSQaTWyIiIiKSDCa3RERERCQZTG6JiIiISDKY3BIRERGRZDC5JSIiIiLJYHJLRERERJLB5JaIiIiIJIPJLRERERFJBpNbIiIiIpIMJrdEREREJBlMbomIiIhIMpjcEhEREZFkMLklIiIiIslgcktEREREksHklohI4vQGPcoryqE36P0dChGR1zG5JSKSKG2pFqkrUhE1Nwox82MQNTcKqStSUVJa4u/QiIi8hsktEZEE5e3IQ9KiJBQdLoJJMAEATIIJRYeLkLgoEfk78/0cIRGRdzC5JSKSGG2pFhnrMiBAQLWputZ11aZqCBCQvjadFVwikiQmt0REEpO9LRsKucLhGoVcgZztOT6KiIjId5jcEhFJiN6gR+GhQquKbV3VpmoUHCzgSWZEJDlMbomIJERXqbPssXXGJJigq9R5OSIiIt9icktEJCHqCDXkMtf+tMtlcqgj1F6OiIjIt5jcEhFJiCpMheS4ZCjlSofrlHIlUnqkQBWm8lFkRES+weSWiEhisvpnwWgyOlxjNBmR2S/TRxEREfkOk1siIokZ0HEAckfmQgaZVQVXKVdCBhlyR+ZC01HjpwiJiLyHyS0RUQDx1KjctIQ0FE8oRnJcsmUPrlwmR3JcMoonFCMtIc0T4RIRBRzHm7KIiMgntKVaZG/LRuGhQpgEkyURnd5/utsVVk1HDTQdNdAb9NBV6qCOUHOPLRFJHiu3RER+5u1RuaowFdpEtWFiS0QhgcktEZEfcVQuEZFnMbklIvIjjsolIvIsJrdERH7CUblERJ7H5JaIyE84KpeIyPOY3BIR+QlH5RIReR6TWyIiP+GoXCIiz2NyS0TkRxyVS0TkWUxuiYj8iKNyiYg8i8ktEZGfcVQuEZHncPwuEVEA4KhcIiLPYHJLRBRAVGEqJrVERA3AbQlEREREJBlMbomIiIhIMpjcEhEREZFkMLklIiIiIslgcktEREREksHkloiIiIgkg8ktEREREUkGk1siIiIikgwmt0REREQkGUGd3B4/fhyTJk1Cly5doFKpcMMNN2DOnDmoqqryd2hERERE5AdBPX734MGDMJlMeO+999CtWzfs378fkydPxpUrVzBv3jx/h0dEREREPhbUye2wYcMwbNgwy+ddu3bFoUOHkJeXx+SWiIiIKAQFdXJry6VLlxAdHe1wTWVlJSorKy2f63Q6y8cGg8FrsZH3mY8fj2Nw43GUBh5HaeBxlAYpHEdXY5cJgiB4ORaf+fXXX3H77bdj3rx5mDx5st11zz//PF544QWry5ctW4ZGjRp5M0QiIiIicsPVq1cxbtw4XLp0CWq12u66gExun376abz22msO1/z888/o0aOH5fPTp09j4MCBGDRoEBYsWODwtrYqtx06dMCyZcuQnJyMsLCwhn0B5DcGgwGbNm3C4MGDeRyDGI+jNPA4SgOPozRI4TjqdDq0bNnSaXIbkNsSpk+fjscee8zhmq5du1o+PnPmDO6++27cddddeP/9953ef0REBCIiImxeFxYWFrQHna7jcZQGHkdp4HGUBh5HaQjm4+hq3AGZ3LZq1QqtWrVyae3p06dx99134/bbb8eiRYsglwd1dzMiIiIiaoCATG5ddfr0aQwaNAidOnXCvHnzcO7cOct1MTExfoyMiIiIiPwhqJPbTZs24ddff8Wvv/6K9u3b17ouALcSExEREZGXBfVr+I899hgEQbD5RkREREShJ6iTWyIiIiKimpjcEhEREZFkMLklIiIiIslgcktEREREksHkloiIiIgkg8ktEREREUkGk1siIiIikgwmt0REREQkGUxuiYiIiEgymNwSERERkWQwuSUiIiIiyWByS0RERESSweSWiIiIiCSDyS0RERERSQaTWyIiIiKSDCa3RERERCQZTG6JiIiISDKY3BIRERGRZDC5JSIiIiLJYHJLRERERJLB5JaIiIiIJIPJLRERERFJBpNbIiIiIpIMJrdEREREJBlMbomIiIhIMpjcEhEREZFkMLklIiIiIslgcktEREREksHklogkT2/Qo7yiHHrD/2/v3sOiuO43gL+zXBeWRRC53wKRRuu1QhHBKKIJIiKSqDReMLVtbPFWjDGNiWhTH0yNmlqjxtqIeYwmlsRSYrQaIwjGWxCviVowCeAVQQSRgOye3x/82LjuchdWxvfzPPsoZ87MfHcOI6/DmdlqU5dCZBrV1cD16/V/Eskcwy0RyVZOYQ7iPo6DKkUF15WuUKWoEPdxHA4VHjJ1aUSdIycHiIsDVCrA1bX+z7g44BDPAZIvhlsikqX1x9fj6c1PI+NiBrRCCwDQCi0yLmZg6Oah2PD1BhNXSNTB1q8Hnn4ayMgAtPXnALTa+q+HDgU28BwgeWK4JSLZySnMQeLniRAQqNPW6S2r09ZBQOAPu/7AK7gkXzk5QGIiIARQp38OoK6uvv0Pf+AVXJIlhlsikp1Vh1fBTGHWZB8zhRlWH1ndSRURdbJVqwCzps8BmJkBq3kOkPww3BKRrFTfq0b6hXSDK7YPqtPWYef5nbzJjOSnuhpITze8Yvugujpg507eZEayw3BLRLJSUVOhm2PbHK3QoqKmooMrIupkFRU/zbFtjlZb359IRhhuiUhW1FZqKKSW/dOmkBRQW6k7uCKiTqZWA4oW/nhXKOr7E8kIwy0RyYrSQolxPxsHc4V5k/3MFeYY/9R4KC2UnVQZUSdRKoFx4wDzps8BmJsD48fX9yeSEYZbIpKdpJAkaLSaJvtotBr8cfAfO6kiok6WlARomj4HoNEAf+Q5QPLDcEtEshPmHYZ1Y9ZBgmRwBddcYQ4JEtaNWYdQ71ATVUjUwcLCgHXrAEkyvIJrbl7fvm4dEMpzgOSH4ZaIZGlm4Exkv5iNcT8bp5uDq5AUGPezcch+MRszA2eauEKiDjZzJpCdXT9FoWEOrkJR/3V2dv1yIhlqZkIOEVHXFeodilDvUFTfq0ZFTQXUVmrOsaXHS2ho/au6uv6pCGo159iS7DHcEpHsKS2UDLX0eFMqGWrpscFpCUREREQkGwy3RERERCQbDLdEREREJBsMt0REREQkGwy3RERERCQbDLdEREREJBsMt0REREQkGwy3RERERCQbDLdEREREJBsMt0REREQkGwy3RERERCQbDLdEREREJBsMt0REREQkGwy3RERERCQbDLdEREREJBsMt0REREQkGwy3RERERCQbDLdEREREJBsMt0REREQkG+amLuBRIIQAANy9excVFRWwsLAwcUXUVvfu3eM4ygDHUR44jvLAcZQHOYxjRUUFgJ9yW2Mk0VyPx0BxcTG8vLxMXQYRERERNaOoqAienp6NLme4BaDVanHhwgX07t0bRUVFUKvVpi6J2qiiogJeXl4cxy6O4ygPHEd54DjKgxzGUQiByspKuLu7Q6FofGYtpyUAUCgU8PDwAACo1eouO+j0E46jPHAc5YHjKA8cR3no6uNob2/fbB/eUEZEREREssFwS0RERESywXD7/6ysrJCcnAwrKytTl0LtwHGUB46jPHAc5YHjKA+P0zjyhjIiIiIikg1euSUiIiIi2WC4JSIiIiLZYLglIiIiItlguCUiIiIi2WC4bURMTAy8vb1hbW0NNzc3TJ06FVeuXDF1WdQK33//PWbMmIEnnngCSqUS/v7+SE5ORm1tralLo1ZatmwZhgwZAhsbG3Tr1s3U5VALvfvuu/D19YW1tTWCg4Nx7NgxU5dErXDw4EGMHTsW7u7ukCQJ//73v01dErVBSkoKgoKCYGdnB2dnZ8TGxuLChQumLqtDMdw2Ijw8HDt27MCFCxfwySefoKCgAM8//7ypy6JWOH/+PLRaLd577z2cO3cOq1evxoYNG/Daa6+ZujRqpdraWkyYMAG///3vTV0KtdDHH3+MpKQkJCcn48SJE+jfvz+effZZ3Lhxw9SlUQtVVVWhf//+ePfdd01dCrVDVlYWEhMTceTIEezbtw/37t3DM888g6qqKlOX1mH4KLAW+s9//oPY2FjU1NTAwsLC1OVQG61YsQLr16/HpUuXTF0KtUFqairmzZuH8vJyU5dCzQgODkZQUBDWrl0LANBqtfDy8sLs2bPx6quvmrg6ai1JkrBz507ExsaauhRqp5KSEjg7OyMrKwtPP/20qcvpELxy2wJlZWX48MMPMWTIEAbbLu727dtwdHQ0dRlEslZbW4vc3FyMHDlS16ZQKDBy5EgcPnzYhJUR0e3btwFA1j8LGW6bsHDhQtja2qJ79+4oLCxEenq6qUuidsjPz8ff//53vPTSS6YuhUjWbt68CY1GAxcXF712FxcXXLt2zURVEZFWq8W8efMQGhqKPn36mLqcDvNYhdtXX30VkiQ1+Tp//ryu/4IFC5CXl4e9e/fCzMwM06ZNA2dxmF5rxxEALl++jMjISEyYMAG//e1vTVQ53a8t40hERG2XmJiIs2fP4qOPPjJ1KR3K3NQFdKb58+dj+vTpTfbx8/PT/d3JyQlOTk4ICAhAr1694OXlhSNHjiAkJKSDK6WmtHYcr1y5gvDwcAwZMgQbN27s4OqopVo7jtR1ODk5wczMDNevX9drv379OlxdXU1UFdHjbdasWfjss89w8OBBeHp6mrqcDvVYhdsePXqgR48ebVpXq9UCAGpqah5mSdQGrRnHy5cvIzw8HIMGDcLmzZuhUDxWv6x4pLXnfKRHm6WlJQYNGoT9+/frbkDSarXYv38/Zs2aZdriiB4zQgjMnj0bO3fuRGZmJp544glTl9ThHqtw21JHjx7F8ePHERYWBgcHBxQUFOCNN96Av78/r9p2IZcvX8bw4cPh4+ODt99+GyUlJbplvHrUtRQWFqKsrAyFhYXQaDQ4efIkAODJJ5+ESqUybXFkVFJSEhISEhAYGIhf/vKXeOedd1BVVYUXX3zR1KVRC925cwf5+fm6r7/77jucPHkSjo6O8Pb2NmFl1BqJiYnYtm0b0tPTYWdnp5v3bm9vD6VSaeLqOgYfBWbEmTNnMHfuXJw6dQpVVVVwc3NDZGQkXn/9dXh4eJi6PGqh1NTURn+Q8tu+a5k+fTq2bNli0H7gwAEMHz688wuiFlm7di1WrFiBa9euYcCAAVizZg2Cg4NNXRa1UGZmJsLDww3aExISkJqa2vkFUZtIkmS0ffPmzc1ODeuqGG6JiIiISDY4AZGIiIiIZIPhloiIiIhkg+GWiIiIiGSD4ZaIiIiIZIPhloiIiIhkg+GWiIiIiGSD4ZaIiIiIZIPhloiIiIhkg+GWiHQkSdJ7KRQKdOvWDUOHDsWmTZta/clumZmZkCTJJJ+CM3z4cEiShO+//77T9w0Avr6+jX4yUHO++eYbzJ49G3369IG9vT2srKzg4eGBmJgYfPDBB6itrX3I1ZKp5ebmYvny5YiLi4Onp6fuHCSi1jM3dQFE9OhJSEgAAGg0GhQUFODQoUPIycnB/v37sX37dhNXJ19CCCxevBgpKSnQaDTw9vZGeHg4lEolioqKsGfPHmRkZGDp0qUoKCgwdbn0AF9fX/zwww9t+njvN998E+np6R1QFdHjhx+/S0Q6DVeKHvxnYd++fYiKikJdXR0yMjIQHR3dou3dvXsXhYWFsLe3h5ub20OvtymFhYW4e/cu/P39YWFh0an7BtoWdP70pz9h+fLlcHFxwfvvv4+oqCi95bdu3cLbb7+NFStW8OrtI6g94fatt95CVVUVgoKCEBQUBF9fX9TU1LRpW0SPO4ZbItJpLNwCwK9//Wts3rwZM2bMwKZNmzq7tC6ntUHn2LFjGDx4MKytrZGbm4tevXo12vfQoUMIDQ19WKXSQ9KecPsga2trhluiNuKcWyJqkYEDBwIAioqKdG2SJMHX1xe1tbX485//jKeeegpWVlaIjY0F0Pic2yVLlkCSJKSmpuLMmTOIiYmBg4MDbG1tMWzYMHz11VeN1nH06FHEx8fDw8MDVlZWcHNzQ0REBP7xj3/o9Wtszu39NScnJ8Pf3x/W1tbw8/PD4sWL8eOPPxrsMz8/H0uWLEFISAhcXV1haWkJT09PTJs2DRcvXmzFUWzcypUrIYTAnDlzmgy2AIwG22+++QaTJ0+Gm5sbLC0t4eHhgWnTpuHChQsGfe8flxs3bmDGjBlwdXWFra0twsLC9I7/hg0b0K9fPyiVSnh5eWHJkiXQarUG22zLcQWA0tJSLFiwAD179oS1tTUcHR0RGRmJvXv3Gu3fsB+NRoO33noLAQEBsLKygpeXFxYuXIiamhqj6929excpKSkYOHAgVCoVVCoVBg8ejC1btrR7Pw3H84cfftCt2/Dy9fU1un0i6kCCiOj/ARCN/bOwbNkyAUCMHTtWr7+Xl5cYPXq0sLW1FVFRUWLChAli5syZQgghDhw4IACIhIQEvW0lJycLACIxMVHY2NiIvn37ikmTJon+/fsLAMLa2lqcOXPGoIZ33nlHKBQKAUAMGjRIxMfHi5EjRwpnZ2dhb2+v13fYsGECgPjuu+8M3qO3t7eIjo4WSqVSREdHi7i4OGFvby8AiIiICFFXV6e3zsKFC4UkSaJv374iOjpaPPfcc6JXr14CgFCr1eLUqVMGtfr4+DR6LB+k0WiEWq0WAIxuqzlffPGFUCqVAoAYOHCgiI+PFwMGDBAAhEqlEgcPHtTr3zAuMTExws/PT/j4+IhJkyaJ4OBgAUDY2NiIs2fPijlz5gilUimioqJEdHS0sLOzEwDEa6+9ZlBDW45rcXGx8PPz0607adIkMWLECGFmZiYAiFWrVhndj4+Pj5g4caJQqVQiOjpaREdH6/YzefJkg3WuX78u+vXrJwAIV1dXERUVJUaPHq1bZ9asWe3az7fffisSEhKEra2t7vu94TV//vwWj+P9rKysWvz9Q0T6eOYQkU5j4Var1YqQkBABQCxatMig/5NPPimKi4sN1msu3AIQf/vb3/SWzZs3TwAQU6dO1WvPysoSkiQJOzs78cUXX+gtu3fvnti1a5deW1PhFoDw9PQUBQUFuvYbN26IPn36CABi9erVeuscPnxYXLp0yeD9vf/++wKACA8PN1jWmnD7v//9TwAQVlZWBgGwOXfu3BEuLi4CgFi7dq3eslWrVunea3V1ta69YVwAiClTpoja2lrdsoax6d27t3B3dxf5+fm6ZefOnROWlpbCxsZGVFZW6u2rLcc1OjpaABAvvPCCqKmp0bVnZ2cLGxsbYWZmJvLy8ozup1evXuLq1au69kuXLolu3boJAHo1CyFEVFSUACDmzp0rfvzxR137tWvXRGBgoAAgdu/e3e79tGbMm8NwS9R2PHOISOfBcFtXVycuXrwopk+frgtf9/9Ab+j/r3/9y+j2mgu3oaGhBuvcvHlTd9XsfqNHjxYAxPLly1v0XpoLtxs3bjRYZ/fu3QKA8Pf3b9E+hBAiNDRUSJIkysvL9dpbE3SOHDmiu6rYWg0BOyQkxOjyQYMGCQBi69aturaGcVGr1aKsrEyvf3l5uZAkSQAQmzZtMtje+PHjBQBx4MABvfbWHteCggLdleXS0lKDdZKSkgQA8Zvf/Mbofvbt22ewzqxZswQAsXnzZl1bXl6eACCCgoKERqMxWOfEiRO6q9jt2Y8QDLdEjwo+CoyIDBh7vqadnR22bNkCf39/g75jx45t036eeeYZg7bu3bvD0dERV69e1bXV1dUhMzMTAPC73/2uTft6UHx8vEFbZGQkHBwcUFBQgKtXr+o94eHOnTvIyMjAyZMnUVZWhnv37gEArl69CiEECgoK8Itf/OKh1NYa2dnZAIDJkycbXT5lyhTk5uYiOzvboE9gYCAcHBz02uzt7eHo6IjS0lKj4+Pn5wcAeuNzv5Ye15ycHN0yR0dHg3WmTp2KVatW6d7f/SwsLBAeHm7QHhAQYFBbw9zd2NhYKBSGt5k0zME9duxYu/ZDRI8OhlsiMtDwnFuFQgG1Wo2+ffsiLi7OIAgBgLOzM6ysrNq0H09PT6PtdnZ2KCsr031dWlqK6upqODo6Gq2htRwcHGBnZ2d0mY+PD27duoUrV67owu2XX36J+Ph4lJSUNLrNysrKNtfTvXt3APWP+tJoNDAzM2vxuleuXAGARm9cami/fPmywTIPDw+j66hUKpSWlhpdrlKpAMDojVutOa7tqdvV1dXoMWrY9/21NdxQuGjRIixatMjovgAYveGtNfshokcHwy0RGUhNTW1xX2tr6zbvx9iVtEfNnTt3MHHiRJSVlWHx4sWIj4+Hj48PlEolJEnCCy+8gO3bt7frkU1+fn5Qq9WoqKjAuXPn0K9fv4dWf1OfctXc8Tfl+LSn7vs1PNkhLCzM4LcOzekK359EZIjhlogeeU5OTlAqlSgrK0N5eTm6devWru3dunULlZWVRq8yFhYWAgDc3d0B1P/av7S0FM8//zyWLl1q0P/SpUvtqgWoD1GRkZHYsWMHtm3b1qpw21Bnw2OoHtRw5bKxq7QPU2uOa2fV3fDbgdjYWMyfP79d2yKiroH/LSWiR56ZmRmGDx8OANi4ceND2eaOHTsM2vbu3YuysjL4+fnppiTcunULgPEpFPn5+Thx4sRDqScpKQmSJGHNmjX49ttvm+x7/3Nohw4dCgCNfizy1q1b9fp1tJYe17CwMADAnj17UF5ebrDOw6p71KhRAICdO3e2azstYWlpCaB+jjgRmQ7DLRF1CQsXLoQkSVi2bBkOHDigt6yurg6ff/55q7a3dOlSvQ94uHnzJhYsWAAASExM1LU33Dz06aef6s25LS8vx4wZM3Q3lrVXcHAwXnnlFVRXV2PEiBFG38/t27eRnJysd5PTxIkT4eLigpycHIPgv2bNGnz99dfw8PDAc88991DqbE5Lj6ufnx/GjBmDyspKzJ07V+84Hj58GOvXr4eZmZneOm0RHByMUaNG4dChQ0hMTERFRYVBn1OnTmHPnj3t2g/w09VoYx+cQUSdh9MSiKhLGDZsGP7617/ilVdewYgRIxAYGIiePXvi5s2bOHXqFGpqaoxeATTG29sb/fr1w89//nNERETAwsICX375JcrLyxEeHo45c+bo+gYGBmLUqFHYt28fAgICdFeQMzMz4eTkhHHjxiE9Pf2hvMeUlBSYm5sjJSUFY8aMgY+PDwYOHAilUoni4mIcPXoUtbW16Nmzp24dW1tbfPjhhxg7dixeeuklbNy4EQEBATh//jzy8vKgUqmwffv2ds2NbqnWHFcAeO+99zB06FB88MEHyMrKQkhICEpKSpCZmQmNRoOVK1diwIAB7a5r69atiIyMxLp167Bt2zYMGDAA7u7uuH37Nk6fPo2ioiLMnTsXkZGR7dpPTEwMsrKyEBERgfDwcNja2sLJyQnLly9vdt1du3bhzTff1H1dW1sLABg8eLCu7Y033sCYMWPaVSPR44BXbomoy3j55ZeRlZWF8ePHo7CwEGlpaTh79iz69u2LlStXtng7kiQhLS0N8+bNw5kzZ/DZZ5/B3t4eixYtwq5du2Burv///vT0dCxatAg9evTA7t27kZubi/j4eBw5cqTd838frOsvf/kLTp8+jcTERNjY2GD//v1IS0tDQUEBnn32WWzduhXnzp3TWy8iIgLHjx/Hr371KxQXFyMtLQ3Xrl3DlClT8PXXX3falITWHlcPDw8cP34c8+fPh7m5OT799FPk5uYiIiIC//3vf5GUlPRQ6nJ2dsZXX32FNWvWoHfv3sjLy0NaWhpOnz4NPz8/rFixAi+//HK79zNnzhy8/vrrUKlU+OSTT/DPf/4TH330UYvWLSkpwdGjR3WvhhsU729r6mkdRPQTSbTnFl8ioi5GkiT4+Pjo/eqc2o/HlYgeFbxyS0RERESywXBLRERERLLBcEtEREREssE5t0REREQkG7xyS0RERESywXBLRERERLLBcEtEREREssFwS0RERESywXBLRERERLLBcEtEREREssFwS0RERESywXBLRERERLLxf3h3lIj59hzSAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 800x800 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize = (8,8))\n",
"ax = fig.add_subplot(1,1,1) \n",
"ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
"ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
"ax.set_title('First two Components of PCA', fontsize = 20)\n",
"\n",
"targets = [0, 1, 2]\n",
"names = ['setosa', 'versicolor','virginica']\n",
"colors = ['r', 'g', 'b'] \n",
"for target, color, name in zip(targets, colors, names):\n",
" indicesToKeep = y == target\n",
" ax.scatter(principalDf.loc[indicesToKeep, 'principal component 1']\n",
" , principalDf.loc[indicesToKeep, 'principal component 2']\n",
" , c = color\n",
" , s = 50)\n",
"ax.legend(names)\n",
"ax.grid()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Based on what is observed in the graph of the first 2 principal components, we notice how the setosa species differentiates from the versicolor and virginica species in principal component 1, which is attributed to the variables petal length (cm), and sepal length (cm)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Among its other applications, here we were able to use PCA to describe a dataset in a dimension smaller than that of the original dataset. As previously discussed, we noticed how we can describe interesting aspects of the original data without the need to address separately all the dimensions of such data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## <span style='color:Black'> **Convert your model to Cairo** </span>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### <span style='color:Black'> **Generating Cairo files** </span>\n",
"\n",
"Now let's generate Cairo files for each tensor in the object."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"def decimal_to_fp16x16(num):\n",
"\n",
" whole_num = int(num)\n",
" fractional_part = int((num - whole_num) * 65536)\n",
" fp_number = (whole_num << 16) + fractional_part\n",
" return fp_number"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"import os"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"current_directory = os.getcwd()\n",
"parent_directory = os.path.dirname(current_directory)\n",
"new_directory_path = os.path.join(parent_directory, \"src/generated\")"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"os.makedirs('src/generated', exist_ok=True) "
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"tensor_name = [\"X\",\"X_std\",\"y\"]\n",
"\n",
"def generate_cairo_files(data, name):\n",
"\n",
" with open(os.path.join('src', 'generated', f\"{name}.cairo\"), \"w\") as f:\n",
" f.write(\n",
" \"use array::{ArrayTrait, SpanTrait};\\n\" +\n",
" \"use orion::operators::tensor::{core::{Tensor, TensorTrait}};\\n\" +\n",
" \"use orion::operators::tensor::FP16x16Tensor;\\n\" +\n",
" \"use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16, FixedTrait};\\n\" +\n",
" \"\\n\" + f\"fn {name}() -> Tensor<FP16x16>\" + \"{\\n\\n\" + \n",
" \"let mut shape = ArrayTrait::new();\\n\"\n",
" )\n",
" for dim in data.shape:\n",
" f.write(f\"shape.append({dim});\\n\")\n",
" \n",
" f.write(\"let mut data = ArrayTrait::new();\\n\")\n",
" for val in np.nditer(data.flatten()):\n",
" f.write(f\"data.append(FixedTrait::new({abs(int(decimal_to_fp16x16(val)))}, {str(val < 0).lower()}));\\n\")\n",
" f.write(\n",
" \"let tensor = TensorTrait::<FP16x16>::new(shape.span(), data.span());\\n\" +\n",
" \"return tensor;\\n}\"\n",
" )\n",
"\n",
"with open(f\"src/generated.cairo\", \"w\") as f:\n",
" for n in tensor_name:\n",
" f.write(f\"mod {n};\\n\")\n",
"\n",
"generate_cairo_files(X, \"X\")\n",
"generate_cairo_files(X_std, \"X_std\")\n",
"generate_cairo_files(y, \"y\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<span style='color:Black'> **Convert some hyperparameters to FP16x16** </span>"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"tol=1e-2\n",
"max_iter=500"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"655\n",
"32768000\n"
]
}
],
"source": [
"print(decimal_to_fp16x16(tol))\n",
"print(decimal_to_fp16x16(max_iter))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<span style='color:Black'> **Get an estimate for the first two values of eigenvalus and first eigenvector in FP16x16** </span>"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First Eigenvalue: 137983\n",
"Second Eigenvalue: 53087\n",
"First Eigenvector: [-38469 36581 -38429]\n"
]
}
],
"source": [
"evec_1 = np.array([decimal_to_fp16x16(evec[0][0]),\n",
" decimal_to_fp16x16(evec[0][1]),\n",
" decimal_to_fp16x16(evec[0][2])])\n",
"\n",
"print(\"First Eigenvalue: {}\".format(decimal_to_fp16x16(evalu[0])))\n",
"print(\"Second Eigenvalue: {}\".format(decimal_to_fp16x16(evalu[1])))\n",
"\n",
"print(\"First Eigenvector: {}\".format(evec_1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the implementation of PCA in Cairo with Orion, please visit the Convert your model section within the **Verifiable Principal Componentes Analysis tutorial**"
]
}
],
"metadata": {
"kernelspec": {
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"name": "python3"
},
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"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_principal_component_analysis/src/generated.cairo | mod X_std;
mod X;
mod y;
mod evalu_sort;
mod evec_sort;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_principal_component_analysis/src/generated/X.cairo | use array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{core::{Tensor, TensorTrait}};
use orion::operators::tensor::FP16x16Tensor;
use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16, FixedTrait};
fn X() -> Tensor<FP16x16> {
let mut shape = ArrayTrait::new();
shape.append(105);
shape.append(3);
let mut data = ArrayTrait::new();
data.append(FixedTrait::new(334233, false));
data.append(FixedTrait::new(229376, false));
data.append(FixedTrait::new(91750, false));
data.append(FixedTrait::new(321126, false));
data.append(FixedTrait::new(196608, false));
data.append(FixedTrait::new(91750, false));
data.append(FixedTrait::new(308019, false));
data.append(FixedTrait::new(209715, false));
data.append(FixedTrait::new(85196, false));
data.append(FixedTrait::new(301465, false));
data.append(FixedTrait::new(203161, false));
data.append(FixedTrait::new(98304, false));
data.append(FixedTrait::new(327680, false));
data.append(FixedTrait::new(235929, false));
data.append(FixedTrait::new(91750, false));
data.append(FixedTrait::new(353894, false));
data.append(FixedTrait::new(255590, false));
data.append(FixedTrait::new(111411, false));
data.append(FixedTrait::new(301465, false));
data.append(FixedTrait::new(222822, false));
data.append(FixedTrait::new(91750, false));
data.append(FixedTrait::new(327680, false));
data.append(FixedTrait::new(222822, false));
data.append(FixedTrait::new(98304, false));
data.append(FixedTrait::new(288358, false));
data.append(FixedTrait::new(190054, false));
data.append(FixedTrait::new(91750, false));
data.append(FixedTrait::new(321126, false));
data.append(FixedTrait::new(203161, false));
data.append(FixedTrait::new(98304, false));
data.append(FixedTrait::new(353894, false));
data.append(FixedTrait::new(242483, false));
data.append(FixedTrait::new(98304, false));
data.append(FixedTrait::new(314572, false));
data.append(FixedTrait::new(222822, false));
data.append(FixedTrait::new(104857, false));
data.append(FixedTrait::new(314572, false));
data.append(FixedTrait::new(196608, false));
data.append(FixedTrait::new(91750, false));
data.append(FixedTrait::new(281804, false));
data.append(FixedTrait::new(196608, false));
data.append(FixedTrait::new(72089, false));
data.append(FixedTrait::new(380108, false));
data.append(FixedTrait::new(262144, false));
data.append(FixedTrait::new(78643, false));
data.append(FixedTrait::new(373555, false));
data.append(FixedTrait::new(288358, false));
data.append(FixedTrait::new(98304, false));
data.append(FixedTrait::new(353894, false));
data.append(FixedTrait::new(255590, false));
data.append(FixedTrait::new(85196, false));
data.append(FixedTrait::new(334233, false));
data.append(FixedTrait::new(229376, false));
data.append(FixedTrait::new(91750, false));
data.append(FixedTrait::new(373555, false));
data.append(FixedTrait::new(249036, false));
data.append(FixedTrait::new(111411, false));
data.append(FixedTrait::new(334233, false));
data.append(FixedTrait::new(249036, false));
data.append(FixedTrait::new(98304, false));
data.append(FixedTrait::new(353894, false));
data.append(FixedTrait::new(222822, false));
data.append(FixedTrait::new(111411, false));
data.append(FixedTrait::new(334233, false));
data.append(FixedTrait::new(242483, false));
data.append(FixedTrait::new(98304, false));
data.append(FixedTrait::new(301465, false));
data.append(FixedTrait::new(235929, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(334233, false));
data.append(FixedTrait::new(216268, false));
data.append(FixedTrait::new(111411, false));
data.append(FixedTrait::new(314572, false));
data.append(FixedTrait::new(222822, false));
data.append(FixedTrait::new(124518, false));
data.append(FixedTrait::new(327680, false));
data.append(FixedTrait::new(196608, false));
data.append(FixedTrait::new(104857, false));
data.append(FixedTrait::new(327680, false));
data.append(FixedTrait::new(222822, false));
data.append(FixedTrait::new(104857, false));
data.append(FixedTrait::new(340787, false));
data.append(FixedTrait::new(229376, false));
data.append(FixedTrait::new(98304, false));
data.append(FixedTrait::new(340787, false));
data.append(FixedTrait::new(222822, false));
data.append(FixedTrait::new(91750, false));
data.append(FixedTrait::new(308019, false));
data.append(FixedTrait::new(209715, false));
data.append(FixedTrait::new(104857, false));
data.append(FixedTrait::new(314572, false));
data.append(FixedTrait::new(203161, false));
data.append(FixedTrait::new(104857, false));
data.append(FixedTrait::new(353894, false));
data.append(FixedTrait::new(222822, false));
data.append(FixedTrait::new(98304, false));
data.append(FixedTrait::new(340787, false));
data.append(FixedTrait::new(268697, false));
data.append(FixedTrait::new(98304, false));
data.append(FixedTrait::new(360448, false));
data.append(FixedTrait::new(275251, false));
data.append(FixedTrait::new(91750, false));
data.append(FixedTrait::new(321126, false));
data.append(FixedTrait::new(203161, false));
data.append(FixedTrait::new(98304, false));
data.append(FixedTrait::new(327680, false));
data.append(FixedTrait::new(209715, false));
data.append(FixedTrait::new(78643, false));
data.append(FixedTrait::new(360448, false));
data.append(FixedTrait::new(229376, false));
data.append(FixedTrait::new(85196, false));
data.append(FixedTrait::new(321126, false));
data.append(FixedTrait::new(235929, false));
data.append(FixedTrait::new(91750, false));
data.append(FixedTrait::new(288358, false));
data.append(FixedTrait::new(196608, false));
data.append(FixedTrait::new(85196, false));
data.append(FixedTrait::new(334233, false));
data.append(FixedTrait::new(222822, false));
data.append(FixedTrait::new(98304, false));
data.append(FixedTrait::new(327680, false));
data.append(FixedTrait::new(229376, false));
data.append(FixedTrait::new(85196, false));
data.append(FixedTrait::new(294912, false));
data.append(FixedTrait::new(150732, false));
data.append(FixedTrait::new(85196, false));
data.append(FixedTrait::new(288358, false));
data.append(FixedTrait::new(209715, false));
data.append(FixedTrait::new(85196, false));
data.append(FixedTrait::new(327680, false));
data.append(FixedTrait::new(229376, false));
data.append(FixedTrait::new(104857, false));
data.append(FixedTrait::new(334233, false));
data.append(FixedTrait::new(249036, false));
data.append(FixedTrait::new(124518, false));
data.append(FixedTrait::new(314572, false));
data.append(FixedTrait::new(196608, false));
data.append(FixedTrait::new(91750, false));
data.append(FixedTrait::new(334233, false));
data.append(FixedTrait::new(249036, false));
data.append(FixedTrait::new(104857, false));
data.append(FixedTrait::new(301465, false));
data.append(FixedTrait::new(209715, false));
data.append(FixedTrait::new(91750, false));
data.append(FixedTrait::new(347340, false));
data.append(FixedTrait::new(242483, false));
data.append(FixedTrait::new(98304, false));
data.append(FixedTrait::new(327680, false));
data.append(FixedTrait::new(216268, false));
data.append(FixedTrait::new(91750, false));
data.append(FixedTrait::new(458752, false));
data.append(FixedTrait::new(209715, false));
data.append(FixedTrait::new(308019, false));
data.append(FixedTrait::new(419430, false));
data.append(FixedTrait::new(209715, false));
data.append(FixedTrait::new(294912, false));
data.append(FixedTrait::new(452198, false));
data.append(FixedTrait::new(203161, false));
data.append(FixedTrait::new(321126, false));
data.append(FixedTrait::new(360448, false));
data.append(FixedTrait::new(150732, false));
data.append(FixedTrait::new(262144, false));
data.append(FixedTrait::new(425984, false));
data.append(FixedTrait::new(183500, false));
data.append(FixedTrait::new(301465, false));
data.append(FixedTrait::new(373555, false));
data.append(FixedTrait::new(183500, false));
data.append(FixedTrait::new(294912, false));
data.append(FixedTrait::new(412876, false));
data.append(FixedTrait::new(216268, false));
data.append(FixedTrait::new(308019, false));
data.append(FixedTrait::new(321126, false));
data.append(FixedTrait::new(157286, false));
data.append(FixedTrait::new(216268, false));
data.append(FixedTrait::new(432537, false));
data.append(FixedTrait::new(190054, false));
data.append(FixedTrait::new(301465, false));
data.append(FixedTrait::new(340787, false));
data.append(FixedTrait::new(176947, false));
data.append(FixedTrait::new(255590, false));
data.append(FixedTrait::new(327680, false));
data.append(FixedTrait::new(131072, false));
data.append(FixedTrait::new(229376, false));
data.append(FixedTrait::new(386662, false));
data.append(FixedTrait::new(196608, false));
data.append(FixedTrait::new(275251, false));
data.append(FixedTrait::new(393216, false));
data.append(FixedTrait::new(144179, false));
data.append(FixedTrait::new(262144, false));
data.append(FixedTrait::new(399769, false));
data.append(FixedTrait::new(190054, false));
data.append(FixedTrait::new(308019, false));
data.append(FixedTrait::new(367001, false));
data.append(FixedTrait::new(190054, false));
data.append(FixedTrait::new(235929, false));
data.append(FixedTrait::new(439091, false));
data.append(FixedTrait::new(203161, false));
data.append(FixedTrait::new(288358, false));
data.append(FixedTrait::new(367001, false));
data.append(FixedTrait::new(196608, false));
data.append(FixedTrait::new(294912, false));
data.append(FixedTrait::new(380108, false));
data.append(FixedTrait::new(176947, false));
data.append(FixedTrait::new(268697, false));
data.append(FixedTrait::new(406323, false));
data.append(FixedTrait::new(144179, false));
data.append(FixedTrait::new(294912, false));
data.append(FixedTrait::new(367001, false));
data.append(FixedTrait::new(163840, false));
data.append(FixedTrait::new(255590, false));
data.append(FixedTrait::new(386662, false));
data.append(FixedTrait::new(209715, false));
data.append(FixedTrait::new(314572, false));
data.append(FixedTrait::new(399769, false));
data.append(FixedTrait::new(183500, false));
data.append(FixedTrait::new(262144, false));
data.append(FixedTrait::new(412876, false));
data.append(FixedTrait::new(163840, false));
data.append(FixedTrait::new(321126, false));
data.append(FixedTrait::new(399769, false));
data.append(FixedTrait::new(183500, false));
data.append(FixedTrait::new(308019, false));
data.append(FixedTrait::new(419430, false));
data.append(FixedTrait::new(190054, false));
data.append(FixedTrait::new(281804, false));
data.append(FixedTrait::new(432537, false));
data.append(FixedTrait::new(196608, false));
data.append(FixedTrait::new(288358, false));
data.append(FixedTrait::new(445644, false));
data.append(FixedTrait::new(183500, false));
data.append(FixedTrait::new(314572, false));
data.append(FixedTrait::new(439091, false));
data.append(FixedTrait::new(196608, false));
data.append(FixedTrait::new(327680, false));
data.append(FixedTrait::new(393216, false));
data.append(FixedTrait::new(190054, false));
data.append(FixedTrait::new(294912, false));
data.append(FixedTrait::new(373555, false));
data.append(FixedTrait::new(170393, false));
data.append(FixedTrait::new(229376, false));
data.append(FixedTrait::new(360448, false));
data.append(FixedTrait::new(157286, false));
data.append(FixedTrait::new(249036, false));
data.append(FixedTrait::new(360448, false));
data.append(FixedTrait::new(157286, false));
data.append(FixedTrait::new(242483, false));
data.append(FixedTrait::new(380108, false));
data.append(FixedTrait::new(176947, false));
data.append(FixedTrait::new(255590, false));
data.append(FixedTrait::new(393216, false));
data.append(FixedTrait::new(176947, false));
data.append(FixedTrait::new(334233, false));
data.append(FixedTrait::new(353894, false));
data.append(FixedTrait::new(196608, false));
data.append(FixedTrait::new(294912, false));
data.append(FixedTrait::new(393216, false));
data.append(FixedTrait::new(222822, false));
data.append(FixedTrait::new(294912, false));
data.append(FixedTrait::new(439091, false));
data.append(FixedTrait::new(203161, false));
data.append(FixedTrait::new(308019, false));
data.append(FixedTrait::new(412876, false));
data.append(FixedTrait::new(150732, false));
data.append(FixedTrait::new(288358, false));
data.append(FixedTrait::new(367001, false));
data.append(FixedTrait::new(196608, false));
data.append(FixedTrait::new(268697, false));
data.append(FixedTrait::new(360448, false));
data.append(FixedTrait::new(163840, false));
data.append(FixedTrait::new(262144, false));
data.append(FixedTrait::new(360448, false));
data.append(FixedTrait::new(170393, false));
data.append(FixedTrait::new(288358, false));
data.append(FixedTrait::new(399769, false));
data.append(FixedTrait::new(196608, false));
data.append(FixedTrait::new(301465, false));
data.append(FixedTrait::new(380108, false));
data.append(FixedTrait::new(170393, false));
data.append(FixedTrait::new(262144, false));
data.append(FixedTrait::new(327680, false));
data.append(FixedTrait::new(150732, false));
data.append(FixedTrait::new(216268, false));
data.append(FixedTrait::new(367001, false));
data.append(FixedTrait::new(176947, false));
data.append(FixedTrait::new(275251, false));
data.append(FixedTrait::new(373555, false));
data.append(FixedTrait::new(196608, false));
data.append(FixedTrait::new(275251, false));
data.append(FixedTrait::new(373555, false));
data.append(FixedTrait::new(190054, false));
data.append(FixedTrait::new(275251, false));
data.append(FixedTrait::new(406323, false));
data.append(FixedTrait::new(190054, false));
data.append(FixedTrait::new(281804, false));
data.append(FixedTrait::new(334233, false));
data.append(FixedTrait::new(163840, false));
data.append(FixedTrait::new(196608, false));
data.append(FixedTrait::new(373555, false));
data.append(FixedTrait::new(183500, false));
data.append(FixedTrait::new(268697, false));
data.append(FixedTrait::new(412876, false));
data.append(FixedTrait::new(216268, false));
data.append(FixedTrait::new(393216, false));
data.append(FixedTrait::new(380108, false));
data.append(FixedTrait::new(176947, false));
data.append(FixedTrait::new(334233, false));
data.append(FixedTrait::new(465305, false));
data.append(FixedTrait::new(196608, false));
data.append(FixedTrait::new(386662, false));
data.append(FixedTrait::new(412876, false));
data.append(FixedTrait::new(190054, false));
data.append(FixedTrait::new(367001, false));
data.append(FixedTrait::new(425984, false));
data.append(FixedTrait::new(196608, false));
data.append(FixedTrait::new(380108, false));
let tensor = TensorTrait::<FP16x16>::new(shape.span(), data.span());
return tensor;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_principal_component_analysis/src/generated/X_std.cairo | use array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{core::{Tensor, TensorTrait}};
use orion::operators::tensor::FP16x16Tensor;
use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16, FixedTrait};
fn X_std() -> Tensor<FP16x16> {
let mut shape = ArrayTrait::new();
shape.append(105);
shape.append(3);
let mut data = ArrayTrait::new();
data.append(FixedTrait::new(41223, true));
data.append(FixedTrait::new(57011, false));
data.append(FixedTrait::new(68250, true));
data.append(FixedTrait::new(61079, true));
data.append(FixedTrait::new(13084, true));
data.append(FixedTrait::new(68250, true));
data.append(FixedTrait::new(80935, true));
data.append(FixedTrait::new(14953, false));
data.append(FixedTrait::new(72529, true));
data.append(FixedTrait::new(90862, true));
data.append(FixedTrait::new(934, false));
data.append(FixedTrait::new(63972, true));
data.append(FixedTrait::new(51151, true));
data.append(FixedTrait::new(71031, false));
data.append(FixedTrait::new(68250, true));
data.append(FixedTrait::new(11440, true));
data.append(FixedTrait::new(113089, false));
data.append(FixedTrait::new(55415, true));
data.append(FixedTrait::new(90862, true));
data.append(FixedTrait::new(42992, false));
data.append(FixedTrait::new(68250, true));
data.append(FixedTrait::new(51151, true));
data.append(FixedTrait::new(42992, false));
data.append(FixedTrait::new(63972, true));
data.append(FixedTrait::new(110718, true));
data.append(FixedTrait::new(27104, true));
data.append(FixedTrait::new(68250, true));
data.append(FixedTrait::new(61079, true));
data.append(FixedTrait::new(934, false));
data.append(FixedTrait::new(63972, true));
data.append(FixedTrait::new(11440, true));
data.append(FixedTrait::new(85050, false));
data.append(FixedTrait::new(63972, true));
data.append(FixedTrait::new(71007, true));
data.append(FixedTrait::new(42992, false));
data.append(FixedTrait::new(59694, true));
data.append(FixedTrait::new(71007, true));
data.append(FixedTrait::new(13084, true));
data.append(FixedTrait::new(68250, true));
data.append(FixedTrait::new(120646, true));
data.append(FixedTrait::new(13084, true));
data.append(FixedTrait::new(81086, true));
data.append(FixedTrait::new(28270, false));
data.append(FixedTrait::new(127108, false));
data.append(FixedTrait::new(76807, true));
data.append(FixedTrait::new(18342, false));
data.append(FixedTrait::new(183185, false));
data.append(FixedTrait::new(63972, true));
data.append(FixedTrait::new(11440, true));
data.append(FixedTrait::new(113089, false));
data.append(FixedTrait::new(72529, true));
data.append(FixedTrait::new(41223, true));
data.append(FixedTrait::new(57011, false));
data.append(FixedTrait::new(68250, true));
data.append(FixedTrait::new(18342, false));
data.append(FixedTrait::new(99069, false));
data.append(FixedTrait::new(55415, true));
data.append(FixedTrait::new(41223, true));
data.append(FixedTrait::new(99069, false));
data.append(FixedTrait::new(63972, true));
data.append(FixedTrait::new(11440, true));
data.append(FixedTrait::new(42992, false));
data.append(FixedTrait::new(55415, true));
data.append(FixedTrait::new(41223, true));
data.append(FixedTrait::new(85050, false));
data.append(FixedTrait::new(63972, true));
data.append(FixedTrait::new(90862, true));
data.append(FixedTrait::new(71031, false));
data.append(FixedTrait::new(85364, true));
data.append(FixedTrait::new(41223, true));
data.append(FixedTrait::new(28973, false));
data.append(FixedTrait::new(55415, true));
data.append(FixedTrait::new(71007, true));
data.append(FixedTrait::new(42992, false));
data.append(FixedTrait::new(46858, true));
data.append(FixedTrait::new(51151, true));
data.append(FixedTrait::new(13084, true));
data.append(FixedTrait::new(59694, true));
data.append(FixedTrait::new(51151, true));
data.append(FixedTrait::new(42992, false));
data.append(FixedTrait::new(59694, true));
data.append(FixedTrait::new(31296, true));
data.append(FixedTrait::new(57011, false));
data.append(FixedTrait::new(63972, true));
data.append(FixedTrait::new(31296, true));
data.append(FixedTrait::new(42992, false));
data.append(FixedTrait::new(68250, true));
data.append(FixedTrait::new(80935, true));
data.append(FixedTrait::new(14953, false));
data.append(FixedTrait::new(59694, true));
data.append(FixedTrait::new(71007, true));
data.append(FixedTrait::new(934, false));
data.append(FixedTrait::new(59694, true));
data.append(FixedTrait::new(11440, true));
data.append(FixedTrait::new(42992, false));
data.append(FixedTrait::new(63972, true));
data.append(FixedTrait::new(31296, true));
data.append(FixedTrait::new(141127, false));
data.append(FixedTrait::new(63972, true));
data.append(FixedTrait::new(1512, true));
data.append(FixedTrait::new(155147, false));
data.append(FixedTrait::new(68250, true));
data.append(FixedTrait::new(61079, true));
data.append(FixedTrait::new(934, false));
data.append(FixedTrait::new(63972, true));
data.append(FixedTrait::new(51151, true));
data.append(FixedTrait::new(14953, false));
data.append(FixedTrait::new(76807, true));
data.append(FixedTrait::new(1512, true));
data.append(FixedTrait::new(57011, false));
data.append(FixedTrait::new(72529, true));
data.append(FixedTrait::new(61079, true));
data.append(FixedTrait::new(71031, false));
data.append(FixedTrait::new(68250, true));
data.append(FixedTrait::new(110718, true));
data.append(FixedTrait::new(13084, true));
data.append(FixedTrait::new(72529, true));
data.append(FixedTrait::new(41223, true));
data.append(FixedTrait::new(42992, false));
data.append(FixedTrait::new(63972, true));
data.append(FixedTrait::new(51151, true));
data.append(FixedTrait::new(57011, false));
data.append(FixedTrait::new(72529, true));
data.append(FixedTrait::new(100790, true));
data.append(FixedTrait::new(111220, true));
data.append(FixedTrait::new(72529, true));
data.append(FixedTrait::new(110718, true));
data.append(FixedTrait::new(14953, false));
data.append(FixedTrait::new(72529, true));
data.append(FixedTrait::new(51151, true));
data.append(FixedTrait::new(57011, false));
data.append(FixedTrait::new(59694, true));
data.append(FixedTrait::new(41223, true));
data.append(FixedTrait::new(99069, false));
data.append(FixedTrait::new(46858, true));
data.append(FixedTrait::new(71007, true));
data.append(FixedTrait::new(13084, true));
data.append(FixedTrait::new(68250, true));
data.append(FixedTrait::new(41223, true));
data.append(FixedTrait::new(99069, false));
data.append(FixedTrait::new(59694, true));
data.append(FixedTrait::new(90862, true));
data.append(FixedTrait::new(14953, false));
data.append(FixedTrait::new(68250, true));
data.append(FixedTrait::new(21368, true));
data.append(FixedTrait::new(85050, false));
data.append(FixedTrait::new(63972, true));
data.append(FixedTrait::new(51151, true));
data.append(FixedTrait::new(28973, false));
data.append(FixedTrait::new(68250, true));
data.append(FixedTrait::new(147403, false));
data.append(FixedTrait::new(14953, false));
data.append(FixedTrait::new(72936, false));
data.append(FixedTrait::new(87837, false));
data.append(FixedTrait::new(14953, false));
data.append(FixedTrait::new(64379, false));
data.append(FixedTrait::new(137476, false));
data.append(FixedTrait::new(934, false));
data.append(FixedTrait::new(81493, false));
data.append(FixedTrait::new(1512, true));
data.append(FixedTrait::new(111220, true));
data.append(FixedTrait::new(42987, false));
data.append(FixedTrait::new(97765, false));
data.append(FixedTrait::new(41123, true));
data.append(FixedTrait::new(68658, false));
data.append(FixedTrait::new(18342, false));
data.append(FixedTrait::new(41123, true));
data.append(FixedTrait::new(64379, false));
data.append(FixedTrait::new(77909, false));
data.append(FixedTrait::new(28973, false));
data.append(FixedTrait::new(72936, false));
data.append(FixedTrait::new(61079, true));
data.append(FixedTrait::new(97200, true));
data.append(FixedTrait::new(13038, false));
data.append(FixedTrait::new(107692, false));
data.append(FixedTrait::new(27104, true));
data.append(FixedTrait::new(68658, false));
data.append(FixedTrait::new(31296, true));
data.append(FixedTrait::new(55142, true));
data.append(FixedTrait::new(38709, false));
data.append(FixedTrait::new(51151, true));
data.append(FixedTrait::new(153278, true));
data.append(FixedTrait::new(21595, false));
data.append(FixedTrait::new(38198, false));
data.append(FixedTrait::new(13084, true));
data.append(FixedTrait::new(51544, false));
data.append(FixedTrait::new(48126, false));
data.append(FixedTrait::new(125239, true));
data.append(FixedTrait::new(42987, false));
data.append(FixedTrait::new(58053, false));
data.append(FixedTrait::new(27104, true));
data.append(FixedTrait::new(72936, false));
data.append(FixedTrait::new(8414, false));
data.append(FixedTrait::new(27104, true));
data.append(FixedTrait::new(25874, false));
data.append(FixedTrait::new(117620, false));
data.append(FixedTrait::new(934, false));
data.append(FixedTrait::new(60101, false));
data.append(FixedTrait::new(8414, false));
data.append(FixedTrait::new(13084, true));
data.append(FixedTrait::new(64379, false));
data.append(FixedTrait::new(28270, false));
data.append(FixedTrait::new(55142, true));
data.append(FixedTrait::new(47266, false));
data.append(FixedTrait::new(67981, false));
data.append(FixedTrait::new(125239, true));
data.append(FixedTrait::new(64379, false));
data.append(FixedTrait::new(8414, false));
data.append(FixedTrait::new(83181, true));
data.append(FixedTrait::new(38709, false));
data.append(FixedTrait::new(38198, false));
data.append(FixedTrait::new(14953, false));
data.append(FixedTrait::new(77215, false));
data.append(FixedTrait::new(58053, false));
data.append(FixedTrait::new(41123, true));
data.append(FixedTrait::new(42987, false));
data.append(FixedTrait::new(77909, false));
data.append(FixedTrait::new(83181, true));
data.append(FixedTrait::new(81493, false));
data.append(FixedTrait::new(58053, false));
data.append(FixedTrait::new(41123, true));
data.append(FixedTrait::new(72936, false));
data.append(FixedTrait::new(87837, false));
data.append(FixedTrait::new(27104, true));
data.append(FixedTrait::new(55823, false));
data.append(FixedTrait::new(107692, false));
data.append(FixedTrait::new(13084, true));
data.append(FixedTrait::new(60101, false));
data.append(FixedTrait::new(127548, false));
data.append(FixedTrait::new(41123, true));
data.append(FixedTrait::new(77215, false));
data.append(FixedTrait::new(117620, false));
data.append(FixedTrait::new(13084, true));
data.append(FixedTrait::new(85772, false));
data.append(FixedTrait::new(48126, false));
data.append(FixedTrait::new(27104, true));
data.append(FixedTrait::new(64379, false));
data.append(FixedTrait::new(18342, false));
data.append(FixedTrait::new(69162, true));
data.append(FixedTrait::new(21595, false));
data.append(FixedTrait::new(1512, true));
data.append(FixedTrait::new(97200, true));
data.append(FixedTrait::new(34431, false));
data.append(FixedTrait::new(1512, true));
data.append(FixedTrait::new(97200, true));
data.append(FixedTrait::new(30152, false));
data.append(FixedTrait::new(28270, false));
data.append(FixedTrait::new(55142, true));
data.append(FixedTrait::new(38709, false));
data.append(FixedTrait::new(48126, false));
data.append(FixedTrait::new(55142, true));
data.append(FixedTrait::new(90050, false));
data.append(FixedTrait::new(11440, true));
data.append(FixedTrait::new(13084, true));
data.append(FixedTrait::new(64379, false));
data.append(FixedTrait::new(48126, false));
data.append(FixedTrait::new(42992, false));
data.append(FixedTrait::new(64379, false));
data.append(FixedTrait::new(117620, false));
data.append(FixedTrait::new(934, false));
data.append(FixedTrait::new(72936, false));
data.append(FixedTrait::new(77909, false));
data.append(FixedTrait::new(111220, true));
data.append(FixedTrait::new(60101, false));
data.append(FixedTrait::new(8414, false));
data.append(FixedTrait::new(13084, true));
data.append(FixedTrait::new(47266, false));
data.append(FixedTrait::new(1512, true));
data.append(FixedTrait::new(83181, true));
data.append(FixedTrait::new(42987, false));
data.append(FixedTrait::new(1512, true));
data.append(FixedTrait::new(69162, true));
data.append(FixedTrait::new(60101, false));
data.append(FixedTrait::new(58053, false));
data.append(FixedTrait::new(13084, true));
data.append(FixedTrait::new(68658, false));
data.append(FixedTrait::new(28270, false));
data.append(FixedTrait::new(69162, true));
data.append(FixedTrait::new(42987, false));
data.append(FixedTrait::new(51151, true));
data.append(FixedTrait::new(111220, true));
data.append(FixedTrait::new(13038, false));
data.append(FixedTrait::new(8414, false));
data.append(FixedTrait::new(55142, true));
data.append(FixedTrait::new(51544, false));
data.append(FixedTrait::new(18342, false));
data.append(FixedTrait::new(13084, true));
data.append(FixedTrait::new(51544, false));
data.append(FixedTrait::new(18342, false));
data.append(FixedTrait::new(27104, true));
data.append(FixedTrait::new(51544, false));
data.append(FixedTrait::new(67981, false));
data.append(FixedTrait::new(27104, true));
data.append(FixedTrait::new(55823, false));
data.append(FixedTrait::new(41223, true));
data.append(FixedTrait::new(83181, true));
data.append(FixedTrait::new(203, false));
data.append(FixedTrait::new(18342, false));
data.append(FixedTrait::new(41123, true));
data.append(FixedTrait::new(47266, false));
data.append(FixedTrait::new(77909, false));
data.append(FixedTrait::new(28973, false));
data.append(FixedTrait::new(128556, false));
data.append(FixedTrait::new(28270, false));
data.append(FixedTrait::new(55142, true));
data.append(FixedTrait::new(90050, false));
data.append(FixedTrait::new(157331, false));
data.append(FixedTrait::new(13084, true));
data.append(FixedTrait::new(124277, false));
data.append(FixedTrait::new(77909, false));
data.append(FixedTrait::new(27104, true));
data.append(FixedTrait::new(111442, false));
data.append(FixedTrait::new(97765, false));
data.append(FixedTrait::new(13084, true));
data.append(FixedTrait::new(119999, false));
let tensor = TensorTrait::<FP16x16>::new(shape.span(), data.span());
return tensor;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_principal_component_analysis/src/generated/evalu_sort.cairo | use array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{core::{Tensor, TensorTrait}};
use orion::operators::tensor::FP16x16Tensor;
use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16, FixedTrait};
fn evalu_sort() -> Tensor<FP16x16> {
let mut shape = ArrayTrait::new();
shape.append(3);
let mut data = ArrayTrait::new();
// evalu Original [ 52513 137534 5393]
// evalu Sorted [137534 52513 5393]
data.append(FixedTrait::new(137534, false));
data.append(FixedTrait::new(52513, false));
data.append(FixedTrait::new(5393, false));
let tensor = TensorTrait::<FP16x16>::new(shape.span(), data.span());
return tensor;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_principal_component_analysis/src/generated/evec_sort.cairo | use array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{core::{Tensor, TensorTrait}};
use orion::operators::tensor::FP16x16Tensor;
use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16, FixedTrait};
fn evec_sort() -> Tensor<FP16x16> {
let mut shape = ArrayTrait::new();
shape.append(3);
shape.append(3);
// evec Original
// [[ 36422 -38024 -38467]
// [ 53777 30012 21440]
// [ 5195 -43789 48143]]
// evec Sorted
// [[-38024 36422 -38467]
// [ 30012 53777 21440]
// [-43789 5195 48143]]
let mut data = ArrayTrait::new();
data.append(FixedTrait::new(38024, true));
data.append(FixedTrait::new(36422, false));
data.append(FixedTrait::new(38467, true));
data.append(FixedTrait::new(30012, false));
data.append(FixedTrait::new(53777, false));
data.append(FixedTrait::new(21440, false));
data.append(FixedTrait::new(43789, true));
data.append(FixedTrait::new(5195, false));
data.append(FixedTrait::new(48143, false));
let tensor = TensorTrait::<FP16x16>::new(shape.span(), data.span());
return tensor;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_principal_component_analysis/src/generated/y.cairo | use array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{core::{Tensor, TensorTrait}};
use orion::operators::tensor::FP16x16Tensor;
use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16, FixedTrait};
fn y() -> Tensor<FP16x16> {
let mut shape = ArrayTrait::new();
shape.append(105);
let mut data = ArrayTrait::new();
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(0, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(131072, false));
data.append(FixedTrait::new(131072, false));
data.append(FixedTrait::new(131072, false));
data.append(FixedTrait::new(131072, false));
data.append(FixedTrait::new(131072, false));
let tensor = TensorTrait::<FP16x16>::new(shape.span(), data.span());
return tensor;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_principal_component_analysis/src/helper.cairo | use traits::TryInto;
use alexandria_data_structures::array_ext::{SpanTraitExt};
use array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{Tensor, TensorTrait};
use orion::numbers::fixed_point::{core::{FixedTrait}};
use orion::operators::tensor::{FP16x16Tensor, FP16x16TensorDiv};
use orion::numbers::fixed_point::implementations::fp16x16::core::{
FP16x16, FP16x16Impl, FP16x16Add, FP16x16AddEq, FP16x16Sub, FP16x16Mul, FP16x16MulEq,
FP16x16TryIntoU128, FP16x16PartialEq, FP16x16PartialOrd, FP16x16SubEq, FP16x16Neg, FP16x16Div,
FP16x16IntoFelt252, FP16x16Print, HALF
};
use orion::numbers::fixed_point::implementations::fp16x16::math::trig;
#[derive(Copy, Drop)]
struct EigenValues<FP16x16> {
p_index: usize,
q_index: usize,
theta: FP16x16,
}
fn div_by_scalar(self: @Tensor<FP16x16>, divisor: u32) -> Tensor<FP16x16> {
let mut data = (*self).data;
let mut data_array = ArrayTrait::new();
loop {
match data.pop_front() {
Option::Some(elem) => {
data_array.append(FixedTrait::new(*elem.mag / divisor, *elem.sign));
},
Option::None(_) => {
break TensorTrait::<FP16x16>::new((*self).shape, data_array.span());
}
};
}
}
fn div_by_fp(self: @Tensor<FP16x16>, divisor: FP16x16) -> Tensor<FP16x16> {
let mut data = (*self).data;
let mut data_array = ArrayTrait::new();
loop {
match data.pop_front() {
Option::Some(elem) => { data_array.append(FP16x16Div::div(*elem, divisor)); },
Option::None(_) => {
break TensorTrait::<FP16x16>::new((*self).shape, data_array.span());
}
};
}
}
// find_max_off_diag: Finds the maximum off-diagonal element in a square Tensor.
fn find_max_off_diag(a: @Tensor<FP16x16>) -> (usize, usize) {
let mut data = *a.data;
let mut shape = *a.shape;
let n = *(*a).shape.at(0);
let mut i = 0_usize;
let mut j = 0_usize;
let mut p = 0_usize;
let mut q = 1_usize;
let mut max_val = FixedTrait::abs((*a).at(indices: array![p, q].span()));
loop {
if i == n {
break (p, q);
};
j = i + 1;
loop {
if j == n {
break;
};
if FixedTrait::abs((a).at(indices: array![i, j].span())) > max_val {
max_val = FixedTrait::abs((a).at(indices: array![i, j].span()));
p = i;
q = j;
};
j += 1;
};
i += 1;
}
}
// jacobi_eigensystem: Implements the Jacobi eigenvalue algorithm to compute the eigenvalues and eigenvectors of a symmetric Tensor.
fn jacobi_eigensystem(
mut a: Tensor<FP16x16>, tol: FP16x16, max_iter: usize
) -> (Tensor<FP16x16>, Tensor<FP16x16>) {
assert(
!((a).shape.len() != 2_usize || ((a).shape.at(0_usize) != (a).shape.at(1_usize))),
'a must be a square matrix'
);
// let two = FixedTrait::new(ONE, false) + FixedTrait::new(ONE, false);
let two = FixedTrait::ONE() + FixedTrait::ONE();
let four = two * two;
let half = FixedTrait::<FP16x16>::new(HALF, false);
let pi = FixedTrait::<FP16x16>::new(trig::PI, false);
let mut data = a.data;
let mut shape = a.shape;
let numRows = *((shape).at(0));
let mut v = eye(numRows: numRows);
let mut i: usize = 0;
loop {
let (p, q) = find_max_off_diag(@a);
if i == max_iter || FixedTrait::abs((a).at(indices: array![p, q].span())) < tol {
break (extract_diagonal(@a), v);
};
let theta = if (a)
.at(indices: array![p, p].span()) == (a)
.at(indices: array![q, q].span()) {
FP16x16Div::div(pi, four)
} else {
half
* trig::atan(
FP16x16Div::div(
two * (a).at(indices: array![p, q].span()),
(FP16x16Sub::sub(
(a).at(indices: array![p, p].span()),
(a).at(indices: array![q, q].span())
))
)
)
};
let eigensystem = EigenValues { p_index: p, q_index: q, theta: theta };
let j_eye = eye(numRows: numRows);
let j = update_eigen_values(self: @j_eye, eigensystem: eigensystem);
let transpose_j = j.transpose(axes: array![1, 0].span());
a = transpose_j.matmul(@a).matmul(@j);
v = v.matmul(@j);
i += 1;
}
}
// eye: Generates an identity Tensor of the specified size
fn eye(numRows: usize) -> Tensor<FP16x16> {
let mut data_array = ArrayTrait::new();
let mut x: usize = 0;
loop {
if x == numRows {
break;
};
let mut y: usize = 0;
loop {
if y == numRows {
break;
};
if x == y {
data_array.append(FixedTrait::ONE());
} else {
data_array.append(FixedTrait::ZERO());
};
y += 1;
};
x += 1;
};
Tensor::<FP16x16> { shape: array![numRows, numRows].span(), data: data_array.span() }
}
// extract_diagonal: Extracts the diagonal elements from a square tensor
fn extract_diagonal(self: @Tensor<FP16x16>) -> Tensor<FP16x16> {
let mut data = (*self).data;
let mut data_array = ArrayTrait::new();
let dims = (*self).shape.at(0);
let mut x: usize = 0;
loop {
if x == *dims {
break;
};
let mut y: usize = 0;
loop {
if y == *dims {
break;
};
match data.pop_front() {
Option::Some(elem) => { if x == y {
data_array.append(*elem);
}; },
Option::None(_) => { break; }
};
y += 1;
};
x += 1;
};
Tensor::<FP16x16> { shape: array![*dims].span(), data: data_array.span() }
}
// update_eigen_values: Updates the eigenvalues of a symmetric tensor
fn update_eigen_values(
self: @Tensor<FP16x16>, eigensystem: EigenValues<FP16x16>
) -> Tensor<FP16x16> {
let mut data = (*self).data;
let mut data_array = ArrayTrait::new();
let mut x: usize = 0;
let mut y: usize = 0;
let mut index: usize = 0;
let dims = (*self).shape.at(0);
let items = *dims * *dims;
let dims_y = (*self).shape.at(1);
loop {
if index == items {
break;
};
if y == *dims_y {
x += 1;
y = 0;
};
match data.pop_front() {
Option::Some(elem) => {
let eigen_values = eigensystem;
let value = if (eigen_values.p_index, eigen_values.p_index) == (x, y) {
trig::cos(eigen_values.theta)
} else if (eigen_values.q_index, eigen_values.q_index) == (x, y) {
trig::cos(eigen_values.theta)
} else if (eigen_values.p_index, eigen_values.q_index) == (x, y) {
trig::sin(eigen_values.theta)
} else if (eigen_values.q_index, eigen_values.p_index) == (x, y) {
trig::sin(-eigen_values.theta)
} else {
*elem
};
data_array.append(value);
y += 1;
index += 1;
},
Option::None(_) => { break; }
};
};
Tensor::<FP16x16> { shape: *self.shape, data: data_array.span() }
}
// check_unit_diagonal_tensor: Checks whether a square tensor has a unit diagonal
fn check_unit_diagonal_tensor(self: @Tensor<FP16x16>) -> bool {
let mut x: usize = 0;
let mut valid: bool = true;
let dim_x = (*self).shape.at(0);
let dim_y = (*self).shape.at(1);
loop {
if x == *dim_x || !valid {
break valid;
};
let mut y: usize = 0;
loop {
if y == *dim_y {
break;
};
if x == y {
if (self).at(indices: array![x, y].span()) != FixedTrait::ONE() {
valid = false;
break;
}
} else {
if (self).at(indices: array![x, y].span()) != FixedTrait::ZERO() {
valid = false;
break;
}
};
y += 1;
};
x += 1;
}
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_principal_component_analysis/src/lib.cairo | mod generated;
mod helper;
mod test;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_principal_component_analysis/src/test.cairo | #[cfg(test)]
mod tests {
use traits::TryInto;
use alexandria_data_structures::array_ext::{SpanTraitExt};
use array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{Tensor, TensorTrait};
use orion::numbers::fixed_point::{core::{FixedTrait}};
use orion::operators::tensor::{FP16x16Tensor, FP16x16TensorDiv, FP16x16TensorSub};
use orion::numbers::fixed_point::implementations::fp16x16::core::{
FP16x16, FP16x16Impl, FP16x16Add, FP16x16AddEq, FP16x16Sub, FP16x16Mul, FP16x16MulEq,
FP16x16TryIntoU128, FP16x16PartialEq, FP16x16PartialOrd, FP16x16SubEq, FP16x16Neg,
FP16x16Div, FP16x16IntoFelt252, FP16x16Print
};
use pca::{
helper::{
EigenValues, extract_diagonal, eye, find_max_off_diag, jacobi_eigensystem,
update_eigen_values, check_unit_diagonal_tensor, div_by_scalar, div_by_fp
}
};
use pca::{generated::{X_std::X_std, X::X, y::y, evalu_sort::evalu_sort, evec_sort::evec_sort}};
#[test]
#[available_gas(99999999999999999)]
fn pca_test() {
let tol = FixedTrait::<FP16x16>::new(655, false); // 655 is 0.01 = 1e-2
let max_iter = 500_usize;
let X_std = X_std();
let X = X();
let y = y();
let mut n: usize = *((X_std).shape.at(0)) - 1;
let size = *(X_std.shape.at(1));
let X_std_transpose = X_std.transpose(axes: array![1, 0].span());
let mut cov_matrix = div_by_scalar(@(X_std_transpose.matmul(@X_std)), n);
let mut stddevs = extract_diagonal(@cov_matrix).sqrt();
let mut stddevs_left = stddevs.reshape(array![size, 1].span());
let mut stddevs_right = stddevs.reshape(array![1, size].span());
let corr_matrix = cov_matrix / stddevs_left.matmul(@stddevs_right);
let (evalu, evec) = jacobi_eigensystem(a: corr_matrix, tol: tol, max_iter: max_iter);
let (evalu, evec) = (evalu_sort(), evec_sort());
let loadings = evec;
let principal_component = X_std.matmul(@loadings);
n = *((principal_component).shape.at(0)) - 1;
let principal_component_transpose = principal_component
.transpose(axes: array![1, 0].span());
let cov_new = div_by_scalar(
@(principal_component_transpose.matmul(@principal_component)), n
);
stddevs = extract_diagonal(@cov_new).sqrt();
stddevs_left = stddevs.reshape(array![size, 1].span());
stddevs_right = stddevs.reshape(array![1, size].span());
let corr_new = cov_new / stddevs_left.matmul(@stddevs_right);
let new_corr = (@corr_new.abs()).round();
// The orthogonality of the tensor is validated.
assert(check_unit_diagonal_tensor(@new_corr), 'orthogonality is invalid');
let evalu_cumsum = evalu.cumsum(0, Option::None(()), Option::None(()));
let sum = evalu_cumsum.data.at(evalu_cumsum.data.len() - 1);
let evalu_div_sum = div_by_fp(@evalu, *sum);
let pc = (*evalu_div_sum.data.at(0) + *evalu_div_sum.data.at(1))
* FixedTrait::<FP16x16>::new_unscaled(100, false);
assert(
FixedTrait::round(pc).mag == 0x610000, 'no match with notebook version'
); // 0x610000 == 97
}
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_support_vector_machine/notebooks/svm.ipynb | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# **Verifiable Support Vector Machine**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Support Vector Machines (SVM) model is a supervised learning technique used for classification and regression. It is employed to solve binary classification problems where it identifies the hyperplane that best divides a data set into classes. This hyperplane results from maximizing the margin between the two classes. By determining this optimal hyperplane, predictions can be made for new data points and understand how the input attributes influence classification.\n",
"\n",
"Below, we provide a brief review of implementing an SVM model using the Gradient Descent method for the linear kernel in Python, which we will later convert to Cairo to transform it into a verifiable ZKML (support vector machine model), using Orion's library. This allows an opportunity to familiarize oneself with the main functions and operators that the framework offers for the implementation of the SVM."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **DataSet Generate** "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the purposes of this tutorial, we generated linearly separable data using make_blobs from Scikit-learn"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(100, 3) (100,) (50, 3) (50,)\n"
]
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.datasets import make_blobs\n",
"\n",
"X, y = make_blobs(n_samples=150, centers=2,\n",
" random_state=0, cluster_std=0.60)\n",
"y[y == 0] = -1\n",
"\n",
"X = np.hstack((X, np.ones((X.shape[0], 1))))\n",
"\n",
"X_train, y_train = X[:100, :], y[:100]\n",
"X_test, y_test = X[100:, :], y[100:]\n",
"\n",
"print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we will visualize the training data using a scatter plot, where the points are colored based on their class labels, which in our case will be 1 and -1"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x145c39360>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X_train[:, 0], X_train[:, 1], c=y_train, s=10, cmap='autumn')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **Loss function, gradient and Weight init** "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will start by generating the key functions for SVM.\n",
"\n",
"Next, we'll define the loss functions and its gradient, with $\\mathbf{L2}$ regularization, both necessary to train our SVM.\n",
"\n",
"In the case of the loss function in SVM, the Hinge Loss ($\\max(0, 1 - y_i \\times (\\mathbf{w} \\cdot \\mathbf{x}_i))$) is used, which measures how far a sample is on the \"wrong side\" of the margin. If the sample is on the correct side of the margin, the loss is 0.\n",
"\n",
"$\\text{Loss Function}$ = $ \\frac{1}{N} \\sum_{i=1}^{N} \\max(0, 1 - y_i \\times (\\mathbf{w} \\cdot \\mathbf{x}_i)) + C \\times \\frac{1}{2} \\times \\mathbf{w} \\cdot \\mathbf{w}$\n",
"\n",
"$\\text{Gradient}$ = $\\frac{1}{N} \\sum_{i=1}^{N} \\left( -y_i \\times \\mathbf{x}_i \\text{ (si } y_i \\times (\\mathbf{w} \\cdot \\mathbf{x}_i) < 1 \\text{) } \\right) + C \\times \\mathbf{w}$\n",
"\n",
"For the purposes of this tutorial, we initialize $\\mathbf{w}$ as an array of $\\mathbf{0's}$"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def loss_function(w, X, y, C): \n",
" hinge_loss = np.maximum(0, 1 - y * np.dot(X, w)) \n",
" regularization_term = 0.5 * np.dot(w, w) # Regularización L2 ###\n",
" total_loss = np.mean(hinge_loss) + C * regularization_term ###\n",
" return total_loss\n",
"\n",
"def loss_gradient(w, X, y, C): \n",
" mask = (y * (np.dot(X, w))) < 1 #<1\n",
" gradient = (-np.dot(mask * y, X) / len(y)) + C*w\n",
" return gradient\n",
"\n",
"# Gradiente descendente\n",
"losses = []\n",
"w = np.zeros(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **Initial hyperparameters** "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we declare the hyperparameters: learning rate (learning_rate), the number of epochs (num_epochs), and the regularization parameter (C). Then, we will use gradient descent to adjust the weights of the SVM model. For the purposes of this tutorial, we stick with the following hyperparameters; however, the hyperplane acquisition could be improved with their adjustment."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"learning_rate = 0.01\n",
"num_epochs = 100\n",
"C = 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **Training** "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 0, Loss: 1.0000\n",
"Epoch 25, Loss: 0.5300\n",
"Epoch 50, Loss: 0.4594\n",
"Epoch 75, Loss: 0.4238\n",
"Epoch 99, Loss: 0.4092\n"
]
}
],
"source": [
"for epoch in range(num_epochs):\n",
" loss = loss_function(w,X_train, y_train, C)\n",
" losses.append(loss)\n",
"\n",
" if epoch % 25 == 0 or epoch == 99:\n",
" print(f\"Epoch {epoch}, Loss: {loss:.4f}\")\n",
"\n",
" gradient_w = loss_gradient(w, X_train, y_train,C)\n",
" w -= learning_rate * gradient_w"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x145d7edd0>]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(losses)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After training the model and observing the decrease of the loss function, we evaluate its performance on both the training and test data. We will calculate the accuracy and display the final loss on the training data. In our case, the weights $\\mathbf{w}$ and the accuracies will be the values against which we compare the SVM implementation in Cairo with Orion."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **Evaluate model on training data** "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.99\n",
"Final loss: 0.4089273002134721\n"
]
}
],
"source": [
"def predict(X, w):\n",
" return np.sign(np.dot(X, w))\n",
"\n",
"predictions = predict(X_train, w)\n",
"final_loss = loss_function(w, X_train, y_train,C)\n",
"\n",
"print(\"Accuracy: {}\".format((predictions == y_train).mean()))\n",
"print(\"Final loss: {}\".format(final_loss))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **Evaluate model on test data**"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.98\n"
]
}
],
"source": [
"predictions = predict(X_test, w)\n",
"\n",
"print(\"Accuracy: {}\".format((predictions == y_test).mean()))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.36715632, -0.35873007, 0.12536368])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"w"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we will visualize the obtained hyperplane, determined by $\\mathbf{w} = (0.367, -0.358, 0.125)$ and the way it separates the classes in the test data."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x145e11cf0>]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, s=50, cmap='autumn')\n",
"\n",
"x_plot = np.linspace(X_test[:, 0].min() - 1, X_test[:, 0].max() + 1, 100)\n",
"y_plot = (-w[0] / w[1]) * x_plot - (w[2]/w[1]) #\n",
"plt.plot(x_plot, y_plot, 'k-')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The equation of the line obtained is $\\mathbf{Y} = 1.023\\mathbf{X} + 0.349$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## **Convert your model to Cairo**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **Generating Cairo files**\n",
"\n",
"Now let's generate Cairo files for each tensor in the object."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def decimal_to_fp16x16(num):\n",
"\n",
" whole_num = int(num)\n",
" fractional_part = int((num - whole_num) * 65536)\n",
" fp_number = (whole_num << 16) + fractional_part\n",
" return fp_number"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"import os"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"os.makedirs(\"src/generated\", exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"tensor_name = [\"X_train\", \"Y_train\", \"X_test\", \"Y_test\"]\n",
"\n",
"def generate_cairo_files(data, name):\n",
"\n",
" with open(os.path.join('src', 'generated', f\"{name}.cairo\"), \"w\") as f:\n",
" f.write(\n",
" \"use array::ArrayTrait;\\n\" +\n",
" \"use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};\\n\" +\n",
" \"use orion::numbers::{FixedTrait, FP16x16, FP16x16Impl};\\n\" +\n",
" \"\\n\" + f\"fn {name}() -> Tensor<FP16x16>\" + \"{\\n\\n\" + \n",
" \"let mut shape = ArrayTrait::new();\\n\"\n",
" )\n",
" for dim in data.shape:\n",
" f.write(f\"shape.append({dim});\\n\")\n",
" \n",
" f.write(\"let mut data = ArrayTrait::new();\")\n",
" for val in np.nditer(data.flatten()):\n",
" f.write(f\"data.append(FixedTrait::new({abs(int(decimal_to_fp16x16(val)))}, {str(val < 0).lower()}));\\n\")\n",
" f.write(\n",
" \"let tensor = TensorTrait::<FP16x16>::new(shape.span(), data.span());\\n\" +\n",
" \"return tensor;\\n}\"\n",
" )\n",
"\n",
"with open(f\"src/generated.cairo\", \"w\") as f:\n",
" for n in tensor_name:\n",
" f.write(f\"mod {n};\\n\")\n",
"\n",
"generate_cairo_files(X_train, \"X_train\")\n",
"generate_cairo_files(X_test, \"X_test\")\n",
"generate_cairo_files(y_train, \"Y_train\")\n",
"generate_cairo_files(y_test, \"Y_test\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" **Convert hyperparameters to FP16x16**\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"655\n",
"65536\n",
"6553600\n"
]
}
],
"source": [
"print(decimal_to_fp16x16(learning_rate))\n",
"print(decimal_to_fp16x16(C))\n",
"print(decimal_to_fp16x16(num_epochs))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" **Get an estimate for the initial and final loss value, and final weights in FP16x16** "
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initial loss: 65536\n",
"Final loss: 26799\n",
"Weights: [ 24061 -23509 8215]\n"
]
}
],
"source": [
"w = np.array([decimal_to_fp16x16(w[0]),\n",
"decimal_to_fp16x16(w[1]),\n",
"decimal_to_fp16x16(w[2])])\n",
"\n",
"print(\"Initial loss: {}\".format(decimal_to_fp16x16(losses[0])))\n",
"print(\"Final loss: {}\".format(decimal_to_fp16x16(final_loss)))\n",
"print(\"Weights: {}\".format(w))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the implementation of SVM in Cairo with Orion, please visit the Convert your model section within the [Verifiable Support Vector Machine tutorial][def]\n",
"\n",
"[def]: ../tutorial/VerifiableSupportVectorMachineTutorial.md"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "cairo",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_support_vector_machine/src/generated.cairo | mod X_train;
mod Y_train;
mod X_test;
mod Y_test;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_support_vector_machine/src/generated/X_test.cairo | use array::ArrayTrait;
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};
use orion::numbers::{FixedTrait, FP16x16, FP16x16Impl};
fn X_test() -> Tensor<FP16x16>{
let mut shape = ArrayTrait::new();
shape.append(50);
shape.append(3);
let mut data = ArrayTrait::new();data.append(FixedTrait::new(87946, false));
data.append(FixedTrait::new(38900, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(34695, false));
data.append(FixedTrait::new(249556, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(159279, false));
data.append(FixedTrait::new(4166, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(83319, false));
data.append(FixedTrait::new(124029, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(154108, false));
data.append(FixedTrait::new(54263, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(81434, false));
data.append(FixedTrait::new(295173, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(101339, false));
data.append(FixedTrait::new(276101, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(51730, false));
data.append(FixedTrait::new(284261, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(130047, false));
data.append(FixedTrait::new(32083, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(112357, false));
data.append(FixedTrait::new(329332, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(103102, false));
data.append(FixedTrait::new(31715, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(111245, false));
data.append(FixedTrait::new(56762, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(77993, false));
data.append(FixedTrait::new(309836, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(180005, false));
data.append(FixedTrait::new(101281, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(62872, false));
data.append(FixedTrait::new(298895, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(36406, true));
data.append(FixedTrait::new(307754, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(150385, false));
data.append(FixedTrait::new(50193, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(106162, false));
data.append(FixedTrait::new(4433, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(79801, false));
data.append(FixedTrait::new(255125, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(95363, false));
data.append(FixedTrait::new(1913, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(121, true));
data.append(FixedTrait::new(300250, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(14718, false));
data.append(FixedTrait::new(312625, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(89573, false));
data.append(FixedTrait::new(41613, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(30116, false));
data.append(FixedTrait::new(357159, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(159865, false));
data.append(FixedTrait::new(4752, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(61297, false));
data.append(FixedTrait::new(349424, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(116464, false));
data.append(FixedTrait::new(77761, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(70534, false));
data.append(FixedTrait::new(307023, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(155183, false));
data.append(FixedTrait::new(36188, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(133737, false));
data.append(FixedTrait::new(29808, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(145701, false));
data.append(FixedTrait::new(54969, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(105613, false));
data.append(FixedTrait::new(119503, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(138536, false));
data.append(FixedTrait::new(81751, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(89231, false));
data.append(FixedTrait::new(89547, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(37538, false));
data.append(FixedTrait::new(267914, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(211603, false));
data.append(FixedTrait::new(74168, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(157689, false));
data.append(FixedTrait::new(319191, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(145953, false));
data.append(FixedTrait::new(82769, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(69644, false));
data.append(FixedTrait::new(339237, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(108783, false));
data.append(FixedTrait::new(233497, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(120774, false));
data.append(FixedTrait::new(4764, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(38978, false));
data.append(FixedTrait::new(308651, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(122543, false));
data.append(FixedTrait::new(7073, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(111206, false));
data.append(FixedTrait::new(49473, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(130347, false));
data.append(FixedTrait::new(98944, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(161886, false));
data.append(FixedTrait::new(86147, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(86653, false));
data.append(FixedTrait::new(273862, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(86507, false));
data.append(FixedTrait::new(92030, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(124252, false));
data.append(FixedTrait::new(339830, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(113347, false));
data.append(FixedTrait::new(75189, false));
data.append(FixedTrait::new(65536, false));
let tensor = TensorTrait::<FP16x16>::new(shape.span(), data.span());
return tensor;
} | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_support_vector_machine/src/generated/X_train.cairo | use array::ArrayTrait;
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};
use orion::numbers::{FixedTrait, FP16x16, FP16x16Impl};
fn X_train() -> Tensor<FP16x16>{
let mut shape = ArrayTrait::new();
shape.append(100);
shape.append(3);
let mut data = ArrayTrait::new();
data.append(FixedTrait::new(165613, false));
data.append(FixedTrait::new(40488, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(101228, false));
data.append(FixedTrait::new(275957, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65780, false));
data.append(FixedTrait::new(274692, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(82291, false));
data.append(FixedTrait::new(221645, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(122469, false));
data.append(FixedTrait::new(62659, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(83856, false));
data.append(FixedTrait::new(69330, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(70073, false));
data.append(FixedTrait::new(296922, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(70957, false));
data.append(FixedTrait::new(266254, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(193459, false));
data.append(FixedTrait::new(22565, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(50300, false));
data.append(FixedTrait::new(288200, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(29071, false));
data.append(FixedTrait::new(204164, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(140848, false));
data.append(FixedTrait::new(67959, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(165042, false));
data.append(FixedTrait::new(91210, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(133149, false));
data.append(FixedTrait::new(12897, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(172026, false));
data.append(FixedTrait::new(62271, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(519, false));
data.append(FixedTrait::new(273687, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(3112, true));
data.append(FixedTrait::new(358760, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(130822, false));
data.append(FixedTrait::new(32740, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(78697, false));
data.append(FixedTrait::new(39431, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(155270, false));
data.append(FixedTrait::new(52083, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(122730, false));
data.append(FixedTrait::new(273985, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(59922, false));
data.append(FixedTrait::new(298198, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(39038, false));
data.append(FixedTrait::new(267789, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(109040, false));
data.append(FixedTrait::new(43456, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(109628, false));
data.append(FixedTrait::new(43207, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(18165, false));
data.append(FixedTrait::new(317474, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(161818, false));
data.append(FixedTrait::new(110019, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(151455, false));
data.append(FixedTrait::new(85446, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(54844, false));
data.append(FixedTrait::new(140008, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(129348, false));
data.append(FixedTrait::new(103533, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(134024, false));
data.append(FixedTrait::new(73738, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(37464, false));
data.append(FixedTrait::new(283304, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(43940, false));
data.append(FixedTrait::new(264827, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(109385, false));
data.append(FixedTrait::new(28598, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(219755, false));
data.append(FixedTrait::new(111383, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(120177, false));
data.append(FixedTrait::new(49416, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(66596, false));
data.append(FixedTrait::new(293946, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(91267, false));
data.append(FixedTrait::new(60880, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(72170, false));
data.append(FixedTrait::new(320456, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(223533, false));
data.append(FixedTrait::new(57167, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(138029, false));
data.append(FixedTrait::new(229056, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(102711, false));
data.append(FixedTrait::new(1167, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(58556, false));
data.append(FixedTrait::new(66252, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(94025, false));
data.append(FixedTrait::new(85630, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(5342, false));
data.append(FixedTrait::new(299330, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(97971, false));
data.append(FixedTrait::new(252869, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(46869, false));
data.append(FixedTrait::new(354769, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(137416, false));
data.append(FixedTrait::new(243624, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(139593, false));
data.append(FixedTrait::new(340269, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(110332, false));
data.append(FixedTrait::new(274978, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(48127, false));
data.append(FixedTrait::new(330121, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(17851, false));
data.append(FixedTrait::new(358479, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(155294, false));
data.append(FixedTrait::new(62306, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(97104, false));
data.append(FixedTrait::new(45224, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(28769, false));
data.append(FixedTrait::new(297266, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(47716, false));
data.append(FixedTrait::new(252661, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(107481, false));
data.append(FixedTrait::new(119242, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(28304, false));
data.append(FixedTrait::new(284095, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(36669, false));
data.append(FixedTrait::new(276169, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(93592, false));
data.append(FixedTrait::new(106453, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(140275, false));
data.append(FixedTrait::new(46272, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(170483, false));
data.append(FixedTrait::new(71302, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(32006, false));
data.append(FixedTrait::new(214172, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(129468, false));
data.append(FixedTrait::new(47119, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(178995, false));
data.append(FixedTrait::new(16364, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(64393, false));
data.append(FixedTrait::new(352276, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(137416, false));
data.append(FixedTrait::new(317680, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(76291, false));
data.append(FixedTrait::new(248468, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(22750, false));
data.append(FixedTrait::new(226215, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(92649, false));
data.append(FixedTrait::new(287124, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(74107, false));
data.append(FixedTrait::new(61316, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(53442, false));
data.append(FixedTrait::new(313606, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(14023, false));
data.append(FixedTrait::new(320171, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(170903, false));
data.append(FixedTrait::new(71362, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(29739, false));
data.append(FixedTrait::new(259291, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(122501, false));
data.append(FixedTrait::new(356602, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(118987, false));
data.append(FixedTrait::new(73380, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(116781, false));
data.append(FixedTrait::new(59516, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(43895, false));
data.append(FixedTrait::new(235628, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(33706, false));
data.append(FixedTrait::new(303257, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(168385, false));
data.append(FixedTrait::new(33229, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(48750, false));
data.append(FixedTrait::new(270165, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(93906, false));
data.append(FixedTrait::new(286837, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(78782, false));
data.append(FixedTrait::new(238822, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(72450, false));
data.append(FixedTrait::new(82830, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(181409, false));
data.append(FixedTrait::new(71291, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(177934, false));
data.append(FixedTrait::new(84595, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(225287, false));
data.append(FixedTrait::new(17147, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(88127, false));
data.append(FixedTrait::new(318315, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(153231, false));
data.append(FixedTrait::new(224865, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(79552, false));
data.append(FixedTrait::new(239072, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(21877, false));
data.append(FixedTrait::new(323515, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(75708, false));
data.append(FixedTrait::new(334208, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(68971, false));
data.append(FixedTrait::new(297859, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(97556, false));
data.append(FixedTrait::new(42705, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(109996, false));
data.append(FixedTrait::new(39914, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(28087, false));
data.append(FixedTrait::new(325975, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(12237, false));
data.append(FixedTrait::new(263902, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(90416, false));
data.append(FixedTrait::new(298075, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(115110, false));
data.append(FixedTrait::new(134701, false));
data.append(FixedTrait::new(65536, false));
let tensor = TensorTrait::<FP16x16>::new(shape.span(), data.span());
return tensor;
} | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_support_vector_machine/src/generated/Y_test.cairo | use array::ArrayTrait;
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};
use orion::numbers::{FixedTrait, FP16x16, FP16x16Impl};
fn Y_test() -> Tensor<FP16x16>{
let mut shape = ArrayTrait::new();
shape.append(50);
let mut data = ArrayTrait::new();data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
let tensor = TensorTrait::<FP16x16>::new(shape.span(), data.span());
return tensor;
} | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_support_vector_machine/src/generated/Y_train.cairo | use array::ArrayTrait;
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};
use orion::numbers::{FixedTrait, FP16x16, FP16x16Impl};
fn Y_train() -> Tensor<FP16x16>{
let mut shape = ArrayTrait::new();
shape.append(100);
let mut data = ArrayTrait::new();data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, false));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, true));
data.append(FixedTrait::new(65536, false));
let tensor = TensorTrait::<FP16x16>::new(shape.span(), data.span());
return tensor;
} | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_support_vector_machine/src/helper.cairo | use debug::PrintTrait;
use traits::TryInto;
use array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{
Tensor, TensorTrait, FP16x16Tensor, FP16x16TensorAdd, FP16x16TensorMul, FP16x16TensorSub,
FP16x16TensorDiv
};
use orion::numbers::{FixedTrait, FP16x16, FP16x16Impl};
use orion::numbers::fixed_point::implementations::fp16x16::core::{
HALF, ONE, FP16x16Mul, FP16x16Div, FP16x16Print, FP16x16IntoI32, FP16x16PartialOrd,
FP16x16PartialEq
};
// Calculates the machine learning model's loss.
fn calculate_loss(
w: @Tensor<FP16x16>,
x_train: @Tensor<FP16x16>,
y_train: @Tensor<FP16x16>,
c: @Tensor<FP16x16>,
one_tensor: @Tensor<FP16x16>,
half_tensor: @Tensor<FP16x16>,
y_train_len: u32
) -> FP16x16 {
let tensor_size = FixedTrait::new_unscaled(y_train_len, false);
let pre_cumsum = *one_tensor - *y_train * x_train.matmul(w);
let cumsum = pre_cumsum.cumsum(0, Option::None(()), Option::None(()));
let sum = cumsum.data[pre_cumsum.data.len() - 1];
let mean = FP16x16Div::div(*sum, tensor_size);
let mean_tensor = TensorTrait::new(
shape: array![1].span(), data: array![mean].span(),
);
let regularization_term = *half_tensor * (w.matmul(w));
let loss_tensor = mean_tensor + *c * regularization_term;
loss_tensor.at(array![0].span())
}
// Calculates the gradient for the machine learning model
fn calculate_gradient(
w: @Tensor<FP16x16>,
x_train: @Tensor<FP16x16>,
y_train: @Tensor<FP16x16>,
c: Tensor<FP16x16>,
one_tensor: @Tensor<FP16x16>,
neg_one_tensor: @Tensor<FP16x16>,
y_train_len: u32
) -> Tensor<FP16x16> {
let tensor_size = TensorTrait::new(
shape: array![1].span(),
data: array![FixedTrait::new_unscaled(y_train_len, false)].span(),
);
let mask = (*y_train * x_train.matmul(w));
let mask = less(@mask, one_tensor);
let gradient = (((mask * *y_train).matmul(x_train) / tensor_size) * *neg_one_tensor) + (c * *w);
gradient
}
// Calculates the accuracy of the machine learning model's predictions.
fn accuracy(y: @Tensor<FP16x16>, z: @Tensor<FP16x16>) -> FP16x16 {
let (mut left, mut right) = (y, z);
let mut right_data = *right.data;
let mut left_data = *left.data;
let mut counter = 0;
loop {
match right_data.pop_front() {
Option::Some(item) => {
let right_current_index = item;
let left_current_index = left_data.pop_front();
let (y_value, z_value) = (left_current_index.unwrap(), right_current_index);
if *y_value == *z_value {
counter += 1;
};
},
Option::None(_) => {
break;
}
};
};
(FixedTrait::new_unscaled(counter, false) / FixedTrait::new_unscaled((*y.data).len(), false))
* FixedTrait::new_unscaled(100, false)
}
// Returns the truth value of (x < y) element-wise.
fn less(y: @Tensor<FP16x16>, z: @Tensor<FP16x16>) -> Tensor<FP16x16> {
let mut data_result = ArrayTrait::<FP16x16>::new();
let mut data_result2 = ArrayTrait::<FP16x16>::new();
let (mut smaller, mut bigger, retains_input_order) = if (*y.data).len() < (*z.data).len() {
(y, z, true)
} else {
(z, y, false)
};
let mut bigger_data = *bigger.data;
let mut smaller_data = *smaller.data;
let mut smaller_index = 0;
loop {
match bigger_data.pop_front() {
Option::Some(item) => {
let bigger_current_index = item;
let smaller_current_index = smaller_data[smaller_index];
let (y_value, z_value) = if retains_input_order {
(smaller_current_index, bigger_current_index)
} else {
(bigger_current_index, smaller_current_index)
};
if *y_value < *z_value {
data_result.append(FixedTrait::ONE());
} else {
data_result.append(FixedTrait::ZERO());
};
smaller_index = (1 + smaller_index) % smaller_data.len();
},
Option::None(_) => {
break;
}
};
};
return TensorTrait::<FP16x16>::new(*bigger.shape, data_result.span());
}
// Returns an element-wise indication of the sign of a number.
fn sign(z: @Tensor<FP16x16>) -> Tensor<FP16x16> {
let mut data_result = ArrayTrait::<FP16x16>::new();
let mut z_data = *z.data;
loop {
match z_data.pop_front() {
Option::Some(item) => {
let result = if *item.sign {
FixedTrait::new(ONE, true)
} else {
FixedTrait::new(ONE, false)
};
data_result.append(result);
},
Option::None(_) => {
break;
}
};
};
TensorTrait::<FP16x16>::new(*z.shape, data_result.span())
}
// Returns predictions using the machine learning model.
fn pred(x: @Tensor<FP16x16>, w: @Tensor<FP16x16>) -> Tensor<FP16x16> {
sign(@(x.matmul(w)))
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_support_vector_machine/src/lib.cairo | mod generated;
mod train;
mod test;
mod helper;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_support_vector_machine/src/test.cairo | use traits::TryInto;
use array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{
Tensor, TensorTrait, FP16x16Tensor, FP16x16TensorAdd, FP16x16TensorMul, FP16x16TensorSub,
FP16x16TensorDiv
};
use orion::numbers::{FixedTrait, FP16x16, FP16x16Impl};
use orion::numbers::fixed_point::implementations::fp16x16::core::{
HALF, ONE, FP16x16Mul, FP16x16Div, FP16x16IntoI32, FP16x16PartialOrd,
FP16x16PartialEq
};
use verifiable_support_vector_machine::{
generated::{X_train::X_train, Y_train::Y_train, X_test::X_test, Y_test::Y_test}, train::{train}
};
use verifiable_support_vector_machine::{helper::{pred, accuracy}};
#[test]
#[available_gas(99999999999999999)]
fn svm_test() {
let x_train = X_train();
let x_test = X_test();
let y_train = Y_train();
let y_test = Y_test();
let feature_size = *x_train.shape[1];
let mut zero_array = ArrayTrait::new();
let learning_rate = FixedTrait::new(655, false); // 655 is 0.01
// 50 %
let average_compare = FixedTrait::new_unscaled(50, false);
let mut i = 0_u32;
loop {
if i >= feature_size {
break ();
}
zero_array.append(FP16x16Impl::ZERO());
i += 1;
};
let initial_w = TensorTrait::new(
shape: array![feature_size].span(), data: zero_array.span()
);
let y_train_len = y_train.data.len();
let (final_w, initial_loss, final_loss) = train(
@x_train, @y_train, @initial_w, learning_rate, y_train_len, 100_u32
);
let final_y_pred = pred(@x_test, @final_w);
let average_pred = accuracy(@final_y_pred, @y_test);
let train_y_pred = pred(@x_train, @final_w);
let average_train = accuracy(@train_y_pred, @y_train);
assert(final_loss < initial_loss, 'No decrease in training loss');
assert(average_pred > average_compare, 'It is better to flip a coin');
assert(average_train > average_compare, 'It was not a good training');
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/basic/verifiable_support_vector_machine/src/train.cairo | use debug::PrintTrait;
use traits::TryInto;
use array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{
Tensor, TensorTrait, FP16x16Tensor, FP16x16TensorAdd, FP16x16TensorMul, FP16x16TensorSub,
FP16x16TensorDiv
};
use orion::numbers::{FixedTrait, FP16x16, FP16x16Impl};
use orion::numbers::fixed_point::implementations::fp16x16::core::{
HALF, ONE, FP16x16Mul, FP16x16Div, FP16x16Print, FP16x16IntoI32, FP16x16PartialOrd,
FP16x16PartialEq
};
use verifiable_support_vector_machine::{helper::{calculate_loss, calculate_gradient}};
// Performs a training step for each iteration during model training
fn train_step(
x: @Tensor<FP16x16>,
y: @Tensor<FP16x16>,
w: @Tensor<FP16x16>,
learning_rate: FP16x16,
one_tensor: @Tensor<FP16x16>,
half_tensor: @Tensor<FP16x16>,
neg_one_tensor: @Tensor<FP16x16>,
y_train_len: u32,
iterations: u32,
index: u32
) -> Tensor<FP16x16> {
let learning_rate_tensor = TensorTrait::new(
shape: array![1].span(), data: array![learning_rate].span()
);
let c = TensorTrait::new(
shape: array![1].span(),
data: array![FP16x16Impl::ONE()].span(),
);
let mut w_recursive = *w;
let gradient = calculate_gradient(
@w_recursive, x, y, c, one_tensor, neg_one_tensor, y_train_len
);
w_recursive = w_recursive - (learning_rate_tensor * gradient);
if index == iterations {
return w_recursive;
}
train_step(
x,
y,
@w_recursive,
learning_rate,
one_tensor,
half_tensor,
neg_one_tensor,
y_train_len,
iterations,
index + 1
)
}
// Trains the machine learning model.
fn train(
x: @Tensor<FP16x16>,
y: @Tensor<FP16x16>,
init_w: @Tensor<FP16x16>,
learning_rate: FP16x16,
y_train_len: u32,
iterations: u32
) -> (Tensor<FP16x16>, FP16x16, FP16x16) {
let iter_w = init_w;
'Iterations'.print();
iterations.print();
let c = TensorTrait::new(
shape: array![1].span(),
data: array![FP16x16Impl::ONE()].span(),
);
let one_tensor = TensorTrait::new(
shape: array![1].span(),
data: array![FP16x16Impl::ONE()].span(),
);
let half_tensor = TensorTrait::new(
shape: array![1].span(),
data: array![FixedTrait::new(HALF, false)].span(),
);
let neg_one_tensor = TensorTrait::new(
shape: array![1].span(),
data: array![FixedTrait::new(ONE, true)].span(),
);
let initial_loss = FixedTrait::<FP16x16>::ZERO();
let final_loss = FixedTrait::<FP16x16>::ZERO();
let initial_loss = calculate_loss(init_w, x, y, @c, @one_tensor, @half_tensor, y_train_len);
let iter_w = train_step(
x,
y,
init_w,
learning_rate,
@one_tensor,
@half_tensor,
@neg_one_tensor,
y_train_len,
iterations,
1
);
let final_loss = calculate_loss(@iter_w, x, y, @c, @one_tensor, @half_tensor, y_train_len);
(iter_w, initial_loss, final_loss)
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/Provable Linear Regression Solver (Linear Regression).ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "c4619365",
"metadata": {},
"source": [
"## Provable Linear Regression Solver"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "4d401fa3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f63f2d5ca10>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np\n",
"import matplotlib.pylab as plt\n",
"from sklearn.metrics import r2_score\n",
"import os\n",
"\n",
"N = 50\n",
"noise = np.random.randn(N)+100\n",
"x = np.random.randn(N)\n",
"y = 8* x + noise\n",
"np.random.seed(1)\n",
"plt.scatter(x, y)\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "ed9e4ef7",
"metadata": {},
"source": [
"# Gradient and bias calculation"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9e4e7430",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gradient: 8.326885863832231\n",
"bias: 100.05814848422345\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f63f2d967d0>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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1PYiISNfupHRM2XxOK5wAQIYiH1M2n8PupPQabU+1BJSioiKMHDkSKSkp2Lt3r9Z5OXK5HIWFhXj48KHWazIzM+Ht7S16P0dHR825O7Zw/s6xY8cglUoRERFh9Gv5BU9ERMZSqgTEJSRDbDBHXRaXkFyjwz0mDyjqcHLt2jXs27cPHh4eWtfbtm0Le3t7rcm06enpSEpKQufOnU3dnCox1zjc+vXr8frrr+Po0aO4detWjbwnERHZrlMpD3R6TkoTAKQr8nEq5UGNtcnogJKTk4PExEQkJiYCAFJSUpCYmIhbt26huLgYI0aMwJkzZ7BlyxYolUpkZGQgIyMDhYWFAEpW+kycOBEzZszA/v37cf78eYwdOxYtWrTQrOqxBLuT0tF10a8Yve4E3tyWiNHrTqDrol+rvYsrNzcXX331FaZMmYJBgwZh48aNOnV++OEHtGvXDk5OTvD09MSwYcMAAD169MDNmzfx73//GxKJRDNkFhsbi9atW2vdY8WKFQgMDNQ8P336NPr27QtPT0/IZDJ0794d586dM7jda9euRaNGjaBSqbTKX3jhBURHRwMAbty4gcGDB8Pb2xv16tXDs88+i3379um9Z2pqKiQSiebPGgA8evQIEokEBw8e1JQlJydj4MCBqFevHry9vfHyyy/j3r17muvffPMNWrRoAWdnZ3h4eKBPnz7Izc01+LMREVm7zGz94aQy9UzB6IBy5swZtGnTBm3atAEATJ8+HW3atMF7772H27dv44cffsDt27fRunVr+Pj4aB6lV+h89NFHGDJkCEaOHIkuXbrAxcUFCQkJVd4DxVTMOQ63fft2NG3aFE2bNsXYsWOxYcMGCMKTnpsff/wRw4YNw/PPP4/z589j//79aNeuHQDgu+++Q+PGjfH+++8jPT0d6emGtzM7OxvR0dE4cuQITpw4gZCQEAwcOBDZ2dkGvf7FF1/EvXv3cODAAU3Zw4cPsWfPHowZMwZASbgdOHAg9u3bh/Pnz6N///6IjIysUi9Reno6unfvjtatW+PMmTPYvXs3/vrrL4wcOVJzffTo0ZgwYQIuX76MgwcPYtiwYVo/UyIiW+flatj+TYbWMwWjV/H06NGj3L/cDfmL38nJCStXrsTKlSuNfftqV9E4nAQl43B9Q+WQ2pl+h9rPP/8cY8eOBQBEREQgJycH+/fv1/QuzZ8/H6NGjdJa1dSqVSsAgLu7O6RSKVxdXSGXy4163169emk9X7t2LRo0aIBDhw5h0KBBFb7e3d0dERERiI+PR+/evQEAX3/9Ndzd3TXPW7VqpWkrAMybNw87duzADz/8gKlTpxrVXrU1a9YgPDwcCxYs0JStX78efn5+uHr1KnJyclBcXIxhw4YhICAAANCiRYtKvRcRkbVqH+QOH5kTMhT5ot9/EgBymRPaB7nXWJu4u1QZ5hyH+/3333Hq1CmMGjUKAFCnTh289NJLWL9+vaZOYmKi5gvflDIzM/Gvf/0LTZo0gUwmg0wmQ05OjlG9G2PGjMG3336LgoICAMCWLVswatQoTc9Ybm4uZs+ejdDQUNSvXx/16tXDlStXqtSDcvbsWRw4cAD16tXTPJ555hkAJUNKrVq1Qu/evdGiRQu8+OKLWLdunc4EbSIiWye1kyAmMhRASRgpTf08JjK0Wv5hro9VHhZYFeYch/v8889RXFyMRo0aacoEQYC9vT0ePnyIBg0awNnZ2ej72tnZ6fRsFRUVaT0fN24c7t69ixUrViAgIACOjo7o1KmTZu6QISIjI6FSqfDjjz/i2WefxZEjR7B8+XLN9VmzZmHPnj1YunQpnn76aTg7O2PEiBF630O9O2vptpdtt0qlQmRkJBYtWqTzeh8fH0ilUuzduxfHjh3DL7/8gpUrV+Kdd97ByZMnERQUZPBnIyKydhFhPlgzNlxnHxS5mfZBYUApw1zjcMXFxfjyyy+xbNkyrV12AWD48OHYsmULpk6dipYtW2L//v0YP3686H0cHBygVCq1yho2bIiMjAytvWZKTzwFgCNHjmD16tUYOHAgACAtLU1roqkhnJ2dMWzYMGzZsgXXr19HkyZN0LZtW633GDduHIYOHQqgZE5Kamqq3vs1bNgQQMk8EvWcp7LtDg8Px7fffovAwEDUqSP+x1kikaBLly7o0qUL3nvvPQQEBGDHjh2YPn26UZ+PiMjaRYT5oG+oHKdSHiAzOx9eriXDOjXZc6LGgFKGucbhdu3ahYcPH2LixImQyWRa10aMGIHPP/8cU6dORUxMDHr37o3g4GCMGjUKxcXF+PnnnzF79mwAJfugHD58GKNGjYKjoyM8PT3Ro0cP3L17F4sXL8aIESOwe/du/Pzzz1r7yTz99NPYtGkT2rVrh6ysLMyaNatSvTVjxoxBZGQkLl26pJlLU/o9vvvuO0RGRkIikWDu3Lk6q35Kc3Z2RseOHfHhhx8iMDAQ9+7dw7vvvqtV57XXXsO6deswevRozJo1C56enrh+/Tq2bduGdevW4cyZM9i/fz/69esHLy8vnDx5Enfv3kWzZs2M/mxERLZAaidBp2CPiitWM85BKcNc43Cff/45+vTpoxNOgJIelMTERJw7dw49evTA119/jR9++AGtW7dGr169cPLkSU3d999/H6mpqQgODtb0QDRr1gyrV6/Gp59+ilatWuHUqVOYOXOm1nusX78eDx8+RJs2bfDyyy/jjTfegJeXl9Gfo1evXnB3d8fvv/+OqKgorWsfffQRGjRogM6dOyMyMhL9+/dHeHh4ufdbv349ioqK0K5dO7z55puYN2+e1nVfX1/873//g1KpRP/+/REWFoY333wTMpkMdnZ2cHNzw+HDhzFw4EA0adIE7777LpYtW6b3wEsiIrIMEqEWrrfMysqCTCaDQqHQ2VU2Pz8fKSkpCAoKqtKx95Z0HgFZPlP9uSMismblfX+XxSEePSxpHI6IiMjWMKCUw1LG4YiIiGwN56AQERGRxWFAISIiIovDgEJEREQWhwGFiIiItFnAAl8GFCIiInri0GBgqx1wdppZm8FVPERERATkpgHf+z95fv+0+doC9qAQERFR8hLtcAIAvfebpy1/Y0CxUbGxsWjdurXm+bhx4zBkyJAab0dqaiokEonOIYCmJpFIsHPnzmp9DyKiWkeZD8RLgMTZT8paLwaiBEBq3l2xGVAsyLhx4yCRSCCRSGBvb4+nnnoKM2fORG5ubrW/98cff4yNGzcaVLemQgUREVWj9L3A9jKHwg5JA0Jnmac9ZXAOioWJiIjAhg0bUFRUhCNHjuCVV15Bbm4u1qxZo1O3qKgI9vb2JnlfsUMKiYjICgkC8Gtv4K8DT8oavQB0/958bRJhGz0oggAU55rnYeRSLUdHR8jlcvj5+SEqKgpjxozRDE2oh2XWr1+Pp556Co6OjhAEAQqFAv/85z/h5eUFNzc39OrVC7/99pvWfT/88EN4e3vD1dUVEydORH5+vtb1skM8KpUKixYtwtNPPw1HR0f4+/tj/vz5AICgoCAAQJs2bSCRSNCjRw/N6zZs2IBmzZrByckJzzzzDFavXq31PqdOnUKbNm3g5OSEdu3a4fz58+X+PObMmYOOHTvqlLds2RIxMTEAgNOnT6Nv377w9PSETCZD9+7dce7cOb33PHjwICQSCR49eqQpS0xMhEQiQWpqqqbs2LFj6NatG5ydneHn54c33nijRnqziIiqTc4fJSt0SoeTPoctLpwAttKDonwMfFXPPO89MgeoU7fSL3d2dkZRUZHm+fXr1/HVV1/h22+/hVQqBQA8//zzcHd3x08//QSZTIa1a9eid+/euHr1Ktzd3fHVV18hJiYGn376KZ577jls2rQJn3zyCZ566im97ztnzhysW7cOH330Ebp27Yr09HRcuXIFQEnIaN++Pfbt24fmzZvDwcEBALBu3TrExMRg1apVaNOmDc6fP49Jkyahbt26iI6ORm5uLgYNGoRevXph8+bNSElJwZtvvlnu5x8zZgw+/PBD3LhxA8HBwQCAS5cu4eLFi/jmm28AANnZ2YiOjsYnn3wCAFi2bBkGDhyIa9euwdXVtVI/94sXL6J///744IMP8Pnnn+Pu3buYOnUqpk6dig0bNlTqnkREZnUxDrgY++R5nbrA8AeA1MFsTSqPbQSUWurUqVOIj49H7969NWWFhYXYtGkTGjZsCAD49ddfcfHiRWRmZsLR0REAsHTpUuzcuRPffPMN/vnPf2LFihWYMGECXnnlFQDAvHnzsG/fPp1eFLXs7Gx8/PHHWLVqFaKjowEAwcHB6Nq1KwBo3tvDwwNyuVzzug8++ADLli3DsGHDAJT0tCQnJ2Pt2rWIjo7Gli1boFQqsX79eri4uKB58+a4ffs2pkyZovdnEBYWhpYtWyI+Ph5z584FAGzZsgXPPvssmjRpAgDo1auX1mvWrl2LBg0a4NChQxg0aJAhP2odS5YsQVRUFKZNmwYACAkJwSeffILu3btjzZo1cHIy7+QxIiKDFefq/iO93adAk1fN0x4D2UZAkbqU9GSY672NsGvXLtSrVw/FxcUoKirC4MGDsXLlSs31gIAATUAAgLNnzyInJwceHtqnLufl5eHGjRsAgMuXL+Nf//qX1vVOnTrhwIEDEHP58mUUFBRoBaOK3L17F2lpaZg4cSImTZqkKS8uLtbMb7l8+TJatWoFF5cnP5NOnTpVeO8xY8Zg/fr1mDt3LgRBwNatWzXBAQAyMzPx3nvv4ddff8Vff/0FpVKJx48f49atWwa3v6yzZ8/i+vXr2LJli6ZMEASoVCqkpKSgWbNmlb43EVGNuf0DcHiwdtnQDMDZ2zztMYJtBBSJpErDLDWpZ8+eWLNmDezt7eHr66szCbZuXe3PoVKp4OPjg4MHD+rcq379+pVqg7Ozc8WVylCpVABKhnk6dOigdU09FCVUcuvkqKgovP322zh37hzy8vKQlpaGUaNGaa6PGzcOd+/exYoVKxAQEABHR0d06tQJhYWFovezs7PTaU/pYTT155k8eTLeeOMNndf7+/vrlBERWRRBAPZ0AB6U2mwtcAzQebP52mQk2wgotUjdunXx9NNPG1w/PDwcGRkZqFOnDgIDA0XrNGvWDCdOnMA//vEPTdmJEyf03jMkJATOzs7Yv3+/ZlioNPWcE6VSqSnz9vZGo0aN8Mcff2DMmDGi9w0NDcWmTZuQl5enCUHltUOtcePG6NatG7Zs2YK8vDz06dMH3t5P0v+RI0ewevVqDBw4EACQlpaGe/fu6b2fugcqPT0dDRo0AACdJdPh4eG4dOmSUb8LIiKLkPU7sOsZ7bJ+JwHP9uZpTyXZxioeK9anTx906tQJQ4YMwZ49e5Camopjx47h3XffxZkzZwAAb775JtavX4/169fj6tWriImJwaVLl/Te08nJCW+99RZmz56NL7/8Ejdu3MCJEyfw+eefAwC8vLzg7OyM3bt346+//oJCoQBQsspo4cKF+Pjjj3H16lVcvHgRGzZswPLlywGU9ITY2dlh4sSJSE5Oxk8//YSlS5ca9DnHjBmDbdu24euvv8bYsWO1rj399NPYtGkTLl++jJMnT2LMmDHl9gI9/fTT8PPzQ2xsLK5evYoff/wRy5Yt06rz1ltv4fjx43jttdeQmJiIa9eu4YcffsDrr79uUHuJiMzi/GztcOLsA4wqqnXhBGBAqfUkEgl++ukndOvWDRMmTECTJk0watQopKamanoZXnrpJbz33nt466230LZtW9y8ebPciakAMHfuXMyYMQPvvfcemjVrhpdeegmZmZkAgDp16uCTTz7B2rVr4evri8GDS8Y3X3nlFfzf//0fNm7ciBYtWqB79+7YuHGjZllyvXr1kJCQgOTkZLRp0wbvvPMOFi1aZNDnfPHFF3H//n08fvxYZ8fb9evX4+HDh2jTpg1efvllvPHGG/Dy8tJ7L3t7e2zduhVXrlxBq1atsGjRIsybN0+rTsuWLXHo0CFcu3YNzz33HNq0aYO5c+fCx8fHoPYSEdWooqySHWEvL3lS1mE9MPQOYFc7B0skQmUnBphRVlYWZDIZFAoF3NzctK7l5+cjJSUFQUFBXGlBNYZ/7ojIbG5+BfzvJe2y4fcARw/x+mZU3vd3WbUzVhEREdk6QQX8GFoy50Tt6clA+8/M1yYTYkAhIiKqbR5eAH5upV02IBFo0Eq0em3EgEJERFSbnH4NuFbqGBHXEGDQFUBiXdNKGVCIiIhqg4IHwLdl5pV03goEjhKvX8tZbUCphXN/qRbjnzciqlZ/fAGcGKddNuIR4GC9J9FbXUBR77z6+PHjSu2ISlQZjx8/BgCdnX+JyHYpVQJOpTxAZnY+vFyd0D7IHVI7iXE3USmB7wOAvD+flD0zHQhfpv81VsLogHL48GEsWbIEZ8+eRXp6Onbs2KG1L8V3332HtWvX4uzZs7h//z7Onz+P1q1ba92jR48eOHTokFbZSy+9hG3btlXqQ5QmlUpRv359zZ4dLi4ukEiM/ANBZCBBEPD48WNkZmaifv36mm39ici27U5KR1xCMtIVTw5l9ZE5ISYyFBFhBu6ndP8MsOdZ7bLnkwGZbZwFZnRAyc3NRatWrTB+/HgMHz5c9HqXLl3w4osvah0aV9akSZPw/vvva56bsrdDfcKuOqQQVbf69etrnexMRLZrd1I6pmw+h7IDvxmKfEzZfA5rxoZXHFKORwMpXz553qANEHG25Gw5G2F0QBkwYAAGDBig9/rLL78MAEhNTS33Pi4uLtX2F7pEIoGPjw+8vLx0DoEjMjV7e3v2nBARgJJhnbiEZJ1wAgACAAmAuIRk9A2Viw/35GcC35U5abjbTqDxYN26Vs5sc1C2bNmCzZs3w9vbGwMGDEBMTAxcXV1F6xYUFKCgoEDzPCsry6D3kEql/OIgIqIacyrlgdawTlkCgHRFPk6lPECn4DIrcq59BpwucwzJi9mAfT3TN7QWMEtAGTNmDIKCgiCXy5GUlIQ5c+bgt99+w969e0XrL1y4EHFxcTXcSiIiIuNkZusPJ3rrqYqAbzyA4uwnZWFzgZbv677QhpgloJSemxIWFoaQkBC0a9cO586dQ3h4uE79OXPmYPr06ZrnWVlZ8PPzq5G2EhERGcrL1bCzuDT17v4P2NtV+2LkdcA12MQtq30sYtu58PBw2Nvb49q1a6LXHR0d4ebmpvUgIiKyNO2D3OEjc4K+qawSlKzmaR/kDhweph1OvLoDo1UMJ3+ziIBy6dIlFBUV8Sh7IiKq1aR2EsREhgKATkhRP18Q0QDSbXbA7R1PLvbYDfQ5aFOrdCpi9BBPTk4Orl+/rnmekpKCxMREuLu7w9/fHw8ePMCtW7dw584dAMDvv5ecsiiXyyGXy3Hjxg1s2bIFAwcOhKenJ5KTkzFjxgy0adMGXbp0MdHHIiIiMo+IMB+sGRuusw+KXOaEDR2P4JnLZeZUjnwM1OHGomVJBCP36D548CB69uypUx4dHY2NGzdi48aNGD9+vM71mJgYxMbGIi0tDWPHjkVSUhJycnLg5+eH559/HjExMXB3dzeoDVlZWZDJZFAoFBzuISIii1R6J1nvukCH036QlF6A3PpDIPQt8zXQDIz5/jY6oFgCBhQiIqo1MvYDv/bRLht8E6jrb572mJEx399WdxYPERGRRRAEYGuZqZ6+zwM9dpmnPbUMAwoREZGp/XUQ2F9mOkTvg4B3dzM0pnZiQCEiIjKlbU6AqkC77KUCQOpgnvbUUhaxzJiIiKjWy78HxEu0w4lrEyBKYDipBPagEBERVdXpqcC1T7XLIs4B7m3M0x4rwIBCRERUWWITYYGSXhOqEg7xEBGRzVGqBBy/cR/fJ/6J4zfuQ6mqRKD480fdcBK+guHERNiDQkRENmV3UrrOLq8+MifERIYiIszAI1fiRbakfykPkBp2WCBVjD0oRERkM3YnpWPK5nNa4QQAMhT5mLL5HHYnpZd/g8d3dMOJZ6e/J8IynJgSe1CIiMgmKFUC4hKSITYAI6DkML+4hGT0DZVDaifSQ7LdGVBqBxs8fxmQPVMNrSX2oBARkU04lfJAp+ekNAFAuiIfp1IeaF9QFZf0mpQNJ1ECw0k1YkAhIiKbkJmtP5zorXfhPWCbvdb1P9zH43iHe5WbWEsG4xAPERFZNfWpwtf+yjGovpfr33NJRCbCNrm4A4WCPXDwhPETa8koDChERGS1xFbs6CMBIJc5ob0sFYj31LkeeEH7kD/1xNo1Y8MZUqoBAwoREVkl9YodQwZi1H0lxwP6AL9oX/vHnc9w+F5jndcYNLGWKo1zUIiIyOqUt2JHjL/MDiktB+mUH+9wTzScqOmdWEtVxoBCRERWp6IVO2pTewbjRJ9vcShgoPaFFrFAlFC5ibVkEhziISIiq2NoYJh5v5lu4ahiwE4KoNSE2QoYWo8Mxx4UIiKyOhUFhvZ1k5BadkinTr2SvU3+DicA0D7IHT4yJ+ibXSJByTb57YPcq9Zg0sGAQkREVqe8YJHachC+Cn5bu3DQVWBktk5dqZ0EMZGhAKBzL/XzmMhQTpCtBgwoRERkdcSChZs0R7fXBCjpNXEL0XuviDAfrBkbDrlMu1dGLnPiEuNqJBEEodZthZeVlQWZTAaFQgE3NzdzN4eIiCyUeh+U4wF9dC+2XgyEzjL4XuoN3zKz8+HlWjKsw54T4xjz/c1JskREZLUiwnwQccFX98JoJSAxbhBBaidBp2APE7WMKsIhHiIisk7HXhbdrj7o4i5M/OIMjt+4z/N0LBh7UIiIyPqIBJMR1xfhzOPmAID9V+5i/5W7PE/HgrEHhYiIrMfDRNFwEnhhlyaclJb+93k6u5PSa6BxZAwGFCIisg7xEuDnNlpFV/P9dQ75ExOXkMzhHgvDIR4iIqr99PSaGKL0eTqcBGs52INCRES1184A0XDyud8No2/F83QsCwMKERHVTvES4PEt7bKIc0CUgJc7BcLYLUp4no5lYUAhIqLaJWO/aK8JogTAvWQOikMdO0x6Lsig2/E8HcvEOShERFR7iAUTr+5An4M6xXMGlmx1v+5ICvTNf+V5OpaLAYWIiCyfoAK2SnXLo8STh3pb+lBfGb4Y3x5XMrJx9PpdnLv1CNn5xZp6cu6DYrGMDiiHDx/GkiVLcPbsWaSnp2PHjh0YMmSI5vp3332HtWvX4uzZs7h//z7Onz+P1q1ba92joKAAM2fOxNatW5GXl4fevXtj9erVaNy4cVU/DxERWRuxXhNAbzhRn7+Trngy6VW9Idv6ce15nk4tYfQclNzcXLRq1QqrVq3Se71Lly748MMP9d5j2rRp2LFjB7Zt24ajR48iJycHgwYNglKpNLY5RERkzcTCyQt/lBtOpmw+pxVOACDj7w3Z9iZnoFOwBwa3boROwR4MJxbM6B6UAQMGYMCAAXqvv/zyywCA1NRU0esKhQKff/45Nm3ahD59Sk6X3Lx5M/z8/LBv3z7079/f2CYREZG1SdkCHB+rW64nmAAlwzpxCckQqyGgZL5JXEIy+obKGUxqgRpfxXP27FkUFRWhX79+mjJfX1+EhYXh2LFjoq8pKChAVlaW1oOIiKxUvEQ3nDw1odxwAgCnUh7o9JyUVnpDNrJ8NR5QMjIy4ODggAYNGmiVe3t7IyMjQ/Q1CxcuhEwm0zz8/PxqoqlERFSTlAX6lw93/LzClxu60Ro3ZKsdLGYfFEEQIJGId7nNmTMHCoVC80hLS6vh1hERUbWKlwDbRTZKq6DXpDRDN1rjhmy1Q40vM5bL5SgsLMTDhw+1elEyMzPRuXNn0dc4OjrC0dGxpppIREQ1SazXZNhdwMnTqNu0D3KHj8wJGYp80XkoEpQsK+aGbLVDjfegtG3bFvb29ti7d6+mLD09HUlJSXoDChERWaHkJfqHdIwMJwAgtZMgJrJkc7ayd+WGbLWP0T0oOTk5uH79uuZ5SkoKEhMT4e7uDn9/fzx48AC3bt3CnTt3AAC///47gJKeE7lcDplMhokTJ2LGjBnw8PCAu7s7Zs6ciRYtWmhW9RARkZUTCyYtYoEWMVW6bUSYD9aMDdfZB4UbstU+EkEQDB/gA3Dw4EH07NlTpzw6OhobN27Exo0bMX78eJ3rMTExiI2NBQDk5+dj1qxZiI+P19qozdDJr1lZWZDJZFAoFHBzczOm+UREZALqnVqN3vCs8BHwTQPdciPmmlRr+6haGfP9bXRAsQQMKERE5lPeTq3l9lAYuSMsWR9jvr8tZhUPERFZvop2at2dlC7+QrFwMjKX4YT0YkAhIiKDVLRTK1CyU6uy9NHBZ17XPxG2jkt1NJOsBAMKEREZxOidWuMlwNUy57a1X8deEzJIje+DQkREtZOhO7CeTPoNnU721r3AYEJGYEAhIiKDGLIDa2rLQUCuyAWGEzISAwoRERmkop1aU1sO0i0cVQTY8auGjMc5KEREZBD1Tq1lw8nngXGi4eR4h3sMJ1RpDChERGSwiDAfTOgSqHme2nIQerud1qozPiUGgRd28dRgqhIGFCIiMkrfUDlCnf4Q7TUJvLALB7KfBcBTg6lq2PdGRERG6XTSEz810S7LUrqg5aWvAPDUYDINBhQiIjKcyKZrgRcSoD4vmKcGk6kwoBARUcW21wWUj3WKO93cB4CnBpPpMaAQEVH5xLaq73MI8OqGozw1mKoJAwoRkY1QGhsm0vcCB/rplpfadE1qJ0GnYI9qaC3ZOgYUIiIbsDspHXEJyVpn6fiUNxwj1mvi7AMMvVONrSR6gsuMiYis3O6kdEzZfE7noL8MRT6mbD6H3UnpTwoFlf7ThxlOqAYxoBARWTGlSkBcQrLo1vTqsriEZChVQkkw2SrVrchzdMgMOMRDRGTFTqU80Ok5KU0AkK7Ih3SbyL9Xn78EyEKrr3FE5WAPChGRFatou/lh9feL7gjb6eY+7E5rUF3NIqoQAwoRkRUrb7v51JaDsNz/I62y4zktEHhhl/j8FKIaxCEeIiIr1j7IHT4yJ2Qo8jVzTuwlRbjWYqhO3cALuzT/LaBkV9i4hGT0DZVzbxOqcexBISKyYlI7CWIiS+aRSFDSa1JROFFTz085lfKgmltJpIs9KEREVi4izAdrxoYj4oKvzrX2yV8gs7j8jdYqmsdCVB3Yg0JEZO3OzxYNJ4EXdlUYToDy57EQVRf2oBAR1SJGb1cvsunajoc98O+0mRW+lwQlh/+1D3KvQouJKocBhYioljBqu/rCh8A3usHi+9Db+Pe2RIPfMyYylBNkySw4xENEVAsYtV19vEQ0nCBKMHi4xr2ufcm8FbFzeohqAHtQiIgsXEXb1WstBxbbEXbEQ8ChPgDxZcdledR1wPE5veFQh/+GJfPhnz4iIgtnyHb1MfXnioeTKEETTgDdZcelSf5+zB8axnBCZsc/gUREFq6iZb6pLQchQnZcuzD0Lb2H/KmXHctl2sM9cpkTh3XIYnCIh4jIwumbN+Jrn4ljzSboXjDg9OGIMB/0DZUbtyKIqAYxoBARWTixeSNiB/wBMCicqEntJOgUXPE+KETmwCEeIqIaoFQJOH7jPr5P/BPHb9yHUmVckCi7XX1Ze8JSjQonRJbO6IBy+PBhREZGwtfXFxKJBDt37tS6LggCYmNj4evrC2dnZ/To0QOXLl3SqtOjRw9IJBKtx6hRo6r0QYiILNXupHR0XfQrRq87gTe3JWL0uhPouuhXo04Kjgjzwe9tXkaKSDjZ3fIO+rcMMGWTiczO6ICSm5uLVq1aYdWqVaLXFy9ejOXLl2PVqlU4ffo05HI5+vbti+zsbK16kyZNQnp6uuaxdu3ayn0CIiILIdZLYtT+JeWJl8BB+VCr6EbgUihHqTiplayS0XNQBgwYgAEDBoheEwQBK1aswDvvvINhw4YBAL744gt4e3sjPj4ekydP1tR1cXGBXC6vZLOJiCyL2C6vcjcn5BcrDdu/RN/k1PungT3tdcujBASbouFEFsqkc1BSUlKQkZGBfv36acocHR3RvXt3HDt2TKvuli1b4OnpiebNm2PmzJk6PSylFRQUICsrS+tBRGQp9PaSZOXj0eMiva8TAKQr8nEq5YF4hXiJ3nBCZO1MuoonIyMDAODt7a1V7u3tjZs3b2qejxkzBkFBQZDL5UhKSsKcOXPw22+/Ye/evaL3XbhwIeLi4kzZVCIikyhvl1dDie5zInLIH0YrAQnXNpBtqJZlxhKJ9v9YgiBolU2aNEnz32FhYQgJCUG7du1w7tw5hIeH69xvzpw5mD59uuZ5VlYW/Pz8qqHlRETGqWiXV0No7XMiFkwA9pqQzTFpFFfPKVH3pKhlZmbq9KqUFh4eDnt7e1y7dk30uqOjI9zc3LQeRESWoKJdXssjQclpxO2D/j7YTyycdP2a4YRskkkDinrYpvRQTWFhIQ4dOoTOnTvrfd2lS5dQVFQEHx/ORCei2sXQ04HLUkeRmMhQSO8kiIeTKAHwH1H5xhHVYkYP8eTk5OD69eua5ykpKUhMTIS7uzv8/f0xbdo0LFiwACEhIQgJCcGCBQvg4uKCqKgoAMCNGzewZcsWDBw4EJ6enkhOTsaMGTPQpk0bdOnSxXSfjIioBlR0OrAEQF3HOpBKAEV+saZcLnNCTGQoIi74it+YvSZk44wOKGfOnEHPnj01z9VzQ6Kjo7Fx40bMnj0beXl5ePXVV/Hw4UN06NABv/zyC1xdXQEADg4O2L9/Pz7++GPk5OTAz88Pzz//PGJiYiCVSk30sYiInlCqhGo7c0a9y+uUzecgAXRCigAgp+BJMKnvbI/xXYIwtWcwpNtF/s5jMCECAEgEQah1/zdkZWVBJpNBoVBwPgoRlUtsfxIfde+FCTc4E3sfMRJAdDdYAAwnZPWM+f5mQCEiq6Xen6TsX3LqvpM1Y8NNGlLUPTUZWfn4YNclPMjV3QNF9JC/vkeBhhziJutnzPc3F9QTkVUqb38SdVlcQrJRh/ZVRH06sFc9R51w8s+G34qHkyiB4YRIRLXsg0JEZG4V7U9SehfXTsEeJnvf3UnpePvbi1plYsGkQGWP3WEpGGyydyayLgwoRGSVDN2fpCr7mJRVdkjJQVKEqy2G6tQLvLALALC1U+WWKBPZAgYUIrJKhu5PUtl9TMoqO6QkOpyDknAiQckyY80GbUSkgwGFiKySIfuTVDYkiC1bLj2kJBZOBlz9BJfzn9I8j4kMNdlSZyJrxIBCRFapvP1JtHZxNTIk6Fu2PDBMjg2BMejpdlbnNeohHfV7/7NbkElXDxFZI67iISKrFRHmgzVjwyGXaQ/jyGVOlVpirJ5jUnbybYYiH3OzwyoMJ0BJUPrv4RTsTko36r2JbA17UIjIqkWE+aBvqLzKO8nqW7Ysk2bjt+ajdeqXDSZlxSUko2+onMM8RHowoBCR1VPvT1IVYsuWy5sIW57qWuJMZE04xENEZICyy5HFwkmH5I143zUJ9Z3tK3VPInqCAYWIyADq5cjfBc8QDSeBF3bhr2JP9A2V49OocKPuSUS6OMRDRGSA9kHuosHk4uNgRF7/WGfZcnUtcSayFexBISKqSO5NSLfp/nUZeGGXJpwAT5Ytq5c4A0+WNKtVZYkzkS1hQCEiKk+8BPg+UKe49ERYbzdHnWXLpl7iTGRrOMRDRLWG2A6uhvRCGPs6df1OJz11rv0Sdh3v7UoFUHqCq/i9TLXEmcgWSQRBMN1Z4zUkKysLMpkMCoUCbm5u5m4OEdUAfTu4xkSGltsbYezrdielo0diEJzsCnSvtbyjdRigmjpusGeEqHzGfH9ziIeILF55O7hO2XxO766sxr5ud1I6Ii746oST1ZkjEHhhF97+7qLopFd1WVxCMpQqw//Np1QJOH7jPr5P/BPHb9w36rVE1o5DPERk0fTt4AqUBAMJxHdlNfZ1yvuJiLjQRqdu6bkmjx4X6W2nsZuvVbZHiMhWsAeFiCya2A6upZUOBpV+XbwE0j3lhxNDGbL5WmV7hIhsCXtQiMiiGbrbatl6hr5ObCJs0IUfIFTy328Vbb5W2R4hIlvDgEJEFs3Q3VbL1qvodZU9R0cfQzdfM6Znh+f0kC3jEA8RWbT2Qe7wkTnpWchbEgx8RIJBea8TCyeqjpvQ6eY+ve8DAPVd7CFB1TZfq2yPEJGtYUAhIotW2V1ZxV73XL1z4j0nUQLsnhqrqa/PS+0aV3nztcr2CBHZGu6DQkS1QlX3QTke0Ee8QpT2X4ELf0rG2sMpolUlKNnrpCqbrylVArou+rXCc3qOvtWLc1DI6hjz/c2AQkS1RqV2khUEYKtIZ3GU7l996vCgb46IqcKDehUPAK2Qwg3fyNoZ8/3NSbJEVGtI7STGTRyN1xMiRMIJUHMTWNXn9JTtEZJzHxQiDQYUIrJOYuGk71GgYRe9L6nJCaw8p4eofAwoRGRWlT0AUK/UeODYGN1yPb0mpdX0BFaje4SIbAgDChGZjdjEV7mbI0a390egZ13jA4tYr4mjBzD8nkEvVy9NrmgCa0V7nRBR1TGgEJFZqCeKlg0CGVkF+GjfNc1zg86nURUD2+x1yw3oNSlNvTR5yuZzkEB8Aqshe50QUdVxHxQiqnHlbfdeVoXn08RLTBJO1NQTWKuy1wkRVR17UIioxlW0Wqa0cs+nERvSGXQFcGtapfZxAiuR+Rndg3L48GFERkbC19cXEokEO3fu1LouCAJiY2Ph6+sLZ2dn9OjRA5cuXdKqU1BQgNdffx2enp6oW7cuXnjhBdy+fbtKH4SIag9jV8HonFicvEg8nEQJVQ4nauoJrINbN0KnYA+GE6IaZnRAyc3NRatWrbBq1SrR64sXL8by5cuxatUqnD59GnK5HH379kV2dramzrRp07Bjxw5s27YNR48eRU5ODgYNGgSlUln5T0JEtUZlV8FkZueXBJPEt8vcsFulh3SIyDJVaSdZiUSCHTt2YMiQIQBKek98fX0xbdo0vPXWWwBKeku8vb2xaNEiTJ48GQqFAg0bNsSmTZvw0ksvAQDu3LkDPz8//PTTT+jfv7/O+xQUFKCgoEDzPCsrC35+ftxJlqiWqmjHVjFOknxcaTFC94KJgonJlzsTkQ5jdpI16STZlJQUZGRkoF+/fpoyR0dHdO/eHceOHQMAnD17FkVFRVp1fH19ERYWpqlT1sKFCyGTyTQPPz8/UzabiGqY1E6CF1oZPtk0teWgag0nu5PS0XXRrxi97gTe3JaI0etOoOuiX/VPzCWiamfSgJKRkQEA8Pb21ir39vbWXMvIyICDgwMaNGigt05Zc+bMgUKh0DzS0tJM2WwiqmG7k9LxXz0H8pUldvrwz00u4HiHe1Cqqh5Q1Mudy/bmVLh6iIiqVbWs4pFItLtFBUHQKSurvDqOjo5wdHQ0WfuIyHwMXWK8oNFKRHns0SkPvLALuHALwC3D9kipZFvKXT1ERNXOpD0ocrkcAHR6QjIzMzW9KnK5HIWFhXj48KHeOkRkvQxZYpzacpBOONl2v19JOCmlqr0cxhwOSEQ1y6QBJSgoCHK5HHv37tWUFRYW4tChQ+jcuTMAoG3btrC3t9eqk56ejqSkJE0dIqodlCoBx2/cx/eJf+L4jfsGDbmUt8TYQ/pIdEin0819ePvPN3TK1e8Wl5BcqeGemjwckIiMY/QQT05ODq5fv655npKSgsTERLi7u8Pf3x/Tpk3DggULEBISgpCQECxYsAAuLi6IiooCAMhkMkycOBEzZsyAh4cH3N3dMXPmTLRo0QJ9+vQx3Scjomoldo6OIUMu+pYYiwUTADje4R7SL5zQe7/SvRzGHrxX04cDEpHhjA4oZ86cQc+ePTXPp0+fDgCIjo7Gxo0bMXv2bOTl5eHVV1/Fw4cP0aFDB/zyyy9wdXXVvOajjz5CnTp1MHLkSOTl5aF3797YuHEjpFKpCT4SEVU3vefo/D3kUt6W8GIH8omFE+WIbEgd6iEz8U+D2lSZXg4eDkhkuaq0D4q5GLOOmohMq6I9TNRf6kff6qV3Yqk64MxrtApjPHbrXm95RxNwjt+4j9Hr9PegqG2d1NHoHpTSbQHEDwfk+TtEpmO2fVCIyPqZYmJpRJgPUloO0gknyx5M1QonwJNeDn1raCQoGVqqbC8HDwckskw8LJCIjFLliaW5t4DvA3SKj3e4h2kiu7dK7SSIiQzFlM3nIIF4L0dMZGiVlgHzcEAiy8OAQkRGqdLEUrED/gAgSkCncu6l7uUoOylXXsV9UEpTHw5IRJaBAYWIjGLMxNLS59sMTm6sW3lUMWBX8eR4pUqAzNkBs/s3xYPcQrjXc4Tcjb0cRNaMAYWIjGLokMve5AzEJSRjkfssDHY9r3sjA8/RKW85M8MJkfXiJFkiMlpFE0sBYMrmczge0AfdyoST6D/isLvlHa0yfRu+8ZwcItvFHhQiqhR9E0sBYMyyjUhpOUHnNYEXdkEC4Gqp82309ZDMfT4UH/zIc3KIbBUDChFVmujE0ngJtjXSLrrw+Gm8cH0FAO1lyIq8Qr0bvr0af67c967KDrJEZPkYUIjIdERW6QReSABEdjHJUORh8Z7f9faQGIrn5BBZJwYUIqq6vd2Au0d0isuePlzag9zCCk81NgTPySGyTgwoRFQ1Ir0mY+6sw7F74nuTqJchu9dzrNLb8pwcIuvGVTxEVDmZR8Q3XosS8HLE8wB0B3ZKL0OWuxne81HefThBlsg6MaAQkfHiJcC+btplPhGavU0MOd/G0DN2Vke14Tk5RDaIpxkTkeEEFbBVZOdXPZuuld5JVux8G0NPEq7oPkRUOxjz/c2AQkSG2e4CKPN0yw3cEVaf8naKZQ8JkXUx5vubk2SJqGJic01e+AOoF1TlW/MkYSISw4BCRPql/wIc6K9T3OnmPsSkOiEizDRvw5OEiagsTpIlInHxEp1w8vFfoxB4YRfPwiGiasceFCLSpioCtjnoFJfedI1n4RBRdWMPChE9sd2lwnCiVvosHCIiU2MPChGVEJkI2/LSNmQp65X7Mp6FQ0TVgT0oRLYuNV40nBzvcK/CcALwLBwiqh7sQSGyZWLLh59dDYRMQXuVAB+ZEzIU+aKnC/MsHCKqTuxBIbJFRTl6z9FByBQAJUt/YyJDAfAsHCKqeQwoRLYmXgJ87apbLrIjrCFn6hARVQcO8RDZErFek5GPgTrOel/CnV6JyBwYUIhsweXlwPkZuuUGnqPDnV6JqKYxoBBZO7Fek67fAP7Da74tREQGYkAhslb5d4HvvHTLq3j6MBFRTWBAIbJGYr0mTnJgGM/OIaLagQGFyNqIhZNRxYCdtObbQkRUSVxmTGQtzs3Uv7cJwwkR1TLVElCys7Mxbdo0BAQEwNnZGZ07d8bp06c118eNGweJRKL16NixY3U0hcg2xEuAK8u0y3of5HwTIqq1qmWI55VXXkFSUhI2bdoEX19fbN68GX369EFycjIaNWoEAIiIiMCGDRs0r3Fw0D1BlcgWKVWC4XuOZF8HEkJ0yxlMiKiWM3lAycvLw7fffovvv/8e3bp1AwDExsZi586dWLNmDebNmwcAcHR0hFwuN/XbE9Vqu5PSEZeQjHTFkxOCfWROiIkM1d21VWw4x7MT0O9YNbeSiKj6mXyIp7i4GEqlEk5O2ltjOzs74+jRo5rnBw8ehJeXF5o0aYJJkyYhMzNT7z0LCgqQlZWl9SCyNruT0jFl8zmtcAIAGYp8TNl8DruTSq3AEQsno1UMJ0RkNUweUFxdXdGpUyd88MEHuHPnDpRKJTZv3oyTJ08iPb3kL9gBAwZgy5Yt+PXXX7Fs2TKcPn0avXr1QkFBgeg9Fy5cCJlMpnn4+fmZutlEZqVUCYhLSBY9NVhdFpeQDOGXLvonwkq49TwRWQ+JIAgmH6y+ceMGJkyYgMOHD0MqlSI8PBxNmjTBuXPnkJycrFM/PT0dAQEB2LZtG4YNG6ZzvaCgQCu8ZGVlwc/PDwqFAm5ubqZuPlGNO37jPkavO1FundSWg3QL+50EPNtXU6uIiEwrKysLMpnMoO/vapkkGxwcjEOHDiE3NxdZWVnw8fHBSy+9hKCgINH6Pj4+CAgIwLVr10SvOzo6wtHRsTqaSmQRMrPz9V5r7fw7doZU/hwdIqLaqFo3aqtbty7q1q2Lhw8fYs+ePVi8eLFovfv37yMtLQ0+Pjy6nWyTl6uTaLlor0nDrkDfI9XcIiIi86qWgLJnzx4IgoCmTZvi+vXrmDVrFpo2bYrx48cjJycHsbGxGD58OHx8fJCamor//Oc/8PT0xNChQ6ujOUQWr32QO3xkTshQ5P8950RAastInXrKUSqtJcdGLUkmIqpFqiWgKBQKzJkzB7dv34a7uzuGDx+O+fPnw97eHsXFxbh48SK+/PJLPHr0CD4+PujZsye2b98OV1fX6mgOUY0zNjhI7SSIiQwtWa0TMhXPOKfq1Nnd8g4iSt3DqCXJRES1TLVMkq1uxkyyIappVQoOIit0Rv65ERMG9NN6rXpJctn/edWvXjM2nCGFiCyOMd/fPIuHyISM2suktL8OiIaT4x3uYeuMf2iFDUOXJCtVte7fHkREGgwoRCZS6eAQLwH299Iq+ixzGDrd3AdFXqHO0NCplAc6Aajse6Ur8nEq5YHxH4KIyEIwoBCZiNHBQVUs2msSeGEXPsyYoLfXpbwlyZWpR0RkiRhQiEzEqODwrRewzV7nWuCFXZr/1tfrom9JclmG1iMiskQMKEQmYmggGJzcGCi4q1XW5tIWrXCiJjZco16SrG9NkAQlk3LbB7kb2HIiIsvDgEJkIhUFh+dlR0U3Xgu8sAsPlbJy7126d0a9JBmAznupn8dEhnI/FCKq1RhQiEykvOCQ2nIQPg34ULsw/CMc73DPoHuX7Z2JCPPBmrHhkMu0y+UyJy4xJiKrUK1b3RPZGnVwePu7i3j0uAhOknxcaTFCt+Lf5+i0VwlldpDVJkFJ6BAbrokI80HfUDl3kiUiq8SAQlQNFI+LxM/Rwd87wv7936V3kJUAWiHFkOEaqZ0EnYI9TNVsIiKLwSEeIgMpVQKO37iP7xP/xPEb90U3QlPvhZIiEk6aXfwGQRd26azK4XANEZEu9qAQGcDQ7etvnF6H4wGTdV5feoWOelVO6Z4PDtcQEWljQCGqgL5zb9QbqWl6OeIlaFKmzqTUd7E3q6POPcX2TOFwDRHREwwoROWoaPt6CYClu04h4sIQneti+5qocRM1IqLyMaAQlaOi7evF5pokFzTB878vF61f3qocIiJ6gpNkicpR3vb1oqt0RhXh1rMHAXATNSKiqmBAISqH2FDMeI/vxcNJlADY1eGqHCIiE+AQD1E51NvXqzdSEwsmk9I/wWf/ngppqTKuyiEiqhr2oBCVQ72RWgOpQjScBF3YheH9R4gGD/WqnMGtG6FTsAfDCRGRERhQiCoQcSUM55qP0Sr7WdEZnW7u45ANEVE14RAPUXnidXs9vm+WBi83ZxzlkA0RUbVhQCESc/VT4MxU3fIoAYNrvjVERDaHAYWoLJFeE7xwA6j3VM23hYjIRjGgEKnlpgHf++uWR4ntI0tERNWJAYUIEO81ab0ICJ1d820hIiIGFLJxggBsFVnMxl4TIiKz4jJjsl0XYnXDiZ09wwkRkQVgDwrZJrEhnaHpgLO85ttCREQ6GFDItiiuAD820y1nrwkRkUVhQCHbIdZr0mE9EDy+5ttCRETlYkAh6yeogK1S3XL2mhARWSwGFLIaSpWge3rwmVeB659pV6wbBAz+Q7w+t64nIrIIDChkFXYnpSMuIRnpinxNmdjpwxjxEHCoL1rfR+aEmMhQHv5HRGQBuMyYar3dSemYsvmcJmwEO6aJh5MoQRNOStdXy1DkY8rmc9idlF4TzSYionJUS0DJzs7GtGnTEBAQAGdnZ3Tu3BmnT5/WXBcEAbGxsfD19YWzszN69OiBS5cuVUdTyAopVQKO37iP7xP/xP+u3UPsD8lQzya5GjYE+5tO0ar/VmYclKNUmtfGJTypX5q6LC4hGUoV56cQEZlTtQzxvPLKK0hKSsKmTZvg6+uLzZs3o0+fPkhOTkajRo2wePFiLF++HBs3bkSTJk0wb9489O3bF7///jtcXV2ro0lkJcSGZgBACiVutNQ9Zzjwwi4AgO+v1/BmnyY4lfJA57WlCQDSFfk4lfIAnYI9TNp2IiIynMl7UPLy8vDtt99i8eLF6NatG55++mnExsYiKCgIa9asgSAIWLFiBd555x0MGzYMYWFh+OKLL/D48WPEx8ebujlkRfQNzUz33qQTTjbci9SEEwD4aN817E5KR2a2/nBSmqH1iIioepi8B6W4uBhKpRJOTk5a5c7Ozjh69ChSUlKQkZGBfv36aa45Ojqie/fuOHbsGCZPnqxzz4KCAhQUFGieZ2VlmbrZZOH0Dc2IzTVpcnEHCgV7nfK4hGQsfbGVQe/n5epUcSUiIqo2Ju9BcXV1RadOnfDBBx/gzp07UCqV2Lx5M06ePIn09HRkZGQAALy9vbVe5+3trblW1sKFCyGTyTQPPz8/UzebLFzZoZkw5+ui4STwwi7RcAKUDN1AKFmto28xsQQl19sHuZug1UREVFnVMkl206ZNEAQBjRo1gqOjIz755BNERUVBKn2yWZZEov0VIQiCTpnanDlzoFAoNI+0tLTqaDZZsNJDLj+GvIFdIdO0rg+8+onWkI4+93ILEBMZCgA6IUX9PCYylPuhEBGZWbUElODgYBw6dAg5OTlIS0vDqVOnUFRUhKCgIMjlJYexle0tyczM1OlVUXN0dISbm5vWg2yLl6sT7CVFSG05CM2d/9C6FnhhF5LznzL4PhFhPlgzNhxymfYwjlzmhDVjw7kPChGRBajWjdrq1q2LunXr4uHDh9izZw8WL16sCSl79+5FmzZtAACFhYU4dOgQFi1aVJ3NoVqsg+pbXGuhPT9pQfp4/PfucM1zCSC6fFh9TV5q6CYizAd9Q+XcSZaIyEJVS0DZs2cPBEFA06ZNcf36dcyaNQtNmzbF+PHjIZFIMG3aNCxYsAAhISEICQnBggUL4OLigqioqOpoDtV28RKdrr6gCz9A+LtUHSn+2S0I/z2cAkA7qOgbupHaSbiUmIjIQlVLQFEoFJgzZw5u374Nd3d3DB8+HPPnz4e9fcnkxdmzZyMvLw+vvvoqHj58iA4dOuCXX37hHiikTZEM/Nhcq+jP+iMw4rd/QcCTOSnyUlvUt/FvoLNPipxb2BMR1ToSQRBq3ZaZWVlZkMlkUCgUnI9irXY/Czw4o1029A7g7FPhIX88BJCIyDIZ8/3NwwLJshTnAV+56JZHPcnRFQ3NcOiGiKj242GBZDkuL9cNJz1+0gonRERkG9iDQpYhXmQIZrQK0LM3DhERWTf2oJB5PTirG06avF7Sa8JwQkRks9iDQubz/VNAbop22fB7gCPnjxAR2ToGFKp5RdnA12Vmb9epC4zMMU97iIjI4nCIh2rWhfd0w0mfQwwnRESkhT0oVHM4EZaIiAzEHhSqfplHdMNJ2FxOhCUiIr3Yg0LV62sZUJSlXfaiArDnDsBERKQfAwpVj4IHwLdlVuPUDQQGp4hWJyIiKo1DPGR6Z97UDSf9TzOcEBGRwdiDQqYjCMBWkczLreqJiMhI7EEh07izWzectFnKcEJERJXCHhSqOrHlwyMfA3Wca74tRERkFdiDQpWXl64bTtzblvSaMJwQEVEVsAeFKud4NJDypXbZwCSgfnPztIeIiKwKAwoZR1ABW6W65ZxrQkREJsQhHjKcIlk3nLRfy3BCREQmxx4UMszZfwO/r9Aue6kAkDqYpTlERGTdGFCofIWPgG8aaJd1+hIIetkszSEiItvAgEL6pW4FjkVpFSmH3sOpO0Bm4p/wcnVC+yB3SO144B8REZkWAwrpUimBhBAgt9TW9E2mYrfTfxC3IhHpinxNsY/MCTGRoYgI8zFDQ4mIyFpxkixpe3Ae2FZHO5wMvIjdTv/BlM3ntMIJAGQo8jFl8znsTkqv4YYSEZE1Y0ChJ05OAnaHP3kuaw6MVkLp1hxxCckQW6ujLotLSIZSxdU8RERkGhziISD/HvBdQ+2yrl8D/iMAAKf+uK/Tc1KaACBdkY9TKQ/QKdhDbz0iIiJDMaDYuhufAydf0S57UQHYu2meZmbrDyelGVqPiIioIgwotkpVDOzwBQruPikLfQto/aFOVS9XJ4NuaWg9IiKiijCg2KJ7J4FfOmqXDfodcGsiWr19kDt8ZE7IUOSLzkORAJDLSpYcExERmQInydqa/0VphxOPDsBold5wAgBSOwliIkMBlISR0tTPYyJDuR8KERGZDAOKrcjLAOIlwM2tT8q6JwD9TwCSioNFRJgP1owNh1ymPYwjlzlhzdhw7oNCREQmxSEeW/D7KuDs69plI3OAOnWNuk1EmA/6hspxKuUBMrPzuZMsERFVGwYUa6YsBL6RAcpSq2tavA+0mFvpW0rtJFxKTERE1c7kQzzFxcV49913ERQUBGdnZzz11FN4//33oVKpNHXGjRsHiUSi9ejYsWM5dyWjZR4Gtjtqh5MX/qhSOCEiIqopJu9BWbRoET777DN88cUXaN68Oc6cOYPx48dDJpPhzTff1NSLiIjAhg0bNM8dHBxM3RTbdegF4M+EJ8/lfYGeewyaa0JERGQJTB5Qjh8/jsGDB+P5558HAAQGBmLr1q04c+aMVj1HR0fI5XJTv71ty00DvvfXLuu1F5D3MU97iIiIKsnkQzxdu3bF/v37cfXqVQDAb7/9hqNHj2LgwIFa9Q4ePAgvLy80adIEkyZNQmZmpt57FhQUICsrS+tBZSQv0g0nL+UxnBARUa1k8h6Ut956CwqFAs888wykUimUSiXmz5+P0aNHa+oMGDAAL774IgICApCSkoK5c+eiV69eOHv2LBwdHXXuuXDhQsTFxZm6qdahOA/4ykW7rM1SoNkM87SHiIjIBCSCIJj0CNpt27Zh1qxZWLJkCZo3b47ExERMmzYNy5cvR3R0tOhr0tPTERAQgG3btmHYsGE61wsKClBQUKB5npWVBT8/PygUCri5uenUtxnpvwAH+muXDUkDXBqbpz1ERETlyMrKgkwmM+j72+Q9KLNmzcLbb7+NUaNGAQBatGiBmzdvYuHChXoDio+PDwICAnDt2jXR646OjqI9KzZLEID9PYHMQ0/KGg8Buu0wW5OIiIhMyeQB5fHjx7Cz057aIpVKtZYZl3X//n2kpaXBx4e7kVYo5w/gh2Dtsj5HAK+u5mkPERFRNTB5QImMjMT8+fPh7++P5s2b4/z581i+fDkmTJgAAMjJyUFsbCyGDx8OHx8fpKam4j//+Q88PT0xdOhQUzfHulyIAZLef/K8jisw/B4g5RJtIiKyLiYPKCtXrsTcuXPx6quvIjMzE76+vpg8eTLee+89ACW9KRcvXsSXX36JR48ewcfHBz179sT27dvh6upq6uZYh6Ic4OsyP5tnVwMhU8zTHiIiompm8kmyNcGYSTa13u0fgMODtcuGZgDO3uZpDxERUSWZdZIsmYggAHueBR6cfVIWOBbovMl8bSIiIqohDCiWSHEF+LGZdln/U4DHs+ZpDxERUQ1jQLE052YCV5Y9ee7sAwy+BdjxV0VERLaD33qWolABfFNfu6zjRuAp8b1jiIiIrBkDSilKlYBTKQ+QmZ0PL1cntA9yh9SuBk4Avrkd+N8o7bLh9wBHj+p/byIiIgvEgPK33UnpiEtIRroiX1PmI3NCTGQoIsKqaQM5QQXsegbILrWD7tP/AtqvqZ73IyIiqiVMfppxbbQ7KR1TNp/TCicAkKHIx5TN57A7Kd30b/rwArBVqh1OBvzGcEJERAQGFChVAuISkiG2GYy6LC4hGUqVCbeLOfUv4OdWT567NQVGK4EGLU33HkRERLWYzQ/xnEp5oNNzUpoAIF2Rj1MpD9ApuIpzQgruA996apd12QYEvFS1+xIREVkZmw8omdn6w0ll6un1x0bgxHjtshGPAAdZ1e5LRERkhWw+oHi5Opm0ng5VMfC9P5BXah7LMzOA8KWVux8REZENsPmA0j7IHT4yJ2Qo8kXnoUgAyGUlS46Ndv80sKe9dtnzlwHZM5VpKhERkc2w+UmyUjsJYiJDAZSEkdLUz2MiQ43fD+XYy9rhxL0dMFrFcEJERGQAmw8oABAR5oM1Y8Mhl2kP48hlTlgzNty4fVDy/gLiJUDq5idl3X4AIk4DkhrY9I2IiMgK2PwQj1pEmA/6hsqrtpPstTXA6Ve1y0bmAHXqmraxREREVo4BpRSpnaRyS4lVRcA3HkBx9pOysBigZazJ2kZERGRLGFCqKvMosO857bIXbgD1njJPe4iIiKwAA0pVHB4K3N755Ll3T6DXfs41ISIiqiIGlMp4fBvY6add1nMP4NPPPO0hIiKyMgwoxrq8DDg/U7ts5GOgjrN52kNERGSFGFAMpcwHtpcJIa0XAaGzzdMeIiIiK8aAYoiMfcCvfbXLBt8C6vqJ1yciIqIqYUApjyCUBJO/9j8p8x0E9EgwX5uIiIhsAAOKPjkpwA9llgr3OQR4dTNPe4iIiGwIA4qYi+8DF2OePJc6AyMeAVIHszWJiIjIljCglLXTr2QZsVrblUDTqeZrDxERkQ1iQCmtKEs7nAxNB5zl5msPERGRjWJAKc3eDej6FVCUDQRPMHdriIiIbBYDSln+L5q7BURERDbPztwNICIiIiqLAYWIiIgsDgMKERERWRwGFCIiIrI4Jg8oxcXFePfddxEUFARnZ2c89dRTeP/996FSqTR1BEFAbGwsfH194ezsjB49euDSpUumbgoRERHVUiYPKIsWLcJnn32GVatW4fLly1i8eDGWLFmClStXauosXrwYy5cvx6pVq3D69GnI5XL07dsX2dnZpm4OERER1UImDyjHjx/H4MGD8fzzzyMwMBAjRoxAv379cObMGQAlvScrVqzAO++8g2HDhiEsLAxffPEFHj9+jPj4eFM3h4iIiGohkweUrl27Yv/+/bh69SoA4LfffsPRo0cxcOBAAEBKSgoyMjLQr18/zWscHR3RvXt3HDt2TPSeBQUFyMrK0noQERGR9TL5Rm1vvfUWFAoFnnnmGUilUiiVSsyfPx+jR48GAGRkZAAAvL29tV7n7e2Nmzdvit5z4cKFiIuLM3VTiYiIyEKZvAdl+/bt2Lx5M+Lj43Hu3Dl88cUXWLp0Kb744gutehKJROu5IAg6ZWpz5syBQqHQPNLS0kzdbCIiIrIgJu9BmTVrFt5++22MGjUKANCiRQvcvHkTCxcuRHR0NOTyksP3MjIy4OPjo3ldZmamTq+KmqOjIxwdHU3dVCIiIrJQJu9Befz4MezstG8rlUo1y4yDgoIgl8uxd+9ezfXCwkIcOnQInTt3NnVziIiIqBYyeQ9KZGQk5s+fD39/fzRv3hznz5/H8uXLMWFCyenAEokE06ZNw4IFCxASEoKQkBAsWLAALi4uiIqKMnVziIiIqBYyeUBZuXIl5s6di1dffRWZmZnw9fXF5MmT8d5772nqzJ49G3l5eXj11Vfx8OFDdOjQAb/88gtcXV0Neg9BEACAq3mIiIhqEfX3tvp7vDwSwZBaFub27dvw8/MzdzOIiIioEtLS0tC4ceNy69TKgKJSqXDnzh24urrqXflTG2RlZcHPzw9paWlwc3Mzd3OoFP5uLBd/N5aLvxvLZEm/F0EQkJ2dDV9fX535qmWZfIinJtjZ2VWYvGoTNzc3s/+hIXH83Vgu/m4sF383lslSfi8ymcygejzNmIiIiCwOAwoRERFZHAYUM3J0dERMTAw3obNA/N1YLv5uLBd/N5aptv5eauUkWSIiIrJu7EEhIiIii8OAQkRERBaHAYWIiIgsDgMKERERWRwGFCIiIrI4DCgWIjU1FRMnTkRQUBCcnZ0RHByMmJgYFBYWmrtpNm/+/Pno3LkzXFxcUL9+fXM3x6atXr0aQUFBcHJyQtu2bXHkyBFzN4kAHD58GJGRkfD19YVEIsHOnTvN3SQCsHDhQjz77LNwdXWFl5cXhgwZgt9//93czTIYA4qFuHLlClQqFdauXYtLly7ho48+wmeffYb//Oc/5m6azSssLMSLL76IKVOmmLspNm379u2YNm0a3nnnHZw/fx7PPfccBgwYgFu3bpm7aTYvNzcXrVq1wqpVq8zdFCrl0KFDeO2113DixAns3bsXxcXF6NevH3Jzc83dNINwHxQLtmTJEqxZswZ//PGHuZtCADZu3Ihp06bh0aNH5m6KTerQoQPCw8OxZs0aTVmzZs0wZMgQLFy40Iwto9IkEgl27NiBIUOGmLspVMbdu3fh5eWFQ4cOoVu3buZuToXYg2LBFAoF3N3dzd0MIrMrLCzE2bNn0a9fP63yfv364dixY2ZqFVHtolAoAKDWfK8woFioGzduYOXKlfjXv/5l7qYQmd29e/egVCrh7e2tVe7t7Y2MjAwztYqo9hAEAdOnT0fXrl0RFhZm7uYYhAGlmsXGxkIikZT7OHPmjNZr7ty5g4iICLz44ot45ZVXzNRy61aZ3wuZn0Qi0XouCIJOGRHpmjp1Ki5cuICtW7eauykGq2PuBli7qVOnYtSoUeXWCQwM1Pz3nTt30LNnT3Tq1An//e9/q7l1tsvY3wuZl6enJ6RSqU5vSWZmpk6vChFpe/311/HDDz/g8OHDaNy4sbmbYzAGlGrm6ekJT09Pg+r++eef6NmzJ9q2bYsNGzbAzo4dXNXFmN8LmZ+DgwPatm2LvXv3YujQoZryvXv3YvDgwWZsGZHlEgQBr7/+Onbs2IGDBw8iKCjI3E0yCgOKhbhz5w569OgBf39/LF26FHfv3tVck8vlZmwZ3bp1Cw8ePMCtW7egVCqRmJgIAHj66adRr1498zbOhkyfPh0vv/wy2rVrp+lhvHXrFudpWYCcnBxcv35d8zwlJQWJiYlwd3eHv7+/GVtm21577TXEx8fj+++/h6urq6YHUiaTwdnZ2cytM4BAFmHDhg0CANEHmVd0dLTo7+XAgQPmbprN+fTTT4WAgADBwcFBCA8PFw4dOmTuJpEgCAcOHBD9fyQ6OtrcTbNp+r5TNmzYYO6mGYT7oBAREZHF4SQHIiIisjgMKERERGRxGFCIiIjI4jCgEBERkcVhQCEiIiKLw4BCREREFocBhYiIiCwOAwoRERFZHAYUIiIisjgMKERERGRxGFCIiIjI4vw/VnmmG8d8dZcAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"numerator = sum((x - x.mean()) * (y - y.mean()))\n",
"denominator = sum((x - x.mean())**2) \n",
"\n",
"w = numerator / denominator\n",
"b = y.mean() - w * x.mean()\n",
"\n",
"print(\"Gradient:\", w) \n",
"print(\"bias:\",b)\n",
"plt.scatter(x, y)\n",
"plt.title(\"Predicted values\")\n",
"plt.plot(x,(w * x)+b, color='orange')\n",
"plt.legend([\"Actual values\", \"Predicted value\"])"
]
},
{
"cell_type": "markdown",
"id": "03421570",
"metadata": {},
"source": [
"# Accuracy of model"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "92aab9c4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R^2 score : 0.9808663599761945\n"
]
}
],
"source": [
"predictions = w*x +b\n",
"accuracy = r2_score(y, predictions)\n",
"print(\"R^2 score :\", accuracy) "
]
},
{
"cell_type": "markdown",
"id": "9641c98b",
"metadata": {},
"source": [
"# Transition to Cairo\n",
"## Create a scarb project\n",
"\n",
"Scarb is the Cairo package manager specifically created to streamline our Cairo and Starknet development process. You can find all information about Scarb and Cairo installation here"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1e527c7f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Created `linear_regression` package.\n"
]
}
],
"source": [
"! scarb new linear_regression"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "5b75bcd0",
"metadata": {},
"outputs": [],
"source": [
"!echo -n > linear_regression/src/lib.cairo"
]
},
{
"cell_type": "markdown",
"id": "b84409f8",
"metadata": {},
"source": [
"A new project folder will be created for you and make sure to replace the content in Scarb.toml file with the following code:\n",
"```toml\n",
"[package]\n",
"name = \"linear_regresion\"\n",
"version = \"0.1.0\"\n",
"\n",
"\n",
"[dependencies]\n",
"orion = { git = \"https://github.com/gizatechxyz/onnx-cairo\" }\n",
"\n",
"[scripts]\n",
"test = \"scarb cairo-test -f linear_regression_test\"\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "0bd602a0",
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (3814475083.py, line 3)",
"output_type": "error",
"traceback": [
"\u001b[0;36m Cell \u001b[0;32mIn [6], line 3\u001b[0;36m\u001b[0m\n\u001b[0;31m mod test;\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"# add reference modules to help our code compile at the end\n",
"%%writefile linear_regression/src/lib.cairo\n",
"mod test;\n",
"mod data_preprocessing;\n",
"mod helper_functions;\n",
"mod datasets;\n",
"mod model;"
]
},
{
"cell_type": "markdown",
"id": "1d2ad43c",
"metadata": {},
"source": [
"\n",
"## Generate Cairo files\n",
"\n",
"Now, we will transition our model to cairo. We will start by converting the the x features and y labels to FP16x16 tensors numbers. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e8dcc4e4",
"metadata": {},
"outputs": [],
"source": [
"\n",
"def generate_cairo_files(data, name, folder_name):\n",
" \n",
" os.makedirs(f'linear_regression/src/datasets/{folder_name}', exist_ok=True)\n",
" with open(os.path.join('linear_regression/src/datasets', f'{folder_name}', f\"{name}.cairo\"), \"w\") as f:\n",
" f.write(\n",
" \"use array::ArrayTrait;\\n\" +\n",
" \"use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16Impl, FP16x16PartialEq };\\n\" +\n",
" \"use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};\\n\" +\n",
" \"use orion::numbers::{FP16x16, FixedTrait};\\n\\n\" +\n",
" \"fn {0}() -> Tensor<FP16x16> \".format(name) + \"{\\n\" +\n",
" \" let tensor = TensorTrait::<FP16x16>::new( \\n\"\n",
" )\n",
" \n",
" if len(data.shape)>1:\n",
" f.write(\" shape: array![{0},\".format(data.shape[0]))\n",
" f.write(\"{0}].span(),\\n\".format(data.shape[1]))\n",
" f.write(\n",
" \" data: array![ \\n\"\n",
" )\n",
" if len(data.shape)==1:\n",
" f.write(\" shape: array![{0}].span(),\\n\".format(data.shape[0]))\n",
" f.write(\n",
" \" data: array![ \\n\"\n",
" )\n",
" for val in np.nditer(data.flatten()):\n",
" f.write(\" FixedTrait::new({0}, {1} ),\\n\".format(abs(int(val * 2**16)), str(val < 0).lower()))\n",
" f.write(\n",
" \"].span() \\n \\n\" +\n",
" \");\\n\\n\"+\n",
" \"return tensor; \\n\"+\n",
" \"}\"\n",
" )\n",
" with open(os.path.join('linear_regression/src/datasets', f'{folder_name}.cairo'), 'a') as f:\n",
" f.write(f\"mod {name};\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "130007b6",
"metadata": {},
"outputs": [],
"source": [
"generate_cairo_files(x, 'x_feature_data', 'linear_data')\n",
"generate_cairo_files(y, 'y_label_data', 'linear_data')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0e56a0d7",
"metadata": {},
"outputs": [],
"source": [
"# add reference modules to help our code compile\n",
"! touch linear_regression/src/datasets.cairo"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "398e349b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting linear_regression/src/datasets.cairo\n"
]
}
],
"source": [
"%%writefile linear_regression/src/datasets.cairo\n",
"mod linear_data;"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "753ad297",
"metadata": {},
"outputs": [],
"source": [
"! touch linear_regression/src/datasets/linear_data.cairo"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "1baa1a26",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting linear_regression/src/datasets/linear_data.cairo\n"
]
}
],
"source": [
"%%writefile linear_regression/src/datasets/linear_data.cairo\n",
"mod x_feature_data;\n",
"mod y_label_data;"
]
},
{
"cell_type": "markdown",
"id": "ce0a391b",
"metadata": {},
"source": [
"## Helper functions \n",
"\n",
"We add some helper functions that we may need during our linear regression model construction and testing"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "e944127f",
"metadata": {},
"outputs": [],
"source": [
"! touch linear_regression/src/helper_functions.cairo"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "649d0a52",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting linear_regression/src/helper_functions.cairo\n"
]
}
],
"source": [
"%%writefile linear_regression/src/helper_functions.cairo\n",
"\n",
"\n",
"use debug::PrintTrait;\n",
"use array::{ArrayTrait, SpanTrait};\n",
"use orion::operators::tensor::{\n",
" Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,\n",
" FP16x16TensorDiv, FP16x16TensorMul\n",
"};\n",
"\n",
"use orion::numbers::{FP16x16, FixedTrait};\n",
"\n",
"// retrieves row data by index in a 2D tensor\n",
"fn get_tensor_data_by_row(tensor_data: Tensor<FP16x16>, row_index: u32,) -> Tensor<FP16x16> {\n",
" let column_len = *tensor_data.shape.at(1); //13\n",
" // crete new array\n",
" let mut result = ArrayTrait::<FP16x16>::new();\n",
" // loop through the x values and append values \n",
" let mut i: u32 = 0;\n",
" loop {\n",
" if i >= column_len {\n",
" break ();\n",
" }\n",
" result.append(tensor_data.at(indices: array![row_index, i].span()));\n",
" i += 1;\n",
" };\n",
" let resultant_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(array![column_len].span(), data: result.span());\n",
" return resultant_tensor;\n",
"}\n",
"\n",
"\n",
"// transposes tensor\n",
"fn transpose_tensor(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {\n",
" let tensor_transposed = tensor_data.transpose(axes: array![1, 0].span());\n",
" return tensor_transposed;\n",
"}\n",
"\n",
"fn calculate_mean(tensor_data: Tensor<FP16x16>) -> FP16x16 {\n",
" let tensor_size = FixedTrait::<FP16x16>::new_unscaled(tensor_data.data.len(), false);\n",
" let cumulated_sum = tensor_data.cumsum(0, Option::None(()), Option::None(()));\n",
" let sum_result = cumulated_sum.data[tensor_data.data.len() - 1];\n",
" let mean = *sum_result / tensor_size;\n",
" return mean;\n",
"}\n",
"\n",
"// Calculates the R-Squared score between two tensors.\n",
"fn calculate_r_score(Y_values: Tensor<FP16x16>, Y_pred_values: Tensor<FP16x16>) -> FP16x16 {\n",
" let mut Y_values_ = Y_values;\n",
" let mean_y_value = calculate_mean(Y_values);\n",
" // creating the appropriate tensor shapes and empty arrays to populate values into\n",
" let mut squared_diff_shape = array::ArrayTrait::new();\n",
" squared_diff_shape.append(Y_values.data.len());\n",
" let mut squared_diff_vals = array::ArrayTrait::new();\n",
" let mut squared_mean_diff_shape = array::ArrayTrait::new();\n",
" squared_mean_diff_shape.append(Y_values.data.len());\n",
" let mut squared_mean_diff_vals = array::ArrayTrait::new();\n",
"\n",
" let mut i: u32 = 0;\n",
"\n",
" loop {\n",
" match Y_values_.data.pop_front() {\n",
" Option::Some(y_value) => {\n",
" let diff_pred = *y_value - *Y_pred_values.data.at(i);\n",
" let squared_diff = diff_pred * diff_pred;\n",
" squared_diff_vals.append(squared_diff);\n",
"\n",
" let diff_mean = *y_value - mean_y_value;\n",
" let squared_mean_diff = diff_mean * diff_mean;\n",
" squared_mean_diff_vals.append(squared_mean_diff);\n",
" i += 1;\n",
" },\n",
" Option::None(_) => { break; }\n",
" }\n",
" };\n",
"\n",
" let squared_diff_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(squared_diff_shape.span(), squared_diff_vals.span());\n",
" let squared_mean_diff_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(squared_mean_diff_shape.span(), squared_mean_diff_vals.span());\n",
" let sum_squared_diff = squared_diff_tensor.cumsum(0, Option::None(()), Option::None(()));\n",
" let sum_squared_mean_diff = squared_mean_diff_tensor\n",
" .cumsum(0, Option::None(()), Option::None(()));\n",
" let r_score = FixedTrait::new_unscaled(1, false)\n",
" - *sum_squared_diff.data.at(Y_values.data.len() - 1)\n",
" / *sum_squared_mean_diff.data.at(Y_values.data.len() - 1);\n",
"\n",
" return r_score;\n",
"}\n",
"\n",
"\n",
"// computes the x_min, x_max and x_range. Used for helping in normalizing and denormalizing user inputed values operations\n",
"fn normalize_user_x_inputs(\n",
" x_inputs: Tensor<FP16x16>, original_x_values: Tensor<FP16x16>\n",
") -> Tensor<FP16x16> {\n",
" let mut x_inputs_normalized = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());\n",
"\n",
" let mut x_min = ArrayTrait::<FP16x16>::new();\n",
" let mut x_max = ArrayTrait::<FP16x16>::new();\n",
" let mut x_range = ArrayTrait::<FP16x16>::new();\n",
" let mut result = ArrayTrait::<FP16x16>::new();\n",
"\n",
" if original_x_values.shape.len() > 1 {\n",
" let transposed_tensor = original_x_values.transpose(axes: array![1, 0].span());\n",
" let data_len = *transposed_tensor.shape.at(0); //13\n",
" // loop through each row calculating the min, max and range row values for each feature columns\n",
" let mut i: u32 = 0;\n",
" loop {\n",
" if i >= data_len {\n",
" break ();\n",
" }\n",
" let mut transposed_tensor_row = get_tensor_data_by_row(transposed_tensor, i);\n",
" x_min.append(transposed_tensor_row.min_in_tensor());\n",
" x_max.append(transposed_tensor_row.max_in_tensor());\n",
" x_range\n",
" .append(\n",
" transposed_tensor_row.max_in_tensor() - transposed_tensor_row.min_in_tensor()\n",
" );\n",
" i += 1;\n",
" };\n",
" let mut x_min_tensor = TensorTrait::new(shape: array![data_len].span(), data: x_min.span());\n",
" let mut x_max_tensor = TensorTrait::new(shape: array![data_len].span(), data: x_max.span());\n",
" let mut x_range_tensor = TensorTrait::new(\n",
" shape: array![data_len].span(), data: x_range.span()\n",
" );\n",
"\n",
" // for normalizing 2D user inputed feature vals\n",
" if x_inputs.shape.len() > 1 {\n",
" let mut j: u32 = 0;\n",
" loop {\n",
" if j >= *x_inputs.shape.at(0) {\n",
" break ();\n",
" };\n",
" let mut row_data = get_tensor_data_by_row(x_inputs, j);\n",
" let mut norm_row_data = (row_data - x_min_tensor) / x_range_tensor;\n",
" let mut k: u32 = 0;\n",
"\n",
" loop {\n",
" if k >= norm_row_data.data.len() {\n",
" break ();\n",
" };\n",
" result.append(*norm_row_data.data.at(k));\n",
" k += 1;\n",
" };\n",
" j += 1;\n",
" };\n",
" x_inputs_normalized =\n",
" TensorTrait::<\n",
" FP16x16\n",
" >::new(\n",
" array![*x_inputs.shape.at(0), *x_inputs.shape.at(1)].span(), data: result.span()\n",
" );\n",
" };\n",
"\n",
" // for normalizing 1D feature input\n",
" if x_inputs.shape.len() == 1 {\n",
" x_inputs_normalized = (x_inputs - x_min_tensor) / x_range_tensor;\n",
" };\n",
" }\n",
"\n",
" if original_x_values.shape.len() == 1 {\n",
" let mut x_min_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![original_x_values.min_in_tensor()].span());\n",
" let mut x_max_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![original_x_values.max_in_tensor()].span());\n",
" let mut x_range_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(\n",
" shape: array![1].span(),\n",
" data: array![original_x_values.max_in_tensor() - original_x_values.min_in_tensor()]\n",
" .span()\n",
" );\n",
" let mut diff = ((x_inputs - x_min_tensor));\n",
" x_inputs_normalized = ((x_inputs - x_min_tensor)) / x_range_tensor;\n",
" };\n",
" return x_inputs_normalized;\n",
"}\n",
"\n",
"\n",
"// rescales model predictions to standard format\n",
"fn rescale_predictions(\n",
" prediction_result: Tensor<FP16x16>, y_values: Tensor<FP16x16>\n",
") -> Tensor<FP16x16> {\n",
" let mut rescale_predictions = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());\n",
"\n",
" let mut y_min_array = ArrayTrait::<FP16x16>::new();\n",
" let mut y_max_array = ArrayTrait::<FP16x16>::new();\n",
" let mut y_range_array = ArrayTrait::<FP16x16>::new();\n",
"\n",
" let mut y_max = y_values.max_in_tensor();\n",
" let mut y_min = y_values.min_in_tensor();\n",
" let mut y_range = y_values.max_in_tensor() - y_values.min_in_tensor();\n",
" // convert to tensor format for ease of math operations\n",
" let y_min_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![y_min].span());\n",
" let y_max_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![y_max].span());\n",
" let y_range_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![y_range].span());\n",
"\n",
" rescale_predictions = (prediction_result * y_range_tensor) + y_min_tensor;\n",
"\n",
" return rescale_predictions;\n",
"}\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "079198a6",
"metadata": {},
"source": [
"## Data-preprocessing functions\n",
"\n",
"We can normalize our data but for this tutorial we will not as we are working with a relitively small and simple dataset. However, it is recommended to normalize data before passing it to the linear regression model since we will be working with 16x16 fixed point numbers in cairo. This is to prevent from having overflow issues as we compute the feature gradient values (some calculations involve squaring x values which can lead to relatively large if not normalized)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "e2f82c96",
"metadata": {},
"outputs": [],
"source": [
"! touch linear_regression/src/data_preprocessing.cairo"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "089f888f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting linear_regression/src/data_preprocessing.cairo\n"
]
}
],
"source": [
"\n",
"%%writefile linear_regression/src/data_preprocessing.cairo\n",
"\n",
"\n",
"use orion::operators::tensor::{\n",
" Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,\n",
" FP16x16TensorDiv, FP16x16TensorMul\n",
"};\n",
"use orion::numbers::{FP16x16, FixedTrait};\n",
"use linear_regresion::helper_functions::{\n",
" get_tensor_data_by_row, transpose_tensor, calculate_mean, calculate_r_score,\n",
" normalize_user_x_inputs, rescale_predictions\n",
"};\n",
"\n",
"#[derive(Copy, Drop)]\n",
"struct Dataset {\n",
" x_values: Tensor<FP16x16>,\n",
" y_values: Tensor<FP16x16>,\n",
"}\n",
"\n",
"#[generate_trait]\n",
"impl DataPreprocessing of DatasetTrait {\n",
" fn normalize_dataset(ref self: Dataset) -> Dataset {\n",
" let mut x_values = TensorTrait::<FP16x16>::new(array![1].span(), array![FixedTrait::new(0, false)].span());\n",
" let mut y_values = TensorTrait::<FP16x16>::new(array![1].span(), array![FixedTrait::new(0, false)].span());\n",
" // used for multiple_linear_regression_models\n",
" if self.x_values.shape.len() > 1 {\n",
" x_values = normalize_feature_data(self.x_values);\n",
" y_values = normalize_label_data(self.y_values);\n",
" }\n",
" // used for linear_regression_models\n",
" if self.x_values.shape.len() == 1 {\n",
" x_values = normalize_label_data(self.x_values);\n",
" y_values = normalize_label_data(self.y_values);\n",
" }\n",
"\n",
" return Dataset { x_values, y_values };\n",
" }\n",
"}\n",
"\n",
"// normalizes 2D Tensor\n",
"fn normalize_feature_data(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {\n",
" let mut x_min_array = ArrayTrait::<FP16x16>::new();\n",
" let mut x_max_array = ArrayTrait::<FP16x16>::new();\n",
" let mut x_range_array = ArrayTrait::<FP16x16>::new();\n",
" let mut normalized_array = ArrayTrait::<FP16x16>::new();\n",
" // transpose to change rows to be columns\n",
" let transposed_tensor = tensor_data.transpose(axes: array![1, 0].span());\n",
" let tensor_shape = transposed_tensor.shape;\n",
" let tensor_row_len = *tensor_shape.at(0); // 13 \n",
" let tensor_column_len = *tensor_shape.at(1); //50\n",
" // loop and append max and min row values to corresponding array\n",
" let mut i: u32 = 0;\n",
" loop {\n",
" if i >= tensor_row_len {\n",
" break ();\n",
" }\n",
" let mut transposed_tensor_row = get_tensor_data_by_row(transposed_tensor, i);\n",
" x_max_array.append(transposed_tensor_row.max_in_tensor());\n",
" x_min_array.append(transposed_tensor_row.min_in_tensor());\n",
" x_range_array\n",
" .append(transposed_tensor_row.max_in_tensor() - transposed_tensor_row.min_in_tensor());\n",
" i += 1;\n",
" };\n",
" // convert array to tensor format for ease of math operation\n",
" let mut x_min = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1, tensor_row_len].span(), data: x_min_array.span());\n",
" let mut x_range = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1, tensor_row_len].span(), data: x_range_array.span());\n",
" let normalized_tensor = (tensor_data - x_min) / x_range;\n",
" return normalized_tensor;\n",
"}\n",
"\n",
"// normalizes 1D tensor\n",
"fn normalize_label_data(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {\n",
" let mut tensor_data_ = tensor_data;\n",
" let mut normalized_array = ArrayTrait::<FP16x16>::new();\n",
" let mut range = tensor_data.max_in_tensor() - tensor_data.min_in_tensor();\n",
" // loop through tensor values normalizing and appending to new array\n",
" let mut i: u32 = 0;\n",
"\n",
" loop {\n",
" match tensor_data_.data.pop_front() {\n",
" Option::Some(tensor_val) => {\n",
" let mut diff = *tensor_val - tensor_data.min_in_tensor();\n",
" normalized_array.append(diff / range);\n",
" i += 1;\n",
" },\n",
" Option::None(_) => { break; }\n",
" };\n",
" };\n",
" // convert normalized array values to tensor format\n",
" let mut normalized_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![tensor_data.data.len()].span(), data: normalized_array.span());\n",
" return normalized_tensor;\n",
"}\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "d2c1d5bf",
"metadata": {},
"source": [
"## Linear Regression Model\n",
"\n",
"Implement the Linear Regression functions"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "ba6e1b1a",
"metadata": {},
"outputs": [],
"source": [
"os.makedirs(f'linear_regression/src/model/', exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "f310112c",
"metadata": {},
"outputs": [],
"source": [
"! touch linear_regression/src/model/linear_regression_model.cairo"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "7703f49a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting linear_regression/src/model/linear_regression_model.cairo\n"
]
}
],
"source": [
"%%writefile linear_regression/src/model/linear_regression_model.cairo\n",
"\n",
"use orion::operators::tensor::{\n",
" Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,\n",
" FP16x16TensorDiv, FP16x16TensorMul\n",
"};\n",
"use orion::numbers::{FP16x16, FixedTrait};\n",
"use linear_regresion::data_preprocessing::{Dataset, DatasetTrait};\n",
"use linear_regresion::helper_functions::{\n",
" get_tensor_data_by_row, transpose_tensor, calculate_mean, calculate_r_score,\n",
" normalize_user_x_inputs, rescale_predictions\n",
"};\n",
"\n",
"#[derive(Copy, Drop)]\n",
"struct LinearRegressionModel {\n",
" gradient: Tensor<FP16x16>,\n",
" bias: Tensor<FP16x16>\n",
"}\n",
"\n",
"#[generate_trait]\n",
"impl RegressionOperation of LinearRegressionModelTrait {\n",
" fn predict(ref self: LinearRegressionModel, x_input: Tensor<FP16x16>) -> Tensor<FP16x16> {\n",
" let gradient = self.gradient;\n",
" let bias = self.bias;\n",
" let mut prediction = (gradient * x_input) + bias;\n",
" return prediction;\n",
" }\n",
"}\n",
"\n",
"fn LinearRegression(dataset: Dataset) -> LinearRegressionModel {\n",
" let gradient = compute_gradient(dataset);\n",
" let bias = compute_bias(dataset);\n",
" return LinearRegressionModel { gradient, bias };\n",
"}\n",
"\n",
"// computes the mean of a given 1D tensor and outputs result as tensor\n",
"fn compute_mean(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {\n",
" let tensor_size = FixedTrait::<FP16x16>::new_unscaled(tensor_data.data.len(), false);\n",
" let cumulated_sum = tensor_data.cumsum(0, Option::None(()), Option::None(()));\n",
" let sum_result = cumulated_sum.data[tensor_data.data.len() - 1];\n",
" let mean = *sum_result / tensor_size;\n",
" let mut result_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![mean].span());\n",
" return result_tensor;\n",
"}\n",
"\n",
"\n",
"/// Calculates the deviation of each element from the mean of the provided 1D tensor.\n",
"fn deviation_from_mean(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {\n",
" let mut tensor_data_ = tensor_data;\n",
" let mean_value = calculate_mean(tensor_data);\n",
" let mut tensor_shape = array::ArrayTrait::new();\n",
" tensor_shape.append(tensor_data.data.len());\n",
" let mut deviation_values = array::ArrayTrait::new();\n",
"\n",
" let mut i: u32 = 0;\n",
"\n",
" loop {\n",
" match tensor_data_.data.pop_front() {\n",
" Option::Some(tensor_val) => {\n",
" let distance_from_mean = *tensor_val - mean_value;\n",
" deviation_values.append(distance_from_mean);\n",
" i += 1;\n",
" },\n",
" Option::None(_) => { break; }\n",
" };\n",
" };\n",
" let distance_from_mean_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(tensor_shape.span(), deviation_values.span());\n",
"\n",
" return distance_from_mean_tensor;\n",
"}\n",
"\n",
"/// Calculates the beta value for linear regression.\n",
"fn compute_gradient(dataset: Dataset) -> Tensor<FP16x16> {\n",
" let x_deviation = deviation_from_mean(dataset.x_values);\n",
" let y_deviation = deviation_from_mean(dataset.y_values);\n",
"\n",
" let x_y_covariance = x_deviation.matmul(@y_deviation);\n",
" let x_variance = x_deviation.matmul(@x_deviation);\n",
"\n",
" let beta_value = x_y_covariance / x_variance;\n",
"\n",
" return beta_value;\n",
"}\n",
"\n",
"\n",
"/// Calculates the intercept for linear regression.\n",
"fn compute_bias(dataset: Dataset) -> Tensor<FP16x16> {\n",
" let x_mean = compute_mean(dataset.x_values);\n",
" let y_mean = compute_mean(dataset.y_values);\n",
" let gradient = compute_gradient(dataset);\n",
" let mx = gradient * x_mean;\n",
" let intercept = y_mean - mx;\n",
" return intercept;\n",
"}\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "8be0b0e6",
"metadata": {},
"outputs": [],
"source": [
"! touch linear_regression/src/model.cairo"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "4fecca6e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting linear_regression/src/model.cairo\n"
]
}
],
"source": [
"%%writefile linear_regression/src/model.cairo\n",
"mod linear_regression_model;"
]
},
{
"cell_type": "markdown",
"id": "e9321f85",
"metadata": {},
"source": [
"## Running tests on model\n",
"\n",
"Running some checks to ensure the model is performing as expected. Some of the checks involve:\n",
"- tensor shape/dimension check\n",
"- gradient and bias value and dimension checks \n",
"- model accuracy deviance checks"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "f32a96db",
"metadata": {},
"outputs": [],
"source": [
"! touch linear_regression/src/test.cairo"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "619cff0f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting linear_regression/src/test.cairo\n"
]
}
],
"source": [
"%%writefile linear_regression/src/test.cairo\n",
"// use traits::Into;\n",
"use debug::PrintTrait;\n",
"use array::{ArrayTrait, SpanTrait};\n",
"\n",
"\n",
"use linear_regresion::datasets::linear_data::x_feature_data::x_feature_data;\n",
"use linear_regresion::datasets::linear_data::y_label_data::y_label_data;\n",
"\n",
"\n",
"use orion::numbers::{FP16x16, FixedTrait};\n",
"use linear_regresion::model::linear_regression_model::{\n",
" LinearRegressionModel, compute_mean, LinearRegression, LinearRegressionModelTrait\n",
"};\n",
"\n",
"use linear_regresion::data_preprocessing::{Dataset, DatasetTrait};\n",
"use linear_regresion::helper_functions::{get_tensor_data_by_row, transpose_tensor, calculate_mean , \n",
"calculate_r_score, normalize_user_x_inputs, rescale_predictions};\n",
"\n",
"use orion::operators::tensor::{\n",
" Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, \n",
" FP16x16TensorSub, FP16x16TensorAdd, FP16x16TensorDiv, FP16x16TensorMul};\n",
"\n",
"#[test]\n",
"#[available_gas(99999999999999999)]\n",
"fn multiple_linear_regression_test() {\n",
"\n",
"\n",
"// // ----------------------------------------------------------------Simple Linear regression tests---------------------------------------------------------------------------------\n",
"\n",
"let mut main_x_vals = x_feature_data();\n",
"let mut main_y_vals = y_label_data();\n",
"let dataset = Dataset{x_values: main_x_vals,y_values:main_y_vals};\n",
"let mut model = LinearRegression(dataset);\n",
"let gradient = model.gradient;\n",
"let mut reconstructed_ys = model.predict(main_x_vals);\n",
"let mut r_squared_score = calculate_r_score(main_y_vals,reconstructed_ys);\n",
"r_squared_score.print(); \n",
"\n",
"// performing checks on shape on coefficient values (gradient vals + bias) \n",
"assert(model.gradient.data.len() == 1, 'gradient data shape mismatch');\n",
"assert(model.bias.data.len() == 1, 'bias data shape mismatch');\n",
"// model accuracy deviance checks\n",
"assert(r_squared_score >= FixedTrait::new(62259, false), 'Linear model acc. less than 95%');\n",
"\n",
"\n",
"// linear regression model new input predictions\n",
"let mut user_value = TensorTrait::<FP16x16>::new(shape: array![2].span(), data: array![FixedTrait::new(65536, false), FixedTrait::new(65536, true)].span());\n",
"let mut prediction_results = model.predict(user_value);\n",
"(*prediction_results.data.at(0)).print(); \n",
"(*prediction_results.data.at(1)).print();\n",
"\n",
"\n",
"}\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a3ac443e",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/Provable Multiple Linear Regression (Boston Housing Value Estimation).ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "97bdc270",
"metadata": {},
"source": [
"# Import necessary libs & prepare dataset"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ae4ed5fb",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import os\n",
"from sklearn.metrics import r2_score"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4768aa34",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>CRIM</th>\n",
" <th>ZN</th>\n",
" <th>INDUS</th>\n",
" <th>CHAS</th>\n",
" <th>NOX</th>\n",
" <th>RM</th>\n",
" <th>AGE</th>\n",
" <th>DIS</th>\n",
" <th>RAD</th>\n",
" <th>TAX</th>\n",
" <th>PTRATIO</th>\n",
" <th>MEDV</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.40771</td>\n",
" <td>0.0</td>\n",
" <td>6.20</td>\n",
" <td>1</td>\n",
" <td>0.507</td>\n",
" <td>6.164</td>\n",
" <td>91.3</td>\n",
" <td>3.0480</td>\n",
" <td>8</td>\n",
" <td>307</td>\n",
" <td>17.4</td>\n",
" <td>21.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.26838</td>\n",
" <td>0.0</td>\n",
" <td>9.69</td>\n",
" <td>0</td>\n",
" <td>0.585</td>\n",
" <td>5.794</td>\n",
" <td>70.6</td>\n",
" <td>2.8927</td>\n",
" <td>6</td>\n",
" <td>391</td>\n",
" <td>19.2</td>\n",
" <td>18.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.05360</td>\n",
" <td>21.0</td>\n",
" <td>5.64</td>\n",
" <td>0</td>\n",
" <td>0.439</td>\n",
" <td>6.511</td>\n",
" <td>21.1</td>\n",
" <td>6.8147</td>\n",
" <td>4</td>\n",
" <td>243</td>\n",
" <td>16.8</td>\n",
" <td>25.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>11.16040</td>\n",
" <td>0.0</td>\n",
" <td>18.10</td>\n",
" <td>0</td>\n",
" <td>0.740</td>\n",
" <td>6.629</td>\n",
" <td>94.6</td>\n",
" <td>2.1247</td>\n",
" <td>24</td>\n",
" <td>666</td>\n",
" <td>20.2</td>\n",
" <td>13.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.31390</td>\n",
" <td>0.0</td>\n",
" <td>19.58</td>\n",
" <td>0</td>\n",
" <td>0.605</td>\n",
" <td>5.880</td>\n",
" <td>97.3</td>\n",
" <td>2.3887</td>\n",
" <td>5</td>\n",
" <td>403</td>\n",
" <td>14.7</td>\n",
" <td>19.1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX PTRATIO \\\n",
"0 0.40771 0.0 6.20 1 0.507 6.164 91.3 3.0480 8 307 17.4 \n",
"1 0.26838 0.0 9.69 0 0.585 5.794 70.6 2.8927 6 391 19.2 \n",
"2 0.05360 21.0 5.64 0 0.439 6.511 21.1 6.8147 4 243 16.8 \n",
"3 11.16040 0.0 18.10 0 0.740 6.629 94.6 2.1247 24 666 20.2 \n",
"4 2.31390 0.0 19.58 0 0.605 5.880 97.3 2.3887 5 403 14.7 \n",
"\n",
" MEDV \n",
"0 21.7 \n",
"1 18.3 \n",
"2 25.0 \n",
"3 13.4 \n",
"4 19.1 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"boston_dataset.csv\")\n",
"df.drop('Unnamed: 0', axis=1, inplace=True)\n",
"\n",
"df.head()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c88f769e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(50, 12)\n"
]
}
],
"source": [
"print(df.shape)"
]
},
{
"cell_type": "markdown",
"id": "66a093ca",
"metadata": {},
"source": [
"##### Column features\n",
"• CRIM per capita crime rate by town <br>\n",
"• ZN proportion of residential land zoned for lots over 25,000 sq.ft. <br>\n",
"• INDUS proportion of non-retail business acres per town <br>\n",
"• CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)<br>\n",
"• NOX nitric oxides concentration (parts per 10 million) <br>\n",
"• RM average number of rooms per dwelling <br>\n",
"• AGE proportion of owner-occupied units built prior to 1940 <br>\n",
"• DIS weighted distances to five Boston employment centres <br>\n",
"• RAD index of accessibility to radial highways <br>\n",
"• TAX full-value property-tax rate per 10,000 dollars <br>\n",
"• PTRATIO pupil-teacher ratio by town <br>\n",
"• MEDV Median value of owner-occupied homes in $1000's <br>\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a3d9d8b1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(50, 11)\n",
"(50,)\n"
]
}
],
"source": [
"features = df.drop(['MEDV',], axis=1)\n",
"target = df['MEDV']\n",
"\n",
"#convert to numpy\n",
"X_original = features.to_numpy()\n",
"Y_original = target.to_numpy()\n",
"\n",
"print(X_original.shape)\n",
"print(Y_original.shape)"
]
},
{
"cell_type": "markdown",
"id": "fc086887",
"metadata": {},
"source": [
"## Normalize the dataset"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "587dcfb9",
"metadata": {},
"outputs": [],
"source": [
"# Normalize the data \n",
"\n",
"def normalize_data(original_data):\n",
" data_min = np.min(original_data, axis=0)\n",
" data_max = np.max(original_data, axis=0)\n",
" data_range = data_max - data_min\n",
" data_normalized = (original_data - data_min) / data_range\n",
" \n",
" return data_normalized"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "fd6c797f",
"metadata": {},
"outputs": [],
"source": [
"X_normalized= normalize_data(X_original)\n",
"y_normalized= normalize_data(Y_original)"
]
},
{
"cell_type": "markdown",
"id": "e2bddf32",
"metadata": {},
"source": [
"# Main Multiple Linear Regression algorythm"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c1fb0d79",
"metadata": {},
"outputs": [],
"source": [
"def transpose_and_add_bias(feature_data):\n",
" #transpose the data\n",
" transposed_data= feature_data.T\n",
" #add bias term\n",
" transposed_data_with_bias = np.vstack((transposed_data, np.ones(transposed_data.shape[1])))\n",
" \n",
" return transposed_data_with_bias"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "feeab27f",
"metadata": {},
"outputs": [],
"source": [
"def decorrelate_features(feature_data):\n",
"\n",
" # Make copy of input matrix\n",
" x_temp = feature_data.copy()\n",
" \n",
" # Get number of features\n",
" feature_rows = feature_data.shape[0]\n",
" \n",
" # Decorrelate features\n",
" for i in range(feature_rows):\n",
" feature_squared = np.sum(x_temp[i]**2)\n",
" for j in range(i+1, feature_rows):\n",
" feature_cross_prod = np.sum(x_temp[i] * x_temp[j])\n",
" if feature_squared == 0:\n",
" print('Warning, division by zero encountered and handled')\n",
" feature_squared = 1e-8 \n",
" feature_grad = feature_cross_prod / feature_squared\n",
" x_temp[j] -= feature_grad * x_temp[i]\n",
" \n",
" decorelated_x_vals = x_temp\n",
"\n",
" return decorelated_x_vals\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "617f3d11",
"metadata": {},
"outputs": [],
"source": [
"def calculate_gradients(decorelated_x_vals, y_values, original_x_features):\n",
" \n",
" # Initialize gradients array\n",
" feature_rows = decorelated_x_vals.shape[0]\n",
" gradients = np.zeros(feature_rows)\n",
"\n",
" # Calculate gradients\n",
" for i in range(feature_rows-1, -1, -1):\n",
" prod = np.sum(y_values * decorelated_x_vals[i])\n",
" squared = np.sum(decorelated_x_vals[i] * decorelated_x_vals[i])\n",
" if squared == 0:\n",
" print('Warning, division by zero encountered and handled')\n",
" squared = 1e-8\n",
"\n",
" gradients[i] = prod / squared\n",
" y_values -= gradients[i] * original_x_features[i]\n",
" \n",
"\n",
" return gradients"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "f07ae5a1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training data shapes:\n",
"X_normalized: (12, 50)\n",
"y_normalized: (50,)\n"
]
}
],
"source": [
"X_normalized_transposed_with_bias = transpose_and_add_bias(X_normalized)\n",
"\n",
"print(\"Training data shapes:\")\n",
"print(\"X_normalized:\", X_normalized_transposed_with_bias.shape)\n",
"print(\"y_normalized:\", y_normalized.shape)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "e3a13196",
"metadata": {},
"outputs": [],
"source": [
"decorrelate_X_features = decorrelate_features(X_normalized_transposed_with_bias)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "21864d6b",
"metadata": {},
"outputs": [],
"source": [
"decorrelate_X_features = decorrelate_features(X_normalized_transposed_with_bias)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "8090dcd8",
"metadata": {},
"outputs": [],
"source": [
"gradient_values = calculate_gradients(decorrelate_X_features, y_normalized, X_normalized_transposed_with_bias )"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "11b052e5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.43365324, -0.18175555, -0.42523263, 0.13711559, -0.19621044,\n",
" -0.06995875, 0.62886834, -0.08972977, -0.0081258 , 0.00121573,\n",
" 0.0482544 , 0.13295955])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"real_gradient_values_reversed = np.flip(gradient_values)\n",
"real_gradient_values_reversed"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "b27055f3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.13295955, 0.0482544 , 0.00121573, -0.0081258 , -0.08972977,\n",
" 0.62886834, -0.06995875, -0.19621044, 0.13711559, -0.42523263,\n",
" -0.18175555, 0.43365324])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gradient_values"
]
},
{
"cell_type": "markdown",
"id": "4f970ffa",
"metadata": {},
"source": [
"# Reconstructing the y labels using the calculated gradients and X feature data"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "7dab4a56",
"metadata": {},
"outputs": [],
"source": [
"def denormalize_data(original_data,normalized_data):\n",
" \n",
" data_min = np.min(original_data)\n",
" data_max = np.max(original_data)\n",
" data_range = data_max - data_min\n",
" \n",
" denormalize_data = ( normalized_data * data_range) + data_min\n",
" return denormalize_data"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "f3a73a20",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R^2 score (denormalized): 0.8497719822024984\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"y_pred_norm = gradient_values @ X_normalized_transposed_with_bias #prediction#\n",
"\n",
"reconstructed_y = denormalize_data(Y_original,y_pred_norm)\n",
"\n",
"# Plot the denormalized y values\n",
"plt.figure(2)\n",
"plt.title(\"Denormalized Prediction\")\n",
"plt.plot(Y_original)\n",
"plt.plot(reconstructed_y)\n",
"plt.legend([\"original y values\", \"reconstructed ys\"])\n",
"\n",
"# Calculate R^2 score for denormalized prediction\n",
"accuracy_denormalized = r2_score(Y_original, reconstructed_y)\n",
"print(\"R^2 score (denormalized):\", accuracy_denormalized)"
]
},
{
"cell_type": "markdown",
"id": "302d8725",
"metadata": {},
"source": [
"# Estimating value of a new property"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "2bfac80e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.40771, 0. , 6.2 , 1. , 0.507 , 6.164 ,\n",
" 91.3 , 3.048 , 8. , 307. , 17.4 ])"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 63.61759948730469 --->0.3379058837890625 #6.1999969482421875, 1, 0.506988525390625\n",
"X_original[0] #0.015472412109375 0.4076995849609375"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "2fbe2acc",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.3628772191870112\n"
]
}
],
"source": [
"# user_input_feature_values = np.array([9.0771e-01, 5.0000e-01, 1.9500e+01, 1.3200e+00, 9.9000e-01,\n",
"# 1.2164e+01, 9.1300e+01, 1.1000e+00, 6.0000e+00, 5.0600e+02,\n",
"# 2.1000e+01])\n",
"\n",
"user_input_feature_values = X_original[0]\n",
"\n",
"X_min = np.min(X_original, axis=0)\n",
"X_max = np.max(X_original, axis=0)\n",
"X_range = X_max - X_min\n",
"# normalize the feature vals\n",
"normalized_vals = (user_input_feature_values - X_min) / X_range\n",
"# add bias term \n",
"user_input_feature_values_normalized_with_bias = np.append(normalized_vals, 1)\n",
"\n",
"# get normalized prediction\n",
"prediction_normalized = gradient_values @ user_input_feature_values_normalized_with_bias\n",
"print(prediction_normalized)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "535f23d1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"24.32395193323655"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#denormalize forecast\n",
"Y_min = np.min(Y_original, axis=0)\n",
"Y_max = np.max(Y_original, axis=0)\n",
"Y_range = Y_max - Y_min\n",
"# rescale prediction\n",
"denormlized_house_value_estimation = (prediction_normalized * Y_range) + Y_min\n",
"denormlized_house_value_estimation"
]
},
{
"cell_type": "markdown",
"id": "ea617854",
"metadata": {},
"source": [
"# Transition to Cairo\n",
"## Create a scarb project\n",
"\n",
"Scarb is the Cairo package manager specifically created to streamline our Cairo and Starknet development process. You can find all information about Scarb and Cairo installation here"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "35d4e483",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Created `multiple_linear_regression` package.\n"
]
}
],
"source": [
"! scarb new multiple_linear_regression"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "e4211294",
"metadata": {},
"outputs": [],
"source": [
"! echo -n > multiple_linear_regression/src/lib.cairo"
]
},
{
"cell_type": "markdown",
"id": "72afd4c8",
"metadata": {},
"source": [
"A new project folder will be created for you and make sure to replace the content in Scarb.toml file with the following code:\n",
"```toml\n",
"[package]\n",
"name = \"multiple_linear_regresion\"\n",
"version = \"0.1.0\"\n",
"\n",
"\n",
"[dependencies]\n",
"orion = { git = \"https://github.com/gizatechxyz/onnx-cairo\" }\n",
"\n",
"[scripts]\n",
"test = \"scarb cairo-test -f multiple_linear_regression_test\"\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2aab745d",
"metadata": {},
"outputs": [],
"source": [
"# add reference modules to help our code compile at the end\n",
"%%writefile -a multiple_linear_regression/src/lib.cairo\n",
"mod test;\n",
"mod data_preprocessing;\n",
"mod helper_functions;\n",
"mod datasets;\n",
"mod model;"
]
},
{
"cell_type": "markdown",
"id": "2ecade3b",
"metadata": {},
"source": [
"## Generate Cairo files\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "4192fdf0",
"metadata": {},
"outputs": [],
"source": [
"# Convert the original data to Cairo \n",
"\n",
"\n",
"def generate_cairo_files(data, name, folder_name):\n",
" \n",
" os.makedirs(f'multiple_linear_regression/src/datasets/{folder_name}', exist_ok=True)\n",
" with open(os.path.join('multiple_linear_regression/src/datasets', f'{folder_name}', f\"{name}.cairo\"), \"w\") as f:\n",
" f.write(\n",
" \"use array::ArrayTrait;\\n\" +\n",
" \"use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16Impl, FP16x16PartialEq };\\n\" +\n",
" \"use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};\\n\" +\n",
" \"use orion::numbers::{FP16x16, FixedTrait};\\n\\n\" +\n",
" \"fn {0}() -> Tensor<FP16x16> \".format(name) + \"{\\n\" +\n",
" \" let tensor = TensorTrait::<FP16x16>::new( \\n\"\n",
" )\n",
" \n",
" if len(data.shape)>1:\n",
" f.write(\" shape: array![{0},\".format(data.shape[0]))\n",
" f.write(\"{0}].span(),\\n\".format(data.shape[1]))\n",
" f.write(\n",
" \" data: array![ \\n\"\n",
" )\n",
" if len(data.shape)==1:\n",
" f.write(\" shape: array![{0}].span(),\\n\".format(data.shape[0]))\n",
" f.write(\n",
" \" data: array![ \\n\"\n",
" )\n",
" for val in np.nditer(data.flatten()):\n",
" f.write(\" FixedTrait::new({0}, {1} ),\\n\".format(abs(int(val * 2**16)), str(val < 0).lower()))\n",
" f.write(\n",
" \"].span() \\n \\n\" +\n",
" \");\\n\\n\"+\n",
" \"return tensor; \\n\"+\n",
" \"}\"\n",
" )\n",
" with open(os.path.join('multiple_linear_regression/src/datasets', f'{folder_name}.cairo'), 'a') as f:\n",
" f.write(f\"mod {name};\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "b05daac1",
"metadata": {},
"outputs": [],
"source": [
"generate_cairo_files(X_original, 'boston_x_features', 'boston_data')\n",
"generate_cairo_files(Y_original, 'boston_y_labels', 'boston_data') "
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "30fe7dca",
"metadata": {},
"outputs": [],
"source": [
"generate_cairo_files(user_input_feature_values, 'user_inputs_boston_data', 'user_inputs_data')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "02e8a2d0",
"metadata": {},
"outputs": [],
"source": [
"# add reference modules to help our code compile\n",
"! touch multiple_linear_regression/src/datasets.cairo"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "f8cdb5fa",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting multiple_linear_regression/src/datasets.cairo\n"
]
}
],
"source": [
"%%writefile multiple_linear_regression/src/datasets.cairo\n",
"mod boston_data;\n",
"mod user_inputs_data;"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "cd1ca29f",
"metadata": {},
"outputs": [],
"source": [
"! touch multiple_linear_regression/src/datasets/boston_data.cairo"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "4350c425",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting multiple_linear_regression/src/datasets/boston_data.cairo\n"
]
}
],
"source": [
"%%writefile multiple_linear_regression/src/datasets/boston_data.cairo\n",
"mod boston_x_features;\n",
"mod boston_y_labels;"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "f034a5e0",
"metadata": {},
"outputs": [],
"source": [
"! touch multiple_linear_regression/src/datasets/user_inputs_data.cairo"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "d3235299",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting multiple_linear_regression/src/datasets/user_inputs_data.cairo\n"
]
}
],
"source": [
"%%writefile multiple_linear_regression/src/datasets/user_inputs_data.cairo\n",
"mod user_inputs_boston_data;"
]
},
{
"cell_type": "markdown",
"id": "ea3dd5d9",
"metadata": {},
"source": [
"## Helper functions \n",
"\n",
"We add some helper functions to make it easier to construct our MLR model"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "24ae59be",
"metadata": {},
"outputs": [],
"source": [
"! touch multiple_linear_regression/src/helper_functions.cairo"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "a51190ec",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting multiple_linear_regression/src/helper_functions.cairo\n"
]
}
],
"source": [
"%%writefile multiple_linear_regression/src/helper_functions.cairo\n",
"\n",
"use debug::PrintTrait;\n",
"use array::{ArrayTrait, SpanTrait};\n",
"use orion::operators::tensor::{\n",
" Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,\n",
" FP16x16TensorDiv, FP16x16TensorMul\n",
"};\n",
"\n",
"use orion::numbers::{FP16x16, FixedTrait};\n",
"\n",
"// retrieves row data by index in a 2D tensor\n",
"fn get_tensor_data_by_row(tensor_data: Tensor<FP16x16>, row_index: u32,) -> Tensor<FP16x16> {\n",
" let column_len = *tensor_data.shape.at(1); //13\n",
" // crete new array\n",
" let mut result = ArrayTrait::<FP16x16>::new();\n",
" // loop through the x values and append values \n",
" let mut i: u32 = 0;\n",
" loop {\n",
" if i >= column_len {\n",
" break ();\n",
" }\n",
" result.append(tensor_data.at(indices: array![row_index, i].span()));\n",
" i += 1;\n",
" };\n",
" let resultant_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(array![column_len].span(), data: result.span());\n",
" return resultant_tensor;\n",
"}\n",
"\n",
"\n",
"// transposes tensor\n",
"fn transpose_tensor(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {\n",
" let tensor_transposed = tensor_data.transpose(axes: array![1, 0].span());\n",
" return tensor_transposed;\n",
"}\n",
"\n",
"fn calculate_mean(tensor_data: Tensor<FP16x16>) -> FP16x16 {\n",
" let tensor_size = FixedTrait::<FP16x16>::new_unscaled(tensor_data.data.len(), false);\n",
" let cumulated_sum = tensor_data.cumsum(0, Option::None(()), Option::None(()));\n",
" let sum_result = cumulated_sum.data[tensor_data.data.len() - 1];\n",
" let mean = *sum_result / tensor_size;\n",
" return mean;\n",
"}\n",
"\n",
"// Calculates the R-Squared score between two tensors.\n",
"fn calculate_r_score(Y_values: Tensor<FP16x16>, Y_pred_values: Tensor<FP16x16>) -> FP16x16 {\n",
" let mut Y_values_ = Y_values;\n",
" let mean_y_value = calculate_mean(Y_values);\n",
" // creating the appropriate tensor shapes and empty arrays to populate values into\n",
" let mut squared_diff_shape = array::ArrayTrait::new();\n",
" squared_diff_shape.append(Y_values.data.len());\n",
" let mut squared_diff_vals = array::ArrayTrait::new();\n",
" let mut squared_mean_diff_shape = array::ArrayTrait::new();\n",
" squared_mean_diff_shape.append(Y_values.data.len());\n",
" let mut squared_mean_diff_vals = array::ArrayTrait::new();\n",
"\n",
" let mut i: u32 = 0;\n",
"\n",
" loop {\n",
" match Y_values_.data.pop_front() {\n",
" Option::Some(y_value) => {\n",
" let diff_pred = *y_value - *Y_pred_values.data.at(i);\n",
" let squared_diff = diff_pred * diff_pred;\n",
" squared_diff_vals.append(squared_diff);\n",
"\n",
" let diff_mean = *y_value - mean_y_value;\n",
" let squared_mean_diff = diff_mean * diff_mean;\n",
" squared_mean_diff_vals.append(squared_mean_diff);\n",
" i += 1;\n",
" },\n",
" Option::None(_) => { break; }\n",
" }\n",
" };\n",
"\n",
" let squared_diff_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(squared_diff_shape.span(), squared_diff_vals.span());\n",
" let squared_mean_diff_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(squared_mean_diff_shape.span(), squared_mean_diff_vals.span());\n",
" let sum_squared_diff = squared_diff_tensor.cumsum(0, Option::None(()), Option::None(()));\n",
" let sum_squared_mean_diff = squared_mean_diff_tensor\n",
" .cumsum(0, Option::None(()), Option::None(()));\n",
" let r_score = FixedTrait::new_unscaled(1, false)\n",
" - *sum_squared_diff.data.at(Y_values.data.len() - 1)\n",
" / *sum_squared_mean_diff.data.at(Y_values.data.len() - 1);\n",
"\n",
" return r_score;\n",
"}\n",
"\n",
"\n",
"// computes the x_min, x_max and x_range. Used for helping in normalizing and denormalizing user inputed values operations\n",
"fn normalize_user_x_inputs(\n",
" x_inputs: Tensor<FP16x16>, original_x_values: Tensor<FP16x16>\n",
") -> Tensor<FP16x16> {\n",
" let mut x_inputs_normalized = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());\n",
"\n",
" let mut x_min = ArrayTrait::<FP16x16>::new();\n",
" let mut x_max = ArrayTrait::<FP16x16>::new();\n",
" let mut x_range = ArrayTrait::<FP16x16>::new();\n",
" let mut result = ArrayTrait::<FP16x16>::new();\n",
"\n",
" if original_x_values.shape.len() > 1 {\n",
" let transposed_tensor = original_x_values.transpose(axes: array![1, 0].span());\n",
" let data_len = *transposed_tensor.shape.at(0); //13\n",
" // loop through each row calculating the min, max and range row values for each feature columns\n",
" let mut i: u32 = 0;\n",
" loop {\n",
" if i >= data_len {\n",
" break ();\n",
" }\n",
" let mut transposed_tensor_row = get_tensor_data_by_row(transposed_tensor, i);\n",
" x_min.append(transposed_tensor_row.min_in_tensor());\n",
" x_max.append(transposed_tensor_row.max_in_tensor());\n",
" x_range\n",
" .append(\n",
" transposed_tensor_row.max_in_tensor() - transposed_tensor_row.min_in_tensor()\n",
" );\n",
" i += 1;\n",
" };\n",
" let mut x_min_tensor = TensorTrait::new(shape: array![data_len].span(), data: x_min.span());\n",
" let mut x_max_tensor = TensorTrait::new(shape: array![data_len].span(), data: x_max.span());\n",
" let mut x_range_tensor = TensorTrait::new(\n",
" shape: array![data_len].span(), data: x_range.span()\n",
" );\n",
"\n",
" // for normalizing 2D user inputed feature vals\n",
" if x_inputs.shape.len() > 1 {\n",
" let mut j: u32 = 0;\n",
" loop {\n",
" if j >= *x_inputs.shape.at(0) {\n",
" break ();\n",
" };\n",
" let mut row_data = get_tensor_data_by_row(x_inputs, j);\n",
" let mut norm_row_data = (row_data - x_min_tensor) / x_range_tensor;\n",
" let mut k: u32 = 0;\n",
"\n",
" loop {\n",
" if k >= norm_row_data.data.len() {\n",
" break ();\n",
" };\n",
" result.append(*norm_row_data.data.at(k));\n",
" k += 1;\n",
" };\n",
" j += 1;\n",
" };\n",
" x_inputs_normalized =\n",
" TensorTrait::<\n",
" FP16x16\n",
" >::new(\n",
" array![*x_inputs.shape.at(0), *x_inputs.shape.at(1)].span(), data: result.span()\n",
" );\n",
" };\n",
"\n",
" // for normalizing 1D feature input\n",
" if x_inputs.shape.len() == 1 {\n",
" x_inputs_normalized = (x_inputs - x_min_tensor) / x_range_tensor;\n",
" };\n",
" }\n",
"\n",
" if original_x_values.shape.len() == 1 {\n",
" let mut x_min_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![original_x_values.min_in_tensor()].span());\n",
" let mut x_max_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![original_x_values.max_in_tensor()].span());\n",
" let mut x_range_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(\n",
" shape: array![1].span(),\n",
" data: array![original_x_values.max_in_tensor() - original_x_values.min_in_tensor()]\n",
" .span()\n",
" );\n",
" let mut diff = ((x_inputs - x_min_tensor));\n",
" x_inputs_normalized = ((x_inputs - x_min_tensor)) / x_range_tensor;\n",
" };\n",
" return x_inputs_normalized;\n",
"}\n",
"\n",
"\n",
"// rescales model predictions to standard format\n",
"fn rescale_predictions(\n",
" prediction_result: Tensor<FP16x16>, y_values: Tensor<FP16x16>\n",
") -> Tensor<FP16x16> {\n",
" let mut rescale_predictions = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());\n",
"\n",
" let mut y_min_array = ArrayTrait::<FP16x16>::new();\n",
" let mut y_max_array = ArrayTrait::<FP16x16>::new();\n",
" let mut y_range_array = ArrayTrait::<FP16x16>::new();\n",
"\n",
" let mut y_max = y_values.max_in_tensor();\n",
" let mut y_min = y_values.min_in_tensor();\n",
" let mut y_range = y_values.max_in_tensor() - y_values.min_in_tensor();\n",
" // convert to tensor format for ease of math operations\n",
" let y_min_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![y_min].span());\n",
" let y_max_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![y_max].span());\n",
" let y_range_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![y_range].span());\n",
"\n",
" rescale_predictions = (prediction_result * y_range_tensor) + y_min_tensor;\n",
"\n",
" return rescale_predictions;\n",
"}\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "f728e6b9",
"metadata": {},
"source": [
"## Data-preprocessing functions\n",
"\n",
"It is <b>recommended</b> to normalize data before passing it to the multiple linear regression model since we will be working with 16x16 fixed point numbers in cairo. This will prevent from having overflow issues as we compute the feature gradient values (some of the calculations involve squaring x values which can be relatively large if not normalized)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "9d23a514",
"metadata": {},
"outputs": [],
"source": [
"! touch multiple_linear_regression/src/data_preprocessing.cairo"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "71453638",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting multiple_linear_regression/src/data_preprocessing.cairo\n"
]
}
],
"source": [
"\n",
"%%writefile multiple_linear_regression/src/data_preprocessing.cairo\n",
"\n",
"use orion::operators::tensor::{\n",
" Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,\n",
" FP16x16TensorDiv, FP16x16TensorMul\n",
"};\n",
"use orion::numbers::{FP16x16, FixedTrait};\n",
"use multiple_linear_regresion::helper_functions::{\n",
" get_tensor_data_by_row, transpose_tensor, calculate_mean, calculate_r_score,\n",
" normalize_user_x_inputs, rescale_predictions\n",
"};\n",
"\n",
"#[derive(Copy, Drop)]\n",
"struct Dataset {\n",
" x_values: Tensor<FP16x16>,\n",
" y_values: Tensor<FP16x16>,\n",
"}\n",
"\n",
"#[generate_trait]\n",
"impl DataPreprocessing of DatasetTrait {\n",
" fn normalize_dataset(ref self: Dataset) -> Dataset {\n",
" let mut x_values = TensorTrait::<FP16x16>::new(array![1].span(), array![FixedTrait::new(0, false)].span());\n",
" let mut y_values = TensorTrait::<FP16x16>::new(array![1].span(), array![FixedTrait::new(0, false)].span());\n",
" // used for multiple_linear_regression_models\n",
" if self.x_values.shape.len() > 1 {\n",
" x_values = normalize_feature_data(self.x_values);\n",
" y_values = normalize_label_data(self.y_values);\n",
" }\n",
" // used for linear_regression_models\n",
" if self.x_values.shape.len() == 1 {\n",
" x_values = normalize_label_data(self.x_values);\n",
" y_values = normalize_label_data(self.y_values);\n",
" }\n",
"\n",
" return Dataset { x_values, y_values };\n",
" }\n",
"}\n",
"\n",
"// normalizes 2D Tensor\n",
"fn normalize_feature_data(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {\n",
" let mut x_min_array = ArrayTrait::<FP16x16>::new();\n",
" let mut x_max_array = ArrayTrait::<FP16x16>::new();\n",
" let mut x_range_array = ArrayTrait::<FP16x16>::new();\n",
" let mut normalized_array = ArrayTrait::<FP16x16>::new();\n",
" // transpose to change rows to be columns\n",
" let transposed_tensor = tensor_data.transpose(axes: array![1, 0].span());\n",
" let tensor_shape = transposed_tensor.shape;\n",
" let tensor_row_len = *tensor_shape.at(0); // 13 \n",
" let tensor_column_len = *tensor_shape.at(1); //50\n",
" // loop and append max and min row values to corresponding array\n",
" let mut i: u32 = 0;\n",
" loop {\n",
" if i >= tensor_row_len {\n",
" break ();\n",
" }\n",
" let mut transposed_tensor_row = get_tensor_data_by_row(transposed_tensor, i);\n",
" x_max_array.append(transposed_tensor_row.max_in_tensor());\n",
" x_min_array.append(transposed_tensor_row.min_in_tensor());\n",
" x_range_array\n",
" .append(transposed_tensor_row.max_in_tensor() - transposed_tensor_row.min_in_tensor());\n",
" i += 1;\n",
" };\n",
" // convert array to tensor format for ease of math operation\n",
" let mut x_min = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1, tensor_row_len].span(), data: x_min_array.span());\n",
" let mut x_range = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1, tensor_row_len].span(), data: x_range_array.span());\n",
" let normalized_tensor = (tensor_data - x_min) / x_range;\n",
" return normalized_tensor;\n",
"}\n",
"\n",
"// normalizes 1D tensor\n",
"fn normalize_label_data(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {\n",
" let mut tensor_data_ = tensor_data;\n",
" let mut normalized_array = ArrayTrait::<FP16x16>::new();\n",
" let mut range = tensor_data.max_in_tensor() - tensor_data.min_in_tensor();\n",
" // loop through tensor values normalizing and appending to new array\n",
" let mut i: u32 = 0;\n",
"\n",
" loop {\n",
" match tensor_data_.data.pop_front() {\n",
" Option::Some(tensor_val) => {\n",
" let mut diff = *tensor_val - tensor_data.min_in_tensor();\n",
" normalized_array.append(diff / range);\n",
" i += 1;\n",
" },\n",
" Option::None(_) => { break; }\n",
" };\n",
" };\n",
" // convert normalized array values to tensor format\n",
" let mut normalized_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![tensor_data.data.len()].span(), data: normalized_array.span());\n",
" return normalized_tensor;\n",
"}\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "97a3109b",
"metadata": {},
"source": [
"## Multiple Linear Regression Model\n",
"\n",
"Implement the Multiple Linear Regression functions"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "253241d2",
"metadata": {},
"outputs": [],
"source": [
"os.makedirs(f'multiple_linear_regression/src/model/', exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "820b7925",
"metadata": {},
"outputs": [],
"source": [
"! touch multiple_linear_regression/src/model/multiple_linear_regression_model.cairo"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "9fe7721f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting multiple_linear_regression/src/model/multiple_linear_regression_model.cairo\n"
]
}
],
"source": [
"%%writefile multiple_linear_regression/src/model/multiple_linear_regression_model.cairo\n",
"\n",
"use orion::operators::tensor::{\n",
" Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,\n",
" FP16x16TensorDiv, FP16x16TensorMul\n",
"};\n",
"use orion::numbers::{FP16x16, FixedTrait};\n",
"use multiple_linear_regresion::data_preprocessing::{Dataset, DatasetTrait};\n",
"use multiple_linear_regresion::helper_functions::{\n",
" get_tensor_data_by_row, transpose_tensor, calculate_mean, calculate_r_score,\n",
" normalize_user_x_inputs, rescale_predictions\n",
"};\n",
"\n",
"\n",
"#[derive(Copy, Drop)]\n",
"struct MultipleLinearRegressionModel {\n",
" coefficients: Tensor<FP16x16>\n",
"}\n",
"\n",
"#[generate_trait]\n",
"impl RegressionOperation of MultipleLinearRegressionModelTrait {\n",
" // reconstruct the y values using the computed gradients and x values\n",
" fn predict(\n",
" ref self: MultipleLinearRegressionModel, feature_inputs: Tensor<FP16x16>\n",
" ) -> Tensor<FP16x16> {\n",
" // random tensor value that we will replace\n",
" let mut prediction_result = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());\n",
"\n",
" let mut result = ArrayTrait::<FP16x16>::new();\n",
" // for multiple predictions\n",
" if feature_inputs.shape.len() > 1 {\n",
" let feature_values = add_bias_term(feature_inputs, 1);\n",
" let mut data_len: u32 = *feature_values.shape.at(0);\n",
" let mut i: u32 = 0;\n",
" loop {\n",
" if i >= data_len {\n",
" break ();\n",
" }\n",
" let feature_row_values = get_tensor_data_by_row(feature_values, i);\n",
" let predicted_values = feature_row_values.matmul(@self.coefficients);\n",
" result.append(*predicted_values.data.at(0));\n",
" i += 1;\n",
" };\n",
" prediction_result =\n",
" TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![result.len()].span(), data: result.span());\n",
" }\n",
"\n",
" // for single predictions \n",
" if feature_inputs.shape.len() == 1 && self.coefficients.data.len() > 1 {\n",
" let feature_values = add_bias_term(feature_inputs, 1);\n",
" prediction_result = feature_values.matmul(@self.coefficients);\n",
" }\n",
"\n",
" return prediction_result;\n",
" }\n",
"}\n",
"\n",
"fn MultipleLinearRegression(dataset: Dataset) -> MultipleLinearRegressionModel {\n",
" let x_values_tranposed = transpose_tensor(dataset.x_values);\n",
" let x_values_tranposed_with_bias = add_bias_term(x_values_tranposed, 0);\n",
" let decorrelated_x_features = decorrelate_x_features(x_values_tranposed_with_bias);\n",
" let coefficients = compute_gradients(\n",
" decorrelated_x_features, dataset.y_values, x_values_tranposed_with_bias\n",
" );\n",
" return MultipleLinearRegressionModel { coefficients };\n",
"}\n",
"\n",
"//Adds bias term to features based on axis\n",
"fn add_bias_term(x_feature: Tensor<FP16x16>, axis: u32) -> Tensor<FP16x16> {\n",
" let mut x_feature_ = x_feature;\n",
" let mut tensor_with_bias = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());\n",
" let mut result = ArrayTrait::<FP16x16>::new();\n",
" // check if feature data has multiple rows and columns\n",
" if x_feature.shape.len() > 1 {\n",
" let mut index: u32 = 0;\n",
" if axis == 1 {\n",
" index = 0;\n",
" } else {\n",
" index = 1;\n",
" }\n",
" let data_len = *x_feature.shape.at(index); // 50\n",
" let mut i: u32 = 0;\n",
" loop {\n",
" if i >= data_len {\n",
" break ();\n",
" }\n",
" result\n",
" .append(FixedTrait::new(65536, false)); //65536=ONE in FP16x16, change accordingly \n",
" i += 1;\n",
" };\n",
" if axis == 0 {\n",
" let res_tensor = TensorTrait::new(\n",
" shape: array![1, data_len].span(), data: result.span()\n",
" );\n",
" tensor_with_bias =\n",
" TensorTrait::concat(tensors: array![x_feature, res_tensor].span(), axis: axis);\n",
" } else {\n",
" let res_tensor = TensorTrait::new(\n",
" shape: array![data_len, 1].span(), data: result.span()\n",
" );\n",
" tensor_with_bias =\n",
" TensorTrait::concat(tensors: array![x_feature, res_tensor].span(), axis: axis);\n",
" }\n",
" }\n",
" // check if feature data is 1D\n",
" if x_feature.shape.len() == 1 {\n",
" let mut j: u32 = 0;\n",
" loop {\n",
" match x_feature_.data.pop_front() {\n",
" Option::Some(x_val) => {\n",
" result.append(*x_val);\n",
" j += 1;\n",
" },\n",
" Option::None(_) => { break; }\n",
" };\n",
" };\n",
" result.append(FixedTrait::new(65536, false)); //65536=ONE in FP16x16, change accordingly \n",
" tensor_with_bias =\n",
" TensorTrait::<FP16x16>::new(shape: array![result.len()].span(), data: result.span());\n",
" }\n",
" return tensor_with_bias;\n",
"}\n",
"\n",
"// decorrelates the feature data (*only the last tensor row of the decorelated feature data will be fully orthogonal)\n",
"fn decorrelate_x_features(x_feature_data: Tensor<FP16x16>) -> Tensor<FP16x16> {\n",
" let mut input_tensor = x_feature_data;\n",
"\n",
" let mut i: u32 = 0;\n",
" loop {\n",
" if i >= *x_feature_data.shape.at(0) {\n",
" break ();\n",
" }\n",
" let mut placeholder = ArrayTrait::<FP16x16>::new();\n",
" let mut feature_row_values = get_tensor_data_by_row(input_tensor, i);\n",
" let mut feature_squared = feature_row_values.matmul(@feature_row_values);\n",
" // avoiding division by zero errors\n",
" if *feature_squared.data.at(0) == FixedTrait::new(0, false) {\n",
" feature_squared =\n",
" TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());\n",
" }\n",
" // loop throgh remaining tensor data and remove the individual tensor factors from one another \n",
" let mut j: u32 = i + 1;\n",
" loop {\n",
" if j >= *x_feature_data.shape.at(0) {\n",
" break ();\n",
" }\n",
" let mut remaining_tensor_values = get_tensor_data_by_row(input_tensor, j);\n",
" let feature_cross_product = feature_row_values.matmul(@remaining_tensor_values);\n",
" let feature_gradients = feature_cross_product / feature_squared;\n",
" remaining_tensor_values = remaining_tensor_values\n",
" - (feature_row_values\n",
" * feature_gradients); //remove the feature factors from one another\n",
" // loop and append the modifieed remaining_tensor_values (after the corelated factor has been removed) to placeholder array\n",
" let mut k: u32 = 0;\n",
" loop {\n",
" if k >= remaining_tensor_values.data.len() {\n",
" break ();\n",
" }\n",
" placeholder.append(*remaining_tensor_values.data.at(k));\n",
" k += 1;\n",
" };\n",
"\n",
" j += 1;\n",
" };\n",
" // convert placeholder array to tensor format and update the original tensor with the new modified decorrelated tensor row values\n",
" let mut decorrelated_tensor = TensorTrait::new(\n",
" shape: array![*x_feature_data.shape.at(0) - 1 - i, *x_feature_data.shape.at(1)].span(),\n",
" data: placeholder.span()\n",
" );\n",
" let mut original_tensor = input_tensor\n",
" .slice(\n",
" starts: array![0, 0].span(),\n",
" ends: array![i + 1, *x_feature_data.shape.at(1)].span(),\n",
" axes: Option::None(()),\n",
" steps: Option::Some(array![1, 1].span())\n",
" );\n",
" input_tensor =\n",
" TensorTrait::concat(\n",
" tensors: array![original_tensor, decorrelated_tensor].span(), axis: 0\n",
" );\n",
" i += 1;\n",
" };\n",
" return input_tensor;\n",
"}\n",
"\n",
"// computes the corresponding MLR gradient using decorrelated feature\n",
"fn compute_gradients(\n",
" decorrelated_x_features: Tensor<FP16x16>,\n",
" y_values: Tensor<FP16x16>,\n",
" original_x_tensor_values: Tensor<FP16x16>\n",
") -> Tensor<FP16x16> {\n",
" let mut gradient_values_flipped = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());\n",
"\n",
" let mut result = ArrayTrait::<FP16x16>::new();\n",
" let mut tensor_y_vals = y_values;\n",
" let mut i: u32 = *decorrelated_x_features.shape.at(0);\n",
" // loop through Decorrelated_x_features starting from the fully orthogonlised last tensor row value\n",
" loop {\n",
" if i <= 0 {\n",
" break ();\n",
" }\n",
" let index_val = i - 1;\n",
" let mut decorelated_feature_row_values = get_tensor_data_by_row(\n",
" decorrelated_x_features, index_val\n",
" ); // 50 vals\n",
" let mut decorelated_features_squared = decorelated_feature_row_values\n",
" .matmul(@decorelated_feature_row_values);\n",
" let mut feature_label_cross_product = tensor_y_vals\n",
" .matmul(@decorelated_feature_row_values); // multiply the tensors\n",
" // avoiding division by zero errors\n",
" if *decorelated_features_squared.data.at(0) == FixedTrait::new(0, false) {\n",
" decorelated_features_squared =\n",
" TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());\n",
" }\n",
" // computing the feature gradient values using the y values and decorrelated x features and appending to array\n",
" let mut single_gradient_value = feature_label_cross_product\n",
" / decorelated_features_squared; // devide the summed value by each other\n",
" result.append(*single_gradient_value.data.at(0));\n",
" // remove the assosciated feature gradient value away from y values\n",
" let mut original_x_tensor_row_values = get_tensor_data_by_row(\n",
" original_x_tensor_values, index_val\n",
" );\n",
" tensor_y_vals = tensor_y_vals\n",
" - (original_x_tensor_row_values\n",
" * single_gradient_value); //remove the first feature from second feature values\n",
" i -= 1;\n",
" };\n",
" // convert the gradient array to tensor format\n",
" let final_gradients = TensorTrait::new(\n",
" shape: array![*decorrelated_x_features.shape.at(0)].span(), data: result.span()\n",
" );\n",
"\n",
" let mut reverse_grad_array = ArrayTrait::<FP16x16>::new();\n",
" let mut data_len: u32 = final_gradients.data.len();\n",
" loop {\n",
" if data_len <= 0 {\n",
" break ();\n",
" }\n",
" let temp_val = data_len - 1;\n",
" reverse_grad_array.append(*final_gradients.data.at(temp_val));\n",
" data_len -= 1;\n",
" };\n",
" // convert gradient values to tensor format\n",
" let gradient_values_flipped = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![reverse_grad_array.len()].span(), data: reverse_grad_array.span());\n",
"\n",
" return gradient_values_flipped;\n",
"}\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "d269825b",
"metadata": {},
"outputs": [],
"source": [
"! touch multiple_linear_regression/src/model.cairo"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "b762a212",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting multiple_linear_regression/src/model.cairo\n"
]
}
],
"source": [
"%%writefile multiple_linear_regression/src/model.cairo\n",
"mod multiple_linear_regression_model;"
]
},
{
"cell_type": "markdown",
"id": "f700ee3a",
"metadata": {},
"source": [
"## Running tests on model\n",
"\n",
"Running some checks to ensure the model is performing as expected. Some of the checks involve:\n",
"- data normalizations checks\n",
"- tensor shape/dimension check\n",
"- coefficient value and dimension checks \n",
"- model accuracy deviance checks"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "6df3b8c5",
"metadata": {},
"outputs": [],
"source": [
"! touch multiple_linear_regression/src/test.cairo"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "6ccda27e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting multiple_linear_regression/src/test.cairo\n"
]
}
],
"source": [
"%%writefile multiple_linear_regression/src/test.cairo\n",
"\n",
"// use traits::Into;\n",
"use debug::PrintTrait;\n",
"use array::{ArrayTrait, SpanTrait};\n",
"\n",
"\n",
"use multiple_linear_regresion::datasets::boston_data::boston_x_features::boston_x_features;\n",
"use multiple_linear_regresion::datasets::boston_data::boston_y_labels::boston_y_labels;\n",
"use multiple_linear_regresion::datasets::user_inputs_data::user_inputs_boston_data::user_inputs_boston_data;\n",
"\n",
"use orion::numbers::{FP16x16, FixedTrait};\n",
"\n",
"use multiple_linear_regresion::model::multiple_linear_regression_model::{\n",
" MultipleLinearRegressionModel, MultipleLinearRegression, MultipleLinearRegressionModelTrait\n",
"};\n",
"use multiple_linear_regresion::data_preprocessing::{Dataset, DatasetTrait};\n",
"use multiple_linear_regresion::helper_functions::{get_tensor_data_by_row, transpose_tensor, calculate_mean , \n",
"calculate_r_score, normalize_user_x_inputs, rescale_predictions};\n",
"\n",
"use orion::operators::tensor::{\n",
" Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, \n",
" FP16x16TensorSub, FP16x16TensorAdd, FP16x16TensorDiv, FP16x16TensorMul};\n",
"\n",
"#[test]\n",
"#[available_gas(99999999999999999)]\n",
"fn multiple_linear_regression_test() {\n",
"\n",
"// -------------------------------------------------------------------Boston dataset tests---------------------------------------------------------------------------------------------\n",
"\n",
"let mut main_x_vals = boston_x_features();\n",
"let mut main_y_vals = boston_y_labels();\n",
"let mut dataset = Dataset{x_values: main_x_vals,y_values:main_y_vals};\n",
"let mut normalized_dataset = dataset.normalize_dataset();\n",
"let mut model = MultipleLinearRegression(normalized_dataset);\n",
"let mut model_coefficients = model.coefficients;\n",
"let mut reconstructed_ys = model.predict (normalized_dataset.x_values);\n",
"let mut r_squared_score = calculate_r_score(normalized_dataset.y_values,reconstructed_ys);\n",
"r_squared_score.print(); \n",
"\n",
"// checking if data has been normalized correctly\n",
"assert(normalized_dataset.x_values.max_in_tensor() <= FixedTrait::new(65536, false), 'normalized x not between 0-1');\n",
"assert(normalized_dataset.x_values.min_in_tensor() >= FixedTrait::new(0, false), 'normalized x not between 0-1');\n",
"assert(normalized_dataset.y_values.max_in_tensor() <= FixedTrait::new(65536, false), 'normalized y not between 0-1');\n",
"assert(normalized_dataset.x_values.min_in_tensor() >= FixedTrait::new(0, false), 'normalized y not between 0-1');\n",
"// performing checks on the shape of normalized data\n",
"assert(normalized_dataset.x_values.data.len()== main_x_vals.data.len() && \n",
"normalized_dataset.y_values.data.len()== main_y_vals.data.len() , 'normalized data shape mismatch');\n",
"// performing checks on shape on coefficient values (gradient vals + bias)\n",
"assert(model.coefficients.data.len() == *main_x_vals.shape.at(1)+1, 'coefficient data shape mismatch');\n",
"// model accuracy deviance checks\n",
"assert(r_squared_score >= FixedTrait::new(55699, false), 'Boston model acc. less than 84%');\n",
"\n",
"\n",
"// boston user inputed house valuation predictions\n",
"let user_input = user_inputs_boston_data();\n",
"let mut normalized_user_x_inputs = normalize_user_x_inputs(user_input, main_x_vals) ;\n",
"let mut prediction_result = model.predict (normalized_user_x_inputs); \n",
"let mut rescale_prediction = rescale_predictions(prediction_result, main_y_vals);\n",
"(*rescale_prediction.data.at(0)).print(); \n",
"\n",
"\n",
"}\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "52ccc81e",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "3a36b2e6",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/Provable Multiple Linear Regression Solver (Forecasting AAVE Business metrics).ipynb | {
"cells": [
{
"cell_type": "markdown",
"id": "151a8d2d",
"metadata": {},
"source": [
" # Provable Multiple Linear Regression Solver\n",
" \n",
"For this particular tutorial, we will build a Multiple Linear Regression algorithm from scratch and use it to <b>forecast the projected the 7-day lifetime repayments for AAVE's WETH Pool</b>. Toward the end of the tutorial, we will convert the data & model to Cairo enabling us to make the entire Multiple Regression Solver as well as the AAVE's forecasts to be <b>fully Provable and Verifiable. </b>\n",
"### Overview & methodology\n",
"In many financial business applications, a vast number of problems can be modeled using <b>Multiple Linear Regression.</b> As we step into the on-chain ProvableML domain, these traditional algorithms may still be essential in addressing forecasting and prediction-related business problems. Interestingly, these may prove to be actually advantageous in on-chain environments as they are relatively interpretable, lightweight and cost-efficient.\n",
"\n",
"The most common method for computing MLR includes the use of pseudo-inverse or SVD (Singular value decomposition), which can be far more complex to implement than the problem being worked on often times. Hence, the next common approach often preferred are gradient-based methods. These are perfect for large datasets, but they too can be excessive due to the intensive iterative approach they take to approximate gradients; which in the context of on-chain environments can be fairly expensive. \n",
"\n",
"<b>This repo presents an intuitive a closed-form (non gradient based) approach to computing MLR gradients to allow builders and end-users to interact with MLR systems with ease and transparency. </b>\n",
"\n",
"The Multiple Linear regression Solver works in three components:\n",
"1. Decorrelates x features from one another (most intensive part)\n",
"2. Computes the exact gradients between each decorrelated x feature and target y variables in one step\n",
"3. Performs predictions and forecasts using the computed gradients\n",
"\n",
"#### Provability and Verifiability\n",
"<b>The key benefit of this Multiple Linear Regression Solver lies in its commitment to <u>Provability and Verifiability</u>. By integrating it into Cairo using the Orion Framework, we allow the entire MLR system to become inherently provable through STARKs, ensuring unparalleled transparency and trustworthiness. This enables every inference of the model construction, execution and prediction to be easily proved using LambdaClass STARK Prover. </b>\n"
]
},
{
"cell_type": "markdown",
"id": "90182f06",
"metadata": {},
"source": [
"## Import necessary libs & prepare dataset"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f09afc09",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import os\n",
"from sklearn.metrics import r2_score"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1f1a3299",
"metadata": {},
"outputs": [],
"source": [
"# dataset pulled from https://app.aavescan.com/ \n",
"df_main= pd.read_csv('AAVE-V3-weth.csv')\n",
"df_main.drop('Unnamed: 0', axis=1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "bb21fa7d",
"metadata": {},
"outputs": [],
"source": [
"# reverse order as the row 1 illustrates the most recent datapoint in terms of date\n",
"df_main= df_main.iloc[::-1]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "1d292060",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>accruedToTreasury</th>\n",
" <th>availableLiquidity</th>\n",
" <th>lifetimeFlashLoans</th>\n",
" <th>lifetimeLiquidity</th>\n",
" <th>lifetimeReserveFactorAccrued</th>\n",
" <th>totalLiquidityAsCollateral</th>\n",
" <th>totalScaledVariableDebt</th>\n",
" <th>lifetimeRepayments</th>\n",
" <th>variableBorrowRate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>9.330000e+18</td>\n",
" <td>7.400000e+22</td>\n",
" <td>1.240000e+23</td>\n",
" <td>2.040000e+24</td>\n",
" <td>5.500000e+20</td>\n",
" <td>1.190000e+24</td>\n",
" <td>2.870000e+23</td>\n",
" <td>7.640000e+23</td>\n",
" <td>3.370000e+25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>1.340000e+19</td>\n",
" <td>7.450000e+22</td>\n",
" <td>1.240000e+23</td>\n",
" <td>2.050000e+24</td>\n",
" <td>5.500000e+20</td>\n",
" <td>1.190000e+24</td>\n",
" <td>2.870000e+23</td>\n",
" <td>7.640000e+23</td>\n",
" <td>3.370000e+25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>1.740000e+19</td>\n",
" <td>8.020000e+22</td>\n",
" <td>1.240000e+23</td>\n",
" <td>2.070000e+24</td>\n",
" <td>5.500000e+20</td>\n",
" <td>1.190000e+24</td>\n",
" <td>2.880000e+23</td>\n",
" <td>7.650000e+23</td>\n",
" <td>3.320000e+25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2.120000e+19</td>\n",
" <td>9.590000e+22</td>\n",
" <td>1.280000e+23</td>\n",
" <td>2.120000e+24</td>\n",
" <td>5.500000e+20</td>\n",
" <td>1.240000e+24</td>\n",
" <td>2.860000e+23</td>\n",
" <td>7.700000e+23</td>\n",
" <td>3.180000e+25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>1.660000e+17</td>\n",
" <td>1.000000e+23</td>\n",
" <td>1.410000e+23</td>\n",
" <td>2.150000e+24</td>\n",
" <td>5.750000e+20</td>\n",
" <td>1.280000e+24</td>\n",
" <td>2.870000e+23</td>\n",
" <td>7.730000e+23</td>\n",
" <td>3.150000e+25</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" accruedToTreasury availableLiquidity lifetimeFlashLoans \\\n",
"30 9.330000e+18 7.400000e+22 1.240000e+23 \n",
"29 1.340000e+19 7.450000e+22 1.240000e+23 \n",
"28 1.740000e+19 8.020000e+22 1.240000e+23 \n",
"27 2.120000e+19 9.590000e+22 1.280000e+23 \n",
"26 1.660000e+17 1.000000e+23 1.410000e+23 \n",
"\n",
" lifetimeLiquidity lifetimeReserveFactorAccrued \\\n",
"30 2.040000e+24 5.500000e+20 \n",
"29 2.050000e+24 5.500000e+20 \n",
"28 2.070000e+24 5.500000e+20 \n",
"27 2.120000e+24 5.500000e+20 \n",
"26 2.150000e+24 5.750000e+20 \n",
"\n",
" totalLiquidityAsCollateral totalScaledVariableDebt lifetimeRepayments \\\n",
"30 1.190000e+24 2.870000e+23 7.640000e+23 \n",
"29 1.190000e+24 2.870000e+23 7.640000e+23 \n",
"28 1.190000e+24 2.880000e+23 7.650000e+23 \n",
"27 1.240000e+24 2.860000e+23 7.700000e+23 \n",
"26 1.280000e+24 2.870000e+23 7.730000e+23 \n",
"\n",
" variableBorrowRate \n",
"30 3.370000e+25 \n",
"29 3.370000e+25 \n",
"28 3.320000e+25 \n",
"27 3.180000e+25 \n",
"26 3.150000e+25 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_main.head()"
]
},
{
"cell_type": "markdown",
"id": "4d06feb5",
"metadata": {},
"source": [
"## Preparing the Features and Labels"
]
},
{
"cell_type": "markdown",
"id": "0eec3df8",
"metadata": {},
"source": [
"Now that we have prepared our AAVE dataset, we will use the features to forecast the <b>lifetime Repayments 7 days in advance</b>. In order to achieve this, we take the lifetime Repayments column and replicate it into a new column whilst shifting the column data upwards by 7 spots. This, will allow us to use this shifted column as our y label and the rest of the data as our X features.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "3a6dc4ba",
"metadata": {},
"outputs": [],
"source": [
"#Since Most of the df values are in wei we devide all values by a fixed factor to make the data easy to work with.\n",
"# Hence, we devide by 1e+22 to have values in thousands of ETH. \n",
"# This will also prevent overflow as we work with 16x16 fixed point numbers in orion as we transition to cairo in later stages \n",
"factor = 1e+22\n",
"df_main = df_main/factor"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "6b94e8bd",
"metadata": {},
"outputs": [],
"source": [
"df= df_main.copy()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "629ee65a",
"metadata": {},
"outputs": [],
"source": [
"days_to_forecast = -7\n",
"df['lifetimeRepayments_7day_forecast'] = df[['lifetimeRepayments']].shift(days_to_forecast) \n",
"df = df[0:days_to_forecast]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1a8ac4af",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>accruedToTreasury</th>\n",
" <th>availableLiquidity</th>\n",
" <th>lifetimeFlashLoans</th>\n",
" <th>lifetimeLiquidity</th>\n",
" <th>lifetimeReserveFactorAccrued</th>\n",
" <th>totalLiquidityAsCollateral</th>\n",
" <th>totalScaledVariableDebt</th>\n",
" <th>lifetimeRepayments</th>\n",
" <th>variableBorrowRate</th>\n",
" <th>lifetimeRepayments_7day_forecast</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>0.000933</td>\n",
" <td>7.40</td>\n",
" <td>12.4</td>\n",
" <td>204.0</td>\n",
" <td>0.0550</td>\n",
" <td>119.0</td>\n",
" <td>28.7</td>\n",
" <td>76.4</td>\n",
" <td>3370.0</td>\n",
" <td>77.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0.001340</td>\n",
" <td>7.45</td>\n",
" <td>12.4</td>\n",
" <td>205.0</td>\n",
" <td>0.0550</td>\n",
" <td>119.0</td>\n",
" <td>28.7</td>\n",
" <td>76.4</td>\n",
" <td>3370.0</td>\n",
" <td>77.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.001740</td>\n",
" <td>8.02</td>\n",
" <td>12.4</td>\n",
" <td>207.0</td>\n",
" <td>0.0550</td>\n",
" <td>119.0</td>\n",
" <td>28.8</td>\n",
" <td>76.5</td>\n",
" <td>3320.0</td>\n",
" <td>77.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0.002120</td>\n",
" <td>9.59</td>\n",
" <td>12.8</td>\n",
" <td>212.0</td>\n",
" <td>0.0550</td>\n",
" <td>124.0</td>\n",
" <td>28.6</td>\n",
" <td>77.0</td>\n",
" <td>3180.0</td>\n",
" <td>77.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0.000017</td>\n",
" <td>10.00</td>\n",
" <td>14.1</td>\n",
" <td>215.0</td>\n",
" <td>0.0575</td>\n",
" <td>128.0</td>\n",
" <td>28.7</td>\n",
" <td>77.3</td>\n",
" <td>3150.0</td>\n",
" <td>78.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" accruedToTreasury availableLiquidity lifetimeFlashLoans \\\n",
"30 0.000933 7.40 12.4 \n",
"29 0.001340 7.45 12.4 \n",
"28 0.001740 8.02 12.4 \n",
"27 0.002120 9.59 12.8 \n",
"26 0.000017 10.00 14.1 \n",
"\n",
" lifetimeLiquidity lifetimeReserveFactorAccrued \\\n",
"30 204.0 0.0550 \n",
"29 205.0 0.0550 \n",
"28 207.0 0.0550 \n",
"27 212.0 0.0550 \n",
"26 215.0 0.0575 \n",
"\n",
" totalLiquidityAsCollateral totalScaledVariableDebt lifetimeRepayments \\\n",
"30 119.0 28.7 76.4 \n",
"29 119.0 28.7 76.4 \n",
"28 119.0 28.8 76.5 \n",
"27 124.0 28.6 77.0 \n",
"26 128.0 28.7 77.3 \n",
"\n",
" variableBorrowRate lifetimeRepayments_7day_forecast \n",
"30 3370.0 77.4 \n",
"29 3370.0 77.4 \n",
"28 3320.0 77.5 \n",
"27 3180.0 77.6 \n",
"26 3150.0 78.0 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "cf992ac6",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(24, 9)\n",
"(24,)\n"
]
}
],
"source": [
"#Drop the y label from dataframe\n",
"features = df.drop(['lifetimeRepayments_7day_forecast'], axis=1)\n",
"#setting our y label\n",
"target = df['lifetimeRepayments_7day_forecast']\n",
"\n",
"\n",
"# convert data to numpy format\n",
"X_original = features.to_numpy()\n",
"Y_original = target.to_numpy()\n",
"\n",
"print(X_original.shape)\n",
"print(Y_original.shape)"
]
},
{
"cell_type": "markdown",
"id": "d5c31a01",
"metadata": {},
"source": [
"## Normalize the data\n",
"Normalize the data so that all the features values are between 0-1"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "e4551bc6",
"metadata": {},
"outputs": [],
"source": [
"def normalize_data(original_data):\n",
" data_min = np.min(original_data, axis=0)\n",
" data_max = np.max(original_data, axis=0)\n",
" data_range = data_max - data_min\n",
" data_normalized = (original_data - data_min) / data_range\n",
" \n",
" return data_normalized\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "9a50f392",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Features max value: 1.0\n",
"Features min value: 0.0\n",
"Labels max value: 1.0\n",
"Labels min value: 0.0\n"
]
}
],
"source": [
"# normalize the data \n",
"X_normalized= normalize_data(X_original)\n",
"y_normalized= normalize_data(Y_original)\n",
"\n",
"print('Features max value:', X_normalized.max())\n",
"print('Features min value:', X_normalized.min())\n",
"print('Labels max value:', y_normalized.max())\n",
"print('Labels min value:', y_normalized.min())\n"
]
},
{
"cell_type": "markdown",
"id": "310c1a0a",
"metadata": {},
"source": [
"# Main Multiple Linear Regression algorythm"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "8a5a52b6",
"metadata": {},
"outputs": [],
"source": [
"def transpose_and_add_bias(feature_data):\n",
" #transpose the data\n",
" transposed_data= feature_data.T\n",
" #add bias term\n",
" transposed_data_with_bias = np.vstack((transposed_data, np.ones(transposed_data.shape[1])))\n",
" \n",
" return transposed_data_with_bias"
]
},
{
"cell_type": "markdown",
"id": "c8d6ed24",
"metadata": {},
"source": [
"#### Decorelate the x features\n",
"It's <b>very important</b> to notice that as we decorrelate the X features, only the last feature row will be fully <b>orthogonal</b>. The rest of the features are decorrelated from one another but <b>are not fully orthogonal to each other</b>. This is done to save on computational cost and make the algorythm more efficient. This will enable us to calculate the following feature gradients much easier and quicker, than if we were to fully orthogonalize all the features and then compute the gradients.\n",
"\n",
"Later when we compute the gradients we will start from the last fully orthogonalised feature values and walk backwards to calculate the correponding gradinets, whilst removing their component from the y variable. "
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "e3c5b44c",
"metadata": {},
"outputs": [],
"source": [
"def decorrelate_features(feature_data):\n",
"\n",
" # Make copy of input matrix\n",
" x_temp = feature_data.copy()\n",
" \n",
" # Get number of features\n",
" feature_rows = feature_data.shape[0]\n",
" \n",
" # Decorrelate features\n",
" for i in range(feature_rows):\n",
" feature_squared = np.sum(x_temp[i]**2)\n",
" for j in range(i+1, feature_rows):\n",
" feature_cross_prod = np.sum(x_temp[i] * x_temp[j])\n",
" if feature_squared == 0:\n",
" print('Warning, division by zero encountered and handled')\n",
" feature_squared = 1e-8 \n",
" feature_grad = feature_cross_prod / feature_squared\n",
" x_temp[j] -= feature_grad * x_temp[i]\n",
" \n",
" decorelated_x_vals = x_temp\n",
"\n",
" return decorelated_x_vals\n"
]
},
{
"cell_type": "markdown",
"id": "74fa9460",
"metadata": {},
"source": [
"#### Calculating Gradients"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "42de0d6e",
"metadata": {},
"outputs": [],
"source": [
"def calculate_gradients(decorelated_x_vals, y_values, original_x_features):\n",
" \n",
" # Initialize gradients array\n",
" # Make copy of input matrix\n",
" y_temp = y_values.copy()\n",
" feature_rows = decorelated_x_vals.shape[0]\n",
" gradients = np.zeros(feature_rows)\n",
"\n",
" # Calculate gradients\n",
" for i in range(feature_rows-1, -1, -1):\n",
" prod = np.sum(y_temp * decorelated_x_vals[i])\n",
" squared = np.sum(decorelated_x_vals[i] * decorelated_x_vals[i])\n",
" if squared == 0:\n",
" print('Warning, division by zero encountered and handled')\n",
" squared = 1e-8\n",
"\n",
" gradients[i] = prod / squared\n",
" y_temp -= gradients[i] * original_x_features[i]\n",
" \n",
"\n",
" return gradients"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "3c7c3fc3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training data shapes:\n",
"X_normalized: (10, 24)\n",
"y_normalized: (24,)\n"
]
}
],
"source": [
"X_normalized_transposed_with_bias = transpose_and_add_bias(X_normalized)\n",
"\n",
"print(\"Training data shapes:\")\n",
"print(\"X_normalized:\", X_normalized_transposed_with_bias.shape)\n",
"print(\"y_normalized:\", y_normalized.shape)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "f96a6e15",
"metadata": {},
"outputs": [],
"source": [
"decorrelated_X_features = decorrelate_features(X_normalized_transposed_with_bias)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "8815700e",
"metadata": {},
"outputs": [],
"source": [
"gradient_values = calculate_gradients(decorrelated_X_features, y_normalized, X_normalized_transposed_with_bias )\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "169a41b4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"All regression coefficient: [-1.27062243 1.15931271 0.173401 -0.31112069 1.09338439 0.93959362\n",
" -1.12956438 -0.08371113 1.18734043 0.3425375 ]\n"
]
}
],
"source": [
"real_gradient_values_reversed = np.flip(gradient_values)\n",
"print('All regression coefficient: ', real_gradient_values_reversed )"
]
},
{
"cell_type": "markdown",
"id": "d6134a53",
"metadata": {},
"source": [
"# Reconstructing the y labels using the calculated gradients and X feature data"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "a1bda8ed",
"metadata": {},
"outputs": [],
"source": [
"def denormalize_data(original_data,normalized_data):\n",
" data_min = np.min(original_data)\n",
" data_max = np.max(original_data)\n",
" data_range = data_max - data_min\n",
" \n",
" denormalize_data = ( normalized_data * data_range) + data_min\n",
" return denormalize_data"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "17b37869",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R^2 score (denormalized): 0.9968099033369738\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"y_pred_norm = gradient_values @ X_normalized_transposed_with_bias #prediction#\n",
"reconstructed_y = denormalize_data(Y_original,y_pred_norm) \n",
"# Plot the denormalized y values\n",
"plt.figure(2)\n",
"plt.title(\" LifetimeRepayment Predictions\")\n",
"plt.plot(reconstructed_y )\n",
"plt.plot(Y_original)\n",
"plt.legend([\" Actual y values\", \"reconstructed ys (predictions)\"])\n",
"plt.xlabel('Days')\n",
"plt.ylabel('Lifetime Repayment (thousands ETH)')\n",
"\n",
"# Calculate R^2 score for denormalized prediction\n",
"accuracy_denormalized = r2_score(Y_original, reconstructed_y)\n",
"print(\"R^2 score (denormalized):\", accuracy_denormalized)"
]
},
{
"cell_type": "markdown",
"id": "b8296c8b",
"metadata": {},
"source": [
"# The upcoming 7 day Total lifetime repayments forecasts for AAVE's WETH Pool"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "bb721ff0",
"metadata": {},
"outputs": [],
"source": [
"df_forecast = df_main[-7:]\n",
"df_forecast_data = df_forecast.to_numpy()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "5ba45802",
"metadata": {},
"outputs": [],
"source": [
"X_min = np.min(X_original, axis=0)\n",
"X_max = np.max(X_original, axis=0)\n",
"X_range = X_max - X_min\n",
"df_forecast_data_normalized = (df_forecast_data - X_min) / X_range\n",
"# print(df_forecast_data_normalized.shape)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "c8d18eb5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training data shapes:\n",
"X_normalized: (9, 7)\n",
"y_normalized: (10, 7)\n"
]
}
],
"source": [
"# tranpose the matrix and add ones \n",
"df_forecast_data_normalized_transposed= df_forecast_data_normalized.T\n",
"df_forecast_data_normalized_transposed_with_bias = np.vstack((df_forecast_data_normalized_transposed, np.ones(df_forecast_data_normalized_transposed.shape[1])))\n",
"print(\"Training data shapes:\")\n",
"print(\"X_normalized:\", df_forecast_data_normalized_transposed.shape)\n",
"print(\"y_normalized:\", df_forecast_data_normalized_transposed_with_bias.shape)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "5df80bde",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 1.0782944838728898\n",
"1 1.1357059848525208\n",
"2 1.1665048712468642\n",
"3 1.193423550976403\n",
"4 1.2455779743568984\n",
"5 1.3032904853814864\n",
"6 1.3668000624508785\n"
]
}
],
"source": [
"forecast_normalized = gradient_values @ df_forecast_data_normalized_transposed_with_bias\n",
"for i in range(len(forecast_normalized)):\n",
" print(i,forecast_normalized[i])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "84ea2dd4",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tomorrow's Lifetime Repayment from Weth pool Forecast: 95.62317677745183\n",
"Day 2 Lifetime Repayment from Weth pool Forecast 96.5934311440076\n",
"Day 3 Lifetime Repayment from Weth pool Forecast 97.113932324072\n",
"Day 4 Lifetime Repayment from Weth pool Forecast 97.5688580115012\n",
"Day 5 Lifetime Repayment from Weth pool Forecast 98.45026776663158\n",
"Day 6 Lifetime Repayment from Weth pool Forecast 99.42560920294711\n",
"Day 7 Lifetime Repayment from Weth pool Forecast 100.49892105541984\n"
]
}
],
"source": [
"#denormalize forecast\n",
"Y_min = np.min(Y_original, axis=0)\n",
"Y_max = np.max(Y_original, axis=0)\n",
"Y_range = Y_max - Y_min\n",
"\n",
"forecast_pred = (forecast_normalized * Y_range) + Y_min\n",
"# # print(forecast_pred)\n",
"for i in range(len(forecast_pred)):\n",
" if i==0:\n",
" print(\"Tomorrow's Lifetime Repayment from Weth pool Forecast: \",forecast_pred[i])\n",
" else:\n",
" print('Day',i+1,'Lifetime Repayment from Weth pool Forecast',forecast_pred[i])\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "01f96e7f",
"metadata": {},
"outputs": [
{
"data": {
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9Xf4fV9R7/VLh4eFq4MCBRa4XoiJI1z4hKplPP/0U4Lq79V1KKeXy3GazMW3aNOrXr4/BYMDNzQ2DwcCRI0dcukZVr16dXr168eGHHzqP8cUXX5CUlFTouJrCDBgwgB07drg88iswrV+/nvr169OqVSuXfYYNG4ZSivXr17ss79Onj0uhiqNHj/LPP/84C2nYbDbno0ePHsTHx3Po0CEAVq1aRefOnalXr94V4/3mm29o164dnp6euLm5odfr+fTTT13uS363vQEDBvD1119z9uzZYt2LK4mLiwMgODi4wLpNmzZx9OhRhg4dik6nA/7XZfDSLjL5vv76a9LS0pxdDSGvCIBSyqUrX3R0NJ07d2bJkiXOohSrVq0iISHBZd9LffbZZwVez+3bt1/x2lq1asWqVauYNGkSGzduJDs7+yp348q2bt1KdnZ2gYpxERER3HnnnQW6RhVlz5499OnTh4CAAHQ6HXq9niFDhmC32zl8+HCJ4+rYsSNardbZvW/jxo20aNECT09PvLy8aN68ubN738aNG3Fzc+OOO+4ocJynn366wD3esWNHsYuWFKaw123Hjh2Fnr+0/fTTT3Tu3JmwsDCX92j+OKpNmzZd87FDQ0O57bbbnM/9/f0JDg6madOmhIWFOZfnv+/zq5haLBZ++eUX+vfvj7u7e4HPDovFwrZt21zO9fzzz6PX6zGZTDRt2pS///6bH3/80aU4Qkmv9d5778VkMjmfe3l50bt3bzZv3ozdbgfK7vO6V69eLs/z79Hl4yLr1avnUv31p59+wtfXl969e7tcY9OmTQkJCSnQvbVp06ZUq1bN+dxkMlG7du0SV5QNDg4ulc9aIUqTJFJCVCK5ubl89tlnNGnSpFQH9J86dcrlS8Wzzz7L5MmT6devHz/++CPbt29nx44dNGnSpMCX3KeffpojR46wdu1aIG9cSZs2bWjevHmxzh0UFESLFi1cHvnlz5OSkgqtwpUfa1JSksvyy7fNH5swYcIE9Hq9y2P06NFAXglwyCv5faXCDADff/89AwYMIDw8nM8//5ytW7eyY8cORowYgcVicW7XoUMHli9fjs1mY8iQIVStWpWGDRtecczP1eTf90u/VOXLT6779+9PSkoKKSkp+Pj4cMcdd/Ddd9+RkpJSYHuTyUS3bt2c2zdu3JioqCgWLlzo/IIGeUUnkpKS+OGHH4C8qnyenp4MGDCg0Djr1atX4PW89ItsYd59912ef/55li9fTufOnfH396dfv34cOXKk2PfnUvl/F0X97Vz+d1OYU6dO0b59e86ePcs777zDr7/+yo4dO5zjY64l2fP19aVp06bOZGnDhg107NjRub5jx47OL5kbNmygRYsWhVZnrFq1aoF7nJ+QXavCXrcWLVqUy/isc+fO8eOPPxZ4jzZo0AD433v0Wvj7+xdYZjAYCiw3GAwAzvdxUlISNpuN9957r0BcPXr0KDSu/AR3y5YtvP322+Tm5tK3b1+Xv7eSXmtISEiB+ENCQrBarc5y+WX1eV3UPSps+aWff+fOnSMlJQWDwVDgOhMSEgpcY0BAQIFzG43GEr/HTCbTdf8II0Rpk6p9QlQiP/30E4mJiUyePLnUjvnHH3+QkJDgUsb6888/Z8iQIUybNs1l2wsXLhQodHDnnXfSsGFD3n//fTw9Pdm9ezeff/55qcQWEBBAfHx8geX5rTOXzzd16eD1S9e/8MIL3HvvvYWeI7/qYVBQEGfOnLliPJ9//jnR0dF89dVXLufKyckpsG3fvn3p27cvOTk5bNu2jTfeeINBgwYRFRVFmzZtrniewuRfy8WLF10ShEuLROS3hF3uiy++cCaOhw8fZsuWLQAuvwJfas2aNc4vi/feey9+fn7Mnz+fjh078tNPPzFkyJDr+tJ+OQ8PD6ZMmcKUKVM4d+6cs3Wqd+/e/PPPPyU+Xv4Xs6L+doozT9ny5cvJzMzk+++/JzIy0rm8pPN+Xa5z587MnDmTv/76i/379zNjxgznuo4dOzJr1iz++usvTpw4wUMPPXRd57pRBAYG0rhxY15//fVC11/6I0958fPzQ6fT8fDDD/Pkk08Wuk10dLTL8/wEF/Kq9oWEhPCvf/2LV155hffffx8o+bVePu9Y/jKDweB8D1aWz+t8gYGBBAQEFFq8Ayj0x4HScPHixWueBkGIsiKJlBCVSH5LQmnN+XTx4kUef/xx9Ho9zzzzjHO5RqMpUDZ8xYoVnD17lpo1axY4ztixY3n88cdJTU2lSpUqzgpL1+uuu+7ijTfeYPfu3S6/mH722WdoNJoC8+tcrk6dOtSqVYs///yzwJeMy3Xv3p3Fixdz6NChIkvK509ofHm1scKq9uUzGo107NgRX19f1qxZw549e2jTpo3z/hb3F9T8uZmOHTvm/PUa8pKk7Oxspk6dWmg3rAceeID58+c7E6n81qt58+YVeC2zs7Pp27cv8+fPdyZSJpOJQYMG8dFHHzF9+nRyc3OL7NZXGqpUqcKwYcP4888/mTNnDllZWbi7uxe6bVH3sE2bNpjNZj7//HOXv8UzZ86wfv16l/m1Lj1Gful3+F9Sfun7QCnFvHnzruv68hOpKVOmoNVqXV6z/H/nz5V0tb/vm0WvXr1YuXIlNWrUKHTOsnwlfc9cD3d3dzp37syePXto3LixszWmJAYPHswnn3zCvHnzmDhxIpGRkcW+1nzff/89b731lrMlOj09nR9//JH27ds7u/FWls/rfL169WLp0qXY7XZat25dKse82mtvs9k4ffq083NLiMpCEikhKon88tYDBw4s1v+AL3fkyBG2bduGw+FwTsj76aefkpaWxmeffeby5bxXr14sXLiQunXr0rhxY3bt2sVbb71VZNe3f/3rX7zwwgts3ryZl1566Zq+dBTmmWee4bPPPqNnz568+uqrREZGsmLFCj788EOeeOIJateufdVjfPzxx3Tv3p2uXbsybNgwwsPDuXjxIgcPHmT37t188803ALz66qusWrWKDh068O9//5tGjRqRkpLC6tWrefbZZ6lbty69evXi+++/Z/To0dx///2cPn2aqVOnEhoa6tIN7eWXX+bMmTPcddddVK1alZSUFN555x30er2zK1eNGjUwm80sWbKEevXq4enpSVhYWJG/vrdu3Rqz2cy2bdvo06ePc/mnn36Kn58fEyZMKLTb35AhQ5g1axZ//vknDRo04LPPPqNevXpFjrHr3bs3P/zwA+fPn3eWmx45ciQffPABs2bNom7durRt27bI+/3333+7lHHOV6NGjSLn+WndujW9evWicePG+Pn5cfDgQRYvXkybNm2KTKIgb84qgP/85z94eXlhMpmIjo4mICCAyZMn8+9//5shQ4bw0EMPkZSUxJQpUzCZTLzyyivOY+TPnTZ9+nS6d++OTqejcePG3H333RgMBh566CGee+45LBYLc+fOJTk5uch4iqNDhw7odDqWLVtWoOuer68vTZo0YdmyZej1etq1a1foMU6dOlVgfA7ktarWqFHjuuKrCK+++ipr166lbdu2jB07ljp16mCxWDhx4gQrV67ko48+omrVqnh5eREZGcl///tf7rrrLvz9/QkMDCyzVoh33nmHO+64g/bt2/PEE08QFRVFeno6R48e5ccffywwRrMw06dPp3Xr1kydOpVPPvmk2NeaT6fTcffdd/Pss8/icDiYPn06aWlpzmQbKs/ndb4HH3yQJUuW0KNHD55++mlatWqFXq/nzJkzbNiwgb59+xZ7Ivl8V3qvA/z1119kZWXdMj8+iBtIRVa6EEL8T3556/Xr15dov/yqffkPNzc3FRAQoNq0aaP+/e9/qxMnThTYJzk5WY0cOVIFBwcrd3d3dccdd6hff/1VdezYUXXs2LHQ8wwbNky5ubmpM2fOFDs2CqngdbmTJ0+qQYMGqYCAAKXX61WdOnXUW2+95ay2p1TR5Yfz/fnnn2rAgAEqODhY6fV6FRISou6880710UcfuWx3+vRpNWLECBUSEqL0er0KCwtTAwYMUOfOnXNu8+abb6qoqChlNBpVvXr11Lx585wV8vL99NNPqnv37io8PFwZDAYVHBysevToUaCq2pdffqnq1q2r9Hp9kdXILvXwww+r+vXru1wXoMaNG1fkPv/884+zAld+1bg5c+YUuX1+ielLyxkrpVSzZs0UoGbMmFHofler/pZfibEwkyZNUi1atFB+fn7KaDSq6tWrq2eeeeaq5fOVUmrOnDkqOjpa6XS6AlUQP/nkE9W4cWNlMBiUj4+P6tu3r7MqWr6cnBz1yCOPqKCgIKXRaFwqv/3444+qSZMmymQyqfDwcDVx4kS1atWqAtXDilu1L1+rVq0UoCZMmFBg3bhx4xRQoCqiUlev2jd48OBCz1ecqn07duwodH3Pnj3LvGqfUnkV+caOHauio6OVXq9X/v7+6rbbblMvvviiysjIcG63bt061axZM2U0GhXgPH5RVfsaNGhQ4FxF3Y/CYouNjVUjRoxQ4eHhSq/Xq6CgINW2bVv12muvuWxzpc+fBx54QLm5uTkrYhbnWi+tBDhlyhRVtWpVZTAYVLNmzdSaNWtcjl/an9dF/U3kf86dP3/eZfnQoUOVh4eHy7Lc3Fz19ttvO98/np6eqm7duuqxxx5TR44ccW5X1GtRWOxXeq9PnjxZBQYGOqcuEKKy0Ch1WTkvIYS4jNVqJSoqijvuuKPQiW5F6di5cyctW7Zk27ZtpdZlRghR+Zw4cYLo6GjeeustJkyYUKrHvtk+r+12OzVr1mTQoEFFjj0ToqJI1T4hRJHOnz/Pli1beOKJJzh37hyTJk2q6JBuai1atGDAgAFMnTq1okMRQtxgbtbP688//5yMjAwmTpxY0aEIUYAkUkKIIq1YsYL27duzatUqPvzww2KXPBfXbubMmbRs2ZL09PSKDkUIcQO5WT+vHQ4HS5YsKVChUIjKQLr2CSGEEEIIIUQJSYuUEEIIIYQQQpSQJFJCCCGEEEIIUUKSSAkhhBBCCCFECcmEvOQNZIyLi8PLy8s5270QQgghhBDi1qOUIj09nbCwMLTaotudJJEC4uLiiIiIqOgwhBBCCCGEEJXE6dOnqVq1apHrJZECvLy8gLyb5e3tXcHRCCGEEEIIISpKWloaERERzhyhKJJIgbM7n7e3tyRSQgghhBBCiKsO+ZFiE0IIIYQQQghRQpJICSGEEEIIIUQJSSIlhBBCCCGEECUkY6SKSSmFzWbDbrdXdChC3BJ0Oh1ubm4yJYEQQgghKiVJpIrBarUSHx9PVlZWRYcixC3F3d2d0NBQDAZDRYcihBBCCOFCEqmrcDgcxMbGotPpCAsLw2AwyC/kQpQxpRRWq5Xz588TGxtLrVq1rjghnhBCCCFEeZNE6iqsVisOh4OIiAjc3d0rOhwhbhlmsxm9Xs/JkyexWq2YTKaKDkkIIYQQwkl+4i0m+TVciPIn7zshhBBCVFbyLUUIIYQQQgghSkgSKSGEEEIIIYQoIUmkbnELFy7E19e3Qs49bNgw+vXrV2rHi4mJoWnTpqV2PCGEEEIIIYoiidRNqqgkZePGjWg0GlJSUgAYOHAghw8fLtYxSzvpeuedd1i4cGGpHe9qTpw4gUajITg4mPT0dJd1TZs2JSYmptjHKu69WLhwIRqNxvmoUqUKvXv3Zv/+/SWMXgghhBBCVCaSSN3izGYzwcHB5XpOu92Ow+HAx8enQlrD0tPTefvtt8vtfN7e3sTHxxMXF8eKFSvIzMykZ8+eWK3WcotBCCGEEEKULkmkSkgpRZbVViEPpVSpX8/lLSt//vknnTt3xsvLC29vb2677TZ27tzJxo0bGT58OKmpqc7WlfwWnOTkZIYMGYKfnx/u7u50796dI0eOFDjHTz/9RP369TEajZw8ebJAq5nD4WD69OnUrFkTo9FItWrVeP31153rn3/+eWrXro27uzvVq1dn8uTJ5Obmlvian3rqKWbNmkViYmKR21itVp577jnCw8Px8PCgdevWbNy4EeCK96IwGo2GkJAQQkNDadGiBc888wwnT57k0KFDzm1+//13OnTogNlsJiIigrFjx5KZmQnACy+8wO23317guI0bN+aVV15xPl+wYAH16tXDZDJRt25dPvzwQ+e6/Na477//ns6dO+Pu7k6TJk3YunWrc5vCukbOmTOHqKgol2VXOo8QQgghxK1C5pEqoexcO/VfXlMh5z7walfcDWX7kg0ePJhmzZoxd+5cdDode/fuRa/X07ZtW+bMmcPLL7/sTAA8PT2BvG6ER44c4YcffsDb25vnn3+eHj16cODAAfR6PQBZWVm88cYbfPLJJwQEBBTaCvbCCy8wb948Zs+ezR133EF8fDz//POPc72XlxcLFy4kLCyMffv2MWrUKLy8vHjuuedKdI0PPfQQa9eu5dVXX+X9998vdJvhw4dz4sQJli5dSlhYGMuWLaNbt27s27fvivfialJSUvjiiy8AnPdm3759dO3alalTp/Lpp59y/vx5xowZw5gxY1iwYAGDBw/mzTff5NixY9SoUQOA/fv3s2/fPr799lsA5s2bxyuvvML7779Ps2bN2LNnD6NGjcLDw4OhQ4c6z//iiy/y9ttvU6tWLV588UUeeughjh49iptb8f6uinseIYQQQojicigHF7MvopQiyCOoosMpNkmkbmI//fRTgS/4drv9ivucOnWKiRMnUrduXQBq1arlXOfj4+NsXcmXn0D99ttvtG3bFoAlS5YQERHB8uXLeeCBBwDIzc3lww8/pEmTJoWeNz09nXfeeYf333/f+YW8Ro0a3HHHHc5tXnrpJee/o6KiGD9+PF999VWJEymNRsObb75J7969eeaZZ5zJSb5jx47x5ZdfcubMGcLCwgCYMGECq1evZsGCBUybNq3Qe1GU1NRUPD0981ozs7IA6NOnj/Mev/XWWwwaNIhx48YBeff83XffpWPHjsydO5eGDRvSuHFjvvjiCyZPngzk3eOWLVtSu3ZtAKZOncrMmTO59957AYiOjubAgQN8/PHHLgnOhAkT6NmzJwBTpkyhQYMGHD161BnL1RT3PEIIIYQQxWGxWTiXcY7ErESCzEGSSN3MzHodB17tWmHnLonOnTszd+5cl2Xbt2/nX//6V5H7PPvsszzyyCMsXryYLl268MADDxRINC518OBB3NzcaN26tXNZQEAAderU4eDBg85lBoOBxo0bX/E4OTk53HXXXUVu8+233zJnzhyOHj1KRkYGNpsNb2/vIre/kq5du3LHHXcwefJkZwtRvt27d6OUciYp+XJycggICCjxuby8vNi9ezc2m41Nmzbx1ltv8dFHHznX79q1i6NHj7JkyRLnMqUUDoeD2NhY6tWrx+DBg5k/fz6TJ09GKcWXX37pTLzOnz/P6dOnGTlyJKNGjXIew2az4ePj4xLLpa9BaGgoAImJicVKpEpyHiGEEEKIK1FKkWJJIT4jnuzcbNw0N15acuNFXME0Gk2Zd68rLR4eHtSsWdNl2ZkzZ664T0xMDIMGDWLFihWsWrWKV155haVLl9K/f/9Cty9q3JZSCo1G43xuNptdnl/ObDZfMa5t27bx4IMPMmXKFLp27YqPjw9Lly5l5syZV9zvSt58803atGnDxIkTXZY7HA50Oh27du1Cp3NNXovbhe9SWq3W+TrUrVuXhIQEBg4cyObNm53ne+yxxxg7dmyBfatVqwbAoEGDmDRpErt37yY7O5vTp0/z4IMPOveHvG53lya0QIH487sTAs7XI39/rVZb4PW8dAxaSc4jhBBCCFGUXHsuiZmJJGYmotfqCXQPJDUntaLDKrEbIyMQ5ap27drUrl2bZ555hoceeogFCxbQv39/DAZDga6B9evXx2azsX37dmfXvqSkJA4fPky9evWKfc5atWphNpv55ZdfeOSRRwqs/+2334iMjOTFF190Ljt58uQ1XmGeVq1ace+99zJp0iSX5c2aNcNut5OYmEj79u0L3bewe1FczzzzDLNmzWLZsmX079+f5s2bs3///gJJ76WqVq1Khw4dWLJkCdnZ2XTp0oUqVaoAUKVKFcLDwzl+/DiDBw++ppgAgoKCSEhIcEmC9+7d61xfWucRQgghxK0rPSed+PR40qxp+Bh9MOgMFR3SNZNESjhlZ2czceJE7r//fqKjozlz5gw7duzgvvvuA/LGJWVkZPDLL7/QpEkT3N3dqVWrFn379mXUqFF8/PHHeHl5MWnSJMLDw+nbt2+xz20ymXj++ed57rnnMBgMtGvXjvPnz7N//35GjhxJzZo1OXXqFEuXLqVly5asWLGCZcuWXfc1v/766zRo0MCl2ELt2rUZPHgwQ4YMYebMmTRr1owLFy6wfv16GjVqRI8ePQq9F+7u7sU6p7e3N4888givvPIK/fr14/nnn+f222/nySefdBZuOHjwIGvXruW9995z7jd48GBiYmKwWq3Mnj3b5ZgxMTGMHTsWb29vunfvTk5ODjt37iQ5OZlnn322WHF16tSJ8+fPM2PGDO6//35Wr17NqlWrXLpPlsZ5hBBCCHHrsTvsXMi6QEJGAgCB5sAr9la6EUj5c+Gk0+lISkpiyJAh1K5dmwEDBtC9e3emTJkCQNu2bXn88ccZOHAgQUFBzJgxA8grh33bbbfRq1cv2rRpg1KKlStXunQjK47Jkyczfvx4Xn75ZerVq8fAgQOdJcr79u3LM888w5gxY2jatCm///67s/DC9ahduzYjRozAYrG4LF+wYAFDhgxh/Pjx1KlThz59+rB9+3YiIiKueC+K6+mnn+bgwYN88803NG7cmE2bNnHkyBHat29Ps2bNmDx5snMMU74HHniApKQksrKyCky2/Mgjj/DJJ5+wcOFCGjVqRMeOHVm4cCHR0dHFjqlevXp8+OGHfPDBBzRp0oQ//viDCRMmlPp5hBBCCHFrycrN4kTKCU6nncbkZsLX5HvDJ1EAGlUWkxPdYNLS0vDx8SE1NbVA8QKLxUJsbCzR0dGYTKYKilCIW5O8/4QQQogbl0M5SM5OJj4jnlx7Lr4mX7SawttxUnNS8TZ4E+UXVb5BFuJKucGlpGufEEIIIYQQolTl2HJIyEjgQtYFzG5m/M3+FR1SqZNESgghhBBCCFFqUiwpxKfHk5Wbha/JFzftzZly3JxXJYQQQgghhChXNoeNxMxEzmWcw03rRqB7YEWHVKYkkRJCCCGEEEJclwxrBnHpcaTlpOFt8MboZqzokMqcJFJCCCGEEEKIa+JQDi5k5pU1dygHAeaAIgtK3GwkkRJCCCGEEEKUWHZuNgkZCSRlJ+Gp98SsN1d0SOVKEikhhBBCCCFEsSmlSLYkE58eT449B3+TPzqtrqLDKneSSAkhhBBCCCGKxWq3ci7jHOezzmPUGQkwB1R0SBVGEikhhBBCCCHEVaXlpBGfHk+6NR1foy96nb6iQ6pQt8ZIMHFD0mg0LF++vKLDKLaoqCjmzJlTpudQSvHoo4/i7++PRqNh7969ZXo+IYQQQohcey5n085y/OJxcmw5BJoDb/kkCiSRuml16tSJcePGFVi+fPlyNBpN+Qd0DeLj4+nevXuZnmPjxo1oNJpCHzt27CjTc1+L1atXs3DhQn766Sfi4+Np2LBhRYd0zcoj8RRCCCHEtVNKkWpJ5XjyceIz4nHXu+Nj8rlhvkuWNenaJyqtkJCQMj9H27ZtiY+Pd1k2efJk1q1bR4sWLcr8/CV17NgxQkNDadu27TUfQymF3W7HzU3e/kIIIYQoXP5YqAtZF9BpdQSaAyWBuoy0SJWUUmDLrJiHUqV+OTExMTRt2pSPP/6YiIgI3N3deeCBB0hJSXHZbv78+TRo0ACj0UhoaChjxoxxrjt16hR9+/bF09MTb29vBgwYwLlz5wqcY/78+VSrVg1PT0+eeOIJ7HY7M2bMICQkhODgYF5//XWXc17ate/EiRNoNBq+//57OnfujLu7O02aNGHr1q0u+8ybN895Hf3792fWrFn4+voWef0Gg4GQkBDnIyAggB9++IERI0Zc8cMiMTGR3r17YzabiY6OZsmSJQW2mTVrFo0aNcLDw4OIiAhGjx5NRkYGAJmZmXh7e/Ptt9+67PPjjz/i4eFBenp6geMNGzaMp556ilOnTqHRaIiKigIgJyeHsWPHEhwcjMlk4o477nBpTctvdVuzZg0tWrTAaDTy66+/opRixowZVK9eHbPZTJMmTQrEs3//fnr27Im3tzdeXl60b9+eY8eOAbBjxw7uvvtuAgMD8fHxoWPHjuzevdtl/5iYGKpVq4bRaCQsLIyxY8cCeS2mJ0+e5JlnnnG2AAohhBCi4imlSM5O5tjFY5zLPIenwRMfo7RCFUZ+ki4pexZ87Vkx5x6QAW4epX7Yo0eP8vXXX/Pjjz+SlpbGyJEjefLJJ53Jwdy5c3n22Wd588036d69O6mpqfz2229A3putX79+eHh4sGnTJmw2G6NHj2bgwIFs3LjReY5jx46xatUqVq9ezbFjx7j//vuJjY2ldu3abNq0id9//50RI0Zw1113cfvttxcZ64svvsjbb79NrVq1ePHFF3nooYc4evQobm5u/Pbbbzz++ONMnz6dPn36sG7dOiZPnlyie/HDDz9w4cIFhg0bdsXthg0bxunTp1m/fj0Gg4GxY8eSmJjoso1Wq+Xdd98lKiqK2NhYRo8ezXPPPceHH36Ih4cHDz74IAsWLOD+++937pP/3MvLq8A533nnHWrUqMF//vMfduzYgU6XV2b0ueee47vvvmPRokVERkYyY8YMunbtytGjR/H393fu/9xzz/H2229TvXp1fH19eemll/j++++ZO3cutWrVYvPmzfzrX/8iKCiIjh07cvbsWTp06ECnTp1Yv3493t7e/Pbbb9hsNgDS09MZOnQo7777LgAzZ86kR48eHDlyBC8vL7799ltmz57N0qVLadCgAQkJCfz5558AfP/99zRp0oRHH32UUaNGleg1EkIIIUTZyLHlkJiZyPms8+i1+nJthdLmpoHOWC7nKi2SSAksFguLFi2iatWqALz33nv07NmTmTNnEhISwmuvvcb48eN5+umnnfu0bNkSgHXr1vHXX38RGxtLREQEAIsXL6ZBgwbs2LHDuZ3D4WD+/Pl4eXlRv359OnfuzKFDh1i5ciVarZY6deowffp0Nm7ceMVEasKECfTs2ROAKVOm0KBBA44ePUrdunV577336N69OxMmTACgdu3a/P777/z000/FvheffvopXbt2dV5LYQ4fPsyqVavYtm0brVu3du5Xr149l+0uHaMWHR3N1KlTeeKJJ/jwww8BeOSRR2jbti1xcXGEhYVx4cIFfvrpJ9auXVvoeX18fPDy8kKn0zm7PWZmZjJ37lwWLlzoHE82b9481q5dy6effsrEiROd+7/66qvcfffdzv1mzZrF+vXradOmDQDVq1dny5YtfPzxx3Ts2JEPPvgAHx8fli5dil6vd97TfHfeeadLfB9//DF+fn5s2rSJXr16cerUKUJCQujSpQt6vZ5q1arRqlUrAPz9/dHpdHh5eZVLF04hhBBCFC1/XqiEjASyc7PxMfqUXzEJpXBPWEnY/lfIiHwYAmeVz3lLgSRSJaVzz2sZqqhzl4Fq1ao5kyiANm3a4HA4OHToEFqtlri4OO66665C9z148CAREREuiUf9+vXx9fXl4MGDzkQqKirKpZWlSpUq6HQ6tFqty7LLW3Uu17hxY+e/Q0NDgbxudnXr1uXQoUP079/fZftWrVoVO5E6c+YMa9as4euvv77idgcPHsTNzc1lDFXdunULdCHcsGED06ZN48CBA6SlpWGz2bBYLGRmZuLh4UGrVq1o0KABn332GZMmTWLx4sVUq1aNDh06FCteyGvpy83NpV27ds5ler2eVq1acfDgQZdtL433wIEDWCwWZ2KVz2q10qxZMwD27t1L+/btnUnU5RITE3n55ZdZv349586dw263k5WVxalTpwB44IEHmDNnDtWrV6dbt2706NGD3r17y9gsIYQQohKx2CzOsVBGnZFA98ByO7cu+yz++1/GPfEXADwSVoFjBmhvjO8KN0aUlYlGUybd60qbt7c3qampBZanpKTg7e19xX3zm3A1Gg1ms/mK2yqlCm3yvXz55V/GNRpNocscDscVz3fpPvnHz9+nsFhUCcaVLViwgICAAPr06XPF7fKPeaWm7pMnT9KjRw8ef/xxpk6dir+/P1u2bGHkyJHk5uY6t3vkkUd4//33mTRpEgsWLGD48OElakIvKpbC7oWHx//+bvPv2YoVKwgPD3fZzmjMa1a/2ms/bNgwzp8/z5w5c4iMjMRoNNKmTRusVisAERERHDp0iLVr17Ju3TpGjx7NW2+9xaZNm4pMzoQQQghRPhzKQXJ2XiuUxWbB1+SLW3klMMqO14mF+B6eidaehdLoSYwaSXatcUTdIEkUSLGJm1bdunXZuXNngeU7duygTp06LstOnTpFXFyc8/nWrVvRarXUrl0bLy8voqKi+OWXXwo9T/369Tl16hSnT592Ljtw4ACpqakFurqVtbp16/LHH3+4LCvsHhRGKcWCBQsYMmTIVb/k16tXD5vN5nLsQ4cOuRTo2LlzJzabjZkzZ3L77bdTu3Ztl3uc71//+henTp3i3XffZf/+/QwdOrRY8earWbMmBoOBLVu2OJfl5uayc+fOK97/+vXrYzQaOXXqFDVr1nR55LcuNm7cmF9//dUl8bvUr7/+ytixY+nRo4ezEMmFCxdctjGbzfTp04d3332XjRs3snXrVvbt2wfkFfqw2+0lul4hhBBCXL+s3CxOppzkRMoJNGgIdA8styRKn/o3Ib/1w//gVLT2LCx+LYlrv4pz1Z+44cZIVWgitXnzZnr37k1YWFihk68qpYiJiSEsLAyz2UynTp3Yv3+/yzY5OTk89dRTBAYG4uHhQZ8+fThz5kw5XkXlNHr0aI4dO8aTTz7Jn3/+yeHDh/nggw8KjJsBMJlMDB06lD///NP55XjAgAHOsSsxMTHMnDmTd999lyNHjrB7927ee+89ALp06ULjxo0ZPHgwu3fv5o8//mDIkCF07Nix3MuHP/XUU6xcuZJZs2Zx5MgRPv74Y1atWlWsFp7169cTGxvLyJEjr7ptnTp16NatG6NGjWL79u3s2rWLRx55xKUFp0aNGthsNt577z2OHz/O4sWL+eijjwocy8/Pj3vvvZeJEydyzz33uHSxLA4PDw+eeOIJJk6cyOrVqzlw4ACjRo0iKyvritfi5eXFhAkTeOaZZ1i0aBHHjh1jz549fPDBByxatAiAMWPGkJaWxoMPPsjOnTs5cuQIixcv5tChQ0BeErd48WIOHjzI9u3bGTx4sMs9WLhwIZ9++il///238x6YzWYiIyOBvO6emzdv5uzZswUSMCGEEEKUPrvDzoWsCxy7eIxkSzJ+Jj88DOXT00pjy8L34DRCf++LMW0fDjcvkhq+wbnbv8LmWbNcYihtFZpIZWZm0qRJE95///1C18+YMYNZs2bx/vvvs2PHDkJCQrj77rtdSkOPGzeOZcuWsXTpUrZs2UJGRga9evW65X/pjoqK4tdff+XYsWPcc889tGzZkoULF7Jw4UIeeOABl21r1qzJvffeS48ePbjnnnto2LChsyACwNChQ5kzZw4ffvghDRo0oFevXhw5cgT4X4lyPz8/OnToQJcuXahevTpfffVVuV4vQLt27fjoo4+YNWsWTZo0YfXq1TzzzDOYTKar7vvpp5/Stm3bYreiLViwgIiICDp27Mi9997Lo48+SnBwsHN906ZNmTVrFtOnT6dhw4YsWbKEN954o9BjjRw5EqvVyogRI4p3oZd58803ue+++3j44Ydp3rw5R48eZc2aNfj5+V1xv6lTp/Lyyy/zxhtvUK9ePbp27cqPP/5IdHQ0AAEBAaxfv56MjAw6duzIbbfdxrx585wtdvPnzyc5OZlmzZrx8MMPO0uw5/P19WXevHm0a9eOxo0b88svv/Djjz8SEBAA5BW/OHHiBDVq1CAoKOiarl0IIYQQxZNpzeREyglOpJzATetGgDkAnVZXLuc2nd9I2K/34BP7HzTKTmZoT+I6/EJGtYdAc+N2kNOokgwiKUMajYZly5bRr18/IK81KiwsjHHjxvH8888Dea1PVapUYfr06Tz22GOkpqYSFBTE4sWLGThwIABxcXFERESwcuVKunbtWqxzp6Wl4ePjQ2pqaoHxQxaLhdjYWKKjo4v1hfxGExMTw/Lly9m7d29Fh1ImRo0axT///MOvv/5a0aEUacmSJTz99NPExcVhMBgqOpxK5WZ//wkhhBBlLb8V6lzmOewOOz5Gn3JLoLQ55/E/MBWP+B8AsJnCudhwKtnBdxbYNjUnFW+DN1F+UeUS25VcKTe4VKVNAWNjY0lISOCee+5xLjMajXTs2JHff/8dgF27dpGbm+uyTVhYGA0bNnRuU5icnBzS0tJcHuLm8Pbbb/Pnn39y9OhR3nvvPRYtWlTicUflJSsri/379/PGG2/w2GOPSRIlhBBCiFKVYc0gNiWW02mnMWgN+Jv9yyeJUgrP018RtrkLHvE/oNCSFjWSuA4/F5pE3agqbSKVkJAA5JXEvlSVKlWc6xISEjAYDAW6MF26TWHeeOMNfHx8nI8rzRkkbix//PEHd999N40aNeKjjz7i3Xff5ZFHHqnosAo1Y8YMmjZtSpUqVXjhhRcqOhwhhBBC3CRsDhsJ6Qkcv3icjJwMAswBmPVXrsZbWtwyjlFl+4ME7HseXW4qOd4NSGj3X5LrT0bdAJWvS6LS1xcsTlnny11tmxdeeIFnn33W+TwtLe2WTaZiYmKIiYmp6DBKzdXmgKpMbrZ7L4QQQoiKl56TTnxGPGk5aXgZvDC5lVPXeHsOPsc/wufYB2gcVhw6Mym1niU9avgNMy9USVXaq8qvGJeQkOCceBXyJgHNb6UKCQnBarWSnJzs0iqVmJhI27Ztizy20Wh0zpUjhBBCCCHEjc5qt3I+8zznM8+j0WgINAeWaG7K62G8uAP/v1/AkHEUgOygjiQ1eA27+83dUFFpu/ZFR0cTEhLC2rVrncusViubNm1yJkm33XYber3eZZv4+Hj+/vvvKyZSQgghhBBC3AwcysHF7IscvXiUhIwE3PXu+Jp8yyWJ0uSm4r/v34RsewBDxlHshkDON32XxBYLb/okCiq4RSojI4OjR486n8fGxrJ37178/f2pVq0a48aNY9q0adSqVYtatWoxbdo03N3dGTRoEAA+Pj6MHDmS8ePHExAQgL+/PxMmTKBRo0Z06dKloi5LCCGEEEKIMpdhzeB85nkuZl/E5GYi0D2wfE6sFO4JK/E7EINbznkA0iMeJKXOJBwG3/KJoRKo0ERq586ddO7c2fk8f9zS0KFDWbhwIc899xzZ2dmMHj2a5ORkWrduzc8//4yXl5dzn9mzZ+Pm5saAAQPIzs7mrrvuYuHCheh05VPWUQghhBBCiPJktVu5kHWBC5kXsCs7fia/citprss+i//+l3FP/AWAXI/qJDWcRk7A7eVy/sqk0swjVZFu5XmkhKjM5P0nhBBC/I9DOUi1pJKQkUBmbmb5FpNQdrxOLMT38Ey09iyURk9qjdGk1hgNuuuvPXAjziNVaYtNVHY2hw2HcpTb+bQaLW43acUTIYQQQghxZZnWTBIzE7mYfRGjzljOxSS243fgVYxp+wGw+LUkqdEb2Dxrlsv5Kyv5Zn4NbA4bx5OPY7FZyu2cJjcT1f2qSzJ1BTExMcydO5fExESWLVvG8uXLSUlJYfny5UXu06lTJ5o2bcqcOXPKLc6SOnHiBNHR0ezZs4emTZtWdDjXLCoqinHjxjFu3LiKDkUIIYS4YeTac0nKSiIxKxG7w46vybfcvg/qsk7j988beCSsBMDh5kVy3X+TETEQNJW2Zl25kTtwDRzKgcVmQa/VY3Yzl/lDr9VjsVlK1AKWnp7OuHHjiIyMxGw207ZtW3bs2OGyzbBhw9BoNC6P22937d/67LPPOot/LF261GXd119/Te/eva/9RpaigwcPMmXKFD7++GPi4+Pp3r0777zzDgsXLqzo0K5bREQE8fHxNGzYsNj7xMTE3NBJlxBCCHGrU0qRYknhePJxzqSfwagz4m/2L5ckSmPLxPfQW4RvvguPhJUotKRXG8zZjhvJqPaQJFH/T5o3roOb1g2DzlAu58p15JZo+0ceeYS///6bxYsXExYWxueff06XLl04cOAA4eHhzu26devGggULnM8Nhv9dz48//sgXX3zBzz//zJEjRxg+fDh33303AQEBpKSk8OKLL/LLL79c/8WVgmPHjgHQt29fZzP3zTJXmE6nc86rVt5yc3PR6/UVcm4hhBDiVpWVm8X5zPMkZSeh1+rLrxufcuBx9nt8D83ALScRgOyAtiTXm0yud72yP/8NRtLJm1B2djbfffcdM2bMoEOHDtSsWZOYmBiio6OZO3euy7ZGo5GQkBDnw9/f37nu4MGDdOrUiRYtWvDQQw/h7e3N8ePHAXjuuecYPXo01apVK1ZMP/zwAy1atMBkMhEYGMi9997rXJecnMyQIUPw8/PD3d2d7t27c+TIEef6hQsX4uvry5o1a6hXrx6enp5069aN+Ph4IK/1Jb9lTKvVOj9ohg0bRr9+/ZzHyczMZMiQIXh6ehIaGsrMmTMLxGm1WnnuuecIDw/Hw8OD1q1bs3HjxmLHkm/+/Pk0aNAAo9FIaGgoY8aMca5LTU3l0UcfJTg4GG9vb+68807+/PPPIu/diRMn0Gg07N27F4CNGzei0Wj45ZdfaNGiBe7u7rRt25ZDhw45Y5wyZQp//vmns6Uxv2XuaufOb8maP38+1atXx2g08vHHHxMeHo7D4doi2qdPH4YOHQrkJbJ9+/alSpUqeHp60rJlS9atW1fkNQkhhBCiIJvDxrmMcxy7eIyk7CR8jD54G73LJYkyJu8i5Pf+BP41AbecRHLdI0ls/jGJrZZIElUESaRuQjabDbvdXqDKmdlsZsuWLS7LNm7cSHBwMLVr12bUqFEkJiY61zVp0oSdO3eSnJzMrl27yM7OpmbNmmzZsoXdu3czduzYYsWzYsUK7r33Xnr27MmePXucCUC+YcOGsXPnTn744Qe2bt2KUooePXqQm/u/VrisrCzefvttFi9ezObNmzl16hQTJkwAYMKECc5Wtfj4+AJJTb6JEyeyYcMGli1bxs8//8zGjRvZtWuXyzbDhw/nt99+Y+nSpfz111888MADdOvWzSWxu1IsAHPnzuXJJ5/k0UcfZd++ffzwww/UrJk3GFMpRc+ePUlISGDlypXs2rWL5s2bc9ddd3Hx4sVi3c98L774IjNnzmTnzp24ubkxYsQIAAYOHMj48eNp0KCB834MHDiw2Oc+evQoX3/9Nd999x179+7l/vvv58KFC2zYsMG5TXJyMmvWrGHw4MFA3pxwPXr0YN26dezZs4euXbvSu3dvTp06VaJrEkIIIW5FSilSLakcv3ic02mn0Wv1BJgDyqUbny47jsA9YwnZeh/G1D9xuHmSXGcSce1/JjukK5RTQYsbkXTtuwl5eXnRpk0bpk6dSr169ahSpQpffvkl27dvp1atWs7tunfvzgMPPEBkZCSxsbFMnjyZO++8k127dmE0GunatSv/+te/aNmyJWazmUWLFuHh4cETTzzBwoULmTt3Lu+99x6BgYH85z//oUGDBoXG8/rrr/Pggw8yZcoU57ImTZoAcOTIEX744Qd+++032rZtC8CSJUuIiIhg+fLlPPDAA0BeF7OPPvqIGjVqADBmzBheffVVADw9PfH19QUosgtcRkYGn376KZ999hl33303AIsWLaJq1arObY4dO8aXX37JmTNnCAsLA/KStNWrV7NgwQKmTZt21VgAXnvtNcaPH8/TTz/tXNayZUsANmzYwL59+0hMTHR2PXz77bdZvnw53377LY8++mih8Rd1Xzt27AjApEmT6NmzJxaLBbPZjKenJ25ubi73Y/369cU6t9VqZfHixQQFBTn37datG1988QV33XUXAN988w3+/v7O502aNHG+pvn3YNmyZfzwww8urXFCCCGEcJWdm+2sxqfT6sqtG5/Gno33sY/wPv4xWocFhYaMqgNIqTMehzG4zM9/M5BE6ia1ePFiRowYQXh4ODqdjubNmzNo0CB2797t3GbgwIHOfzds2JAWLVoQGRnpbEGCvK5eMTExzu1iYmLo0qULer2e1157jX379vHTTz8xZMiQAq07+fbu3cuoUaMKXXfw4EHc3Nxo3bq1c1lAQAB16tTh4MGDzmXu7u7OxAUgNDTUpfXsao4dO4bVaqVNmzbOZf7+/tSpU8f5fPfu3SilqF27tsu+OTk5BAQEFCuWxMRE4uLinAnG5Xbt2kVGRobL8SCvO2b+OK/iaty4sUsM+ecvqrtlcc8dGRnpkkQBDB48mEcffZQPP/wQo9HIkiVLePDBB50TX2dmZjJlyhR++ukn4uLisNlsZGdnS4uUEEIIUQSbw8bFrIskZiWSY8vBx+iDXlcO45KVwj3uB/wOvYmbJa8Xj8WvFcn1X8bqU/zCVkISqZtWjRo12LRpE5mZmaSlpREaGsrAgQOJjo4ucp/Q0FAiIyNdurFd6p9//mHJkiXs2bOH+fPn06FDB4KCghgwYAAjRowgLS2t0EnLzGZzkecsaj5opZTLrzGXFzzQaDRF7luS81zK4XCg0+nYtWuXM0HI5+npWaxYrnSt+ecIDQ11GXeVL79VrbgujSP/Xl0+julazu3h4VFgfe/evXE4HKxYsYKWLVvy66+/MmvWLOf6iRMnsmbNGt5++21q1qyJ2Wzm/vvvx2q1luiahBBCiJudUop0azoJGQmk5aThofcg0D2wXM5tSPkTvwNTMKXk/bBuM4eTXPffZIX0kC5810ASqZuch4cHHh4ezjEtM2bMKHLbpKQkTp8+7WzduJRSikcffZSZM2fi6emJ3W53jmHK/29RX+IbN27ML7/8wvDhwwusq1+/Pjabje3btzu79iUlJXH48GHq1Su9gY01a9ZEr9ezbds2Z4tNcnIyhw8fdnaPa9asGXa7ncTERNq3b39N5/Hy8iIqKopffvmFzp07F1jfvHlzEhIScHNzIyoq6pqv52oMBgN2u73Uzm02m7n33ntZsmQJR48epXbt2tx2223O9b/++ivDhg2jf//+QF5XyhMnTlzvZQghhBA3DZvDRqY1kxRLSl43Po2OAHMA2nIoJa6znMP30HQ8z34PgEPnTmqN0aRHP4LSma6yd9mz2CxY7Va02hurfIMkUtfB5rBV2vOsWbMGpRR16tTh6NGjTJw4kTp16jiTmYyMDGJiYrjvvvsIDQ3lxIkT/Pvf/yYwMND5ZfhS8+bNIzg4mD59+gDQrl07YmJi2LZtG6tWraJ+/fpFtqi88sor3HXXXdSoUYMHH3wQm83GqlWreO6556hVqxZ9+/Zl1KhRfPzxx3h5eTFp0iTCw8Pp27dvia+7KJ6enowcOZKJEycSEBBAlSpVePHFF13esLVr12bw4MEMGTKEmTNn0qxZMy5cuMD69etp1KgRPXr0KNa5YmJiePzxxwkODqZ79+6kp6fz22+/8dRTT9GlSxfatGlDv379mD59OnXq1CEuLo6VK1fSr18/lyIc1yMqKorY2Fj27t1L1apV8fLyuu5zDx48mN69e7N//37+9a9/uayrWbMm33//Pb1790aj0TB58uQrto4JIYQQtwKHcpCVm0VGTgbJlmSycrPQarR4GjzLZQodjd2CV+wn+Bz7EK09C4CM8HtJqfM8dlOVMj//1eTac0mzpuGmcSPMK6zcWuZKS4kSKaUUmzZt4tdff+XEiRNkZWURFBREs2bN6NKlCxEREWUVZ6Wi1WgxuZmw2Cwlnt/pWpncTCX6xSI1NZUXXniBM2fO4O/vz3333cfrr7/u7A6m0+nYt28fn332GSkpKYSGhtK5c2e++uorvLy8XI517tw5pk2bxu+//+5c1qpVK8aPH0/Pnj0JDg5m0aJFRcbSqVMnvvnmG6ZOncqbb76Jt7c3HTp0cK5fsGABTz/9NL169cJqtdKhQwdWrlxZ6vMXvfXWW2RkZNCnTx+8vLwYP348qampLtssWLDAWSzi7NmzBAQE0KZNm2InUQBDhw7FYrEwe/ZsJkyYQGBgIPfffz+Q1wVv5cqVvPjii4wYMYLz588TEhJChw4dqFKl9D7Q7rvvPr7//ns6d+5MSkoKCxYsYNiwYdd17jvvvBN/f38OHTrEoEGDXNbNnj2bESNG0LZtWwIDA3n++edJS0srtesRQgghbiQWm4UMawYXsy+Sac3EoRyY3cz4m/3LpQUKpXBPWInfP9Nwyz6bF5Nv87xxUL5Ny/78V2F32Em3puNQDgLMAQR5BOGud6/osEpMo4oxeCQ7O5vZs2fz4YcfkpSURJMmTQgPD8dsNnPx4kX+/vtv4uLiuOeee3j55Ze5/fbbyyP2UpOWloaPjw+pqakFxvhYLBZiY2OJjo52KSduc9hwqPL7xV2r0ZZLCUwhKpOi3n9CCCFEZZNrzyXDmkGKJYX0nHSsDitGnRF3vXu5fofTp/6N/4FXMSX/AYDNFEpynUlkhfWp8HFQ+ePDrHYrPiYfgj2C8TJ4lc9kwyVwpdzgUsV6VWvXrk3r1q356KOP6Nq1a6EtBSdPnuSLL75g4MCBvPTSS0VWabtZSFIjhBBCCHFrszvsZOZmkmZJIzUnlWxbNnqtHrObGR83n3KNRZcdj8+ROXie+RoNCofWRFr1x0ir8ThKd+ViWOUhKzeLzNxMPA2ehHuH42vyLZ/WuTJUrGxg1apVNGx45XKIkZGRvPDCC4wfP56TJ0+WSnBCCCGEEEJUJkopsm3ZpOekO8c9ocBd715uc0BdSptzAZ9jc/E6tRiNI69abmZoH5LrTsJuDivXWAqTY8shzZqGyc1ENe9q+Jn9yqfMezkoViJ1tSTqUgaDwWXSVyGEEEIIIW50ObYcMqx5RSMycjKwKzsmNxN+Jr8KaVnR5qbiffw/eJ1Y4CwkYfFrRUrd58nxu+0qe5c9m8NGWk4aWo2WUM9QAtwDMLndXN30i90/7a+//irWdpdOEiqEEEIIIcSNyuawkWHNINWSSmpOKla7FYPOgKfBs8JaVTS2TLxOLMD7+H/Q2fIKO+X4NCal9gQsge0rfByUQzlIz0nHruz4mf0Icg/Cw1BwjsqbQbETqaZNm7pMPJrfbJk/cWr+fy+fu0YIIYQQQogbhUM5yLRmkm5NJ8WSQnZuNjqNDrPejI+xfMc9XUpjt+B58nN8jn+IznoRAKtnHVJqP0t2lXsqPIFSSpFhzcBit+Bt9KaKRxW8jd6VrpBEaSp2IhUbG+v8t1KKhg0bsnLlSiIjI8skMCGEEEIIIcpLrj2XdGs6F7IukJGTARowu5kJMAdUbDLgsOJ5+mt8jr2PmyUhL1b3aFJqjyMrtBdodBUX2//Lzs0mIzcDD70H0V7R+Jp80WkrPq6yVuxE6vKESaPRULVqVUmkhBBCCCHEDSsrN4s0SxpJ2Ulk27Ix6AyVIxFQdjzOLsfnyBz02acBsJnCSK31NBnh90ElqCBttVtJy0nDoDMQ4R2Bv9n/pikkURwV/woIIYQQQghRjvLH8aRYUkixpGBz2Cqs6l4ByoF7wip8D89Cn3kMALshkNSaY0iPeAh0xoqNj/8VktCgIdgjmED3QMz6ii+xXt4kkRJCCCGEELcEq91Kek46SVlJZORmoEWLh8EDg85Q0aGBUpjPb8Dn8EyMafsBsOt9Sav+OOlRQyvFXFAO5SDdmo7NYcPP5EeQRxCeBs+KDqvCXFetxgrP2EWF2rhxIxqNhpSUlGLvExUVxZw5c8osprKycOFCfH19nc9jYmJo2rTpdR2zNI4hhBBCiCtTSpFpzSQuPY7DSYeJTYnFarfiZ/LDz+xXKZIoY9LvVNl6H8E7R2BM24/DzZOUmk9zttPmSjGhrlKK9Jx0LmZfxKQzUd2vOpG+kbd0EgUlSKSaNWtG8+bNnY/s7Gx69+7tsqx58+ZlGasogWHDhqHRaHj88ccLrBs9ejQajYZhw4aVf2CXiYqKYuPGjde0b0xMDBqNBo1Gg06nIyIigkceeYTz58+XbpCFmDBhAr/88kuxt9doNCxfvvy6jiGEEEKI4rM77KRYUjiRcoKjF48Snx6Pm8aNIPcgvIxeFTL30+UMybsJ3j6YkO2DMKXsxqE1kVr9Mc522kxq7WdQeu8Kjc+hHKTlpHEh+wJ6nZ4o3yiq+1fH1+RbKe5fRSt2175+/fq5PO/bt29pxyJKWUREBEuXLmX27NmYzXm/ZFgsFr788kuqVatWwdGVjgYNGrBu3Trsdjt79uxh5MiRnD17llWrVhXY1m63o9Fo0Gqv/43v6emJp+f1/QpTGscQQgghhKscW46z+l6mNRM3rRseeo9KVQRBn7Yf38OzcE/M+0FVafSkVxtEWo0nsZuCKzi6vAQqw5pBjj0Hb4M34d7heBu9casEBS4qk2J/oxw+fDiTJ0/mlVdeueLjZqeUwuZwVMgjfw6v4mrevDnVqlXj+++/dy77/vvviYiIoFmzZi7b5uTkMHbsWIKDgzGZTNxxxx3s2LHDZZuVK1dSu3ZtzGYznTt35sSJEwXO+fvvv9OhQwfMZjMRERGMHTuWzMzMYsVrtVoZM2YMoaGhmEwmoqKieOONN664j5ubGyEhIYSHh9OrVy/Gjh3Lzz//THZ2trM73k8//UT9+vUxGo2cPHkSq9XKc889R3h4OB4eHrRu3bpAq9jChQupVq0a7u7u9O/fn6SkJJf1hXXLmz9/Pg0aNMBoNBIaGsqYMWOAvFY3gP79+6PRaJzPLz+Gw+Hg1VdfpWrVqhiNRpo2bcrq1aud60+cOIFGo+H777+nc+fOuLu706RJE7Zu3erc5uTJk/Tu3Rs/Pz88PDxo0KABK1euLMbdF0IIIW5c+XMYnUk9w+Gkw5xIOYHdYcff7I+vybdyJFGOXIwXdxK450nCtvTEPfEXlEZHRtUBnO20geQGUyo8icpvgUrKTsKoM1LdrzrV/avjb/aXJKoQxb4j0dHRxMfHExxc8VlyRbIrxQ9HzlXIufvUqoJbCcelDR8+nAULFjB48GAg78v+iBEjCiQOzz33HN999x2LFi0iMjKSGTNm0LVrV44ePYq/vz+nT5/m3nvv5fHHH+eJJ55g586djB8/3uUY+/bto2vXrkydOpVPP/2U8+fPM2bMGMaMGcOCBQuuGuu7777LDz/8wNdff021atU4ffo0p0+fLtH1ms1mHA4HNpsNgKysLN544w0++eQTAgICCA4OZvjw4Zw4cYKlS5cSFhbGsmXL6NatG/v27aNWrVps376dESNGMG3aNO69915Wr1591R8J5s6dy7PPPsubb75J9+7dSU1N5bfffgNgx44dBAcHs2DBArp164ZOV3g51XfeeYeZM2fy8ccf06xZM+bPn0+fPn3Yv38/tWrVcm734osv8vbbb1OrVi1efPFFHnroIY4ePYqbmxtPPvkkVquVzZs34+HhwYEDB6TVSwghxE3L5rA5x+6k5aShUHjoPfA2VmyXOACUwi3zGOYLWzBd2ILp4ja0tgzn6szQ3qTUegabZ/UKDDJPfguU1W7Fy+BFuHc4Pkafii8BX8kVO5EqaWuIqBwefvhhXnjhBWdrxm+//cbSpUtdEqnMzEzmzp3LwoUL6d69OwDz5s1j7dq1fPrpp0ycOJG5c+dSvXp1Zs+ejUajoU6dOuzbt4/p06c7j/PWW28xaNAgxo0bB0CtWrV499136dixI3PnzsVkMhWI79JWrVOnTlGrVi3uuOMONBpNieco++eff5g7dy6tWrXCy8sLgNzcXD788EOaNGkCwLFjx/jyyy85c+YMYWFhQN5YpdWrV7NgwQKmTZvGO++8Q9euXZk0aRIAtWvX5vfff3dpHbrca6+9xvjx43n66aedy1q2bAlAUFAQAL6+voSEhBR5jLfffpvnn3+eBx98EIDp06ezYcMG5syZwwcffODcbsKECfTs2ROAKVOm0KBBA44ePUrdunU5deoU9913H40aNQKgevWK/3AWQgghSpvFZiHVksrF7Itk5Wah1+orRdczbc55TBd+x3zhV0xJv+FmiXdZb9f7YgnqSGr1x8n1rldBUf5PfhW+XHsuXgYvqnpXxdvoLQlUMUkbXQnpNBr61KpSYecuqcDAQHr27MmiRYtQStGzZ08CAwNdtjl27Bi5ubm0a9fOuUyv19OqVSsOHjwIwMGDB7n99ttdKjW2adPG5Ti7du3i6NGjLFmyxLlMKYXD4SA2NpZ69a78gTFs2DDuvvtu6tSpQ7du3ejVqxf33HPPFffZt28fnp6e2O12cnJy6NSpE//5z3+c6w0GA40bN3Y+3717N0opateu7XKcnJwcAgICnNfav39/l/Vt2rQpMpFKTEwkLi6Ou+6664qxXklaWhpxcXEurwFAu3bt+PPPP12WXXo9oaGhzhjq1q3L2LFjeeKJJ/j555/p0qUL9913n8v2QgghxI1MKUWyJZn49Hiybdm4u7kTYA6osErSGns2xovbna1OhvR/XOPVGrD4tcQS2A5LYHus3vVBU/FJiksCZfQiwjtCEqhrUKJE6pNPPrlqN6GxY8deV0CVnUajKXH3uoo2YsQI53idS1s28uW3Nl7+IaSUci4rToukw+HgscceK/RvoDjFLZo3b05sbCyrVq1i3bp1DBgwgC5duvDtt98WuU+dOnX44Ycf0Ol0hIWFYTS6TlJnNptdrsvhcKDT6di1a1eBLnb5f9slbX3NL+RRGq70GuTT6/UFtnc4HAA88sgjdO3alRUrVvDzzz/zxhtvMHPmTJ566qlSi1EIIYSoCLn2XM5lnCMxKxGjzkiQe1D5B6HsGFL3YbrwG+YLv2JM2Y3GYXXZxOpdn+zA9lgC7iDHv0WFly6/lCRQpatEidRHH31U5PgOyPtSd7MnUjeibt26YbXmvcm7du1aYH3NmjUxGAxs2bKFQYMGAXld4nbu3Onsple/fv0C5bu3bdvm8rx58+bs37+fmjVrXnOs3t7eDBw4kIEDB3L//ffTrVs3Ll68iL+/f6HbGwyGEp2vWbNm2O12EhMTad++faHb1K9fv8C1Xf78Ul5eXkRFRfHLL7/QuXPnQrfR6/XY7fYij+Ht7U1YWBhbtmyhQ4cOzuW///47rVq1utIlFRAREcHjjz/O448/zgsvvMC8efMkkRJCCHFDy7BmEJceR1pOGj5Gn/Kb+0kp3LJO5o1xurAFU9Lv6GxpLpvYTOFkB96R1+oU0A6HMaB8YisBu8NOhjWDXIckUKWpRInUzp07b/liEzcinU7n7KJXWCLs4eHBE088wcSJE/H396datWrMmDGDrKwsRo4cCcDjjz/OzJkzefbZZ3nsscfYtWsXCxcudDnO888/z+23386TTz7JqFGj8PDw4ODBg6xdu5b33nvvqnHOnj2b0NBQmjZtilar5ZtvviEkJMRlItzrVbt2bQYPHsyQIUOYOXMmzZo148KFC6xfv55GjRrRo0cPxo4dS9u2bZkxYwb9+vXj559/vuL4KMirwPf4448THBxM9+7dSU9P57fffnMmMPmJVrt27TAajfj5+RU4xsSJE3nllVeoUaMGTZs2ZcGCBezdu9elq+TVjBs3ju7du1O7dm2Sk5NZv379VbtUCiGEEJWVQzm4kHmBhIwEFIpAc2CZd+PTWpMxJf3mbHVyyz7jGpObF5aAtv+fPN2BzT0KKmlvpcsTqGoe1fA2esscUKWk2IlURfU9FaXD2/vK1WvefPNNHA4HDz/8MOnp6bRo0YI1a9Y4v/BXq1aN7777jmeeeYYPP/yQVq1aMW3aNEaMGOE8RuPGjdm0aRMvvvgi7du3RylFjRo1GDhwYLFi9PT0ZPr06Rw5cgSdTkfLli1ZuXJlqcz7dKkFCxY4i0OcPXuWgIAA2rRpQ48ePQC4/fbb+eSTT3jllVeIiYmhS5cuvPTSS0ydOrXIYw4dOhSLxcLs2bOZMGECgYGB3H///c71+UnovHnzCA8PL7R0/NixY0lLS2P8+PEkJiZSv359fvjhB5eKfVdjt9t58sknOXPmDN7e3nTr1o3Zs2cX/+YIIYQQlUSOLYeEjATOZ53HU++JWV92XeTcMo7hnrgO87l1GJN3ocHhXKc0enL8mmEJbE92QDusPo2hkpcCvzSB8jH5EOgeKAlUGdCoYg4I0Wq1JCQk3JQtUmlpafj4+JCamlog4bBYLMTGxhIdHV1o1TkhRNmR958QQtyaUiwpxKfHk5Wbha/Jt/Sr8Sk7xuQ9mBPX4n5uLfrM4y6rrZ61sQTeQXZge3L8W6HcPEr3/GUkP4GyKRveRm9JoK7RlXKDSxX7r/KVV16R+WiEEEIIIUSZsTlsJGYmci7jHG5aNwLdA6++UzFpbFmYLvyK+7m1mM+vR2e96FynNHosAbeTFXw32VXuwm4OL7XzloccWw4Z1rw5qrxN3gS5B+Fl9JIEqowVO5G6vPrZ5s2bad26tbNKWnp6Os8//zwffvhh6UcphBBCCCFualm5WcSlx5FiScHb4I3RzXj1na5Cm5OI+7lfMCeuw3RhC1pHjnOd3c2b7OA7ya7ShezADih9JZjEtwSUUmTbssnMzcSgMxDoEYifyQ8Pg4ckUOWk2F37dDod8fHxzq593t7e7N271znh57lz5wgLC7tiZbLKSrr2CVE5yftPCCFufkopLmZfJC49jlx7Ln5mv2tPBJRCn3EE87m1uCeuxZiy12W1zVyVrCp3k1XlbnL8WoJWX/hxKjG7w05GbgZWuxV3fd48Wt5G7zIdQ3arKfWufZfnWyWda0cIIYQQQohLWe3WvLmhMhMxu5nxdr+GViGHDWPyDtzPrcOcuBZ91imX1Tk+TciqcjfZwV3I9apTaSvsXY3VbnV23/MweFDVuypeBi/0uhsvGbxZVO6SI5WIJI5ClD953wkhxM0rLSeNhPQE0qxp+Bp9S5QQaGwZmM9vwnxuXd54p9xU5zqlNZAd0I7sKneTHXwXdlOVsgi/XOR338vKzUKv1eNv9sfP7IenwVO671UCkkhdhV6f96bOysrCbJYmUyHKU1ZWFvC/96EQQogbn91h50JW3txQGjQlmhtKZzmH76G38Ij/AY3D+r9j6v3IDr6TrCp3Ywlsf8NU2SuK3WEnMzeTHHsOZjcz4V7h+Jh8pPteJVOiROqTTz5xVu6z2WwsXLiQwMC8airp6emlH10loNPp8PX1JTExEQB3d3eZU0uIMqaUIisri8TERHx9fQudSFoIIcSNx2KzEJ8eT1J2El4GL0xuxRv/qrFb8I6dh/exD9HaswHIdY/O67JXpQs5freB5sb/f4XVbiXTmokDB556T8K8wvA2ekv3vUqq2MUmoqKiipVAxMbGXndQ5e1qA8qUUiQkJJCSklL+wQlxC/P19SUkJER+vBBCiBucUooUSwpx6XFYbBb8TH7otMVIfJTCPf5H/P55EzdLHAAWv9tIrvcSVt9mZRx1+cnOzeu+p9Pq8DX5Sve9ClbqxSZOnDhRGnHdkDQaDaGhoQQHB5Obm1vR4QhxS9Dr9dISJYQQNwGbw+YsKKHX6os9N5Qh5U/8Dr6KKXlX3nFM4STXnURWaK8btmDEpRzKQaY1E4vdgtnNTIhXCL4mX9z17hUdmigmGSNVAjqdTr7YCSGEEEIUU6Y1k7PpZ0nLScPH6INBZ7jqPjpLAr6HZuB59nsAHDozaTVGkxY9CqW7safCUEqR68gl05qJXdnxMngR6hWKl9GrWPdGVC6SSAkhhBBCiFLlUA7n3FAOh4MAc8BVu6lp7Nl4H5+H9/G5znFQGeH3kVLnuRuy8l6uPRebw0auI5dcey6KvNE0ep0eH5MP/mZ/PA2exeviKColSaSEEEIIIUSpUUqRmJFIXHocZr0Zd+NVuqophXv8D/j9M/2ycVAvY/VtUg4RXx+bw0auPZdcR17i5FAONBoNbho33HRueBg8cHdzx+BmQK/Vo9fpMeqMMv73JiCJlBBCCCGEKDVJ2UnEZcThYfC4alU+Q8pe/A68iillN1C5x0HZHDZsDhtWu9WZMAHotXrcdG55SaPeHZObyZkw6bV6aXG6iUkiJYQQQgghSkWKJYWzaWcxu5mvmEQVHAflTmqN0aRHP1Lh46DsDruzO57NYcOu7AC4ad1w0/4vYTLqjBh0BkmYbmElTqR2796NXq+nUaNGAPz3v/9lwYIF1K9fn5iYGAwGGSgnhBBCCHGrybBmcCbtDFqNtsjKc3njoP6D9/GPKsU4qPwuefn/VSjcNG7odXoMbgb8Df4YdUZnsmTQGSRhEk4lTqQee+wxJk2aRKNGjTh+/DgPPvgg/fv355tvviErK4s5c+aUQZhCCCGEEKKystgsnEk7g81uw8/sV3CDQsdBtfj/cVCNyzw+pRRWu9U5jsnmsAE4u+B5G73xMHjkJVA6AwadATetdNwSV1biv5DDhw/TtGlTAL755hs6dOjAF198wW+//caDDz4oiZQQQgghxC0k157LmbQzZOVm4W/yL7DekLIHvwNTy20c1KVd83IduXnFH9A4kyQvoxfuendnwmTQGWTiW3FNSpxIKaVwOPIG161bt45evXoBEBERwYULF0o3OiGEEEIIUWnZHXbi0uNIsaQQYA5wqUSny47PGwcVtwwom3FQ+RXzrI68AhBKKXQaHW46N2fXPJObyZkw6bV6qZYnSk2JE6kWLVrw2muv0aVLFzZt2sTcuXMBiI2NpUqVG6/GvxBCCCGEKDmHcpCQkUBiZqLrPFGOXHyOzS2T+aCUUlhsFrJsWSilcNPmjWfyNHjioffA6GaUrnmi3JT4L2zOnDkMHjyY5cuX8+KLL1KzZk0Avv32W9q2bVvqAQohhBBCiMrnfOZ5EjIS8DX5Ogsw6CyJBO4ZjSl5J1B646DsDjuZuZnk2HMwu5kJ8QzBQ+/hTJqkAISoCBqllCqNA1ksFnQ6HXq9vjQOV67S0tLw8fEhNTUVb2/vig5HCCGEEKJSS8pK4mTKSZe5oowXtxO0+0l01gs43LxIavAaWWF9rmscVI4th8zcTJRSeBo9CTAH4GX0wqCTKtGi7BQ3Nyi1Nk+TqWJr/gshhBBCiLKXaknlbNpZTG6mvCRKKbxiP8Hv0JtolB2rZx3O3/YRNo/oazq+Uoqs3CyybdnodXoC3APwM/nhYfCQohCiUilWIuXn51fsgXkXL168roCEEEIIIUTllGnN5EzaGdCAh8EDjS2DgL+ewyNhJQAZYf242HAayq3weaSuJNeeS2ZuJjaHDXe9OxHeEXibvK84sa8QFalYidSlJc2TkpJ47bXX6Nq1K23atAFg69atrFmzhsmTJ5dJkEIIIYQQomJZbBbOpp3Farfib/bHLeMowbseQ595DKVx42K9l8mIfLjEXfmyc7PJzM1Ep9HhZfQiwD0AT4OnFIsQlV6Jx0jdd999dO7cmTFjxrgsf//991m3bh3Lly8vzfhIT09n8uTJLFu2jMTERJo1a8Y777xDy5YtARg2bBiLFi1y2ad169Zs27at2OeQMVJCCCGEEEXLtedyKvWUs8y5R8JKAv56Dq09E5uxCuebf4jV77ZiH+/S4hEmNxP+Zn98jD64692lPLmocGU2RmrNmjVMnz69wPKuXbsyadKkkh7uqh555BH+/vtvFi9eTFhYGJ9//jldunThwIEDhIeHA9CtWzcWLFjg3MdgkAGIQgghhBClIX+uqGRLMgFGb/wPvob3iU8BsPjfzvlm7+EwBhXrWM7iESg89Z6EeYVJ8QhxwyrxiL2AgACWLVtWYPny5csJCAgolaDyZWdn89133zFjxgw6dOhAzZo1iYmJITo62jl/FYDRaCQkJMT58PcvOKu2EEIIIYQoGaUU5zLPcT7rPIEaG6F/POxMolKrP8a5Vp9fNYlSSpFpzeR81nksNgv+Zn9q+tekZkBNAtwDJIkSN6wSt0hNmTKFkSNHsnHjRucYqW3btrF69Wo++eSTUg3OZrNht9sLVAQ0m81s2bLF+Xzjxo0EBwfj6+tLx44def311wkODi7yuDk5OeTk5Difp6WllWrcQgghhBA3g/OZ54lPj6dK1hFC/3wat5zzONw8udD4bbJDul1xX5vDRqY1E6vD+r/iEUZvzHpzOUUvRNm6pnmktm/fzrvvvsvBgwdRSlG/fn3Gjh1L69atSz3Atm3bYjAY+OKLL6hSpQpffvklQ4YMoVatWhw6dIivvvoKT09PIiMjiY2NZfLkydhsNnbt2oXRaCz0mDExMUyZMqXAchkjJYQQQgiR52L2RU4mnyA8/luCDr+FRtmwetbmfPO52DxrFLmfxWYhMzcTLVq8jF74m/3xMnpJ8QhxwyjuGKlSm5C3rBw7dowRI0awefNmdDodzZs3p3bt2uzevZsDBw4U2D4+Pp7IyEiWLl3KvffeW+gxC2uRioiIkERKCCGEEAJIy0nj1IUDRPwzBZ9zqwHIDO1DUqM3UG4eBba/vHiEn8kPH5MPHnoPKR4hbjhlOiGvw+Hg6NGjJCYm4nA4XNZ16NDhWg5ZpBo1arBp0yYyMzNJS0sjNDSUgQMHEh1d+CRvoaGhREZGcuTIkSKPaTQai2ytEkIIIYS4lWXlZpEYv5mafz6NKfM4SuNGcr0XSY8cVqC0ucVmIdOaCYCnUYpHiFtLiROpbdu2MWjQIE6ePMnljVkajQa73V5qwV3Kw8MDDw8PkpOTWbNmDTNmzCh0u6SkJE6fPk1oaGiZxCGEEEIIcbPKseWQcvhTov+ehM6ehc0YzIVmH5Lj38K5jUM5yLRmYrFbMOqMBHkE4WvyxcPggVZT4jpmQtywSpxIPf7447Ro0YIVK1YQGhpa5s21a9asQSlFnTp1OHr0KBMnTqROnToMHz6cjIwMYmJiuO+++wgNDeXEiRP8+9//JjAwkP79+5dpXEIIIYQQNxObzUL2H08RdiKveJjFv/X/lzbPK+BltVvJtGbiwIGH3oMQrxA8DZ6Y3ExXOqwQN60SJ1JHjhzh22+/pWbNmmURTwGpqam88MILnDlzBn9/f+677z5ef/119Ho9NpuNffv28dlnn5GSkkJoaCidO3fmq6++wsvLq1ziE0IIIYS40Tmy4rFtvhffi9sASI1+lJQ6z6E0OrKsmWTZsjBoDfiZ/fA1+eJp8ESn1VVw1EJUrBIXm7jzzjt57rnn6NbtyiUvbyTFHVAmhBBCCHEzsDkcnE23UM3bDOd/x7HlfnSWBBw6D5Iav0Vq8N1k5mZic9hw17vjb/aX0uXillFmxSaeeuopxo8fT0JCAo0aNUKv17usb9y4ccmjFUIIIYQQZS4r187xlExiU7LIdShMCT8SvPtf6JSNHI8anGnyDimGENxyM52lyz0NnlK6XIhClLhFSqstOIhQo9GglCrTYhNlSVqkhBBCCHEzu5ht5WhyJmfTLeR/8TPlJtLs5ARCU9eRFNSFk3UmYzQFOFuf3PXuUrpc3JLKrEUqNjb2ugITQgghhBBlTylFXEYOR5MzSMrOdS5PuJhJy4QYWmR/gULLmZrjsdR4jCj3ALwMXuh1+iscVQiRr8SJVGRkZFnEIYQQQgghSoHVbmf32RTOZuSg0eW1KNnsDvbEJrN5fyJnL2bzfEgO0QF+7K42m7ZN75WJc4W4Btfc4fXAgQOcOnUKq9XqsrxPnz7XHZQQQgghhCgeq83B33Gp7D6VTIrNRliAOyaDDo1OQ6bFxu+HLrD1n/OE+5q5u24VWkT50bJaRwJNWdxjCq7o8IW4YZU4kTp+/Dj9+/dn3759zrFRgPNXjBtxjJQQQgghxI0i3ZLLnlMp7DhxkR0nLpKUbeX2OkE0jvTFX5v3fSwx1cLpc5kEmPQ82LQqM/o0xMt0eZc9z/IPXoibSIkTqaeffpro6GjWrVtH9erV+eOPP0hKSmL8+PG8/fbbZRGjEEIIIcQtKzHNwo4Tyc7E6WB8GgCNIn3p0CCY6OD/JUTWHDtVvUz0bBaMUS/zPAlRlkqcSG3dupX169cTFBSEVqtFq9Vyxx138MYbbzB27Fj27NlTFnEKIYQQQtzUlFIkpudwMD6NQwnp/JOQzq6TyZy6mOXcxqTX0r5+MJ0aBuPjbgBAA0R4manp74FvgVYnIURZKXEiZbfb8fTM++UjMDCQuLg46tSpQ2RkJIcOHSr1AIUQQgghbjYZOTYOJaT//yONfxLSOXQunZSs3ALbajTQMtqfuxpVIcjPlLcAMOi0VPd1p7qvOyY3aX0SoryVOJFq2LAhf/31F9WrV6d169bMmDEDg8HAf/7zH6pXr14WMQohhBBC3JBsdgexFzLzEqX/b2U6dC6N0xezC91eq4HoQA/qhnhTJ8SLelW9Mbu7kZj1v+JeXgY3avp5UM3bjE4rlfaEqCglTqReeuklMjMzAXjttdfo1asX7du3JyAggK+++qrUAxRCCCGEqOwu75aXnzQdu5CBRqPBpNdi1Osw6XWYTW40jvQl2NtIVX93Qn1MBHoZ8XU34GHS4VBgcygsdjvpVjvp/59EBbsbqOnvQRV3o5QqF6IS0Kj8snvX4eLFi/j5+d2wb+rizl4shBBCCJFv79kU/jidTIbVRo7NgU6rwXhJwpT/7+uh1UCEt5mafh74GGX8kxDlobi5wTXPI3XpiTZv3kzdunWpW7fu9R5OCCGEEKLS+/mfc1y02wj0MxFYjO21GnDTanHTatBrNZf9W4P+/5+7/f+6/OV+Jj1GGf8kRKVU4kRqwIABdOjQgTFjxpCdnU2LFi04ceIESimWLl3KfffdVxZxCiGEEEJUCl/tPg0mLQY3LeeSswlyNxLibSLMx4S7XldowiRjmYS4+ZQ4kdq8eTMvvvgiAMuWLUMpRUpKCosWLeK1116TREoIIYQQNyWlFB//Fou/vxE3nZakVAvDbovELPM1CXFL0pZ0h9TUVPz9/QFYvXo19913H+7u7vTs2ZMjR46UeoBCCCGEEBUt1+7gzbWHCAgw4abTkpGZy4gWkkQJcSsrcSIVERHB1q1byczMZPXq1dxzzz0AJCcnYzKZSj1AIYQQQoiKlJFj45WVB6gV4Y1Oq8FudfCvZhHotCX+GiWEuImUuGvfuHHjGDx4MJ6enkRGRtKpUycgr8tfo0aNSjs+IYQQQogKk5hm4Y21h2jXIAitRoNeQf+GYTdspWIhROkpcSI1evRoWrVqxenTp7n77rvR/v+vMdWrV+e1114r9QCFEEIIISrC0cR0Zm88yl1NQgDwdtNxV/UgSaKEEEApzSN1o5N5pIQQQghxqT9iLzJ/eyz3NAsDoIrZQNsIf0mihLgFlNk8UiNGjLji+vnz55f0kEIIIYQQlcaKv+L58UC8M4mK9DLTPNRHkighhIsSJ1LJyckuz3Nzc/n7779JSUnhzjvvLLXAhBBCCCHK2ye/HmdXfAr3NAsFoJavB42qSG8VIURBJU6kli1bVmCZw+Fg9OjRVK9evVSCEkIIIYQoT3aH4rUVB7hgzeXuJnlJVINAL+oEeFZwZEKIyqpU6nZqtVqeeeYZZs+eXRqHE0IIIYQoN5ZcO2O+2E2q3UbnhlUAaBIsSZQQ4spKbQKEY8eOYbPZSutwQgghhBBlLiXLysOfbMfL28Ad9YJBKZpV8aGGnyRRQogrK3HXvmeffdbluVKK+Ph4VqxYwdChQ0stMCGEEEKIsnT6YhbDF/5ByzqBtKwZAECLUF+q+bhXcGRCiBtBiROpPXv2uDzXarUEBQUxc+bMq1b0E0IIIYSoDP4+m8rIRTu4p3kYzaL9AGgV6ktVb3MFRyaEuFGUOJHasGFDWcQhhBBCCFEuNh5K5Kkv93B/22o0ivRFA7QO8yPMy1TRoQkhbiAlTqTynT9/nkOHDqHRaKhduzZBQUGlGZcQQgghRKn7esdpJv/wN0M6RVOvqg9a4PZwP0I8JYkSQpRMiYtNZGZmMmLECEJDQ+nQoQPt27cnLCyMkSNHkpWVVRYxCiGEEEJcF6UUc9Yd5qX//s3wO6vnJVEaaFvVX5IoIcQ1KXEi9eyzz7Jp0yZ+/PFHUlJSSElJ4b///S+bNm1i/PjxZRGjEEIIIcQ1y7U7eP67v5i76Rij7q5B7TBv3DQa7qgaQLCHsaLDE0LcoDRKKVWSHQIDA/n222/p1KmTy/INGzYwYMAAzp8/X5rxlYu0tDR8fHxITU3F21tmLxdCCCFuFpk5NkYv2c32Exd57J6aRAZ5oNdqaFfVH3+zoaLDE0JUQsXNDUo8RiorK4sqVaoUWB4cHCxd+4QQQghRaRxNzODppXuITcrkyW61CA9wx6DV0C4iAD+TvqLDE0Lc4Ercta9Nmza88sorWCwW57Ls7GymTJlCmzZtSjU4IYQQQoiSyrU7eH/9EXq88ysnL2bxVI86hAe4Y9RpaV9NkighROkocYvUO++8Q7du3ahatSpNmjRBo9Gwd+9eTCYTa9asKYsYhRBCCCGKZd+ZVCZ++ydnUrJpVTuArk1C8DDrMem03BHhj7dRkighROko8RgpyGuB+vzzz/nnn39QSlG/fn0GDx6M2XxjTmInY6SEEEKIG1u21c47vxxmx5lkmlf3p06YN1qtBgCzm5b2EQF4Gq551hchxC2kuLnBNSVSNxtJpIQQQogbk0MpNh29wNZTSURX8cSo1znX+Zn0RHibqeZtxqAr8WgGIcQtqsyKTQAcPnyYjRs3kpiYiMPhcFn38ssvX8shhRBCCCGKRSlFsiWXo8mZxCZnoXfTUreqDwAaBXUCPYnwNuMlLVBCiDJU4k+YefPm8cQTTxAYGEhISAgajca5TqPRSCIlhBBCiDKRbrVxOi2b02nZZObaAdC7aUnPziUry0avBiFU9Ta7fDcRQoiyUuJE6rXXXuP111/n+eefL4t4hBBCCCGcLDY7Z9ItnE7LJtmS61yek2vn71OpnDmfyWPtqtO2aWAFRimEuBWVOJFKTk7mgQceKItYhBBCCCGwORzEpVs4lWbhfFYOzsHcSnEkPoPtRy5w4HQqQ9tE8dFDt2E26K50OCGEKBMlTqQeeOABfv75Zx5//PGyiEcIIYQQtyCHUpzLzOF0WjbxGRbsl5TC8tDr2HboAt9sP02GxUbdEC++fqwNjav6Vli8QghRrETq3Xffdf67Zs2aTJ48mW3bttGoUSP0etf5GMaOHVu6EQohhBDippWZa+PIxUzOpFuw2v9XwMpTr6Oql5ndxy8yecVBMq12DDotE+6pzWMda6CXKnxCiApWrPLn0dHRxTuYRsPx48evO6jyJuXPhRBCiPKXlpPLr6cvkvP/CZRRp6Wql4kIbzMX03N44ft97DiRDMBtkX5Mv68RNYO9KjJkIcQtoFTLn8fGxpZaYEIIIYQQqZZctpzJS6K8DW40DPYi2N2I3aH4z+bjvPPLEaw2B+4GHc93q8vDt0c6J9gVQojKoMTt4q+++ipZWVkFlmdnZ/Pqq6+WSlBCCCGEuHklW3L59XQSOXYHvkY32lcLIMTDxIG4NPq+/xtvrTmE1eagQ+0gfn6mA0PbRkkSJYSodIrVte9SOp2O+Ph4goODXZYnJSURHByM3W4v1QDLg3TtE0IIIcrHxWwrv525SK5D4WfS066qPw6H4p1fjvCfzcexOxS+7nom96zPvc3DZU4oIUS5K9WufZdSShX6ofbnn3/i7+9f0sMJIYQQ4haR9P9JlM2h8P//JGrPqRQmffcXxy9kAtCzcSgxvRsQ5GWs4GiFEOLKip1I+fn5odFo0Gg01K5d2yWZstvtZGRkSEl0IYQQQhTqQlYOv59JxqYUgWYDjQK9mPLjfj7fdgqAYC8jU/s1pGuDkAqOVAghiqfYidScOXNQSjFixAimTJmCj4+Pc53BYCAqKoo2bdqUSZBCCCGEuHElZuaw9WwydqUIcjdgzcylxzu/Ep9qAeDBlhG80KMePmb9VY4khBCVR7ETqaFDhwJ5pdDbtWuHm1uJewUKIYQQ4hZzLjOHrWcv4lDgZ9Tz/e+nWLYnDoBq/u68eW8j2tYMrOAohRCi5IqVDWVmZuLh4QFAx44dS7S9EEIIIW5N8RkWtscl41CAXfHckr2cT89Bq4FH2lfnmS61MRt0FR2mEEJck2KVP69ZsybTpk0jLi6uyG2UUqxdu5bu3bvz7rvvllqAQgghhLjxxKVb2HY2L4mKT8pm4ud5SVTdEC+WjW7Hv3vUkyRKCHFDK1aL1MaNG3nppZeYMmUKTZs2pUWLFoSFhWEymUhOTubAgQNs3boVvV7PCy+8wKOPPlrWcQshhBCikjqTns2OuBQUsO9kCos2HMdNq+Xpu2vxeMcaGNxKPI2lEEJUOiWaR+rMmTN88803bN68mRMnTpCdnU1gYCDNmjWja9eu9OjRA632xvtwlHmkhBBCiNJxOi2bHfEpAOw8msTSLSdpGuHL9PsaU6uKV8UGJ4QQxVDc3KDEE/LejCSREkIIIa7f8eRM9pxLRaPRsP3wBX7aeZYJ99RhSJsodFqZWFcIcWMoswl5hRBCCCEut+VEEucsOWg0GrYeOk/C+WxWP92BCH/3ig5NCCHKhCRSQgghhLhmllw7C/84SXCgGY1Gwx+HL9A63J/7eoej0UgrlBDi5lXpBzSlp6czbtw4IiMjMZvNtG3blh07djjXK6WIiYkhLCwMs9lMp06d2L9/fwVGLIQQQtwa/oi9yAs//E1woBmAkwkZPNuxFvffVlWSKCHETa/SJ1KPPPIIa9euZfHixezbt4977rmHLl26cPbsWQBmzJjBrFmzeP/999mxYwchISHcfffdpKenV3DkQgghxM0p3ZLL5OV/M/e347RvGAyA3gHjOtQk2NtUwdEJIUT5KHGxiVOnThEREVHglyalFKdPn6ZatWqlFlx2djZeXl7897//pWfPns7lTZs2pVevXkydOpWwsDDGjRvH888/D0BOTg5VqlRh+vTpPPbYY8U6jxSbEEIIIYpnwz+JvLhsH/WjfOnRPAyAaG8zTUN8pBVKCHFTKG5uUOIWqejoaM6fP19g+cWLF4mOji7p4a7IZrNht9sxmVx/3TKbzWzZsoXY2FgSEhK45557nOuMRiMdO3bk999/L/K4OTk5pKWluTyEEEIIUbSLmVbGLd3D8IU7aFzdz5lE1Q/0pFmoryRRQohbTokTKaVUoR+WGRkZBRKe6+Xl5UWbNm2YOnUqcXFx2O12Pv/8c7Zv3058fDwJCQkAVKlSxWW/KlWqONcV5o033sDHx8f5iIiIKNW4hRBCiJtFjs3Od7vO0GXWJpbvjaNXizDuaRoKQMMgL+oGyNxQQohbU7Gr9j377LMAaDQaJk+ejLv7/8qZ2u12tm/fTtOmTUs9wMWLFzNixAjCw8PR6XQ0b96cQYMGsXv3buc2hXUzvNIvYy+88ILzeiCv+U6SKSGEECKP3aHYHpvEf/fEsfLveNItNgCGdYqmcbQfAI2Dvanp51GRYQohRIUqdiK1Z88eIC9J2bdvHwaDwbnOYDDQpEkTJkyYUOoB1qhRg02bNpGZmUlaWhqhoaEMHDiQ6OhoQkJCAEhISCA0NNS5T2JiYoFWqksZjUaMRmOpxyqEEELcqJRS7I9L4797z/Ljn/EkpFmc66p4GxnTrTYeHnoAmgR7U0OSKCHELa7YidSGDRsAGD58OO+88065F2Xw8PDAw8OD5ORk1qxZw4wZM5zJ1Nq1a2nWrBkAVquVTZs2MX369HKNTwghhLgRnUzK5L974/jv3rMcO5/pXO5tcqNvszDurB+Ch4cbZ9LzEqtmVXyI9pVJdoUQosQT8i5YsKAs4ijSmjVrUEpRp04djh49ysSJE6lTpw7Dhw9Ho9Ewbtw4pk2bRq1atahVqxbTpk3D3d2dQYMGlWucQgghxI3ifHoOP/0Vx3/3xrH3dAoAfh4GmkX7cUftQGqGeOGm15Jjd5DssJGcnte177YQHyJ9JIkSQgi4hkQqMzOTN998k19++YXExEQcDofL+uPHj5dacACpqam88MILnDlzBn9/f+677z5ef/119Pq87gXPPfcc2dnZjB49muTkZFq3bs3PP/+Ml5cMfhVCCCHypVty+Xn/OZbvPcuhxHTC/N2pWsWd2+sHEhXkgUGvc25rB+z2vP+/exnc8DXpqeZtpoqHdIsXQoh8JZ5H6qGHHmLTpk08/PDDhIaGFijq8PTTT5dqgOVB5pESQghxM8rOtbHx8Hl2nk4hxZJLiJ+JcH93TAZdgW01gLcxL2nyNerxNenxMbrhpi1xgV8hhLihFTc3KHGL1KpVq1ixYgXt2rW7rgCFEEIIUXrsDkWa1UZytpXD5zNIzMzBaNChd9PSINrXZVsN5CVMlyRN3gY3dFqZC0oIIYqrxImUn58f/v7+ZRGLEEIIIUrI5lD8fT6N4ylZLss93fO6wOfk2rHbHIR5m6kR4IGvSY+XwQ2tTKArhBDXpcSJ1NSpU3n55ZdZtGiRy1xSQgghhChfyRYrW88kY/n/8UyZFhtnkrI4n2ohxNPE7dH+tK1ZBTeddM8TQojSVuJEaubMmRw7dowqVaoQFRXlLPqQ79KJcoUQQghR+hxKcSgpgwMX0tFoNKRmWfnu99NU8zXTt2k4nTrUxOhWcByUEEKI0lPiRKpfv35lEIYQQgghiiPDamPLqSSy7A40Gg17Y5M5diaNdwY0pUaQZ0WHJ4QQt4wSV+27GUnVPiGEEJWdUorjKVnsSUhFq9VgsdpZsfMs3epWYXDrSLRSKEIIIUpFmVXtA0hJSeHbb7/l2LFjTJw4EX9/f3bv3k2VKlUIDw+/5qCFEEIIUVCOzc7GE0lk2u1otRqOJaRz/Gw6M/o3JtzXXNHhCSHELanEidRff/1Fly5d8PHx4cSJE4waNQp/f3+WLVvGyZMn+eyzz8oiTiGEEOKWdCo1i21nknFz02KzO9j09zm61KrChA61CszlKIQQovyUuIzPs88+y7Bhwzhy5Agmk8m5vHv37mzevLlUgxNCCCFuVTaHYvXhc+xMSMXNTUtCcjZ7D1/k1R4N6N8sXJIoIYSoYCVukdqxYwcff/xxgeXh4eEkJCSUSlBCCCHErex0aha/nkzCZMz73/TOI0l0rh7I6NurV3BkQggh8pU4kTKZTKSlpRVYfujQIYKCgkolKCGEEOJW5FCKVf+cIwsHJqMbqZlWzl3IZlKXOnib9Fc/gBBCiHJT4q59ffv25dVXXyU3NxcAjUbDqVOnmDRpEvfdd1+pByiEEELcCk6nZLFo9ylytAqdVsORs2nU8fFg0j11JYkSQohKqMTlz9PS0ujRowf79+8nPT2dsLAwEhISaNOmDStXrsTDw6OsYi0zUv5cCCFERXE4HCz/Ox6rVmHQ68i22klJsTCidRRmwzUV1xVCCHEdyqz8ube3N1u2bGH9+vXs3r0bh8NB8+bN6dKly3UFLIQQQtxqTlzMZMU/CYQG/F97dx4eVXn3f/x9Zskkmcxk34Ek7LKLgIiKopWi1rrWvaKoxVpb1MfaaquiVag+LW615Wn7E/el7rS1FlrQiksF2UERkCUQQiDLzGSS2c/vj0AwsmUkYbJ8Xtc1V5IzZ4ZvPB7IJ/d9f+9UkoAdNY2cUJzJyKE9El2aiIgchjbkRSNSIiJydMViJi8tLSdsA1eKnUg0RoM/wuXH9iDJZk10eSIi3Vq7bsj7ySef8O6771JVVUUsFmvx3KxZs77JW4qIiHQLX1T5eGN1Bf2K3SQDdfUhRhdmMHRQeqJLExGROMQdpGbMmMEvf/lLBgwYQH5+fot9LLSnhYiIyIGFozHmfLQZ02HQr7jpN5yhxgjfH94Tuy3u3k8iIpJgcQepRx99lCeffJKrr766HcoRERHpOio9AT7ZXMOyrbU0mDFG9cvGajFoDEYYkZ/O4AGaTi4i0lnFHaQsFgsnnnhie9QiIiLSacViJht31fPJ5hqWbK5l8eYaQjGTkwflMqpPFg77nrVPkRgXDSrCobVQIiKdWtxB6pZbbuGJJ57gkUceaYdyREREOodQJMaq7R4Wb65hyeYalmyppa4hjAEMKHZz1qgiBvbYt+7JiMHgXBf9sp2aCi8i0gXEHaRuu+02zj77bPr06cOgQYOw21tuEvj666+3WXEiIiIdhTcQZumWppGmxZtrWVFeRzCyr+FSks3CqYPzmDAkH1fqvn8bC9Mc9M10kpOSpAAlItKFxB2kfvzjH7Nw4UImTJhAdna2/lEQEZEuqdITaB5t+mRzLZ9Xevn6hiFZziRO7p/DmH7ZOJ129sYqm8WgND2VPhmpOLWprohIlxT33+7PPPMMr732GmeffXZ71CMiIpIQnsYwb6/aweJNNSzeUkN5TeN+55RkpzKqJIvRpZkc08NNQyzGDn8QgBiQZrfSJ9NJr/QU7BZ14hMR6criDlJZWVn06dOnPWoRERFJiH+uqeSuN1dT5Qs2H7MYMKjIvSc4ZTGqNJOcNAflvkY21vrZ6NsXtPJSk+iT6aTA6dBMDRGRbiLuIDV9+nTuuece5syZQ2pqanvUJCIiclRU+QJMn7uGt1dVAlCancp3hxcxqjSLY3tl4EpuWuvUGInyZW0Di6s8hKJNE/isBvRyp9InMxW3w37QP0NERLqmuIPUY489xsaNG8nPz6e0tHS/ZhNLly5ts+JERETag2mavLZ0O7/621o8jWGsFoOp43vzk9P7kWzf15a8pjHExlo/23wB9i6PSrFZ6JPhpDQjlSSrpu+JiHRXcQep8847rx3KEBEROTrKaxq4841VvL9+NwCDi9w8dNEwBhc1tSqPmSbbfQE21vqpCYSbX5edYqdvppPCtGQsmr4nItLtGab59R5E3Y/X6yU9PR2Px4PbrV3mRUS6omjM5JmPNvO//1xHQyiKw2bh5m/15/qTy7BZLfhDEcp9Ab6s8xPY09bcYkAPVwp9Mp1kJmv6nohId9DabBD3iNTVV1/NlClTGD9+/BEVKCIicrSs3+nj9tdWsmxrHQBjyrJ44PwhpKXaWbPbx05/kPpwtPl8h9VCWUYqvTNSSbZZD/KuIiLSncUdpHw+HxMnTqRnz55cc801TJ48meLi4vaoTURE5IiEIjFmv7eR3y3YQCgao09+Gtef2pvi7FTW1NUTq913rgFkpSRRmp5CD1cKVoum74mIyMF9o6l91dXVPPfcczz11FOsXr2ab33rW1x77bWce+65+zWf6Aw0tU9EpOtZUV7H3XNXY9gsDCx2M6wkg+SklqNLqTYreU4H+U4HealJ2NU8QkSk22ttNjjiNVLLli3jySef5M9//jNpaWlceeWV3HjjjfTr1+9I3vaoUpASEekaYqZJpS/AX9fsoDEao0dOaovGEFYDclKbglO+00Ga3ap9n0REpIV2WyP1VTt27GDevHnMmzcPq9XKWWedxZo1axg0aBAPPfQQt9xyy5G8vYiIyGE1hKPs9AfZ6Q+yo76pTXluVkrz806blSJXMvlOB9kpSZqyJyIibSLuIBUOh5k7dy5z5sxh3rx5DBs2jFtuuYUrrrgCl8sFwEsvvcQPf/hDBSkREWlzkZjJ7sam4FTlD+ILRVs87w9E2LrLz8geGZzaJ5cUu5pFiIhI24s7SBUWFhKLxbjsssv45JNPGDFixH7nfPvb3yYjI6MNyhMREWkSjMZYvtPDjvoAsa9OSjdhW3UDq7fW8fl2L+P75vCzSQNxqV25iIi0o7iD1MMPP8z3vvc9kpOTD3pOZmYmmzZtOqLCRERE9vKFIny4rQb/nhblKTYL6Ul25q2q5KWPtxIIRSnLcfK/Fwzj+N7ZCa5WRES6g7iD1Pe///32qENEROSAqvxB/ltRSzhmkmqzMrownXc/28XP/7YCT2MYq8Xgh6f2Ydrp/UjWND4RETlKvlGzicWLF/PKK6+wdetWQqFQi+def/31NilMRERkU10Dy3d6MIGsZDslacnc8tIK/vPFLgAGFbp56KJhDClOT2yhIiLS7cS9YcZLL73EiSeeyNq1a3njjTcIh8OsXbuWBQsWkJ6uf8hEROTImabJqiovy/aEqB6uZGwhk+8+9gH/+WIXSTYLt08awFs3nagQJSIiCRH3iNSMGTN4+OGH+dGPfoTL5eLRRx+lrKyMqVOnUlhY2B41iohINxKJxVi8o44d9UEAjslO47OtHm5/dSWRmMmxvTL4zfeG0yc3LcGViohIdxb3iNTGjRs5++yzAXA4HPj9fgzD4JZbbuGPf/xjmxcoIiLdR0M4yntbq9lRH8RiwOiCdBas2smtf1lBJGZyzvAiXvrBWIUoERFJuLiDVFZWFj6fD4Di4mJWr14NQF1dHQ0NDW1bnYiIdBs1jSEWbtmNJxjBYbUwrjiT2Qs28r//XAfA1PG9efSSEThsaighIiKJF/fUvpNPPpn58+czdOhQLr74YqZNm8aCBQuYP38+p59+envUKCIiXdx2XyNLdtQRNcGdZOPYPDc/e3Ul//qsCsOAe74ziKtPLEt0mSIiIs3iDlK/+93vCAQCANxxxx3Y7XYWLVrEBRdcwF133dXmBYqISNdlmiZf1PhZs7tppkO+00EfdypTnlrCivI6HDYLj146gklDtAZXREQ6FsM0TfPwp3VtXq+X9PR0PB4Pbrc70eWIiHQL0ZjJsp0etnobAeiTkYrLYuWapxazpbqBjFQ7f75qFKNKsxJcqYiIdCetzQbfaB+paDTKG2+8wWeffYZhGBxzzDGce+652Gzf6O1ERKSbCUZifFxRQ3VjGAMYlufG4wtx0dOfUOMP0SMzhaenjFFTCRER6bDiTj6rV6/m3HPPpbKykgEDBgDwxRdfkJuby9y5cxk6dGibFykiIl2HNxjmo+21+MNRbBaD44syWbG5lp+8tIxAOMbQ4nT+39WjyHMlJ7pUERGRg4q7a991113H4MGD2bZtG0uXLmXp0qWUl5czbNgwfvCDH7RHjSIi0kXs9Ad5b2s1/nCUVLuVU3tlM2/lDm547lMC4RinDsjlpR+MVYgSEZEOL+4RqRUrVrBkyRIyMzObj2VmZvLAAw8wevToNi1ORES6ji/r/KzY6cUEslPsjCnI5NF/r2f2exsBuHR0T+4/bwg2a9y/4xMRETnq4g5SAwYMYOfOnQwePLjF8aqqKvr27dtmhYmISNdgmiardvnYUOsHoKc7hSE5Lu54bSVvLq8A4NYz+vPj0/piGEYiSxUREWm1uIPUjBkz+MlPfsL06dMZO3YsAB9//DH33XcfDz74IF6vt/lcdcATEenewrEYiyvqqPQHARiUk0ZhqoNrn1rMhxursVkMZl4wlO+N6pngSkVEROITd/tzi2XflIu9vznc+xZf/dowDKLRaFvV2a7U/lxEpO01hKN8uK0GbyiCxYBRBRlYTbhmzmI+r/ThTLLy+yuP45T+uYkuVUREpFm7tT9fuHDhERUmIiJdX01jiI+21xKMxnBYLZxQnEmVJ8DVTy6m0hsg1+VgztWjGVKcnuhSRUREvpG4g9Qpp5zSHnWIiEgXsc3byJLKOmImpDtsnFCcxfIttUx99lN8wQh989J46prR9MhMTXSpIiIi39g3ao30/vvvc+WVVzJu3Di2b98OwLPPPsuiRYvatDgREek8TNNk7W4fn+xoClEFTgfje2Uzf00lk+d8gi8YYUxpFq/ecIJClIiIdHpxB6nXXnuNb3/726SkpLB06VKCwaYFxD6fjxkzZrR5gSIi0vGFozE+rqjl8+p6APpmOhlblMGf/7OJaS8tJxw1OXtoIc9cO4aM1KQEVysiInLk4g5S999/P7Nnz+ZPf/oTdru9+fi4ceNYunRpmxYnIiIdny8U4d2t1eyoD2IxYGRBOoNzXNwzdy0PvvM5ANedVMbjlx1Lst2a4GpFRETaRtxrpNatW8f48eP3O+52u6mrq2uLmkREpJOorA+weEcd4ZhJss3C2KJMUqxWbnjuU+av3YlhwC/PHsS1J5UlulQREZE2FXeQKiwsZMOGDZSWlrY4vmjRInr37t1WdYmISAdmmiZf1PhZs9sHQFaynbHFmdQHIlw+52OWba0jyWbhkUtGcNbQwgRXKyIi0vbinto3depUpk2bxn//+18Mw6CiooLnn3+e2267jRtvvLFNi4tEIvzyl7+krKyMlJQUevfuzX333UcsFms+5+qrr8YwjBaPvRsFi4hI24vEYnyyo645RJWmpzK+VzYfb6zmzEffZ9nWOtJT7Dx/3fEKUSIi0mXFPSJ1++234/F4mDBhAoFAgPHjx+NwOLjtttu46aab2rS4Bx98kNmzZ/P0008zePBglixZwjXXXEN6ejrTpk1rPm/SpEnMmTOn+eukJC1kFhFpD/5QhI+21+INRTCA4fluipzJ3PfXtTz14WYA+uWl8Ycrj6NvXlpCaxUREWlPcQcpgAceeIBf/OIXrF27llgsxqBBg0hLS6OhoYHU1LZrafvRRx9x7rnncvbZZwNQWlrKiy++yJIlS1qc53A4KCgoaPX7BoPB5m6D0LR7sYiIHFqVP8gnFbWEYiYOq4XjizLZWdfIOU8uYn1VU7e+q8eV8vMzB6qphIiIdHnfaB8pgNTUVEaNGsWYMWOw2WzMmjWrzddInXTSSfz73//miy++AGDFihUsWrSIs846q8V57777Lnl5efTv35/rr7+eqqqqQ77vzJkzSU9Pb3707NmzTesWEelKTNNkfU09i7bVEIqZZCbbObVnNq8vKef8Jz5kfVU9OWkO5lwzmunfHawQJSIi3YJhmqbZmhNDoRD33nsv8+bNw263c/vtt3PeeecxZ84cfvGLX2AYBjfddBN33HFHmxVnmiZ33nknDz74IFarlWg0ygMPPNDiz3j55ZdJS0ujpKSETZs2cddddxGJRPj0009xOBwHfN8DjUj17NkTj8eD2+1us/pFRDq7aMxkaWUd5b4AAL3cKRSmJPHTV1by4cZqAL51TD4PXjiU7LQD/50rIiLSmXi9XtLT0w+bDVo9tW/69Ok88cQTnHHGGXzwwQd873vfY8qUKbz77rvMnDmTyy+/vMW+Um3h5Zdf5rnnnuOFF15g8ODBLF++nJtvvpmioiImT54MwCWXXNJ8/pAhQxg1ahQlJSX8/e9/54ILLjjg+zocjoOGLBERadIQjvLx9hrqgk3roYblufms3Mu1b3yCpzFMit3K3ecM4tLRPTEMI9HlioiIHFWtDlJ/+ctfeOqppzj//PNZsWIFxx57LF6vlzVr1mCzfaOlVof105/+lJ///OdceumlAAwdOpQtW7Ywc+bM5iD1dYWFhZSUlLB+/fp2qUlEpDvY1RDkk4o6gtEYSVYLQ3PSeGzeBl5bug2AYT3SeeSSEfTOVUMJERHpnlqdgMrLyxk9ejQAw4cPJykpiZ/97GftFqIAGhoasFhaLuOyWq0t2p9/XXV1NeXl5RQWquWuiEi8TNPky7oGVlZ5MYF0h400LFz5x/9SXtOIxYAbT+3LtG/1w279xstsRUREOr1Wp6BwONyirbjdbic9Pb1ditrrnHPO4YEHHqBXr14MHjyYZcuWMWvWLKZMmQJAfX0906dP58ILL6SwsJDNmzdz5513kpOTw/nnn9+utYmIdDXRmMnyKg9bPI0AFKcl8+HaKh5fsIGYCcUZKTx8yQjGlGUluFIREZHEi2s46e67725ubx4Khbj//vv3C1OzZs1qs+Ief/xx7rrrLm688UaqqqooKipi6tSp3H333UDT6NSqVat45plnqKuro7CwkAkTJvDyyy/jcrnarA4Rka6uMRzl44paagNhAIpTHcx4cy0ryusAOP/YYu49dzDu5LZdCysiItJZtbpr36mnnnrYxcSGYbBgwYI2Kexoam1nDhGRrqi6McTH22sJRmPYLQYNvjD3vrmGhlAUV7KNB84fyneHFyW6TBERkaOizbv2vfvuu21Rl4iIdCCb6hpYvtODCThtVt5esp23llUAMLZ3Fr+9eATFGSmJLVJERKQDar9OESIi0mHFTJMVO71s8jQAkITBfa+sZntdI3arwf9MHMD1J/fGalFbcxERkQNRkBIR6WYCkSj/railurFpPdSOXQ38798+B6BPrpNHLz2WIcXt20xIRESks1OQEhHpJqIxk02eBtZV1xOMxrAAf1u8nX+t3gnAVSeUcMeZx5CSZE1soSIiIp2AgpSISBcXM0027wlQjZGmffiikRi/+ds6KmobyUlL4qGLhnHawPwEVyoiItJ5KEiJiHRRMdOk3NvI2t2+5gAVCkf55Itq3lqynWjM5PSBeTx40TBy0hwJrlZERKRz+UZB6v333+f//u//2LhxI6+++irFxcU8++yzlJWVcdJJJ7V1jSIi0grhaIxNu/18Vulju7eRNJedtJSmfZ+8DWH+vbKSj77YTSRqkmy3cO93B3PF8b0Ou7WFiIiI7C/uIPXaa6/x/e9/nyuuuIJly5YRDAYB8Pl8zJgxg7fffrvNixQRkX1M06TSG+DzSh/r9jw+2+Hly11++he7mHRsEUV5TZun+wMRFqzayYbtXnrnpnHtiWUMLHQxtnc2helqay4iIvJNtXpD3r2OPfZYbrnlFq666ipcLhcrVqygd+/eLF++nEmTJlFZWdletbYbbcgrIh2VLxDmi52+5tC096NnT8e9vQYWu5l0bCG9cp0ARKIxosEYpe4Ujil0k5GalIjyRUREOp0235B3r3Xr1jF+/Pj9jrvdburq6uJ9OxER+YrNu/0s+LyKDzdW83mll221jQc8z2ox6J3j5Pi+2QwuzcDhaOq0ZzUM+mY66ZflJMlqOZqli4iIdCtxB6nCwkI2bNhAaWlpi+OLFi2id+/ebVWXiEi3EIrEWLK5hgWfV7Hg8yq+3O3f75wCdzIDClwMLHAxYM8jy+1gQ62fXQ0hACwG9Mlw0j/LicOm9uUiIiLtLe4gNXXqVKZNm8aTTz6JYRhUVFTw0Ucfcdttt3H33Xe3R40iIl3KLl+Qd9dVsXBdFf/5Yjf1wUjzczaLwZiyLE4dkMuwHhkMLHC1mJZXGwizdrePddtrATCAsoxUBmSnkaIAJSIictTEHaRuv/12PB4PEyZMIBAIMH78eBwOB7fddhs33XRTe9QoItKpxWImayq8TaNO66pYua2Or65OzXYmMWFgHqcNzOOkfjm4k+37vYcnGOaz3T4q6psa/BhASXoKA7PTSLVrJwsREZGjLe5mE3s1NDSwdu1aYrEYgwYNIi0tra1rO2rUbEJE2po/GGHRht0s+Kxp5KnKF2zx/JBiN6cNyOO0Y/IZVpyOxXLgFuS+UITPdvvY5gs0H+vpTuGY7DTSkhSgRERE2lq7NZvYKzU1lVGjRn3Tl4uIdDlbqv3Na53++2UNoWis+bnUJCsn9c3htIF5TBiYR747+ZDv5Q9F+Ky6nq3efc0mitOSOSYnDbdj/xErERERObriDlKBQIDHH3+chQsXUlVVRSwWa/H80qVL26w4EZGOLByNsXhzDQs/r+Lfn1fx5a6WjSJ6ZaVy2p4pe8f3zjpoE4hwNIYnGMETDOMJhqkLRvAEwuydLlDgdDAox0XGAab8iYiISGLEHaSmTJnC/PnzueiiixgzZgyGceDpKCIiXZUvEGbG25/ztxUV+L7WKGJUaSanD8xnwsA8+uQ6W/wdaZomjZFYU1gKhJvDkz8cPeCfk5eaxKAcF1kp2gNKRESko4k7SP3973/n7bff5sQTT2yPekREOrTPK7388LmlbNrTpjzbmcQpA3I5fWA+J/ff1ygiZpp4gxHqgvsCkycQJhQ78LLUFJuFdIeddIedjGQbGQ47Tq2BEhER6bDi/le6uLgYl8vVHrWIiHRor366jV++uYpAOEZhejL/e9FwTuiTTdQ08QTDVDWG+KLWjycYxheKcKDMZACuJBsZyXbSHbbm8OSwafNcERGRziTuIPXb3/6Wn/3sZ8yePZuSkpL2qElEpEMJhKNMn7uGlxaXA3DqgFxuPWsA/kiU+Zt30XCQqXk2i0GGY09gSm4KTO4kG9aDdOgTERGRziPuIDVq1CgCgQC9e/cmNTUVu73l4ueampo2K05EJNG2VPv54XNLWbvDi2HAHd85hpLCNDbUNbQ4L9VmJT25aYQpw2EnPdlGqs2qdaQiIiJdVNxB6rLLLmP79u3MmDGD/Px8/ZAgIl3WP9dUctsrK/AFIhxT7ObGiX0JA/5wlGSrhb5ZTjL3jDQlWTU1T0REpDuJO0h9+OGHfPTRRwwfPrw96hERSbhwNMZD73zOn97fhCvFxo++3Y8+RS7CgMWA/llp9M9yYrMoPImIiHRXcQepgQMH0tjYePgTRUQ6oUpPgJteWMqKbXV8a1gBk44txLJnTVNPVzKDc92k2g+8H5SIiIh0H3EHqV//+tf8z//8Dw888ABDhw7db42U2+1us+JERI6mDzbs5icvLqNnnpM7LhxMhrNp/6asZDvD8tzaz0lERESaGaZpHnhTk4Ow7JnK8vW1UaZpYhgG0eiBu1d1ZF6vl/T0dDwej4KgSDcUi5k8sXADryzfxrmje1CWnwY07e00JNdND1ey1oOKiIh0E63NBnGPSC1cuPCIChMR6Uhq/SHueHMV2ZnJ3PydgQBYDYMB2U76ZqZhU6tyEREROYC4g9Qpp5zSHnWIiBx1n26p4cVl2zh1RAFJNgumaVKSnsrgXBcpNq2DEhERkYNrVZBauXIlQ4YMwWKxsHLlykOeO2zYsDYpTESkvcRiMV5cto2QxWTcMbkApFgtjO2RRWay/TCvFhEREWllkBoxYgSVlZXk5eUxYsQIDMPgQEurOusaKRHpPso9jfx7YxXutCRSgIZAhON7ZNI3y6l1UCIiItJqrQpSmzZtIjc3t/lzEZHOxh+K8FF5Dd5IFLcziUAoSiwU49IRPbBpM10RERGJU6uCVElJSfPnW7ZsYdy4cdhsLV8aiUT48MMPW5wrIpJo4WiMdTX1fFFdD4ZBLGayaksdZw/MZ0xpdqLLExERkU4q7mYTEyZMYMeOHeTl5bU47vF4mDBhgqb2iUjCRWMmgUiUqoYQa3Z7CUVNMAy+qPBSXunnvnMGk53mSHSZIiIi0onFHaT27hf1ddXV1TidzjYpSkTkYPaGpMZIlMZIjIbw3s+jNIZjNEaiBKOxFq+p8gT46+LtnDEgj8cuORarWpqLiIjIEWp1kLrggguApoYSV199NQ7Hvt/mRqNRVq5cybhx49q+QhHpFGIxk/pQBF8ggi8QbvHR+7Vj3sZ9X4e/EnosBqQm23Am20h1NH10OmxfOWYlxdG6v7ai0Rg19SE++Hw3a7bW8fDFIxjfP7e9vn0RERHpZlodpNLT04GmESmXy0VKSkrzc0lJSYwdO5brr7++7SsUkYQJRWK8vWoHFZ7GrwWklp97A2HqgxEO0MzzoHLdDkb2zqI4O4X01CQynHZcKa1rPR6OxKhrCOHxh6n1N32sawhR5w/j8Td99AcjAIzslcFfbzqJooyUw7yriIiISOu1OkjNmTMHgNLSUm677TZN4xPp4kzT5Ja/LOfvK3fE9Tq71cCVbMeVbGt6OJo+d6fYyXDayc9MId2VhM1+kE55JlgAg6aPFsAwjX2fA4bFSk5aKqQdupaUJCujS7OwqyufiIiItLG410jdc889RCIR/vWvf7Fx40Yuv/xyXC4XFRUVuN1u0tIO85ONiHQKry/dzt9X7sBqMTh3RBHuZDvuPYGoKSR9/aMNd7Idh83SYh1lJGayoz7AVm8jVf4gewetDCDP6aDA6SDVbiXFZiXFbiXJYmg/JxEREenw4g5SW7ZsYdKkSWzdupVgMMgZZ5yBy+XioYceIhAIMHv27PaoU0SOoq3VDdz91moAbj69Hz8+vV9crzdNk10NIbZ6G6nwBYh8Zc5fZrKdXu4Uil3JJNusbVq3iIiIyNESd5CaNm0ao0aNYsWKFWRn79uD5fzzz+e6665r0+JE5OiLRGPc/PIy/KEoo0szuXFC31a/1hMIs9XbSLmvkUBkXxOJVLuVnu4UerlTcCXF/deOiIiISIcT9080ixYt4oMPPiApKanF8ZKSErZv395mhYlIYjyxcCNLt9bhctiYdfGIw7YKbwxHKfc1Uu5txLOnwQOA3WLQw5VCT3cK2Sl2TdcTERGRLiXuIBWLxQ646e62bdtwuVxtUpSIJMbSrbU8tmA9APedN5ieWakHPC8ci1Hha1r3tKsh1HzcYkCBM5me7hQKnA7t1yQiIiJdVtxB6owzzuCRRx7hj3/8I9C0r1R9fT333HMPZ511VpsXKCJHR30wwi0vLycaMzlneBHnjShu8XzMNKnyB9nqbWRHfYDoV1qdZ6fY6eVOpdiVTJI65ImIiEg3EHeQevjhh5kwYQKDBg0iEAhw+eWXs379enJycnjxxRfbo0YROQrunbuGLdUNFGekcP95QzAMA9M0qQuG2eppZJsvQPArm+em2a30Sk+hpysFp9Y9iYiISDcT908/RUVFLF++nJdeeolPP/2UWCzGtddeyxVXXNFik14R6Tz+sWoHr3y6DcOA3148nPQUO75QhJVVXnb6g83nOawWeriapu5lJmvdk4iIiHRfhml+pS/xQYwcOZJ///vfZGZmct9993HbbbeRmnrgtROdkdfrJT09HY/Hg9vtTnQ5IkdVpSfAtx/5D57GMD88tQ//M7E/n1fXs77Gj0nTfk/FrmR6uVPIczqwKDyJiIhIF9babNCqxQyfffYZfr8fgHvvvZf6+vq2qVJEEioWM/mfV5bjaQwzpNjNxcf3ZN6mXXyxJ0TlOx18qyyXMUWZFKQlK0SJiIiI7NGqqX0jRozgmmuu4aSTTsI0TX7zm9+QlpZ2wHPvvvvuNi1QRNrPkx9s4oMN1ZTmpvLjswawrMoLgNNuZViemwKnQ9P3RERERA6gVVP71q1bxz333MPGjRtZunQpgwYNwmbbP4MZhsHSpUvbpdD2pKl90h2trfBy2Z8+5vThBZw4MAfDMLAaBgOynfTLTFPrchEREemWWpsNWhWkvspisVBZWUleXt4RF9lRKEhJd9MYivDzuas5rl82aclNvxQpdiUzNNdNqt2a4OpEREREEqe12eAbbcgrIp1XdWOIf3yxk1OG5gPgtFkZWZhObqojwZWJiIiIdB6tClJz587lzDPPxG63M3fu3EOe+93vfrdNChORttUYibJml4+t3kZSk200BiNkOeyc0TtXTSRERERE4tSqqX1fnc5nsRy80Z9hGESj0TYt8GjQ1D7pymKmyYZaP59X1xOJmZimyX/XV+OyWLnr7EGJLk9ERESkQ2nTqX1fnc6nqX0incdOf5AVVR7qQ02/4KirDzFn4ZekWC3MvemkBFcnIiIi0nm1ah+p1igvL2fKlClt9XYicgT84Qgfb6/hg2011IeiOKwWQg0RfvXKanbWBnjkkmNJVlMJERERkW+szYJUTU0NTz/9dFu9nYh8A9GYydrdPuZv2kVFfRAD6JvppJ8rlemvr8YEfvrtAQwq0hRWERERkSMRd9c+Eel4TNOkoj7AqiofDZGmaXy5qUkMz3OTbLVy4R8+JBCOcWLfbK49qSzB1YqIiIh0fgpSIp2cNxhmZZWXqoYQACk2C8Py3BSlJWMYBg+98zmrtnvISLXz2++NwKKNdkVERESOWJtN7WsPkUiEX/7yl5SVlZGSkkLv3r257777WjS8ME2T6dOnU1RUREpKCqeeeipr1qxJYNUiR4e5pxvfgi27qWoIYTFgYHYaZ5TlUexKwTAMPv6ymj+8txGAmecPpSA9OcFVi4iIiHQNrR6RuuCCCw75fF1d3ZHWsp8HH3yQ2bNn8/TTTzN48GCWLFnCNddcQ3p6OtOmTQPgoYceYtasWTz11FP079+f+++/nzPOOIN169bhcrnavCaRjqAxEuXTHXXNo1D5Tgcj8tw4k/bd0p7GMLe+vBzThO8d14MzhxYmqlwRERGRLqfVQSo9Pf2wz1911VVHXNBXffTRR5x77rmcffbZAJSWlvLiiy+yZMkSoOk38o888gi/+MUvmoPe008/TX5+Pi+88AJTp05t03pEOoIKX4ClO+sIRU2sBgzNdVOWkYrxtU1173pzNRWeACXZqdzz3cEJqlZERESka2p1kJozZ0571nFAJ510ErNnz+aLL76gf//+rFixgkWLFvHII48AsGnTJiorK5k4cWLzaxwOB6eccgoffvjhQYNUMBgkGAw2f+31etv1+xBpC5FYjJVVXjZ7GgFId9gYXZiB22Hf79w3l21n7ooKrBaDhy8ZQZpDyyFFRERE2lKH/unqZz/7GR6Ph4EDB2K1WolGozzwwANcdtllAFRWVgKQn5/f4nX5+fls2bLloO87c+ZM7r333vYrXKSN1QZCLK6ooz7c1JGvf5aTQTkuLMb+jSPKaxq4683VAPzktH6M7JV5VGsVERER6Q46dLOJl19+meeee44XXniBpUuX8vTTT/Ob3/xmv/2qvj6lyTTN/Y591R133IHH42l+lJeXt0v9IkfKNE3WVdfz7pZq6sNRkm0WTu6ZxZBc9wFDVDRmcutfluMLRjiuJJMfTeiTgKpFREREur4OPSL105/+lJ///OdceumlAAwdOpQtW7Ywc+ZMJk+eTEFBAdA0MlVYuG8hfVVV1X6jVF/lcDhwOBztW7zIEWoIR1iyw8PuxqaGEsWuZI7NTyfJevDff/zh3Q0s3lxLmsPGwxePwHaIc0VERETkm+vQP2U1NDRgsbQs0Wq1Nrc/Lysro6CggPnz5zc/HwqFeO+99xg3btxRrVWkLZV7G/n35t3sbgxhMwyOK0hnTGHGIUPUivI6HvnXegDu/e5gemWnHq1yRURERLqdDj0idc455/DAAw/Qq1cvBg8ezLJly5g1axZTpkwBmqb03XzzzcyYMYN+/frRr18/ZsyYQWpqKpdffnmCqxeJXzgaY3mVl3JvU0OJrGQ7owozSEs69K3qD0a4+eXlRGImZw8r5IKRxUejXBEREZFuq0MHqccff5y77rqLG2+8kaqqKoqKipg6dSp333138zm33347jY2N3HjjjdTW1nL88cczb9487SElnc7uhhBLdtTREGlqKDEwO42B2WkHXAv1dff/fS2bdvspTE9mxnlDD7lGUERERESOnGGappnoIhLN6/WSnp6Ox+PB7XYnuhzpZmKmyWfV9ayrrgcg1W5ldGEG2SlJrXr9P9dUMvXZTzEMeP664xnXJ6c9yxURERHp0lqbDTr0iJRIV1cfirB4Rx21gTAAvdwpDM9zYz9Mk4hYzGTRht089/EW/v15FQA/OLm3QpSIiIjIUaIgJZIApmmyxdPIiiovUdPEbjE4Nj+dHu6UQ76uriHEK0u28fx/t7C5uqH5+MRB+dw6sX97ly0iIiIieyhIiRxlwWiMZZUeKuoDAOSkJDGqMINUu/WA55umyYptHp79aAt/W1lBMNLUtdLlsHHhcT244vhe9MvXmkARERGRo0lBSuQoqvIHWVJZRyASwwAG57jol+U8YHOIxlCUuSu28+zHW1i93dt8fFChm++fUMK5I4pIPUw3PxERERFpH/opTOQoiMZM1u72sb7WD0BakpXRhZlkJtv3O3dDVT3P/3cLr366DV8gAkCSzcJ3hhVy5dgSju2Zoa58IiIiIgmmICXSzjzBMEt21OEJNoWisvRUhua5sVn2haFwNMb8tTt59qMtfPRldfPxXlmpXDm2F987rieZztZ18RMRERGR9qcgJdJOTNNkfY2ftdU+YiYkWS0cV5BOYVpy8zk7PI28+Ek5L32ylSpfEACLAacfk8+VY0s4uW8OFotGn0REREQ6GgUpkXZQH4rwaWUd1Y1Nbc0LnA5GFqSTbLMSi5l8sHE3z37U1Lo8Gmvayi0nzcFlY3py6ZheFGccunufiIiIiCSWgpRIGzJNk011Daza5SNqmtgsBsPy3JS4U/A0hnnuoy0893HL1uXHl2Xx/RNKmDiogCTbofePEhEREZGOQUFKpI00hKMsrayjqiEENLU1H1ngZsNOP7+bt56/rmjZuvyCkcVcMbaE/mpdLiIiItLpKEiJHCHTNCn3Nm2uG46ZWAwYkJnGys21XPbWZ6za7mk+d1ChmyvHNrUudzp0+4mIiIh0VvpJTuQIBCNRlu30Nm+um2q1sHxDDXd/vBLvV1uXDy3kirEljOyl1uUiIiIiXYGClMg3VOELsGynh2A0Bias3lLHU+9+yZ7eEfTKSuWK43vxvVE9yVLrchEREZEuRUFKJE7haIwVVV62ehsBqPIEePbdTWyvacRiwLeOyefKsb0Y3y9XrctFREREuigFKZE47KwP8NH2WmJALGaycPVO3lm2g8xUOzdN6Mtlx6t1uYiIiEh3oCAl0go19SHeWb+T5NSmW2a3N8AL728hz+ng4UtGMGmwWpeLiIiIdCcKUiKHsKK8jjdXbqcwL5UcdzIA//1iNymmwf9dfhwDCtS6XERERKQ7UpAS+ZrGUJS/rqjghU+2UJTr5LSh+VgsBvWBMPYI3DXxGNLUulxERESkW9NPgyJ7fLmrnuc+3sqrn5bjTLFzxfgSirJSAXBaLJw9uBiHzZrgKkVERESkI1CQkm6r1h9iyZZalmyu4b+balheXofFgAlD8pk0sgirxcBuMRhZkE6xSw0kRERERGQfBSnpFkzTZFttI4s317B4cy2LN9ewoaq+xTm5bgc3TOxLpssBQGGag2Pz00nWKJSIiIiIfI2ClHRooWiMXQ1BKv1BqhtDmCYYgGGAgYGxZ5smi2G0OA7QEIrgbYxQ1xiizh+mIRwlFjOxpVg4fmAOowdkk+awkZOWRE6aA6vdQgywWQyG57np5U7BMLQPlIiIiIjsT0GqG4mZJrWBMA6rBafd2iFDgmma1AUj7PQH2OkPUtMYxjyC97MlWchJSiYnPfmw58aAvNQkRhZkkGrXKJSIiIiIHJyCVDdgmiYV9QHW7vbhC0UBSLJayEy2k5VsJyslicxkO0nWxOyDFIxE2dkQYqc/SJU/SDAaa/G8K8lKvjOZvNQk7FYLpgm+QJh1lT7WVflYv9PHluoGYjSNTFkMMAyDFLuFstw0euc4Kctx0iMzFZvVwKTpv0nTR5q/diXZKHYld8iAKSIiIiIdi4JUF1flD7Jmt4/aQBhomrYWM01C0Rg7/UF2+oPN56bZrc2hKivFTrrDjqUdQsXekbG9f/7e2vayGQaZyXbSbFZsGARDUaprAqzb6mFZeR1LNtfwxc76/d431+VgTGkWo0ozGV2axcACF7YEhUMRERER6doUpLqo2kCYNbu8VDWEALAaBn2znPTPdGIxDDzBMLWBMDWNIWoCYfzhKPXhKPXhRrZ6GwGwGJDh2DdilZVsJ/UwUwJjMRN/KIIvsPcRxheI4AmEaYzFMC1gT7JitbZ8j93eABt31LOm3MPnFV4i0cNP6OuT62R0aRajSrMYU5pFzyytaRIRERGRo0NBqovxhSKs3e1juy8ANDVmKMtIZWB2Wovuc1kpSWSlJNEn0wlAMBqjpjHEzvqmpg714QhRE2oCYWq+MmIUicbwN0So9QXZWRegvLqBmvoQvkAEbyCMPxghZoLVYlCW52Rgj3QGFrspykrBwb7RoYZghHXbvXy+5+FrjOz3vditBq5kO65kG65kG+5kO4OL3IwqzWJUSSbZaY52+q8oIiIiInJohmmaR7KWv0vwer2kp6fj8Xhwu90JreXX//gcf3D/UHE4dpuFgpwUsjMcGIaBaZrUekPs2NVAKLxvzVHMNPEH940YefeMGPkCYer3hKC9ctwOSnKc9MpNpSTXSVFWygGnyu2sC7Bll5+tu/wADOzhpl+hC8dXGjaYponHH8ZbHyYUjGDjqyFpX1hyJdtx7/mYbLdohElEREREjqrWZgONSHUwr35azu76UKvPT02yctqwfE7ul4fd1hRy1pR7ePvTCnbUNn6jGmwWA1eyjVSblWAwQuWuRvy+MNt2+MlLd5DpcpCWYiMpyYphNcjPSCY/I5kx/bJbvI/DaiHf6SDf6SDP6cCh9UoiIiIi0kUoSHUw15/cm4Y9nfUOxTDA5Uoi3Z2ExdI0ahMIRKjzhEgzLVw8ssdBX+dMsrXZSFAwEqUmsG+9lQnkpTaFp3SHTSNKIiIiItIlKUh1MFNP6XPI52OmyWZPA5/trm9uE+5OsjE410WB03HUg4vDZqUwzUph2uH3aRIRERER6SoUpDoJ0zTZ5mvaC8ofbhqxSrVbGZSdRk+3utWJiIiIiBxNClIdnGma7GwIsmaXD8+eJhQOq4WB2WmUZaS2yz5PIiIiIiJyaApSHVh1Y4g1u3zsbmxqPmGzGPTPctI304nNosYNIiIiIiKJoiDVAXmDYdbs9rGjPgg0bYzbJ8NJ/6w0HDYFKBERERGRRFOQ6kBC0Rgrq7xs9e5rW16SnsIx2S5Sv7Ink4iIiIiIJJaCVAdiNQx2NTRN4ytKS2ZQThpuhz3BVYmIiIiIyNcpSHUgVovByIJ07BaDrJSkRJcjIiIiIiIHoSDVweQ7HYkuQUREREREDkOdC0REREREROKkICUiIiIiIhInBSkREREREZE4KUiJiIiIiIjESUFKREREREQkTgpSIiIiIiIicVKQEhERERERiZOClIiIiIiISJwUpEREREREROKkICUiIiIiIhInBSkREREREZE4KUiJiIiIiIjESUFKREREREQkTgpSIiIiIiIicbIluoCOwDRNALxeb4IrERERERGRRNqbCfZmhINRkAJ8Ph8APXv2THAlIiIiIiLSEfh8PtLT0w/6vGEeLmp1A7FYjIqKClwuF4ZhJLQWr9dLz549KS8vx+12J7QWaTu6rl2PrmnXo2vaNem6dj26pl1TR7qupmni8/koKirCYjn4SiiNSAEWi4UePXokuowW3G53wv8nkran69r16Jp2PbqmXZOua9eja9o1dZTreqiRqL3UbEJERERERCROClIiIiIiIiJxUpDqYBwOB/fccw8OhyPRpUgb0nXtenRNux5d065J17Xr0TXtmjrjdVWzCRERERERkThpREpERERERCROClIiIiIiIiJxUpASERERERGJk4KUiIiIiIhInBSkOpjf//73lJWVkZyczHHHHcf777+f6JLkG5o+fTqGYbR4FBQUJLosidN//vMfzjnnHIqKijAMgzfffLPF86ZpMn36dIqKikhJSeHUU09lzZo1iSlWWuVw1/Tqq6/e794dO3ZsYoqVVpk5cyajR4/G5XKRl5fHeeedx7p161qco3u1c2nNNdW92vn84Q9/YNiwYc2b7p5wwgn84x//aH6+s92nClIdyMsvv8zNN9/ML37xC5YtW8bJJ5/MmWeeydatWxNdmnxDgwcPZseOHc2PVatWJbokiZPf72f48OH87ne/O+DzDz30ELNmzeJ3v/sdixcvpqCggDPOOAOfz3eUK5XWOtw1BZg0aVKLe/ftt98+ihVKvN577z1+9KMf8fHHHzN//nwikQgTJ07E7/c3n6N7tXNpzTUF3audTY8ePfj1r3/NkiVLWLJkCaeddhrnnntuc1jqdPepKR3GmDFjzBtuuKHFsYEDB5o///nPE1SRHIl77rnHHD58eKLLkDYEmG+88Ubz17FYzCwoKDB//etfNx8LBAJmenq6OXv27ARUKPH6+jU1TdOcPHmyee655yakHmkbVVVVJmC+9957pmnqXu0Kvn5NTVP3aleRmZlp/vnPf+6U96lGpDqIUCjEp59+ysSJE1scnzhxIh9++GGCqpIjtX79eoqKiigrK+PSSy/lyy+/THRJ0oY2bdpEZWVli/vW4XBwyimn6L7t5N59913y8vLo378/119/PVVVVYkuSeLg8XgAyMrKAnSvdgVfv6Z76V7tvKLRKC+99BJ+v58TTjihU96nClIdxO7du4lGo+Tn57c4np+fT2VlZYKqkiNx/PHH88wzz/DPf/6TP/3pT1RWVjJu3Diqq6sTXZq0kb33pu7bruXMM8/k+eefZ8GCBfz2t79l8eLFnHbaaQSDwUSXJq1gmia33norJ510EkOGDAF0r3Z2B7qmoHu1s1q1ahVpaWk4HA5uuOEG3njjDQYNGtQp71NboguQlgzDaPG1aZr7HZPO4cwzz2z+fOjQoZxwwgn06dOHp59+mltvvTWBlUlb033btVxyySXNnw8ZMoRRo0ZRUlLC3//+dy644IIEViatcdNNN7Fy5UoWLVq033O6Vzung11T3aud04ABA1i+fDl1dXW89tprTJ48mffee6/5+c50n2pEqoPIycnBarXul7irqqr2S+bSOTmdToYOHcr69esTXYq0kb1dGHXfdm2FhYWUlJTo3u0EfvzjHzN37lwWLlxIjx49mo/rXu28DnZND0T3aueQlJRE3759GTVqFDNnzmT48OE8+uijnfI+VZDqIJKSkjjuuOOYP39+i+Pz589n3LhxCapK2lIwGOSzzz6jsLAw0aVIGykrK6OgoKDFfRsKhXjvvfd033Yh1dXVlJeX697twEzT5KabbuL1119nwYIFlJWVtXhe92rnc7hreiC6Vzsn0zQJBoOd8j7V1L4O5NZbb+X73/8+o0aN4oQTTuCPf/wjW7du5YYbbkh0afIN3HbbbZxzzjn06tWLqqoq7r//frxeL5MnT050aRKH+vp6NmzY0Pz1pk2bWL58OVlZWfTq1Yubb76ZGTNm0K9fP/r168eMGTNITU3l8ssvT2DVciiHuqZZWVlMnz6dCy+8kMLCQjZv3sydd95JTk4O559/fgKrlkP50Y9+xAsvvMBbb72Fy+Vq/o12eno6KSkpGIahe7WTOdw1ra+v173aCd15552ceeaZ9OzZE5/Px0svvcS7777LO++80znv04T1C5QDeuKJJ8ySkhIzKSnJHDlyZIs2n9K5XHLJJWZhYaFpt9vNoqIi84ILLjDXrFmT6LIkTgsXLjSB/R6TJ082TbOprfI999xjFhQUmA6Hwxw/fry5atWqxBYth3Soa9rQ0GBOnDjRzM3NNe12u9mrVy9z8uTJ5tatWxNdthzCga4nYM6ZM6f5HN2rncvhrqnu1c5pypQpzT/n5ubmmqeffro5b9685uc7231qmKZpHs3gJiIiIiIi0tlpjZSIiIiIiEicFKRERERERETipCAlIiIiIiISJwUpERERERGROClIiYiIiIiIxElBSkREREREJE4KUiIiIiIiInFSkBIREREREYmTgpSIiIiIiEicFKRERKTLuPrqqzEMA8MwsNvt5Ofnc8YZZ/Dkk08Si8USXZ6IiHQhClIiItKlTJo0iR07drB582b+8Y9/MGHCBKZNm8Z3vvMdIpFIossTEZEuQkFKRES6FIfDQUFBAcXFxYwcOZI777yTt956i3/84x889dRTAMyaNYuhQ4fidDrp2bMnN954I/X19QD4/X7cbjevvvpqi/f961//itPpxOfzEQqFuOmmmygsLCQ5OZnS0lJmzpx5tL9VERFJIAUpERHp8k477TSGDx/O66+/DoDFYuGxxx5j9erVPP300yxYsIDbb78dAKfTyaWXXsqcOXNavMecOXO46KKLcLlcPPbYY8ydO5e//OUvrFu3jueee47S0tKj/W2JiEgC2RJdgIiIyNEwcOBAVq5cCcDNN9/cfLysrIxf/epX/PCHP+T3v/89ANdddx3jxo2joqKCoqIidu/ezd/+9jfmz58PwNatW+nXrx8nnXQShmFQUlJy1L8fERFJLI1IiYhIt2CaJoZhALBw4ULOOOMMiouLcblcXHXVVVRXV+P3+wEYM2YMgwcP5plnngHg2WefpVevXowfPx5oamqxfPlyBgwYwE9+8hPmzZuXmG9KREQSRkFKRES6hc8++4yysjK2bNnCWWedxZAhQ3jttdf49NNPeeKJJwAIh8PN51933XXN0/vmzJnDNddc0xzERo4cyaZNm/jVr35FY2MjF198MRdddNHR/6ZERCRhFKRERKTLW7BgAatWreLCCy9kyZIlRCIRfvvb3zJ27Fj69+9PRUXFfq+58sor2bp1K4899hhr1qxh8uTJLZ53u91ccskl/OlPf+Lll1/mtddeo6am5mh9SyIikmBaIyUiIl1KMBiksrKSaDTKzp07eeedd5g5cybf+c53uOqqq1i1ahWRSITHH3+cc845hw8++IDZs2fv9z6ZmZlccMEF/PSnP2XixIn06NGj+bmHH36YwsJCRowYgcVi4ZVXXqGgoICMjIyj+J2KiEgiaURKRES6lHfeeYfCwkJKS0uZNGkSCxcu5LHHHuOtt97CarUyYsQIZs2axYMPPsiQIUN4/vnnD9q6/NprryUUCjFlypQWx9PS0njwwQcZNWoUo0ePZvPmzbz99ttYLPpnVUSkuzBM0zQTXYSIiEhH9PzzzzNt2jQqKipISkpKdDkiItKBaGqfiIjI1zQ0NLBp0yZmzpzJ1KlTFaJERGQ/moMgIiLyNQ899BAjRowgPz+fO+64I9HliIhIB6SpfSIiIiIiInHSiJSIiIiIiEicFKRERERERETipCAlIiIiIiISJwUpERERERGROClIiYiIiIiIxElBSkREREREJE4KUiIiIiIiInFSkBIREREREYnT/we1mC6bTb13xgAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"forecast_plot_data = np.insert(forecast_pred, 0, Y_original[-1])\n",
"\n",
"# Calculate expanding confidence intervals\n",
"residual = Y_original - reconstructed_y\n",
"stderr = np.std(residual)\n",
"z_score = 1.96 # z-score for 95% CI\n",
"intervals = z_score *stderr * np.sqrt(np.arange(len(forecast_plot_data)))\n",
"\n",
"\n",
"# Creating the plot\n",
"plt.figure(figsize=(10, 5))\n",
"plt.plot(Y_original , label='Historical lifetime repayments')\n",
"plt.plot(len(Y_original)-1 + np.arange(len(forecast_plot_data)), forecast_plot_data , color='orange', label='Upcoming 7 day forecast')\n",
"plt.fill_between(len(Y_original)-1 + np.arange(len(forecast_plot_data)), \n",
" (forecast_plot_data - intervals),\n",
" (forecast_plot_data + intervals),\n",
" alpha=0.12,\n",
" color='green',\n",
" label='95% confidence interval')\n",
"\n",
"\n",
"plt.plot(reconstructed_y , label=\"Model's Predictions\", color='lightblue')\n",
"# Adding labels and title\n",
"plt.xlabel('Days')\n",
"plt.ylabel('Lifetime Repayment (thousands ETH)')\n",
"plt.title(\" 7 Day Forecast (AAVE's total WETH lifetimeRepayment)\")\n",
"plt.legend()\n",
"\n",
"# Display the plot\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "8af06223",
"metadata": {},
"source": [
"# Transition to Cairo\n",
"## Create a scarb project\n",
"\n",
"Scarb is the Cairo package manager specifically created to streamline our Cairo and Starknet development process. You can find all information about Scarb and Cairo installation here"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "9d3cb826",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Created `multiple_linear_regression_aave` package.\n"
]
}
],
"source": [
"! scarb new multiple_linear_regression_aave"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "df9e40c3",
"metadata": {},
"outputs": [],
"source": [
"!echo -n > multiple_linear_regression_aave/src/lib.cairo"
]
},
{
"cell_type": "markdown",
"id": "38ac62e4",
"metadata": {},
"source": [
"A new project folder will be created for you and make sure to replace the content in Scarb.toml file with the following code:\n",
"```toml\n",
"[package]\n",
"name = \"multiple_linear_regresion\"\n",
"version = \"0.1.0\"\n",
"\n",
"\n",
"[dependencies]\n",
"orion = { git = \"https://github.com/gizatechxyz/onnx-cairo\" }\n",
"\n",
"[scripts]\n",
"test = \"scarb cairo-test -f multiple_linear_regression_test\"\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "e366fe1f",
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (252576425.py, line 3)",
"output_type": "error",
"traceback": [
"\u001b[0;36m Cell \u001b[0;32mIn [29], line 3\u001b[0;36m\u001b[0m\n\u001b[0;31m mod test;\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"# add reference modules to help our code compile at the end\n",
"%%writefile -a src/lib.cairo \n",
"mod test;\n",
"mod data_preprocessing;\n",
"mod helper_functions;\n",
"mod datasets;\n",
"mod model;"
]
},
{
"cell_type": "markdown",
"id": "c0340f15",
"metadata": {},
"source": [
"\n",
"## Generate Cairo files\n",
"\n",
"Now, we will transition our model to cairo. We will start by converting the the X features and Y labels to FP16x16 tensors numbers. "
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "39b6b561",
"metadata": {},
"outputs": [],
"source": [
"# Convert the original data to Cairo \n",
"\n",
"\n",
"def generate_cairo_files(data, name, folder_name):\n",
" \n",
" os.makedirs(f'multiple_linear_regression_aave/src/datasets/{folder_name}', exist_ok=True)\n",
" with open(os.path.join('multiple_linear_regression_aave/src/datasets', f'{folder_name}', f\"{name}.cairo\"), \"w\") as f:\n",
" f.write(\n",
" \"use array::ArrayTrait;\\n\" +\n",
" \"use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16Impl, FP16x16PartialEq };\\n\" +\n",
" \"use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};\\n\" +\n",
" \"use orion::numbers::{FP16x16, FixedTrait};\\n\\n\" +\n",
" \"fn {0}() -> Tensor<FP16x16> \".format(name) + \"{\\n\" +\n",
" \" let tensor = TensorTrait::<FP16x16>::new( \\n\"\n",
" )\n",
" \n",
" if len(data.shape)>1:\n",
" f.write(\" shape: array![{0},\".format(data.shape[0]))\n",
" f.write(\"{0}].span(),\\n\".format(data.shape[1]))\n",
" f.write(\n",
" \" data: array![ \\n\"\n",
" )\n",
" if len(data.shape)==1:\n",
" f.write(\" shape: array![{0}].span(),\\n\".format(data.shape[0]))\n",
" f.write(\n",
" \" data: array![ \\n\"\n",
" )\n",
" for val in np.nditer(data.flatten()):\n",
" f.write(\" FixedTrait::new({0}, {1} ),\\n\".format(abs(int(val * 2**16)), str(val < 0).lower()))\n",
" f.write(\n",
" \"].span() \\n \\n\" +\n",
" \");\\n\\n\"+\n",
" \"return tensor; \\n\"+\n",
" \"}\"\n",
" )\n",
" with open(os.path.join('multiple_linear_regression_aave/src/datasets', f'{folder_name}.cairo'), 'a') as f:\n",
" f.write(f\"mod {name};\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "1a0e7a71",
"metadata": {},
"outputs": [],
"source": [
"generate_cairo_files(X_original, 'aave_x_features', 'aave_data')\n",
"generate_cairo_files(Y_original, 'aave_y_labels', 'aave_data')\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "b8c2b804",
"metadata": {},
"outputs": [],
"source": [
"generate_cairo_files(df_forecast_data, 'aave_weth_revenue_data_input', 'user_inputs_data')"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "fb316caf",
"metadata": {},
"outputs": [],
"source": [
"# add reference modules to help our code compile\n",
"! touch multiple_linear_regression_aave/src/datasets.cairo"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "fa5fb2be",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting multiple_linear_regression_aave/src/datasets.cairo\n"
]
}
],
"source": [
"%%writefile multiple_linear_regression_aave/src/datasets.cairo\n",
"mod aave_data;\n",
"mod user_inputs_data;"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "b9af5716",
"metadata": {},
"outputs": [],
"source": [
"! touch multiple_linear_regression_aave/src/datasets/aave_data.cairo"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "230ac6ed",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting multiple_linear_regression_aave/src/datasets/aave_data.cairo\n"
]
}
],
"source": [
"%%writefile multiple_linear_regression_aave/src/datasets/aave_data.cairo\n",
"mod aave_x_features;\n",
"mod aave_y_labels;"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "4d57b462",
"metadata": {},
"outputs": [],
"source": [
"! touch multiple_linear_regression_aave/src/datasets/user_inputs_data.cairo"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "fe4d136a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting multiple_linear_regression_aave/src/datasets/user_inputs_data.cairo\n"
]
}
],
"source": [
"%%writefile multiple_linear_regression_aave/src/datasets/user_inputs_data.cairo\n",
"mod aave_weth_revenue_data_input;"
]
},
{
"cell_type": "markdown",
"id": "8cb7f589",
"metadata": {},
"source": [
"## Helper functions \n",
"\n",
"We add some helper functions to make it easier to construct our MLR model"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "cd34a054",
"metadata": {},
"outputs": [],
"source": [
"! touch multiple_linear_regression_aave/src/helper_functions.cairo"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "a9d33776",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting multiple_linear_regression_aave/src/helper_functions.cairo\n"
]
}
],
"source": [
"%%writefile multiple_linear_regression_aave/src/helper_functions.cairo\n",
"\n",
"use debug::PrintTrait;\n",
"use array::{ArrayTrait, SpanTrait};\n",
"use orion::operators::tensor::{\n",
" Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,\n",
" FP16x16TensorDiv, FP16x16TensorMul\n",
"};\n",
"\n",
"use orion::numbers::{FP16x16, FixedTrait};\n",
"\n",
"// retrieves row data by index in a 2D tensor\n",
"fn get_tensor_data_by_row(tensor_data: Tensor<FP16x16>, row_index: u32,) -> Tensor<FP16x16> {\n",
" let column_len = *tensor_data.shape.at(1); //13\n",
" // crete new array\n",
" let mut result = ArrayTrait::<FP16x16>::new();\n",
" // loop through the x values and append values \n",
" let mut i: u32 = 0;\n",
" loop {\n",
" if i >= column_len {\n",
" break ();\n",
" }\n",
" result.append(tensor_data.at(indices: array![row_index, i].span()));\n",
" i += 1;\n",
" };\n",
" let resultant_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(array![column_len].span(), data: result.span());\n",
" return resultant_tensor;\n",
"}\n",
"\n",
"\n",
"// transposes tensor\n",
"fn transpose_tensor(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {\n",
" let tensor_transposed = tensor_data.transpose(axes: array![1, 0].span());\n",
" return tensor_transposed;\n",
"}\n",
"\n",
"fn calculate_mean(tensor_data: Tensor<FP16x16>) -> FP16x16 {\n",
" let tensor_size = FixedTrait::<FP16x16>::new_unscaled(tensor_data.data.len(), false);\n",
" let cumulated_sum = tensor_data.cumsum(0, Option::None(()), Option::None(()));\n",
" let sum_result = cumulated_sum.data[tensor_data.data.len() - 1];\n",
" let mean = *sum_result / tensor_size;\n",
" return mean;\n",
"}\n",
"\n",
"// Calculates the R-Squared score between two tensors.\n",
"fn calculate_r_score(Y_values: Tensor<FP16x16>, Y_pred_values: Tensor<FP16x16>) -> FP16x16 {\n",
" let mut Y_values_ = Y_values;\n",
" let mean_y_value = calculate_mean(Y_values);\n",
" // creating the appropriate tensor shapes and empty arrays to populate values into\n",
" let mut squared_diff_shape = array::ArrayTrait::new();\n",
" squared_diff_shape.append(Y_values.data.len());\n",
" let mut squared_diff_vals = array::ArrayTrait::new();\n",
" let mut squared_mean_diff_shape = array::ArrayTrait::new();\n",
" squared_mean_diff_shape.append(Y_values.data.len());\n",
" let mut squared_mean_diff_vals = array::ArrayTrait::new();\n",
"\n",
" let mut i: u32 = 0;\n",
"\n",
" loop {\n",
" match Y_values_.data.pop_front() {\n",
" Option::Some(y_value) => {\n",
" let diff_pred = *y_value - *Y_pred_values.data.at(i);\n",
" let squared_diff = diff_pred * diff_pred;\n",
" squared_diff_vals.append(squared_diff);\n",
"\n",
" let diff_mean = *y_value - mean_y_value;\n",
" let squared_mean_diff = diff_mean * diff_mean;\n",
" squared_mean_diff_vals.append(squared_mean_diff);\n",
" i += 1;\n",
" },\n",
" Option::None(_) => { break; }\n",
" }\n",
" };\n",
"\n",
" let squared_diff_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(squared_diff_shape.span(), squared_diff_vals.span());\n",
" let squared_mean_diff_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(squared_mean_diff_shape.span(), squared_mean_diff_vals.span());\n",
" let sum_squared_diff = squared_diff_tensor.cumsum(0, Option::None(()), Option::None(()));\n",
" let sum_squared_mean_diff = squared_mean_diff_tensor\n",
" .cumsum(0, Option::None(()), Option::None(()));\n",
" let r_score = FixedTrait::new_unscaled(1, false)\n",
" - *sum_squared_diff.data.at(Y_values.data.len() - 1)\n",
" / *sum_squared_mean_diff.data.at(Y_values.data.len() - 1);\n",
"\n",
" return r_score;\n",
"}\n",
"\n",
"\n",
"// computes the x_min, x_max and x_range. Used for helping in normalizing and denormalizing user inputed values operations\n",
"fn normalize_user_x_inputs(\n",
" x_inputs: Tensor<FP16x16>, original_x_values: Tensor<FP16x16>\n",
") -> Tensor<FP16x16> {\n",
" let mut x_inputs_normalized = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());\n",
"\n",
" let mut x_min = ArrayTrait::<FP16x16>::new();\n",
" let mut x_max = ArrayTrait::<FP16x16>::new();\n",
" let mut x_range = ArrayTrait::<FP16x16>::new();\n",
" let mut result = ArrayTrait::<FP16x16>::new();\n",
"\n",
" if original_x_values.shape.len() > 1 {\n",
" let transposed_tensor = original_x_values.transpose(axes: array![1, 0].span());\n",
" let data_len = *transposed_tensor.shape.at(0); //13\n",
" // loop through each row calculating the min, max and range row values for each feature columns\n",
" let mut i: u32 = 0;\n",
" loop {\n",
" if i >= data_len {\n",
" break ();\n",
" }\n",
" let mut transposed_tensor_row = get_tensor_data_by_row(transposed_tensor, i);\n",
" x_min.append(transposed_tensor_row.min_in_tensor());\n",
" x_max.append(transposed_tensor_row.max_in_tensor());\n",
" x_range\n",
" .append(\n",
" transposed_tensor_row.max_in_tensor() - transposed_tensor_row.min_in_tensor()\n",
" );\n",
" i += 1;\n",
" };\n",
" let mut x_min_tensor = TensorTrait::new(shape: array![data_len].span(), data: x_min.span());\n",
" let mut x_max_tensor = TensorTrait::new(shape: array![data_len].span(), data: x_max.span());\n",
" let mut x_range_tensor = TensorTrait::new(\n",
" shape: array![data_len].span(), data: x_range.span()\n",
" );\n",
"\n",
" // for normalizing 2D user inputed feature vals\n",
" if x_inputs.shape.len() > 1 {\n",
" let mut j: u32 = 0;\n",
" loop {\n",
" if j >= *x_inputs.shape.at(0) {\n",
" break ();\n",
" };\n",
" let mut row_data = get_tensor_data_by_row(x_inputs, j);\n",
" let mut norm_row_data = (row_data - x_min_tensor) / x_range_tensor;\n",
" let mut k: u32 = 0;\n",
"\n",
" loop {\n",
" if k >= norm_row_data.data.len() {\n",
" break ();\n",
" };\n",
" result.append(*norm_row_data.data.at(k));\n",
" k += 1;\n",
" };\n",
" j += 1;\n",
" };\n",
" x_inputs_normalized =\n",
" TensorTrait::<\n",
" FP16x16\n",
" >::new(\n",
" array![*x_inputs.shape.at(0), *x_inputs.shape.at(1)].span(), data: result.span()\n",
" );\n",
" };\n",
"\n",
" // for normalizing 1D feature input\n",
" if x_inputs.shape.len() == 1 {\n",
" x_inputs_normalized = (x_inputs - x_min_tensor) / x_range_tensor;\n",
" };\n",
" }\n",
"\n",
" if original_x_values.shape.len() == 1 {\n",
" let mut x_min_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![original_x_values.min_in_tensor()].span());\n",
" let mut x_max_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![original_x_values.max_in_tensor()].span());\n",
" let mut x_range_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(\n",
" shape: array![1].span(),\n",
" data: array![original_x_values.max_in_tensor() - original_x_values.min_in_tensor()]\n",
" .span()\n",
" );\n",
" let mut diff = ((x_inputs - x_min_tensor));\n",
" x_inputs_normalized = ((x_inputs - x_min_tensor)) / x_range_tensor;\n",
" };\n",
" return x_inputs_normalized;\n",
"}\n",
"\n",
"\n",
"// rescales model predictions to standard format\n",
"fn rescale_predictions(\n",
" prediction_result: Tensor<FP16x16>, y_values: Tensor<FP16x16>\n",
") -> Tensor<FP16x16> {\n",
" let mut rescale_predictions = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());\n",
"\n",
" let mut y_min_array = ArrayTrait::<FP16x16>::new();\n",
" let mut y_max_array = ArrayTrait::<FP16x16>::new();\n",
" let mut y_range_array = ArrayTrait::<FP16x16>::new();\n",
"\n",
" let mut y_max = y_values.max_in_tensor();\n",
" let mut y_min = y_values.min_in_tensor();\n",
" let mut y_range = y_values.max_in_tensor() - y_values.min_in_tensor();\n",
" // convert to tensor format for ease of math operations\n",
" let y_min_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![y_min].span());\n",
" let y_max_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![y_max].span());\n",
" let y_range_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![y_range].span());\n",
"\n",
" rescale_predictions = (prediction_result * y_range_tensor) + y_min_tensor;\n",
"\n",
" return rescale_predictions;\n",
"}\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "22a08abf",
"metadata": {},
"source": [
"## Data-preprocessing functions\n",
"\n",
"It is <b>recommended</b> to normalize data before passing it to the multiple linear regression model since we will be working with 16x16 fixed point numbers in cairo. This will prevent from having overflow issues as we compute the feature gradient values (some of the calculations involve squaring x values which can be relatively large if not normalized)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "9995e498",
"metadata": {},
"outputs": [],
"source": [
"! touch multiple_linear_regression_aave/src/data_preprocessing.cairo"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "f559dab1",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting multiple_linear_regression_aave/src/data_preprocessing.cairo\n"
]
}
],
"source": [
"\n",
"%%writefile multiple_linear_regression_aave/src/data_preprocessing.cairo\n",
"\n",
"use orion::operators::tensor::{\n",
" Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,\n",
" FP16x16TensorDiv, FP16x16TensorMul\n",
"};\n",
"use orion::numbers::{FP16x16, FixedTrait};\n",
"use multiple_linear_regresion::helper_functions::{\n",
" get_tensor_data_by_row, transpose_tensor, calculate_mean, calculate_r_score,\n",
" normalize_user_x_inputs, rescale_predictions\n",
"};\n",
"\n",
"#[derive(Copy, Drop)]\n",
"struct Dataset {\n",
" x_values: Tensor<FP16x16>,\n",
" y_values: Tensor<FP16x16>,\n",
"}\n",
"\n",
"#[generate_trait]\n",
"impl DataPreprocessing of DatasetTrait {\n",
" fn normalize_dataset(ref self: Dataset) -> Dataset {\n",
" let mut x_values = TensorTrait::<FP16x16>::new(array![1].span(), array![FixedTrait::new(0, false)].span());\n",
" let mut y_values = TensorTrait::<FP16x16>::new(array![1].span(), array![FixedTrait::new(0, false)].span());\n",
" // used for multiple_linear_regression_models\n",
" if self.x_values.shape.len() > 1 {\n",
" x_values = normalize_feature_data(self.x_values);\n",
" y_values = normalize_label_data(self.y_values);\n",
" }\n",
" // used for linear_regression_models\n",
" if self.x_values.shape.len() == 1 {\n",
" x_values = normalize_label_data(self.x_values);\n",
" y_values = normalize_label_data(self.y_values);\n",
" }\n",
"\n",
" return Dataset { x_values, y_values };\n",
" }\n",
"}\n",
"\n",
"// normalizes 2D Tensor\n",
"fn normalize_feature_data(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {\n",
" let mut x_min_array = ArrayTrait::<FP16x16>::new();\n",
" let mut x_max_array = ArrayTrait::<FP16x16>::new();\n",
" let mut x_range_array = ArrayTrait::<FP16x16>::new();\n",
" let mut normalized_array = ArrayTrait::<FP16x16>::new();\n",
" // transpose to change rows to be columns\n",
" let transposed_tensor = tensor_data.transpose(axes: array![1, 0].span());\n",
" let tensor_shape = transposed_tensor.shape;\n",
" let tensor_row_len = *tensor_shape.at(0); // 13 \n",
" let tensor_column_len = *tensor_shape.at(1); //50\n",
" // loop and append max and min row values to corresponding array\n",
" let mut i: u32 = 0;\n",
" loop {\n",
" if i >= tensor_row_len {\n",
" break ();\n",
" }\n",
" let mut transposed_tensor_row = get_tensor_data_by_row(transposed_tensor, i);\n",
" x_max_array.append(transposed_tensor_row.max_in_tensor());\n",
" x_min_array.append(transposed_tensor_row.min_in_tensor());\n",
" x_range_array\n",
" .append(transposed_tensor_row.max_in_tensor() - transposed_tensor_row.min_in_tensor());\n",
" i += 1;\n",
" };\n",
" // convert array to tensor format for ease of math operation\n",
" let mut x_min = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1, tensor_row_len].span(), data: x_min_array.span());\n",
" let mut x_range = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1, tensor_row_len].span(), data: x_range_array.span());\n",
" let normalized_tensor = (tensor_data - x_min) / x_range;\n",
" return normalized_tensor;\n",
"}\n",
"\n",
"// normalizes 1D tensor\n",
"fn normalize_label_data(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {\n",
" let mut tensor_data_ = tensor_data;\n",
" let mut normalized_array = ArrayTrait::<FP16x16>::new();\n",
" let mut range = tensor_data.max_in_tensor() - tensor_data.min_in_tensor();\n",
" // loop through tensor values normalizing and appending to new array\n",
" let mut i: u32 = 0;\n",
"\n",
" loop {\n",
" match tensor_data_.data.pop_front() {\n",
" Option::Some(tensor_val) => {\n",
" let mut diff = *tensor_val - tensor_data.min_in_tensor();\n",
" normalized_array.append(diff / range);\n",
" i += 1;\n",
" },\n",
" Option::None(_) => { break; }\n",
" };\n",
" };\n",
" // convert normalized array values to tensor format\n",
" let mut normalized_tensor = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![tensor_data.data.len()].span(), data: normalized_array.span());\n",
" return normalized_tensor;\n",
"}\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "12784736",
"metadata": {},
"source": [
"## Multiple Linear Regression Model\n",
"\n",
"Implement the Multiple Linear Regression functions"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "ea7c8acc",
"metadata": {},
"outputs": [],
"source": [
"os.makedirs(f'multiple_linear_regression_aave/src/model/', exist_ok=True)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "543d3c63",
"metadata": {},
"outputs": [],
"source": [
"! touch multiple_linear_regression_aave/src/model/multiple_linear_regression_model.cairo"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "24d21d37",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting multiple_linear_regression_aave/src/model/multiple_linear_regression_model.cairo\n"
]
}
],
"source": [
"%%writefile multiple_linear_regression_aave/src/model/multiple_linear_regression_model.cairo\n",
"\n",
"use orion::operators::tensor::{\n",
" Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,\n",
" FP16x16TensorDiv, FP16x16TensorMul\n",
"};\n",
"use orion::numbers::{FP16x16, FixedTrait};\n",
"use multiple_linear_regresion::data_preprocessing::{Dataset, DatasetTrait};\n",
"use multiple_linear_regresion::helper_functions::{\n",
" get_tensor_data_by_row, transpose_tensor, calculate_mean, calculate_r_score,\n",
" normalize_user_x_inputs, rescale_predictions\n",
"};\n",
"\n",
"\n",
"#[derive(Copy, Drop)]\n",
"struct MultipleLinearRegressionModel {\n",
" coefficients: Tensor<FP16x16>\n",
"}\n",
"\n",
"#[generate_trait]\n",
"impl RegressionOperation of MultipleLinearRegressionModelTrait {\n",
" // reconstruct the y values using the computed gradients and x values\n",
" fn predict(\n",
" ref self: MultipleLinearRegressionModel, feature_inputs: Tensor<FP16x16>\n",
" ) -> Tensor<FP16x16> {\n",
" // random tensor value that we will replace\n",
" let mut prediction_result = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());\n",
"\n",
" let mut result = ArrayTrait::<FP16x16>::new();\n",
" // for multiple predictions\n",
" if feature_inputs.shape.len() > 1 {\n",
" let feature_values = add_bias_term(feature_inputs, 1);\n",
" let mut data_len: u32 = *feature_values.shape.at(0);\n",
" let mut i: u32 = 0;\n",
" loop {\n",
" if i >= data_len {\n",
" break ();\n",
" }\n",
" let feature_row_values = get_tensor_data_by_row(feature_values, i);\n",
" let predicted_values = feature_row_values.matmul(@self.coefficients);\n",
" result.append(*predicted_values.data.at(0));\n",
" i += 1;\n",
" };\n",
" prediction_result =\n",
" TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![result.len()].span(), data: result.span());\n",
" }\n",
"\n",
" // for single predictions \n",
" if feature_inputs.shape.len() == 1 && self.coefficients.data.len() > 1 {\n",
" let feature_values = add_bias_term(feature_inputs, 1);\n",
" prediction_result = feature_values.matmul(@self.coefficients);\n",
" }\n",
"\n",
" return prediction_result;\n",
" }\n",
"}\n",
"\n",
"fn MultipleLinearRegression(dataset: Dataset) -> MultipleLinearRegressionModel {\n",
" let x_values_tranposed = transpose_tensor(dataset.x_values);\n",
" let x_values_tranposed_with_bias = add_bias_term(x_values_tranposed, 0);\n",
" let decorrelated_x_features = decorrelate_x_features(x_values_tranposed_with_bias);\n",
" let coefficients = compute_gradients(\n",
" decorrelated_x_features, dataset.y_values, x_values_tranposed_with_bias\n",
" );\n",
" return MultipleLinearRegressionModel { coefficients };\n",
"}\n",
"\n",
"//Adds bias term to features based on axis\n",
"fn add_bias_term(x_feature: Tensor<FP16x16>, axis: u32) -> Tensor<FP16x16> {\n",
" let mut x_feature_ = x_feature;\n",
" let mut tensor_with_bias = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());\n",
" let mut result = ArrayTrait::<FP16x16>::new();\n",
" // check if feature data has multiple rows and columns\n",
" if x_feature.shape.len() > 1 {\n",
" let mut index: u32 = 0;\n",
" if axis == 1 {\n",
" index = 0;\n",
" } else {\n",
" index = 1;\n",
" }\n",
" let data_len = *x_feature.shape.at(index); // 50\n",
" let mut i: u32 = 0;\n",
" loop {\n",
" if i >= data_len {\n",
" break ();\n",
" }\n",
" result\n",
" .append(FixedTrait::new(65536, false)); //65536=ONE in FP16x16, change accordingly \n",
" i += 1;\n",
" };\n",
" if axis == 0 {\n",
" let res_tensor = TensorTrait::new(\n",
" shape: array![1, data_len].span(), data: result.span()\n",
" );\n",
" tensor_with_bias =\n",
" TensorTrait::concat(tensors: array![x_feature, res_tensor].span(), axis: axis);\n",
" } else {\n",
" let res_tensor = TensorTrait::new(\n",
" shape: array![data_len, 1].span(), data: result.span()\n",
" );\n",
" tensor_with_bias =\n",
" TensorTrait::concat(tensors: array![x_feature, res_tensor].span(), axis: axis);\n",
" }\n",
" }\n",
" // check if feature data is 1D\n",
" if x_feature.shape.len() == 1 {\n",
" let mut j: u32 = 0;\n",
" loop {\n",
" match x_feature_.data.pop_front() {\n",
" Option::Some(x_val) => {\n",
" result.append(*x_val);\n",
" j += 1;\n",
" },\n",
" Option::None(_) => { break; }\n",
" };\n",
" };\n",
" result.append(FixedTrait::new(65536, false)); //65536=ONE in FP16x16, change accordingly \n",
" tensor_with_bias =\n",
" TensorTrait::<FP16x16>::new(shape: array![result.len()].span(), data: result.span());\n",
" }\n",
" return tensor_with_bias;\n",
"}\n",
"\n",
"// decorrelates the feature data (*only the last tensor row of the decorelated feature data will be fully orthogonal)\n",
"fn decorrelate_x_features(x_feature_data: Tensor<FP16x16>) -> Tensor<FP16x16> {\n",
" let mut input_tensor = x_feature_data;\n",
"\n",
" let mut i: u32 = 0;\n",
" loop {\n",
" if i >= *x_feature_data.shape.at(0) {\n",
" break ();\n",
" }\n",
" let mut placeholder = ArrayTrait::<FP16x16>::new();\n",
" let mut feature_row_values = get_tensor_data_by_row(input_tensor, i);\n",
" let mut feature_squared = feature_row_values.matmul(@feature_row_values);\n",
" // avoiding division by zero errors\n",
" if *feature_squared.data.at(0) == FixedTrait::new(0, false) {\n",
" feature_squared =\n",
" TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());\n",
" }\n",
" // loop throgh remaining tensor data and remove the individual tensor factors from one another \n",
" let mut j: u32 = i + 1;\n",
" loop {\n",
" if j >= *x_feature_data.shape.at(0) {\n",
" break ();\n",
" }\n",
" let mut remaining_tensor_values = get_tensor_data_by_row(input_tensor, j);\n",
" let feature_cross_product = feature_row_values.matmul(@remaining_tensor_values);\n",
" let feature_gradients = feature_cross_product / feature_squared;\n",
" remaining_tensor_values = remaining_tensor_values\n",
" - (feature_row_values\n",
" * feature_gradients); //remove the feature factors from one another\n",
" // loop and append the modifieed remaining_tensor_values (after the corelated factor has been removed) to placeholder array\n",
" let mut k: u32 = 0;\n",
" loop {\n",
" if k >= remaining_tensor_values.data.len() {\n",
" break ();\n",
" }\n",
" placeholder.append(*remaining_tensor_values.data.at(k));\n",
" k += 1;\n",
" };\n",
"\n",
" j += 1;\n",
" };\n",
" // convert placeholder array to tensor format and update the original tensor with the new modified decorrelated tensor row values\n",
" let mut decorrelated_tensor = TensorTrait::new(\n",
" shape: array![*x_feature_data.shape.at(0) - 1 - i, *x_feature_data.shape.at(1)].span(),\n",
" data: placeholder.span()\n",
" );\n",
" let mut original_tensor = input_tensor\n",
" .slice(\n",
" starts: array![0, 0].span(),\n",
" ends: array![i + 1, *x_feature_data.shape.at(1)].span(),\n",
" axes: Option::None(()),\n",
" steps: Option::Some(array![1, 1].span())\n",
" );\n",
" input_tensor =\n",
" TensorTrait::concat(\n",
" tensors: array![original_tensor, decorrelated_tensor].span(), axis: 0\n",
" );\n",
" i += 1;\n",
" };\n",
" return input_tensor;\n",
"}\n",
"\n",
"// computes the corresponding MLR gradient using decorrelated feature\n",
"fn compute_gradients(\n",
" decorrelated_x_features: Tensor<FP16x16>,\n",
" y_values: Tensor<FP16x16>,\n",
" original_x_tensor_values: Tensor<FP16x16>\n",
") -> Tensor<FP16x16> {\n",
" let mut gradient_values_flipped = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());\n",
"\n",
" let mut result = ArrayTrait::<FP16x16>::new();\n",
" let mut tensor_y_vals = y_values;\n",
" let mut i: u32 = *decorrelated_x_features.shape.at(0);\n",
" // loop through Decorrelated_x_features starting from the fully orthogonlised last tensor row value\n",
" loop {\n",
" if i <= 0 {\n",
" break ();\n",
" }\n",
" let index_val = i - 1;\n",
" let mut decorelated_feature_row_values = get_tensor_data_by_row(\n",
" decorrelated_x_features, index_val\n",
" ); // 50 vals\n",
" let mut decorelated_features_squared = decorelated_feature_row_values\n",
" .matmul(@decorelated_feature_row_values);\n",
" let mut feature_label_cross_product = tensor_y_vals\n",
" .matmul(@decorelated_feature_row_values); // multiply the tensors\n",
" // avoiding division by zero errors\n",
" if *decorelated_features_squared.data.at(0) == FixedTrait::new(0, false) {\n",
" decorelated_features_squared =\n",
" TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());\n",
" }\n",
" // computing the feature gradient values using the y values and decorrelated x features and appending to array\n",
" let mut single_gradient_value = feature_label_cross_product\n",
" / decorelated_features_squared; // devide the summed value by each other\n",
" result.append(*single_gradient_value.data.at(0));\n",
" // remove the assosciated feature gradient value away from y values\n",
" let mut original_x_tensor_row_values = get_tensor_data_by_row(\n",
" original_x_tensor_values, index_val\n",
" );\n",
" tensor_y_vals = tensor_y_vals\n",
" - (original_x_tensor_row_values\n",
" * single_gradient_value); //remove the first feature from second feature values\n",
" i -= 1;\n",
" };\n",
" // convert the gradient array to tensor format\n",
" let final_gradients = TensorTrait::new(\n",
" shape: array![*decorrelated_x_features.shape.at(0)].span(), data: result.span()\n",
" );\n",
"\n",
" let mut reverse_grad_array = ArrayTrait::<FP16x16>::new();\n",
" let mut data_len: u32 = final_gradients.data.len();\n",
" loop {\n",
" if data_len <= 0 {\n",
" break ();\n",
" }\n",
" let temp_val = data_len - 1;\n",
" reverse_grad_array.append(*final_gradients.data.at(temp_val));\n",
" data_len -= 1;\n",
" };\n",
" // convert gradient values to tensor format\n",
" let gradient_values_flipped = TensorTrait::<\n",
" FP16x16\n",
" >::new(shape: array![reverse_grad_array.len()].span(), data: reverse_grad_array.span());\n",
"\n",
" return gradient_values_flipped;\n",
"}\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "6b37fbe5",
"metadata": {},
"outputs": [],
"source": [
"! touch multiple_linear_regression_aave/src/model.cairo"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "22f961a5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting multiple_linear_regression_aave/src/model.cairo\n"
]
}
],
"source": [
"%%writefile multiple_linear_regression_aave/src/model.cairo\n",
"mod multiple_linear_regression_model;"
]
},
{
"cell_type": "markdown",
"id": "8c1f41c6",
"metadata": {},
"source": [
"## Running tests on model\n",
"\n",
"Running some checks to ensure the model is performing as expected. Some of the checks involve:\n",
"- data normalizations checks\n",
"- tensor shape/dimension check\n",
"- coefficient value and dimension checks \n",
"- model accuracy deviance checks"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "dfb70ccd",
"metadata": {},
"outputs": [],
"source": [
"! touch multiple_linear_regression_aave/src/test.cairo"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "4dd10050",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Overwriting multiple_linear_regression_aave/src/test.cairo\n"
]
}
],
"source": [
"%%writefile multiple_linear_regression_aave/src/test.cairo\n",
"\n",
"\n",
"\n",
"// use traits::Into;\n",
"use debug::PrintTrait;\n",
"use array::{ArrayTrait, SpanTrait};\n",
"\n",
"use multiple_linear_regresion::datasets::aave_data::aave_x_features::aave_x_features;\n",
"use multiple_linear_regresion::datasets::aave_data::aave_y_labels::aave_y_labels; \n",
"use multiple_linear_regresion::datasets::user_inputs_data::aave_weth_revenue_data_input::{aave_weth_revenue_data_input }; \n",
"\n",
"use multiple_linear_regresion::model::multiple_linear_regression_model::{\n",
" MultipleLinearRegressionModel, MultipleLinearRegression, MultipleLinearRegressionModelTrait\n",
"};\n",
"use multiple_linear_regresion::data_preprocessing::{Dataset, DatasetTrait};\n",
"use multiple_linear_regresion::helper_functions::{get_tensor_data_by_row, transpose_tensor, calculate_mean , \n",
"calculate_r_score, normalize_user_x_inputs, rescale_predictions};\n",
"\n",
"use orion::numbers::{FP16x16, FixedTrait};\n",
"\n",
"\n",
"use orion::operators::tensor::{\n",
" Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, \n",
" FP16x16TensorSub, FP16x16TensorAdd, FP16x16TensorDiv, FP16x16TensorMul};\n",
"\n",
"#[test]\n",
"#[available_gas(99999999999999999)]\n",
"fn multiple_linear_regression_test() {\n",
"\n",
"\n",
"// -------------------------------------------------------------------AAVE dataset tests---------------------------------------------------------------------------------------------\n",
"\n",
"let mut main_x_vals = aave_x_features();\n",
"let mut main_y_vals = aave_y_labels();\n",
"let mut dataset = Dataset{x_values: main_x_vals,y_values:main_y_vals};\n",
"let mut normalized_dataset = dataset.normalize_dataset();\n",
"let mut model = MultipleLinearRegression(normalized_dataset);\n",
"let mut model_coefficients = model.coefficients;\n",
"let mut reconstructed_ys = model.predict (normalized_dataset.x_values);\n",
"let mut r_squared_score = calculate_r_score(normalized_dataset.y_values,reconstructed_ys);\n",
"r_squared_score.print(); \n",
"\n",
"// checking if data has been normalized correctly\n",
"assert(normalized_dataset.x_values.max_in_tensor() <= FixedTrait::new(65536, false), 'normalized x not between 0-1');\n",
"assert(normalized_dataset.x_values.min_in_tensor() >= FixedTrait::new(0, false), 'normalized x not between 0-1');\n",
"assert(normalized_dataset.y_values.max_in_tensor() <= FixedTrait::new(65536, false), 'normalized y not between 0-1');\n",
"assert(normalized_dataset.x_values.min_in_tensor() >= FixedTrait::new(0, false), 'normalized y not between 0-1');\n",
"// performing checks on the shape of normalized data\n",
"assert(normalized_dataset.x_values.data.len()== main_x_vals.data.len() && \n",
"normalized_dataset.y_values.data.len()== main_y_vals.data.len() , 'normalized data shape mismatch');\n",
"// performing checks on shape on coefficient values (gradient vals + bias)\n",
"assert(model.coefficients.data.len() == *main_x_vals.shape.at(1)+1, 'coefficient data shape mismatch');\n",
"// model accuracy deviance checks\n",
"assert(r_squared_score >= FixedTrait::new(62259, false), 'AAVE model acc. less than 95%');\n",
"\n",
"// using model to forecast aave's 7 day WETH lifetime repayments forecast \n",
"let last_7_days_aave_data = aave_weth_revenue_data_input();\n",
"let last_7_days_aave_data_normalized = normalize_user_x_inputs(last_7_days_aave_data, main_x_vals );\n",
"let mut forecast_results = model.predict (last_7_days_aave_data_normalized); \n",
"let mut rescale_forecasts = rescale_predictions(forecast_results, main_y_vals); // PS. ** the rescaled forecasted ouputs are in denominated thousands of ETH\n",
"(*rescale_forecasts.data.at(0)).print(); \n",
"(*rescale_forecasts.data.at(1)).print(); \n",
"(*rescale_forecasts.data.at(2)).print(); \n",
"(*rescale_forecasts.data.at(5)).print(); \n",
"(*rescale_forecasts.data.at(6)).print(); \n",
"}\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4ae8fd10",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/linear_regression/src/data_preprocessing.cairo |
use orion::operators::tensor::{
Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,
FP16x16TensorDiv, FP16x16TensorMul
};
use orion::numbers::{FP16x16, FixedTrait};
use linear_regresion::helper_functions::{
get_tensor_data_by_row, transpose_tensor, calculate_mean, calculate_r_score,
normalize_user_x_inputs, rescale_predictions
};
#[derive(Copy, Drop)]
struct Dataset {
x_values: Tensor<FP16x16>,
y_values: Tensor<FP16x16>,
}
#[generate_trait]
impl DataPreprocessing of DatasetTrait {
fn normalize_dataset(ref self: Dataset) -> Dataset {
let mut x_values = TensorTrait::<FP16x16>::new(array![1].span(), array![FixedTrait::new(0, false)].span());
let mut y_values = TensorTrait::<FP16x16>::new(array![1].span(), array![FixedTrait::new(0, false)].span());
// used for multiple_linear_regression_models
if self.x_values.shape.len() > 1 {
x_values = normalize_feature_data(self.x_values);
y_values = normalize_label_data(self.y_values);
}
// used for linear_regression_models
if self.x_values.shape.len() == 1 {
x_values = normalize_label_data(self.x_values);
y_values = normalize_label_data(self.y_values);
}
return Dataset { x_values, y_values };
}
}
// normalizes 2D Tensor
fn normalize_feature_data(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {
let mut x_min_array = ArrayTrait::<FP16x16>::new();
let mut x_max_array = ArrayTrait::<FP16x16>::new();
let mut x_range_array = ArrayTrait::<FP16x16>::new();
let mut normalized_array = ArrayTrait::<FP16x16>::new();
// transpose to change rows to be columns
let transposed_tensor = tensor_data.transpose(axes: array![1, 0].span());
let tensor_shape = transposed_tensor.shape;
let tensor_row_len = *tensor_shape.at(0); // 13
let tensor_column_len = *tensor_shape.at(1); //50
// loop and append max and min row values to corresponding array
let mut i: u32 = 0;
loop {
if i >= tensor_row_len {
break ();
}
let mut transposed_tensor_row = get_tensor_data_by_row(transposed_tensor, i);
x_max_array.append(transposed_tensor_row.max_in_tensor());
x_min_array.append(transposed_tensor_row.min_in_tensor());
x_range_array
.append(transposed_tensor_row.max_in_tensor() - transposed_tensor_row.min_in_tensor());
i += 1;
};
// convert array to tensor format for ease of math operation
let mut x_min = TensorTrait::<
FP16x16
>::new(shape: array![1, tensor_row_len].span(), data: x_min_array.span());
let mut x_range = TensorTrait::<
FP16x16
>::new(shape: array![1, tensor_row_len].span(), data: x_range_array.span());
let normalized_tensor = (tensor_data - x_min) / x_range;
return normalized_tensor;
}
// normalizes 1D tensor
fn normalize_label_data(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {
let mut tensor_data_ = tensor_data;
let mut normalized_array = ArrayTrait::<FP16x16>::new();
let mut range = tensor_data.max_in_tensor() - tensor_data.min_in_tensor();
// loop through tensor values normalizing and appending to new array
let mut i: u32 = 0;
loop {
match tensor_data_.data.pop_front() {
Option::Some(tensor_val) => {
let mut diff = *tensor_val - tensor_data.min_in_tensor();
normalized_array.append(diff / range);
i += 1;
},
Option::None(_) => { break; }
};
};
// convert normalized array values to tensor format
let mut normalized_tensor = TensorTrait::<
FP16x16
>::new(shape: array![tensor_data.data.len()].span(), data: normalized_array.span());
return normalized_tensor;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/linear_regression/src/datasets.cairo | mod linear_data;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/linear_regression/src/datasets/linear_data.cairo | mod x_feature_data;
mod y_label_data;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/linear_regression/src/datasets/linear_data/x_feature_data.cairo | use array::ArrayTrait;
use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16Impl, FP16x16PartialEq };
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};
use orion::numbers::{FP16x16, FixedTrait};
fn x_feature_data() -> Tensor<FP16x16> {
let tensor = TensorTrait::<FP16x16>::new(
shape: array![50].span(),
data: array![
FixedTrait::new(90639, false ),
FixedTrait::new(12581, true ),
FixedTrait::new(33595, false ),
FixedTrait::new(92893, false ),
FixedTrait::new(64841, false ),
FixedTrait::new(21784, false ),
FixedTrait::new(93600, false ),
FixedTrait::new(139107, false ),
FixedTrait::new(46680, true ),
FixedTrait::new(148678, true ),
FixedTrait::new(55700, false ),
FixedTrait::new(63442, false ),
FixedTrait::new(16625, false ),
FixedTrait::new(15088, false ),
FixedTrait::new(109945, false ),
FixedTrait::new(22098, false ),
FixedTrait::new(28923, false ),
FixedTrait::new(55032, true ),
FixedTrait::new(29968, false ),
FixedTrait::new(17353, false ),
FixedTrait::new(126, true ),
FixedTrait::new(6705, true ),
FixedTrait::new(81234, true ),
FixedTrait::new(38498, true ),
FixedTrait::new(75536, true ),
FixedTrait::new(984, true ),
FixedTrait::new(45491, true ),
FixedTrait::new(88496, false ),
FixedTrait::new(8992, false ),
FixedTrait::new(28549, false ),
FixedTrait::new(61676, true ),
FixedTrait::new(54096, true ),
FixedTrait::new(91046, false ),
FixedTrait::new(53660, false ),
FixedTrait::new(6145, true ),
FixedTrait::new(26994, false ),
FixedTrait::new(90657, false ),
FixedTrait::new(21638, true ),
FixedTrait::new(50848, false ),
FixedTrait::new(4550, true ),
FixedTrait::new(7560, true ),
FixedTrait::new(41550, false ),
FixedTrait::new(200, false ),
FixedTrait::new(102341, false ),
FixedTrait::new(25789, false ),
FixedTrait::new(9158, false ),
FixedTrait::new(102276, true ),
FixedTrait::new(76823, true ),
FixedTrait::new(69440, true ),
FixedTrait::new(17547, true ),
].span()
);
return tensor;
} | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/linear_regression/src/datasets/linear_data/y_label_data.cairo | use array::ArrayTrait;
use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16Impl, FP16x16PartialEq };
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};
use orion::numbers::{FP16x16, FixedTrait};
fn y_label_data() -> Tensor<FP16x16> {
let tensor = TensorTrait::<FP16x16>::new(
shape: array![50].span(),
data: array![
FixedTrait::new(7282724, false ),
FixedTrait::new(6435011, false ),
FixedTrait::new(6662231, false ),
FixedTrait::new(7271410, false ),
FixedTrait::new(7099095, false ),
FixedTrait::new(6751687, false ),
FixedTrait::new(7403695, false ),
FixedTrait::new(7831893, false ),
FixedTrait::new(6135683, false ),
FixedTrait::new(5448106, false ),
FixedTrait::new(6992113, false ),
FixedTrait::new(7129256, false ),
FixedTrait::new(6678313, false ),
FixedTrait::new(6524452, false ),
FixedTrait::new(7538849, false ),
FixedTrait::new(6685568, false ),
FixedTrait::new(6749158, false ),
FixedTrait::new(6149931, false ),
FixedTrait::new(6876758, false ),
FixedTrait::new(6623147, false ),
FixedTrait::new(6679189, false ),
FixedTrait::new(6578635, false ),
FixedTrait::new(5894520, false ),
FixedTrait::new(6161430, false ),
FixedTrait::new(5887716, false ),
FixedTrait::new(6440009, false ),
FixedTrait::new(6209384, false ),
FixedTrait::new(7208597, false ),
FixedTrait::new(6679473, false ),
FixedTrait::new(6809111, false ),
FixedTrait::new(6068970, false ),
FixedTrait::new(6089744, false ),
FixedTrait::new(7360056, false ),
FixedTrait::new(6971060, false ),
FixedTrait::new(6419231, false ),
FixedTrait::new(6780044, false ),
FixedTrait::new(7279453, false ),
FixedTrait::new(6350620, false ),
FixedTrait::new(7023820, false ),
FixedTrait::new(6568475, false ),
FixedTrait::new(6528424, false ),
FixedTrait::new(6936953, false ),
FixedTrait::new(6511689, false ),
FixedTrait::new(7367935, false ),
FixedTrait::new(6860285, false ),
FixedTrait::new(6800462, false ),
FixedTrait::new(5650037, false ),
FixedTrait::new(5915425, false ),
FixedTrait::new(5913912, false ),
FixedTrait::new(6491295, false ),
].span()
);
return tensor;
} | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/linear_regression/src/helper_functions.cairo |
use debug::PrintTrait;
use array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{
Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,
FP16x16TensorDiv, FP16x16TensorMul
};
use orion::numbers::{FP16x16, FixedTrait};
// retrieves row data by index in a 2D tensor
fn get_tensor_data_by_row(tensor_data: Tensor<FP16x16>, row_index: u32,) -> Tensor<FP16x16> {
let column_len = *tensor_data.shape.at(1); //13
// crete new array
let mut result = ArrayTrait::<FP16x16>::new();
// loop through the x values and append values
let mut i: u32 = 0;
loop {
if i >= column_len {
break ();
}
result.append(tensor_data.at(indices: array![row_index, i].span()));
i += 1;
};
let resultant_tensor = TensorTrait::<
FP16x16
>::new(array![column_len].span(), data: result.span());
return resultant_tensor;
}
// transposes tensor
fn transpose_tensor(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {
let tensor_transposed = tensor_data.transpose(axes: array![1, 0].span());
return tensor_transposed;
}
fn calculate_mean(tensor_data: Tensor<FP16x16>) -> FP16x16 {
let tensor_size = FixedTrait::<FP16x16>::new_unscaled(tensor_data.data.len(), false);
let cumulated_sum = tensor_data.cumsum(0, Option::None(()), Option::None(()));
let sum_result = cumulated_sum.data[tensor_data.data.len() - 1];
let mean = *sum_result / tensor_size;
return mean;
}
// Calculates the R-Squared score between two tensors.
fn calculate_r_score(Y_values: Tensor<FP16x16>, Y_pred_values: Tensor<FP16x16>) -> FP16x16 {
let mut Y_values_ = Y_values;
let mean_y_value = calculate_mean(Y_values);
// creating the appropriate tensor shapes and empty arrays to populate values into
let mut squared_diff_shape = array::ArrayTrait::new();
squared_diff_shape.append(Y_values.data.len());
let mut squared_diff_vals = array::ArrayTrait::new();
let mut squared_mean_diff_shape = array::ArrayTrait::new();
squared_mean_diff_shape.append(Y_values.data.len());
let mut squared_mean_diff_vals = array::ArrayTrait::new();
let mut i: u32 = 0;
loop {
match Y_values_.data.pop_front() {
Option::Some(y_value) => {
let diff_pred = *y_value - *Y_pred_values.data.at(i);
let squared_diff = diff_pred * diff_pred;
squared_diff_vals.append(squared_diff);
let diff_mean = *y_value - mean_y_value;
let squared_mean_diff = diff_mean * diff_mean;
squared_mean_diff_vals.append(squared_mean_diff);
i += 1;
},
Option::None(_) => { break; }
}
};
let squared_diff_tensor = TensorTrait::<
FP16x16
>::new(squared_diff_shape.span(), squared_diff_vals.span());
let squared_mean_diff_tensor = TensorTrait::<
FP16x16
>::new(squared_mean_diff_shape.span(), squared_mean_diff_vals.span());
let sum_squared_diff = squared_diff_tensor.cumsum(0, Option::None(()), Option::None(()));
let sum_squared_mean_diff = squared_mean_diff_tensor
.cumsum(0, Option::None(()), Option::None(()));
let r_score = FixedTrait::new_unscaled(1, false)
- *sum_squared_diff.data.at(Y_values.data.len() - 1)
/ *sum_squared_mean_diff.data.at(Y_values.data.len() - 1);
return r_score;
}
// computes the x_min, x_max and x_range. Used for helping in normalizing and denormalizing user inputed values operations
fn normalize_user_x_inputs(
x_inputs: Tensor<FP16x16>, original_x_values: Tensor<FP16x16>
) -> Tensor<FP16x16> {
let mut x_inputs_normalized = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());
let mut x_min = ArrayTrait::<FP16x16>::new();
let mut x_max = ArrayTrait::<FP16x16>::new();
let mut x_range = ArrayTrait::<FP16x16>::new();
let mut result = ArrayTrait::<FP16x16>::new();
if original_x_values.shape.len() > 1 {
let transposed_tensor = original_x_values.transpose(axes: array![1, 0].span());
let data_len = *transposed_tensor.shape.at(0); //13
// loop through each row calculating the min, max and range row values for each feature columns
let mut i: u32 = 0;
loop {
if i >= data_len {
break ();
}
let mut transposed_tensor_row = get_tensor_data_by_row(transposed_tensor, i);
x_min.append(transposed_tensor_row.min_in_tensor());
x_max.append(transposed_tensor_row.max_in_tensor());
x_range
.append(
transposed_tensor_row.max_in_tensor() - transposed_tensor_row.min_in_tensor()
);
i += 1;
};
let mut x_min_tensor = TensorTrait::new(shape: array![data_len].span(), data: x_min.span());
let mut x_max_tensor = TensorTrait::new(shape: array![data_len].span(), data: x_max.span());
let mut x_range_tensor = TensorTrait::new(
shape: array![data_len].span(), data: x_range.span()
);
// for normalizing 2D user inputed feature vals
if x_inputs.shape.len() > 1 {
let mut j: u32 = 0;
loop {
if j >= *x_inputs.shape.at(0) {
break ();
};
let mut row_data = get_tensor_data_by_row(x_inputs, j);
let mut norm_row_data = (row_data - x_min_tensor) / x_range_tensor;
let mut k: u32 = 0;
loop {
if k >= norm_row_data.data.len() {
break ();
};
result.append(*norm_row_data.data.at(k));
k += 1;
};
j += 1;
};
x_inputs_normalized =
TensorTrait::<
FP16x16
>::new(
array![*x_inputs.shape.at(0), *x_inputs.shape.at(1)].span(), data: result.span()
);
};
// for normalizing 1D feature input
if x_inputs.shape.len() == 1 {
x_inputs_normalized = (x_inputs - x_min_tensor) / x_range_tensor;
};
}
if original_x_values.shape.len() == 1 {
let mut x_min_tensor = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![original_x_values.min_in_tensor()].span());
let mut x_max_tensor = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![original_x_values.max_in_tensor()].span());
let mut x_range_tensor = TensorTrait::<
FP16x16
>::new(
shape: array![1].span(),
data: array![original_x_values.max_in_tensor() - original_x_values.min_in_tensor()]
.span()
);
let mut diff = ((x_inputs - x_min_tensor));
x_inputs_normalized = ((x_inputs - x_min_tensor)) / x_range_tensor;
};
return x_inputs_normalized;
}
// rescales model predictions to standard format
fn rescale_predictions(
prediction_result: Tensor<FP16x16>, y_values: Tensor<FP16x16>
) -> Tensor<FP16x16> {
let mut rescale_predictions = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());
let mut y_min_array = ArrayTrait::<FP16x16>::new();
let mut y_max_array = ArrayTrait::<FP16x16>::new();
let mut y_range_array = ArrayTrait::<FP16x16>::new();
let mut y_max = y_values.max_in_tensor();
let mut y_min = y_values.min_in_tensor();
let mut y_range = y_values.max_in_tensor() - y_values.min_in_tensor();
// convert to tensor format for ease of math operations
let y_min_tensor = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![y_min].span());
let y_max_tensor = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![y_max].span());
let y_range_tensor = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![y_range].span());
rescale_predictions = (prediction_result * y_range_tensor) + y_min_tensor;
return rescale_predictions;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/linear_regression/src/lib.cairo | mod test;
mod data_preprocessing;
mod helper_functions;
mod datasets;
mod model; | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/linear_regression/src/model.cairo | mod linear_regression_model;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/linear_regression/src/model/linear_regression_model.cairo |
use orion::operators::tensor::{
Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,
FP16x16TensorDiv, FP16x16TensorMul
};
use orion::numbers::{FP16x16, FixedTrait};
use linear_regresion::data_preprocessing::{Dataset, DatasetTrait};
use linear_regresion::helper_functions::{
get_tensor_data_by_row, transpose_tensor, calculate_mean, calculate_r_score,
normalize_user_x_inputs, rescale_predictions
};
#[derive(Copy, Drop)]
struct LinearRegressionModel {
gradient: Tensor<FP16x16>,
bias: Tensor<FP16x16>
}
#[generate_trait]
impl RegressionOperation of LinearRegressionModelTrait {
fn predict(ref self: LinearRegressionModel, x_input: Tensor<FP16x16>) -> Tensor<FP16x16> {
let gradient = self.gradient;
let bias = self.bias;
let mut prediction = (gradient * x_input) + bias;
return prediction;
}
}
fn LinearRegression(dataset: Dataset) -> LinearRegressionModel {
let gradient = compute_gradient(dataset);
let bias = compute_bias(dataset);
return LinearRegressionModel { gradient, bias };
}
// computes the mean of a given 1D tensor and outputs result as tensor
fn compute_mean(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {
let tensor_size = FixedTrait::<FP16x16>::new_unscaled(tensor_data.data.len(), false);
let cumulated_sum = tensor_data.cumsum(0, Option::None(()), Option::None(()));
let sum_result = cumulated_sum.data[tensor_data.data.len() - 1];
let mean = *sum_result / tensor_size;
let mut result_tensor = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![mean].span());
return result_tensor;
}
/// Calculates the deviation of each element from the mean of the provided 1D tensor.
fn deviation_from_mean(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {
let mut tensor_data_ = tensor_data;
let mean_value = calculate_mean(tensor_data);
let mut tensor_shape = array::ArrayTrait::new();
tensor_shape.append(tensor_data.data.len());
let mut deviation_values = array::ArrayTrait::new();
let mut i: u32 = 0;
loop {
match tensor_data_.data.pop_front() {
Option::Some(tensor_val) => {
let distance_from_mean = *tensor_val - mean_value;
deviation_values.append(distance_from_mean);
i += 1;
},
Option::None(_) => { break; }
};
};
let distance_from_mean_tensor = TensorTrait::<
FP16x16
>::new(tensor_shape.span(), deviation_values.span());
return distance_from_mean_tensor;
}
/// Calculates the beta value for linear regression.
fn compute_gradient(dataset: Dataset) -> Tensor<FP16x16> {
let x_deviation = deviation_from_mean(dataset.x_values);
let y_deviation = deviation_from_mean(dataset.y_values);
let x_y_covariance = x_deviation.matmul(@y_deviation);
let x_variance = x_deviation.matmul(@x_deviation);
let beta_value = x_y_covariance / x_variance;
return beta_value;
}
/// Calculates the intercept for linear regression.
fn compute_bias(dataset: Dataset) -> Tensor<FP16x16> {
let x_mean = compute_mean(dataset.x_values);
let y_mean = compute_mean(dataset.y_values);
let gradient = compute_gradient(dataset);
let mx = gradient * x_mean;
let intercept = y_mean - mx;
return intercept;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/linear_regression/src/test.cairo | // use traits::Into;
use debug::PrintTrait;
use array::{ArrayTrait, SpanTrait};
use linear_regresion::datasets::linear_data::x_feature_data::x_feature_data;
use linear_regresion::datasets::linear_data::y_label_data::y_label_data;
use orion::numbers::{FP16x16, FixedTrait};
use linear_regresion::model::linear_regression_model::{
LinearRegressionModel, compute_mean, LinearRegression, LinearRegressionModelTrait
};
use linear_regresion::data_preprocessing::{Dataset, DatasetTrait};
use linear_regresion::helper_functions::{get_tensor_data_by_row, transpose_tensor, calculate_mean ,
calculate_r_score, normalize_user_x_inputs, rescale_predictions};
use orion::operators::tensor::{
Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd,
FP16x16TensorSub, FP16x16TensorAdd, FP16x16TensorDiv, FP16x16TensorMul};
#[test]
#[available_gas(99999999999999999)]
fn multiple_linear_regression_test() {
// // ----------------------------------------------------------------Simple Linear regression tests---------------------------------------------------------------------------------
let mut main_x_vals = x_feature_data();
let mut main_y_vals = y_label_data();
let dataset = Dataset{x_values: main_x_vals,y_values:main_y_vals};
let mut model = LinearRegression(dataset);
let gradient = model.gradient;
let mut reconstructed_ys = model.predict(main_x_vals);
let mut r_squared_score = calculate_r_score(main_y_vals,reconstructed_ys);
r_squared_score.print();
// performing checks on shape on coefficient values (gradient vals + bias)
assert(model.gradient.data.len() == 1, 'gradient data shape mismatch');
assert(model.bias.data.len() == 1, 'bias data shape mismatch');
// model accuracy deviance checks
assert(r_squared_score >= FixedTrait::new(62259, false), 'Linear model acc. less than 95%');
// linear regression model new input predictions
let mut user_value = TensorTrait::<FP16x16>::new(shape: array![2].span(), data: array![FixedTrait::new(65536, false), FixedTrait::new(65536, true)].span());
let mut prediction_results = model.predict(user_value);
(*prediction_results.data.at(0)).print();
(*prediction_results.data.at(1)).print();
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_aave/src/data_preprocessing.cairo |
use orion::operators::tensor::{
Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,
FP16x16TensorDiv, FP16x16TensorMul
};
use orion::numbers::{FP16x16, FixedTrait};
use multiple_linear_regresion::helper_functions::{
get_tensor_data_by_row, transpose_tensor, calculate_mean, calculate_r_score,
normalize_user_x_inputs, rescale_predictions
};
#[derive(Copy, Drop)]
struct Dataset {
x_values: Tensor<FP16x16>,
y_values: Tensor<FP16x16>,
}
#[generate_trait]
impl DataPreprocessing of DatasetTrait {
fn normalize_dataset(ref self: Dataset) -> Dataset {
let mut x_values = TensorTrait::<FP16x16>::new(array![1].span(), array![FixedTrait::new(0, false)].span());
let mut y_values = TensorTrait::<FP16x16>::new(array![1].span(), array![FixedTrait::new(0, false)].span());
// used for multiple_linear_regression_models
if self.x_values.shape.len() > 1 {
x_values = normalize_feature_data(self.x_values);
y_values = normalize_label_data(self.y_values);
}
// used for linear_regression_models
if self.x_values.shape.len() == 1 {
x_values = normalize_label_data(self.x_values);
y_values = normalize_label_data(self.y_values);
}
return Dataset { x_values, y_values };
}
}
// normalizes 2D Tensor
fn normalize_feature_data(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {
let mut x_min_array = ArrayTrait::<FP16x16>::new();
let mut x_max_array = ArrayTrait::<FP16x16>::new();
let mut x_range_array = ArrayTrait::<FP16x16>::new();
let mut normalized_array = ArrayTrait::<FP16x16>::new();
// transpose to change rows to be columns
let transposed_tensor = tensor_data.transpose(axes: array![1, 0].span());
let tensor_shape = transposed_tensor.shape;
let tensor_row_len = *tensor_shape.at(0); // 13
let tensor_column_len = *tensor_shape.at(1); //50
// loop and append max and min row values to corresponding array
let mut i: u32 = 0;
loop {
if i >= tensor_row_len {
break ();
}
let mut transposed_tensor_row = get_tensor_data_by_row(transposed_tensor, i);
x_max_array.append(transposed_tensor_row.max_in_tensor());
x_min_array.append(transposed_tensor_row.min_in_tensor());
x_range_array
.append(transposed_tensor_row.max_in_tensor() - transposed_tensor_row.min_in_tensor());
i += 1;
};
// convert array to tensor format for ease of math operation
let mut x_min = TensorTrait::<
FP16x16
>::new(shape: array![1, tensor_row_len].span(), data: x_min_array.span());
let mut x_range = TensorTrait::<
FP16x16
>::new(shape: array![1, tensor_row_len].span(), data: x_range_array.span());
let normalized_tensor = (tensor_data - x_min) / x_range;
return normalized_tensor;
}
// normalizes 1D tensor
fn normalize_label_data(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {
let mut tensor_data_ = tensor_data;
let mut normalized_array = ArrayTrait::<FP16x16>::new();
let mut range = tensor_data.max_in_tensor() - tensor_data.min_in_tensor();
// loop through tensor values normalizing and appending to new array
let mut i: u32 = 0;
loop {
match tensor_data_.data.pop_front() {
Option::Some(tensor_val) => {
let mut diff = *tensor_val - tensor_data.min_in_tensor();
normalized_array.append(diff / range);
i += 1;
},
Option::None(_) => { break; }
};
};
// convert normalized array values to tensor format
let mut normalized_tensor = TensorTrait::<
FP16x16
>::new(shape: array![tensor_data.data.len()].span(), data: normalized_array.span());
return normalized_tensor;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_aave/src/datasets.cairo | mod aave_data;
mod user_inputs_data;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_aave/src/datasets/aave_data.cairo | mod aave_x_features;
mod aave_y_labels;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_aave/src/datasets/aave_data/aave_x_features.cairo | use array::ArrayTrait;
use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16Impl, FP16x16PartialEq };
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};
use orion::numbers::{FP16x16, FixedTrait};
fn aave_x_features() -> Tensor<FP16x16> {
let tensor = TensorTrait::<FP16x16>::new(
shape: array![24,9].span(),
data: array![
FixedTrait::new(61, false ),
FixedTrait::new(484966, false ),
FixedTrait::new(812646, false ),
FixedTrait::new(13369344, false ),
FixedTrait::new(3604, false ),
FixedTrait::new(7798784, false ),
FixedTrait::new(1880883, false ),
FixedTrait::new(5006950, false ),
FixedTrait::new(220856320, false ),
FixedTrait::new(87, false ),
FixedTrait::new(488243, false ),
FixedTrait::new(812646, false ),
FixedTrait::new(13434880, false ),
FixedTrait::new(3604, false ),
FixedTrait::new(7798784, false ),
FixedTrait::new(1880883, false ),
FixedTrait::new(5006950, false ),
FixedTrait::new(220856320, false ),
FixedTrait::new(114, false ),
FixedTrait::new(525598, false ),
FixedTrait::new(812646, false ),
FixedTrait::new(13565952, false ),
FixedTrait::new(3604, false ),
FixedTrait::new(7798784, false ),
FixedTrait::new(1887436, false ),
FixedTrait::new(5013504, false ),
FixedTrait::new(217579519, false ),
FixedTrait::new(138, false ),
FixedTrait::new(628490, false ),
FixedTrait::new(838860, false ),
FixedTrait::new(13893632, false ),
FixedTrait::new(3604, false ),
FixedTrait::new(8126463, false ),
FixedTrait::new(1874329, false ),
FixedTrait::new(5046272, false ),
FixedTrait::new(208404480, false ),
FixedTrait::new(1, false ),
FixedTrait::new(655360, false ),
FixedTrait::new(924057, false ),
FixedTrait::new(14090240, false ),
FixedTrait::new(3768, false ),
FixedTrait::new(8388608, false ),
FixedTrait::new(1880883, false ),
FixedTrait::new(5065932, false ),
FixedTrait::new(206438400, false ),
FixedTrait::new(25, false ),
FixedTrait::new(688128, false ),
FixedTrait::new(924057, false ),
FixedTrait::new(14155776, false ),
FixedTrait::new(3768, false ),
FixedTrait::new(8454144, false ),
FixedTrait::new(1893990, false ),
FixedTrait::new(5065932, false ),
FixedTrait::new(204472320, false ),
FixedTrait::new(50, false ),
FixedTrait::new(681574, false ),
FixedTrait::new(924057, false ),
FixedTrait::new(14286848, false ),
FixedTrait::new(3768, false ),
FixedTrait::new(8585216, false ),
FixedTrait::new(1900544, false ),
FixedTrait::new(5072486, false ),
FixedTrait::new(205127680, false ),
FixedTrait::new(76, false ),
FixedTrait::new(640942, false ),
FixedTrait::new(924057, false ),
FixedTrait::new(14352384, false ),
FixedTrait::new(3768, false ),
FixedTrait::new(8650752, false ),
FixedTrait::new(1933312, false ),
FixedTrait::new(5072486, false ),
FixedTrait::new(209059840, false ),
FixedTrait::new(100, false ),
FixedTrait::new(747110, false ),
FixedTrait::new(924057, false ),
FixedTrait::new(14483456, false ),
FixedTrait::new(3768, false ),
FixedTrait::new(8716288, false ),
FixedTrait::new(1939865, false ),
FixedTrait::new(5072486, false ),
FixedTrait::new(201195519, false ),
FixedTrait::new(126, false ),
FixedTrait::new(650117, false ),
FixedTrait::new(989593, false ),
FixedTrait::new(14614528, false ),
FixedTrait::new(3768, false ),
FixedTrait::new(8781824, false ),
FixedTrait::new(1966080, false ),
FixedTrait::new(5079040, false ),
FixedTrait::new(209059840, false ),
FixedTrait::new(152, false ),
FixedTrait::new(645529, false ),
FixedTrait::new(989593, false ),
FixedTrait::new(14876672, false ),
FixedTrait::new(3768, false ),
FixedTrait::new(8978432, false ),
FixedTrait::new(1979187, false ),
FixedTrait::new(5085593, false ),
FixedTrait::new(209715200, false ),
FixedTrait::new(1, false ),
FixedTrait::new(653393, false ),
FixedTrait::new(1002700, false ),
FixedTrait::new(14876672, false ),
FixedTrait::new(3951, false ),
FixedTrait::new(8978432, false ),
FixedTrait::new(1959526, false ),
FixedTrait::new(5111808, false ),
FixedTrait::new(209059840, false ),
FixedTrait::new(26, false ),
FixedTrait::new(614072, false ),
FixedTrait::new(1009254, false ),
FixedTrait::new(15007744, false ),
FixedTrait::new(3951, false ),
FixedTrait::new(9043968, false ),
FixedTrait::new(1926758, false ),
FixedTrait::new(5157683, false ),
FixedTrait::new(211025919, false ),
FixedTrait::new(54, false ),
FixedTrait::new(523632, false ),
FixedTrait::new(1009254, false ),
FixedTrait::new(15073280, false ),
FixedTrait::new(3951, false ),
FixedTrait::new(9043968, false ),
FixedTrait::new(2011955, false ),
FixedTrait::new(5203558, false ),
FixedTrait::new(220856320, false ),
FixedTrait::new(78, false ),
FixedTrait::new(688128, false ),
FixedTrait::new(1009254, false ),
FixedTrait::new(15138816, false ),
FixedTrait::new(3951, false ),
FixedTrait::new(9109504, false ),
FixedTrait::new(1861222, false ),
FixedTrait::new(5360844, false ),
FixedTrait::new(203816960, false ),
FixedTrait::new(102, false ),
FixedTrait::new(688128, false ),
FixedTrait::new(1028915, false ),
FixedTrait::new(15204352, false ),
FixedTrait::new(3951, false ),
FixedTrait::new(9109504, false ),
FixedTrait::new(1861222, false ),
FixedTrait::new(5367398, false ),
FixedTrait::new(203816960, false ),
FixedTrait::new(126, false ),
FixedTrait::new(694681, false ),
FixedTrait::new(1028915, false ),
FixedTrait::new(15204352, false ),
FixedTrait::new(3951, false ),
FixedTrait::new(9109504, false ),
FixedTrait::new(1861222, false ),
FixedTrait::new(5367398, false ),
FixedTrait::new(203161600, false ),
FixedTrait::new(151, false ),
FixedTrait::new(681574, false ),
FixedTrait::new(1028915, false ),
FixedTrait::new(15466496, false ),
FixedTrait::new(3951, false ),
FixedTrait::new(9371648, false ),
FixedTrait::new(1848115, false ),
FixedTrait::new(5452595, false ),
FixedTrait::new(203161600, false ),
FixedTrait::new(1, false ),
FixedTrait::new(591790, false ),
FixedTrait::new(1048576, false ),
FixedTrait::new(15663104, false ),
FixedTrait::new(4128, false ),
FixedTrait::new(9568256, false ),
FixedTrait::new(1985740, false ),
FixedTrait::new(5485363, false ),
FixedTrait::new(214302719, false ),
FixedTrait::new(29, false ),
FixedTrait::new(565575, false ),
FixedTrait::new(1048576, false ),
FixedTrait::new(15859712, false ),
FixedTrait::new(4128, false ),
FixedTrait::new(9764864, false ),
FixedTrait::new(2025062, false ),
FixedTrait::new(5505024, false ),
FixedTrait::new(217579519, false ),
FixedTrait::new(57, false ),
FixedTrait::new(681574, false ),
FixedTrait::new(1048576, false ),
FixedTrait::new(16187392, false ),
FixedTrait::new(4128, false ),
FixedTrait::new(9961472, false ),
FixedTrait::new(1979187, false ),
FixedTrait::new(5583667, false ),
FixedTrait::new(207093760, false ),
FixedTrait::new(83, false ),
FixedTrait::new(547225, false ),
FixedTrait::new(1048576, false ),
FixedTrait::new(16580607, false ),
FixedTrait::new(4128, false ),
FixedTrait::new(10223616, false ),
FixedTrait::new(1998848, false ),
FixedTrait::new(5681971, false ),
FixedTrait::new(218234879, false ),
FixedTrait::new(110, false ),
FixedTrait::new(753664, false ),
FixedTrait::new(1048576, false ),
FixedTrait::new(16777216, false ),
FixedTrait::new(4128, false ),
FixedTrait::new(10289152, false ),
FixedTrait::new(1966080, false ),
FixedTrait::new(5754060, false ),
FixedTrait::new(201195519, false ),
FixedTrait::new(135, false ),
FixedTrait::new(747110, false ),
FixedTrait::new(1048576, false ),
FixedTrait::new(16842752, false ),
FixedTrait::new(4128, false ),
FixedTrait::new(10289152, false ),
FixedTrait::new(1992294, false ),
FixedTrait::new(5780275, false ),
FixedTrait::new(202506239, false ),
].span()
);
return tensor;
} | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_aave/src/datasets/aave_data/aave_y_labels.cairo | use array::ArrayTrait;
use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16Impl, FP16x16PartialEq };
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};
use orion::numbers::{FP16x16, FixedTrait};
fn aave_y_labels() -> Tensor<FP16x16> {
let tensor = TensorTrait::<FP16x16>::new(
shape: array![24].span(),
data: array![
FixedTrait::new(5072486, false ),
FixedTrait::new(5072486, false ),
FixedTrait::new(5079040, false ),
FixedTrait::new(5085593, false ),
FixedTrait::new(5111808, false ),
FixedTrait::new(5157683, false ),
FixedTrait::new(5203558, false ),
FixedTrait::new(5360844, false ),
FixedTrait::new(5367398, false ),
FixedTrait::new(5367398, false ),
FixedTrait::new(5452595, false ),
FixedTrait::new(5485363, false ),
FixedTrait::new(5505024, false ),
FixedTrait::new(5583667, false ),
FixedTrait::new(5681971, false ),
FixedTrait::new(5754060, false ),
FixedTrait::new(5780275, false ),
FixedTrait::new(5852364, false ),
FixedTrait::new(5891686, false ),
FixedTrait::new(5963776, false ),
FixedTrait::new(6035865, false ),
FixedTrait::new(6134169, false ),
FixedTrait::new(6153830, false ),
FixedTrait::new(6180044, false ),
].span()
);
return tensor;
} | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_aave/src/datasets/user_inputs_data.cairo | mod aave_weth_revenue_data_input;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_aave/src/datasets/user_inputs_data/aave_weth_revenue_data_input.cairo | use array::ArrayTrait;
use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16Impl, FP16x16PartialEq };
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};
use orion::numbers::{FP16x16, FixedTrait};
fn aave_weth_revenue_data_input() -> Tensor<FP16x16> {
let tensor = TensorTrait::<FP16x16>::new(
shape: array![7,9].span(),
data: array![
FixedTrait::new(160, false ),
FixedTrait::new(786432, false ),
FixedTrait::new(1048576, false ),
FixedTrait::new(16973824, false ),
FixedTrait::new(4128, false ),
FixedTrait::new(10354688, false ),
FixedTrait::new(1952972, false ),
FixedTrait::new(5852364, false ),
FixedTrait::new(198574079, false ),
FixedTrait::new(185, false ),
FixedTrait::new(681574, false ),
FixedTrait::new(1048576, false ),
FixedTrait::new(17170432, false ),
FixedTrait::new(4128, false ),
FixedTrait::new(10420224, false ),
FixedTrait::new(1959526, false ),
FixedTrait::new(5891686, false ),
FixedTrait::new(207093760, false ),
FixedTrait::new(211, false ),
FixedTrait::new(688128, false ),
FixedTrait::new(1055129, false ),
FixedTrait::new(17301504, false ),
FixedTrait::new(4128, false ),
FixedTrait::new(10420224, false ),
FixedTrait::new(1952972, false ),
FixedTrait::new(5963776, false ),
FixedTrait::new(206438400, false ),
FixedTrait::new(236, false ),
FixedTrait::new(707788, false ),
FixedTrait::new(1055129, false ),
FixedTrait::new(17367040, false ),
FixedTrait::new(4128, false ),
FixedTrait::new(10420224, false ),
FixedTrait::new(1907097, false ),
FixedTrait::new(6035865, false ),
FixedTrait::new(203161600, false ),
FixedTrait::new(261, false ),
FixedTrait::new(792985, false ),
FixedTrait::new(1061683, false ),
FixedTrait::new(17432576, false ),
FixedTrait::new(4128, false ),
FixedTrait::new(10420224, false ),
FixedTrait::new(1880883, false ),
FixedTrait::new(6134169, false ),
FixedTrait::new(195952639, false ),
FixedTrait::new(285, false ),
FixedTrait::new(792985, false ),
FixedTrait::new(1061683, false ),
FixedTrait::new(17432576, false ),
FixedTrait::new(4128, false ),
FixedTrait::new(10420224, false ),
FixedTrait::new(1880883, false ),
FixedTrait::new(6153830, false ),
FixedTrait::new(195952639, false ),
FixedTrait::new(308, false ),
FixedTrait::new(792985, false ),
FixedTrait::new(1061683, false ),
FixedTrait::new(17498112, false ),
FixedTrait::new(4128, false ),
FixedTrait::new(10420224, false ),
FixedTrait::new(1887436, false ),
FixedTrait::new(6180044, false ),
FixedTrait::new(196607999, false ),
].span()
);
return tensor;
} | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_aave/src/helper_functions.cairo |
use debug::PrintTrait;
use array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{
Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,
FP16x16TensorDiv, FP16x16TensorMul
};
use orion::numbers::{FP16x16, FixedTrait};
// retrieves row data by index in a 2D tensor
fn get_tensor_data_by_row(tensor_data: Tensor<FP16x16>, row_index: u32,) -> Tensor<FP16x16> {
let column_len = *tensor_data.shape.at(1); //13
// crete new array
let mut result = ArrayTrait::<FP16x16>::new();
// loop through the x values and append values
let mut i: u32 = 0;
loop {
if i >= column_len {
break ();
}
result.append(tensor_data.at(indices: array![row_index, i].span()));
i += 1;
};
let resultant_tensor = TensorTrait::<
FP16x16
>::new(array![column_len].span(), data: result.span());
return resultant_tensor;
}
// transposes tensor
fn transpose_tensor(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {
let tensor_transposed = tensor_data.transpose(axes: array![1, 0].span());
return tensor_transposed;
}
fn calculate_mean(tensor_data: Tensor<FP16x16>) -> FP16x16 {
let tensor_size = FixedTrait::<FP16x16>::new_unscaled(tensor_data.data.len(), false);
let cumulated_sum = tensor_data.cumsum(0, Option::None(()), Option::None(()));
let sum_result = cumulated_sum.data[tensor_data.data.len() - 1];
let mean = *sum_result / tensor_size;
return mean;
}
// Calculates the R-Squared score between two tensors.
fn calculate_r_score(Y_values: Tensor<FP16x16>, Y_pred_values: Tensor<FP16x16>) -> FP16x16 {
let mut Y_values_ = Y_values;
let mean_y_value = calculate_mean(Y_values);
// creating the appropriate tensor shapes and empty arrays to populate values into
let mut squared_diff_shape = array::ArrayTrait::new();
squared_diff_shape.append(Y_values.data.len());
let mut squared_diff_vals = array::ArrayTrait::new();
let mut squared_mean_diff_shape = array::ArrayTrait::new();
squared_mean_diff_shape.append(Y_values.data.len());
let mut squared_mean_diff_vals = array::ArrayTrait::new();
let mut i: u32 = 0;
loop {
match Y_values_.data.pop_front() {
Option::Some(y_value) => {
let diff_pred = *y_value - *Y_pred_values.data.at(i);
let squared_diff = diff_pred * diff_pred;
squared_diff_vals.append(squared_diff);
let diff_mean = *y_value - mean_y_value;
let squared_mean_diff = diff_mean * diff_mean;
squared_mean_diff_vals.append(squared_mean_diff);
i += 1;
},
Option::None(_) => { break; }
}
};
let squared_diff_tensor = TensorTrait::<
FP16x16
>::new(squared_diff_shape.span(), squared_diff_vals.span());
let squared_mean_diff_tensor = TensorTrait::<
FP16x16
>::new(squared_mean_diff_shape.span(), squared_mean_diff_vals.span());
let sum_squared_diff = squared_diff_tensor.cumsum(0, Option::None(()), Option::None(()));
let sum_squared_mean_diff = squared_mean_diff_tensor
.cumsum(0, Option::None(()), Option::None(()));
let r_score = FixedTrait::new_unscaled(1, false)
- *sum_squared_diff.data.at(Y_values.data.len() - 1)
/ *sum_squared_mean_diff.data.at(Y_values.data.len() - 1);
return r_score;
}
// computes the x_min, x_max and x_range. Used for helping in normalizing and denormalizing user inputed values operations
fn normalize_user_x_inputs(
x_inputs: Tensor<FP16x16>, original_x_values: Tensor<FP16x16>
) -> Tensor<FP16x16> {
let mut x_inputs_normalized = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());
let mut x_min = ArrayTrait::<FP16x16>::new();
let mut x_max = ArrayTrait::<FP16x16>::new();
let mut x_range = ArrayTrait::<FP16x16>::new();
let mut result = ArrayTrait::<FP16x16>::new();
if original_x_values.shape.len() > 1 {
let transposed_tensor = original_x_values.transpose(axes: array![1, 0].span());
let data_len = *transposed_tensor.shape.at(0); //13
// loop through each row calculating the min, max and range row values for each feature columns
let mut i: u32 = 0;
loop {
if i >= data_len {
break ();
}
let mut transposed_tensor_row = get_tensor_data_by_row(transposed_tensor, i);
x_min.append(transposed_tensor_row.min_in_tensor());
x_max.append(transposed_tensor_row.max_in_tensor());
x_range
.append(
transposed_tensor_row.max_in_tensor() - transposed_tensor_row.min_in_tensor()
);
i += 1;
};
let mut x_min_tensor = TensorTrait::new(shape: array![data_len].span(), data: x_min.span());
let mut x_max_tensor = TensorTrait::new(shape: array![data_len].span(), data: x_max.span());
let mut x_range_tensor = TensorTrait::new(
shape: array![data_len].span(), data: x_range.span()
);
// for normalizing 2D user inputed feature vals
if x_inputs.shape.len() > 1 {
let mut j: u32 = 0;
loop {
if j >= *x_inputs.shape.at(0) {
break ();
};
let mut row_data = get_tensor_data_by_row(x_inputs, j);
let mut norm_row_data = (row_data - x_min_tensor) / x_range_tensor;
let mut k: u32 = 0;
loop {
if k >= norm_row_data.data.len() {
break ();
};
result.append(*norm_row_data.data.at(k));
k += 1;
};
j += 1;
};
x_inputs_normalized =
TensorTrait::<
FP16x16
>::new(
array![*x_inputs.shape.at(0), *x_inputs.shape.at(1)].span(), data: result.span()
);
};
// for normalizing 1D feature input
if x_inputs.shape.len() == 1 {
x_inputs_normalized = (x_inputs - x_min_tensor) / x_range_tensor;
};
}
if original_x_values.shape.len() == 1 {
let mut x_min_tensor = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![original_x_values.min_in_tensor()].span());
let mut x_max_tensor = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![original_x_values.max_in_tensor()].span());
let mut x_range_tensor = TensorTrait::<
FP16x16
>::new(
shape: array![1].span(),
data: array![original_x_values.max_in_tensor() - original_x_values.min_in_tensor()]
.span()
);
let mut diff = ((x_inputs - x_min_tensor));
x_inputs_normalized = ((x_inputs - x_min_tensor)) / x_range_tensor;
};
return x_inputs_normalized;
}
// rescales model predictions to standard format
fn rescale_predictions(
prediction_result: Tensor<FP16x16>, y_values: Tensor<FP16x16>
) -> Tensor<FP16x16> {
let mut rescale_predictions = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());
let mut y_min_array = ArrayTrait::<FP16x16>::new();
let mut y_max_array = ArrayTrait::<FP16x16>::new();
let mut y_range_array = ArrayTrait::<FP16x16>::new();
let mut y_max = y_values.max_in_tensor();
let mut y_min = y_values.min_in_tensor();
let mut y_range = y_values.max_in_tensor() - y_values.min_in_tensor();
// convert to tensor format for ease of math operations
let y_min_tensor = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![y_min].span());
let y_max_tensor = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![y_max].span());
let y_range_tensor = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![y_range].span());
rescale_predictions = (prediction_result * y_range_tensor) + y_min_tensor;
return rescale_predictions;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_aave/src/lib.cairo | mod test;
mod data_preprocessing;
mod helper_functions;
mod datasets;
mod model; | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_aave/src/model.cairo | mod multiple_linear_regression_model;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_aave/src/model/multiple_linear_regression_model.cairo |
use orion::operators::tensor::{
Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,
FP16x16TensorDiv, FP16x16TensorMul
};
use orion::numbers::{FP16x16, FixedTrait};
use multiple_linear_regresion::data_preprocessing::{Dataset, DatasetTrait};
use multiple_linear_regresion::helper_functions::{
get_tensor_data_by_row, transpose_tensor, calculate_mean, calculate_r_score,
normalize_user_x_inputs, rescale_predictions
};
#[derive(Copy, Drop)]
struct MultipleLinearRegressionModel {
coefficients: Tensor<FP16x16>
}
#[generate_trait]
impl RegressionOperation of MultipleLinearRegressionModelTrait {
// reconstruct the y values using the computed gradients and x values
fn predict(
ref self: MultipleLinearRegressionModel, feature_inputs: Tensor<FP16x16>
) -> Tensor<FP16x16> {
// random tensor value that we will replace
let mut prediction_result = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());
let mut result = ArrayTrait::<FP16x16>::new();
// for multiple predictions
if feature_inputs.shape.len() > 1 {
let feature_values = add_bias_term(feature_inputs, 1);
let mut data_len: u32 = *feature_values.shape.at(0);
let mut i: u32 = 0;
loop {
if i >= data_len {
break ();
}
let feature_row_values = get_tensor_data_by_row(feature_values, i);
let predicted_values = feature_row_values.matmul(@self.coefficients);
result.append(*predicted_values.data.at(0));
i += 1;
};
prediction_result =
TensorTrait::<
FP16x16
>::new(shape: array![result.len()].span(), data: result.span());
}
// for single predictions
if feature_inputs.shape.len() == 1 && self.coefficients.data.len() > 1 {
let feature_values = add_bias_term(feature_inputs, 1);
prediction_result = feature_values.matmul(@self.coefficients);
}
return prediction_result;
}
}
fn MultipleLinearRegression(dataset: Dataset) -> MultipleLinearRegressionModel {
let x_values_tranposed = transpose_tensor(dataset.x_values);
let x_values_tranposed_with_bias = add_bias_term(x_values_tranposed, 0);
let decorrelated_x_features = decorrelate_x_features(x_values_tranposed_with_bias);
let coefficients = compute_gradients(
decorrelated_x_features, dataset.y_values, x_values_tranposed_with_bias
);
return MultipleLinearRegressionModel { coefficients };
}
//Adds bias term to features based on axis
fn add_bias_term(x_feature: Tensor<FP16x16>, axis: u32) -> Tensor<FP16x16> {
let mut x_feature_ = x_feature;
let mut tensor_with_bias = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());
let mut result = ArrayTrait::<FP16x16>::new();
// check if feature data has multiple rows and columns
if x_feature.shape.len() > 1 {
let mut index: u32 = 0;
if axis == 1 {
index = 0;
} else {
index = 1;
}
let data_len = *x_feature.shape.at(index); // 50
let mut i: u32 = 0;
loop {
if i >= data_len {
break ();
}
result
.append(FixedTrait::new(65536, false)); //65536=ONE in FP16x16, change accordingly
i += 1;
};
if axis == 0 {
let res_tensor = TensorTrait::new(
shape: array![1, data_len].span(), data: result.span()
);
tensor_with_bias =
TensorTrait::concat(tensors: array![x_feature, res_tensor].span(), axis: axis);
} else {
let res_tensor = TensorTrait::new(
shape: array![data_len, 1].span(), data: result.span()
);
tensor_with_bias =
TensorTrait::concat(tensors: array![x_feature, res_tensor].span(), axis: axis);
}
}
// check if feature data is 1D
if x_feature.shape.len() == 1 {
let mut j: u32 = 0;
loop {
match x_feature_.data.pop_front() {
Option::Some(x_val) => {
result.append(*x_val);
j += 1;
},
Option::None(_) => { break; }
};
};
result.append(FixedTrait::new(65536, false)); //65536=ONE in FP16x16, change accordingly
tensor_with_bias =
TensorTrait::<FP16x16>::new(shape: array![result.len()].span(), data: result.span());
}
return tensor_with_bias;
}
// decorrelates the feature data (*only the last tensor row of the decorelated feature data will be fully orthogonal)
fn decorrelate_x_features(x_feature_data: Tensor<FP16x16>) -> Tensor<FP16x16> {
let mut input_tensor = x_feature_data;
let mut i: u32 = 0;
loop {
if i >= *x_feature_data.shape.at(0) {
break ();
}
let mut placeholder = ArrayTrait::<FP16x16>::new();
let mut feature_row_values = get_tensor_data_by_row(input_tensor, i);
let mut feature_squared = feature_row_values.matmul(@feature_row_values);
// avoiding division by zero errors
if *feature_squared.data.at(0) == FixedTrait::new(0, false) {
feature_squared =
TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());
}
// loop throgh remaining tensor data and remove the individual tensor factors from one another
let mut j: u32 = i + 1;
loop {
if j >= *x_feature_data.shape.at(0) {
break ();
}
let mut remaining_tensor_values = get_tensor_data_by_row(input_tensor, j);
let feature_cross_product = feature_row_values.matmul(@remaining_tensor_values);
let feature_gradients = feature_cross_product / feature_squared;
remaining_tensor_values = remaining_tensor_values
- (feature_row_values
* feature_gradients); //remove the feature factors from one another
// loop and append the modifieed remaining_tensor_values (after the corelated factor has been removed) to placeholder array
let mut k: u32 = 0;
loop {
if k >= remaining_tensor_values.data.len() {
break ();
}
placeholder.append(*remaining_tensor_values.data.at(k));
k += 1;
};
j += 1;
};
// convert placeholder array to tensor format and update the original tensor with the new modified decorrelated tensor row values
let mut decorrelated_tensor = TensorTrait::new(
shape: array![*x_feature_data.shape.at(0) - 1 - i, *x_feature_data.shape.at(1)].span(),
data: placeholder.span()
);
let mut original_tensor = input_tensor
.slice(
starts: array![0, 0].span(),
ends: array![i + 1, *x_feature_data.shape.at(1)].span(),
axes: Option::None(()),
steps: Option::Some(array![1, 1].span())
);
input_tensor =
TensorTrait::concat(
tensors: array![original_tensor, decorrelated_tensor].span(), axis: 0
);
i += 1;
};
return input_tensor;
}
// computes the corresponding MLR gradient using decorrelated feature
fn compute_gradients(
decorrelated_x_features: Tensor<FP16x16>,
y_values: Tensor<FP16x16>,
original_x_tensor_values: Tensor<FP16x16>
) -> Tensor<FP16x16> {
let mut gradient_values_flipped = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());
let mut result = ArrayTrait::<FP16x16>::new();
let mut tensor_y_vals = y_values;
let mut i: u32 = *decorrelated_x_features.shape.at(0);
// loop through Decorrelated_x_features starting from the fully orthogonlised last tensor row value
loop {
if i <= 0 {
break ();
}
let index_val = i - 1;
let mut decorelated_feature_row_values = get_tensor_data_by_row(
decorrelated_x_features, index_val
); // 50 vals
let mut decorelated_features_squared = decorelated_feature_row_values
.matmul(@decorelated_feature_row_values);
let mut feature_label_cross_product = tensor_y_vals
.matmul(@decorelated_feature_row_values); // multiply the tensors
// avoiding division by zero errors
if *decorelated_features_squared.data.at(0) == FixedTrait::new(0, false) {
decorelated_features_squared =
TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());
}
// computing the feature gradient values using the y values and decorrelated x features and appending to array
let mut single_gradient_value = feature_label_cross_product
/ decorelated_features_squared; // devide the summed value by each other
result.append(*single_gradient_value.data.at(0));
// remove the assosciated feature gradient value away from y values
let mut original_x_tensor_row_values = get_tensor_data_by_row(
original_x_tensor_values, index_val
);
tensor_y_vals = tensor_y_vals
- (original_x_tensor_row_values
* single_gradient_value); //remove the first feature from second feature values
i -= 1;
};
// convert the gradient array to tensor format
let final_gradients = TensorTrait::new(
shape: array![*decorrelated_x_features.shape.at(0)].span(), data: result.span()
);
let mut reverse_grad_array = ArrayTrait::<FP16x16>::new();
let mut data_len: u32 = final_gradients.data.len();
loop {
if data_len <= 0 {
break ();
}
let temp_val = data_len - 1;
reverse_grad_array.append(*final_gradients.data.at(temp_val));
data_len -= 1;
};
// convert gradient values to tensor format
let gradient_values_flipped = TensorTrait::<
FP16x16
>::new(shape: array![reverse_grad_array.len()].span(), data: reverse_grad_array.span());
return gradient_values_flipped;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_aave/src/test.cairo |
// use traits::Into;
use debug::PrintTrait;
use array::{ArrayTrait, SpanTrait};
use multiple_linear_regresion::datasets::aave_data::aave_x_features::aave_x_features;
use multiple_linear_regresion::datasets::aave_data::aave_y_labels::aave_y_labels;
use multiple_linear_regresion::datasets::user_inputs_data::aave_weth_revenue_data_input::{aave_weth_revenue_data_input };
use multiple_linear_regresion::model::multiple_linear_regression_model::{
MultipleLinearRegressionModel, MultipleLinearRegression, MultipleLinearRegressionModelTrait
};
use multiple_linear_regresion::data_preprocessing::{Dataset, DatasetTrait};
use multiple_linear_regresion::helper_functions::{get_tensor_data_by_row, transpose_tensor, calculate_mean ,
calculate_r_score, normalize_user_x_inputs, rescale_predictions};
use orion::numbers::{FP16x16, FixedTrait};
use orion::operators::tensor::{
Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd,
FP16x16TensorSub, FP16x16TensorAdd, FP16x16TensorDiv, FP16x16TensorMul};
#[test]
#[available_gas(99999999999999999)]
fn multiple_linear_regression_test() {
// -------------------------------------------------------------------AAVE dataset tests---------------------------------------------------------------------------------------------
let mut main_x_vals = aave_x_features();
let mut main_y_vals = aave_y_labels();
let mut dataset = Dataset{x_values: main_x_vals,y_values:main_y_vals};
let mut normalized_dataset = dataset.normalize_dataset();
let mut model = MultipleLinearRegression(normalized_dataset);
let mut model_coefficients = model.coefficients;
let mut reconstructed_ys = model.predict (normalized_dataset.x_values);
let mut r_squared_score = calculate_r_score(normalized_dataset.y_values,reconstructed_ys);
r_squared_score.print();
// checking if data has been normalized correctly
assert(normalized_dataset.x_values.max_in_tensor() <= FixedTrait::new(65536, false), 'normalized x not between 0-1');
assert(normalized_dataset.x_values.min_in_tensor() >= FixedTrait::new(0, false), 'normalized x not between 0-1');
assert(normalized_dataset.y_values.max_in_tensor() <= FixedTrait::new(65536, false), 'normalized y not between 0-1');
assert(normalized_dataset.x_values.min_in_tensor() >= FixedTrait::new(0, false), 'normalized y not between 0-1');
// performing checks on the shape of normalized data
assert(normalized_dataset.x_values.data.len()== main_x_vals.data.len() &&
normalized_dataset.y_values.data.len()== main_y_vals.data.len() , 'normalized data shape mismatch');
// performing checks on shape on coefficient values (gradient vals + bias)
assert(model.coefficients.data.len() == *main_x_vals.shape.at(1)+1, 'coefficient data shape mismatch');
// model accuracy deviance checks
assert(r_squared_score >= FixedTrait::new(62259, false), 'AAVE model acc. less than 95%');
// using model to forecast aave's 7 day WETH net lifetime repayments forecast
let last_7_days_aave_data = aave_weth_revenue_data_input();
let last_7_days_aave_data_normalized = normalize_user_x_inputs(last_7_days_aave_data, main_x_vals );
let mut forecast_results = model.predict (last_7_days_aave_data_normalized);
let mut rescale_forecasts = rescale_predictions(forecast_results, main_y_vals); // PS. ** the rescaled forecasted ouputs are in denominated thousands of ETH
(*rescale_forecasts.data.at(0)).print();
(*rescale_forecasts.data.at(1)).print();
(*rescale_forecasts.data.at(2)).print();
(*rescale_forecasts.data.at(5)).print();
(*rescale_forecasts.data.at(6)).print();
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_boston/src/data_preprocessing.cairo |
use orion::operators::tensor::{
Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,
FP16x16TensorDiv, FP16x16TensorMul
};
use orion::numbers::{FP16x16, FixedTrait};
use multiple_linear_regresion::helper_functions::{
get_tensor_data_by_row, transpose_tensor, calculate_mean, calculate_r_score,
normalize_user_x_inputs, rescale_predictions
};
#[derive(Copy, Drop)]
struct Dataset {
x_values: Tensor<FP16x16>,
y_values: Tensor<FP16x16>,
}
#[generate_trait]
impl DataPreprocessing of DatasetTrait {
fn normalize_dataset(ref self: Dataset) -> Dataset {
let mut x_values = TensorTrait::<FP16x16>::new(array![1].span(), array![FixedTrait::new(0, false)].span());
let mut y_values = TensorTrait::<FP16x16>::new(array![1].span(), array![FixedTrait::new(0, false)].span());
// used for multiple_linear_regression_models
if self.x_values.shape.len() > 1 {
x_values = normalize_feature_data(self.x_values);
y_values = normalize_label_data(self.y_values);
}
// used for linear_regression_models
if self.x_values.shape.len() == 1 {
x_values = normalize_label_data(self.x_values);
y_values = normalize_label_data(self.y_values);
}
return Dataset { x_values, y_values };
}
}
// normalizes 2D Tensor
fn normalize_feature_data(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {
let mut x_min_array = ArrayTrait::<FP16x16>::new();
let mut x_max_array = ArrayTrait::<FP16x16>::new();
let mut x_range_array = ArrayTrait::<FP16x16>::new();
let mut normalized_array = ArrayTrait::<FP16x16>::new();
// transpose to change rows to be columns
let transposed_tensor = tensor_data.transpose(axes: array![1, 0].span());
let tensor_shape = transposed_tensor.shape;
let tensor_row_len = *tensor_shape.at(0); // 13
let tensor_column_len = *tensor_shape.at(1); //50
// loop and append max and min row values to corresponding array
let mut i: u32 = 0;
loop {
if i >= tensor_row_len {
break ();
}
let mut transposed_tensor_row = get_tensor_data_by_row(transposed_tensor, i);
x_max_array.append(transposed_tensor_row.max_in_tensor());
x_min_array.append(transposed_tensor_row.min_in_tensor());
x_range_array
.append(transposed_tensor_row.max_in_tensor() - transposed_tensor_row.min_in_tensor());
i += 1;
};
// convert array to tensor format for ease of math operation
let mut x_min = TensorTrait::<
FP16x16
>::new(shape: array![1, tensor_row_len].span(), data: x_min_array.span());
let mut x_range = TensorTrait::<
FP16x16
>::new(shape: array![1, tensor_row_len].span(), data: x_range_array.span());
let normalized_tensor = (tensor_data - x_min) / x_range;
return normalized_tensor;
}
// normalizes 1D tensor
fn normalize_label_data(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {
let mut tensor_data_ = tensor_data;
let mut normalized_array = ArrayTrait::<FP16x16>::new();
let mut range = tensor_data.max_in_tensor() - tensor_data.min_in_tensor();
// loop through tensor values normalizing and appending to new array
let mut i: u32 = 0;
loop {
match tensor_data_.data.pop_front() {
Option::Some(tensor_val) => {
let mut diff = *tensor_val - tensor_data.min_in_tensor();
normalized_array.append(diff / range);
i += 1;
},
Option::None(_) => { break; }
};
};
// convert normalized array values to tensor format
let mut normalized_tensor = TensorTrait::<
FP16x16
>::new(shape: array![tensor_data.data.len()].span(), data: normalized_array.span());
return normalized_tensor;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_boston/src/datasets.cairo | mod boston_data;
mod user_inputs_data;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_boston/src/datasets/boston_data.cairo | mod boston_x_features;
mod boston_y_labels;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_boston/src/datasets/boston_data/boston_x_features.cairo | use array::ArrayTrait;
use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16Impl, FP16x16PartialEq };
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};
use orion::numbers::{FP16x16, FixedTrait};
fn boston_x_features() -> Tensor<FP16x16> {
let tensor = TensorTrait::<FP16x16>::new(
shape: array![50,11].span(),
data: array![
FixedTrait::new(26719, false ),
FixedTrait::new(0, false ),
FixedTrait::new(406323, false ),
FixedTrait::new(65536, false ),
FixedTrait::new(33226, false ),
FixedTrait::new(403963, false ),
FixedTrait::new(5983436, false ),
FixedTrait::new(199753, false ),
FixedTrait::new(524288, false ),
FixedTrait::new(20119552, false ),
FixedTrait::new(1140326, false ),
FixedTrait::new(17588, false ),
FixedTrait::new(0, false ),
FixedTrait::new(635043, false ),
FixedTrait::new(0, false ),
FixedTrait::new(38338, false ),
FixedTrait::new(379715, false ),
FixedTrait::new(4626841, false ),
FixedTrait::new(189575, false ),
FixedTrait::new(393216, false ),
FixedTrait::new(25624576, false ),
FixedTrait::new(1258291, false ),
FixedTrait::new(3512, false ),
FixedTrait::new(1376256, false ),
FixedTrait::new(369623, false ),
FixedTrait::new(0, false ),
FixedTrait::new(28770, false ),
FixedTrait::new(426704, false ),
FixedTrait::new(1382809, false ),
FixedTrait::new(446608, false ),
FixedTrait::new(262144, false ),
FixedTrait::new(15925248, false ),
FixedTrait::new(1101004, false ),
FixedTrait::new(731407, false ),
FixedTrait::new(0, false ),
FixedTrait::new(1186201, false ),
FixedTrait::new(0, false ),
FixedTrait::new(48496, false ),
FixedTrait::new(434438, false ),
FixedTrait::new(6199705, false ),
FixedTrait::new(139244, false ),
FixedTrait::new(1572864, false ),
FixedTrait::new(43646976, false ),
FixedTrait::new(1323827, false ),
FixedTrait::new(151643, false ),
FixedTrait::new(0, false ),
FixedTrait::new(1283194, false ),
FixedTrait::new(0, false ),
FixedTrait::new(39649, false ),
FixedTrait::new(385351, false ),
FixedTrait::new(6376652, false ),
FixedTrait::new(156545, false ),
FixedTrait::new(327680, false ),
FixedTrait::new(26411008, false ),
FixedTrait::new(963379, false ),
FixedTrait::new(637283, false ),
FixedTrait::new(0, false ),
FixedTrait::new(1186201, false ),
FixedTrait::new(0, false ),
FixedTrait::new(48496, false ),
FixedTrait::new(419823, false ),
FixedTrait::new(6370099, false ),
FixedTrait::new(135338, false ),
FixedTrait::new(1572864, false ),
FixedTrait::new(43646976, false ),
FixedTrait::new(1323827, false ),
FixedTrait::new(6860, false ),
FixedTrait::new(2621440, false ),
FixedTrait::new(420085, false ),
FixedTrait::new(65536, false ),
FixedTrait::new(29294, false ),
FixedTrait::new(476250, false ),
FixedTrait::new(3211264, false ),
FixedTrait::new(313733, false ),
FixedTrait::new(262144, false ),
FixedTrait::new(16646144, false ),
FixedTrait::new(1153433, false ),
FixedTrait::new(1598672, false ),
FixedTrait::new(0, false ),
FixedTrait::new(1186201, false ),
FixedTrait::new(0, false ),
FixedTrait::new(45875, false ),
FixedTrait::new(304873, false ),
FixedTrait::new(6553600, false ),
FixedTrait::new(96154, false ),
FixedTrait::new(1572864, false ),
FixedTrait::new(43646976, false ),
FixedTrait::new(1323827, false ),
FixedTrait::new(264661, false ),
FixedTrait::new(0, false ),
FixedTrait::new(1186201, false ),
FixedTrait::new(0, false ),
FixedTrait::new(34865, false ),
FixedTrait::new(408223, false ),
FixedTrait::new(5944115, false ),
FixedTrait::new(203115, false ),
FixedTrait::new(1572864, false ),
FixedTrait::new(43646976, false ),
FixedTrait::new(1323827, false ),
FixedTrait::new(10624, false ),
FixedTrait::new(1310720, false ),
FixedTrait::new(456130, false ),
FixedTrait::new(0, false ),
FixedTrait::new(30408, false ),
FixedTrait::new(408944, false ),
FixedTrait::new(1068236, false ),
FixedTrait::new(290258, false ),
FixedTrait::new(196608, false ),
FixedTrait::new(14614528, false ),
FixedTrait::new(1218969, false ),
FixedTrait::new(6768, false ),
FixedTrait::new(1638400, false ),
FixedTrait::new(336199, false ),
FixedTrait::new(0, false ),
FixedTrait::new(29687, false ),
FixedTrait::new(388431, false ),
FixedTrait::new(3093299, false ),
FixedTrait::new(454295, false ),
FixedTrait::new(524288, false ),
FixedTrait::new(18612224, false ),
FixedTrait::new(1291059, false ),
FixedTrait::new(40077, false ),
FixedTrait::new(1310720, false ),
FixedTrait::new(260177, false ),
FixedTrait::new(0, false ),
FixedTrait::new(42401, false ),
FixedTrait::new(570425, false ),
FixedTrait::new(5695078, false ),
FixedTrait::new(118030, false ),
FixedTrait::new(327680, false ),
FixedTrait::new(17301504, false ),
FixedTrait::new(851968, false ),
FixedTrait::new(527944, false ),
FixedTrait::new(0, false ),
FixedTrait::new(1186201, false ),
FixedTrait::new(0, false ),
FixedTrait::new(38273, false ),
FixedTrait::new(355663, false ),
FixedTrait::new(6252134, false ),
FixedTrait::new(159239, false ),
FixedTrait::new(1572864, false ),
FixedTrait::new(43646976, false ),
FixedTrait::new(1323827, false ),
FixedTrait::new(14030, false ),
FixedTrait::new(1441792, false ),
FixedTrait::new(384040, false ),
FixedTrait::new(0, false ),
FixedTrait::new(28246, false ),
FixedTrait::new(421920, false ),
FixedTrait::new(583270, false ),
FixedTrait::new(484750, false ),
FixedTrait::new(458752, false ),
FixedTrait::new(21626880, false ),
FixedTrait::new(1251737, false ),
FixedTrait::new(22353, false ),
FixedTrait::new(0, false ),
FixedTrait::new(483655, false ),
FixedTrait::new(0, false ),
FixedTrait::new(32309, false ),
FixedTrait::new(420413, false ),
FixedTrait::new(2627993, false ),
FixedTrait::new(309402, false ),
FixedTrait::new(327680, false ),
FixedTrait::new(18808832, false ),
FixedTrait::new(1284505, false ),
FixedTrait::new(51800, false ),
FixedTrait::new(0, false ),
FixedTrait::new(648806, false ),
FixedTrait::new(0, false ),
FixedTrait::new(35651, false ),
FixedTrait::new(401211, false ),
FixedTrait::new(3460300, false ),
FixedTrait::new(173034, false ),
FixedTrait::new(262144, false ),
FixedTrait::new(19922944, false ),
FixedTrait::new(1205862, false ),
FixedTrait::new(1998, false ),
FixedTrait::new(3604480, false ),
FixedTrait::new(247726, false ),
FixedTrait::new(0, false ),
FixedTrait::new(31719, false ),
FixedTrait::new(450494, false ),
FixedTrait::new(1841561, false ),
FixedTrait::new(423716, false ),
FixedTrait::new(327680, false ),
FixedTrait::new(24248320, false ),
FixedTrait::new(1153433, false ),
FixedTrait::new(22898, false ),
FixedTrait::new(0, false ),
FixedTrait::new(648806, false ),
FixedTrait::new(0, false ),
FixedTrait::new(35651, false ),
FixedTrait::new(391380, false ),
FixedTrait::new(5026611, false ),
FixedTrait::new(203325, false ),
FixedTrait::new(262144, false ),
FixedTrait::new(19922944, false ),
FixedTrait::new(1205862, false ),
FixedTrait::new(24195, false ),
FixedTrait::new(0, false ),
FixedTrait::new(648806, false ),
FixedTrait::new(0, false ),
FixedTrait::new(35651, false ),
FixedTrait::new(430374, false ),
FixedTrait::new(5721292, false ),
FixedTrait::new(236080, false ),
FixedTrait::new(262144, false ),
FixedTrait::new(19922944, false ),
FixedTrait::new(1205862, false ),
FixedTrait::new(623485, false ),
FixedTrait::new(0, false ),
FixedTrait::new(1186201, false ),
FixedTrait::new(0, false ),
FixedTrait::new(46727, false ),
FixedTrait::new(440926, false ),
FixedTrait::new(6166937, false ),
FixedTrait::new(163584, false ),
FixedTrait::new(1572864, false ),
FixedTrait::new(43646976, false ),
FixedTrait::new(1323827, false ),
FixedTrait::new(52606, false ),
FixedTrait::new(0, false ),
FixedTrait::new(533463, false ),
FixedTrait::new(0, false ),
FixedTrait::new(35258, false ),
FixedTrait::new(357564, false ),
FixedTrait::new(2398617, false ),
FixedTrait::new(248807, false ),
FixedTrait::new(262144, false ),
FixedTrait::new(20119552, false ),
FixedTrait::new(1376256, false ),
FixedTrait::new(3709, false ),
FixedTrait::new(0, false ),
FixedTrait::new(223477, false ),
FixedTrait::new(0, false ),
FixedTrait::new(32047, false ),
FixedTrait::new(459210, false ),
FixedTrait::new(5655756, false ),
FixedTrait::new(224244, false ),
FixedTrait::new(131072, false ),
FixedTrait::new(17694720, false ),
FixedTrait::new(1166540, false ),
FixedTrait::new(28554, false ),
FixedTrait::new(0, false ),
FixedTrait::new(694026, false ),
FixedTrait::new(65536, false ),
FixedTrait::new(32047, false ),
FixedTrait::new(350224, false ),
FixedTrait::new(6553600, false ),
FixedTrait::new(253952, false ),
FixedTrait::new(262144, false ),
FixedTrait::new(18153472, false ),
FixedTrait::new(1218969, false ),
FixedTrait::new(34087, false ),
FixedTrait::new(1310720, false ),
FixedTrait::new(260177, false ),
FixedTrait::new(0, false ),
FixedTrait::new(42401, false ),
FixedTrait::new(550371, false ),
FixedTrait::new(5996544, false ),
FixedTrait::new(149979, false ),
FixedTrait::new(327680, false ),
FixedTrait::new(17301504, false ),
FixedTrait::new(851968, false ),
FixedTrait::new(802632, false ),
FixedTrait::new(0, false ),
FixedTrait::new(1186201, false ),
FixedTrait::new(0, false ),
FixedTrait::new(38273, false ),
FixedTrait::new(382533, false ),
FixedTrait::new(3912499, false ),
FixedTrait::new(130914, false ),
FixedTrait::new(1572864, false ),
FixedTrait::new(43646976, false ),
FixedTrait::new(1323827, false ),
FixedTrait::new(17654, false ),
FixedTrait::new(0, false ),
FixedTrait::new(648806, false ),
FixedTrait::new(0, false ),
FixedTrait::new(35651, false ),
FixedTrait::new(410648, false ),
FixedTrait::new(5426380, false ),
FixedTrait::new(213830, false ),
FixedTrait::new(262144, false ),
FixedTrait::new(19922944, false ),
FixedTrait::new(1205862, false ),
FixedTrait::new(2988, false ),
FixedTrait::new(0, false ),
FixedTrait::new(910295, false ),
FixedTrait::new(65536, false ),
FixedTrait::new(36044, false ),
FixedTrait::new(385875, false ),
FixedTrait::new(3670016, false ),
FixedTrait::new(203954, false ),
FixedTrait::new(327680, false ),
FixedTrait::new(18087936, false ),
FixedTrait::new(1074790, false ),
FixedTrait::new(3787, false ),
FixedTrait::new(0, false ),
FixedTrait::new(161218, false ),
FixedTrait::new(0, false ),
FixedTrait::new(31981, false ),
FixedTrait::new(457441, false ),
FixedTrait::new(3827302, false ),
FixedTrait::new(185401, false ),
FixedTrait::new(196608, false ),
FixedTrait::new(12648448, false ),
FixedTrait::new(1166540, false ),
FixedTrait::new(54084, false ),
FixedTrait::new(1310720, false ),
FixedTrait::new(260177, false ),
FixedTrait::new(0, false ),
FixedTrait::new(42401, false ),
FixedTrait::new(480182, false ),
FixedTrait::new(6193152, false ),
FixedTrait::new(136236, false ),
FixedTrait::new(327680, false ),
FixedTrait::new(17301504, false ),
FixedTrait::new(851968, false ),
FixedTrait::new(227690, false ),
FixedTrait::new(0, false ),
FixedTrait::new(1186201, false ),
FixedTrait::new(65536, false ),
FixedTrait::new(47054, false ),
FixedTrait::new(575406, false ),
FixedTrait::new(5432934, false ),
FixedTrait::new(124826, false ),
FixedTrait::new(1572864, false ),
FixedTrait::new(43646976, false ),
FixedTrait::new(1323827, false ),
FixedTrait::new(35685, false ),
FixedTrait::new(0, false ),
FixedTrait::new(1434583, false ),
FixedTrait::new(0, false ),
FixedTrait::new(40894, false ),
FixedTrait::new(403111, false ),
FixedTrait::new(6415974, false ),
FixedTrait::new(109359, false ),
FixedTrait::new(262144, false ),
FixedTrait::new(28639232, false ),
FixedTrait::new(1389363, false ),
FixedTrait::new(9209, false ),
FixedTrait::new(0, false ),
FixedTrait::new(694026, false ),
FixedTrait::new(0, false ),
FixedTrait::new(32047, false ),
FixedTrait::new(417792, false ),
FixedTrait::new(2116812, false ),
FixedTrait::new(258565, false ),
FixedTrait::new(262144, false ),
FixedTrait::new(18153472, false ),
FixedTrait::new(1218969, false ),
FixedTrait::new(3069, false ),
FixedTrait::new(0, false ),
FixedTrait::new(223477, false ),
FixedTrait::new(0, false ),
FixedTrait::new(32047, false ),
FixedTrait::new(420544, false ),
FixedTrait::new(4331929, false ),
FixedTrait::new(202656, false ),
FixedTrait::new(131072, false ),
FixedTrait::new(17694720, false ),
FixedTrait::new(1166540, false ),
FixedTrait::new(4016, false ),
FixedTrait::new(1310720, false ),
FixedTrait::new(218234, false ),
FixedTrait::new(65536, false ),
FixedTrait::new(29025, false ),
FixedTrait::new(501022, false ),
FixedTrait::new(3257139, false ),
FixedTrait::new(341567, false ),
FixedTrait::new(327680, false ),
FixedTrait::new(14155776, false ),
FixedTrait::new(976486, false ),
FixedTrait::new(63974, false ),
FixedTrait::new(0, false ),
FixedTrait::new(1434583, false ),
FixedTrait::new(0, false ),
FixedTrait::new(40894, false ),
FixedTrait::new(377290, false ),
FixedTrait::new(6448742, false ),
FixedTrait::new(153747, false ),
FixedTrait::new(262144, false ),
FixedTrait::new(28639232, false ),
FixedTrait::new(1389363, false ),
FixedTrait::new(14556, false ),
FixedTrait::new(0, false ),
FixedTrait::new(656015, false ),
FixedTrait::new(0, false ),
FixedTrait::new(35848, false ),
FixedTrait::new(399245, false ),
FixedTrait::new(6252134, false ),
FixedTrait::new(166985, false ),
FixedTrait::new(393216, false ),
FixedTrait::new(28311552, false ),
FixedTrait::new(1166540, false ),
FixedTrait::new(11358, false ),
FixedTrait::new(0, false ),
FixedTrait::new(635043, false ),
FixedTrait::new(0, false ),
FixedTrait::new(38338, false ),
FixedTrait::new(374013, false ),
FixedTrait::new(3538944, false ),
FixedTrait::new(156087, false ),
FixedTrait::new(393216, false ),
FixedTrait::new(25624576, false ),
FixedTrait::new(1258291, false ),
FixedTrait::new(3758, false ),
FixedTrait::new(0, false ),
FixedTrait::new(294256, false ),
FixedTrait::new(0, false ),
FixedTrait::new(29425, false ),
FixedTrait::new(434503, false ),
FixedTrait::new(3676569, false ),
FixedTrait::new(290829, false ),
FixedTrait::new(196608, false ),
FixedTrait::new(16187392, false ),
FixedTrait::new(1212416, false ),
FixedTrait::new(11143, false ),
FixedTrait::new(819200, false ),
FixedTrait::new(515768, false ),
FixedTrait::new(0, false ),
FixedTrait::new(34340, false ),
FixedTrait::new(393478, false ),
FixedTrait::new(5629542, false ),
FixedTrait::new(432019, false ),
FixedTrait::new(327680, false ),
FixedTrait::new(20381696, false ),
FixedTrait::new(996147, false ),
FixedTrait::new(2121, false ),
FixedTrait::new(0, false ),
FixedTrait::new(142868, false ),
FixedTrait::new(0, false ),
FixedTrait::new(30015, false ),
FixedTrait::new(458620, false ),
FixedTrait::new(3001548, false ),
FixedTrait::new(397292, false ),
FixedTrait::new(196608, false ),
FixedTrait::new(14548992, false ),
FixedTrait::new(1225523, false ),
FixedTrait::new(10443, false ),
FixedTrait::new(0, false ),
FixedTrait::new(452853, false ),
FixedTrait::new(0, false ),
FixedTrait::new(29360, false ),
FixedTrait::new(407044, false ),
FixedTrait::new(425984, false ),
FixedTrait::new(374924, false ),
FixedTrait::new(196608, false ),
FixedTrait::new(15269888, false ),
FixedTrait::new(1173094, false ),
FixedTrait::new(3057, false ),
FixedTrait::new(5242880, false ),
FixedTrait::new(99614, false ),
FixedTrait::new(0, false ),
FixedTrait::new(26476, false ),
FixedTrait::new(465764, false ),
FixedTrait::new(2398617, false ),
FixedTrait::new(479002, false ),
FixedTrait::new(131072, false ),
FixedTrait::new(21561344, false ),
FixedTrait::new(825753, false ),
FixedTrait::new(2325, false ),
FixedTrait::new(5242880, false ),
FixedTrait::new(238551, false ),
FixedTrait::new(0, false ),
FixedTrait::new(25690, false ),
FixedTrait::new(385089, false ),
FixedTrait::new(1251737, false ),
FixedTrait::new(604261, false ),
FixedTrait::new(65536, false ),
FixedTrait::new(20643840, false ),
FixedTrait::new(1074790, false ),
FixedTrait::new(4601, false ),
FixedTrait::new(0, false ),
FixedTrait::new(265420, false ),
FixedTrait::new(0, false ),
FixedTrait::new(33423, false ),
FixedTrait::new(394526, false ),
FixedTrait::new(3093299, false ),
FixedTrait::new(232973, false ),
FixedTrait::new(327680, false ),
FixedTrait::new(19398656, false ),
FixedTrait::new(1087897, false ),
FixedTrait::new(2816, false ),
FixedTrait::new(3440640, false ),
FixedTrait::new(348651, false ),
FixedTrait::new(0, false ),
FixedTrait::new(26542, false ),
FixedTrait::new(430243, false ),
FixedTrait::new(1500774, false ),
FixedTrait::new(479540, false ),
FixedTrait::new(393216, false ),
FixedTrait::new(19202048, false ),
FixedTrait::new(1087897, false ),
FixedTrait::new(90963, false ),
FixedTrait::new(0, false ),
FixedTrait::new(533463, false ),
FixedTrait::new(0, false ),
FixedTrait::new(35258, false ),
FixedTrait::new(389939, false ),
FixedTrait::new(5373952, false ),
FixedTrait::new(261488, false ),
FixedTrait::new(262144, false ),
FixedTrait::new(20119552, false ),
FixedTrait::new(1376256, false ),
FixedTrait::new(12499, false ),
FixedTrait::new(1441792, false ),
FixedTrait::new(384040, false ),
FixedTrait::new(0, false ),
FixedTrait::new(28246, false ),
FixedTrait::new(440270, false ),
FixedTrait::new(1146880, false ),
FixedTrait::new(512917, false ),
FixedTrait::new(458752, false ),
FixedTrait::new(21626880, false ),
FixedTrait::new(1251737, false ),
FixedTrait::new(12805, false ),
FixedTrait::new(0, false ),
FixedTrait::new(708444, false ),
FixedTrait::new(0, false ),
FixedTrait::new(27066, false ),
FixedTrait::new(409272, false ),
FixedTrait::new(406323, false ),
FixedTrait::new(346508, false ),
FixedTrait::new(262144, false ),
FixedTrait::new(19988480, false ),
FixedTrait::new(1258291, false ),
FixedTrait::new(758769, false ),
FixedTrait::new(0, false ),
FixedTrait::new(1186201, false ),
FixedTrait::new(0, false ),
FixedTrait::new(45875, false ),
FixedTrait::new(330039, false ),
FixedTrait::new(6356992, false ),
FixedTrait::new(115998, false ),
FixedTrait::new(1572864, false ),
FixedTrait::new(43646976, false ),
FixedTrait::new(1323827, false ),
FixedTrait::new(2369, false ),
FixedTrait::new(5242880, false ),
FixedTrait::new(324403, false ),
FixedTrait::new(0, false ),
FixedTrait::new(26935, false ),
FixedTrait::new(434503, false ),
FixedTrait::new(1533542, false ),
FixedTrait::new(335328, false ),
FixedTrait::new(262144, false ),
FixedTrait::new(16056320, false ),
FixedTrait::new(1258291, false ),
].span()
);
return tensor;
} | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_boston/src/datasets/boston_data/boston_y_labels.cairo | use array::ArrayTrait;
use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16Impl, FP16x16PartialEq };
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};
use orion::numbers::{FP16x16, FixedTrait};
fn boston_y_labels() -> Tensor<FP16x16> {
let tensor = TensorTrait::<FP16x16>::new(
shape: array![50].span(),
data: array![
FixedTrait::new(1422131, false ),
FixedTrait::new(1199308, false ),
FixedTrait::new(1638400, false ),
FixedTrait::new(878182, false ),
FixedTrait::new(1251737, false ),
FixedTrait::new(1120665, false ),
FixedTrait::new(2175795, false ),
FixedTrait::new(688128, false ),
FixedTrait::new(1284505, false ),
FixedTrait::new(1651507, false ),
FixedTrait::new(1284505, false ),
FixedTrait::new(3276800, false ),
FixedTrait::new(904396, false ),
FixedTrait::new(1625292, false ),
FixedTrait::new(1638400, false ),
FixedTrait::new(1448345, false ),
FixedTrait::new(2044723, false ),
FixedTrait::new(1330380, false ),
FixedTrait::new(1559756, false ),
FixedTrait::new(976486, false ),
FixedTrait::new(1323827, false ),
FixedTrait::new(1546649, false ),
FixedTrait::new(1310720, false ),
FixedTrait::new(3198156, false ),
FixedTrait::new(668467, false ),
FixedTrait::new(1415577, false ),
FixedTrait::new(1526988, false ),
FixedTrait::new(2437939, false ),
FixedTrait::new(2031616, false ),
FixedTrait::new(1435238, false ),
FixedTrait::new(1166540, false ),
FixedTrait::new(1841561, false ),
FixedTrait::new(1481113, false ),
FixedTrait::new(3014656, false ),
FixedTrait::new(1022361, false ),
FixedTrait::new(1225523, false ),
FixedTrait::new(1428684, false ),
FixedTrait::new(1743257, false ),
FixedTrait::new(1238630, false ),
FixedTrait::new(2188902, false ),
FixedTrait::new(1618739, false ),
FixedTrait::new(1985740, false ),
FixedTrait::new(1369702, false ),
FixedTrait::new(1520435, false ),
FixedTrait::new(1625292, false ),
FixedTrait::new(865075, false ),
FixedTrait::new(1717043, false ),
FixedTrait::new(1533542, false ),
FixedTrait::new(635699, false ),
FixedTrait::new(1828454, false ),
].span()
);
return tensor;
} | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_boston/src/datasets/user_inputs_data.cairo | mod user_inputs_boston_data;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_boston/src/datasets/user_inputs_data/user_inputs_boston_data.cairo | use array::ArrayTrait;
use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16Impl, FP16x16PartialEq };
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};
use orion::numbers::{FP16x16, FixedTrait};
fn user_inputs_boston_data() -> Tensor<FP16x16> {
let tensor = TensorTrait::<FP16x16>::new(
shape: array![11].span(),
data: array![
FixedTrait::new(26719, false ),
FixedTrait::new(0, false ),
FixedTrait::new(406323, false ),
FixedTrait::new(65536, false ),
FixedTrait::new(33226, false ),
FixedTrait::new(403963, false ),
FixedTrait::new(5983436, false ),
FixedTrait::new(199753, false ),
FixedTrait::new(524288, false ),
FixedTrait::new(20119552, false ),
FixedTrait::new(1140326, false ),
].span()
);
return tensor;
} | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_boston/src/helper_functions.cairo |
use debug::PrintTrait;
use array::{ArrayTrait, SpanTrait};
use orion::operators::tensor::{
Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,
FP16x16TensorDiv, FP16x16TensorMul
};
use orion::numbers::{FP16x16, FixedTrait};
// retrieves row data by index in a 2D tensor
fn get_tensor_data_by_row(tensor_data: Tensor<FP16x16>, row_index: u32,) -> Tensor<FP16x16> {
let column_len = *tensor_data.shape.at(1); //13
// crete new array
let mut result = ArrayTrait::<FP16x16>::new();
// loop through the x values and append values
let mut i: u32 = 0;
loop {
if i >= column_len {
break ();
}
result.append(tensor_data.at(indices: array![row_index, i].span()));
i += 1;
};
let resultant_tensor = TensorTrait::<
FP16x16
>::new(array![column_len].span(), data: result.span());
return resultant_tensor;
}
// transposes tensor
fn transpose_tensor(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {
let tensor_transposed = tensor_data.transpose(axes: array![1, 0].span());
return tensor_transposed;
}
fn calculate_mean(tensor_data: Tensor<FP16x16>) -> FP16x16 {
let tensor_size = FixedTrait::<FP16x16>::new_unscaled(tensor_data.data.len(), false);
let cumulated_sum = tensor_data.cumsum(0, Option::None(()), Option::None(()));
let sum_result = cumulated_sum.data[tensor_data.data.len() - 1];
let mean = *sum_result / tensor_size;
return mean;
}
// Calculates the R-Squared score between two tensors.
fn calculate_r_score(Y_values: Tensor<FP16x16>, Y_pred_values: Tensor<FP16x16>) -> FP16x16 {
let mut Y_values_ = Y_values;
let mean_y_value = calculate_mean(Y_values);
// creating the appropriate tensor shapes and empty arrays to populate values into
let mut squared_diff_shape = array::ArrayTrait::new();
squared_diff_shape.append(Y_values.data.len());
let mut squared_diff_vals = array::ArrayTrait::new();
let mut squared_mean_diff_shape = array::ArrayTrait::new();
squared_mean_diff_shape.append(Y_values.data.len());
let mut squared_mean_diff_vals = array::ArrayTrait::new();
let mut i: u32 = 0;
loop {
match Y_values_.data.pop_front() {
Option::Some(y_value) => {
let diff_pred = *y_value - *Y_pred_values.data.at(i);
let squared_diff = diff_pred * diff_pred;
squared_diff_vals.append(squared_diff);
let diff_mean = *y_value - mean_y_value;
let squared_mean_diff = diff_mean * diff_mean;
squared_mean_diff_vals.append(squared_mean_diff);
i += 1;
},
Option::None(_) => { break; }
}
};
let squared_diff_tensor = TensorTrait::<
FP16x16
>::new(squared_diff_shape.span(), squared_diff_vals.span());
let squared_mean_diff_tensor = TensorTrait::<
FP16x16
>::new(squared_mean_diff_shape.span(), squared_mean_diff_vals.span());
let sum_squared_diff = squared_diff_tensor.cumsum(0, Option::None(()), Option::None(()));
let sum_squared_mean_diff = squared_mean_diff_tensor
.cumsum(0, Option::None(()), Option::None(()));
let r_score = FixedTrait::new_unscaled(1, false)
- *sum_squared_diff.data.at(Y_values.data.len() - 1)
/ *sum_squared_mean_diff.data.at(Y_values.data.len() - 1);
return r_score;
}
// computes the x_min, x_max and x_range. Used for helping in normalizing and denormalizing user inputed values operations
fn normalize_user_x_inputs(
x_inputs: Tensor<FP16x16>, original_x_values: Tensor<FP16x16>
) -> Tensor<FP16x16> {
let mut x_inputs_normalized = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());
let mut x_min = ArrayTrait::<FP16x16>::new();
let mut x_max = ArrayTrait::<FP16x16>::new();
let mut x_range = ArrayTrait::<FP16x16>::new();
let mut result = ArrayTrait::<FP16x16>::new();
if original_x_values.shape.len() > 1 {
let transposed_tensor = original_x_values.transpose(axes: array![1, 0].span());
let data_len = *transposed_tensor.shape.at(0); //13
// loop through each row calculating the min, max and range row values for each feature columns
let mut i: u32 = 0;
loop {
if i >= data_len {
break ();
}
let mut transposed_tensor_row = get_tensor_data_by_row(transposed_tensor, i);
x_min.append(transposed_tensor_row.min_in_tensor());
x_max.append(transposed_tensor_row.max_in_tensor());
x_range
.append(
transposed_tensor_row.max_in_tensor() - transposed_tensor_row.min_in_tensor()
);
i += 1;
};
let mut x_min_tensor = TensorTrait::new(shape: array![data_len].span(), data: x_min.span());
let mut x_max_tensor = TensorTrait::new(shape: array![data_len].span(), data: x_max.span());
let mut x_range_tensor = TensorTrait::new(
shape: array![data_len].span(), data: x_range.span()
);
// for normalizing 2D user inputed feature vals
if x_inputs.shape.len() > 1 {
let mut j: u32 = 0;
loop {
if j >= *x_inputs.shape.at(0) {
break ();
};
let mut row_data = get_tensor_data_by_row(x_inputs, j);
let mut norm_row_data = (row_data - x_min_tensor) / x_range_tensor;
let mut k: u32 = 0;
loop {
if k >= norm_row_data.data.len() {
break ();
};
result.append(*norm_row_data.data.at(k));
k += 1;
};
j += 1;
};
x_inputs_normalized =
TensorTrait::<
FP16x16
>::new(
array![*x_inputs.shape.at(0), *x_inputs.shape.at(1)].span(), data: result.span()
);
};
// for normalizing 1D feature input
if x_inputs.shape.len() == 1 {
x_inputs_normalized = (x_inputs - x_min_tensor) / x_range_tensor;
};
}
if original_x_values.shape.len() == 1 {
let mut x_min_tensor = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![original_x_values.min_in_tensor()].span());
let mut x_max_tensor = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![original_x_values.max_in_tensor()].span());
let mut x_range_tensor = TensorTrait::<
FP16x16
>::new(
shape: array![1].span(),
data: array![original_x_values.max_in_tensor() - original_x_values.min_in_tensor()]
.span()
);
let mut diff = ((x_inputs - x_min_tensor));
x_inputs_normalized = ((x_inputs - x_min_tensor)) / x_range_tensor;
};
return x_inputs_normalized;
}
// rescales model predictions to standard format
fn rescale_predictions(
prediction_result: Tensor<FP16x16>, y_values: Tensor<FP16x16>
) -> Tensor<FP16x16> {
let mut rescale_predictions = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());
let mut y_min_array = ArrayTrait::<FP16x16>::new();
let mut y_max_array = ArrayTrait::<FP16x16>::new();
let mut y_range_array = ArrayTrait::<FP16x16>::new();
let mut y_max = y_values.max_in_tensor();
let mut y_min = y_values.min_in_tensor();
let mut y_range = y_values.max_in_tensor() - y_values.min_in_tensor();
// convert to tensor format for ease of math operations
let y_min_tensor = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![y_min].span());
let y_max_tensor = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![y_max].span());
let y_range_tensor = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![y_range].span());
rescale_predictions = (prediction_result * y_range_tensor) + y_min_tensor;
return rescale_predictions;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_boston/src/lib.cairo | mod test;
mod data_preprocessing;
mod helper_functions;
mod datasets;
mod model; | https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_boston/src/model.cairo | mod multiple_linear_regression_model;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_boston/src/model/multiple_linear_regression_model.cairo |
use orion::operators::tensor::{
Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,
FP16x16TensorDiv, FP16x16TensorMul
};
use orion::numbers::{FP16x16, FixedTrait};
use multiple_linear_regresion::data_preprocessing::{Dataset, DatasetTrait};
use multiple_linear_regresion::helper_functions::{
get_tensor_data_by_row, transpose_tensor, calculate_mean, calculate_r_score,
normalize_user_x_inputs, rescale_predictions
};
#[derive(Copy, Drop)]
struct MultipleLinearRegressionModel {
coefficients: Tensor<FP16x16>
}
#[generate_trait]
impl RegressionOperation of MultipleLinearRegressionModelTrait {
// reconstruct the y values using the computed gradients and x values
fn predict(
ref self: MultipleLinearRegressionModel, feature_inputs: Tensor<FP16x16>
) -> Tensor<FP16x16> {
// random tensor value that we will replace
let mut prediction_result = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());
let mut result = ArrayTrait::<FP16x16>::new();
// for multiple predictions
if feature_inputs.shape.len() > 1 {
let feature_values = add_bias_term(feature_inputs, 1);
let mut data_len: u32 = *feature_values.shape.at(0);
let mut i: u32 = 0;
loop {
if i >= data_len {
break ();
}
let feature_row_values = get_tensor_data_by_row(feature_values, i);
let predicted_values = feature_row_values.matmul(@self.coefficients);
result.append(*predicted_values.data.at(0));
i += 1;
};
prediction_result =
TensorTrait::<
FP16x16
>::new(shape: array![result.len()].span(), data: result.span());
}
// for single predictions
if feature_inputs.shape.len() == 1 && self.coefficients.data.len() > 1 {
let feature_values = add_bias_term(feature_inputs, 1);
prediction_result = feature_values.matmul(@self.coefficients);
}
return prediction_result;
}
}
fn MultipleLinearRegression(dataset: Dataset) -> MultipleLinearRegressionModel {
let x_values_tranposed = transpose_tensor(dataset.x_values);
let x_values_tranposed_with_bias = add_bias_term(x_values_tranposed, 0);
let decorrelated_x_features = decorrelate_x_features(x_values_tranposed_with_bias);
let coefficients = compute_gradients(
decorrelated_x_features, dataset.y_values, x_values_tranposed_with_bias
);
return MultipleLinearRegressionModel { coefficients };
}
//Adds bias term to features based on axis
fn add_bias_term(x_feature: Tensor<FP16x16>, axis: u32) -> Tensor<FP16x16> {
let mut x_feature_ = x_feature;
let mut tensor_with_bias = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());
let mut result = ArrayTrait::<FP16x16>::new();
// check if feature data has multiple rows and columns
if x_feature.shape.len() > 1 {
let mut index: u32 = 0;
if axis == 1 {
index = 0;
} else {
index = 1;
}
let data_len = *x_feature.shape.at(index); // 50
let mut i: u32 = 0;
loop {
if i >= data_len {
break ();
}
result
.append(FixedTrait::new(65536, false)); //65536=ONE in FP16x16, change accordingly
i += 1;
};
if axis == 0 {
let res_tensor = TensorTrait::new(
shape: array![1, data_len].span(), data: result.span()
);
tensor_with_bias =
TensorTrait::concat(tensors: array![x_feature, res_tensor].span(), axis: axis);
} else {
let res_tensor = TensorTrait::new(
shape: array![data_len, 1].span(), data: result.span()
);
tensor_with_bias =
TensorTrait::concat(tensors: array![x_feature, res_tensor].span(), axis: axis);
}
}
// check if feature data is 1D
if x_feature.shape.len() == 1 {
let mut j: u32 = 0;
loop {
match x_feature_.data.pop_front() {
Option::Some(x_val) => {
result.append(*x_val);
j += 1;
},
Option::None(_) => { break; }
};
};
result.append(FixedTrait::new(65536, false)); //65536=ONE in FP16x16, change accordingly
tensor_with_bias =
TensorTrait::<FP16x16>::new(shape: array![result.len()].span(), data: result.span());
}
return tensor_with_bias;
}
// decorrelates the feature data (*only the last tensor row of the decorelated feature data will be fully orthogonal)
fn decorrelate_x_features(x_feature_data: Tensor<FP16x16>) -> Tensor<FP16x16> {
let mut input_tensor = x_feature_data;
let mut i: u32 = 0;
loop {
if i >= *x_feature_data.shape.at(0) {
break ();
}
let mut placeholder = ArrayTrait::<FP16x16>::new();
let mut feature_row_values = get_tensor_data_by_row(input_tensor, i);
let mut feature_squared = feature_row_values.matmul(@feature_row_values);
// avoiding division by zero errors
if *feature_squared.data.at(0) == FixedTrait::new(0, false) {
feature_squared =
TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());
}
// loop throgh remaining tensor data and remove the individual tensor factors from one another
let mut j: u32 = i + 1;
loop {
if j >= *x_feature_data.shape.at(0) {
break ();
}
let mut remaining_tensor_values = get_tensor_data_by_row(input_tensor, j);
let feature_cross_product = feature_row_values.matmul(@remaining_tensor_values);
let feature_gradients = feature_cross_product / feature_squared;
remaining_tensor_values = remaining_tensor_values
- (feature_row_values
* feature_gradients); //remove the feature factors from one another
// loop and append the modifieed remaining_tensor_values (after the corelated factor has been removed) to placeholder array
let mut k: u32 = 0;
loop {
if k >= remaining_tensor_values.data.len() {
break ();
}
placeholder.append(*remaining_tensor_values.data.at(k));
k += 1;
};
j += 1;
};
// convert placeholder array to tensor format and update the original tensor with the new modified decorrelated tensor row values
let mut decorrelated_tensor = TensorTrait::new(
shape: array![*x_feature_data.shape.at(0) - 1 - i, *x_feature_data.shape.at(1)].span(),
data: placeholder.span()
);
let mut original_tensor = input_tensor
.slice(
starts: array![0, 0].span(),
ends: array![i + 1, *x_feature_data.shape.at(1)].span(),
axes: Option::None(()),
steps: Option::Some(array![1, 1].span())
);
input_tensor =
TensorTrait::concat(
tensors: array![original_tensor, decorrelated_tensor].span(), axis: 0
);
i += 1;
};
return input_tensor;
}
// computes the corresponding MLR gradient using decorrelated feature
fn compute_gradients(
decorrelated_x_features: Tensor<FP16x16>,
y_values: Tensor<FP16x16>,
original_x_tensor_values: Tensor<FP16x16>
) -> Tensor<FP16x16> {
let mut gradient_values_flipped = TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());
let mut result = ArrayTrait::<FP16x16>::new();
let mut tensor_y_vals = y_values;
let mut i: u32 = *decorrelated_x_features.shape.at(0);
// loop through Decorrelated_x_features starting from the fully orthogonlised last tensor row value
loop {
if i <= 0 {
break ();
}
let index_val = i - 1;
let mut decorelated_feature_row_values = get_tensor_data_by_row(
decorrelated_x_features, index_val
); // 50 vals
let mut decorelated_features_squared = decorelated_feature_row_values
.matmul(@decorelated_feature_row_values);
let mut feature_label_cross_product = tensor_y_vals
.matmul(@decorelated_feature_row_values); // multiply the tensors
// avoiding division by zero errors
if *decorelated_features_squared.data.at(0) == FixedTrait::new(0, false) {
decorelated_features_squared =
TensorTrait::<
FP16x16
>::new(shape: array![1].span(), data: array![FixedTrait::new(10, false)].span());
}
// computing the feature gradient values using the y values and decorrelated x features and appending to array
let mut single_gradient_value = feature_label_cross_product
/ decorelated_features_squared; // devide the summed value by each other
result.append(*single_gradient_value.data.at(0));
// remove the assosciated feature gradient value away from y values
let mut original_x_tensor_row_values = get_tensor_data_by_row(
original_x_tensor_values, index_val
);
tensor_y_vals = tensor_y_vals
- (original_x_tensor_row_values
* single_gradient_value); //remove the first feature from second feature values
i -= 1;
};
// convert the gradient array to tensor format
let final_gradients = TensorTrait::new(
shape: array![*decorrelated_x_features.shape.at(0)].span(), data: result.span()
);
let mut reverse_grad_array = ArrayTrait::<FP16x16>::new();
let mut data_len: u32 = final_gradients.data.len();
loop {
if data_len <= 0 {
break ();
}
let temp_val = data_len - 1;
reverse_grad_array.append(*final_gradients.data.at(temp_val));
data_len -= 1;
};
// convert gradient values to tensor format
let gradient_values_flipped = TensorTrait::<
FP16x16
>::new(shape: array![reverse_grad_array.len()].span(), data: reverse_grad_array.span());
return gradient_values_flipped;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/notebook_tutorials/multiple_linear_regression_boston/src/test.cairo |
// use traits::Into;
use debug::PrintTrait;
use array::{ArrayTrait, SpanTrait};
use multiple_linear_regresion::datasets::boston_data::boston_x_features::boston_x_features;
use multiple_linear_regresion::datasets::boston_data::boston_y_labels::boston_y_labels;
use multiple_linear_regresion::datasets::user_inputs_data::user_inputs_boston_data::user_inputs_boston_data;
use orion::numbers::{FP16x16, FixedTrait};
use multiple_linear_regresion::model::multiple_linear_regression_model::{
MultipleLinearRegressionModel, MultipleLinearRegression, MultipleLinearRegressionModelTrait
};
use multiple_linear_regresion::data_preprocessing::{Dataset, DatasetTrait};
use multiple_linear_regresion::helper_functions::{get_tensor_data_by_row, transpose_tensor, calculate_mean ,
calculate_r_score, normalize_user_x_inputs, rescale_predictions};
use orion::operators::tensor::{
Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd,
FP16x16TensorSub, FP16x16TensorAdd, FP16x16TensorDiv, FP16x16TensorMul};
#[test]
#[available_gas(99999999999999999)]
fn multiple_linear_regression_test() {
// -------------------------------------------------------------------Boston dataset tests---------------------------------------------------------------------------------------------
let mut main_x_vals = boston_x_features();
let mut main_y_vals = boston_y_labels();
let mut dataset = Dataset{x_values: main_x_vals,y_values:main_y_vals};
let mut normalized_dataset = dataset.normalize_dataset();
let mut model = MultipleLinearRegression(normalized_dataset);
let mut model_coefficients = model.coefficients;
let mut reconstructed_ys = model.predict (normalized_dataset.x_values);
let mut r_squared_score = calculate_r_score(normalized_dataset.y_values,reconstructed_ys);
r_squared_score.print();
// checking if data has been normalized correctly
assert(normalized_dataset.x_values.max_in_tensor() <= FixedTrait::new(65536, false), 'normalized x not between 0-1');
assert(normalized_dataset.x_values.min_in_tensor() >= FixedTrait::new(0, false), 'normalized x not between 0-1');
assert(normalized_dataset.y_values.max_in_tensor() <= FixedTrait::new(65536, false), 'normalized y not between 0-1');
assert(normalized_dataset.x_values.min_in_tensor() >= FixedTrait::new(0, false), 'normalized y not between 0-1');
// performing checks on the shape of normalized data
assert(normalized_dataset.x_values.data.len()== main_x_vals.data.len() &&
normalized_dataset.y_values.data.len()== main_y_vals.data.len() , 'normalized data shape mismatch');
// performing checks on shape on coefficient values (gradient vals + bias)
assert(model.coefficients.data.len() == *main_x_vals.shape.at(1)+1, 'coefficient data shape mismatch');
// model accuracy deviance checks
assert(r_squared_score >= FixedTrait::new(55699, false), 'Boston model acc. less than 84%');
// boston user inputed house valuation predictions
let user_input = user_inputs_boston_data();
let mut normalized_user_x_inputs = normalize_user_x_inputs(user_input, main_x_vals) ;
let mut prediction_result = model.predict (normalized_user_x_inputs);
let mut rescale_prediction = rescale_predictions(prediction_result, main_y_vals);
(*rescale_prediction.data.at(0)).print();
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/src/data_preprocessing.cairo | use orion::operators::tensor::{
Tensor, TensorTrait, FP16x16Tensor, U32Tensor, U32TensorAdd, FP16x16TensorSub, FP16x16TensorAdd,
FP16x16TensorDiv, FP16x16TensorMul
};
use orion::numbers::{FP16x16, FixedTrait};
use multiple_linear_regresion::helper_functions::{
get_tensor_data_by_row, transpose_tensor, calculate_mean, calculate_r_score,
normalize_user_x_inputs, rescale_predictions
};
#[derive(Copy, Drop)]
struct Dataset {
x_values: Tensor<FP16x16>,
y_values: Tensor<FP16x16>,
}
#[generate_trait]
impl DataPreprocessing of DatasetTrait {
fn normalize_dataset(ref self: Dataset) -> Dataset {
let mut x_values = TensorTrait::<FP16x16>::new(array![1].span(), array![FixedTrait::new(0, false)].span());
let mut y_values = TensorTrait::<FP16x16>::new(array![1].span(), array![FixedTrait::new(0, false)].span());
// used for multiple_linear_regression_models
if self.x_values.shape.len() > 1 {
x_values = normalize_feature_data(self.x_values);
y_values = normalize_label_data(self.y_values);
}
// used for linear_regression_models
if self.x_values.shape.len() == 1 {
x_values = normalize_label_data(self.x_values);
y_values = normalize_label_data(self.y_values);
}
return Dataset { x_values, y_values };
}
}
// normalizes 2D Tensor
fn normalize_feature_data(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {
let mut x_min_array = ArrayTrait::<FP16x16>::new();
let mut x_max_array = ArrayTrait::<FP16x16>::new();
let mut x_range_array = ArrayTrait::<FP16x16>::new();
let mut normalized_array = ArrayTrait::<FP16x16>::new();
// transpose to change rows to be columns
let transposed_tensor = tensor_data.transpose(axes: array![1, 0].span());
let tensor_shape = transposed_tensor.shape;
let tensor_row_len = *tensor_shape.at(0); // 13
let tensor_column_len = *tensor_shape.at(1); //50
// loop and append max and min row values to corresponding array
let mut i: u32 = 0;
loop {
if i >= tensor_row_len {
break ();
}
let mut transposed_tensor_row = get_tensor_data_by_row(transposed_tensor, i);
x_max_array.append(transposed_tensor_row.max_in_tensor());
x_min_array.append(transposed_tensor_row.min_in_tensor());
x_range_array
.append(transposed_tensor_row.max_in_tensor() - transposed_tensor_row.min_in_tensor());
i += 1;
};
// convert array to tensor format for ease of math operation
let mut x_min = TensorTrait::<
FP16x16
>::new(shape: array![1, tensor_row_len].span(), data: x_min_array.span());
let mut x_range = TensorTrait::<
FP16x16
>::new(shape: array![1, tensor_row_len].span(), data: x_range_array.span());
let normalized_tensor = (tensor_data - x_min) / x_range;
return normalized_tensor;
}
// normalizes 1D tensor
fn normalize_label_data(tensor_data: Tensor<FP16x16>) -> Tensor<FP16x16> {
let mut tensor_data_ = tensor_data;
let mut normalized_array = ArrayTrait::<FP16x16>::new();
let mut range = tensor_data.max_in_tensor() - tensor_data.min_in_tensor();
// loop through tensor values normalizing and appending to new array
let mut i: u32 = 0;
loop {
match tensor_data_.data.pop_front() {
Option::Some(tensor_val) => {
let mut diff = *tensor_val - tensor_data.min_in_tensor();
normalized_array.append(diff / range);
i += 1;
},
Option::None(_) => { break; }
};
};
// convert normalized array values to tensor format
let mut normalized_tensor = TensorTrait::<
FP16x16
>::new(shape: array![tensor_data.data.len()].span(), data: normalized_array.span());
return normalized_tensor;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/src/datasets.cairo | mod aave_data;
mod boston_data;
mod linear_data;
mod user_inputs_data;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/src/datasets/aave_data.cairo | mod aave_x_features;
mod aave_y_labels;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/src/datasets/aave_data/aave_x_features.cairo | use array::ArrayTrait;
use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16Impl, FP16x16PartialEq};
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};
use orion::numbers::{FP16x16, FixedTrait};
fn aave_x_features() -> Tensor<FP16x16> {
let tensor = TensorTrait::<
FP16x16
>::new(
shape: array![24, 9].span(),
data: array![
FixedTrait::new(61, false),
FixedTrait::new(484966, false),
FixedTrait::new(812646, false),
FixedTrait::new(13369344, false),
FixedTrait::new(3604, false),
FixedTrait::new(7798784, false),
FixedTrait::new(1880883, false),
FixedTrait::new(5006950, false),
FixedTrait::new(220856320, false),
FixedTrait::new(87, false),
FixedTrait::new(488243, false),
FixedTrait::new(812646, false),
FixedTrait::new(13434880, false),
FixedTrait::new(3604, false),
FixedTrait::new(7798784, false),
FixedTrait::new(1880883, false),
FixedTrait::new(5006950, false),
FixedTrait::new(220856320, false),
FixedTrait::new(114, false),
FixedTrait::new(525598, false),
FixedTrait::new(812646, false),
FixedTrait::new(13565952, false),
FixedTrait::new(3604, false),
FixedTrait::new(7798784, false),
FixedTrait::new(1887436, false),
FixedTrait::new(5013504, false),
FixedTrait::new(217579519, false),
FixedTrait::new(138, false),
FixedTrait::new(628490, false),
FixedTrait::new(838860, false),
FixedTrait::new(13893632, false),
FixedTrait::new(3604, false),
FixedTrait::new(8126463, false),
FixedTrait::new(1874329, false),
FixedTrait::new(5046272, false),
FixedTrait::new(208404480, false),
FixedTrait::new(1, false),
FixedTrait::new(655360, false),
FixedTrait::new(924057, false),
FixedTrait::new(14090240, false),
FixedTrait::new(3768, false),
FixedTrait::new(8388608, false),
FixedTrait::new(1880883, false),
FixedTrait::new(5065932, false),
FixedTrait::new(206438400, false),
FixedTrait::new(25, false),
FixedTrait::new(688128, false),
FixedTrait::new(924057, false),
FixedTrait::new(14155776, false),
FixedTrait::new(3768, false),
FixedTrait::new(8454144, false),
FixedTrait::new(1893990, false),
FixedTrait::new(5065932, false),
FixedTrait::new(204472320, false),
FixedTrait::new(50, false),
FixedTrait::new(681574, false),
FixedTrait::new(924057, false),
FixedTrait::new(14286848, false),
FixedTrait::new(3768, false),
FixedTrait::new(8585216, false),
FixedTrait::new(1900544, false),
FixedTrait::new(5072486, false),
FixedTrait::new(205127680, false),
FixedTrait::new(76, false),
FixedTrait::new(640942, false),
FixedTrait::new(924057, false),
FixedTrait::new(14352384, false),
FixedTrait::new(3768, false),
FixedTrait::new(8650752, false),
FixedTrait::new(1933312, false),
FixedTrait::new(5072486, false),
FixedTrait::new(209059840, false),
FixedTrait::new(100, false),
FixedTrait::new(747110, false),
FixedTrait::new(924057, false),
FixedTrait::new(14483456, false),
FixedTrait::new(3768, false),
FixedTrait::new(8716288, false),
FixedTrait::new(1939865, false),
FixedTrait::new(5072486, false),
FixedTrait::new(201195519, false),
FixedTrait::new(126, false),
FixedTrait::new(650117, false),
FixedTrait::new(989593, false),
FixedTrait::new(14614528, false),
FixedTrait::new(3768, false),
FixedTrait::new(8781824, false),
FixedTrait::new(1966080, false),
FixedTrait::new(5079040, false),
FixedTrait::new(209059840, false),
FixedTrait::new(152, false),
FixedTrait::new(645529, false),
FixedTrait::new(989593, false),
FixedTrait::new(14876672, false),
FixedTrait::new(3768, false),
FixedTrait::new(8978432, false),
FixedTrait::new(1979187, false),
FixedTrait::new(5085593, false),
FixedTrait::new(209715200, false),
FixedTrait::new(1, false),
FixedTrait::new(653393, false),
FixedTrait::new(1002700, false),
FixedTrait::new(14876672, false),
FixedTrait::new(3951, false),
FixedTrait::new(8978432, false),
FixedTrait::new(1959526, false),
FixedTrait::new(5111808, false),
FixedTrait::new(209059840, false),
FixedTrait::new(26, false),
FixedTrait::new(614072, false),
FixedTrait::new(1009254, false),
FixedTrait::new(15007744, false),
FixedTrait::new(3951, false),
FixedTrait::new(9043968, false),
FixedTrait::new(1926758, false),
FixedTrait::new(5157683, false),
FixedTrait::new(211025919, false),
FixedTrait::new(54, false),
FixedTrait::new(523632, false),
FixedTrait::new(1009254, false),
FixedTrait::new(15073280, false),
FixedTrait::new(3951, false),
FixedTrait::new(9043968, false),
FixedTrait::new(2011955, false),
FixedTrait::new(5203558, false),
FixedTrait::new(220856320, false),
FixedTrait::new(78, false),
FixedTrait::new(688128, false),
FixedTrait::new(1009254, false),
FixedTrait::new(15138816, false),
FixedTrait::new(3951, false),
FixedTrait::new(9109504, false),
FixedTrait::new(1861222, false),
FixedTrait::new(5360844, false),
FixedTrait::new(203816960, false),
FixedTrait::new(102, false),
FixedTrait::new(688128, false),
FixedTrait::new(1028915, false),
FixedTrait::new(15204352, false),
FixedTrait::new(3951, false),
FixedTrait::new(9109504, false),
FixedTrait::new(1861222, false),
FixedTrait::new(5367398, false),
FixedTrait::new(203816960, false),
FixedTrait::new(126, false),
FixedTrait::new(694681, false),
FixedTrait::new(1028915, false),
FixedTrait::new(15204352, false),
FixedTrait::new(3951, false),
FixedTrait::new(9109504, false),
FixedTrait::new(1861222, false),
FixedTrait::new(5367398, false),
FixedTrait::new(203161600, false),
FixedTrait::new(151, false),
FixedTrait::new(681574, false),
FixedTrait::new(1028915, false),
FixedTrait::new(15466496, false),
FixedTrait::new(3951, false),
FixedTrait::new(9371648, false),
FixedTrait::new(1848115, false),
FixedTrait::new(5452595, false),
FixedTrait::new(203161600, false),
FixedTrait::new(1, false),
FixedTrait::new(591790, false),
FixedTrait::new(1048576, false),
FixedTrait::new(15663104, false),
FixedTrait::new(4128, false),
FixedTrait::new(9568256, false),
FixedTrait::new(1985740, false),
FixedTrait::new(5485363, false),
FixedTrait::new(214302719, false),
FixedTrait::new(29, false),
FixedTrait::new(565575, false),
FixedTrait::new(1048576, false),
FixedTrait::new(15859712, false),
FixedTrait::new(4128, false),
FixedTrait::new(9764864, false),
FixedTrait::new(2025062, false),
FixedTrait::new(5505024, false),
FixedTrait::new(217579519, false),
FixedTrait::new(57, false),
FixedTrait::new(681574, false),
FixedTrait::new(1048576, false),
FixedTrait::new(16187392, false),
FixedTrait::new(4128, false),
FixedTrait::new(9961472, false),
FixedTrait::new(1979187, false),
FixedTrait::new(5583667, false),
FixedTrait::new(207093760, false),
FixedTrait::new(83, false),
FixedTrait::new(547225, false),
FixedTrait::new(1048576, false),
FixedTrait::new(16580607, false),
FixedTrait::new(4128, false),
FixedTrait::new(10223616, false),
FixedTrait::new(1998848, false),
FixedTrait::new(5681971, false),
FixedTrait::new(218234879, false),
FixedTrait::new(110, false),
FixedTrait::new(753664, false),
FixedTrait::new(1048576, false),
FixedTrait::new(16777216, false),
FixedTrait::new(4128, false),
FixedTrait::new(10289152, false),
FixedTrait::new(1966080, false),
FixedTrait::new(5754060, false),
FixedTrait::new(201195519, false),
FixedTrait::new(135, false),
FixedTrait::new(747110, false),
FixedTrait::new(1048576, false),
FixedTrait::new(16842752, false),
FixedTrait::new(4128, false),
FixedTrait::new(10289152, false),
FixedTrait::new(1992294, false),
FixedTrait::new(5780275, false),
FixedTrait::new(202506239, false),
]
.span()
);
return tensor;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/src/datasets/aave_data/aave_y_labels.cairo | use array::ArrayTrait;
use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16Impl, FP16x16PartialEq};
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};
use orion::numbers::{FP16x16, FixedTrait};
fn aave_y_labels() -> Tensor<FP16x16> {
let tensor = TensorTrait::<
FP16x16
>::new(
shape: array![24].span(),
data: array![
FixedTrait::new(5072486, false),
FixedTrait::new(5072486, false),
FixedTrait::new(5079040, false),
FixedTrait::new(5085593, false),
FixedTrait::new(5111808, false),
FixedTrait::new(5157683, false),
FixedTrait::new(5203558, false),
FixedTrait::new(5360844, false),
FixedTrait::new(5367398, false),
FixedTrait::new(5367398, false),
FixedTrait::new(5452595, false),
FixedTrait::new(5485363, false),
FixedTrait::new(5505024, false),
FixedTrait::new(5583667, false),
FixedTrait::new(5681971, false),
FixedTrait::new(5754060, false),
FixedTrait::new(5780275, false),
FixedTrait::new(5852364, false),
FixedTrait::new(5891686, false),
FixedTrait::new(5963776, false),
FixedTrait::new(6035865, false),
FixedTrait::new(6134169, false),
FixedTrait::new(6153830, false),
FixedTrait::new(6180044, false),
]
.span()
);
return tensor;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/src/datasets/boston_data.cairo | mod boston_x_features;
mod boston_y_labels;
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/src/datasets/boston_data/boston_x_features.cairo | use array::ArrayTrait;
use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16Impl, FP16x16PartialEq};
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};
use orion::numbers::{FP16x16, FixedTrait};
fn boston_x_features() -> Tensor<FP16x16> {
let tensor = TensorTrait::<
FP16x16
>::new(
shape: array![50, 11].span(),
data: array![
FixedTrait::new(26719, false),
FixedTrait::new(0, false),
FixedTrait::new(406323, false),
FixedTrait::new(65536, false),
FixedTrait::new(33226, false),
FixedTrait::new(403963, false),
FixedTrait::new(5983436, false),
FixedTrait::new(199753, false),
FixedTrait::new(524288, false),
FixedTrait::new(20119552, false),
FixedTrait::new(1140326, false),
FixedTrait::new(17588, false),
FixedTrait::new(0, false),
FixedTrait::new(635043, false),
FixedTrait::new(0, false),
FixedTrait::new(38338, false),
FixedTrait::new(379715, false),
FixedTrait::new(4626841, false),
FixedTrait::new(189575, false),
FixedTrait::new(393216, false),
FixedTrait::new(25624576, false),
FixedTrait::new(1258291, false),
FixedTrait::new(3512, false),
FixedTrait::new(1376256, false),
FixedTrait::new(369623, false),
FixedTrait::new(0, false),
FixedTrait::new(28770, false),
FixedTrait::new(426704, false),
FixedTrait::new(1382809, false),
FixedTrait::new(446608, false),
FixedTrait::new(262144, false),
FixedTrait::new(15925248, false),
FixedTrait::new(1101004, false),
FixedTrait::new(731407, false),
FixedTrait::new(0, false),
FixedTrait::new(1186201, false),
FixedTrait::new(0, false),
FixedTrait::new(48496, false),
FixedTrait::new(434438, false),
FixedTrait::new(6199705, false),
FixedTrait::new(139244, false),
FixedTrait::new(1572864, false),
FixedTrait::new(43646976, false),
FixedTrait::new(1323827, false),
FixedTrait::new(151643, false),
FixedTrait::new(0, false),
FixedTrait::new(1283194, false),
FixedTrait::new(0, false),
FixedTrait::new(39649, false),
FixedTrait::new(385351, false),
FixedTrait::new(6376652, false),
FixedTrait::new(156545, false),
FixedTrait::new(327680, false),
FixedTrait::new(26411008, false),
FixedTrait::new(963379, false),
FixedTrait::new(637283, false),
FixedTrait::new(0, false),
FixedTrait::new(1186201, false),
FixedTrait::new(0, false),
FixedTrait::new(48496, false),
FixedTrait::new(419823, false),
FixedTrait::new(6370099, false),
FixedTrait::new(135338, false),
FixedTrait::new(1572864, false),
FixedTrait::new(43646976, false),
FixedTrait::new(1323827, false),
FixedTrait::new(6860, false),
FixedTrait::new(2621440, false),
FixedTrait::new(420085, false),
FixedTrait::new(65536, false),
FixedTrait::new(29294, false),
FixedTrait::new(476250, false),
FixedTrait::new(3211264, false),
FixedTrait::new(313733, false),
FixedTrait::new(262144, false),
FixedTrait::new(16646144, false),
FixedTrait::new(1153433, false),
FixedTrait::new(1598672, false),
FixedTrait::new(0, false),
FixedTrait::new(1186201, false),
FixedTrait::new(0, false),
FixedTrait::new(45875, false),
FixedTrait::new(304873, false),
FixedTrait::new(6553600, false),
FixedTrait::new(96154, false),
FixedTrait::new(1572864, false),
FixedTrait::new(43646976, false),
FixedTrait::new(1323827, false),
FixedTrait::new(264661, false),
FixedTrait::new(0, false),
FixedTrait::new(1186201, false),
FixedTrait::new(0, false),
FixedTrait::new(34865, false),
FixedTrait::new(408223, false),
FixedTrait::new(5944115, false),
FixedTrait::new(203115, false),
FixedTrait::new(1572864, false),
FixedTrait::new(43646976, false),
FixedTrait::new(1323827, false),
FixedTrait::new(10624, false),
FixedTrait::new(1310720, false),
FixedTrait::new(456130, false),
FixedTrait::new(0, false),
FixedTrait::new(30408, false),
FixedTrait::new(408944, false),
FixedTrait::new(1068236, false),
FixedTrait::new(290258, false),
FixedTrait::new(196608, false),
FixedTrait::new(14614528, false),
FixedTrait::new(1218969, false),
FixedTrait::new(6768, false),
FixedTrait::new(1638400, false),
FixedTrait::new(336199, false),
FixedTrait::new(0, false),
FixedTrait::new(29687, false),
FixedTrait::new(388431, false),
FixedTrait::new(3093299, false),
FixedTrait::new(454295, false),
FixedTrait::new(524288, false),
FixedTrait::new(18612224, false),
FixedTrait::new(1291059, false),
FixedTrait::new(40077, false),
FixedTrait::new(1310720, false),
FixedTrait::new(260177, false),
FixedTrait::new(0, false),
FixedTrait::new(42401, false),
FixedTrait::new(570425, false),
FixedTrait::new(5695078, false),
FixedTrait::new(118030, false),
FixedTrait::new(327680, false),
FixedTrait::new(17301504, false),
FixedTrait::new(851968, false),
FixedTrait::new(527944, false),
FixedTrait::new(0, false),
FixedTrait::new(1186201, false),
FixedTrait::new(0, false),
FixedTrait::new(38273, false),
FixedTrait::new(355663, false),
FixedTrait::new(6252134, false),
FixedTrait::new(159239, false),
FixedTrait::new(1572864, false),
FixedTrait::new(43646976, false),
FixedTrait::new(1323827, false),
FixedTrait::new(14030, false),
FixedTrait::new(1441792, false),
FixedTrait::new(384040, false),
FixedTrait::new(0, false),
FixedTrait::new(28246, false),
FixedTrait::new(421920, false),
FixedTrait::new(583270, false),
FixedTrait::new(484750, false),
FixedTrait::new(458752, false),
FixedTrait::new(21626880, false),
FixedTrait::new(1251737, false),
FixedTrait::new(22353, false),
FixedTrait::new(0, false),
FixedTrait::new(483655, false),
FixedTrait::new(0, false),
FixedTrait::new(32309, false),
FixedTrait::new(420413, false),
FixedTrait::new(2627993, false),
FixedTrait::new(309402, false),
FixedTrait::new(327680, false),
FixedTrait::new(18808832, false),
FixedTrait::new(1284505, false),
FixedTrait::new(51800, false),
FixedTrait::new(0, false),
FixedTrait::new(648806, false),
FixedTrait::new(0, false),
FixedTrait::new(35651, false),
FixedTrait::new(401211, false),
FixedTrait::new(3460300, false),
FixedTrait::new(173034, false),
FixedTrait::new(262144, false),
FixedTrait::new(19922944, false),
FixedTrait::new(1205862, false),
FixedTrait::new(1998, false),
FixedTrait::new(3604480, false),
FixedTrait::new(247726, false),
FixedTrait::new(0, false),
FixedTrait::new(31719, false),
FixedTrait::new(450494, false),
FixedTrait::new(1841561, false),
FixedTrait::new(423716, false),
FixedTrait::new(327680, false),
FixedTrait::new(24248320, false),
FixedTrait::new(1153433, false),
FixedTrait::new(22898, false),
FixedTrait::new(0, false),
FixedTrait::new(648806, false),
FixedTrait::new(0, false),
FixedTrait::new(35651, false),
FixedTrait::new(391380, false),
FixedTrait::new(5026611, false),
FixedTrait::new(203325, false),
FixedTrait::new(262144, false),
FixedTrait::new(19922944, false),
FixedTrait::new(1205862, false),
FixedTrait::new(24195, false),
FixedTrait::new(0, false),
FixedTrait::new(648806, false),
FixedTrait::new(0, false),
FixedTrait::new(35651, false),
FixedTrait::new(430374, false),
FixedTrait::new(5721292, false),
FixedTrait::new(236080, false),
FixedTrait::new(262144, false),
FixedTrait::new(19922944, false),
FixedTrait::new(1205862, false),
FixedTrait::new(623485, false),
FixedTrait::new(0, false),
FixedTrait::new(1186201, false),
FixedTrait::new(0, false),
FixedTrait::new(46727, false),
FixedTrait::new(440926, false),
FixedTrait::new(6166937, false),
FixedTrait::new(163584, false),
FixedTrait::new(1572864, false),
FixedTrait::new(43646976, false),
FixedTrait::new(1323827, false),
FixedTrait::new(52606, false),
FixedTrait::new(0, false),
FixedTrait::new(533463, false),
FixedTrait::new(0, false),
FixedTrait::new(35258, false),
FixedTrait::new(357564, false),
FixedTrait::new(2398617, false),
FixedTrait::new(248807, false),
FixedTrait::new(262144, false),
FixedTrait::new(20119552, false),
FixedTrait::new(1376256, false),
FixedTrait::new(3709, false),
FixedTrait::new(0, false),
FixedTrait::new(223477, false),
FixedTrait::new(0, false),
FixedTrait::new(32047, false),
FixedTrait::new(459210, false),
FixedTrait::new(5655756, false),
FixedTrait::new(224244, false),
FixedTrait::new(131072, false),
FixedTrait::new(17694720, false),
FixedTrait::new(1166540, false),
FixedTrait::new(28554, false),
FixedTrait::new(0, false),
FixedTrait::new(694026, false),
FixedTrait::new(65536, false),
FixedTrait::new(32047, false),
FixedTrait::new(350224, false),
FixedTrait::new(6553600, false),
FixedTrait::new(253952, false),
FixedTrait::new(262144, false),
FixedTrait::new(18153472, false),
FixedTrait::new(1218969, false),
FixedTrait::new(34087, false),
FixedTrait::new(1310720, false),
FixedTrait::new(260177, false),
FixedTrait::new(0, false),
FixedTrait::new(42401, false),
FixedTrait::new(550371, false),
FixedTrait::new(5996544, false),
FixedTrait::new(149979, false),
FixedTrait::new(327680, false),
FixedTrait::new(17301504, false),
FixedTrait::new(851968, false),
FixedTrait::new(802632, false),
FixedTrait::new(0, false),
FixedTrait::new(1186201, false),
FixedTrait::new(0, false),
FixedTrait::new(38273, false),
FixedTrait::new(382533, false),
FixedTrait::new(3912499, false),
FixedTrait::new(130914, false),
FixedTrait::new(1572864, false),
FixedTrait::new(43646976, false),
FixedTrait::new(1323827, false),
FixedTrait::new(17654, false),
FixedTrait::new(0, false),
FixedTrait::new(648806, false),
FixedTrait::new(0, false),
FixedTrait::new(35651, false),
FixedTrait::new(410648, false),
FixedTrait::new(5426380, false),
FixedTrait::new(213830, false),
FixedTrait::new(262144, false),
FixedTrait::new(19922944, false),
FixedTrait::new(1205862, false),
FixedTrait::new(2988, false),
FixedTrait::new(0, false),
FixedTrait::new(910295, false),
FixedTrait::new(65536, false),
FixedTrait::new(36044, false),
FixedTrait::new(385875, false),
FixedTrait::new(3670016, false),
FixedTrait::new(203954, false),
FixedTrait::new(327680, false),
FixedTrait::new(18087936, false),
FixedTrait::new(1074790, false),
FixedTrait::new(3787, false),
FixedTrait::new(0, false),
FixedTrait::new(161218, false),
FixedTrait::new(0, false),
FixedTrait::new(31981, false),
FixedTrait::new(457441, false),
FixedTrait::new(3827302, false),
FixedTrait::new(185401, false),
FixedTrait::new(196608, false),
FixedTrait::new(12648448, false),
FixedTrait::new(1166540, false),
FixedTrait::new(54084, false),
FixedTrait::new(1310720, false),
FixedTrait::new(260177, false),
FixedTrait::new(0, false),
FixedTrait::new(42401, false),
FixedTrait::new(480182, false),
FixedTrait::new(6193152, false),
FixedTrait::new(136236, false),
FixedTrait::new(327680, false),
FixedTrait::new(17301504, false),
FixedTrait::new(851968, false),
FixedTrait::new(227690, false),
FixedTrait::new(0, false),
FixedTrait::new(1186201, false),
FixedTrait::new(65536, false),
FixedTrait::new(47054, false),
FixedTrait::new(575406, false),
FixedTrait::new(5432934, false),
FixedTrait::new(124826, false),
FixedTrait::new(1572864, false),
FixedTrait::new(43646976, false),
FixedTrait::new(1323827, false),
FixedTrait::new(35685, false),
FixedTrait::new(0, false),
FixedTrait::new(1434583, false),
FixedTrait::new(0, false),
FixedTrait::new(40894, false),
FixedTrait::new(403111, false),
FixedTrait::new(6415974, false),
FixedTrait::new(109359, false),
FixedTrait::new(262144, false),
FixedTrait::new(28639232, false),
FixedTrait::new(1389363, false),
FixedTrait::new(9209, false),
FixedTrait::new(0, false),
FixedTrait::new(694026, false),
FixedTrait::new(0, false),
FixedTrait::new(32047, false),
FixedTrait::new(417792, false),
FixedTrait::new(2116812, false),
FixedTrait::new(258565, false),
FixedTrait::new(262144, false),
FixedTrait::new(18153472, false),
FixedTrait::new(1218969, false),
FixedTrait::new(3069, false),
FixedTrait::new(0, false),
FixedTrait::new(223477, false),
FixedTrait::new(0, false),
FixedTrait::new(32047, false),
FixedTrait::new(420544, false),
FixedTrait::new(4331929, false),
FixedTrait::new(202656, false),
FixedTrait::new(131072, false),
FixedTrait::new(17694720, false),
FixedTrait::new(1166540, false),
FixedTrait::new(4016, false),
FixedTrait::new(1310720, false),
FixedTrait::new(218234, false),
FixedTrait::new(65536, false),
FixedTrait::new(29025, false),
FixedTrait::new(501022, false),
FixedTrait::new(3257139, false),
FixedTrait::new(341567, false),
FixedTrait::new(327680, false),
FixedTrait::new(14155776, false),
FixedTrait::new(976486, false),
FixedTrait::new(63974, false),
FixedTrait::new(0, false),
FixedTrait::new(1434583, false),
FixedTrait::new(0, false),
FixedTrait::new(40894, false),
FixedTrait::new(377290, false),
FixedTrait::new(6448742, false),
FixedTrait::new(153747, false),
FixedTrait::new(262144, false),
FixedTrait::new(28639232, false),
FixedTrait::new(1389363, false),
FixedTrait::new(14556, false),
FixedTrait::new(0, false),
FixedTrait::new(656015, false),
FixedTrait::new(0, false),
FixedTrait::new(35848, false),
FixedTrait::new(399245, false),
FixedTrait::new(6252134, false),
FixedTrait::new(166985, false),
FixedTrait::new(393216, false),
FixedTrait::new(28311552, false),
FixedTrait::new(1166540, false),
FixedTrait::new(11358, false),
FixedTrait::new(0, false),
FixedTrait::new(635043, false),
FixedTrait::new(0, false),
FixedTrait::new(38338, false),
FixedTrait::new(374013, false),
FixedTrait::new(3538944, false),
FixedTrait::new(156087, false),
FixedTrait::new(393216, false),
FixedTrait::new(25624576, false),
FixedTrait::new(1258291, false),
FixedTrait::new(3758, false),
FixedTrait::new(0, false),
FixedTrait::new(294256, false),
FixedTrait::new(0, false),
FixedTrait::new(29425, false),
FixedTrait::new(434503, false),
FixedTrait::new(3676569, false),
FixedTrait::new(290829, false),
FixedTrait::new(196608, false),
FixedTrait::new(16187392, false),
FixedTrait::new(1212416, false),
FixedTrait::new(11143, false),
FixedTrait::new(819200, false),
FixedTrait::new(515768, false),
FixedTrait::new(0, false),
FixedTrait::new(34340, false),
FixedTrait::new(393478, false),
FixedTrait::new(5629542, false),
FixedTrait::new(432019, false),
FixedTrait::new(327680, false),
FixedTrait::new(20381696, false),
FixedTrait::new(996147, false),
FixedTrait::new(2121, false),
FixedTrait::new(0, false),
FixedTrait::new(142868, false),
FixedTrait::new(0, false),
FixedTrait::new(30015, false),
FixedTrait::new(458620, false),
FixedTrait::new(3001548, false),
FixedTrait::new(397292, false),
FixedTrait::new(196608, false),
FixedTrait::new(14548992, false),
FixedTrait::new(1225523, false),
FixedTrait::new(10443, false),
FixedTrait::new(0, false),
FixedTrait::new(452853, false),
FixedTrait::new(0, false),
FixedTrait::new(29360, false),
FixedTrait::new(407044, false),
FixedTrait::new(425984, false),
FixedTrait::new(374924, false),
FixedTrait::new(196608, false),
FixedTrait::new(15269888, false),
FixedTrait::new(1173094, false),
FixedTrait::new(3057, false),
FixedTrait::new(5242880, false),
FixedTrait::new(99614, false),
FixedTrait::new(0, false),
FixedTrait::new(26476, false),
FixedTrait::new(465764, false),
FixedTrait::new(2398617, false),
FixedTrait::new(479002, false),
FixedTrait::new(131072, false),
FixedTrait::new(21561344, false),
FixedTrait::new(825753, false),
FixedTrait::new(2325, false),
FixedTrait::new(5242880, false),
FixedTrait::new(238551, false),
FixedTrait::new(0, false),
FixedTrait::new(25690, false),
FixedTrait::new(385089, false),
FixedTrait::new(1251737, false),
FixedTrait::new(604261, false),
FixedTrait::new(65536, false),
FixedTrait::new(20643840, false),
FixedTrait::new(1074790, false),
FixedTrait::new(4601, false),
FixedTrait::new(0, false),
FixedTrait::new(265420, false),
FixedTrait::new(0, false),
FixedTrait::new(33423, false),
FixedTrait::new(394526, false),
FixedTrait::new(3093299, false),
FixedTrait::new(232973, false),
FixedTrait::new(327680, false),
FixedTrait::new(19398656, false),
FixedTrait::new(1087897, false),
FixedTrait::new(2816, false),
FixedTrait::new(3440640, false),
FixedTrait::new(348651, false),
FixedTrait::new(0, false),
FixedTrait::new(26542, false),
FixedTrait::new(430243, false),
FixedTrait::new(1500774, false),
FixedTrait::new(479540, false),
FixedTrait::new(393216, false),
FixedTrait::new(19202048, false),
FixedTrait::new(1087897, false),
FixedTrait::new(90963, false),
FixedTrait::new(0, false),
FixedTrait::new(533463, false),
FixedTrait::new(0, false),
FixedTrait::new(35258, false),
FixedTrait::new(389939, false),
FixedTrait::new(5373952, false),
FixedTrait::new(261488, false),
FixedTrait::new(262144, false),
FixedTrait::new(20119552, false),
FixedTrait::new(1376256, false),
FixedTrait::new(12499, false),
FixedTrait::new(1441792, false),
FixedTrait::new(384040, false),
FixedTrait::new(0, false),
FixedTrait::new(28246, false),
FixedTrait::new(440270, false),
FixedTrait::new(1146880, false),
FixedTrait::new(512917, false),
FixedTrait::new(458752, false),
FixedTrait::new(21626880, false),
FixedTrait::new(1251737, false),
FixedTrait::new(12805, false),
FixedTrait::new(0, false),
FixedTrait::new(708444, false),
FixedTrait::new(0, false),
FixedTrait::new(27066, false),
FixedTrait::new(409272, false),
FixedTrait::new(406323, false),
FixedTrait::new(346508, false),
FixedTrait::new(262144, false),
FixedTrait::new(19988480, false),
FixedTrait::new(1258291, false),
FixedTrait::new(758769, false),
FixedTrait::new(0, false),
FixedTrait::new(1186201, false),
FixedTrait::new(0, false),
FixedTrait::new(45875, false),
FixedTrait::new(330039, false),
FixedTrait::new(6356992, false),
FixedTrait::new(115998, false),
FixedTrait::new(1572864, false),
FixedTrait::new(43646976, false),
FixedTrait::new(1323827, false),
FixedTrait::new(2369, false),
FixedTrait::new(5242880, false),
FixedTrait::new(324403, false),
FixedTrait::new(0, false),
FixedTrait::new(26935, false),
FixedTrait::new(434503, false),
FixedTrait::new(1533542, false),
FixedTrait::new(335328, false),
FixedTrait::new(262144, false),
FixedTrait::new(16056320, false),
FixedTrait::new(1258291, false),
]
.span()
);
return tensor;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/src/datasets/boston_data/boston_y_labels.cairo | use array::ArrayTrait;
use orion::numbers::fixed_point::implementations::fp16x16::core::{FP16x16Impl, FP16x16PartialEq};
use orion::operators::tensor::{Tensor, TensorTrait, FP16x16Tensor};
use orion::numbers::{FP16x16, FixedTrait};
fn boston_y_labels() -> Tensor<FP16x16> {
let tensor = TensorTrait::<
FP16x16
>::new(
shape: array![50].span(),
data: array![
FixedTrait::new(1422131, false),
FixedTrait::new(1199308, false),
FixedTrait::new(1638400, false),
FixedTrait::new(878182, false),
FixedTrait::new(1251737, false),
FixedTrait::new(1120665, false),
FixedTrait::new(2175795, false),
FixedTrait::new(688128, false),
FixedTrait::new(1284505, false),
FixedTrait::new(1651507, false),
FixedTrait::new(1284505, false),
FixedTrait::new(3276800, false),
FixedTrait::new(904396, false),
FixedTrait::new(1625292, false),
FixedTrait::new(1638400, false),
FixedTrait::new(1448345, false),
FixedTrait::new(2044723, false),
FixedTrait::new(1330380, false),
FixedTrait::new(1559756, false),
FixedTrait::new(976486, false),
FixedTrait::new(1323827, false),
FixedTrait::new(1546649, false),
FixedTrait::new(1310720, false),
FixedTrait::new(3198156, false),
FixedTrait::new(668467, false),
FixedTrait::new(1415577, false),
FixedTrait::new(1526988, false),
FixedTrait::new(2437939, false),
FixedTrait::new(2031616, false),
FixedTrait::new(1435238, false),
FixedTrait::new(1166540, false),
FixedTrait::new(1841561, false),
FixedTrait::new(1481113, false),
FixedTrait::new(3014656, false),
FixedTrait::new(1022361, false),
FixedTrait::new(1225523, false),
FixedTrait::new(1428684, false),
FixedTrait::new(1743257, false),
FixedTrait::new(1238630, false),
FixedTrait::new(2188902, false),
FixedTrait::new(1618739, false),
FixedTrait::new(1985740, false),
FixedTrait::new(1369702, false),
FixedTrait::new(1520435, false),
FixedTrait::new(1625292, false),
FixedTrait::new(865075, false),
FixedTrait::new(1717043, false),
FixedTrait::new(1533542, false),
FixedTrait::new(635699, false),
FixedTrait::new(1828454, false),
]
.span()
);
return tensor;
}
| https://github.com/gizatechxyz/Giza-Hub |
awesome-orion/finance/provable_multiple_linear_regression_solver/src/datasets/linear_data.cairo | mod feature_data;
mod label_data;
| https://github.com/gizatechxyz/Giza-Hub |
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