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#!/usr/bin/env python3 | |
from typing import TypeVar, Generic, NoReturn, Callable | |
import functools | |
T = TypeVar('T') | |
class Maybe(Generic[T]): | |
def __init__(self, value: T) -> None: | |
self.value = value |
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from sklearn.neighbors import KNeighborsRegressor | |
from sklearn.linear_model import LogisticRegressionCV | |
def X_learner(df, model, y, D, X): | |
temp = dgp.generate_data(true_te=True).sort_values(X) | |
# Mu | |
mu0 = model.fit(temp.loc[temp[D]==0, X], temp.loc[temp[D]==0, y]) | |
temp['mu0_hat_'] = mu0.predict(temp[X]) | |
mu1 = model.fit(temp.loc[temp[D]==1, X], temp.loc[temp[D]==1, y]) |
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#!/usr/bin/env python3 | |
# Use: ./subnet.py <ip/cidr> | |
# Alt: ./subnet.py <ip> <mask> | |
import sys | |
if __name__=="__main__": | |
addr = [0, 0, 0, 0] | |
mask = [0, 0, 0, 0] | |
cidr = 0 |