yulkang

import unittest
import numpy as np
import numpy.linalg as linalg


def makeUnit(x):
    """Normalize entire input to norm 1. Not what you want for 2D arrays!"""
    return x / linalg.norm(x)

yulkang

from __future__ import division
import numpy as np
import pandas as pd
import random

def sample(data):
    sample = [random.choice(data) for _ in xrange(len(data))]
    return sample

def bootstrap_t_test(treatment, control, nboot = 1000, direction = "less"):

yulkang

"""
You can use PyCharm's Python console and use Ctrl + C, 
if you catch the exception that PyCharm raises when Ctrl + C is pressed. 
I wrote a short function below called `is_keyboard_interrupt` 
that tells you whether the exception is KeyboardInterrupt, 
including PyCharm's. 
If it is not, simply re-raise it. 
I paste a simplified version of the code below.

When it is run:

yulkang

# On PyCharm Debugger console, \r needs to come before the text.
# Otherwise, the text may not appear at all, or appear inconsistently.
# tested on PyCharm 2019.3, Python 3.6
# Modification of https://stackoverflow.com/a/517523/2565317

import time

print('Start.')
for i in range(100):
    time.sleep(0.02)

yulkang

*.pyc

yulkang

"""A part of the pylabyk library: numpytorch.py at https://github.com/yulkang/pylabyk"""
import torch
def block_diag(m):
    """
    Make a block diagonal matrix along dim=-3
    EXAMPLE:
    block_diag(torch.ones(4,3,2))
    should give a 12 x 8 matrix with blocks of 3 x 2 ones.
    Prepend batch dimensions if needed.
    You can also give a list of matrices.

yulkang

"""A part of the pylabyk library: numpytorch.py at https://github.com/yulkang/pylabyk"""
import torch
def kron(a, b):
    """
    Kronecker product of matrices a and b with leading batch dimensions.
    Batch dimensions are broadcast. The number of them mush
    :type a: torch.Tensor
    :type b: torch.Tensor
    :rtype: torch.Tensor
    """

yulkang

def get_jacobian(net, x, noutputs):
    x = x.squeeze()
    n = x.size()[0]
    x = x.repeat(noutputs, 1)
    x.requires_grad_(True)
    y = net(x)
    y.backward(torch.eye(noutputs))
    return x.grad.data