Yul Kang yulkang

## torch_jacobian.py
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

## .gitignore
*.pyc

## bootstrap.py
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"):

## rotateVectors.py
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)
	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
	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"):
	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)