johnb30

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"):

fasiha

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)

zhiyzuo

import numpy as np
from scipy import stats

def pearsonr_ci(x,y,alpha=0.05):
    ''' calculate Pearson correlation along with the confidence interval using scipy and numpy

    Parameters
    ----------
    x, y : iterable object such as a list or np.array
      Input for correlation calculation

sbarratt

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