Yul Kang yulkang

Assistant Professor, Bio and Brain Engineering, KAIST; Cognitive computational neuroscience of decision-making under uncertainty

sbarratt / torch_jacobian.py

Created May 9, 2019 19:40

Get the jacobian of a vector-valued function that takes batch inputs, in pytorch.

	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

zhiyzuo / pearsonr_ci.py

Last active January 4, 2023 06:19

calculate Pearson correlation along with the confidence interval using scipy and numpy

	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

fasiha / rotateVectors.py

Last active June 22, 2024 23:13

I frequently have to re-implement projecting vectors onto other vectors, and rotating vectors about other vectors. So here we go. Public domain.

	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)

johnb30 / bootstrap.py

Created November 29, 2012 01:06

Bootstrapped two-sample t-test in Python

	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"):