-
-
Save SherazKhan/4bc6611de4257d66d62de5cd6489ae08 to your computer and use it in GitHub Desktop.
Distance correlation with p-value
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
from scipy.spatial.distance import pdist, squareform | |
import numpy as np | |
import random | |
import copy | |
def distcorr(Xval, Yval, pval=True, nruns=500): | |
""" Compute the distance correlation function, returning the p-value. | |
Based on Satra/distcorr.py (gist aa3d19a12b74e9ab7941) | |
>>> a = [1,2,3,4,5] | |
>>> b = np.array([1,2,9,4,4]) | |
>>> distcorr(a, b) | |
(0.76267624241686671, 0.268) | |
""" | |
X = np.atleast_1d(Xval) | |
Y = np.atleast_1d(Yval) | |
if np.prod(X.shape) == len(X): | |
X = X[:, None] | |
if np.prod(Y.shape) == len(Y): | |
Y = Y[:, None] | |
X = np.atleast_2d(X) | |
Y = np.atleast_2d(Y) | |
n = X.shape[0] | |
if Y.shape[0] != X.shape[0]: | |
raise ValueError('Number of samples must match') | |
a = squareform(pdist(X)) | |
b = squareform(pdist(Y)) | |
A = a - a.mean(axis=0)[None, :] - a.mean(axis=1)[:, None] + a.mean() | |
B = b - b.mean(axis=0)[None, :] - b.mean(axis=1)[:, None] + b.mean() | |
dcov2_xy = (A * B).sum()/float(n * n) | |
dcov2_xx = (A * A).sum()/float(n * n) | |
dcov2_yy = (B * B).sum()/float(n * n) | |
dcor = np.sqrt(dcov2_xy)/np.sqrt(np.sqrt(dcov2_xx) * np.sqrt(dcov2_yy)) | |
if pval: | |
greater = 0 | |
for i in range(nruns): | |
Y_r = copy.copy(Yval) | |
random.shuffle(Y_r) | |
if distcorr(Xval, Y_r, pval=False) > dcor: | |
greater += 1 | |
return (dcor, greater/float(n)) | |
else: | |
return dcor |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment