Created
January 4, 2017 17:34
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Distance correlation
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from scipy.spatial.distance import pdist, squareform | |
import numpy as np | |
def distcorr(X, Y): | |
""" Compute the distance correlation function | |
""" | |
X = np.atleast_1d(X) | |
Y = np.atleast_1d(Y) | |
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)) | |
return dcor |
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