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@satra
Created October 16, 2014 15:40
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Distance Correlation in Python
from scipy.spatial.distance import pdist, squareform
import numpy as np
from numbapro import jit, float32
def distcorr(X, Y):
""" Compute the distance correlation function
>>> a = [1,2,3,4,5]
>>> b = np.array([1,2,9,4,4])
>>> distcorr(a, b)
0.762676242417
"""
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
@JazGit1
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JazGit1 commented Nov 22, 2023

Thanks a lot.

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