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Distance Correlation in Python
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| 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 |
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