import numpy as np
[ドキュメント]def centering( X ):
"""
Normalize the mean and standard deviation along the each column of X to 0 and 1, respectively
Parameters
----------
X: numpy array
N x d dimensional matrix. Each row of X denotes the d-dimensional feature vector of search candidate.
Returns
-------
X_normalized: numpy array
normalized N x d dimensional matrix.
"""
stdX = np.std( X, 0 )
index = np.where( stdX !=0 )
X_normalized = ( X[:,index[0]] - np.mean( X[:,index[0]], 0 ) ) / stdX[ index[0] ]
return X_normalized