import numpy as np
[docs]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