.. currentmodule:: mlpy

Short Guide to Centering and Scaling
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Centering:

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1d array	    >>> x - np.mean(x)
2d array along rows >>> x - np.mean(x, axis=1).reshape(-1, 1)
2d array along cols >>> x - np.mean(x, axis=0)
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Unit length scaling (normalization). Elements are scaled to
have and unit length (:math:`\sum_{i=1}^n {x_{i}^2} = 1`):

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1d array	    >>> x / np.sqrt(np.sum((x)**2))
2d array along rows >>> x / np.sqrt(np.sum((x)**2, axis=1)).reshape(-1, 1)
2d array along cols >>> x / np.sqrt(np.sum((x)**2, axis=0))
=================== ======================================================

Standardization. Elements are scaled to have unit standard deviation.
The standard deviation is computed using :math:`n-1` instead of
:math:`n` (Bessel's correction).

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1d array	    >>> x / np.std(x, ddof=1) # ddof=1: Bessel's correction
2d array along rows >>> x / np.std(x, axis=1, ddof=1).reshape(-1, 1)
2d array along cols >>> x / np.std(x, axis=0, ddof=1)
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