watex.utils.normalizer#

watex.utils.normalizer(arr, /, method='naive')[source]#

Normalize values to be between 0 and 1.

This normlizer handles NaN values translates data individually such that it is in the given range on the training set, e.g. between zero and one.

Note that when the transformation is set to the method ='MinMax', The transformation is given by:

X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
X_normed = X_std * (max - min) + min

where min, max = feature_range.

This transformation is often used as an alternative to zero mean, unit variance scaling.

Parameters

  • arr (Arraylike,) – Array to normalize, can contain NaN values.

  • method (str,) – Can be use ‘scikit-learn’ MinMaxScaler for normalization. Any other values used the naive normalization.

Returns

arr_norm

Return type

Normalized array.

Examples

>>> import numpy as np
>>> from watex.utils.funcutils import normalizer
>>> np.random.seed (42)
>>> arr = np.random.randn (3, 2 )
array([[ 0.49671415, -0.1382643 ],
       [ 0.64768854,  1.52302986],
       [-0.23415337, -0.23413696]])
>>> normalizer (arr )
array([[4.15931313e-01, 5.45697636e-02],
       [5.01849720e-01, 1.00000000e+00],
       [0.00000000e+00, 9.34323403e-06]])
>>> normalizer (arr , method ='min-max')  # normalize data along axis=0
array([[0.82879654, 0.05456093],
       [1.        , 1.        ],
       [0.        , 0.        ]])
>>> arr [0, 1] = np.nan; arr [1, 0] = np.nan
>>> normalizer (arr )
array([[4.15931313e-01,            nan],
       [           nan, 1.00000000e+00],
       [0.00000000e+00, 9.34323403e-06]])
>>> normalizer (arr , method ='min-max')
array([[ 1., nan],
       [nan,  1.],
       [ 0.,  0.]])