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To normalize the matrix elements, you can use the following formula:

It will set the value of each element between 0 and 1.

Here is an example:

>>> import numpy as np

>>> X = np.array([[4, 1, 2, 2],[1, 3, 9, 3], [5, 7, 5, 1]])

>>> X

array([[4, 1, 2, 2],

[1, 3, 9, 3],

[5, 7, 5, 1]])>>> np.divide(X-X.min(axis=0),(X.max(axis=0)-X.min(axis=0)))

array([[0.75 , 0. , 0. , 0.5 ],

[0. , 0.33333333, 1. , 1. ],

[1. , 1. , 0.42857143, 0. ]])

You can also use sklearn's **Normalizer() **to normalize the matrix elements.

Here is an example:

>>> import numpy as np

>>> from sklearn.preprocessing import Normalizer

>>> X = np.array([[4, 1, 2, 2],[1, 3, 9, 3], [5, 7, 5, 1]])

>>> X

array([[4, 1, 2, 2],

[1, 3, 9, 3],

[5, 7, 5, 1]])>>> transformer = Normalizer().fit(X)

>>> transformer.transform(X)

array([[0.8, 0.2, 0.4, 0.4],

[0.1, 0.3, 0.9, 0.3],

[0.5, 0.7, 0.5, 0.1]])