September 2019
Intermediate to advanced
420 pages
10h 29m
English
book
Machine Learning for OpenCV 4 - Second Edition
by Aditya Sharma, Michael Beyeler (USD), Vishwesh Ravi Shrimali, Michael Beyeler
Content preview from Machine Learning for OpenCV 4 - Second Edition
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Normalizing features
Similar to standardization, normalization is the process of scaling individual samples to have a unit norm. I'm sure you know that the norm stands for the length of a vector and can be defined in different ways. We discussed two of them in the previous chapter: the L1 norm (or Manhattan distance), and the L2 norm (or Euclidean distance).
In scikit-learn, our data matrix, X, can be normalized using the normalize function, and the l1 norm is specified by the norm keyword:
In [5]: X_normalized_l1 = preprocessing.normalize(X, norm='l1')... X_normalized_l1Out[5]: array([[ 0.2, -0.4, 0.4], [ 1. , 0. , 0. ], [ 0. , 0.5, -0.5]])
Similarly, the L2 norm can be computed by specifying norm='l2':
In [6]: X_normalized_l2 = preprocessing.normalize(X, ...
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