How to do Independent component analysis in shogun

In this recipe, we shall learn how to do Independent component analysis in shogun with the help of a simple and detailed example.

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Recipe Objective: How to do Independent component analysis in shogun?

This recipe explains how to do Independent component analysis in shogun
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Independent Component Analysis

We use independent component analysis (ICA) for separating superimposed signals. It separates a multivariate signal into additive subcomponents that are maximally independent.

f = RealFeatures(feats)

z = FastICA()
z.set_max_iter(200)
z.set_tol(0.00001)

c = z.apply(f)

matrix = z.get_mixing_matrix()
casted = RealFeatures.obtain_from_generic(c)
signal = casted.get_feature_matrix()

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I come from Northwestern University, which is ranked 9th in the US. Although the high-quality academics at school taught me all the basics I needed, obtaining practical experience was a challenge.... Read More

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