Implement Linear Discriminant Analysis using Shogun.

This recipe helps you to implement Linear Discriminant Analysis using Shogun.

Recipe Objective

This recipe explains how we can implement Linear Discriminant Analysis using Shogun.
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Linear Discriminant Analysis

Linear Discriminant Analysis finds a linear classifier by finding a projection matrix that separates the provided classes. We assume that both classes are a Gaussian distributed with equal co-variance then the learned linear classification rule is optimal under this case.

x_train = RealFeatures(feats_train)
x_test = RealFeatures(feats_test)
y_train = MulticlassLabels(labels_train)
y_test = MulticlassLabels(labels_test)

z = MCLDA(x_train, y_train, 0.0001, True)
z.train()
predict = z.apply_multiclass(x_test)

eval = MulticlassAccuracy()
accuracy = eval.evaluate(predict, y_test)

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