Linear Discriminant Analysis using Shogun.

Recipe Objective

This recipe explains what is Linear Discriminant Analysis, how to implement it 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 = BinaryLabels(labels_train)
y_test = BinaryLabels(labels_test)

z = LDA()
z.set_features(x_train)
z.set_labels(y_train)

z.train()
predict = lda.apply_binary(x_test)

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