How to implement Multiclass Logistic Regression using Shogun?

Recipe Objective

This recipe explains how we can implement a Multi-class Logistic Regression using Shogun.
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Multi-class Logistic Regression

In the below code snippet initially, we have a file with training and testing data set. We have created CMulticlassLabels and CDenseFeatures, after that, we create an instance of the CMulticlassLogisticRegression classifier by passing it the dataset further we will train and apply it to the dataset finally we will evaluate test performance via CMulticlassAccuracy.

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

c = MulticlassLogisticRegression(1, x_train, y_train)
c.train()
predict = c.apply_multiclass(x_test)

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