shogun-toolbox#3555 add meta_example for classifier_perceptron undocu…

…mented-sample (shogun-toolbox#4140)

examples/meta/src/neural_nets/classification_perceptron.sg

@@ -0,0 +1,35 @@
+CSVFile f_feats_train("../../data/classifier_binary_2d_linear_features_train.dat")
+CSVFile f_feats_test("../../data/classifier_binary_2d_linear_features_test.dat")
+CSVFile f_labels_train("../../data/classifier_binary_2d_linear_labels_train.dat")
+CSVFile f_labels_test("../../data/classifier_binary_2d_linear_labels_test.dat")
+
+#![create_features]
+RealFeatures features_train(f_feats_train)
+RealFeatures features_test(f_feats_test)
+BinaryLabels labels_train(f_labels_train)
+BinaryLabels labels_test(f_labels_test)
+#![create_features]
+
+#![create_instance]
+real learn_rate=1.0
+int max_iter=1000
+Perceptron perceptron(features_train, labels_train)
+perceptron.set_learn_rate(learn_rate)
+perceptron.set_max_iter(max_iter)
+#![create_instance]
+
+#![train_and_apply]
+perceptron.train()
+perceptron.set_features(features_test)
+Labels labels_predict = perceptron.apply()
+#![train_and_apply]
+
+#![extract_weights]
+RealVector weights = perceptron.get_w()
+real bias = perceptron.get_bias()
+#![extract_weights]
+
+#![evaluate_accuracy]
+AccuracyMeasure eval()
+real accuracy = eval.evaluate(labels_predict, labels_test)
+#![evaluate_accuracy]