made example way better (random input data means non-sense output) an…

…d prepared for unit-test

examples/undocumented/libshogun/classifier_larank.cpp

@@ -1,3 +1,12 @@
+/*
+ * This program is free software; you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation; either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * Written (W) 2013 Heiko Strathmann and others
+ */
+
 #include <shogun/labels/MulticlassLabels.h>
 #include <shogun/features/DenseFeatures.h>
 #include <shogun/kernel/GaussianKernel.h>
@@ -6,28 +15,42 @@
 
 using namespace shogun;
 
-void print_message(FILE* target, const char* str)
+void test()
 {
-	fprintf(target, "%s", str);
-}
+	index_t num_vec=10;
+	index_t num_feat=3;
+	index_t num_class=num_feat; // to make data easy
+	float64_t distance=15;
 
-int main(int argc, char** argv)
-{
-	init_shogun(&print_message);
-	index_t num_vec=3;
-	index_t num_feat=2;
-	index_t num_class=2;
+	// create some linearly seperable data
+	SGMatrix<float64_t> matrix(num_class, num_vec);
+	SGMatrix<float64_t> matrix_test(num_class, num_vec);
+	CMulticlassLabels* labels=new CMulticlassLabels(num_vec);
+	CMulticlassLabels* labels_test=new CMulticlassLabels(num_vec);
+	for (index_t i=0; i<num_vec; ++i)
+	{
+		index_t label=i%num_class;
+		for (index_t j=0; j<num_feat; ++j)
+		{
+			matrix(j,i)=CMath::randn_double();
+			matrix_test(j,i)=CMath::randn_double();
+			labels->set_label(i, label);
+			labels_test->set_label(i, label);
+		}
 
-	// create some data
-	SGMatrix<float64_t> matrix(num_feat, num_vec);
-	SGVector<float64_t>::range_fill_vector(matrix.matrix, num_feat*num_vec);
+		/* make sure data is linearly seperable per class */
+		matrix(label,i)+=distance;
+		matrix_test(label,i)+=distance;
+	}
+	matrix.display_matrix("matrix");
+	labels->get_int_labels().display_vector("labels");
 
-	// create vectors
 	// shogun will now own the matrix created
 	CDenseFeatures<float64_t>* features=new CDenseFeatures<float64_t>(matrix);
+	CDenseFeatures<float64_t>* features_test=
+			new CDenseFeatures<float64_t>(matrix_test);
 
 	// create three labels
-	CMulticlassLabels* labels=new CMulticlassLabels(num_vec);
 	for (index_t i=0; i<num_vec; ++i)
 		labels->set_label(i, i%num_class);
 
@@ -41,21 +64,39 @@ int main(int argc, char** argv)
 	svm->train();
 
 	// classify on training examples
-	CMulticlassLabels* output=CMulticlassLabels::obtain_from_generic(svm->apply());
-	SGVector<float64_t>::display_vector(output->get_labels().vector, output->get_num_labels(),
-			"batch output");
+	CMulticlassLabels* output=(CMulticlassLabels*)svm->apply();
+	output->get_labels().display_vector("batch output");
 
 	/* assert that batch apply and apply(index_t) give same result */
+	SGVector<float64_t> single_outputs(output->get_num_labels());
 	for (index_t i=0; i<output->get_num_labels(); ++i)
-	{
-		float64_t label=svm->apply_one(i);
-		SG_SPRINT("single output[%d]=%f\n", i, label);
-		ASSERT(output->get_label(i)==label);
-	}
-	SG_UNREF(output);
+		single_outputs[i]=svm->apply_one(i);
+
+	single_outputs.display_vector("single_outputs");
+
+	for (index_t i=0; i<output->get_num_labels(); ++i)
+		ASSERT(output->get_label(i)==single_outputs[i]);
+
+	CMulticlassLabels* output_test=
+			(CMulticlassLabels*)svm->apply(features_test);
+	labels_test->get_labels().display_vector("labels_test");
+	output_test->get_labels().display_vector("output_test");
+
+	for (index_t i=0; i<output->get_num_labels(); ++i)
+		ASSERT(labels_test->get_label(i)==output_test->get_label(i));
 
 	// free up memory
+	SG_UNREF(output);
+	SG_UNREF(labels_test);
+	SG_UNREF(output_test);
 	SG_UNREF(svm);
+}
+
+int main(int argc, char** argv)
+{
+	init_shogun_with_defaults();
+
+	test();
 
 	exit_shogun();
 	return 0;