Add unit tests on lmnn termination

tests/unit/metric/LMNN_unittest.cc

@@ -62,6 +62,57 @@ TEST(LMNN,train_identity_init)
 	SG_UNREF(lmnn)
 }
 
+TEST(LMNN, train_termination)
+{
+	// create features, each column is a feature vector
+	SGMatrix<float64_t> feat_mat(2,4);
+	// 1st feature vector
+	feat_mat(0,0)=0;
+	feat_mat(1,0)=0;
+	// 2nd feature vector
+	feat_mat(0,1)=0;
+	feat_mat(1,1)=-1;
+	// 3rd feature vector
+	feat_mat(0,2)=1;
+	feat_mat(1,2)=1;
+	// 4th feature vector
+	feat_mat(0,3)=-1;
+	feat_mat(1,3)=1;
+	// wrap feat_mat in Shogun features
+	CDenseFeatures<float64_t>* features=new CDenseFeatures<float64_t>(feat_mat);
+
+	// create labels
+	SGVector<float64_t> lab_vec(4);
+	lab_vec[0]=0;
+	lab_vec[1]=0;
+	lab_vec[2]=1;
+	lab_vec[3]=1;
+	// two-class data, use MulticlassLabels because it works in general for more than
+	// two classes
+	CMulticlassLabels* labels=new CMulticlassLabels(lab_vec);
+
+	// create LMNN metric machine
+	int32_t k=1;	// number of target neighbors per example
+	CLMNN* lmnn=new CLMNN(features,labels,k);
+	// use the identity matrix as initial transform for LMNN
+	SGMatrix<float64_t> init_transform=SGMatrix<float64_t>::create_identity_matrix(2,1);
+	// set number of maximum iterations and train
+	lmnn->set_maxiter(1500);
+	lmnn->train(init_transform);
+
+	// check linear transform solution
+	SGMatrix<float64_t> L=lmnn->get_linear_transform();
+	EXPECT_NEAR(L(0,0),0.000041647483219,1e-10);
+	EXPECT_NEAR(L(0,1),0,1e-10);
+	EXPECT_NEAR(L(1,0),0,1e-10);
+	EXPECT_NEAR(L(1,1),0.988162395685451,1e-10);
+
+	// check number of iterations
+	auto stat = lmnn->get_statistics();
+	EXPECT_EQ(stat->obj.vlen, 1234);
+	SG_UNREF(lmnn)
+}
+
 TEST(LMNN,train_pca_init)
 {
 	// create features, each column is a feature vector