port kernel_svm meta example to use put/get

doc/cookbook/source/examples/binary/kernel_support_vector_machine.rst

@@ -28,15 +28,12 @@ Imagine we have files with training and test data. We create CDenseFeatures (her
 
 .. sgexample:: kernel_support_vector_machine.sg:create_features
 
-In order to run :sgclass:`CLibSVM`, we need to initialize a kernel like :sgclass:`CGaussianKernel` with training features and some parameters like :math:`C` and epsilon i.e. residual convergence parameter which is optional.
-
-.. sgexample:: kernel_support_vector_machine.sg:set_parameters
-
-We create an instance of the :sgclass:`CLibSVM` classifier by passing it regularization coefficient, kernel and labels.
+In order to run :sgclass:`CLibSVM`, we first need to initialize a :sgclass:`CKernel` instance, such as :sgclass:`CGaussianKernel`.
+We then create a :sgclass:`CKernelMachine` instance, here :sgclass:`CLibSVM`, and provide it with parameters like the regularization coefficient :math:`C`, the kernel, the training labels, and an optional residual convergence parameter epsilon.
 
 .. sgexample:: kernel_support_vector_machine.sg:create_instance
 
-Then we train and apply it to test data, which here gives :sgclass:`CBinaryLabels`.
+Then we train it on training data and apply it to test data, which here gives :sgclass:`CBinaryLabels`.
 
 .. sgexample:: kernel_support_vector_machine.sg:train_and_apply
 

examples/meta/src/binary/kernel_support_vector_machine.sg

@@ -11,24 +11,21 @@ BinaryLabels labels_test(f_labels_test)
 #![create_features]
 
 #![set_parameters]
-real C = 1.0
-real epsilon = 0.001
-GaussianKernel gauss_kernel(features_train, features_train, 15)
+Kernel k = kernel("GaussianKernel", log_width=1.0074515102711323)
 #![set_parameters]
 
 #![create_instance]
-LibSVM svm(C, gauss_kernel, labels_train)
-svm.set_epsilon(epsilon)
+KernelMachine svm = kernel_machine("LibSVM", C1=1.0, C2=1.0, kernel=k, labels=labels_train, epsilon=0.001)
 #![create_instance]
 
 #![train_and_apply]
-svm.train()
+svm.train(features_train)
 BinaryLabels labels_predict = svm.apply_binary(features_test)
 #![train_and_apply]
 
 #![extract_weights_bias]
-RealVector alphas = svm.get_alphas()
-real b = svm.get_bias()
+RealVector alphas = svm.get_real_vector("m_alpha")
+real b = svm.get_real("m_bias")
 #![extract_weights_bias]
 
 #![evaluate_accuracy]
@@ -37,4 +34,4 @@ real accuracy = eval.evaluate(labels_predict, labels_test)
 #![evaluate_accuracy]
 
 # additional integration testing variables
-RealVector output = labels_predict.get_labels()
+RealVector output = labels_predict.get_real_vector("labels")

src/shogun/classifier/svm/LibSVM.cpp

@@ -60,10 +60,14 @@ bool CLibSVM::train_machine(CFeatures* data)
 		}
 		kernel->init(data, data);
 	}
+	REQUIRE(kernel->get_num_vec_lhs()==m_labels->get_num_labels(),
+			"Number of training data (%d) must match number of labels (%d)\n",
+			kernel->get_num_vec_lhs(), m_labels->get_num_labels())
 
 	problem.l=m_labels->get_num_labels();
 	SG_INFO("%d trainlabels\n", problem.l)
 
+
 	// set linear term
 	if (m_linear_term.vlen>0)
 	{
@@ -100,7 +104,6 @@ bool CLibSVM::train_machine(CFeatures* data)
 	float64_t weights[2]={1.0,get_C2()/get_C1()};
 
 	ASSERT(kernel && kernel->has_features())
-	ASSERT(kernel->get_num_vec_lhs()==problem.l)
 
 	switch (solver_type)
 	{

src/shogun/kernel/Kernel.cpp

@@ -925,10 +925,17 @@ void CKernel::save_serializable_post() throw (ShogunException)
 void CKernel::register_params()   {
 	SG_ADD(&cache_size, "cache_size",
 	    "Cache size in MB.", MS_NOT_AVAILABLE);
-	SG_ADD((CSGObject**) &lhs, "lhs",
-      "Feature vectors to occur on left hand side.", MS_NOT_AVAILABLE);
-	SG_ADD((CSGObject**) &rhs, "rhs",
-      "Feature vectors to occur on right hand side.", MS_NOT_AVAILABLE);
+
+//	SG_ADD((CSGObject**) &lhs, "lhs",
+//      "Feature vectors to occur on left hand side.", MS_NOT_AVAILABLE);
+	m_parameters->add((CSGObject**)&lhs, "lhs", "Feature vectors to occur on left hand side.");
+	watch_param("lhs", &lhs, AnyParameterProperties("Feature vectors to occur on left hand side."));
+
+//	SG_ADD((CSGObject**) &rhs, "rhs",
+//      "Feature vectors to occur on right hand side.", MS_NOT_AVAILABLE);
+	m_parameters->add((CSGObject**)&rhs, "rhs", "Feature vectors to occur on right hand side.");
+	watch_param("rhs", &rhs, AnyParameterProperties("Feature vectors to occur on right hand side."));
+
 	SG_ADD(&lhs_equals_rhs, "lhs_equals_rhs",
 	    "If features on lhs are the same as on rhs.", MS_NOT_AVAILABLE);
 	SG_ADD(&num_lhs, "num_lhs", "Number of feature vectors on left hand side.",

src/shogun/machine/Machine.cpp

@@ -24,8 +24,12 @@ CMachine::CMachine()
 	       "Maximum training time.", MS_NOT_AVAILABLE);
 	SG_ADD((machine_int_t*) &m_solver_type, "solver_type",
 	       "Type of solver.", MS_NOT_AVAILABLE);
-	SG_ADD((CSGObject**) &m_labels, "labels",
-	       "Labels to be used.", MS_NOT_AVAILABLE);
+
+//	SG_ADD((CSGObject**) &m_labels, "labels",
+//	       "Labels to be used.", MS_NOT_AVAILABLE);
+	m_parameters->add((CSGObject**)&m_labels, "labels", "Labels to be used.");
+	watch_param("labels", &m_labels, AnyParameterProperties("Labels to be used."));
+
 	SG_ADD(&m_store_model_features, "store_model_features",
 	       "Should feature data of model be stored after training?", MS_NOT_AVAILABLE);
 	SG_ADD(&m_data_locked, "data_locked",