GP regression memory issues fixes

shogun-toolbox · Sep 2, 2012 · 684cd3a · 684cd3a
1 parent 7e780c2
commit 684cd3a
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Showing 7 changed files with 20 additions and 17 deletions.
diff --git a/examples/undocumented/libshogun/regression_gaussian_process_ard.cpp b/examples/undocumented/libshogun/regression_gaussian_process_ard.cpp
@@ -219,7 +219,6 @@ int main(int argc, char **argv)
 	SG_UNREF(grad_search);
 	SG_UNREF(best_combination);
 	SG_UNREF(result);
-	SG_UNREF(mean);
 
 	exit_shogun();
 

diff --git a/examples/undocumented/libshogun/regression_gaussian_process_fitc.cpp b/examples/undocumented/libshogun/regression_gaussian_process_fitc.cpp
@@ -115,9 +115,6 @@ int main(int argc, char **argv)
 	CDenseFeatures<float64_t>* features2=new CDenseFeatures<float64_t> ();
 	features2->set_feature_matrix(matrix2);
 
-	SG_REF(features);
-	SG_REF(features2);
-
 	SG_REF(labels);
 
 	/*Allocate our Kernel*/
@@ -127,14 +124,17 @@ int main(int argc, char **argv)
 
 	/*Allocate our mean function*/
 	CZeroMean* mean = new CZeroMean();
-	
+
 	/*Allocate our likelihood function*/
 	CGaussianLikelihood* lik = new CGaussianLikelihood();
+
+	//SG_SPRINT("features2 bef inf rc= %d\n",features2->ref_count());
 
 	/*Allocate our inference method*/
 	CFITCInferenceMethod* inf =
 			new CFITCInferenceMethod(test_kernel, 
 						  features, mean, labels, lik, features2);
+	//SG_SPRINT("features2 aft inf rc= %d\n",features2->ref_count());
 
 	SG_REF(inf);
 
@@ -191,11 +191,10 @@ int main(int argc, char **argv)
 	SGVector<float64_t> diagonal = inf->get_diagonal_vector();
 	SGMatrix<float64_t> cholesky = inf->get_cholesky();
 	gp->set_return_type(CGaussianProcessRegression::GP_RETURN_COV);
-
 	CRegressionLabels* covariance = gp->apply_regression(features);
 
 	gp->set_return_type(CGaussianProcessRegression::GP_RETURN_MEANS);
-	
+
 	CRegressionLabels* predictions = gp->apply_regression();
 
 	alpha.display_vector("Alpha Vector");

diff --git a/examples/undocumented/libshogun/regression_gaussian_process_gaussian.cpp b/examples/undocumented/libshogun/regression_gaussian_process_gaussian.cpp
@@ -218,7 +218,6 @@ int main(int argc, char **argv)
 	SG_UNREF(grad_search);
 	SG_UNREF(best_combination);
 	SG_UNREF(result);
-	SG_UNREF(mean);
 
 	exit_shogun();
 

diff --git a/src/shogun/lib/Map.h b/src/shogun/lib/Map.h
@@ -439,8 +439,8 @@ IGNORE_IN_CLASSLIST template<class K, class T> class CMap: public CSGObject
 				CMapNode<K, T>* element = array->get_element(i);
 				if (element!=NULL)
 				{
-					//element->key.~K();
-					//element->data.~T();
+					element->key.~K();
+					element->data.~T();
 
 					if (use_sg_mallocs)
 						SG_FREE(element);

diff --git a/src/shogun/regression/GaussianProcessRegression.cpp b/src/shogun/regression/GaussianProcessRegression.cpp
@@ -237,6 +237,7 @@ SGVector<float64_t> CGaussianProcessRegression::get_mean_vector()
 	result_vector = lik->evaluate_means(result_vector);
 
 	SG_UNREF(lik);
+	SG_UNREF(mean_function);
 
 	return result_vector;
 }

diff --git a/src/shogun/regression/gp/FITCInferenceMethod.cpp b/src/shogun/regression/gp/FITCInferenceMethod.cpp
@@ -319,6 +319,8 @@ get_marginal_likelihood_derivatives(CMap<TParameter*,
 				m_kernel->init(m_latent_features, m_latent_features);
 				derivuu = m_kernel->get_parameter_gradient(param, obj);
 
+				m_kernel->remove_lhs_and_rhs();
+
 				mean_derivatives = m_mean->get_parameter_derivative(
 						param, obj, m_feature_matrix, g);
 
@@ -342,6 +344,8 @@ get_marginal_likelihood_derivatives(CMap<TParameter*,
 
 				m_kernel->init(m_latent_features, m_latent_features);
 				derivuu = m_kernel->get_parameter_gradient(param, obj);
+
+				m_kernel->remove_lhs_and_rhs();
 			}
 
 			sum[0] = 0;
@@ -440,6 +444,8 @@ get_marginal_likelihood_derivatives(CMap<TParameter*,
 		m_kernel->init(m_latent_features, m_latent_features);
 		derivuu = m_kernel->get_kernel_matrix();
 
+		m_kernel->remove_lhs_and_rhs();
+
 		MatrixXd ddiagKi(deriv.num_cols, deriv.num_rows);
 		MatrixXd dKuui(derivuu.num_cols, derivuu.num_rows);
 		MatrixXd dKui(derivtru.num_cols, derivtru.num_rows);
@@ -620,17 +626,13 @@ SGMatrix<float64_t> CFITCInferenceMethod::get_cholesky()
 
 void CFITCInferenceMethod::update_train_kernel()
 {
-	m_kernel->cleanup();
-
 	m_kernel->init(m_features, m_features);
 
 	//K(X, X)
 	SGMatrix<float64_t> kernel_matrix = m_kernel->get_kernel_matrix();
 
 	m_ktrtr=kernel_matrix.clone();
 
-	m_kernel->cleanup();
-
 	m_kernel->init(m_latent_features, m_latent_features);
 
 	//K(X, X)
@@ -643,12 +645,12 @@ void CFITCInferenceMethod::update_train_kernel()
 			m_kuu(i,j) = kernel_matrix(i,j)*m_scale*m_scale;
 	}
 
-	m_kernel->cleanup();
-
 	m_kernel->init(m_latent_features, m_features);
 
 	kernel_matrix = m_kernel->get_kernel_matrix();
 
+	m_kernel->remove_lhs_and_rhs();
+
 	m_ktru = SGMatrix<float64_t>(kernel_matrix.num_rows, kernel_matrix.num_cols);
 
 	for (index_t i = 0; i < kernel_matrix.num_rows; i++)

diff --git a/src/shogun/regression/gp/InferenceMethod.cpp b/src/shogun/regression/gp/InferenceMethod.cpp
@@ -19,6 +19,8 @@ using namespace shogun;
 
 CInferenceMethod::CInferenceMethod()
 {
+	init();
+
 	m_kernel = NULL;
 	m_model = NULL;
 	m_labels = NULL;
@@ -39,7 +41,8 @@ CInferenceMethod::CInferenceMethod(CKernel* kern, CFeatures* feat,
 	set_mean(m);
 }
 
-CInferenceMethod::~CInferenceMethod() {
+CInferenceMethod::~CInferenceMethod() 
+{
 	SG_UNREF(m_kernel);
 	SG_UNREF(m_features);
 	SG_UNREF(m_latent_features);