remove destroy / free vectro functions

shogun-toolbox · May 3, 2012 · 9d458c8 · 9d458c8
1 parent 52b1bf0
commit 9d458c8
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Showing 29 changed files with 56 additions and 132 deletions.
diff --git a/src/shogun/classifier/svm/GPBTSVM.cpp b/src/shogun/classifier/svm/GPBTSVM.cpp
@@ -125,7 +125,6 @@ bool CGPBTSVM::train_machine(CFeatures* data)
 	}
 
 	delete prob.KER;
-	lab.free_vector();
 	SG_FREE(solution);
 
 	return true;

diff --git a/src/shogun/classifier/svm/LibLinear.cpp b/src/shogun/classifier/svm/LibLinear.cpp
@@ -72,7 +72,6 @@ void CLibLinear::init()
 
 CLibLinear::~CLibLinear()
 {
-	m_linear_term.destroy_vector();
 }
 
 bool CLibLinear::train_machine(CFeatures* data)
@@ -1133,6 +1132,23 @@ void CLibLinear::solve_l1r_lr(
 	delete [] xjpos_sum;
 }
 
+void CLibLinear::set_linear_term(const SGVector<float64_t> linear_term)
+{
+	if (!m_labels)
+		SG_ERROR("Please assign labels first!\n");
+
+	int32_t num_labels=m_labels->get_num_labels();
+
+	if (num_labels!=linear_term.vlen)
+	{
+		SG_ERROR("Number of labels (%d) does not match number"
+				" of entries (%d) in linear term \n", num_labels,
+				linear_term.vlen);
+	}
+
+	m_linear_term=linear_term;
+}
+
 SGVector<float64_t> CLibLinear::get_linear_term()
 {
 	if (!m_linear_term.vlen || !m_linear_term.vector)
@@ -1146,8 +1162,6 @@ void CLibLinear::init_linear_term()
 	if (!m_labels)
 		SG_ERROR("Please assign labels first!\n");
 
-	m_linear_term.destroy_vector();
-
 	m_linear_term=SGVector<float64_t>(m_labels->get_num_labels());
 	CMath::fill_vector(m_linear_term.vector, m_linear_term.vlen, -1.0);
 }

diff --git a/src/shogun/classifier/svm/LibLinear.h b/src/shogun/classifier/svm/LibLinear.h
@@ -142,25 +142,7 @@ class CLibLinear : public CLinearMachine
 		}
 
 		/** set the linear term for qp */
-		inline void set_linear_term(const SGVector<float64_t>& linear_term)
-		{
-			if (!m_labels)
-				SG_ERROR("Please assign labels first!\n");
-
-			int32_t num_labels=m_labels->get_num_labels();
-
-			if (num_labels!=linear_term.vlen)
-			{
-				SG_ERROR("Number of labels (%d) does not match number"
-						" of entries (%d) in linear term \n", num_labels,
-						linear_term.vlen);
-			}
-
-			m_linear_term.destroy_vector();
-			m_linear_term.vector=CMath::clone_vector(linear_term.vector,
-				linear_term.vlen);
-			m_linear_term.vlen=linear_term.vlen;
-		}
+		void set_linear_term(const SGVector<float64_t> linear_term);
 
 		/** get the linear term for qp */
 		SGVector<float64_t> get_linear_term();

diff --git a/src/shogun/classifier/svm/SVMLight.cpp b/src/shogun/classifier/svm/SVMLight.cpp
@@ -301,7 +301,6 @@ void CSVMLight::svm_learn()
 	int32_t totdoc=lab.vlen;
 	ASSERT(lab.vector && lab.vlen);
 	int32_t* label=CMath::clone_vector(lab.vector, lab.vlen);
-	lab.free_vector();
 
 	int32_t* docs=SG_MALLOC(int32_t, totdoc);
 	SG_FREE(W);

diff --git a/src/shogun/converter/MultidimensionalScaling.cpp b/src/shogun/converter/MultidimensionalScaling.cpp
@@ -78,7 +78,6 @@ void CMultidimensionalScaling::init()
 
 CMultidimensionalScaling::~CMultidimensionalScaling()
 {
-	m_eigenvalues.destroy_vector();
 }
 
 SGVector<float64_t> CMultidimensionalScaling::get_eigenvalues() const
@@ -222,8 +221,7 @@ SGMatrix<float64_t> CMultidimensionalScaling::classic_embedding(SGMatrix<float64
 	}
 
 	// set eigenvalues vector
-	m_eigenvalues.destroy_vector();
-	m_eigenvalues = SGVector<float64_t>(eigenvalues_vector,m_target_dim,true);
+	m_eigenvalues = SGVector<float64_t>(eigenvalues_vector,m_target_dim);
 #else /* not HAVE_ARPACK */
 	// using LAPACK
 	float64_t* eigenvalues_vector = SG_MALLOC(float64_t, N);
@@ -234,16 +232,12 @@ SGMatrix<float64_t> CMultidimensionalScaling::classic_embedding(SGMatrix<float64
 	ASSERT(eigenproblem_status==0);
 
 	// set eigenvalues vector
-	m_eigenvalues.destroy_vector();
 	m_eigenvalues = SGVector<float64_t>(m_target_dim);
-	m_eigenvalues.do_free = false;
 
 	// fill eigenvalues vector in backwards order
 	for (i=0; i<m_target_dim; i++)
 		m_eigenvalues.vector[i] = eigenvalues_vector[m_target_dim-i-1];
 
-	SG_FREE(eigenvalues_vector);
-
 	// construct embedding
 	for (i=0; i<m_target_dim; i++)
 	{
@@ -399,10 +393,8 @@ SGMatrix<float64_t> CMultidimensionalScaling::landmark_embedding(SGMatrix<float6
 	// cleanup
 	lmk_feature_matrix.destroy_matrix();
 	SG_FREE(current_dist_to_lmks);
-	lmk_idxs.destroy_vector();
 	SG_FREE(mean_sq_dist_vector);
 	SG_FREE(to_process);
-	lmk_idxs.destroy_vector();
 
 	return SGMatrix<float64_t>(new_feature_matrix,m_target_dim,total_N);
 }

diff --git a/src/shogun/converter/StochasticProximityEmbedding.cpp b/src/shogun/converter/StochasticProximityEmbedding.cpp
@@ -352,15 +352,11 @@ CSimpleFeatures< float64_t >* CStochasticProximityEmbedding::embed_distance(CDis
 	}
 
 	// Free memory
-	scale.destroy_vector();
-	D.destroy_vector();
 	Yd.destroy_matrix();
-	Rt.destroy_vector();
 	if ( m_strategy == SPE_LOCAL )
 	{
 		ind1Neighbors.destroy_matrix();
 		neighbors_mat.destroy_matrix();
-		J2.destroy_vector();
 		delete[] ind2;
 	}
 

diff --git a/src/shogun/distributions/Gaussian.cpp b/src/shogun/distributions/Gaussian.cpp
@@ -59,9 +59,7 @@ void CGaussian::init()
 
 CGaussian::~CGaussian()
 {
-	m_d.destroy_vector();
 	m_u.destroy_matrix();
-	m_mean.free_vector();
 }
 
 bool CGaussian::train(CFeatures* data)
@@ -75,8 +73,6 @@ bool CGaussian::train(CFeatures* data)
 	}
 	CDotFeatures* dotdata=(CDotFeatures *) data;
 
-	m_mean.destroy_vector();
-
 	m_mean=dotdata->get_mean();
 	SGMatrix<float64_t> cov=dotdata->get_cov();
 
@@ -119,7 +115,6 @@ float64_t CGaussian::get_log_likelihood_example(int32_t num_example)
 	ASSERT(features->has_property(FP_DOT));
 	SGVector<float64_t> v=((CDotFeatures *)features)->get_computed_dot_feature_vector(num_example);
 	float64_t answer=compute_log_PDF(v);
-	v.free_vector();
 	return answer;
 }
 
@@ -162,6 +157,35 @@ float64_t CGaussian::compute_log_PDF(const SGVector<float64_t>& point)
 	return -0.5*answer;
 }
 
+SGVector<float64_t> CGaussian::get_mean()
+{
+	return m_mean;
+}
+
+void CGaussian::set_mean(const SGVector<float64_t> mean)
+{
+	if (mean.vlen==1)
+		m_cov_type=SPHERICAL;
+
+	m_mean=mean;
+}
+
+void CGaussian::set_cov(SGMatrix<float64_t> cov)
+{
+	ASSERT(cov.num_rows==cov.num_cols);
+	ASSERT(cov.num_rows==m_mean.vlen);
+	decompose_cov(cov);
+	init();
+	if (cov.do_free)
+		cov.free_matrix();
+}
+
+void CGaussian::set_d(const SGVector<float64_t> d)
+{
+	m_d = d;
+	init();
+}
+
 SGMatrix<float64_t> CGaussian::get_cov()
 {
 	float64_t* cov=SG_MALLOC(float64_t, m_mean.vlen*m_mean.vlen);
@@ -212,7 +236,6 @@ void CGaussian::register_params()
 
 void CGaussian::decompose_cov(SGMatrix<float64_t> cov)
 {
-	m_d.destroy_vector();
 	switch (m_cov_type)
 	{
 		case FULL:

diff --git a/src/shogun/distributions/Gaussian.h b/src/shogun/distributions/Gaussian.h
@@ -120,23 +120,13 @@ class CGaussian : public CDistribution
 		 *
 		 * @return mean
 		 */
-		virtual inline SGVector<float64_t> get_mean()
-		{
-			return m_mean;
-		}
+		virtual SGVector<float64_t> get_mean();
 
 		/** set mean
 		 *
 		 * @param mean new mean
 		 */
-		virtual inline void set_mean(const SGVector<float64_t>& mean)
-		{
-			m_mean.destroy_vector();
-			if (mean.vlen==1)
-				m_cov_type=SPHERICAL;
-
-			m_mean=mean;
-		}
+		virtual void set_mean(const SGVector<float64_t> mean);
 
 		/** get covariance
 		 *
@@ -150,15 +140,7 @@ class CGaussian : public CDistribution
 		 *
 		 * @param cov new covariance
 		 */
-		virtual inline void set_cov(SGMatrix<float64_t> cov)
-		{
-			ASSERT(cov.num_rows==cov.num_cols);
-			ASSERT(cov.num_rows==m_mean.vlen);
-			decompose_cov(cov);
-			init();
-			if (cov.do_free)
-				cov.free_matrix();
-		}
+		virtual void set_cov(SGMatrix<float64_t> cov);
 
 		/** get covariance type
 		 *
@@ -193,12 +175,7 @@ class CGaussian : public CDistribution
 		 *
 		 * @param d new diagonal
 		 */
-		inline void set_d(const SGVector<float64_t>& d)
-		{
-			m_d.destroy_vector();
-			m_d = d;
-			init();
-		}
+		void set_d(const SGVector<float64_t> d);
 
 		/** get unitary matrix
 		 *

diff --git a/src/shogun/evaluation/ClusteringEvaluation.cpp b/src/shogun/evaluation/ClusteringEvaluation.cpp
@@ -78,7 +78,5 @@ void CClusteringEvaluation::best_map(CLabels* predicted, CLabels* ground_truth)
 	for (int32_t i= 0; i < predicted_ilabels.vlen; ++i)
 		predicted->set_int_label(i, label_map[predicted_ilabels[i]]);
 
-	label_p.free_vector();
-	label_g.free_vector();
 	G.destroy_matrix();
 }
diff --git a/src/shogun/evaluation/ClusteringMutualInformation.cpp b/src/shogun/evaluation/ClusteringMutualInformation.cpp
@@ -65,8 +65,6 @@ float64_t CClusteringMutualInformation::evaluate(CLabels* predicted, CLabels* gr
 		entropy_p += -G_colsum[i] * log(G_colsum[i])/log(2.);
 	}
 
-	label_p.free_vector();
-	label_g.free_vector();
 	G.destroy_matrix();
 
 	return mutual_info / std::max(entropy_g, entropy_p);

diff --git a/src/shogun/evaluation/CrossValidation.cpp b/src/shogun/evaluation/CrossValidation.cpp
@@ -259,8 +259,6 @@ float64_t CCrossValidation::evaluate_one_run()
 
 			/* clean up */
 			SG_UNREF(result_labels);
-			inverse_subset_indices.destroy_vector();
-			subset_indices.destroy_vector();
 		}
 	}
 	else
@@ -304,8 +302,6 @@ float64_t CCrossValidation::evaluate_one_run()
 			/* clean up, remove subsets */
 			SG_UNREF(result_labels);
 			m_labels->remove_subset();
-			inverse_subset_indices.destroy_vector();
-			subset_indices.destroy_vector();
 		}
 	}
 

diff --git a/src/shogun/evaluation/CrossValidationSplitting.cpp b/src/shogun/evaluation/CrossValidationSplitting.cpp
@@ -64,7 +64,4 @@ void CCrossValidationSplitting::build_subsets()
 	 * elements, which happens if the number of class labels is not equal to
 	 * the number of subsets */
 	m_subset_indices->shuffle();
-
-	/* clean up */
-	indices.destroy_vector();
 }
diff --git a/src/shogun/evaluation/PRCEvaluation.cpp b/src/shogun/evaluation/PRCEvaluation.cpp
@@ -35,7 +35,6 @@ float64_t CPRCEvaluation::evaluate(CLabels* predicted, CLabels* ground_truth)
 	SGVector<float64_t> orig_labels = predicted->get_labels();
 	int32_t length = orig_labels.vlen;
 	float64_t* labels = CMath::clone_vector(orig_labels.vector, length);
-	orig_labels.free_vector();
 
 	// get indexes for sort
 	int32_t* idxs = SG_MALLOC(int32_t, length);

diff --git a/src/shogun/evaluation/ROCEvaluation.cpp b/src/shogun/evaluation/ROCEvaluation.cpp
@@ -40,7 +40,6 @@ float64_t CROCEvaluation::evaluate(CLabels* predicted, CLabels* ground_truth)
 	SGVector<float64_t> orig_labels = predicted->get_labels();
 	int32_t length = orig_labels.vlen;
 	float64_t* labels = CMath::clone_vector(orig_labels.vector, length);
-	orig_labels.free_vector();
 
 	// get sorted indexes
 	int32_t* idxs = SG_MALLOC(int32_t, length);

diff --git a/src/shogun/evaluation/StratifiedCrossValidationSplitting.cpp b/src/shogun/evaluation/StratifiedCrossValidationSplitting.cpp
@@ -49,9 +49,6 @@ CStratifiedCrossValidationSplitting::CStratifiedCrossValidationSplitting(
 					"subset!\n", labels_per_class.vector[i], classes.vector[i], num_subsets);
 		}
 	}
-
-	labels_per_class.destroy_vector();
-	classes.destroy_vector();
 }
 
 void CStratifiedCrossValidationSplitting::build_subsets()
@@ -116,7 +113,4 @@ void CStratifiedCrossValidationSplitting::build_subsets()
 	 * elements, which happens if the number of class labels is not equal to
 	 * the number of subsets */
 	m_subset_indices->shuffle();
-
-	/* clean up */
-	unique_labels.destroy_vector();
 }
diff --git a/src/shogun/lib/SGVector.h b/src/shogun/lib/SGVector.h
@@ -25,7 +25,7 @@ template<class T> class SGVector
 		SGVector(T* v, index_t len, bool ref_counting=true)
 			: vector(v), vlen(len), m_refcount(NULL)
 		{
-			if(ref_counting)
+			if (ref_counting)
 				m_refcount=SG_CALLOC(int32_t, 1); 
 		}
 
@@ -263,18 +263,6 @@ template<class T> class SGVector
 			return vector[index];
 		}
 
-		/** free vector */
-		virtual void free_vector()
-		{
-			unref();
-		}
-
-		/** destroy vector */
-		virtual void destroy_vector()
-		{
-			free_vector();
-		}
-
 		/** display array size */
 		void display_size() const
 		{

diff --git a/src/shogun/machine/KernelMachine.cpp b/src/shogun/machine/KernelMachine.cpp
@@ -191,8 +191,8 @@ SGVector<float64_t> CKernelMachine::get_alphas()
 
 bool CKernelMachine::create_new_model(int32_t num)
 {
-    m_alpha.destroy_vector();
-    m_svs.destroy_vector();
+    m_alpha.unref();
+    m_svs.unref();
 
     m_bias=0;