use get_prng and move to C-11

benchmarks/hasheddoc_benchmarks.cpp

@@ -26,13 +26,14 @@ int main(int argv, char** argc)
 	int32_t num_strings = 5000;
 	int32_t max_str_length = 10000;
 	SGStringList<char> string_list(num_strings, max_str_length);
-	auto m_rng = std::unique_ptr<CRandom>(new CRandom());
+	auto prng = get_prng();
+	std::uniform_int_distribution<index_t> dist('A', 'Z');
 	SG_SPRINT("Creating features...\n");
 	for (index_t i=0; i<num_strings; i++)
 	{
 		string_list.strings[i] = SGString<char>(max_str_length);
 		for (index_t j=0; j<max_str_length; j++)
-			string_list.strings[i].string[j] = (char)m_rng->random('A', 'Z');
+			string_list.strings[i].string[j] = (char)dist(prng);
 	}
 	SG_SPRINT("Features were created.\n");
 

benchmarks/rf_feats_benchmark.cpp

@@ -16,7 +16,8 @@ int main(int argv, char** argc)
 
 	int32_t dims[] = {100, 300, 600};
 	CTime* timer = new CTime();
-	auto m_rng = std::unique_ptr<CRandom>(new CRandom());
+	auto prng = get_prng();
+	std::uniform_int_distribution<int32_t> dist(0, 1);
 	for (index_t d=0; d<3; d++)
 	{
 		int32_t num_dim = dims[d];
@@ -28,7 +29,7 @@ int main(int argv, char** argc)
 		{
 			for (index_t j=0; j<num_dim; j++)
 			{
-				mat(j, i) = m_rng->random(0, 1) + 0.5;
+				mat(j, i) = dist(prng) + 0.5;
 			}
 		}
 

benchmarks/rf_feats_kernel_comp.cpp

@@ -29,7 +29,8 @@ int main(int argv, char** argc)
 	float64_t lin_C = 0.1;
 	float64_t non_lin_C = 0.1;
 	CPRCEvaluation* evaluator = new CPRCEvaluation();
-	auto m_rng = std::unique_ptr<CRandom>(new CRandom());
+	auto prng = get_prng();
+	std::uniform_int_distribution<int32_t> dist(0, 1);
 	CSqrtDiagKernelNormalizer* normalizer = new CSqrtDiagKernelNormalizer(true);
 	SG_REF(normalizer);
 	for (index_t d=0; d<4; d++)
@@ -49,12 +50,12 @@ int main(int argv, char** argc)
 					if ((i+j)%2==0)
 					{
 						labs[i] = -1;
-						mat(j, i) = m_rng->random(0, 1) + 0.5;
+						mat(j, i) = dist(prng) + 0.5;
 					}
 					else
 					{
 						labs[i] = 1;
-						mat(j, i) = m_rng->random(0, 1) - 0.5;
+						mat(j, i) = dist(prng) - 0.5;
 					}
 				}
 			}

examples/undocumented/libshogun/classifier_larank.cpp

@@ -27,14 +27,15 @@ void test()
 	SGMatrix<float64_t> matrix_test(num_class, num_vec);
 	CMulticlassLabels* labels=new CMulticlassLabels(num_vec);
 	CMulticlassLabels* labels_test=new CMulticlassLabels(num_vec);
-	auto m_rng = std::unique_ptr<CRandom>(new CRandom());
+	auto prng = get_prng();
+	std::normal_distribution<float64_t> dist(0, 1);
 	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) = m_rng->std_normal_distrib();
-			matrix_test(j, i) = m_rng->std_normal_distrib();
+			matrix(j, i) = dist(prng);
+			matrix_test(j, i) = dist(prng);
 			labels->set_label(i, label);
 			labels_test->set_label(i, label);
 		}

examples/undocumented/libshogun/classifier_latent_svm.cpp

@@ -110,7 +110,7 @@ static void read_dataset(char* fname, CLatentFeatures*& feats, CLatentLabels*& l
 	SG_REF(labels);
 
 	CBinaryLabels* ys = new CBinaryLabels(num_examples);
-	auto m_rng = std::unique_ptr<CRandom>(new CRandom());
+	auto prng = get_prng();
 	feats = new CLatentFeatures(num_examples);
 	SG_REF(feats);
 
@@ -144,10 +144,11 @@ static void read_dataset(char* fname, CLatentFeatures*& feats, CLatentLabels*& l
 		while ((*pchar)!='\n') pchar++;
 		*pchar = '\0';
 		height = atoi(last_pchar);
-
+		std::uniform_int_distribution<index_t> dist_w(0, width - 1);
+		std::uniform_int_distribution<index_t> dist_h(0, height - 1);
 		/* create latent label */
-		int x = m_rng->random(0, width - 1);
-		int y = m_rng->random(0, height - 1);
+		int x = dist_w(prng);
+		int y = dist_h(prng);
 		CBoundingBox* bb = new CBoundingBox(x,y);
 		labels->add_latent_label(bb);
 

examples/undocumented/libshogun/classifier_libsvm_probabilities.cpp

@@ -10,7 +10,8 @@ using namespace shogun;
 //generates data points (of different classes) randomly
 void gen_rand_data(SGMatrix<float64_t> features, SGVector<float64_t> labels, float64_t distance)
 {
-	auto m_rng = std::unique_ptr<CRandom>(new CRandom());
+	auto prng = get_prng();
+	std::uniform_real_distribution<float64_t> dist(0, 1.0);
 	index_t num_samples = labels.vlen;
 	index_t dimensions = features.num_rows;
 	for (int32_t i = 0; i < num_samples; i++)
@@ -19,13 +20,13 @@ void gen_rand_data(SGMatrix<float64_t> features, SGVector<float64_t> labels, flo
 		{
 			labels[i] = -1.0;
 			for (int32_t j = 0; j < dimensions; j++)
-				features(j, i) = m_rng->random(0.0, 1.0) + distance;
+				features(j, i) = dist(prng) + distance;
 		}
 		else
 		{
 			labels[i] = 1.0;
 			for (int32_t j = 0; j < dimensions; j++)
-				features(j, i) = m_rng->random(0.0, 1.0) - distance;
+				features(j, i) = dist(prng) - distance;
 		}
 	}
 	labels.display_vector("labels");

examples/undocumented/libshogun/classifier_mkl_svmlight_modelselection_bug.cpp

@@ -68,9 +68,10 @@ void test()
 	/* create some data and labels */
 	SGMatrix<float64_t> matrix(dim_vectors, num_vectors);
 	CBinaryLabels* labels=new CBinaryLabels(num_vectors);
-	auto m_rng = std::unique_ptr<CRandom>(new CRandom());
+	auto prng = get_prng();
+	std::normal_distribution<float64_t> dist(0, 1);
 	for (int32_t i=0; i<num_vectors*dim_vectors; i++)
-		matrix.matrix[i] = m_rng->std_normal_distrib();
+		matrix.matrix[i] = dist(prng);
 
 	/* create num_feautres 2-dimensional vectors */
 	CDenseFeatures<float64_t>* features=new CDenseFeatures<float64_t>();

examples/undocumented/libshogun/classifier_svmlight_string_features_precomputed_kernel.cpp

@@ -30,24 +30,26 @@ void test_svmlight()
 	float64_t p_x=0.5; // probability for class A
 	float64_t mostly_prob=0.8;
 	CDenseLabels* labels=new CBinaryLabels(num_train+num_test);
-	auto m_rng = std::unique_ptr<CRandom>(new CRandom(17));
+	auto prng = get_prng();
+	std::uniform_real_distribution<float64_t> dist_real(0.0, 1.0);
+	std::uniform_int_distribution<index_t> dist_int(1, max_length);
 
 	SGStringList<char> data(num_train+num_test, max_length);
 	for (index_t i=0; i<num_train+num_test; ++i)
 	{
 		/* determine length */
-		index_t length = m_rng->random(1, max_length);
+		index_t length = dist_int(prng);
 
 		/* allocate string */
 		data.strings[i]=SGString<char>(length);
 
 		/* fill with elements and set label */
-		if (p_x < m_rng->random(0.0, 1.0))
+		if (p_x < dist_real(prng))
 		{
 			labels->set_label(i, 1);
 			for (index_t j=0; j<length; ++j)
 			{
-				char c = mostly_prob < m_rng->random(0.0, 1.0) ? '0' : '1';
+				char c = mostly_prob < dist_real(prng) ? '0' : '1';
 				data.strings[i].string[j]=c;
 			}
 		}
@@ -56,7 +58,7 @@ void test_svmlight()
 			labels->set_label(i, -1);
 			for (index_t j=0; j<length; ++j)
 			{
-				char c = mostly_prob < m_rng->random(0.0, 1.0) ? '1' : '0';
+				char c = mostly_prob < dist_real(prng) ? '1' : '0';
 				data.strings[i].string[j]=c;
 			}
 		}

examples/undocumented/libshogun/clustering_kmeans.cpp

@@ -39,7 +39,7 @@ int main(int argc, char **argv)
 	int32_t dim_features=3;
 	int32_t num_vectors_per_cluster=5;
 	float64_t cluster_std_dev=2.0;
-	auto m_rng = std::unique_ptr<CRandom>(new CRandom());
+	auto prng = get_prng();
 
 	/* build random cluster centers */
 	SGMatrix<float64_t> cluster_centers(dim_features, num_clusters);
@@ -60,7 +60,9 @@ int main(int argc, char **argv)
 				idx+=j;
 				idx+=k*dim_features;
 				float64_t entry=cluster_centers.matrix[i*dim_features+j];
-				data.matrix[idx] = m_rng->normal_random(entry, cluster_std_dev);
+				std::normal_distribution<float64_t> dist(
+				    entry, cluster_std_dev);
+				data.matrix[idx] = dist(prng);
 			}
 		}
 	}

examples/undocumented/libshogun/converter_jade_bss.cpp

@@ -32,7 +32,8 @@ using namespace Eigen;
 void test()
 {
 	// Generate sample data
-	auto m_rng = std::unique_ptr<CRandom>(new CRandom(0));
+	auto prng = get_prng();
+	std::normal_distribution<float64_t> dist(0, 1);
 	int n_samples = 2000;
 	VectorXd time(n_samples, true);
 	time.setLinSpaced(n_samples,0,10);
@@ -43,11 +44,11 @@ void test()
 	{
 		// Sin wave
 		S(0,i) = sin(2*time[i]);
-		S(0, i) += 0.2 * m_rng->std_normal_distrib();
+		S(0, i) += 0.2 * dist(prng);
 
 		// Square wave
 		S(1,i) = sin(3*time[i]) < 0 ? -1 : 1;
-		S(1, i) += 0.2 * m_rng->std_normal_distrib();
+		S(1, i) += 0.2 * dist(prng);
 	}
 
 	// Standardize data

examples/undocumented/libshogun/evaluation_cross_validation_classification.cpp

@@ -29,7 +29,7 @@ void test_cross_validation()
 	/* data matrix dimensions */
 	index_t num_vectors=40;
 	index_t num_features=5;
-	auto m_rng = std::unique_ptr<CRandom>(new CRandom());
+	auto prng = get_prng();
 	/* data means -1, 1 in all components, std deviation of 3 */
 	SGVector<float64_t> mean_1(num_features);
 	SGVector<float64_t> mean_2(num_features);
@@ -47,8 +47,8 @@ void test_cross_validation()
 		for (index_t j=0; j<num_features; ++j)
 		{
 			float64_t mean=i<num_vectors/2 ? mean_1.vector[0] : mean_2.vector[0];
-			train_dat.matrix[i * num_features + j] =
-			    m_rng->normal_random(mean, sigma);
+			std::normal_distribution<float64_t> dist(mean, sigma);
+			train_dat.matrix[i * num_features + j] = dist(prng);
 		}
 	}
 

examples/undocumented/libshogun/evaluation_cross_validation_locked_comparison.cpp

@@ -37,7 +37,8 @@ void test_cross_validation()
 	SGVector<float64_t>::fill_vector(mean_1.vector, mean_1.vlen, -1.0);
 	SGVector<float64_t>::fill_vector(mean_2.vector, mean_2.vlen, 1.0);
 	float64_t sigma=1.5;
-	auto m_rng = std::unique_ptr<CRandom>(new CRandom());
+	auto prng = get_prng();
+	std::normal_distribution<float64_t> dist(0, 1);
 
 	/* fill data matrix around mean */
 	SGMatrix<float64_t> train_dat(num_features, num_vectors);
@@ -46,8 +47,7 @@ void test_cross_validation()
 		for (index_t j=0; j<num_features; ++j)
 		{
 			float64_t mean=i<num_vectors/2 ? mean_1.vector[0] : mean_2.vector[0];
-			train_dat.matrix[i * num_features + j] =
-			    m_rng->normal_random(mean, sigma);
+			train_dat.matrix[i * num_features + j] = dist(prng);
 		}
 	}
 

examples/undocumented/libshogun/evaluation_cross_validation_mkl_weight_storage.cpp

@@ -28,22 +28,23 @@ void gen_rand_data(SGVector<float64_t> lab, SGMatrix<float64_t> feat,
 {
 	index_t dims=feat.num_rows;
 	index_t num=lab.vlen;
-	auto m_rng = std::unique_ptr<CRandom>(new CRandom());
+	auto prng = get_prng();
+	std::uniform_real_distribution<float64_t> uniform_dist(0.0, 1.0);
 	for (int32_t i=0; i<num; i++)
 	{
 		if (i<num/2)
 		{
 			lab[i]=-1.0;
 
 			for (int32_t j=0; j<dims; j++)
-				feat(j, i) = m_rng->random(0.0, 1.0) + dist;
+				feat(j, i) = uniform_dist(prng) + dist;
 		}
 		else
 		{
 			lab[i]=1.0;
 
 			for (int32_t j=0; j<dims; j++)
-				feat(j, i) = m_rng->random(0.0, 1.0) - dist;
+				feat(j, i) = uniform_dist(prng) - dist;
 		}
 	}
 	lab.display_vector("lab");

examples/undocumented/libshogun/evaluation_cross_validation_regression.cpp

@@ -33,15 +33,16 @@ void test_cross_validation()
 
 	/* training label data */
 	SGVector<float64_t> lab(num_vectors);
-	auto m_rng = std::unique_ptr<CRandom>(new CRandom());
+	auto prng = get_prng();
+	std::uniform_real_distribution<float64_t> dist(0.0, 1.0);
 
 	/* fill data matrix and labels */
 	SGMatrix<float64_t> train_dat(num_features, num_vectors);
 	SGVector<float64_t>::range_fill_vector(train_dat.matrix, num_vectors);
 	for (index_t i=0; i<num_vectors; ++i)
 	{
 		/* labels are linear plus noise */
-		lab.vector[i] = i + m_rng->normal_random(0, 1.0);
+		lab.vector[i] = i + dist(prng);
 	}
 
 	/* training features */

examples/undocumented/libshogun/features_subset_labels.cpp

@@ -24,8 +24,9 @@ const int32_t num_classes=3;
 
 void test()
 {
-	auto m_rng = std::unique_ptr<CRandom>(new CRandom());
-	const int32_t num_subset_idx = m_rng->random(1, num_labels);
+	auto prng = get_prng();
+	std::uniform_int_distribution<index_t> dist(1, num_labels);
+	const int32_t num_subset_idx = dist(prng);
 
 	/* create labels */
 	CMulticlassLabels* labels=new CMulticlassLabels(num_labels);

examples/undocumented/libshogun/features_subset_simple_features.cpp

@@ -49,17 +49,19 @@ const int32_t dim_features=6;
 
 void test()
 {
-	auto m_rng = std::unique_ptr<CRandom>(new CRandom());
-	const int32_t num_subset_idx = m_rng->random(1, num_vectors);
+	auto prng = get_prng();
+	std::uniform_int_distribution<index_t> dist(1, num_vectors);
+	const int32_t num_subset_idx = dist(prng);
 
 	/* create feature data matrix */
 	SGMatrix<int32_t> data(dim_features, num_vectors);
 
 	/* fill matrix with random data */
+	std::uniform_int_distribution<index_t> dist_s(-5, 5);
 	for (index_t i=0; i<num_vectors; ++i)
 	{
 		for (index_t j=0; j<dim_features; ++j)
-			data.matrix[i * dim_features + j] = m_rng->random(-5, 5);
+			data.matrix[i * dim_features + j] = dist_s(prng);
 	}
 
 	/* create simple features */

examples/undocumented/libshogun/hashed_features_example.cpp

@@ -12,12 +12,13 @@ int main()
 
 	int32_t num_vectors = 5;
 	int32_t dim = 20;
-	auto m_rng = std::unique_ptr<CRandom>(new CRandom());
+	auto prng = get_prng();
+	std::uniform_int_distribution<index_t> dist(-dim, dim);
 	SGMatrix<int32_t> mat(dim, num_vectors);
 	for (index_t v=0; v<num_vectors; v++)
 	{
 		for (index_t d=0; d<dim; d++)
-			mat(d, v) = m_rng->random(-dim, dim);
+			mat(d, v) = dist(prng);
 	}
 
 	int32_t hashing_dim = 12;

examples/undocumented/libshogun/kernel_custom.cpp

@@ -33,11 +33,11 @@ void test_custom_kernel_subsets()
 
 	/* create a random permutation */
 	SGVector<index_t> subset(m);
-	auto prng = std::unique_ptr<CRandom>(new CRandom());
+	auto prng = get_prng();
 	for (index_t run=0; run<100; ++run)
 	{
 		subset.range_fill();
-		CMath::permute(subset, prng.get());
+		CMath::permute(subset, prng);
 		//		subset.display_vector("permutation");
 		features->add_subset(subset);
 		k->init(features, features);

examples/undocumented/libshogun/kernel_custom_kernel.cpp

@@ -31,11 +31,11 @@ void test_custom_kernel_subsets()
 
 	/* create a random permutation */
 	SGVector<index_t> subset(m);
-	auto prng = std::unique_ptr<CRandom>(new CRandom());
+	auto prng = get_prng();
 	for (index_t run=0; run<100; ++run)
 	{
 		subset.range_fill();
-		CMath::permute(subset, prng.get());
+		CMath::permute(subset, prng);
 		//		subset.display_vector("permutation");
 		features->add_subset(subset);
 		k->init(features, features);