remove random functions in CMath

benchmarks/hasheddoc_benchmarks.cpp

@@ -13,7 +13,6 @@
 #include <shogun/features/HashedDocDotFeatures.h>
 #include <shogun/features/DenseFeatures.h>
 #include <shogun/lib/NGramTokenizer.h>
-#include <shogun/mathematics/Math.h>
 
 using namespace shogun;
 
@@ -27,13 +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 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) CMath::random('A', 'Z');
+			string_list.strings[i].string[j] = (char)dist(prng);
 	}
 	SG_SPRINT("Features were created.\n");
 

benchmarks/rf_feats_benchmark.cpp

@@ -16,6 +16,8 @@ int main(int argv, char** argc)
 
 	int32_t dims[] = {100, 300, 600};
 	CTime* timer = new CTime();
+	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];
@@ -27,7 +29,7 @@ int main(int argv, char** argc)
 		{
 			for (index_t j=0; j<num_dim; j++)
 			{
-				mat(j,i) = CMath::random(0,1) + 0.5;
+				mat(j, i) = dist(prng) + 0.5;
 			}
 		}
 

benchmarks/rf_feats_kernel_comp.cpp

@@ -29,6 +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 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++)
@@ -48,12 +50,12 @@ int main(int argv, char** argc)
 					if ((i+j)%2==0)
 					{
 						labs[i] = -1;
-						mat(j,i) = CMath::random(0,1) + 0.5;
+						mat(j, i) = dist(prng) + 0.5;
 					}
 					else
 					{
 						labs[i] = 1;
-						mat(j,i) = CMath::random(0,1) - 0.5;
+						mat(j, i) = dist(prng) - 0.5;
 					}
 				}
 			}

benchmarks/sparse_test.cpp

@@ -115,7 +115,7 @@ int main(int argc, char** argv)
 	v.set_const(1.0);
 	Map<VectorXd> map_v(v.vector, v.vlen);
 	CTime time;
-	CMath::init_random(17);
+	set_global_seed(17);
 
 	SG_SPRINT("time\tshogun (s)\teigen3 (s)\n\n");
 	for (index_t t=0; t<times; ++t)

examples/meta/generator/translate.py

@@ -483,7 +483,7 @@ def translateExpr(self, expr):
             method = expr[key][0]["Identifier"]
             argsList = None
             try:
-                argsList = expr[key][2]
+                argsList = expr[key][1]
             except IndexError:
                 pass
             translatedArgsList = self.translateArgumentList(argsList)

examples/meta/src/clustering/gmm.sg

@@ -1,6 +1,6 @@
 CSVFile f_feats_train("../../data/classifier_4class_2d_linear_features_train.dat")
 
-Math:init_random(1)
+set_global_seed(1)
 
 #![create_features]
 RealFeatures features_train(f_feats_train)

examples/meta/src/clustering/kmeans.sg

@@ -1,5 +1,5 @@
 CSVFile f_feats_train("../../data/classifier_binary_2d_linear_features_train.dat")
-Math:init_random(1)
+set_global_seed(1)
 
 #![create_features]
 RealFeatures features_train(f_feats_train)

examples/meta/src/converter/ica_fast.sg

@@ -1,6 +1,6 @@
 CSVFile f_feats("../../data/ica_2_sources.dat")
 
-Math:init_random(1)
+set_global_seed(1)
 
 #![create_features]
 RealFeatures features(f_feats)

examples/meta/src/converter/ica_ff_sep.sg

@@ -1,6 +1,6 @@
 CSVFile f_feats("../../data/ica_2_sources.dat")
 
-Math:init_random(1)
+set_global_seed(1)
 
 #![create_features]
 RealFeatures features(f_feats)

examples/meta/src/converter/ica_jade.sg

@@ -1,6 +1,6 @@
 CSVFile f_feats("../../data/ica_2_sources.dat")
 
-Math:init_random(1)
+set_global_seed(1)
 
 #![create_features]
 RealFeatures features(f_feats)

examples/meta/src/converter/ica_jedi_sep.sg

@@ -1,6 +1,6 @@
 CSVFile f_feats("../../data/ica_2_sources.dat")
 
-Math:init_random(1)
+set_global_seed(1)
 
 #![create_features]
 RealFeatures features(f_feats)

examples/meta/src/converter/ica_sobi.sg

@@ -1,6 +1,6 @@
 CSVFile f_feats("../../data/ica_2_sources.dat")
 
-Math:init_random(1)
+set_global_seed(1)
 
 #![create_features]
 RealFeatures features(f_feats)

examples/meta/src/gaussian_processes/gaussian_process_classifier.sg

@@ -2,7 +2,7 @@ CSVFile f_feats_train("../../data/classifier_4class_2d_linear_features_train.dat
 CSVFile f_feats_test("../../data/classifier_4class_2d_linear_features_test.dat")
 CSVFile f_labels_train("../../data/classifier_4class_2d_linear_labels_train.dat")
 CSVFile f_labels_test("../../data/classifier_4class_2d_linear_labels_test.dat")
-Math:init_random(1)
+set_global_seed(1)
 
 #![create_features]
 RealFeatures features_train(f_feats_train)

examples/meta/src/meta_api/calls.sg

@@ -1,9 +1,11 @@
 # static call
-Math:init_random(1)
+# Math:init_random(1)
 
 # global function call
 get_global_io()
 
+set_global_seed(1)
+
 # member function call
 GaussianKernel k()
 k.set_width(1)

examples/meta/src/multiclass_classifier/cartree.sg

@@ -2,7 +2,7 @@ CSVFile f_feats_train("../../data/classifier_4class_2d_linear_features_train.dat
 CSVFile f_feats_test("../../data/classifier_4class_2d_linear_features_test.dat")
 CSVFile f_labels_train("../../data/classifier_4class_2d_linear_labels_train.dat")
 CSVFile f_labels_test("../../data/classifier_4class_2d_linear_labels_test.dat")
-Math:init_random(1)
+set_global_seed(1)
 
 #![create_features]
 RealFeatures features_train(f_feats_train)

examples/meta/src/multiclass_classifier/multiclass_ecoc_random.sg

@@ -1,4 +1,4 @@
-Math:init_random(1)
+set_global_seed(1)
 
 CSVFile f_feats_train("../../data/classifier_4class_2d_linear_features_train.dat")
 CSVFile f_feats_test("../../data/classifier_4class_2d_linear_features_test.dat")

examples/meta/src/multiclass_classifier/random_forest.sg

@@ -2,7 +2,7 @@ CSVFile f_feats_train("../../data/classifier_4class_2d_linear_features_train.dat
 CSVFile f_feats_test("../../data/classifier_4class_2d_linear_features_test.dat")
 CSVFile f_labels_train("../../data/classifier_4class_2d_linear_labels_train.dat")
 CSVFile f_labels_test("../../data/classifier_4class_2d_linear_labels_test.dat")
-Math:init_random(1)
+set_global_seed(1)
 
 #![create_features]
 RealFeatures features_train(f_feats_train)

examples/meta/src/neural_nets/feedforward_net_classification.sg

@@ -3,7 +3,7 @@ CSVFile f_feats_test("../../data/classifier_binary_2d_nonlinear_features_test.da
 CSVFile f_labels_train("../../data/classifier_binary_2d_nonlinear_labels_train.dat")
 CSVFile f_labels_test("../../data/classifier_binary_2d_nonlinear_labels_test.dat")
 
-Math:init_random(1)
+set_global_seed(1)
 
 #![create_features]
 RealFeatures features_train(f_feats_train)

examples/meta/src/neural_nets/feedforward_net_regression.sg

@@ -3,7 +3,7 @@ CSVFile f_feats_test("../../data/regression_1d_sinc_features_test_with_9d_noise.
 CSVFile f_labels_train("../../data/regression_1d_sinc_labels_train.dat")
 CSVFile f_labels_test("../../data/regression_1d_sinc_labels_test.dat")
 
-Math:init_random(1)
+set_global_seed(1)
 
 #![create_features]
 RealFeatures features_train(f_feats_train)

examples/meta/src/regression/random_forest_regression.sg

@@ -2,7 +2,7 @@ CSVFile f_feats_train("../../data/regression_1d_linear_features_train.dat")
 CSVFile f_feats_test("../../data/regression_1d_linear_features_test.dat")
 CSVFile f_labels_train("../../data/regression_1d_linear_labels_train.dat")
 CSVFile f_labels_test("../../data/regression_1d_linear_labels_test.dat")
-Math:init_random(1)
+set_global_seed(1)
 
 #![create_features]
 RealFeatures features_train(f_feats_train)

examples/undocumented/libshogun/classifier_larank.cpp

@@ -27,13 +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 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)=CMath::randn_double();
-			matrix_test(j,i)=CMath::randn_double();
+			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 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 = CMath::random(0, width-1);
-		int y = CMath::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

@@ -10,27 +10,29 @@ 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)
 {
-    index_t num_samples=labels.vlen;
-    index_t dimensions=features.num_rows;
-    for (int32_t i=0; i<num_samples; i++)
-    {
-        if (i<num_samples/2)
-        {
-            labels[i]=-1.0;
-            for(int32_t j=0; j<dimensions; j++)
-                features(j,i)=CMath::random(0.0,1.0)+distance;
-        }
-        else
-        {
-            labels[i]=1.0;
-            for(int32_t j=0; j<dimensions; j++)
-                features(j,i)=CMath::random(0.0,1.0)-distance;
-        }
-    }
-    labels.display_vector("labels");
-    std::cout<<std::endl;
-    features.display_matrix("features");
-    std::cout<<std::endl;
+	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++)
+	{
+		if (i < num_samples / 2)
+		{
+			labels[i] = -1.0;
+			for (int32_t j = 0; j < dimensions; j++)
+				features(j, i) = dist(prng) + distance;
+		}
+		else
+		{
+			labels[i] = 1.0;
+			for (int32_t j = 0; j < dimensions; j++)
+				features(j, i) = dist(prng) - distance;
+		}
+	}
+	labels.display_vector("labels");
+	std::cout << std::endl;
+	features.display_matrix("features");
+	std::cout << std::endl;
 }
 
 int main(int argc, char** argv)

examples/undocumented/libshogun/classifier_mkl_svmlight_modelselection_bug.cpp

@@ -68,8 +68,10 @@ void test()
 	/* create some data and labels */
 	SGMatrix<float64_t> matrix(dim_vectors, num_vectors);
 	CBinaryLabels* labels=new CBinaryLabels(num_vectors);
+	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]=CMath::randn_double();
+		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);
-	CMath::init_random(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=CMath::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<CMath::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<CMath::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<CMath::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

@@ -39,6 +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 prng = get_prng();
 
 	/* build random cluster centers */
 	SGMatrix<float64_t> cluster_centers(dim_features, num_clusters);
@@ -59,7 +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]=CMath::normal_random(entry, cluster_std_dev);
+				std::normal_distribution<float64_t> dist(
+				    entry, cluster_std_dev);
+				data.matrix[idx] = dist(prng);
 			}
 		}
 	}