remove RealFeatures from SWIG

examples/meta/generator/targets/cpp.json

@@ -92,14 +92,16 @@
         "FloatLiteral": "${number}f",
         "MethodCall": {
             "Default": "$object->$method($arguments)",
+            "get_int": "$object->get<int32_t>($arguments)",
+            "get_int_vector": "$object->get<SGVector<int32_t>>($arguments)",
             "get_real": "$object->get<float64_t>($arguments)",
             "get_real_vector": "$object->get<SGVector<float64_t>>($arguments)",
             "get_real_matrix": "$object->get<SGMatrix<float64_t>>($arguments)"
         },
         "StaticCall": "C$typeName::$method($arguments)",
         "GlobalCall": "$method($arguments)",
         "Identifier": "$identifier",
-        "Enum":"$typeName::$value"
+        "Enum":"(machine_int_t)$typeName::$value"
     },
     "Element": {
         "Access": {

examples/meta/generator/targets/csharp.json

@@ -80,7 +80,7 @@
         "StaticCall": "$typeName.$method($arguments)",
         "GlobalCall": "shogun.$method($arguments)",
         "Identifier": "$identifier",
-        "Enum":"$typeName.$value"
+        "Enum":"(int)$typeName.$value"
     },
     "Element": {
         "Access": {

examples/meta/src/base_api/factory.sg

@@ -7,7 +7,7 @@ RealMatrix real_matrix(2,2)
 Features features_from_matrix = features(real_matrix)
 
 File features_file = csv_file("../../data/classifier_binary_2d_nonlinear_features_train.dat")
-Features features_from_file = features(features_file, enum EPrimitiveType.PT_FLOAT64)
+Features features_from_file = features(features_file)
 
 File labels_file = csv_file("../../data/classifier_binary_2d_nonlinear_labels_train.dat")
 Labels labels_from_file = labels(labels_file)

examples/meta/src/base_api/put_get.sg

@@ -21,10 +21,10 @@ matrix[0,1] = 0.1
 matrix[1,0] = 0.2
 matrix[1,1] = 0.4
 
-RealFeatures features()
-features.put("feature_matrix", matrix)
-RealMatrix matrix2 = features.get_real_matrix("feature_matrix")
-features.put("feature_matrix", matrix2)
+# TODO: replace this with another class that has matrix parameters as features are immutable
+#Features feats = features("DenseFeatures", enum EPrimitiveType.PT_FLOAT64, feature_matrix=matrix)
+#RealMatrix matrix2 = feats.get_real_matrix("feature_matrix")
+#feats.put("feature_matrix", matrix2)
 
 EuclideanDistance distance()
 knn.put("distance", distance)

examples/meta/src/binary/averaged_perceptron.sg

@@ -4,29 +4,26 @@ CSVFile f_labels_train("../../data/classifier_binary_2d_linear_labels_train.dat"
 CSVFile f_labels_test("../../data/classifier_binary_2d_linear_labels_test.dat")
 
 #![create_features]
-RealFeatures features_train(f_feats_train)
-RealFeatures features_test(f_feats_test)
+Features features_train = features(f_feats_train)
+Features features_test = features(f_feats_test)
 BinaryLabels labels_train(f_labels_train)
 BinaryLabels labels_test(f_labels_test)
 #![create_features]
 
 #![set_parameters]
 real learn_rate=1.0
 int max_iter=1000
-AveragedPerceptron perceptron(features_train, labels_train)
-perceptron.set_learn_rate(learn_rate)
-perceptron.set_max_iter(max_iter)
+Machine perceptron = machine("AveragedPerceptron", labels=labels_train, learn_rate=learn_rate, max_iter=max_iter)
 #![set_parameters]
 
 #![train_and_apply]
-perceptron.train()
-perceptron.set_features(features_test)
-Labels labels_predict = perceptron.apply()
+perceptron.train(features_train)
+Labels labels_predict = perceptron.apply(features_test)
 #![train_and_apply]
 
 #![extract_weights]
-RealVector weights = perceptron.get_w()
-real bias = perceptron.get_bias()
+RealVector weights = perceptron.get_real_vector("w")
+real bias = perceptron.get_real("bias")
 #![extract_weights]
 
 #![evaluate_accuracy]

examples/meta/src/binary/linear_discriminant_analysis.sg

@@ -4,25 +4,23 @@ CSVFile f_labels_train("../../data/classifier_binary_2d_linear_labels_train.dat"
 CSVFile f_labels_test("../../data/classifier_binary_2d_linear_labels_test.dat")
 
 #![create_features]
-RealFeatures features_train(f_feats_train)
-RealFeatures features_test(f_feats_test)
+Features features_train = features(f_feats_train)
+Features features_test = features(f_feats_test)
 BinaryLabels labels_train(f_labels_train)
 BinaryLabels labels_test(f_labels_test)
 #![create_features]
 
 #![create_instance]
-LDA lda()
-lda.set_features(features_train)
-lda.set_labels(labels_train)
+Machine lda = machine("LDA", labels=labels_train)
 #![create_instance]
 
 #![train_and_apply]
-lda.train()
+lda.train(features_train)
 BinaryLabels labels_predict = lda.apply_binary(features_test)
 #![train_and_apply]
 
 #![extract_weights]
-RealVector w = lda.get_w()
+RealVector w = lda.get_real_vector("w")
 #![extract_weights]
 
 #![evaluate_accuracy]

examples/meta/src/binary/linear_support_vector_machine.sg

@@ -4,8 +4,8 @@ CSVFile f_labels_train("../../data/classifier_binary_2d_linear_labels_train.dat"
 CSVFile f_labels_test("../../data/classifier_binary_2d_linear_labels_test.dat")
 
 #![create_features]
-RealFeatures features_train(f_feats_train)
-RealFeatures features_test(f_feats_test)
+Features features_train = features(f_feats_train)
+Features features_test = features(f_feats_test)
 BinaryLabels labels_train(f_labels_train)
 BinaryLabels labels_test(f_labels_test)
 #![create_features]
@@ -16,19 +16,17 @@ real epsilon = 0.001
 #![set_parameters]
 
 #![create_instance]
-LibLinear svm(C, features_train, labels_train)
-svm.set_liblinear_solver_type(enum LIBLINEAR_SOLVER_TYPE.L2R_L2LOSS_SVC)
-svm.set_epsilon(epsilon)
+Machine svm = machine("LibLinear", C1=C, C2=C, labels=labels_train, epsilon=epsilon, liblinear_solver_type=enum LIBLINEAR_SOLVER_TYPE.L2R_L2LOSS_SVC, use_bias=True)
 #![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 w = svm.get_w()
-real b = svm.get_bias()
+RealVector w = svm.get_real_vector("w")
+real b = svm.get_real("bias")
 #![extract_weights_bias]
 
 #![evaluate_accuracy]

examples/meta/src/binary/multiple_kernel_learning.sg

@@ -4,8 +4,8 @@ CSVFile f_labels_train("../../data/classifier_binary_2d_nonlinear_labels_train.d
 CSVFile f_labels_test("../../data/classifier_binary_2d_nonlinear_labels_test.dat")
 
 #![create_features]
-RealFeatures features_train(f_feats_train)
-RealFeatures features_test(f_feats_test)
+Features features_train = features(f_feats_train)
+Features features_test = features(f_feats_test)
 BinaryLabels labels_train(f_labels_train)
 BinaryLabels labels_test(f_labels_test)
 #![create_features]

examples/meta/src/binary/perceptron.sg

@@ -4,29 +4,26 @@ CSVFile f_labels_train("../../data/classifier_binary_2d_linear_labels_train.dat"
 CSVFile f_labels_test("../../data/classifier_binary_2d_linear_labels_test.dat")
 
 #![create_features]
-RealFeatures features_train(f_feats_train)
-RealFeatures features_test(f_feats_test)
+Features features_train = features(f_feats_train)
+Features features_test = features(f_feats_test)
 BinaryLabels labels_train(f_labels_train)
 BinaryLabels labels_test(f_labels_test)
 #![create_features]
 
 #![create_instance]
 real learn_rate=1.0
 int max_iter=1000
-Perceptron perceptron(features_train, labels_train)
-perceptron.set_learn_rate(learn_rate)
-perceptron.set_max_iter(max_iter)
+Machine perceptron = machine("Perceptron", labels=labels_train, learn_rate=learn_rate, max_iter=max_iter)
 #![create_instance]
 
 #![train_and_apply]
-perceptron.train()
-perceptron.set_features(features_test)
-Labels labels_predict = perceptron.apply()
+perceptron.train(features_train)
+Labels labels_predict = perceptron.apply(features_test)
 #![train_and_apply]
 
 #![extract_weights]
-RealVector weights = perceptron.get_w()
-real bias = perceptron.get_bias()
+RealVector weights = perceptron.get_real_vector("w")
+real bias = perceptron.get_real("bias")
 #![extract_weights]
 
 #![evaluate_accuracy]

examples/meta/src/clustering/gaussian_mixture_models.sg

@@ -3,7 +3,7 @@ CSVFile f_feats_train("../../data/classifier_4class_2d_linear_features_train.dat
 Math:init_random(1)
 
 #![create_features]
-RealFeatures features_train(f_feats_train)
+Features features_train = features(f_feats_train)
 #![create_features]
 
 #![create_gmm_instance]

examples/meta/src/clustering/kmeans.sg

@@ -2,15 +2,15 @@ CSVFile f_feats_train("../../data/classifier_binary_2d_linear_features_train.dat
 Math:init_random(1)
 
 #![create_features]
-RealFeatures features_train(f_feats_train)
+Features features_train = features(f_feats_train)
 #![create_features]
 
 #![choose_distance]
-EuclideanDistance distance(features_train, features_train)
+Distance d = distance("EuclideanDistance", lhs=features_train, rhs=features_train)
 #![choose_distance]
 
 #![create_instance_lloyd]
-KMeans kmeans(2, distance)
+KMeans kmeans(2, d)
 #![create_instance_lloyd]
 
 #![train_dataset]
@@ -23,6 +23,6 @@ RealVector r = kmeans.get_radiuses()
 #![extract_centers_and_radius]
 
 #![create_instance_mb]
-KMeansMiniBatch kmeans_mb(2, distance)
+KMeansMiniBatch kmeans_mb(2, d)
 kmeans_mb.set_mb_params(4, 1000)
 #![create_instance_mb]

examples/meta/src/converter/independent_component_analysis_fast.sg

@@ -3,7 +3,7 @@ CSVFile f_feats("../../data/ica_2_sources.dat")
 Math:init_random(1)
 
 #![create_features]
-RealFeatures features(f_feats)
+Features feats = features(f_feats)
 #![create_features]
 
 #![set_parameters]
@@ -13,7 +13,7 @@ ica.set_tol(0.00001)
 #![set_parameters]
 
 #![apply_convert]
-Features converted = ica.apply(features)
+Features converted = ica.apply(feats)
 #![apply_convert]
 
 #![extract]

examples/meta/src/converter/independent_component_analysis_ff_sep.sg

@@ -3,7 +3,7 @@ CSVFile f_feats("../../data/ica_2_sources.dat")
 Math:init_random(1)
 
 #![create_features]
-RealFeatures features(f_feats)
+Features feats = features(f_feats)
 #![create_features]
 
 #![set_parameters]
@@ -13,12 +13,11 @@ ica.set_tol(0.00001)
 #![set_parameters]
 
 #![apply_convert]
-Features converted = ica.apply(features)
+Features converted = ica.apply(feats)
 #![apply_convert]
 
 #![extract]
 RealMatrix mixing_matrix = ica.get_mixing_matrix()
-RealFeatures casted = RealFeatures:obtain_from_generic(converted)
-RealMatrix unmixed_signal = casted.get_feature_matrix()
+RealMatrix unmixed_signal = converted.get_real_matrix("feature_matrix")
 #![extract]
 

examples/meta/src/converter/independent_component_analysis_jade.sg

@@ -3,7 +3,7 @@ CSVFile f_feats("../../data/ica_2_sources.dat")
 Math:init_random(1)
 
 #![create_features]
-RealFeatures features(f_feats)
+Features feats = features(f_feats)
 #![create_features]
 
 #![set_parameters]
@@ -13,12 +13,11 @@ ica.set_tol(0.00001)
 #![set_parameters]
 
 #![apply_convert]
-Features converted = ica.apply(features)
+Features converted = ica.apply(feats)
 #![apply_convert]
 
 #![extract]
 RealMatrix mixing_matrix = ica.get_mixing_matrix()
-RealFeatures casted = RealFeatures:obtain_from_generic(converted)
-RealMatrix unmixed_signal = casted.get_feature_matrix()
+RealMatrix unmixed_signal = converted.get_real_matrix("feature_matrix")
 #![extract]
 

examples/meta/src/converter/independent_component_analysis_jedi_sep.sg

@@ -3,7 +3,7 @@ CSVFile f_feats("../../data/ica_2_sources.dat")
 Math:init_random(1)
 
 #![create_features]
-RealFeatures features(f_feats)
+Features feats = features(f_feats)
 #![create_features]
 
 #![set_parameters]
@@ -13,12 +13,11 @@ ica.set_tol(0.00001)
 #![set_parameters]
 
 #![apply_convert]
-Features converted = ica.apply(features)
+Features converted = ica.apply(feats)
 #![apply_convert]
 
 #![extract]
 RealMatrix mixing_matrix = ica.get_mixing_matrix()
-RealFeatures casted = RealFeatures:obtain_from_generic(converted)
-RealMatrix unmixed_signal = casted.get_feature_matrix()
+RealMatrix unmixed_signal = converted.get_real_matrix("feature_matrix")
 #![extract]
 

examples/meta/src/converter/independent_component_analysis_sobi.sg

@@ -3,7 +3,7 @@ CSVFile f_feats("../../data/ica_2_sources.dat")
 Math:init_random(1)
 
 #![create_features]
-RealFeatures features(f_feats)
+Features feats = features(f_feats)
 #![create_features]
 
 #![set_parameters]
@@ -13,12 +13,11 @@ ica.set_tol(0.00001)
 #![set_parameters]
 
 #![apply_convert]
-Features converted = ica.apply(features)
+Features converted = ica.apply(feats)
 #![apply_convert]
 
 #![extract]
 RealMatrix mixing_matrix = ica.get_mixing_matrix()
-RealFeatures casted = RealFeatures:obtain_from_generic(converted)
-RealMatrix unmixed_signal = casted.get_feature_matrix()
+RealMatrix unmixed_signal = converted.get_real_matrix("feature_matrix")
 #![extract]
 

examples/meta/src/distance/braycurtis.sg

@@ -2,19 +2,19 @@ CSVFile f_feats_a("../../data/fm_train_real.dat")
 CSVFile f_feats_b("../../data/fm_test_real.dat")
 
 #![create_features]
-RealFeatures features_a(f_feats_a)
-RealFeatures features_b(f_feats_b)
+Features features_a = features(f_feats_a)
+Features features_b = features(f_feats_b)
 #![create_features]
 
 #![create_instance]
-BrayCurtisDistance distance(features_a, features_a)
+Distance d = distance("BrayCurtisDistance", lhs=features_a, rhs=features_a)
 #![create_instance]
 
 #![extract_distance]
-RealMatrix distance_matrix_aa = distance.get_distance_matrix()
+RealMatrix distance_matrix_aa = d.get_distance_matrix()
 #![extract_distance]
 
 #![refresh_distance]
-distance.init(features_a, features_b)
-RealMatrix distance_matrix_ab = distance.get_distance_matrix()
-#![refresh_distance]
+d.init(features_a, features_b)
+RealMatrix distance_matrix_ab = d.get_distance_matrix()
+#![refresh_distance]