Cleaned up SVC.

mlpp/svc/svc.cpp

@@ -5,48 +5,84 @@
 //
 
 #include "svc.h"
+
 #include "../activation/activation.h"
 #include "../cost/cost.h"
 #include "../lin_alg/lin_alg.h"
 #include "../regularization/reg.h"
 #include "../utilities/utilities.h"
 
-#include <iostream>
 #include <random>
 
-std::vector<real_t> MLPPSVC::modelSetTest(std::vector<std::vector<real_t>> X) {
+Ref<MLPPMatrix> MLPPSVC::get_input_set() {
-	return Evaluate(X);
+	return _input_set;
+}
+void MLPPSVC::set_input_set(const Ref<MLPPMatrix> &val) {
+	_input_set = val;
+
+	_initialized = false;
 }
 
-real_t MLPPSVC::modelTest(std::vector<real_t> x) {
+Ref<MLPPVector> MLPPSVC::get_output_set() {
-	return Evaluate(x);
+	return _output_set;
+}
+void MLPPSVC::set_output_set(const Ref<MLPPMatrix> &val) {
+	_output_set = val;
+
+	_initialized = false;
 }
 
-void MLPPSVC::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
+real_t MLPPSVC::get_c() {
-	class MLPPCost cost;
+	return _c;
+}
+void MLPPSVC::set_c(const real_t val) {
+	_c = val;
+
+	_initialized = false;
+}
+
+Ref<MLPPVector> MLPPSVC::model_set_test(const Ref<MLPPMatrix> &X) {
+	ERR_FAIL_COND_V(!_initialized, Ref<MLPPVector>());
+
+	return evaluatem(X);
+}
+
+real_t MLPPSVC::model_test(const Ref<MLPPVector> &x) {
+	ERR_FAIL_COND_V(!_initialized, 0);
+
+	return evaluatev(x);
+}
+
+void MLPPSVC::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
+	ERR_FAIL_COND(!_initialized);
+
+	MLPPCost mlpp_cost;
 	MLPPActivation avn;
 	MLPPLinAlg alg;
 	MLPPReg regularization;
+
 	real_t cost_prev = 0;
 	int epoch = 1;
-	forwardPass();
+
+	forward_pass();
 
 	while (true) {
-		cost_prev = Cost(y_hat, outputSet, weights, C);
+		cost_prev = cost(_y_hat, _output_set, _weights, _c);
 
-		weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate / n, alg.mat_vec_mult(alg.transpose(inputSet), cost.HingeLossDeriv(z, outputSet, C))));
+		_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate / _n, alg.mat_vec_multv(alg.transposem(_input_set), mlpp_cost.hinge_loss_derivwv(_z, _output_set, _c))));
-		weights = regularization.regWeights(weights, learning_rate / n, 0, "Ridge");
+		_weights = regularization.reg_weightsv(_weights, learning_rate / _n, 0, MLPPReg::REGULARIZATION_TYPE_RIDGE);
 
 		// Calculating the bias gradients
-		bias += learning_rate * alg.sum_elements(cost.HingeLossDeriv(y_hat, outputSet, C)) / n;
+		_bias += learning_rate * alg.sum_elementsv(mlpp_cost.hinge_loss_derivwv(_y_hat, _output_set, _c)) / _n;
 
-		forwardPass();
+		forward_pass();
 
 		// UI PORTION
-		if (UI) {
+		if (ui) {
-			MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet, weights, C));
+			MLPPUtilities::cost_info(epoch, cost_prev, cost(_y_hat, _output_set, _weights, _c));
-			MLPPUtilities::UI(weights, bias);
+			MLPPUtilities::print_ui_vb(_weights, _bias);
 		}
+
 		epoch++;
 
 		if (epoch > max_epoch) {
@@ -55,39 +91,66 @@ void MLPPSVC::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
 	}
 }
 
-void MLPPSVC::SGD(real_t learning_rate, int max_epoch, bool UI) {
+void MLPPSVC::sgd(real_t learning_rate, int max_epoch, bool ui) {
-	class MLPPCost cost;
+	ERR_FAIL_COND(!_initialized);
+
+	MLPPCost mlpp_cost;
 	MLPPActivation avn;
 	MLPPLinAlg alg;
 	MLPPReg regularization;
 
+	std::random_device rd;
+	std::default_random_engine generator(rd());
+	std::uniform_int_distribution<int> distribution(0, int(_n - 1));
+
+	Ref<MLPPVector> input_set_row_tmp;
+	input_set_row_tmp.instance();
+	input_set_row_tmp->resize(_input_set->size().x);
+
+	Ref<MLPPVector> output_set_row_tmp;
+	output_set_row_tmp.instance();
+	output_set_row_tmp->resize(1);
+
+	Ref<MLPPVector> z_row_tmp;
+	z_row_tmp.instance();
+	z_row_tmp->resize(1);
+
 	real_t cost_prev = 0;
 	int epoch = 1;
 
+	forward_pass();
+
 	while (true) {
-		std::random_device rd;
+		int output_index = distribution(generator);
-		std::default_random_engine generator(rd());
-		std::uniform_int_distribution<int> distribution(0, int(n - 1));
-		int outputIndex = distribution(generator);
 
-		//real_t y_hat = Evaluate(inputSet[outputIndex]);
+		_input_set->get_row_into_mlpp_vector(output_index, input_set_row_tmp);
-		real_t z = propagate(inputSet[outputIndex]);
-		cost_prev = Cost({ z }, { outputSet[outputIndex] }, weights, C);
 
-		real_t costDeriv = cost.HingeLossDeriv(std::vector<real_t>({ z }), std::vector<real_t>({ outputSet[outputIndex] }), C)[0]; // Explicit conversion to avoid ambiguity with overloaded function. Error occured on Ubuntu.
+		real_t output_set_indx = _output_set->get_element(output_index);
+		output_set_row_tmp->set_element(0, output_set_indx);
+
+		//real_t y_hat = Evaluate(input_set_row_tmp);
+		real_t z = propagatev(input_set_row_tmp);
+
+		z_row_tmp->set_element(0, z);
+
+		cost_prev = cost(z_row_tmp, output_set_row_tmp, _weights, _c);
+
+		Ref<MLPPVector> cost_deriv_vec = mlpp_cost.hinge_loss_derivwv(z_row_tmp, output_set_row_tmp, _c);
+
+		real_t cost_deriv = cost_deriv_vec->get_element(0);
 
 		// Weight Updation
-		weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate * costDeriv, inputSet[outputIndex]));
+		_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate * cost_deriv, input_set_row_tmp));
-		weights = regularization.regWeights(weights, learning_rate, 0, "Ridge");
+		_weights = regularization.reg_weightsv(_weights, learning_rate, 0, MLPPReg::REGULARIZATION_TYPE_RIDGE);
 
 		// Bias updation
-		bias -= learning_rate * costDeriv;
+		_bias -= learning_rate * cost_deriv;
 
-		//y_hat = Evaluate({ inputSet[outputIndex] });
+		//y_hat = Evaluate({ _input_set[output_index] });
 
-		if (UI) {
+		if (ui) {
-			MLPPUtilities::CostInfo(epoch, cost_prev, Cost({ z }, { outputSet[outputIndex] }, weights, C));
+			MLPPUtilities::cost_info(epoch, cost_prev, cost(z_row_tmp, output_set_row_tmp, _weights, _c));
-			MLPPUtilities::UI(weights, bias);
+			MLPPUtilities::print_ui_vb(_weights, _bias);
 		}
 
 		epoch++;
@@ -96,108 +159,207 @@ void MLPPSVC::SGD(real_t learning_rate, int max_epoch, bool UI) {
 			break;
 		}
 	}
-	forwardPass();
+
+	forward_pass();
 }
 
-void MLPPSVC::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
+void MLPPSVC::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui) {
-	class MLPPCost cost;
+	ERR_FAIL_COND(!_initialized);
+
+	MLPPCost mlpp_cost;
 	MLPPActivation avn;
 	MLPPLinAlg alg;
 	MLPPReg regularization;
+
 	real_t cost_prev = 0;
 	int epoch = 1;
 
 	// Creating the mini-batches
-	int n_mini_batch = n / mini_batch_size;
+	int n_mini_batch = _n / mini_batch_size;
-	auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
+	MLPPUtilities::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, n_mini_batch);
-	auto inputMiniBatches = std::get<0>(batches);
+
-	auto outputMiniBatches = std::get<1>(batches);
+	forward_pass();
 
 	while (true) {
 		for (int i = 0; i < n_mini_batch; i++) {
-			std::vector<real_t> y_hat = Evaluate(inputMiniBatches[i]);
+			Ref<MLPPMatrix> current_input_batch_entry = batches.input_sets[i];
-			std::vector<real_t> z = propagate(inputMiniBatches[i]);
+			Ref<MLPPVector> current_output_batch_entry = batches.output_sets[i];
-			cost_prev = Cost(z, outputMiniBatches[i], weights, C);
+
+			Ref<MLPPVector> y_hat = evaluatem(current_input_batch_entry);
+			Ref<MLPPVector> z = propagatem(current_input_batch_entry);
+			cost_prev = cost(z, current_output_batch_entry, _weights, _c);
 
 			// Calculating the weight gradients
-			weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate / n, alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), cost.HingeLossDeriv(z, outputMiniBatches[i], C))));
+			_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate / _n, alg.mat_vec_multv(alg.transposem(current_input_batch_entry), mlpp_cost.hinge_loss_derivwv(z, current_output_batch_entry, _c))));
-			weights = regularization.regWeights(weights, learning_rate / n, 0, "Ridge");
+			_weights = regularization.reg_weightsv(_weights, learning_rate / _n, 0, MLPPReg::REGULARIZATION_TYPE_RIDGE);
 
 			// Calculating the bias gradients
-			bias -= learning_rate * alg.sum_elements(cost.HingeLossDeriv(y_hat, outputMiniBatches[i], C)) / n;
+			_bias -= learning_rate * alg.sum_elementsv(mlpp_cost.hinge_loss_derivwv(y_hat, current_output_batch_entry, _c)) / _n;
 
-			forwardPass();
+			forward_pass();
 
-			y_hat = Evaluate(inputMiniBatches[i]);
+			y_hat = evaluatem(current_input_batch_entry);
 
-			if (UI) {
+			if (ui) {
-				MLPPUtilities::CostInfo(epoch, cost_prev, Cost(z, outputMiniBatches[i], weights, C));
+				MLPPUtilities::cost_info(epoch, cost_prev, cost(z, current_output_batch_entry, _weights, _c));
-				MLPPUtilities::UI(weights, bias);
+				MLPPUtilities::print_ui_vb(_weights, _bias);
 			}
 		}
+
 		epoch++;
+
 		if (epoch > max_epoch) {
 			break;
 		}
 	}
-	forwardPass();
+
+	forward_pass();
 }
 
 real_t MLPPSVC::score() {
+	ERR_FAIL_COND_V(!_initialized, 0);
+
 	MLPPUtilities util;
-	return util.performance(y_hat, outputSet);
+	return util.performance_vec(_y_hat, _output_set);
 }
 
-void MLPPSVC::save(std::string fileName) {
+void MLPPSVC::save(const String &file_name) {
+	ERR_FAIL_COND(!_initialized);
+
 	MLPPUtilities util;
-	util.saveParameters(fileName, weights, bias);
+
+	//util.saveParameters(_file_name, _weights, _bias);
 }
 
-MLPPSVC::MLPPSVC(std::vector<std::vector<real_t>> p_inputSet, std::vector<real_t> p_outputSet, real_t p_C) {
+bool MLPPSVC::is_initialized() {
-	inputSet = p_inputSet;
+	return _initialized;
-	outputSet = p_outputSet;
+}
-	n = inputSet.size();
+void MLPPSVC::initialize() {
-	k = inputSet[0].size();
+	if (_initialized) {
-	C = p_C;
+		return;
-
-	y_hat.resize(n);
-	weights = MLPPUtilities::weightInitialization(k);
-	bias = MLPPUtilities::biasInitialization();
 	}
 
-real_t MLPPSVC::Cost(std::vector<real_t> z, std::vector<real_t> y, std::vector<real_t> weights, real_t C) {
+	ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
-	class MLPPCost cost;
+
-	return cost.HingeLoss(z, y, weights, C);
+	_n = _input_set->size().y;
+	_k = _input_set->size().x;
+
+	if (!_y_hat.is_valid()) {
+		_y_hat.instance();
 	}
 
-std::vector<real_t> MLPPSVC::Evaluate(std::vector<std::vector<real_t>> X) {
+	_y_hat->resize(_n);
+
+	MLPPUtilities util;
+
+	if (!_weights.is_valid()) {
+		_weights.instance();
+	}
+
+	_weights->resize(_k);
+
+	util.weight_initializationv(_weights);
+	_bias = util.bias_initializationr();
+
+	_initialized = true;
+}
+
+MLPPSVC::MLPPSVC(const Ref<MLPPMatrix> &input_set, const Ref<MLPPVector> &output_set, real_t c) {
+	_input_set = input_set;
+	_output_set = output_set;
+
+	_n = _input_set->size().y;
+	_k = _input_set->size().x;
+	_c = c;
+
+	_y_hat.instance();
+
+	_y_hat->resize(_n);
+
+	MLPPUtilities util;
+
+	_weights.instance();
+	_weights->resize(_k);
+	util.weight_initializationv(_weights);
+	_bias = util.bias_initializationr();
+
+	_initialized = true;
+}
+
+MLPPSVC::MLPPSVC() {
+	_y_hat.instance();
+	_weights.instance();
+
+	_c = 0;
+	_n = 0;
+	_k = 0;
+
+	_initialized = false;
+}
+MLPPSVC::~MLPPSVC() {
+}
+
+real_t MLPPSVC::cost(const Ref<MLPPVector> &z, const Ref<MLPPVector> &y, const Ref<MLPPVector> &weights, real_t c) {
+	MLPPCost mlpp_cost;
+	return mlpp_cost.hinge_losswv(z, y, weights, c);
+}
+
+Ref<MLPPVector> MLPPSVC::evaluatem(const Ref<MLPPMatrix> &X) {
 	MLPPLinAlg alg;
 	MLPPActivation avn;
-	return avn.sign(alg.scalarAdd(bias, alg.mat_vec_mult(X, weights)));
+	return avn.sign_normv(alg.scalar_addnv(_bias, alg.mat_vec_multv(X, _weights)));
 }
 
-std::vector<real_t> MLPPSVC::propagate(std::vector<std::vector<real_t>> X) {
+Ref<MLPPVector> MLPPSVC::propagatem(const Ref<MLPPMatrix> &X) {
 	MLPPLinAlg alg;
 	MLPPActivation avn;
-	return alg.scalarAdd(bias, alg.mat_vec_mult(X, weights));
+	return alg.scalar_addnv(_bias, alg.mat_vec_multv(X, _weights));
 }
 
-real_t MLPPSVC::Evaluate(std::vector<real_t> x) {
+real_t MLPPSVC::evaluatev(const Ref<MLPPVector> &x) {
 	MLPPLinAlg alg;
 	MLPPActivation avn;
-	return avn.sign(alg.dot(weights, x) + bias);
+	return avn.sign_normr(alg.dotv(_weights, x) + _bias);
 }
 
-real_t MLPPSVC::propagate(std::vector<real_t> x) {
+real_t MLPPSVC::propagatev(const Ref<MLPPVector> &x) {
 	MLPPLinAlg alg;
 	MLPPActivation avn;
-	return alg.dot(weights, x) + bias;
+	return alg.dotv(_weights, x) + _bias;
 }
 
 // sign ( wTx + b )
-void MLPPSVC::forwardPass() {
+void MLPPSVC::forward_pass() {
 	MLPPActivation avn;
 
-	z = propagate(inputSet);
+	_z = propagatem(_input_set);
-	y_hat = avn.sign(z);
+	_y_hat = avn.sign_normv(_z);
+}
+
+void MLPPSVC::_bind_methods() {
+	ClassDB::bind_method(D_METHOD("get_input_set"), &MLPPSVC::get_input_set);
+	ClassDB::bind_method(D_METHOD("set_input_set", "val"), &MLPPSVC::set_input_set);
+	ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "input_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_input_set", "get_input_set");
+
+	ClassDB::bind_method(D_METHOD("get_output_set"), &MLPPSVC::get_output_set);
+	ClassDB::bind_method(D_METHOD("set_output_set", "val"), &MLPPSVC::set_output_set);
+	ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "output_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "set_output_set", "get_output_set");
+
+	ClassDB::bind_method(D_METHOD("get_c"), &MLPPSVC::get_c);
+	ClassDB::bind_method(D_METHOD("set_c", "val"), &MLPPSVC::set_c);
+	ADD_PROPERTY(PropertyInfo(Variant::REAL, "c"), "set_c", "get_c");
+
+	ClassDB::bind_method(D_METHOD("model_set_test", "X"), &MLPPSVC::model_set_test);
+	ClassDB::bind_method(D_METHOD("model_test", "x"), &MLPPSVC::model_test);
+
+	ClassDB::bind_method(D_METHOD("gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPSVC::gradient_descent, false);
+	ClassDB::bind_method(D_METHOD("sgd", "learning_rate", "max_epoch", "ui"), &MLPPSVC::sgd, false);
+	ClassDB::bind_method(D_METHOD("mbgd", "learning_rate", "max_epoch", "mini_batch_size", "ui"), &MLPPSVC::mbgd, false);
+
+	ClassDB::bind_method(D_METHOD("score"), &MLPPSVC::score);
+
+	ClassDB::bind_method(D_METHOD("save", "file_name"), &MLPPSVC::save);
+
+	ClassDB::bind_method(D_METHOD("is_initialized"), &MLPPSVC::is_initialized);
+	ClassDB::bind_method(D_METHOD("initialize"), &MLPPSVC::initialize);
 }

mlpp/svc/svc.h

@@ -13,43 +13,71 @@
 
 #include "core/math/math_defs.h"
 
-#include <string>
+#include "core/object/reference.h"
-#include <vector>
+
+#include "../lin_alg/mlpp_matrix.h"
+#include "../lin_alg/mlpp_vector.h"
+
+#include "../regularization/reg.h"
+
+class MLPPSVC : public Reference {
+	GDCLASS(MLPPSVC, Reference);
 
-class MLPPSVC {
 public:
-	std::vector<real_t> modelSetTest(std::vector<std::vector<real_t>> X);
+	Ref<MLPPMatrix> get_input_set();
-	real_t modelTest(std::vector<real_t> x);
+	void set_input_set(const Ref<MLPPMatrix> &val);
-	void gradientDescent(real_t learning_rate, int max_epoch, bool UI = false);
+
-	void SGD(real_t learning_rate, int max_epoch, bool UI = false);
+	Ref<MLPPVector> get_output_set();
-	void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = false);
+	void set_output_set(const Ref<MLPPMatrix> &val);
+
+	real_t get_c();
+	void set_c(const real_t val);
+
+	Ref<MLPPVector> model_set_test(const Ref<MLPPMatrix> &X);
+	real_t model_test(const Ref<MLPPVector> &x);
+
+	void gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
+	void sgd(real_t learning_rate, int max_epoch, bool ui = false);
+	void mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);
+
 	real_t score();
-	void save(std::string fileName);
 
-	MLPPSVC(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, real_t C);
+	void save(const String &file_name);
 
-private:
+	bool is_initialized();
-	real_t Cost(std::vector<real_t> y_hat, std::vector<real_t> y, std::vector<real_t> weights, real_t C);
+	void initialize();
 
-	std::vector<real_t> Evaluate(std::vector<std::vector<real_t>> X);
+	MLPPSVC(const Ref<MLPPMatrix> &input_set, const Ref<MLPPVector> &output_set, real_t c);
-	std::vector<real_t> propagate(std::vector<std::vector<real_t>> X);
-	real_t Evaluate(std::vector<real_t> x);
-	real_t propagate(std::vector<real_t> x);
-	void forwardPass();
 
-	std::vector<std::vector<real_t>> inputSet;
+	MLPPSVC();
-	std::vector<real_t> outputSet;
+	~MLPPSVC();
-	std::vector<real_t> z;
-	std::vector<real_t> y_hat;
-	std::vector<real_t> weights;
-	real_t bias;
 
-	real_t C;
+protected:
-	int n;
+	real_t cost(const Ref<MLPPVector> &z, const Ref<MLPPVector> &y, const Ref<MLPPVector> &weights, real_t c);
-	int k;
 
-	// UI Portion
+	Ref<MLPPVector> evaluatem(const Ref<MLPPMatrix> &X);
-	void UI(int epoch, real_t cost_prev);
+	Ref<MLPPVector> propagatem(const Ref<MLPPMatrix> &X);
+
+	real_t evaluatev(const Ref<MLPPVector> &x);
+	real_t propagatev(const Ref<MLPPVector> &x);
+
+	void forward_pass();
+
+	static void _bind_methods();
+
+	Ref<MLPPMatrix> _input_set;
+	Ref<MLPPVector> _output_set;
+
+	Ref<MLPPVector> _z;
+	Ref<MLPPVector> _y_hat;
+	Ref<MLPPVector> _weights;
+	real_t _bias;
+
+	real_t _c;
+	int _n;
+	int _k;
+
+	bool _initialized;
 };
 
 #endif /* SVC_hpp */

register_types.cpp

@@ -43,6 +43,7 @@ SOFTWARE.
 #include "mlpp/uni_lin_reg/uni_lin_reg.h"
 #include "mlpp/wgan/wgan.h"
 #include "mlpp/probit_reg/probit_reg.h"
+#include "mlpp/svc/svc.h"
 
 #include "mlpp/mlp/mlp.h"
 
@@ -71,6 +72,7 @@ void register_pmlpp_types(ModuleRegistrationLevel p_level) {
 		ClassDB::register_class<MLPPUniLinReg>();
 		ClassDB::register_class<MLPPOutlierFinder>();
 		ClassDB::register_class<MLPPProbitReg>();
+		ClassDB::register_class<MLPPSVC>();
 
 		ClassDB::register_class<MLPPDataESimple>();
 		ClassDB::register_class<MLPPDataSimple>();

test/mlpp_tests.cpp

@@ -51,9 +51,9 @@
 #include "../mlpp/outlier_finder/outlier_finder_old.h"
 #include "../mlpp/pca/pca_old.h"
 #include "../mlpp/probit_reg/probit_reg_old.h"
+#include "../mlpp/svc/svc_old.h"
 #include "../mlpp/uni_lin_reg/uni_lin_reg_old.h"
 #include "../mlpp/wgan/wgan_old.h"
-#include "../mlpp/svc/svc_old.h"
 
 Vector<real_t> dstd_vec_to_vec(const std::vector<real_t> &in) {
 	Vector<real_t> r;
@@ -414,10 +414,16 @@ void MLPPTests::test_support_vector_classification(bool ui) {
 
 	// SUPPORT VECTOR CLASSIFICATION
 	Ref<MLPPDataSimple> dt = data.load_breast_cancer_svc(_breast_cancer_svm_data_path);
+
 	MLPPSVCOld model_old(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector(), ui);
 	model_old.SGD(0.00001, 100000, ui);
 	alg.printVector(model_old.modelSetTest(dt->get_input()->to_std_vector()));
-	std::cout << "ACCURACY: " << 100 * model_old.score() << "%" << std::endl;
+	std::cout << "ACCURACY (old): " << 100 * model_old.score() << "%" << std::endl;
+
+	MLPPSVC model(dt->get_input(), dt->get_output(), ui);
+	model.sgd(0.00001, 100000, ui);
+	PLOG_MSG((model.model_set_test(dt->get_input())->to_string()));
+	PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%");
 }
 
 void MLPPTests::test_mlp(bool ui) {