Now MLPPExpReg uses engine classes.

mlpp/exp_reg/exp_reg.cpp

@@ -14,11 +14,11 @@
 #include <iostream>
 #include <random>
 
-std::vector<real_t> MLPPExpReg::model_set_test(std::vector<std::vector<real_t>> X) {
+Ref<MLPPVector> MLPPExpReg::model_set_test(const Ref<MLPPMatrix> &X) {
 	return evaluatem(X);
 }
 
-real_t MLPPExpReg::model_test(std::vector<real_t> x) {
+real_t MLPPExpReg::model_test(const Ref<MLPPVector> &x) {
 	return evaluatev(x);
 }
 
@@ -34,35 +34,35 @@ void MLPPExpReg::gradient_descent(real_t learning_rate, int max_epoch, bool ui)
 	while (true) {
 		cost_prev = cost(_y_hat, _output_set);
 
-		std::vector<real_t> error = alg.subtraction(_y_hat, _output_set);
+		Ref<MLPPVector> error = alg.subtractionnv(_y_hat, _output_set);
 
 		for (int i = 0; i < _k; i++) {
 			// Calculating the weight gradient
 			real_t sum = 0;
 			for (int j = 0; j < _n; j++) {
-				sum += error[j] * _input_set[j][i] * std::pow(_weights[i], _input_set[j][i] - 1);
+				sum += error->get_element(j) * _input_set->get_element(j, i) * Math::pow(_weights->get_element(i), _input_set->get_element(j, i) - 1);
 			}
 			real_t w_gradient = sum / _n;
 
 			// Calculating the initial gradient
 			real_t sum2 = 0;
 			for (int j = 0; j < _n; j++) {
-				sum2 += error[j] * std::pow(_weights[i], _input_set[j][i]);
+				sum2 += error->get_element(j) * Math::pow(_weights->get_element(i), _input_set->get_element(j, i));
 			}
 
 			real_t i_gradient = sum2 / _n;
 
 			// Weight/initial updation
-			_weights[i] -= learning_rate * w_gradient;
+			_weights->set_element(i, _weights->get_element(i) - learning_rate * w_gradient);
-			_initial[i] -= learning_rate * i_gradient;
+			_initial->set_element(i, _initial->get_element(i) - learning_rate * i_gradient);
 		}
 
-		_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
+		_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
 
 		// Calculating the bias gradient
 		real_t sum = 0;
 		for (int j = 0; j < _n; j++) {
-			sum += (_y_hat[j] - _output_set[j]);
+			sum += (_y_hat->get_element(j) - _output_set->get_element(j));
 		}
 		real_t b_gradient = sum / _n;
 
@@ -72,8 +72,8 @@ void MLPPExpReg::gradient_descent(real_t learning_rate, int max_epoch, bool ui)
 		forward_pass();
 
 		if (ui) {
-			MLPPUtilities::CostInfo(epoch, cost_prev, cost(_y_hat, _output_set));
+			MLPPUtilities::cost_info(epoch, cost_prev, cost(_y_hat, _output_set));
-			MLPPUtilities::UI(_weights, _bias);
+			MLPPUtilities::print_ui_vb(_weights, _bias);
 		}
 
 		epoch++;
@@ -94,35 +94,53 @@ void MLPPExpReg::sgd(real_t learning_rate, int max_epoch, bool ui) {
 	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> y_hat_row_tmp;
+	y_hat_row_tmp.instance();
+	y_hat_row_tmp->resize(1);
+
+	Ref<MLPPVector> output_set_row_tmp;
+	output_set_row_tmp.instance();
+	output_set_row_tmp->resize(1);
+
 	while (true) {
 		int output_index = distribution(generator);
 
-		real_t y_hat = evaluatev(_input_set[output_index]);
+		_input_set->get_row_into_mlpp_vector(output_index, input_set_row_tmp);
-		cost_prev = cost({ y_hat }, { _output_set[output_index] });
+		real_t output_set_element = _output_set->get_element(output_index);
+		output_set_row_tmp->set_element(0, output_set_element);
+
+		real_t y_hat = evaluatev(input_set_row_tmp);
+		y_hat_row_tmp->set_element(0, y_hat);
+
+		cost_prev = cost(y_hat_row_tmp, output_set_row_tmp);
 
 		for (int i = 0; i < _k; i++) {
 			// Calculating the weight gradients
 
-			real_t w_gradient = (y_hat - _output_set[output_index]) * _input_set[output_index][i] * std::pow(_weights[i], _input_set[output_index][i] - 1);
+			real_t w_gradient = (y_hat - output_set_element) * input_set_row_tmp->get_element(i) * Math::pow(_weights->get_element(i), _input_set->get_element(output_index, i) - 1);
-			real_t i_gradient = (y_hat - _output_set[output_index]) * std::pow(_weights[i], _input_set[output_index][i]);
+			real_t i_gradient = (y_hat - output_set_element) * Math::pow(_weights->get_element(i), _input_set->get_element(output_index, i));
 
 			// Weight/initial updation
-			_weights[i] -= learning_rate * w_gradient;
+			_weights->set_element(i, _weights->get_element(i) - learning_rate * w_gradient);
-			_initial[i] -= learning_rate * i_gradient;
+			_initial->set_element(i, _initial->get_element(i) - learning_rate * i_gradient);
 		}
 
-		_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
+		_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
 
 		// Calculating the bias gradients
-		real_t b_gradient = (y_hat - _output_set[output_index]);
+		real_t b_gradient = (y_hat - output_set_element);
 
 		// Bias updation
 		_bias -= learning_rate * b_gradient;
-		y_hat = evaluatev(_input_set[output_index]);
+		y_hat = evaluatev(input_set_row_tmp);
 
 		if (ui) {
-			MLPPUtilities::CostInfo(epoch, cost_prev, cost({ y_hat }, { _output_set[output_index] }));
+			MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat_row_tmp, output_set_row_tmp));
-			MLPPUtilities::UI(_weights, _bias);
+			MLPPUtilities::print_ui_vb(_weights, _bias);
 		}
 
 		epoch++;
@@ -144,51 +162,52 @@ void MLPPExpReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size,
 
 	// Creating the mini-batches
 	int n_mini_batch = _n / mini_batch_size;
-	auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
+	MLPPUtilities::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, n_mini_batch);
-	auto input_mini_batches = std::get<0>(batches);
-	auto output_mini_batches = std::get<1>(batches);
 
 	while (true) {
 		for (int i = 0; i < n_mini_batch; i++) {
-			std::vector<real_t> y_hat = evaluatem(input_mini_batches[i]);
+			Ref<MLPPMatrix> current_input_batch = batches.input_sets[i];
-			cost_prev = cost(y_hat, output_mini_batches[i]);
+			Ref<MLPPVector> current_output_batch = batches.output_sets[i];
-			std::vector<real_t> error = alg.subtraction(y_hat, output_mini_batches[i]);
+
+			Ref<MLPPVector> y_hat = evaluatem(current_input_batch);
+			cost_prev = cost(y_hat, current_output_batch);
+			Ref<MLPPVector> error = alg.subtractionnv(y_hat, current_output_batch);
 
 			for (int j = 0; j < _k; j++) {
 				// Calculating the weight gradient
 				real_t sum = 0;
-				for (uint32_t k = 0; k < output_mini_batches[i].size(); k++) {
+				for (int k = 0; k < current_output_batch->size(); k++) {
-					sum += error[k] * input_mini_batches[i][k][j] * std::pow(_weights[j], input_mini_batches[i][k][j] - 1);
+					sum += error->get_element(k) * current_input_batch->get_element(k, j) * Math::pow(_weights->get_element(j), current_input_batch->get_element(k, j) - 1);
 				}
-				real_t w_gradient = sum / output_mini_batches[i].size();
+				real_t w_gradient = sum / current_output_batch->size();
 
 				// Calculating the initial gradient
 				real_t sum2 = 0;
-				for (uint32_t k = 0; k < output_mini_batches[i].size(); k++) {
+				for (int k = 0; k < current_output_batch->size(); k++) {
-					sum2 += error[k] * std::pow(_weights[j], input_mini_batches[i][k][j]);
+					sum2 += error->get_element(k) * Math::pow(_weights->get_element(j), current_input_batch->get_element(k, j));
 				}
 
-				real_t i_gradient = sum2 / output_mini_batches[i].size();
+				real_t i_gradient = sum2 / current_output_batch->size();
 
 				// Weight/initial updation
-				_weights[j] -= learning_rate * w_gradient;
+				_weights->set_element(i, _weights->get_element(i) - learning_rate * w_gradient);
-				_initial[j] -= learning_rate * i_gradient;
+				_initial->set_element(i, _initial->get_element(i) - learning_rate * i_gradient);
 			}
 
-			_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
+			_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
 
 			// Calculating the bias gradient
 			real_t sum = 0;
-			for (uint32_t j = 0; j < output_mini_batches[i].size(); j++) {
+			for (int j = 0; j < current_output_batch->size(); j++) {
-				sum += (y_hat[j] - output_mini_batches[i][j]);
+				sum += (y_hat->get_element(j) - current_output_batch->get_element(j));
 			}
 
 			//real_t b_gradient = sum / output_mini_batches[i].size();
-			y_hat = evaluatem(input_mini_batches[i]);
+			y_hat = evaluatem(current_input_batch);
 
 			if (ui) {
-				MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, output_mini_batches[i]));
+				MLPPUtilities::cost_info(epoch, cost_prev, cost(y_hat, current_output_batch));
-				MLPPUtilities::UI(_weights, _bias);
+				MLPPUtilities::print_ui_vb(_weights, _bias);
 			}
 		}
 
@@ -205,28 +224,40 @@ void MLPPExpReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size,
 real_t MLPPExpReg::score() {
 	MLPPUtilities util;
 
-	return util.performance(_y_hat, _output_set);
+	return util.performance_vec(_y_hat, _output_set);
 }
 
-void MLPPExpReg::save(std::string file_name) {
+void MLPPExpReg::save(const String &file_name) {
 	MLPPUtilities util;
 
-	util.saveParameters(file_name, _weights, _initial, _bias);
+	//util.saveParameters(file_name, _weights, _initial, _bias);
 }
 
-MLPPExpReg::MLPPExpReg(std::vector<std::vector<real_t>> p_input_set, std::vector<real_t> p_output_set, std::string p_reg, real_t p_lambda, real_t p_alpha) {
+MLPPExpReg::MLPPExpReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, MLPPReg::RegularizationType p_reg, real_t p_lambda, real_t p_alpha) {
 	_input_set = p_input_set;
 	_output_set = p_output_set;
-	_n = p_input_set.size();
+	_n = p_input_set->size().y;
-	_k = p_input_set[0].size();
+	_k = p_input_set->size().x;
 	_reg = p_reg;
 	_lambda = p_lambda;
 	_alpha = p_alpha;
 
-	_y_hat.resize(_n);
+	_y_hat.instance();
-	_weights = MLPPUtilities::weightInitialization(_k);
+	_y_hat->resize(_n);
-	_initial = MLPPUtilities::weightInitialization(_k);
+
-	_bias = MLPPUtilities::biasInitialization();
+	MLPPUtilities util;
+
+	_weights.instance();
+	_weights->resize(_k);
+
+	util.weight_initializationv(_weights);
+
+	_initial.instance();
+	_initial->resize(_k);
+
+	util.weight_initializationv(_initial);
+
+	_bias = util.bias_initializationr();
 }
 
 MLPPExpReg::MLPPExpReg() {
@@ -234,33 +265,38 @@ MLPPExpReg::MLPPExpReg() {
 MLPPExpReg::~MLPPExpReg() {
 }
 
-real_t MLPPExpReg::cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
+real_t MLPPExpReg::cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
 	MLPPReg regularization;
 	MLPPCost mlpp_cost;
 
-	return mlpp_cost.MSE(y_hat, y) + regularization.regTerm(_weights, _lambda, _alpha, _reg);
+	return mlpp_cost.msev(y_hat, y) + regularization.reg_termv(_weights, _lambda, _alpha, _reg);
 }
 
-real_t MLPPExpReg::evaluatev(std::vector<real_t> x) {
+real_t MLPPExpReg::evaluatev(const Ref<MLPPVector> &x) {
 	real_t y_hat = 0;
 
-	for (uint32_t i = 0; i < x.size(); i++) {
+	for (int i = 0; i < x->size(); i++) {
-		y_hat += _initial[i] * std::pow(_weights[i], x[i]);
+		y_hat += _initial->get_element(i) * Math::pow(_weights->get_element(i), x->get_element(i));
 	}
 
 	return y_hat + _bias;
 }
 
-std::vector<real_t> MLPPExpReg::evaluatem(std::vector<std::vector<real_t>> X) {
+Ref<MLPPVector> MLPPExpReg::evaluatem(const Ref<MLPPMatrix> &X) {
-	std::vector<real_t> y_hat;
+	Ref<MLPPVector> y_hat;
-	y_hat.resize(X.size());
+	y_hat.instance();
+	y_hat->resize(X->size().y);
 
-	for (uint32_t i = 0; i < X.size(); i++) {
+	for (int i = 0; i < X->size().y; i++) {
-		y_hat[i] = 0;
+		real_t y;
-		for (uint32_t j = 0; j < X[i].size(); j++) {
+
-			y_hat[i] += _initial[j] * std::pow(_weights[j], X[i][j]);
+		for (int j = 0; j < X->size().x; j++) {
+			y += _initial->get_element(j) * Math::pow(_weights->get_element(j), X->get_element(i, j));
 		}
-		y_hat[i] += _bias;
+
+		y += _bias;
+
+		y_hat->set_element(i, y);
 	}
 
 	return y_hat;

mlpp/exp_reg/exp_reg.h

@@ -12,15 +12,17 @@
 
 #include "core/object/reference.h"
 
-#include <string>
+#include "../regularization/reg.h"
-#include <vector>
+
+#include "../lin_alg/mlpp_matrix.h"
+#include "../lin_alg/mlpp_vector.h"
 
 class MLPPExpReg : public Reference {
 	GDCLASS(MLPPExpReg, Reference);
 
 public:
-	std::vector<real_t> model_set_test(std::vector<std::vector<real_t>> X);
+	Ref<MLPPVector> model_set_test(const Ref<MLPPMatrix> &X);
-	real_t model_test(std::vector<real_t> 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);
@@ -28,35 +30,35 @@ public:
 
 	real_t score();
 
-	void save(std::string file_name);
+	void save(const String &file_name);
 
-	MLPPExpReg(std::vector<std::vector<real_t>> p_input_set, std::vector<real_t> p_output_set, std::string p_reg = "None", real_t p_lambda = 0.5, real_t p_alpha = 0.5);
+	MLPPExpReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5);
 
 	MLPPExpReg();
 	~MLPPExpReg();
 
 protected:
-	real_t cost(std::vector<real_t> y_hat, std::vector<real_t> y);
+	real_t cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
 
-	real_t evaluatev(std::vector<real_t> x);
+	real_t evaluatev(const Ref<MLPPVector> &x);
-	std::vector<real_t> evaluatem(std::vector<std::vector<real_t>> X);
+	Ref<MLPPVector> evaluatem(const Ref<MLPPMatrix> &X);
 
 	void forward_pass();
 
 	static void _bind_methods();
 
-	std::vector<std::vector<real_t>> _input_set;
+	Ref<MLPPMatrix> _input_set;
-	std::vector<real_t> _output_set;
+	Ref<MLPPVector> _output_set;
-	std::vector<real_t> _y_hat;
+	Ref<MLPPVector> _y_hat;
-	std::vector<real_t> _weights;
+	Ref<MLPPVector> _weights;
-	std::vector<real_t> _initial;
+	Ref<MLPPVector> _initial;
 	real_t _bias;
 
 	int _n;
 	int _k;
 
 	// Regularization Params
-	std::string _reg;
+	MLPPReg::RegularizationType _reg;
 	real_t _lambda;
 	real_t _alpha; /* This is the controlling param for Elastic Net*/
 };

test/mlpp_tests.cpp

@@ -447,10 +447,18 @@ void MLPPTests::test_exp_reg_regression(bool ui) {
 	alg.printVector(model_old.modelSetTest(alg.transpose(inputSet)));
 	std::cout << "ACCURACY: " << 100 * model_old.score() << "%" << std::endl;
 
-	MLPPExpReg model(alg.transpose(inputSet), outputSet);
+	Ref<MLPPMatrix> input_set;
+	input_set.instance();
+	input_set->set_from_std_vectors(inputSet);
+
+	Ref<MLPPVector> output_set;
+	output_set.instance();
+	output_set->set_from_std_vector(outputSet);
+
+	MLPPExpReg model(alg.transposem(input_set), output_set);
 	model.sgd(0.001, 10000, ui);
-	alg.printVector(model.model_set_test(alg.transpose(inputSet)));
+	PLOG_MSG(model.model_set_test(alg.transposem(input_set))->to_string());
-	std::cout << "ACCURACY: " << 100 * model.score() << "%" << std::endl;
+	PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%");
 }
 void MLPPTests::test_tanh_regression(bool ui) {
 	MLPPLinAlg alg;