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Initial cleanup pass on MLPPLinReg.
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2a5c278f40
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afedf90694
@ -5,6 +5,7 @@
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//
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#include "lin_reg.h"
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#include "../cost/cost.h"
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#include "../lin_alg/lin_alg.h"
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#include "../regularization/reg.h"
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@ -15,315 +16,395 @@
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#include <iostream>
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#include <random>
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MLPPLinReg::MLPPLinReg(std::vector<std::vector<real_t>> p_inputSet, std::vector<real_t> p_outputSet, std::string p_reg, real_t p_lambda, real_t p_alpha) {
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inputSet = p_inputSet;
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outputSet = p_outputSet;
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n = p_inputSet.size();
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k = p_inputSet[0].size();
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reg = p_reg;
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lambda = p_lambda;
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alpha = p_alpha;
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/*
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Ref<MLPPMatrix> MLPPLinReg::get_input_set() {
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return _input_set;
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}
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void MLPPLinReg::set_input_set(const Ref<MLPPMatrix> &val) {
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_input_set = val;
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y_hat.resize(n);
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weights = MLPPUtilities::weightInitialization(k);
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bias = MLPPUtilities::biasInitialization();
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_initialized = false;
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}
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std::vector<real_t> MLPPLinReg::modelSetTest(std::vector<std::vector<real_t>> X) {
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return Evaluate(X);
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Ref<MLPPVector> MLPPLinReg::get_output_set() {
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return _output_set;
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}
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void MLPPLinReg::set_output_set(const Ref<MLPPVector> &val) {
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_output_set = val;
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_initialized = false;
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}
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real_t MLPPLinReg::modelTest(std::vector<real_t> x) {
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return Evaluate(x);
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MLPPReg::RegularizationType MLPPLinReg::get_reg() {
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return _reg;
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}
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void MLPPLinReg::set_reg(const MLPPReg::RegularizationType val) {
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_reg = val;
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_initialized = false;
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}
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void MLPPLinReg::NewtonRaphson(real_t learning_rate, int max_epoch, bool UI) {
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real_t MLPPLinReg::get_lambda() {
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return _lambda;
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}
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void MLPPLinReg::set_lambda(const real_t val) {
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_lambda = val;
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_initialized = false;
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}
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real_t MLPPLinReg::get_alpha() {
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return _alpha;
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}
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void MLPPLinReg::set_alpha(const real_t val) {
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_alpha = val;
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_initialized = false;
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}
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*/
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std::vector<real_t> MLPPLinReg::model_set_test(std::vector<std::vector<real_t>> X) {
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ERR_FAIL_COND_V(!_initialized, std::vector<real_t>());
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return evaluatem(X);
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}
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real_t MLPPLinReg::model_test(std::vector<real_t> x) {
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ERR_FAIL_COND_V(!_initialized, 0);
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return evaluatev(x);
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}
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void MLPPLinReg::newton_raphson(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_initialized);
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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forwardPass();
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while (true) {
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cost_prev = Cost(y_hat, outputSet);
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std::vector<real_t> error = alg.subtraction(y_hat, outputSet);
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forward_pass();
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while (true) {
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cost_prev = cost(_y_hat, _output_set);
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std::vector<real_t> error = alg.subtraction(_y_hat, _output_set);
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// Calculating the weight gradients (2nd derivative)
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std::vector<real_t> first_derivative = alg.mat_vec_mult(alg.transpose(inputSet), error);
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std::vector<std::vector<real_t>> second_derivative = alg.matmult(alg.transpose(inputSet), inputSet);
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weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate / n, alg.mat_vec_mult(alg.transpose(alg.inverse(second_derivative)), first_derivative)));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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std::vector<real_t> first_derivative = alg.mat_vec_mult(alg.transpose(_input_set), error);
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std::vector<std::vector<real_t>> second_derivative = alg.matmult(alg.transpose(_input_set), _input_set);
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_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate / _n, alg.mat_vec_mult(alg.transpose(alg.inverse(second_derivative)), first_derivative)));
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_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradients (2nd derivative)
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bias -= learning_rate * alg.sum_elements(error) / n; // We keep this the same. The 2nd derivative is just [1].
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forwardPass();
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_bias -= learning_rate * alg.sum_elements(error) / _n; // We keep this the same. The 2nd derivative is just [1].
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
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MLPPUtilities::UI(weights, bias);
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forward_pass();
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(_y_hat, _output_set));
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MLPPUtilities::UI(_weights, _bias);
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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}
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void MLPPLinReg::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
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void MLPPLinReg::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_initialized);
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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forwardPass();
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forward_pass();
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while (true) {
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cost_prev = Cost(y_hat, outputSet);
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cost_prev = cost(_y_hat, _output_set);
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std::vector<real_t> error = alg.subtraction(y_hat, outputSet);
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std::vector<real_t> error = alg.subtraction(_y_hat, _output_set);
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// Calculating the weight gradients
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weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate / n, alg.mat_vec_mult(alg.transpose(inputSet), error)));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate / _n, alg.mat_vec_mult(alg.transpose(_input_set), error)));
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_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradients
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bias -= learning_rate * alg.sum_elements(error) / n;
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forwardPass();
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_bias -= learning_rate * alg.sum_elements(error) / _n;
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet));
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MLPPUtilities::UI(weights, bias);
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forward_pass();
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(_y_hat, _output_set));
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MLPPUtilities::UI(_weights, _bias);
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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}
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void MLPPLinReg::SGD(real_t learning_rate, int max_epoch, bool UI) {
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void MLPPLinReg::sgd(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_initialized);
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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std::random_device rd;
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std::default_random_engine generator(rd());
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std::uniform_int_distribution<int> distribution(0, int(_n - 1));
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while (true) {
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std::random_device rd;
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std::default_random_engine generator(rd());
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std::uniform_int_distribution<int> distribution(0, int(n - 1));
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int outputIndex = distribution(generator);
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real_t y_hat = Evaluate(inputSet[outputIndex]);
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cost_prev = Cost({ y_hat }, { outputSet[outputIndex] });
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real_t y_hat = evaluatev(_input_set[outputIndex]);
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cost_prev = cost({ y_hat }, { _output_set[outputIndex] });
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real_t error = y_hat - outputSet[outputIndex];
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real_t error = y_hat - _output_set[outputIndex];
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// Weight updation
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weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate * error, inputSet[outputIndex]));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate * error, _input_set[outputIndex]));
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_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
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// Bias updation
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bias -= learning_rate * error;
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_bias -= learning_rate * error;
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y_hat = Evaluate({ inputSet[outputIndex] });
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y_hat = evaluatev(_input_set[outputIndex]);
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost({ y_hat }, { outputSet[outputIndex] }));
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MLPPUtilities::UI(weights, bias);
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost({ y_hat }, { _output_set[outputIndex] }));
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MLPPUtilities::UI(_weights, _bias);
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forwardPass();
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forward_pass();
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}
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void MLPPLinReg::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
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void MLPPLinReg::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui) {
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ERR_FAIL_COND(!_initialized);
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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// Creating the mini-batches
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int n_mini_batch = n / mini_batch_size;
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auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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int n_mini_batch = _n / mini_batch_size;
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auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
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auto inputMiniBatches = std::get<0>(batches);
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auto outputMiniBatches = std::get<1>(batches);
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while (true) {
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for (int i = 0; i < n_mini_batch; i++) {
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std::vector<real_t> y_hat = Evaluate(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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std::vector<real_t> y_hat = evaluatem(inputMiniBatches[i]);
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cost_prev = cost(y_hat, outputMiniBatches[i]);
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std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]);
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// Calculating the weight gradients
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weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate / outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error)));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate / outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error)));
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_weights = regularization.regWeights(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradients
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bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size();
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y_hat = Evaluate(inputMiniBatches[i]);
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_bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size();
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y_hat = evaluatem(inputMiniBatches[i]);
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i]));
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MLPPUtilities::UI(weights, bias);
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputMiniBatches[i]));
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MLPPUtilities::UI(_weights, _bias);
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}
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forwardPass();
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forward_pass();
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}
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void MLPPLinReg::Momentum(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool UI) {
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void MLPPLinReg::momentum(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool ui) {
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ERR_FAIL_COND(!_initialized);
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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// Creating the mini-batches
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int n_mini_batch = n / mini_batch_size;
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auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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int n_mini_batch = _n / mini_batch_size;
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auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
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auto inputMiniBatches = std::get<0>(batches);
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auto outputMiniBatches = std::get<1>(batches);
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// Initializing necessary components for Momentum.
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std::vector<real_t> v = alg.zerovec(weights.size());
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std::vector<real_t> v = alg.zerovec(_weights.size());
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while (true) {
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for (int i = 0; i < n_mini_batch; i++) {
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std::vector<real_t> y_hat = Evaluate(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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std::vector<real_t> y_hat = evaluatem(inputMiniBatches[i]);
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cost_prev = cost(y_hat, outputMiniBatches[i]);
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std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]);
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// Calculating the weight gradients
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std::vector<real_t> gradient = alg.scalarMultiply(1 / outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error));
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std::vector<real_t> RegDerivTerm = regularization.regDerivTerm(weights, lambda, alpha, reg);
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std::vector<real_t> RegDerivTerm = regularization.regDerivTerm(_weights, _lambda, _alpha, _reg);
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std::vector<real_t> weight_grad = alg.addition(gradient, RegDerivTerm); // Weight_grad_final
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v = alg.addition(alg.scalarMultiply(gamma, v), alg.scalarMultiply(learning_rate, weight_grad));
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weights = alg.subtraction(weights, v);
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_weights = alg.subtraction(_weights, v);
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// Calculating the bias gradients
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bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal
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y_hat = Evaluate(inputMiniBatches[i]);
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_bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal
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y_hat = evaluatem(inputMiniBatches[i]);
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i]));
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MLPPUtilities::UI(weights, bias);
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if (ui) {
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MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputMiniBatches[i]));
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MLPPUtilities::UI(_weights, _bias);
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}
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forwardPass();
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forward_pass();
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}
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void MLPPLinReg::NAG(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool UI) {
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void MLPPLinReg::nag(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool ui) {
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ERR_FAIL_COND(!_initialized);
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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// Creating the mini-batches
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int n_mini_batch = n / mini_batch_size;
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auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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int n_mini_batch = _n / mini_batch_size;
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auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
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auto inputMiniBatches = std::get<0>(batches);
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auto outputMiniBatches = std::get<1>(batches);
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// Initializing necessary components for Momentum.
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std::vector<real_t> v = alg.zerovec(weights.size());
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std::vector<real_t> v = alg.zerovec(_weights.size());
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while (true) {
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for (int i = 0; i < n_mini_batch; i++) {
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weights = alg.subtraction(weights, alg.scalarMultiply(gamma, v)); // "Aposterori" calculation
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_weights = alg.subtraction(_weights, alg.scalarMultiply(gamma, v)); // "Aposterori" calculation
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std::vector<real_t> y_hat = Evaluate(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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std::vector<real_t> y_hat = evaluatem(inputMiniBatches[i]);
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cost_prev = cost(y_hat, outputMiniBatches[i]);
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std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]);
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// Calculating the weight gradients
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std::vector<real_t> gradient = alg.scalarMultiply(1 / outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error));
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std::vector<real_t> RegDerivTerm = regularization.regDerivTerm(weights, lambda, alpha, reg);
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std::vector<real_t> RegDerivTerm = regularization.regDerivTerm(_weights, _lambda, _alpha, _reg);
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std::vector<real_t> weight_grad = alg.addition(gradient, RegDerivTerm); // Weight_grad_final
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v = alg.addition(alg.scalarMultiply(gamma, v), alg.scalarMultiply(learning_rate, weight_grad));
|
||||
|
||||
weights = alg.subtraction(weights, v);
|
||||
_weights = alg.subtraction(_weights, v);
|
||||
|
||||
// Calculating the bias gradients
|
||||
bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal
|
||||
y_hat = Evaluate(inputMiniBatches[i]);
|
||||
_bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal
|
||||
y_hat = evaluatem(inputMiniBatches[i]);
|
||||
|
||||
if (UI) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i]));
|
||||
MLPPUtilities::UI(weights, bias);
|
||||
if (ui) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputMiniBatches[i]));
|
||||
MLPPUtilities::UI(_weights, _bias);
|
||||
}
|
||||
}
|
||||
|
||||
epoch++;
|
||||
|
||||
if (epoch > max_epoch) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
forwardPass();
|
||||
|
||||
forward_pass();
|
||||
}
|
||||
|
||||
void MLPPLinReg::Adagrad(real_t learning_rate, int max_epoch, int mini_batch_size, real_t e, bool UI) {
|
||||
void MLPPLinReg::adagrad(real_t learning_rate, int max_epoch, int mini_batch_size, real_t e, bool ui) {
|
||||
ERR_FAIL_COND(!_initialized);
|
||||
|
||||
MLPPLinAlg alg;
|
||||
MLPPReg regularization;
|
||||
real_t cost_prev = 0;
|
||||
int epoch = 1;
|
||||
|
||||
// Creating the mini-batches
|
||||
int n_mini_batch = n / mini_batch_size;
|
||||
auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
|
||||
int n_mini_batch = _n / mini_batch_size;
|
||||
auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
|
||||
auto inputMiniBatches = std::get<0>(batches);
|
||||
auto outputMiniBatches = std::get<1>(batches);
|
||||
|
||||
// Initializing necessary components for Adagrad.
|
||||
std::vector<real_t> v = alg.zerovec(weights.size());
|
||||
std::vector<real_t> v = alg.zerovec(_weights.size());
|
||||
while (true) {
|
||||
for (int i = 0; i < n_mini_batch; i++) {
|
||||
std::vector<real_t> y_hat = Evaluate(inputMiniBatches[i]);
|
||||
cost_prev = Cost(y_hat, outputMiniBatches[i]);
|
||||
std::vector<real_t> y_hat = evaluatem(inputMiniBatches[i]);
|
||||
cost_prev = cost(y_hat, outputMiniBatches[i]);
|
||||
|
||||
std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]);
|
||||
|
||||
// Calculating the weight gradients
|
||||
std::vector<real_t> gradient = alg.scalarMultiply(1 / outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error));
|
||||
std::vector<real_t> RegDerivTerm = regularization.regDerivTerm(weights, lambda, alpha, reg);
|
||||
std::vector<real_t> RegDerivTerm = regularization.regDerivTerm(_weights, _lambda, _alpha, _reg);
|
||||
std::vector<real_t> weight_grad = alg.addition(gradient, RegDerivTerm); // Weight_grad_final
|
||||
|
||||
v = alg.hadamard_product(weight_grad, weight_grad);
|
||||
|
||||
weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(weight_grad, alg.sqrt(alg.scalarAdd(e, v)))));
|
||||
_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(weight_grad, alg.sqrt(alg.scalarAdd(e, v)))));
|
||||
|
||||
// Calculating the bias gradients
|
||||
bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal
|
||||
y_hat = Evaluate(inputMiniBatches[i]);
|
||||
_bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal
|
||||
y_hat = evaluatem(inputMiniBatches[i]);
|
||||
|
||||
if (UI) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i]));
|
||||
MLPPUtilities::UI(weights, bias);
|
||||
if (ui) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputMiniBatches[i]));
|
||||
MLPPUtilities::UI(_weights, _bias);
|
||||
}
|
||||
}
|
||||
|
||||
epoch++;
|
||||
|
||||
if (epoch > max_epoch) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
forwardPass();
|
||||
|
||||
forward_pass();
|
||||
}
|
||||
|
||||
void MLPPLinReg::Adadelta(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t e, bool UI) {
|
||||
void MLPPLinReg::adadelta(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t e, bool ui) {
|
||||
ERR_FAIL_COND(!_initialized);
|
||||
|
||||
// Adagrad upgrade. Momentum is applied.
|
||||
MLPPLinAlg alg;
|
||||
MLPPReg regularization;
|
||||
@ -331,72 +412,77 @@ void MLPPLinReg::Adadelta(real_t learning_rate, int max_epoch, int mini_batch_si
|
||||
int epoch = 1;
|
||||
|
||||
// Creating the mini-batches
|
||||
int n_mini_batch = n / mini_batch_size;
|
||||
auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
|
||||
int n_mini_batch = _n / mini_batch_size;
|
||||
auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
|
||||
auto inputMiniBatches = std::get<0>(batches);
|
||||
auto outputMiniBatches = std::get<1>(batches);
|
||||
|
||||
// Initializing necessary components for Adagrad.
|
||||
std::vector<real_t> v = alg.zerovec(weights.size());
|
||||
std::vector<real_t> v = alg.zerovec(_weights.size());
|
||||
while (true) {
|
||||
for (int i = 0; i < n_mini_batch; i++) {
|
||||
std::vector<real_t> y_hat = Evaluate(inputMiniBatches[i]);
|
||||
cost_prev = Cost(y_hat, outputMiniBatches[i]);
|
||||
std::vector<real_t> y_hat = evaluatem(inputMiniBatches[i]);
|
||||
cost_prev = cost(y_hat, outputMiniBatches[i]);
|
||||
|
||||
std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]);
|
||||
|
||||
// Calculating the weight gradients
|
||||
std::vector<real_t> gradient = alg.scalarMultiply(1 / outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error));
|
||||
std::vector<real_t> RegDerivTerm = regularization.regDerivTerm(weights, lambda, alpha, reg);
|
||||
std::vector<real_t> RegDerivTerm = regularization.regDerivTerm(_weights, _lambda, _alpha, _reg);
|
||||
std::vector<real_t> weight_grad = alg.addition(gradient, RegDerivTerm); // Weight_grad_final
|
||||
|
||||
v = alg.addition(alg.scalarMultiply(b1, v), alg.scalarMultiply(1 - b1, alg.hadamard_product(weight_grad, weight_grad)));
|
||||
|
||||
weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(weight_grad, alg.sqrt(alg.scalarAdd(e, v)))));
|
||||
_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(weight_grad, alg.sqrt(alg.scalarAdd(e, v)))));
|
||||
|
||||
// Calculating the bias gradients
|
||||
bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal
|
||||
y_hat = Evaluate(inputMiniBatches[i]);
|
||||
_bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal
|
||||
y_hat = evaluatem(inputMiniBatches[i]);
|
||||
|
||||
if (UI) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i]));
|
||||
MLPPUtilities::UI(weights, bias);
|
||||
if (ui) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputMiniBatches[i]));
|
||||
MLPPUtilities::UI(_weights, _bias);
|
||||
}
|
||||
}
|
||||
|
||||
epoch++;
|
||||
|
||||
if (epoch > max_epoch) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
forwardPass();
|
||||
|
||||
forward_pass();
|
||||
}
|
||||
|
||||
void MLPPLinReg::Adam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool UI) {
|
||||
void MLPPLinReg::adam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui) {
|
||||
ERR_FAIL_COND(!_initialized);
|
||||
|
||||
MLPPLinAlg alg;
|
||||
MLPPReg regularization;
|
||||
real_t cost_prev = 0;
|
||||
int epoch = 1;
|
||||
|
||||
// Creating the mini-batches
|
||||
int n_mini_batch = n / mini_batch_size;
|
||||
auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
|
||||
int n_mini_batch = _n / mini_batch_size;
|
||||
auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
|
||||
auto inputMiniBatches = std::get<0>(batches);
|
||||
auto outputMiniBatches = std::get<1>(batches);
|
||||
|
||||
// Initializing necessary components for Adam.
|
||||
std::vector<real_t> m = alg.zerovec(weights.size());
|
||||
std::vector<real_t> m = alg.zerovec(_weights.size());
|
||||
|
||||
std::vector<real_t> v = alg.zerovec(weights.size());
|
||||
std::vector<real_t> v = alg.zerovec(_weights.size());
|
||||
while (true) {
|
||||
for (int i = 0; i < n_mini_batch; i++) {
|
||||
std::vector<real_t> y_hat = Evaluate(inputMiniBatches[i]);
|
||||
cost_prev = Cost(y_hat, outputMiniBatches[i]);
|
||||
std::vector<real_t> y_hat = evaluatem(inputMiniBatches[i]);
|
||||
cost_prev = cost(y_hat, outputMiniBatches[i]);
|
||||
|
||||
std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]);
|
||||
|
||||
// Calculating the weight gradients
|
||||
std::vector<real_t> gradient = alg.scalarMultiply(1 / outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error));
|
||||
std::vector<real_t> RegDerivTerm = regularization.regDerivTerm(weights, lambda, alpha, reg);
|
||||
std::vector<real_t> RegDerivTerm = regularization.regDerivTerm(_weights, _lambda, _alpha, _reg);
|
||||
std::vector<real_t> weight_grad = alg.addition(gradient, RegDerivTerm); // Weight_grad_final
|
||||
|
||||
m = alg.addition(alg.scalarMultiply(b1, m), alg.scalarMultiply(1 - b1, weight_grad));
|
||||
@ -405,50 +491,55 @@ void MLPPLinReg::Adam(real_t learning_rate, int max_epoch, int mini_batch_size,
|
||||
std::vector<real_t> m_hat = alg.scalarMultiply(1 / (1 - pow(b1, epoch)), m);
|
||||
std::vector<real_t> v_hat = alg.scalarMultiply(1 / (1 - pow(b2, epoch)), v);
|
||||
|
||||
weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(m_hat, alg.scalarAdd(e, alg.sqrt(v_hat)))));
|
||||
_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(m_hat, alg.scalarAdd(e, alg.sqrt(v_hat)))));
|
||||
|
||||
// Calculating the bias gradients
|
||||
bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal
|
||||
y_hat = Evaluate(inputMiniBatches[i]);
|
||||
_bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal
|
||||
y_hat = evaluatem(inputMiniBatches[i]);
|
||||
|
||||
if (UI) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i]));
|
||||
MLPPUtilities::UI(weights, bias);
|
||||
if (ui) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputMiniBatches[i]));
|
||||
MLPPUtilities::UI(_weights, _bias);
|
||||
}
|
||||
}
|
||||
|
||||
epoch++;
|
||||
|
||||
if (epoch > max_epoch) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
forwardPass();
|
||||
|
||||
forward_pass();
|
||||
}
|
||||
|
||||
void MLPPLinReg::Adamax(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool UI) {
|
||||
void MLPPLinReg::adamax(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui) {
|
||||
ERR_FAIL_COND(!_initialized);
|
||||
|
||||
MLPPLinAlg alg;
|
||||
MLPPReg regularization;
|
||||
real_t cost_prev = 0;
|
||||
int epoch = 1;
|
||||
|
||||
// Creating the mini-batches
|
||||
int n_mini_batch = n / mini_batch_size;
|
||||
auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
|
||||
int n_mini_batch = _n / mini_batch_size;
|
||||
auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
|
||||
auto inputMiniBatches = std::get<0>(batches);
|
||||
auto outputMiniBatches = std::get<1>(batches);
|
||||
|
||||
std::vector<real_t> m = alg.zerovec(weights.size());
|
||||
std::vector<real_t> m = alg.zerovec(_weights.size());
|
||||
|
||||
std::vector<real_t> u = alg.zerovec(weights.size());
|
||||
std::vector<real_t> u = alg.zerovec(_weights.size());
|
||||
while (true) {
|
||||
for (int i = 0; i < n_mini_batch; i++) {
|
||||
std::vector<real_t> y_hat = Evaluate(inputMiniBatches[i]);
|
||||
cost_prev = Cost(y_hat, outputMiniBatches[i]);
|
||||
std::vector<real_t> y_hat = evaluatem(inputMiniBatches[i]);
|
||||
cost_prev = cost(y_hat, outputMiniBatches[i]);
|
||||
|
||||
std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]);
|
||||
|
||||
// Calculating the weight gradients
|
||||
std::vector<real_t> gradient = alg.scalarMultiply(1 / outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error));
|
||||
std::vector<real_t> RegDerivTerm = regularization.regDerivTerm(weights, lambda, alpha, reg);
|
||||
std::vector<real_t> RegDerivTerm = regularization.regDerivTerm(_weights, _lambda, _alpha, _reg);
|
||||
std::vector<real_t> weight_grad = alg.addition(gradient, RegDerivTerm); // Weight_grad_final
|
||||
|
||||
m = alg.addition(alg.scalarMultiply(b1, m), alg.scalarMultiply(1 - b1, weight_grad));
|
||||
@ -456,51 +547,57 @@ void MLPPLinReg::Adamax(real_t learning_rate, int max_epoch, int mini_batch_size
|
||||
|
||||
std::vector<real_t> m_hat = alg.scalarMultiply(1 / (1 - pow(b1, epoch)), m);
|
||||
|
||||
weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(m_hat, u)));
|
||||
_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(m_hat, u)));
|
||||
|
||||
// Calculating the bias gradients
|
||||
bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal
|
||||
y_hat = Evaluate(inputMiniBatches[i]);
|
||||
_bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal
|
||||
y_hat = evaluatem(inputMiniBatches[i]);
|
||||
|
||||
if (UI) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i]));
|
||||
MLPPUtilities::UI(weights, bias);
|
||||
if (ui) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputMiniBatches[i]));
|
||||
MLPPUtilities::UI(_weights, _bias);
|
||||
}
|
||||
}
|
||||
|
||||
epoch++;
|
||||
|
||||
if (epoch > max_epoch) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
forwardPass();
|
||||
|
||||
forward_pass();
|
||||
}
|
||||
|
||||
void MLPPLinReg::Nadam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool UI) {
|
||||
void MLPPLinReg::nadam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui) {
|
||||
ERR_FAIL_COND(!_initialized);
|
||||
|
||||
MLPPLinAlg alg;
|
||||
MLPPReg regularization;
|
||||
real_t cost_prev = 0;
|
||||
int epoch = 1;
|
||||
|
||||
// Creating the mini-batches
|
||||
int n_mini_batch = n / mini_batch_size;
|
||||
auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
|
||||
int n_mini_batch = _n / mini_batch_size;
|
||||
auto batches = MLPPUtilities::createMiniBatches(_input_set, _output_set, n_mini_batch);
|
||||
auto inputMiniBatches = std::get<0>(batches);
|
||||
auto outputMiniBatches = std::get<1>(batches);
|
||||
|
||||
// Initializing necessary components for Adam.
|
||||
std::vector<real_t> m = alg.zerovec(weights.size());
|
||||
std::vector<real_t> v = alg.zerovec(weights.size());
|
||||
std::vector<real_t> m_final = alg.zerovec(weights.size());
|
||||
std::vector<real_t> m = alg.zerovec(_weights.size());
|
||||
std::vector<real_t> v = alg.zerovec(_weights.size());
|
||||
std::vector<real_t> m_final = alg.zerovec(_weights.size());
|
||||
|
||||
while (true) {
|
||||
for (int i = 0; i < n_mini_batch; i++) {
|
||||
std::vector<real_t> y_hat = Evaluate(inputMiniBatches[i]);
|
||||
cost_prev = Cost(y_hat, outputMiniBatches[i]);
|
||||
std::vector<real_t> y_hat = evaluatem(inputMiniBatches[i]);
|
||||
cost_prev = cost(y_hat, outputMiniBatches[i]);
|
||||
|
||||
std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]);
|
||||
|
||||
// Calculating the weight gradients
|
||||
std::vector<real_t> gradient = alg.scalarMultiply(1 / outputMiniBatches[i].size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), error));
|
||||
std::vector<real_t> RegDerivTerm = regularization.regDerivTerm(weights, lambda, alpha, reg);
|
||||
std::vector<real_t> RegDerivTerm = regularization.regDerivTerm(_weights, _lambda, _alpha, _reg);
|
||||
std::vector<real_t> weight_grad = alg.addition(gradient, RegDerivTerm); // Weight_grad_final
|
||||
|
||||
m = alg.addition(alg.scalarMultiply(b1, m), alg.scalarMultiply(1 - b1, weight_grad));
|
||||
@ -510,88 +607,168 @@ void MLPPLinReg::Nadam(real_t learning_rate, int max_epoch, int mini_batch_size,
|
||||
std::vector<real_t> m_hat = alg.scalarMultiply(1 / (1 - pow(b1, epoch)), m);
|
||||
std::vector<real_t> v_hat = alg.scalarMultiply(1 / (1 - pow(b2, epoch)), v);
|
||||
|
||||
weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(m_final, alg.scalarAdd(e, alg.sqrt(v_hat)))));
|
||||
_weights = alg.subtraction(_weights, alg.scalarMultiply(learning_rate, alg.elementWiseDivision(m_final, alg.scalarAdd(e, alg.sqrt(v_hat)))));
|
||||
|
||||
// Calculating the bias gradients
|
||||
bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal
|
||||
y_hat = Evaluate(inputMiniBatches[i]);
|
||||
_bias -= learning_rate * alg.sum_elements(error) / outputMiniBatches[i].size(); // As normal
|
||||
y_hat = evaluatem(inputMiniBatches[i]);
|
||||
|
||||
if (UI) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputMiniBatches[i]));
|
||||
MLPPUtilities::UI(weights, bias);
|
||||
if (ui) {
|
||||
MLPPUtilities::CostInfo(epoch, cost_prev, cost(y_hat, outputMiniBatches[i]));
|
||||
MLPPUtilities::UI(_weights, _bias);
|
||||
}
|
||||
}
|
||||
|
||||
epoch++;
|
||||
|
||||
if (epoch > max_epoch) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
forwardPass();
|
||||
|
||||
forward_pass();
|
||||
}
|
||||
|
||||
void MLPPLinReg::normalEquation() {
|
||||
void MLPPLinReg::normal_equation() {
|
||||
ERR_FAIL_COND(!_initialized);
|
||||
|
||||
MLPPLinAlg alg;
|
||||
MLPPStat stat;
|
||||
std::vector<real_t> x_means;
|
||||
std::vector<std::vector<real_t>> inputSetT = alg.transpose(inputSet);
|
||||
std::vector<std::vector<real_t>> _input_setT = alg.transpose(_input_set);
|
||||
|
||||
x_means.resize(inputSetT.size());
|
||||
for (uint32_t i = 0; i < inputSetT.size(); i++) {
|
||||
x_means[i] = (stat.mean(inputSetT[i]));
|
||||
x_means.resize(_input_setT.size());
|
||||
for (uint32_t i = 0; i < _input_setT.size(); i++) {
|
||||
x_means[i] = (stat.mean(_input_setT[i]));
|
||||
}
|
||||
|
||||
//try {
|
||||
std::vector<real_t> temp;
|
||||
temp.resize(k);
|
||||
temp = alg.mat_vec_mult(alg.inverse(alg.matmult(alg.transpose(inputSet), inputSet)), alg.mat_vec_mult(alg.transpose(inputSet), outputSet));
|
||||
if (std::isnan(temp[0])) {
|
||||
//throw 99;
|
||||
//TODO ERR_FAIL_COND
|
||||
std::cout << "ERR: Resulting matrix was noninvertible/degenerate, and so the normal equation could not be performed. Try utilizing gradient descent." << std::endl;
|
||||
return;
|
||||
temp.resize(_k);
|
||||
temp = alg.mat_vec_mult(alg.inverse(alg.matmult(alg.transpose(_input_set), _input_set)), alg.mat_vec_mult(alg.transpose(_input_set), _output_set));
|
||||
|
||||
ERR_FAIL_COND_MSG(std::isnan(temp[0]), "ERR: Resulting matrix was noninvertible/degenerate, and so the normal equation could not be performed. Try utilizing gradient descent.");
|
||||
|
||||
if (_reg == "Ridge") {
|
||||
_weights = alg.mat_vec_mult(alg.inverse(alg.addition(alg.matmult(alg.transpose(_input_set), _input_set), alg.scalarMultiply(_lambda, alg.identity(_k)))), alg.mat_vec_mult(alg.transpose(_input_set), _output_set));
|
||||
} else {
|
||||
if (reg == "Ridge") {
|
||||
weights = alg.mat_vec_mult(alg.inverse(alg.addition(alg.matmult(alg.transpose(inputSet), inputSet), alg.scalarMultiply(lambda, alg.identity(k)))), alg.mat_vec_mult(alg.transpose(inputSet), outputSet));
|
||||
} else {
|
||||
weights = alg.mat_vec_mult(alg.inverse(alg.matmult(alg.transpose(inputSet), inputSet)), alg.mat_vec_mult(alg.transpose(inputSet), outputSet));
|
||||
}
|
||||
|
||||
bias = stat.mean(outputSet) - alg.dot(weights, x_means);
|
||||
|
||||
forwardPass();
|
||||
_weights = alg.mat_vec_mult(alg.inverse(alg.matmult(alg.transpose(_input_set), _input_set)), alg.mat_vec_mult(alg.transpose(_input_set), _output_set));
|
||||
}
|
||||
//} catch (int err_num) {
|
||||
// std::cout << "ERR " << err_num << ": Resulting matrix was noninvertible/degenerate, and so the normal equation could not be performed. Try utilizing gradient descent." << std::endl;
|
||||
//}
|
||||
|
||||
_bias = stat.mean(_output_set) - alg.dot(_weights, x_means);
|
||||
|
||||
forward_pass();
|
||||
}
|
||||
|
||||
real_t MLPPLinReg::score() {
|
||||
ERR_FAIL_COND_V(!_initialized, 0);
|
||||
|
||||
MLPPUtilities util;
|
||||
return util.performance(y_hat, outputSet);
|
||||
|
||||
return util.performance(_y_hat, _output_set);
|
||||
}
|
||||
|
||||
void MLPPLinReg::save(std::string fileName) {
|
||||
ERR_FAIL_COND(!_initialized);
|
||||
|
||||
MLPPUtilities util;
|
||||
util.saveParameters(fileName, weights, bias);
|
||||
|
||||
util.saveParameters(fileName, _weights, _bias);
|
||||
}
|
||||
|
||||
real_t MLPPLinReg::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
|
||||
bool MLPPLinReg::is_initialized() {
|
||||
return _initialized;
|
||||
}
|
||||
void MLPPLinReg::initialize() {
|
||||
if (_initialized) {
|
||||
return;
|
||||
}
|
||||
|
||||
//ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
|
||||
|
||||
_initialized = true;
|
||||
}
|
||||
|
||||
MLPPLinReg::MLPPLinReg(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) {
|
||||
_input_set = p_input_set;
|
||||
_output_set = p_output_set;
|
||||
_n = p_input_set.size();
|
||||
_k = p_input_set[0].size();
|
||||
_reg = p_reg;
|
||||
_lambda = p_lambda;
|
||||
_alpha = p_alpha;
|
||||
|
||||
_y_hat.resize(_n);
|
||||
|
||||
_weights = MLPPUtilities::weightInitialization(_k);
|
||||
_bias = MLPPUtilities::biasInitialization();
|
||||
|
||||
_initialized = true;
|
||||
}
|
||||
|
||||
MLPPLinReg::MLPPLinReg() {
|
||||
_initialized = false;
|
||||
}
|
||||
MLPPLinReg::~MLPPLinReg() {
|
||||
}
|
||||
|
||||
real_t MLPPLinReg::cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
|
||||
MLPPReg regularization;
|
||||
class MLPPCost cost;
|
||||
return cost.MSE(y_hat, y) + regularization.regTerm(weights, lambda, alpha, reg);
|
||||
MLPPCost mlpp_cost;
|
||||
|
||||
return mlpp_cost.MSE(y_hat, y) + regularization.regTerm(_weights, _lambda, _alpha, _reg);
|
||||
}
|
||||
|
||||
std::vector<real_t> MLPPLinReg::Evaluate(std::vector<std::vector<real_t>> X) {
|
||||
real_t MLPPLinReg::evaluatev(std::vector<real_t> x) {
|
||||
MLPPLinAlg alg;
|
||||
return alg.scalarAdd(bias, alg.mat_vec_mult(X, weights));
|
||||
|
||||
return alg.dot(_weights, x) + _bias;
|
||||
}
|
||||
|
||||
real_t MLPPLinReg::Evaluate(std::vector<real_t> x) {
|
||||
std::vector<real_t> MLPPLinReg::evaluatem(std::vector<std::vector<real_t>> X) {
|
||||
MLPPLinAlg alg;
|
||||
return alg.dot(weights, x) + bias;
|
||||
|
||||
return alg.scalarAdd(_bias, alg.mat_vec_mult(X, _weights));
|
||||
}
|
||||
|
||||
// wTx + b
|
||||
void MLPPLinReg::forwardPass() {
|
||||
y_hat = Evaluate(inputSet);
|
||||
void MLPPLinReg::forward_pass() {
|
||||
_y_hat = evaluatem(_input_set);
|
||||
}
|
||||
|
||||
void MLPPLinReg::_bind_methods() {
|
||||
/*
|
||||
ClassDB::bind_method(D_METHOD("get_input_set"), &MLPPLinReg::get_input_set);
|
||||
ClassDB::bind_method(D_METHOD("set_input_set", "val"), &MLPPLinReg::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"), &MLPPLinReg::get_output_set);
|
||||
ClassDB::bind_method(D_METHOD("set_output_set", "val"), &MLPPLinReg::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_reg"), &MLPPLinReg::get_reg);
|
||||
ClassDB::bind_method(D_METHOD("set_reg", "val"), &MLPPLinReg::set_reg);
|
||||
ADD_PROPERTY(PropertyInfo(Variant::INT, "reg"), "set_reg", "get_reg");
|
||||
|
||||
ClassDB::bind_method(D_METHOD("get_lambda"), &MLPPLinReg::get_lambda);
|
||||
ClassDB::bind_method(D_METHOD("set_lambda", "val"), &MLPPLinReg::set_lambda);
|
||||
ADD_PROPERTY(PropertyInfo(Variant::REAL, "lambda"), "set_lambda", "get_lambda");
|
||||
|
||||
ClassDB::bind_method(D_METHOD("get_alpha"), &MLPPLinReg::get_alpha);
|
||||
ClassDB::bind_method(D_METHOD("set_alpha", "val"), &MLPPLinReg::set_alpha);
|
||||
ADD_PROPERTY(PropertyInfo(Variant::REAL, "alpha"), "set_alpha", "get_alpha");
|
||||
|
||||
ClassDB::bind_method(D_METHOD("model_test", "x"), &MLPPLinReg::model_test);
|
||||
ClassDB::bind_method(D_METHOD("model_set_test", "X"), &MLPPLinReg::model_set_test);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPLinReg::gradient_descent, false);
|
||||
ClassDB::bind_method(D_METHOD("sgd", "learning_rate", "max_epoch", "ui"), &MLPPLinReg::sgd, false);
|
||||
ClassDB::bind_method(D_METHOD("mbgd", "learning_rate", "max_epoch", "mini_batch_size", "ui"), &MLPPLinReg::mbgd, false);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("score"), &MLPPLinReg::score);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("save", "file_name"), &MLPPLinReg::save);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("is_initialized"), &MLPPLinReg::is_initialized);
|
||||
ClassDB::bind_method(D_METHOD("initialize"), &MLPPLinReg::initialize);
|
||||
*/
|
||||
}
|
||||
|
||||
@ -10,51 +10,91 @@
|
||||
|
||||
#include "core/math/math_defs.h"
|
||||
|
||||
#include "core/object/reference.h"
|
||||
|
||||
#include "../lin_alg/mlpp_matrix.h"
|
||||
#include "../lin_alg/mlpp_vector.h"
|
||||
|
||||
#include "../regularization/reg.h"
|
||||
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
class MLPPLinReg {
|
||||
class MLPPLinReg : public Reference {
|
||||
GDCLASS(MLPPLinReg, Reference);
|
||||
|
||||
public:
|
||||
MLPPLinReg(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, std::string reg = "None", real_t lambda = 0.5, real_t alpha = 0.5);
|
||||
std::vector<real_t> modelSetTest(std::vector<std::vector<real_t>> X);
|
||||
real_t modelTest(std::vector<real_t> x);
|
||||
void NewtonRaphson(real_t learning_rate, int max_epoch, bool UI);
|
||||
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<MLPPMatrix> get_input_set();
|
||||
void set_input_set(const Ref<MLPPMatrix> &val);
|
||||
|
||||
void Momentum(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool UI = false);
|
||||
void NAG(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool UI = false);
|
||||
void Adagrad(real_t learning_rate, int max_epoch, int mini_batch_size, real_t e, bool UI = false);
|
||||
void Adadelta(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t e, bool UI = false);
|
||||
void Adam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool UI = false);
|
||||
void Adamax(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool UI = false);
|
||||
void Nadam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool UI = false);
|
||||
Ref<MLPPVector> get_output_set();
|
||||
void set_output_set(const Ref<MLPPVector> &val);
|
||||
|
||||
MLPPReg::RegularizationType get_reg();
|
||||
void set_reg(const MLPPReg::RegularizationType val);
|
||||
|
||||
real_t get_lambda();
|
||||
void set_lambda(const real_t val);
|
||||
|
||||
real_t get_alpha();
|
||||
void set_alpha(const real_t val);
|
||||
*/
|
||||
|
||||
std::vector<real_t> model_set_test(std::vector<std::vector<real_t>> X);
|
||||
real_t model_test(std::vector<real_t> x);
|
||||
|
||||
void newton_raphson(real_t learning_rate, int max_epoch, bool ui = false);
|
||||
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 momentum(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool ui = false);
|
||||
void nag(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool ui = false);
|
||||
void adagrad(real_t learning_rate, int max_epoch, int mini_batch_size, real_t e, bool ui = false);
|
||||
void adadelta(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t e, bool ui = false);
|
||||
void adam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui = false);
|
||||
void adamax(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui = false);
|
||||
void nadam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool ui = false);
|
||||
void mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);
|
||||
|
||||
void normal_equation();
|
||||
|
||||
void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = false);
|
||||
void normalEquation();
|
||||
real_t score();
|
||||
|
||||
void save(std::string fileName);
|
||||
|
||||
private:
|
||||
real_t Cost(std::vector<real_t> y_hat, std::vector<real_t> y);
|
||||
bool is_initialized();
|
||||
void initialize();
|
||||
|
||||
std::vector<real_t> Evaluate(std::vector<std::vector<real_t>> X);
|
||||
real_t Evaluate(std::vector<real_t> x);
|
||||
void forwardPass();
|
||||
MLPPLinReg(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);
|
||||
|
||||
std::vector<std::vector<real_t>> inputSet;
|
||||
std::vector<real_t> outputSet;
|
||||
std::vector<real_t> y_hat;
|
||||
std::vector<real_t> weights;
|
||||
real_t bias;
|
||||
MLPPLinReg();
|
||||
~MLPPLinReg();
|
||||
|
||||
int n;
|
||||
int k;
|
||||
protected:
|
||||
real_t cost(std::vector<real_t> y_hat, std::vector<real_t> y);
|
||||
|
||||
real_t evaluatev(std::vector<real_t> x);
|
||||
std::vector<real_t> evaluatem(std::vector<std::vector<real_t>> X);
|
||||
|
||||
void forward_pass();
|
||||
|
||||
static void _bind_methods();
|
||||
|
||||
std::vector<std::vector<real_t>> _input_set;
|
||||
std::vector<real_t> _output_set;
|
||||
std::vector<real_t> _y_hat;
|
||||
std::vector<real_t> _weights;
|
||||
real_t _bias;
|
||||
|
||||
int _n;
|
||||
int _k;
|
||||
|
||||
// Regularization Params
|
||||
std::string reg;
|
||||
int lambda;
|
||||
int alpha; /* This is the controlling param for Elastic Net*/
|
||||
std::string _reg;
|
||||
int _lambda;
|
||||
int _alpha; /* This is the controlling param for Elastic Net*/
|
||||
|
||||
bool _initialized;
|
||||
};
|
||||
|
||||
#endif /* LinReg_hpp */
|
||||
|
||||
@ -243,10 +243,13 @@ void MLPPTests::test_multivariate_linear_regression_gradient_descent(bool ui) {
|
||||
|
||||
Ref<MLPPDataSimple> ds = data.load_california_housing(_california_housing_data_path);
|
||||
|
||||
MLPPLinReg model(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg
|
||||
MLPPLinRegOld model_old(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg
|
||||
model_old.gradientDescent(0.001, 30, ui);
|
||||
alg.printVector(model_old.modelSetTest(ds->get_input()->to_std_vector()));
|
||||
|
||||
model.gradientDescent(0.001, 30, ui);
|
||||
alg.printVector(model.modelSetTest(ds->get_input()->to_std_vector()));
|
||||
MLPPLinReg model(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg
|
||||
model.gradient_descent(0.001, 30, ui);
|
||||
alg.printVector(model.model_set_test(ds->get_input()->to_std_vector()));
|
||||
}
|
||||
|
||||
void MLPPTests::test_multivariate_linear_regression_sgd(bool ui) {
|
||||
@ -255,10 +258,13 @@ void MLPPTests::test_multivariate_linear_regression_sgd(bool ui) {
|
||||
|
||||
Ref<MLPPDataSimple> ds = data.load_california_housing(_california_housing_data_path);
|
||||
|
||||
MLPPLinReg model(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg
|
||||
MLPPLinRegOld model_old(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg
|
||||
model_old.SGD(0.00000001, 300000, ui);
|
||||
alg.printVector(model_old.modelSetTest(ds->get_input()->to_std_vector()));
|
||||
|
||||
model.SGD(0.00000001, 300000, ui);
|
||||
alg.printVector(model.modelSetTest(ds->get_input()->to_std_vector()));
|
||||
MLPPLinReg model(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg
|
||||
model.sgd(0.00000001, 300000, ui);
|
||||
alg.printVector(model.model_set_test(ds->get_input()->to_std_vector()));
|
||||
}
|
||||
|
||||
void MLPPTests::test_multivariate_linear_regression_mbgd(bool ui) {
|
||||
@ -267,10 +273,13 @@ void MLPPTests::test_multivariate_linear_regression_mbgd(bool ui) {
|
||||
|
||||
Ref<MLPPDataSimple> ds = data.load_california_housing(_california_housing_data_path);
|
||||
|
||||
MLPPLinReg model(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg
|
||||
MLPPLinRegOld model_old(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg
|
||||
model_old.MBGD(0.001, 10000, 2, ui);
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alg.printVector(model_old.modelSetTest(ds->get_input()->to_std_vector()));
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model.MBGD(0.001, 10000, 2, ui);
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alg.printVector(model.modelSetTest(ds->get_input()->to_std_vector()));
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MLPPLinReg model(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg
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model.mbgd(0.001, 10000, 2, ui);
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alg.printVector(model.model_set_test(ds->get_input()->to_std_vector()));
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}
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void MLPPTests::test_multivariate_linear_regression_normal_equation(bool ui) {
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@ -279,10 +288,13 @@ void MLPPTests::test_multivariate_linear_regression_normal_equation(bool ui) {
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Ref<MLPPDataSimple> ds = data.load_california_housing(_california_housing_data_path);
|
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MLPPLinReg model(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg
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MLPPLinRegOld model_old(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg
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model_old.normalEquation();
|
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alg.printVector(model_old.modelSetTest(ds->get_input()->to_std_vector()));
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model.normalEquation();
|
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alg.printVector(model.modelSetTest(ds->get_input()->to_std_vector()));
|
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MLPPLinReg model(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg
|
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model.normal_equation();
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alg.printVector(model.model_set_test(ds->get_input()->to_std_vector()));
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}
|
||||
|
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void MLPPTests::test_multivariate_linear_regression_adam() {
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@ -291,9 +303,13 @@ void MLPPTests::test_multivariate_linear_regression_adam() {
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|
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Ref<MLPPDataSimple> ds = data.load_california_housing(_california_housing_data_path);
|
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|
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MLPPLinReg adamModel(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector());
|
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alg.printVector(adamModel.modelSetTest(ds->get_input()->to_std_vector()));
|
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std::cout << "ACCURACY: " << 100 * adamModel.score() << "%" << std::endl;
|
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MLPPLinRegOld adamModelOld(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector());
|
||||
alg.printVector(adamModelOld.modelSetTest(ds->get_input()->to_std_vector()));
|
||||
std::cout << "ACCURACY: " << 100 * adamModelOld.score() << "%" << std::endl;
|
||||
|
||||
MLPPLinReg adam_model(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector());
|
||||
alg.printVector(adam_model.model_set_test(ds->get_input()->to_std_vector()));
|
||||
std::cout << "ACCURACY: " << 100 * adam_model.score() << "%" << std::endl;
|
||||
}
|
||||
|
||||
void MLPPTests::test_multivariate_linear_regression_score_sgd_adam(bool ui) {
|
||||
@ -307,12 +323,20 @@ void MLPPTests::test_multivariate_linear_regression_score_sgd_adam(bool ui) {
|
||||
real_t scoreSGD = 0;
|
||||
real_t scoreADAM = 0;
|
||||
for (int i = 0; i < TRIAL_NUM; i++) {
|
||||
MLPPLinRegOld modelf_old(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector());
|
||||
modelf_old.MBGD(0.001, 5, 1, ui);
|
||||
scoreSGD += modelf_old.score();
|
||||
|
||||
MLPPLinReg modelf(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector());
|
||||
modelf.MBGD(0.001, 5, 1, ui);
|
||||
modelf.mbgd(0.001, 5, 1, ui);
|
||||
scoreSGD += modelf.score();
|
||||
|
||||
MLPPLinRegOld adamModelf_old(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector());
|
||||
adamModelf_old.Adam(0.1, 5, 1, 0.9, 0.999, 1e-8, ui); // Change batch size = sgd, bgd
|
||||
scoreADAM += adamModelf_old.score();
|
||||
|
||||
MLPPLinReg adamModelf(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector());
|
||||
adamModelf.Adam(0.1, 5, 1, 0.9, 0.999, 1e-8, ui); // Change batch size = sgd, bgd
|
||||
adamModelf.adam(0.1, 5, 1, 0.9, 0.999, 1e-8, ui); // Change batch size = sgd, bgd
|
||||
scoreADAM += adamModelf.score();
|
||||
}
|
||||
|
||||
@ -330,9 +354,13 @@ void MLPPTests::test_multivariate_linear_regression_epochs_gradient_descent(bool
|
||||
std::cout << "Total epoch num: 300" << std::endl;
|
||||
std::cout << "Method: 1st Order w/ Jacobians" << std::endl;
|
||||
|
||||
MLPPLinRegOld model3_old(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg
|
||||
model3_old.gradientDescent(0.001, 300, ui);
|
||||
alg.printVector(model3_old.modelSetTest(ds->get_input()->to_std_vector()));
|
||||
|
||||
MLPPLinReg model3(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg
|
||||
model3.gradientDescent(0.001, 300, ui);
|
||||
alg.printVector(model3.modelSetTest(ds->get_input()->to_std_vector()));
|
||||
model3.gradient_descent(0.001, 300, ui);
|
||||
alg.printVector(model3.model_set_test(ds->get_input()->to_std_vector()));
|
||||
}
|
||||
|
||||
void MLPPTests::test_multivariate_linear_regression_newton_raphson(bool ui) {
|
||||
@ -344,10 +372,14 @@ void MLPPTests::test_multivariate_linear_regression_newton_raphson(bool ui) {
|
||||
std::cout << "--------------------------------------------" << std::endl;
|
||||
std::cout << "Total epoch num: 300" << std::endl;
|
||||
std::cout << "Method: Newtonian 2nd Order w/ Hessians" << std::endl;
|
||||
MLPPLinReg model2(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector());
|
||||
|
||||
model2.NewtonRaphson(1.5, 300, ui);
|
||||
alg.printVector(model2.modelSetTest(ds->get_input()->to_std_vector()));
|
||||
MLPPLinRegOld model2_old(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector());
|
||||
model2_old.NewtonRaphson(1.5, 300, ui);
|
||||
alg.printVector(model2_old.modelSetTest(ds->get_input()->to_std_vector()));
|
||||
|
||||
MLPPLinReg model2(alg.transpose(ds->get_input()->to_std_vector()), ds->get_output()->to_std_vector());
|
||||
model2.newton_raphson(1.5, 300, ui);
|
||||
alg.printVector(model2.model_set_test(ds->get_input()->to_std_vector()));
|
||||
}
|
||||
|
||||
void MLPPTests::test_logistic_regression(bool ui) {
|
||||
|
||||
Loading…
Reference in New Issue
Block a user