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Cleaned up ProbitReg.
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@ -5,55 +5,98 @@
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//
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#include "probit_reg.h"
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#include "../activation/activation.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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#include "../utilities/utilities.h"
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#include <iostream>
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#include <random>
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MLPPProbitReg::MLPPProbitReg(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, std::string reg, real_t lambda, real_t alpha) :
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inputSet(inputSet), outputSet(outputSet), n(inputSet.size()), k(inputSet[0].size()), reg(reg), lambda(lambda), alpha(alpha) {
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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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Ref<MLPPMatrix> MLPPProbitReg::get_input_set() {
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return _input_set;
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}
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void MLPPProbitReg::set_input_set(const Ref<MLPPMatrix> &val) {
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_input_set = val;
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_initialized = false;
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}
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std::vector<real_t> MLPPProbitReg::modelSetTest(std::vector<std::vector<real_t>> X) {
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return Evaluate(X);
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Ref<MLPPVector> MLPPProbitReg::get_output_set() {
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return _output_set;
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}
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void MLPPProbitReg::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 MLPPProbitReg::modelTest(std::vector<real_t> x) {
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return Evaluate(x);
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MLPPReg::RegularizationType MLPPProbitReg::get_reg() {
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return _reg;
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}
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void MLPPProbitReg::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 MLPPProbitReg::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
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real_t MLPPProbitReg::get_lambda() {
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return _lambda;
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}
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void MLPPProbitReg::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 MLPPProbitReg::get_alpha() {
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return _alpha;
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}
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void MLPPProbitReg::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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Ref<MLPPVector> MLPPProbitReg::model_set_test(const Ref<MLPPMatrix> &X) {
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return evaluatem(X);
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}
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real_t MLPPProbitReg::model_test(const Ref<MLPPVector> &x) {
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return evaluatev(x);
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}
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void MLPPProbitReg::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_initialized);
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MLPPActivation avn;
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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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Ref<MLPPVector> error = alg.subtractionnv(_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), alg.hadamard_product(error, avn.gaussianCDF(z, 1)))));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate / _n, alg.mat_vec_multv(alg.transposem(_input_set), alg.hadamard_productnv(error, avn.gaussian_cdf_derivv(_z)))));
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_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradients
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bias -= learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.gaussianCDF(z, 1))) / n;
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forwardPass();
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_bias -= learning_rate * alg.sum_elementsv(alg.hadamard_productnv(error, avn.gaussian_cdf_derivv(_z))) / _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::cost_info(epoch, cost_prev, cost(_y_hat, _output_set));
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MLPPUtilities::print_ui_vb(_weights, _bias);
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}
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epoch++;
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if (epoch > max_epoch) {
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@ -62,31 +105,36 @@ void MLPPProbitReg::gradientDescent(real_t learning_rate, int max_epoch, bool UI
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}
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}
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void MLPPProbitReg::MLE(real_t learning_rate, int max_epoch, bool UI) {
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void MLPPProbitReg::mle(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_initialized);
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MLPPActivation avn;
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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(outputSet, y_hat);
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Ref<MLPPVector> error = alg.subtractionnv(_output_set, _y_hat);
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// Calculating the weight gradients
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weights = alg.addition(weights, alg.scalarMultiply(learning_rate / n, alg.mat_vec_mult(alg.transpose(inputSet), alg.hadamard_product(error, avn.gaussianCDF(z, 1)))));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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_weights = alg.additionnv(_weights, alg.scalar_multiplynv(learning_rate / _n, alg.mat_vec_multv(alg.transposem(_input_set), alg.hadamard_productnv(error, avn.gaussian_cdf_derivv(_z)))));
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_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradients
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bias += learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.gaussianCDF(z, 1))) / n;
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forwardPass();
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_bias += learning_rate * alg.sum_elementsv(alg.hadamard_productnv(error, avn.gaussian_cdf_derivv(_z))) / _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::cost_info(epoch, cost_prev, cost(_y_hat, _output_set));
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MLPPUtilities::print_ui_vb(_weights, _bias);
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}
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epoch++;
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if (epoch > max_epoch) {
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@ -95,7 +143,9 @@ void MLPPProbitReg::MLE(real_t learning_rate, int max_epoch, bool UI) {
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}
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}
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void MLPPProbitReg::SGD(real_t learning_rate, int max_epoch, bool UI) {
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void MLPPProbitReg::sgd(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_initialized);
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// NOTE: ∂y_hat/∂z is sparse
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MLPPActivation avn;
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MLPPLinAlg alg;
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@ -103,143 +153,280 @@ void MLPPProbitReg::SGD(real_t learning_rate, int max_epoch, bool UI) {
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real_t cost_prev = 0;
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int epoch = 1;
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Ref<MLPPVector> input_set_row_tmp;
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input_set_row_tmp.instance();
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input_set_row_tmp->resize(_input_set->size().x);
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Ref<MLPPVector> output_set_tmp;
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output_set_tmp.instance();
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output_set_tmp->resize(1);
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Ref<MLPPVector> y_hat_tmp;
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y_hat_tmp.instance();
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y_hat_tmp->resize(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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int output_index = distribution(generator);
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real_t y_hat = Evaluate(inputSet[outputIndex]);
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real_t z = propagate(inputSet[outputIndex]);
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cost_prev = Cost({ y_hat }, { outputSet[outputIndex] });
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_input_set->get_row_into_mlpp_vector(output_index, input_set_row_tmp);
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real_t output_set_entry = _output_set->get_element(output_index);
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real_t error = y_hat - outputSet[outputIndex];
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real_t y_hat = evaluatev(input_set_row_tmp);
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real_t z = propagatev(input_set_row_tmp);
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y_hat_tmp->set_element(0, y_hat);
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output_set_tmp->set_element(0, output_set_entry);
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cost_prev = cost(y_hat_tmp, output_set_tmp);
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real_t error = y_hat - output_set_entry;
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// Weight Updation
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weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate * error * ((1 / sqrt(2 * M_PI)) * exp(-z * z / 2)), inputSet[outputIndex]));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate * error * ((1 / Math::sqrt(2 * M_PI)) * Math::exp(-z * z / 2)), input_set_row_tmp));
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_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
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// Bias updation
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bias -= learning_rate * error * ((1 / sqrt(2 * M_PI)) * exp(-z * z / 2));
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_bias -= learning_rate * error * ((1 / Math::sqrt(2 * M_PI)) * Math::exp(-z * z / 2));
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y_hat = Evaluate({ inputSet[outputIndex] });
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y_hat = evaluatev(input_set_row_tmp);
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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::cost_info(epoch, cost_prev, cost(y_hat_tmp, output_set_tmp));
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MLPPUtilities::print_ui_vb(_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 MLPPProbitReg::MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI) {
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void MLPPProbitReg::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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MLPPActivation avn;
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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 createMiniBatchesResult = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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auto inputMiniBatches = std::get<0>(createMiniBatchesResult);
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auto outputMiniBatches = std::get<1>(createMiniBatchesResult);
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Ref<MLPPVector> z_tmp;
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z_tmp.instance();
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z_tmp->resize(1);
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// Creating the mini-batches
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for (int i = 0; i < n_mini_batch; i++) {
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std::vector<std::vector<real_t>> currentInputSet;
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std::vector<real_t> currentOutputSet;
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for (int j = 0; j < n / n_mini_batch; j++) {
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currentInputSet.push_back(inputSet[n / n_mini_batch * i + j]);
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currentOutputSet.push_back(outputSet[n / n_mini_batch * i + j]);
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}
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inputMiniBatches.push_back(currentInputSet);
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outputMiniBatches.push_back(currentOutputSet);
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}
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int n_mini_batch = _n / mini_batch_size;
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if (real_t(n) / real_t(n_mini_batch) - int(n / n_mini_batch) != 0) {
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for (int i = 0; i < n - n / n_mini_batch * n_mini_batch; i++) {
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inputMiniBatches[n_mini_batch - 1].push_back(inputSet[n / n_mini_batch * n_mini_batch + i]);
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outputMiniBatches[n_mini_batch - 1].push_back(outputSet[n / n_mini_batch * n_mini_batch + i]);
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}
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}
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MLPPUtilities::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, n_mini_batch);
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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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std::vector<real_t> z = propagate(inputMiniBatches[i]);
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cost_prev = Cost(y_hat, outputMiniBatches[i]);
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Ref<MLPPMatrix> current_input = batches.input_sets[i];
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Ref<MLPPVector> current_output = batches.output_sets[i];
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std::vector<real_t> error = alg.subtraction(y_hat, outputMiniBatches[i]);
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Ref<MLPPVector> y_hat = evaluatem(current_input);
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real_t z = propagatev(current_output);
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z_tmp->set_element(0, z);
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cost_prev = cost(y_hat, current_output);
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Ref<MLPPVector> error = alg.subtractionnv(y_hat, current_output);
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// Calculating the weight gradients
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weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate / outputMiniBatches.size(), alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), alg.hadamard_product(error, avn.gaussianCDF(z, 1)))));
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weights = regularization.regWeights(weights, lambda, alpha, reg);
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_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate / batches.input_sets.size(), alg.mat_vec_multv(alg.transposem(current_input), alg.hadamard_productnv(error, avn.gaussian_cdf_derivv(z_tmp)))));
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_weights = regularization.reg_weightsv(_weights, _lambda, _alpha, _reg);
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// Calculating the bias gradients
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bias -= learning_rate * alg.sum_elements(alg.hadamard_product(error, avn.gaussianCDF(z, 1))) / outputMiniBatches.size();
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y_hat = Evaluate(inputMiniBatches[i]);
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_bias -= learning_rate * alg.sum_elementsv(alg.hadamard_productnv(error, avn.gaussian_cdf_derivv(z_tmp))) / batches.input_sets.size();
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y_hat = evaluatev(current_input);
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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::cost_info(epoch, cost_prev, cost(y_hat, current_output));
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MLPPUtilities::print_ui_vb(_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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real_t MLPPProbitReg::score() {
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MLPPUtilities util;
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return util.performance(y_hat, outputSet);
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return util.performance_vec(_y_hat, _output_set);
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}
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void MLPPProbitReg::save(std::string fileName) {
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void MLPPProbitReg::save(const String &file_name) {
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MLPPUtilities util;
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util.saveParameters(fileName, weights, bias);
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//util.saveParameters(file_name, _weights, _bias);
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}
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real_t MLPPProbitReg::Cost(std::vector<real_t> y_hat, std::vector<real_t> y) {
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bool MLPPProbitReg::is_initialized() {
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return _initialized;
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}
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void MLPPProbitReg::initialize() {
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if (_initialized) {
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return;
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}
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ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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_n = _input_set->size().y;
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_k = _input_set->size().x;
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if (!_y_hat.is_valid()) {
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_y_hat.instance();
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}
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_y_hat->resize(_n);
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MLPPUtilities util;
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if (!_weights.is_valid()) {
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_weights.instance();
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}
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_weights->resize(_k);
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util.weight_initializationv(_weights);
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_bias = util.bias_initializationr();
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_initialized = true;
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}
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MLPPProbitReg::MLPPProbitReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, MLPPReg::RegularizationType p_reg, real_t p_lambda, real_t p_alpha) {
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_input_set = p_input_set;
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_output_set = p_output_set;
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_n = _input_set->size().y;
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_k = _input_set->size().x;
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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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_y_hat.instance();
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_y_hat->resize(_n);
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MLPPUtilities util;
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_weights.instance();
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_weights->resize(_k);
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util.weight_initializationv(_weights);
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_bias = util.bias_initializationr();
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_initialized = true;
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}
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MLPPProbitReg::MLPPProbitReg() {
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||||
_y_hat.instance();
|
||||
|
||||
_bias = 0;
|
||||
|
||||
_n = 0;
|
||||
_k = 0;
|
||||
|
||||
// Regularization Params
|
||||
_reg = MLPPReg::REGULARIZATION_TYPE_NONE;
|
||||
_lambda = 0.5;
|
||||
_alpha = 0.5;
|
||||
|
||||
_initialized = false;
|
||||
}
|
||||
MLPPProbitReg::~MLPPProbitReg() {
|
||||
}
|
||||
|
||||
real_t MLPPProbitReg::cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
|
||||
MLPPReg regularization;
|
||||
class MLPPCost cost;
|
||||
return cost.MSE(y_hat, y) + regularization.regTerm(weights, lambda, alpha, reg);
|
||||
|
||||
return cost.msev(y_hat, y) + regularization.reg_termv(_weights, _lambda, _alpha, _reg);
|
||||
}
|
||||
|
||||
std::vector<real_t> MLPPProbitReg::Evaluate(std::vector<std::vector<real_t>> X) {
|
||||
Ref<MLPPVector> MLPPProbitReg::evaluatem(const Ref<MLPPMatrix> &X) {
|
||||
MLPPLinAlg alg;
|
||||
MLPPActivation avn;
|
||||
return avn.gaussianCDF(alg.scalarAdd(bias, alg.mat_vec_mult(X, weights)));
|
||||
|
||||
return avn.gaussian_cdf_normv(alg.scalar_addnv(_bias, alg.mat_vec_multv(X, _weights)));
|
||||
}
|
||||
|
||||
std::vector<real_t> MLPPProbitReg::propagate(std::vector<std::vector<real_t>> X) {
|
||||
Ref<MLPPVector> MLPPProbitReg::propagatem(const Ref<MLPPMatrix> &X) {
|
||||
MLPPLinAlg alg;
|
||||
return alg.scalarAdd(bias, alg.mat_vec_mult(X, weights));
|
||||
|
||||
return alg.scalar_addnv(_bias, alg.mat_vec_multv(X, _weights));
|
||||
}
|
||||
|
||||
real_t MLPPProbitReg::Evaluate(std::vector<real_t> x) {
|
||||
real_t MLPPProbitReg::evaluatev(const Ref<MLPPVector> &x) {
|
||||
MLPPLinAlg alg;
|
||||
MLPPActivation avn;
|
||||
return avn.gaussianCDF(alg.dot(weights, x) + bias);
|
||||
|
||||
return avn.gaussian_cdf_normr(alg.dotv(_weights, x) + _bias);
|
||||
}
|
||||
|
||||
real_t MLPPProbitReg::propagate(std::vector<real_t> x) {
|
||||
real_t MLPPProbitReg::propagatev(const Ref<MLPPVector> &x) {
|
||||
MLPPLinAlg alg;
|
||||
return alg.dot(weights, x) + bias;
|
||||
|
||||
return alg.dotv(_weights, x) + _bias;
|
||||
}
|
||||
|
||||
// gaussianCDF ( wTx + b )
|
||||
void MLPPProbitReg::forwardPass() {
|
||||
void MLPPProbitReg::forward_pass() {
|
||||
MLPPActivation avn;
|
||||
|
||||
z = propagate(inputSet);
|
||||
y_hat = avn.gaussianCDF(z);
|
||||
_z = propagatem(_input_set);
|
||||
_y_hat = avn.gaussian_cdf_normv(_z);
|
||||
}
|
||||
|
||||
void MLPPProbitReg::_bind_methods() {
|
||||
ClassDB::bind_method(D_METHOD("get_input_set"), &MLPPProbitReg::get_input_set);
|
||||
ClassDB::bind_method(D_METHOD("set_input_set", "val"), &MLPPProbitReg::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"), &MLPPProbitReg::get_output_set);
|
||||
ClassDB::bind_method(D_METHOD("set_output_set", "val"), &MLPPProbitReg::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"), &MLPPProbitReg::get_reg);
|
||||
ClassDB::bind_method(D_METHOD("set_reg", "val"), &MLPPProbitReg::set_reg);
|
||||
ADD_PROPERTY(PropertyInfo(Variant::INT, "reg"), "set_reg", "get_reg");
|
||||
|
||||
ClassDB::bind_method(D_METHOD("get_lambda"), &MLPPProbitReg::get_lambda);
|
||||
ClassDB::bind_method(D_METHOD("set_lambda", "val"), &MLPPProbitReg::set_lambda);
|
||||
ADD_PROPERTY(PropertyInfo(Variant::REAL, "lambda"), "set_lambda", "get_lambda");
|
||||
|
||||
ClassDB::bind_method(D_METHOD("get_alpha"), &MLPPProbitReg::get_alpha);
|
||||
ClassDB::bind_method(D_METHOD("set_alpha", "val"), &MLPPProbitReg::set_alpha);
|
||||
ADD_PROPERTY(PropertyInfo(Variant::REAL, "alpha"), "set_alpha", "get_alpha");
|
||||
|
||||
ClassDB::bind_method(D_METHOD("model_set_test", "X"), &MLPPProbitReg::model_set_test);
|
||||
ClassDB::bind_method(D_METHOD("model_test", "x"), &MLPPProbitReg::model_test);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPProbitReg::gradient_descent, 0, false);
|
||||
ClassDB::bind_method(D_METHOD("mle", "learning_rate", "max_epoch", "ui"), &MLPPProbitReg::mle, 0, false);
|
||||
ClassDB::bind_method(D_METHOD("sgd", "learning_rate", "max_epoch", "ui"), &MLPPProbitReg::sgd, 0, false);
|
||||
ClassDB::bind_method(D_METHOD("mbgd", "learning_rate", "max_epoch", "mini_batch_size", "ui"), &MLPPProbitReg::mbgd, false);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("score"), &MLPPProbitReg::score);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("save", "file_name"), &MLPPProbitReg::save);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("is_initialized"), &MLPPProbitReg::is_initialized);
|
||||
ClassDB::bind_method(D_METHOD("initialize"), &MLPPProbitReg::initialize);
|
||||
}
|
||||
|
||||
@ -10,44 +10,82 @@
|
||||
|
||||
#include "core/math/math_defs.h"
|
||||
|
||||
#include <string>
|
||||
#include <vector>
|
||||
#include "core/object/reference.h"
|
||||
|
||||
#include "../lin_alg/mlpp_matrix.h"
|
||||
#include "../lin_alg/mlpp_vector.h"
|
||||
|
||||
#include "../regularization/reg.h"
|
||||
|
||||
class MLPPProbitReg : public Reference {
|
||||
GDCLASS(MLPPProbitReg, Reference);
|
||||
|
||||
class MLPPProbitReg {
|
||||
public:
|
||||
MLPPProbitReg(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 gradientDescent(real_t learning_rate, int max_epoch = 0, bool UI = false);
|
||||
void MLE(real_t learning_rate, int max_epoch = 0, bool UI = false);
|
||||
void SGD(real_t learning_rate, int max_epoch = 0, bool UI = false);
|
||||
void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool UI = false);
|
||||
Ref<MLPPMatrix> get_input_set();
|
||||
void set_input_set(const Ref<MLPPMatrix> &val);
|
||||
|
||||
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);
|
||||
|
||||
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 = 0, bool ui = false);
|
||||
void mle(real_t learning_rate, int max_epoch = 0, bool ui = false);
|
||||
void sgd(real_t learning_rate, int max_epoch = 0, 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);
|
||||
|
||||
private:
|
||||
real_t Cost(std::vector<real_t> y_hat, std::vector<real_t> y);
|
||||
void save(const String &file_name);
|
||||
|
||||
std::vector<real_t> Evaluate(std::vector<std::vector<real_t>> X);
|
||||
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();
|
||||
bool is_initialized();
|
||||
void initialize();
|
||||
|
||||
std::vector<std::vector<real_t>> inputSet;
|
||||
std::vector<real_t> outputSet;
|
||||
std::vector<real_t> z;
|
||||
std::vector<real_t> y_hat;
|
||||
std::vector<real_t> weights;
|
||||
real_t bias;
|
||||
MLPPProbitReg(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);
|
||||
|
||||
int n;
|
||||
int k;
|
||||
MLPPProbitReg();
|
||||
~MLPPProbitReg();
|
||||
|
||||
protected:
|
||||
real_t cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y);
|
||||
|
||||
Ref<MLPPVector> evaluatem(const Ref<MLPPMatrix> &X);
|
||||
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;
|
||||
|
||||
int _n;
|
||||
int _k;
|
||||
|
||||
// Regularization Params
|
||||
std::string reg;
|
||||
real_t lambda;
|
||||
real_t alpha; /* This is the controlling param for Elastic Net*/
|
||||
MLPPReg::RegularizationType _reg;
|
||||
real_t _lambda;
|
||||
real_t _alpha; /* This is the controlling param for Elastic Net*/
|
||||
|
||||
bool _initialized;
|
||||
};
|
||||
|
||||
#endif /* ProbitReg_hpp */
|
||||
|
||||
@ -42,6 +42,7 @@ SOFTWARE.
|
||||
#include "mlpp/pca/pca.h"
|
||||
#include "mlpp/uni_lin_reg/uni_lin_reg.h"
|
||||
#include "mlpp/wgan/wgan.h"
|
||||
#include "mlpp/probit_reg/probit_reg.h"
|
||||
|
||||
#include "mlpp/mlp/mlp.h"
|
||||
|
||||
@ -69,6 +70,7 @@ void register_pmlpp_types(ModuleRegistrationLevel p_level) {
|
||||
ClassDB::register_class<MLPPPCA>();
|
||||
ClassDB::register_class<MLPPUniLinReg>();
|
||||
ClassDB::register_class<MLPPOutlierFinder>();
|
||||
ClassDB::register_class<MLPPProbitReg>();
|
||||
|
||||
ClassDB::register_class<MLPPDataESimple>();
|
||||
ClassDB::register_class<MLPPDataSimple>();
|
||||
|
||||
@ -50,9 +50,9 @@
|
||||
#include "../mlpp/mlp/mlp_old.h"
|
||||
#include "../mlpp/outlier_finder/outlier_finder_old.h"
|
||||
#include "../mlpp/pca/pca_old.h"
|
||||
#include "../mlpp/probit_reg/probit_reg_old.h"
|
||||
#include "../mlpp/uni_lin_reg/uni_lin_reg_old.h"
|
||||
#include "../mlpp/wgan/wgan_old.h"
|
||||
#include "../mlpp/probit_reg/probit_reg_old.h"
|
||||
|
||||
Vector<real_t> dstd_vec_to_vec(const std::vector<real_t> &in) {
|
||||
Vector<real_t> r;
|
||||
@ -353,6 +353,11 @@ void MLPPTests::test_probit_regression(bool ui) {
|
||||
model_old.SGD(0.001, 10000, ui);
|
||||
alg.printVector(model_old.modelSetTest(dt->get_input()->to_std_vector()));
|
||||
std::cout << "ACCURACY: " << 100 * model_old.score() << "%" << std::endl;
|
||||
|
||||
MLPPProbitReg model(dt->get_input(), dt->get_output());
|
||||
model.sgd(0.001, 10000, ui);
|
||||
PLOG_MSG(model.model_set_test(dt->get_input())->to_string());
|
||||
PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%");
|
||||
}
|
||||
void MLPPTests::test_c_log_log_regression(bool ui) {
|
||||
MLPPLinAlg alg;
|
||||
|
||||
Loading…
Reference in New Issue
Block a user