mirror of
https://github.com/Relintai/pmlpp.git
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488 lines
15 KiB
C++
488 lines
15 KiB
C++
/*************************************************************************/
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/* probit_reg.cpp */
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/*************************************************************************/
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/* This file is part of: */
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/* PMLPP Machine Learning Library */
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/* https://github.com/Relintai/pmlpp */
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/*************************************************************************/
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/* Copyright (c) 2023-present Péter Magyar. */
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/* Copyright (c) 2022-2023 Marc Melikyan */
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/* */
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/* Permission is hereby granted, free of charge, to any person obtaining */
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/* a copy of this software and associated documentation files (the */
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/* "Software"), to deal in the Software without restriction, including */
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/* without limitation the rights to use, copy, modify, merge, publish, */
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/* distribute, sublicense, and/or sell copies of the Software, and to */
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/* permit persons to whom the Software is furnished to do so, subject to */
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/* the following conditions: */
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/* */
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/* The above copyright notice and this permission notice shall be */
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/* included in all copies or substantial portions of the Software. */
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/* */
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/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, */
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/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF */
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/* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.*/
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/* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY */
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/* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, */
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/* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE */
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/* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */
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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 "../regularization/reg.h"
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#include "../utilities/utilities.h"
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#include <random>
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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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}
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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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}
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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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}
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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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}
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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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}
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Ref<MLPPVector> MLPPProbitReg::data_z_get() const {
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return _z;
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}
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void MLPPProbitReg::data_z_set(const Ref<MLPPVector> &val) {
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if (!val.is_valid()) {
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return;
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}
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_z = val;
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}
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Ref<MLPPVector> MLPPProbitReg::data_y_hat_get() const {
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return _y_hat;
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}
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void MLPPProbitReg::data_y_hat_set(const Ref<MLPPVector> &val) {
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if (!val.is_valid()) {
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return;
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}
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_y_hat = val;
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}
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Ref<MLPPVector> MLPPProbitReg::data_weights_get() const {
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return _weights;
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}
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void MLPPProbitReg::data_weights_set(const Ref<MLPPVector> &val) {
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if (!val.is_valid()) {
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return;
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}
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_weights = val;
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}
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real_t MLPPProbitReg::data_bias_get() const {
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return _bias;
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}
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void MLPPProbitReg::data_bias_set(const real_t val) {
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_bias = val;
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}
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Ref<MLPPVector> MLPPProbitReg::model_set_test(const Ref<MLPPMatrix> &X) {
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ERR_FAIL_COND_V(needs_init(), Ref<MLPPVector>());
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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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ERR_FAIL_COND_V(needs_init(), 0);
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return evaluatev(x);
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}
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void MLPPProbitReg::train_gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(needs_init());
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MLPPActivation avn;
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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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int n = _input_set->size().y;
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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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Ref<MLPPVector> error = _y_hat->subn(_output_set);
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// Calculating the weight gradients
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_weights->sub(_input_set->transposen()->mult_vec(error->hadamard_productn(avn.gaussian_cdf_derivv(_z)))->scalar_multiplyn(learning_rate / n));
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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 * error->hadamard_productn(avn.gaussian_cdf_derivv(_z))->sum_elements() / n;
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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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break;
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}
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}
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}
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void MLPPProbitReg::train_mle(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(needs_init());
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MLPPActivation avn;
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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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int n = _input_set->size().y;
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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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Ref<MLPPVector> error = _output_set->subn(_y_hat);
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// Calculating the weight gradients
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_weights->add(_input_set->transposen()->mult_vec(error->hadamard_productn(avn.gaussian_cdf_derivv(_z)))->scalar_multiplyn(learning_rate / n));
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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 * error->hadamard_productn(avn.gaussian_cdf_derivv(_z))->sum_elements() / n;
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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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break;
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}
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}
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}
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void MLPPProbitReg::train_sgd(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(needs_init());
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// NOTE: ∂y_hat/∂z is sparse
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MLPPActivation avn;
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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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int n = _input_set->size().y;
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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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int output_index = distribution(generator);
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_input_set->row_get_into_mlpp_vector(output_index, input_set_row_tmp);
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real_t output_set_entry = _output_set->element_get(output_index);
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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->element_set(0, y_hat);
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output_set_tmp->element_set(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->sub(input_set_row_tmp->scalar_multiplyn(learning_rate * error * ((1 / Math::sqrt(2 * Math_PI)) * Math::exp(-z * z / 2))));
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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 / Math::sqrt(2 * Math_PI)) * Math::exp(-z * z / 2));
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y_hat = evaluatev(input_set_row_tmp);
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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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forward_pass();
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}
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void MLPPProbitReg::train_mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui) {
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ERR_FAIL_COND(needs_init());
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MLPPActivation avn;
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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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int n = _input_set->size().y;
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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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int n_mini_batch = n / mini_batch_size;
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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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Ref<MLPPMatrix> current_input = batches.input_sets[i];
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Ref<MLPPVector> current_output = batches.output_sets[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->element_set(0, z);
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cost_prev = cost(y_hat, current_output);
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Ref<MLPPVector> error = y_hat->subn(current_output);
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// Calculating the weight gradients
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_weights->sub(current_input->transposen()->mult_vec(error->hadamard_productn(avn.gaussian_cdf_derivv(z_tmp)))->scalar_multiplyn(learning_rate / batches.input_sets.size()));
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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 * error->hadamard_productn(avn.gaussian_cdf_derivv(z_tmp))->sum_elements() / batches.input_sets.size();
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y_hat = evaluatev(current_input);
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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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forward_pass();
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}
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real_t MLPPProbitReg::score() {
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ERR_FAIL_COND_V(needs_init(), 0);
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MLPPUtilities util;
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return util.performance_vec(_y_hat, _output_set);
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}
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bool MLPPProbitReg::needs_init() const {
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if (!_input_set.is_valid()) {
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return true;
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}
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if (!_output_set.is_valid()) {
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return true;
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}
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int n = _input_set->size().y;
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int k = _input_set->size().x;
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if (_y_hat->size() != n) {
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return true;
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}
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if (_weights->size() != k) {
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return true;
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}
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return false;
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}
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void MLPPProbitReg::initialize() {
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ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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int n = _input_set->size().y;
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int k = _input_set->size().x;
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_y_hat->resize(n);
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MLPPUtilities util;
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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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}
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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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_reg = p_reg;
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_lambda = p_lambda;
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_alpha = p_alpha;
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_z.instance();
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_y_hat.instance();
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_weights.instance();
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_bias = 0;
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initialize();
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}
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MLPPProbitReg::MLPPProbitReg() {
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// Regularization Params
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_reg = MLPPReg::REGULARIZATION_TYPE_NONE;
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_lambda = 0.5;
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_alpha = 0.5;
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_z.instance();
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_y_hat.instance();
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_weights.instance();
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_bias = 0;
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}
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MLPPProbitReg::~MLPPProbitReg() {
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}
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real_t MLPPProbitReg::cost(const Ref<MLPPVector> &y_hat, const Ref<MLPPVector> &y) {
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MLPPReg regularization;
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class MLPPCost cost;
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return cost.msev(y_hat, y) + regularization.reg_termv(_weights, _lambda, _alpha, _reg);
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}
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Ref<MLPPVector> MLPPProbitReg::evaluatem(const Ref<MLPPMatrix> &X) {
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MLPPActivation avn;
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return avn.gaussian_cdf_normv(X->mult_vec(_weights)->scalar_addn(_bias));
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}
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Ref<MLPPVector> MLPPProbitReg::propagatem(const Ref<MLPPMatrix> &X) {
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return X->mult_vec(_weights)->scalar_addn(_bias);
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}
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real_t MLPPProbitReg::evaluatev(const Ref<MLPPVector> &x) {
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MLPPActivation avn;
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return avn.gaussian_cdf_normr(_weights->dot(x) + _bias);
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}
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real_t MLPPProbitReg::propagatev(const Ref<MLPPVector> &x) {
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return _weights->dot(x) + _bias;
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}
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// gaussianCDF ( wTx + b )
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void MLPPProbitReg::forward_pass() {
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MLPPActivation avn;
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_z = propagatem(_input_set);
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_y_hat = avn.gaussian_cdf_normv(_z);
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}
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void MLPPProbitReg::_bind_methods() {
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ClassDB::bind_method(D_METHOD("get_input_set"), &MLPPProbitReg::get_input_set);
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ClassDB::bind_method(D_METHOD("set_input_set", "val"), &MLPPProbitReg::set_input_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "input_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_input_set", "get_input_set");
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ClassDB::bind_method(D_METHOD("get_output_set"), &MLPPProbitReg::get_output_set);
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ClassDB::bind_method(D_METHOD("set_output_set", "val"), &MLPPProbitReg::set_output_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "output_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "set_output_set", "get_output_set");
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ClassDB::bind_method(D_METHOD("get_reg"), &MLPPProbitReg::get_reg);
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ClassDB::bind_method(D_METHOD("set_reg", "val"), &MLPPProbitReg::set_reg);
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ADD_PROPERTY(PropertyInfo(Variant::INT, "reg"), "set_reg", "get_reg");
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ClassDB::bind_method(D_METHOD("get_lambda"), &MLPPProbitReg::get_lambda);
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ClassDB::bind_method(D_METHOD("set_lambda", "val"), &MLPPProbitReg::set_lambda);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "lambda"), "set_lambda", "get_lambda");
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ClassDB::bind_method(D_METHOD("get_alpha"), &MLPPProbitReg::get_alpha);
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ClassDB::bind_method(D_METHOD("set_alpha", "val"), &MLPPProbitReg::set_alpha);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "alpha"), "set_alpha", "get_alpha");
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ADD_GROUP("Data", "data");
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ClassDB::bind_method(D_METHOD("data_z_get"), &MLPPProbitReg::data_z_get);
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ClassDB::bind_method(D_METHOD("data_z_set", "val"), &MLPPProbitReg::set_output_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_z", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "data_z_set", "data_z_get");
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ClassDB::bind_method(D_METHOD("data_y_hat_get"), &MLPPProbitReg::data_y_hat_get);
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ClassDB::bind_method(D_METHOD("data_y_hat_set", "val"), &MLPPProbitReg::data_y_hat_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_y_hat", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "data_y_hat_set", "data_y_hat_get");
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ClassDB::bind_method(D_METHOD("data_weights_get"), &MLPPProbitReg::data_weights_get);
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ClassDB::bind_method(D_METHOD("data_weights_set", "val"), &MLPPProbitReg::data_weights_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_weights", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "data_weights_set", "data_weights_get");
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ClassDB::bind_method(D_METHOD("data_bias_get"), &MLPPProbitReg::data_bias_get);
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ClassDB::bind_method(D_METHOD("data_bias_set", "val"), &MLPPProbitReg::data_bias_set);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "data_bias"), "data_bias_set", "data_bias_get");
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ClassDB::bind_method(D_METHOD("model_set_test", "X"), &MLPPProbitReg::model_set_test);
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ClassDB::bind_method(D_METHOD("model_test", "x"), &MLPPProbitReg::model_test);
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ClassDB::bind_method(D_METHOD("train_gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPProbitReg::train_gradient_descent, 0, false);
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ClassDB::bind_method(D_METHOD("train_mle", "learning_rate", "max_epoch", "ui"), &MLPPProbitReg::train_mle, 0, false);
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ClassDB::bind_method(D_METHOD("train_sgd", "learning_rate", "max_epoch", "ui"), &MLPPProbitReg::train_sgd, 0, false);
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ClassDB::bind_method(D_METHOD("train_mbgd", "learning_rate", "max_epoch", "mini_batch_size", "ui"), &MLPPProbitReg::train_mbgd, false);
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ClassDB::bind_method(D_METHOD("score"), &MLPPProbitReg::score);
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ClassDB::bind_method(D_METHOD("needs_init"), &MLPPProbitReg::needs_init);
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ClassDB::bind_method(D_METHOD("initialize"), &MLPPProbitReg::initialize);
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}
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