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228 lines
6.5 KiB
C++
228 lines
6.5 KiB
C++
/*************************************************************************/
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/* bernoulli_nb.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) 2022-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 "bernoulli_nb.h"
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#include "../data/data.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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Ref<MLPPVector> MLPPBernoulliNB::model_set_test(const Ref<MLPPMatrix> &X) {
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Ref<MLPPVector> y_hat;
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y_hat.instance();
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y_hat->resize(X->size().y);
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Ref<MLPPVector> x_row_tmp;
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x_row_tmp.instance();
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x_row_tmp->resize(X->size().x);
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for (int i = 0; i < X->size().y; i++) {
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X->row_get_into_mlpp_vector(i, x_row_tmp);
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y_hat->element_set(i, model_test(x_row_tmp));
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}
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return y_hat;
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}
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real_t MLPPBernoulliNB::model_test(const Ref<MLPPVector> &x) {
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real_t score_0 = 1;
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real_t score_1 = 1;
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Vector<int> found_indices;
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for (int j = 0; j < x->size(); j++) {
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for (int k = 0; k < _vocab->size(); k++) {
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if (x->element_get(j) == _vocab->element_get(k)) {
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score_0 *= _theta[0][_vocab->element_get(k)];
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score_1 *= _theta[1][_vocab->element_get(k)];
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found_indices.push_back(k);
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}
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}
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}
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for (int i = 0; i < _vocab->size(); i++) {
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bool found = false;
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for (int j = 0; j < found_indices.size(); j++) {
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if (_vocab->element_get(i) == _vocab->element_get(found_indices[j])) {
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found = true;
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}
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}
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if (!found) {
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score_0 *= 1 - _theta[0][_vocab->element_get(i)];
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score_1 *= 1 - _theta[1][_vocab->element_get(i)];
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}
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}
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score_0 *= _prior_0;
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score_1 *= _prior_1;
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// Assigning the traning example to a class
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if (score_0 > score_1) {
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return 0;
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} else {
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return 1;
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}
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}
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real_t MLPPBernoulliNB::score() {
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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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MLPPBernoulliNB::MLPPBernoulliNB(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set) {
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_input_set = p_input_set;
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_output_set = p_output_set;
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_class_num = 2;
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_prior_1 = 0;
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_prior_0 = 0;
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_vocab.instance();
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_y_hat.instance();
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_y_hat->resize(_output_set->size());
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evaluate();
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}
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MLPPBernoulliNB::MLPPBernoulliNB() {
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_prior_1 = 0;
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_prior_0 = 0;
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}
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MLPPBernoulliNB::~MLPPBernoulliNB() {
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}
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void MLPPBernoulliNB::compute_vocab() {
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MLPPData data;
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_vocab = data.vec_to_setnv(_input_set->flatten());
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}
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void MLPPBernoulliNB::compute_theta() {
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// Resizing theta for the sake of ease & proper access of the elements.
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_theta.resize(_class_num);
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// Setting all values in the hasmap by default to 0.
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for (int i = _class_num - 1; i >= 0; i--) {
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for (int j = 0; j < _vocab->size(); j++) {
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_theta.write[i][_vocab->element_get(j)] = 0;
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}
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}
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for (int i = 0; i < _input_set->size().y; i++) {
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for (int j = 0; j < _input_set->size().x; j++) {
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_theta.write[_output_set->element_get(i)][_input_set->element_get(i, j)]++;
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}
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}
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for (int i = 0; i < _theta.size(); i++) {
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for (uint32_t j = 0; j < _theta[i].size(); j++) {
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if (i == 0) {
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_theta.write[i][j] /= _prior_0 * _y_hat->size();
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} else {
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_theta.write[i][j] /= _prior_1 * _y_hat->size();
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}
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}
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}
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}
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void MLPPBernoulliNB::evaluate() {
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for (int i = 0; i < _output_set->size(); i++) {
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// Pr(B | A) * Pr(A)
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real_t score_0 = 1;
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real_t score_1 = 1;
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real_t sum = 0;
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for (int ii = 0; ii < _output_set->size(); ii++) {
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if (_output_set->element_get(ii) == 1) {
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sum += 1;
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}
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}
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// Easy computation of priors, i.e. Pr(C_k)
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_prior_1 = sum / _y_hat->size();
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_prior_0 = 1 - _prior_1;
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// Evaluating Theta...
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compute_theta();
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// Evaluating the vocab set...
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compute_vocab();
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Vector<int> found_indices;
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for (int j = 0; j < _input_set->size().x; j++) {
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for (int k = 0; k < _vocab->size(); k++) {
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if (_input_set->element_get(i, j) == _vocab->element_get(k)) {
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score_0 += Math::log(static_cast<real_t>(_theta[0][_vocab->element_get(k)]));
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score_1 += Math::log(static_cast<real_t>(_theta[1][_vocab->element_get(k)]));
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found_indices.push_back(k);
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}
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}
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}
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for (int ii = 0; ii < _vocab->size(); ii++) {
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bool found = false;
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for (int j = 0; j < found_indices.size(); j++) {
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if (_vocab->element_get(ii) == _vocab->element_get(found_indices[j])) {
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found = true;
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}
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}
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if (!found) {
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score_0 += Math::log(1.0 - _theta[0][_vocab->element_get(ii)]);
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score_1 += Math::log(1.0 - _theta[1][_vocab->element_get(ii)]);
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}
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}
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score_0 += Math::log(_prior_0);
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score_1 += Math::log(_prior_1);
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score_0 = Math::exp(score_0);
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score_1 = Math::exp(score_1);
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// Assigning the traning example to a class
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if (score_0 > score_1) {
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_y_hat->element_set(i, 0);
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} else {
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_y_hat->element_set(i, 1);
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}
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}
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}
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void MLPPBernoulliNB::_bind_methods() {
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}
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