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Now MLPPGaussianNB uses engine classes.

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Relintai 2023-02-15 13:30:22 +01:00
parent 3bc48624b5
commit 3c8ee1ffea
3 changed files with 103 additions and 50 deletions
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129
mlpp/gaussian_nb/gaussian_nb.cpp
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@ -9,10 +9,6 @@
#include "../stat/stat.h"
#include "../utilities/utilities.h"
#include <algorithm>
#include <iostream>
#include <random>
/*
Ref<MLPPMatrix> MLPPGaussianNB::get_input_set() {
return _input_set;
@ -36,29 +32,57 @@ void MLPPGaussianNB::set_class_num(const int val) {
}
*/
std::vector<real_t> MLPPGaussianNB::model_set_test(std::vector<std::vector<real_t>> X) {
std::vector<real_t> y_hat;
for (uint32_t i = 0; i < X.size(); i++) {
y_hat.push_back(model_test(X[i]));
Ref<MLPPVector> MLPPGaussianNB::model_set_test(const Ref<MLPPMatrix> &X) {
Ref<MLPPVector> y_hat;
y_hat.instance();
y_hat->resize(X->size().y);
Ref<MLPPVector> x_row_tmp;
x_row_tmp.instance();
x_row_tmp->resize(X->size().x);
for (int i = 0; i < X->size().y; i++) {
X->get_row_into_mlpp_vector(i, x_row_tmp);
y_hat->set_element(i, model_test(x_row_tmp));
}
return y_hat;
}
real_t MLPPGaussianNB::model_test(std::vector<real_t> x) {
real_t score[_class_num];
real_t MLPPGaussianNB::model_test(const Ref<MLPPVector> &x) {
LocalVector<real_t> score;
score.resize(_class_num);
real_t y_hat_i = 1;
for (int i = _class_num - 1; i >= 0; i--) {
y_hat_i += std::log(_priors[i] * (1 / sqrt(2 * M_PI * _sigma[i] * _sigma[i])) * exp(-(x[i] * _mu[i]) * (x[i] * _mu[i]) / (2 * _sigma[i] * _sigma[i])));
score[i] = exp(y_hat_i);
real_t sigma_i = _sigma->get_element(i);
real_t x_i = x->get_element(i);
real_t mu_i = _mu->get_element(i);
y_hat_i += Math::log(_priors->get_element(i) * (1 / Math::sqrt(2 * M_PI * sigma_i * sigma_i)) * Math::exp(-(x_i * mu_i) * (x_i * mu_i) / (2 * sigma_i * sigma_i)));
score[i] = Math::exp(y_hat_i);
}
return std::distance(score, std::max_element(score, score + sizeof(score) / sizeof(real_t)));
real_t max_element = -Math_INF;
int max_element_index = 0;
for (int i = 0; i < _class_num; ++i) {
real_t score_i = score[i];
if (score_i > max_element) {
max_element = score_i;
max_element_index = i;
}
}
return max_element_index;
}
real_t MLPPGaussianNB::score() {
MLPPUtilities util;
return util.performance(_y_hat, _output_set);
return util.performance_vec(_y_hat, _output_set);
}
bool MLPPGaussianNB::is_initialized() {
@ -74,12 +98,17 @@ void MLPPGaussianNB::initialize() {
_initialized = true;
}
MLPPGaussianNB::MLPPGaussianNB(std::vector<std::vector<real_t>> p_input_set, std::vector<real_t> p_output_set, int p_class_num) {
MLPPGaussianNB::MLPPGaussianNB(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, int p_class_num) {
_input_set = p_input_set;
_output_set = p_output_set;
_class_num = p_class_num;
_y_hat.resize(_output_set.size());
_mu.instance();
_sigma.instance();
_priors.instance();
_y_hat.instance();
_y_hat->resize(_output_set->size());
evaluate();
@ -97,44 +126,70 @@ void MLPPGaussianNB::evaluate() {
MLPPLinAlg alg;
// Computing mu_k_y and sigma_k_y
_mu.resize(_class_num);
_sigma.resize(_class_num);
_mu->resize(_class_num);
_sigma->resize(_class_num);
Ref<MLPPVector> set_vec;
set_vec.instance();
for (int i = _class_num - 1; i >= 0; i--) {
std::vector<real_t> set;
for (uint32_t j = 0; j < _input_set.size(); j++) {
for (uint32_t k = 0; k < _input_set[j].size(); k++) {
if (_output_set[j] == i) {
set.push_back(_input_set[j][k]);
PoolRealArray set;
for (int j = 0; j < _input_set->size().y; j++) {
for (int k = 0; k < _input_set->size().x; k++) {
if (_output_set->get_element(j) == i) {
set.push_back(_input_set->get_element(j, k));
}
}
}
_mu[i] = stat.mean(set);
_sigma[i] = stat.standardDeviation(set);
set_vec->set_from_pool_vector(set);
_mu->set_element(i, stat.meanv(set_vec));
_sigma->set_element(i, stat.standard_deviationv(set_vec));
}
// Priors
_priors.resize(_class_num);
for (uint32_t i = 0; i < _output_set.size(); i++) {
_priors[int(_output_set[i])]++;
_priors->resize(_class_num);
_priors->fill(0);
for (int i = 0; i < _output_set->size(); i++) {
int indx = static_cast<int>(_output_set->get_element(i));
_priors->set_element(indx, _priors->get_element(indx));
}
_priors = alg.scalarMultiply(real_t(1) / real_t(_output_set.size()), _priors);
for (uint32_t i = 0; i < _output_set.size(); i++) {
real_t score[_class_num];
_priors = alg.scalar_multiplynv(real_t(1) / real_t(_output_set->size()), _priors);
for (int i = 0; i < _output_set->size(); i++) {
LocalVector<real_t> score;
score.resize(_class_num);
real_t y_hat_i = 1;
for (int j = _class_num - 1; j >= 0; j--) {
for (uint32_t k = 0; k < _input_set[i].size(); k++) {
y_hat_i += std::log(_priors[j] * (1 / sqrt(2 * M_PI * _sigma[j] * _sigma[j])) * exp(-(_input_set[i][k] * _mu[j]) * (_input_set[i][k] * _mu[j]) / (2 * _sigma[j] * _sigma[j])));
for (int k = 0; k < _input_set->size().x; k++) {
real_t sigma_j = _sigma->get_element(j);
real_t mu_j = _mu->get_element(j);
real_t input_set_i_k = _input_set->get_element(i, k);
y_hat_i += Math::log(_priors->get_element(j) * (1 / Math::sqrt(2 * M_PI * sigma_j * sigma_j)) * Math::exp(-(input_set_i_k * mu_j) * (input_set_i_k * mu_j) / (2 * sigma_j * sigma_j)));
}
score[j] = exp(y_hat_i);
std::cout << score[j] << std::endl;
score[j] = Math::exp(y_hat_i);
}
_y_hat[i] = std::distance(score, std::max_element(score, score + sizeof(score) / sizeof(real_t)));
std::cout << std::distance(score, std::max_element(score, score + sizeof(score) / sizeof(real_t))) << std::endl;
real_t max_element = -Math_INF;
int max_element_index = 0;
for (int ii = 0; ii < _class_num; ++ii) {
real_t score_ii = score[ii];
if (score_ii > max_element) {
max_element = score_ii;
max_element_index = ii;
}
}
_y_hat->set_element(i, max_element_index);
}
}

20
mlpp/gaussian_nb/gaussian_nb.h
View File

@ -15,8 +15,6 @@
#include "../lin_alg/mlpp_matrix.h"
#include "../lin_alg/mlpp_vector.h"
#include <vector>
class MLPPGaussianNB : public Reference {
GDCLASS(MLPPGaussianNB, Reference);
@ -32,15 +30,15 @@ public:
void set_class_num(const int val);
*/
std::vector<real_t> model_set_test(std::vector<std::vector<real_t>> X);
real_t model_test(std::vector<real_t> x);
Ref<MLPPVector> model_set_test(const Ref<MLPPMatrix> &X);
real_t model_test(const Ref<MLPPVector> &x);
real_t score();
bool is_initialized();
void initialize();
MLPPGaussianNB(std::vector<std::vector<real_t>> p_input_set, std::vector<real_t> p_output_set, int p_class_num);
MLPPGaussianNB(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, int p_class_num);
MLPPGaussianNB();
~MLPPGaussianNB();
@ -52,14 +50,14 @@ protected:
int _class_num;
std::vector<real_t> _priors;
std::vector<real_t> _mu;
std::vector<real_t> _sigma;
Ref<MLPPVector> _priors;
Ref<MLPPVector> _mu;
Ref<MLPPVector> _sigma;
std::vector<std::vector<real_t>> _input_set;
std::vector<real_t> _output_set;
Ref<MLPPMatrix> _input_set;
Ref<MLPPVector> _output_set;
std::vector<real_t> _y_hat;
Ref<MLPPVector> _y_hat;
bool _initialized;
};

4
test/mlpp_tests.cpp
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@ -789,8 +789,8 @@ void MLPPTests::test_naive_bayes() {
MLPPGaussianNBOld GNBOld(alg.transpose(inputSet), outputSet, 2);
alg.printVector(GNBOld.modelSetTest(alg.transpose(inputSet)));
MLPPGaussianNB GNB(alg.transpose(inputSet), outputSet, 2);
alg.printVector(GNB.model_set_test(alg.transpose(inputSet)));
MLPPGaussianNB GNB(alg.transposem(input_set), output_set, 2);
PLOG_MSG(GNB.model_set_test(alg.transposem(input_set))->to_string());
}
void MLPPTests::test_k_means(bool ui) {
// KMeans