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Initial cleanup pass on MLPPGaussianNB.

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Relintai 2023-02-12 02:52:05 +01:00
parent afedf90694
commit f7c3506734
3 changed files with 152 additions and 48 deletions
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139
mlpp/gaussian_nb/gaussian_nb.cpp
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@ -13,76 +13,147 @@
#include <iostream>
#include <random>
MLPPGaussianNB::MLPPGaussianNB(std::vector<std::vector<real_t>> p_inputSet, std::vector<real_t> p_outputSet, int p_class_num) {
inputSet = p_inputSet;
outputSet = p_outputSet;
class_num = p_class_num;
y_hat.resize(outputSet.size());
Evaluate();
/*
Ref<MLPPMatrix> MLPPGaussianNB::get_input_set() {
return _input_set;
}
void MLPPGaussianNB::set_input_set(const Ref<MLPPMatrix> &val) {
_input_set = val;
}
std::vector<real_t> MLPPGaussianNB::modelSetTest(std::vector<std::vector<real_t>> X) {
Ref<MLPPVector> MLPPGaussianNB::get_output_set() {
return _output_set;
}
void MLPPGaussianNB::set_output_set(const Ref<MLPPVector> &val) {
_output_set = val;
}
int MLPPGaussianNB::get_class_num() {
return _class_num;
}
void MLPPGaussianNB::set_class_num(const int val) {
_class_num = 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(modelTest(X[i]));
y_hat.push_back(model_test(X[i]));
}
return y_hat;
}
real_t MLPPGaussianNB::modelTest(std::vector<real_t> x) {
real_t score[class_num];
real_t MLPPGaussianNB::model_test(std::vector<real_t> x) {
real_t score[_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])));
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);
}
return std::distance(score, std::max_element(score, score + sizeof(score) / sizeof(real_t)));
}
real_t MLPPGaussianNB::score() {
MLPPUtilities util;
return util.performance(y_hat, outputSet);
return util.performance(_y_hat, _output_set);
}
void MLPPGaussianNB::Evaluate() {
bool MLPPGaussianNB::is_initialized() {
return _initialized;
}
void MLPPGaussianNB::initialize() {
if (_initialized) {
return;
}
//ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
_initialized = true;
}
MLPPGaussianNB::MLPPGaussianNB(std::vector<std::vector<real_t>> p_input_set, std::vector<real_t> 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());
evaluate();
_initialized = true;
}
MLPPGaussianNB::MLPPGaussianNB() {
_initialized = false;
}
MLPPGaussianNB::~MLPPGaussianNB() {
}
void MLPPGaussianNB::evaluate() {
MLPPStat stat;
MLPPLinAlg alg;
// Computing mu_k_y and sigma_k_y
mu.resize(class_num);
sigma.resize(class_num);
for (int i = class_num - 1; i >= 0; i--) {
_mu.resize(_class_num);
_sigma.resize(_class_num);
for (int i = _class_num - 1; i >= 0; i--) {
std::vector<real_t> set;
for (uint32_t j = 0; j < inputSet.size(); j++) {
for (uint32_t k = 0; k < inputSet[j].size(); k++) {
if (outputSet[j] == i) {
set.push_back(inputSet[j][k]);
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]);
}
}
}
mu[i] = stat.mean(set);
sigma[i] = stat.standardDeviation(set);
_mu[i] = stat.mean(set);
_sigma[i] = stat.standardDeviation(set);
}
// Priors
priors.resize(class_num);
for (uint32_t i = 0; i < outputSet.size(); i++) {
priors[int(outputSet[i])]++;
_priors.resize(_class_num);
for (uint32_t i = 0; i < _output_set.size(); i++) {
_priors[int(_output_set[i])]++;
}
priors = alg.scalarMultiply(real_t(1) / real_t(outputSet.size()), priors);
_priors = alg.scalarMultiply(real_t(1) / real_t(_output_set.size()), _priors);
for (uint32_t i = 0; i < outputSet.size(); i++) {
real_t score[class_num];
for (uint32_t i = 0; i < _output_set.size(); i++) {
real_t score[_class_num];
real_t y_hat_i = 1;
for (int j = class_num - 1; j >= 0; j--) {
for (uint32_t k = 0; k < inputSet[i].size(); k++) {
y_hat_i += std::log(priors[j] * (1 / sqrt(2 * M_PI * sigma[j] * sigma[j])) * exp(-(inputSet[i][k] * mu[j]) * (inputSet[i][k] * mu[j]) / (2 * sigma[j] * sigma[j])));
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])));
}
score[j] = exp(y_hat_i);
std::cout << score[j] << std::endl;
}
y_hat[i] = std::distance(score, std::max_element(score, score + sizeof(score) / sizeof(real_t)));
_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;
}
}
void MLPPGaussianNB::_bind_methods() {
/*
ClassDB::bind_method(D_METHOD("get_input_set"), &MLPPGaussianNB::get_input_set);
ClassDB::bind_method(D_METHOD("set_input_set", "value"), &MLPPGaussianNB::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"), &MLPPGaussianNB::get_output_set);
ClassDB::bind_method(D_METHOD("set_output_set", "value"), &MLPPGaussianNB::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_k"), &MLPPGaussianNB::get_k);
ClassDB::bind_method(D_METHOD("set_k", "value"), &MLPPGaussianNB::set_k);
ADD_PROPERTY(PropertyInfo(Variant::INT, "k"), "set_k", "get_k");
ClassDB::bind_method(D_METHOD("model_set_test", "X"), &MLPPGaussianNB::model_set_test);
ClassDB::bind_method(D_METHOD("model_test", "x"), &MLPPGaussianNB::model_test);
ClassDB::bind_method(D_METHOD("score"), &MLPPGaussianNB::score);
*/
}

56
mlpp/gaussian_nb/gaussian_nb.h
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@ -10,28 +10,58 @@
#include "core/math/math_defs.h"
#include "core/object/reference.h"
#include "../lin_alg/mlpp_matrix.h"
#include "../lin_alg/mlpp_vector.h"
#include <vector>
class MLPPGaussianNB {
class MLPPGaussianNB : public Reference {
GDCLASS(MLPPGaussianNB, Reference);
public:
MLPPGaussianNB(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, int class_num);
std::vector<real_t> modelSetTest(std::vector<std::vector<real_t>> X);
real_t modelTest(std::vector<real_t> x);
/*
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);
int get_class_num();
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);
real_t score();
private:
void Evaluate();
bool is_initialized();
void initialize();
int class_num;
MLPPGaussianNB(std::vector<std::vector<real_t>> p_input_set, std::vector<real_t> p_output_set, int p_class_num);
std::vector<real_t> priors;
std::vector<real_t> mu;
std::vector<real_t> sigma;
MLPPGaussianNB();
~MLPPGaussianNB();
std::vector<std::vector<real_t>> inputSet;
std::vector<real_t> outputSet;
protected:
void evaluate();
std::vector<real_t> y_hat;
static void _bind_methods();
int _class_num;
std::vector<real_t> _priors;
std::vector<real_t> _mu;
std::vector<real_t> _sigma;
std::vector<std::vector<real_t>> _input_set;
std::vector<real_t> _output_set;
std::vector<real_t> _y_hat;
bool _initialized;
};
#endif /* GaussianNB_hpp */

5
test/mlpp_tests.cpp
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@ -738,8 +738,11 @@ void MLPPTests::test_naive_bayes() {
MLPPBernoulliNB BNB(alg.transpose(inputSet), outputSet);
alg.printVector(BNB.modelSetTest(alg.transpose(inputSet)));
MLPPGaussianNBOld GNBOld(alg.transpose(inputSet), outputSet, 2);
alg.printVector(GNBOld.modelSetTest(alg.transpose(inputSet)));
MLPPGaussianNB GNB(alg.transpose(inputSet), outputSet, 2);
alg.printVector(GNB.modelSetTest(alg.transpose(inputSet)));
alg.printVector(GNB.model_set_test(alg.transpose(inputSet)));
}
void MLPPTests::test_k_means(bool ui) {
// KMeans