mirror of
https://github.com/Relintai/pmlpp.git
synced 2024-12-30 16:07:12 +01:00
MLPPSVC api rework.
This commit is contained in:
parent
19c9107309
commit
a025a0828d
179
mlpp/svc/svc.cpp
179
mlpp/svc/svc.cpp
@ -14,47 +14,72 @@
|
||||
|
||||
#include <random>
|
||||
|
||||
Ref<MLPPMatrix> MLPPSVC::get_input_set() {
|
||||
Ref<MLPPMatrix> MLPPSVC::get_input_set() const {
|
||||
return _input_set;
|
||||
}
|
||||
void MLPPSVC::set_input_set(const Ref<MLPPMatrix> &val) {
|
||||
_input_set = val;
|
||||
|
||||
_initialized = false;
|
||||
}
|
||||
|
||||
Ref<MLPPVector> MLPPSVC::get_output_set() {
|
||||
Ref<MLPPVector> MLPPSVC::get_output_set() const {
|
||||
return _output_set;
|
||||
}
|
||||
void MLPPSVC::set_output_set(const Ref<MLPPMatrix> &val) {
|
||||
_output_set = val;
|
||||
|
||||
_initialized = false;
|
||||
}
|
||||
|
||||
real_t MLPPSVC::get_c() {
|
||||
real_t MLPPSVC::get_c() const {
|
||||
return _c;
|
||||
}
|
||||
void MLPPSVC::set_c(const real_t val) {
|
||||
_c = val;
|
||||
}
|
||||
|
||||
_initialized = false;
|
||||
Ref<MLPPVector> MLPPSVC::data_z_get() const {
|
||||
return _z;
|
||||
}
|
||||
void MLPPSVC::data_z_set(const Ref<MLPPVector> &val) {
|
||||
_z = val;
|
||||
}
|
||||
|
||||
Ref<MLPPVector> MLPPSVC::data_y_hat_get() const {
|
||||
return _y_hat;
|
||||
}
|
||||
void MLPPSVC::data_y_hat_set(const Ref<MLPPVector> &val) {
|
||||
_y_hat = val;
|
||||
}
|
||||
|
||||
Ref<MLPPVector> MLPPSVC::data_weights_get() const {
|
||||
return _weights;
|
||||
}
|
||||
void MLPPSVC::data_weights_set(const Ref<MLPPVector> &val) {
|
||||
_weights = val;
|
||||
}
|
||||
|
||||
real_t MLPPSVC::data_bias_get() const {
|
||||
return _bias;
|
||||
}
|
||||
void MLPPSVC::data_bias_set(const real_t val) {
|
||||
_bias = val;
|
||||
}
|
||||
|
||||
Ref<MLPPVector> MLPPSVC::model_set_test(const Ref<MLPPMatrix> &X) {
|
||||
ERR_FAIL_COND_V(!_initialized, Ref<MLPPVector>());
|
||||
ERR_FAIL_COND_V(needs_init(), Ref<MLPPVector>());
|
||||
|
||||
return evaluatem(X);
|
||||
}
|
||||
|
||||
real_t MLPPSVC::model_test(const Ref<MLPPVector> &x) {
|
||||
ERR_FAIL_COND_V(!_initialized, 0);
|
||||
ERR_FAIL_COND_V(needs_init(), 0);
|
||||
|
||||
return evaluatev(x);
|
||||
}
|
||||
|
||||
void MLPPSVC::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
|
||||
ERR_FAIL_COND(!_initialized);
|
||||
void MLPPSVC::train_gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
|
||||
ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
|
||||
ERR_FAIL_COND(needs_init());
|
||||
|
||||
int n = _input_set->size().y;
|
||||
|
||||
MLPPCost mlpp_cost;
|
||||
MLPPActivation avn;
|
||||
@ -68,11 +93,11 @@ void MLPPSVC::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
|
||||
while (true) {
|
||||
cost_prev = cost(_y_hat, _output_set, _weights, _c);
|
||||
|
||||
_weights->sub(_input_set->transposen()->mult_vec(mlpp_cost.hinge_loss_derivwv(_z, _output_set, _c))->scalar_multiplyn(learning_rate / _n));
|
||||
_weights = regularization.reg_weightsv(_weights, learning_rate / _n, 0, MLPPReg::REGULARIZATION_TYPE_RIDGE);
|
||||
_weights->sub(_input_set->transposen()->mult_vec(mlpp_cost.hinge_loss_derivwv(_z, _output_set, _c))->scalar_multiplyn(learning_rate / n));
|
||||
_weights = regularization.reg_weightsv(_weights, learning_rate / n, 0, MLPPReg::REGULARIZATION_TYPE_RIDGE);
|
||||
|
||||
// Calculating the bias gradients
|
||||
_bias += learning_rate * mlpp_cost.hinge_loss_derivwv(_y_hat, _output_set, _c)->sum_elements() / _n;
|
||||
_bias += learning_rate * mlpp_cost.hinge_loss_derivwv(_y_hat, _output_set, _c)->sum_elements() / n;
|
||||
|
||||
forward_pass();
|
||||
|
||||
@ -90,8 +115,11 @@ void MLPPSVC::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
|
||||
}
|
||||
}
|
||||
|
||||
void MLPPSVC::sgd(real_t learning_rate, int max_epoch, bool ui) {
|
||||
ERR_FAIL_COND(!_initialized);
|
||||
void MLPPSVC::train_sgd(real_t learning_rate, int max_epoch, bool ui) {
|
||||
ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
|
||||
ERR_FAIL_COND(needs_init());
|
||||
|
||||
int n = _input_set->size().y;
|
||||
|
||||
MLPPCost mlpp_cost;
|
||||
MLPPActivation avn;
|
||||
@ -99,7 +127,7 @@ void MLPPSVC::sgd(real_t learning_rate, int max_epoch, bool ui) {
|
||||
|
||||
std::random_device rd;
|
||||
std::default_random_engine generator(rd());
|
||||
std::uniform_int_distribution<int> distribution(0, int(_n - 1));
|
||||
std::uniform_int_distribution<int> distribution(0, int(n - 1));
|
||||
|
||||
Ref<MLPPVector> input_set_row_tmp;
|
||||
input_set_row_tmp.instance();
|
||||
@ -161,8 +189,11 @@ void MLPPSVC::sgd(real_t learning_rate, int max_epoch, bool ui) {
|
||||
forward_pass();
|
||||
}
|
||||
|
||||
void MLPPSVC::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui) {
|
||||
ERR_FAIL_COND(!_initialized);
|
||||
void MLPPSVC::train_mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui) {
|
||||
ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
|
||||
ERR_FAIL_COND(needs_init());
|
||||
|
||||
int n = _input_set->size().y;
|
||||
|
||||
MLPPCost mlpp_cost;
|
||||
MLPPActivation avn;
|
||||
@ -172,7 +203,7 @@ void MLPPSVC::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, boo
|
||||
int epoch = 1;
|
||||
|
||||
// Creating the mini-batches
|
||||
int n_mini_batch = _n / mini_batch_size;
|
||||
int n_mini_batch = n / mini_batch_size;
|
||||
MLPPUtilities::CreateMiniBatchMVBatch batches = MLPPUtilities::create_mini_batchesmv(_input_set, _output_set, n_mini_batch);
|
||||
|
||||
forward_pass();
|
||||
@ -187,11 +218,11 @@ void MLPPSVC::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, boo
|
||||
cost_prev = cost(z, current_output_batch_entry, _weights, _c);
|
||||
|
||||
// Calculating the weight gradients
|
||||
_weights->subn(current_input_batch_entry->transposen()->mult_vec(mlpp_cost.hinge_loss_derivwv(z, current_output_batch_entry, _c))->scalar_multiplyn(learning_rate / _n));
|
||||
_weights = regularization.reg_weightsv(_weights, learning_rate / _n, 0, MLPPReg::REGULARIZATION_TYPE_RIDGE);
|
||||
_weights->subn(current_input_batch_entry->transposen()->mult_vec(mlpp_cost.hinge_loss_derivwv(z, current_output_batch_entry, _c))->scalar_multiplyn(learning_rate / n));
|
||||
_weights = regularization.reg_weightsv(_weights, learning_rate / n, 0, MLPPReg::REGULARIZATION_TYPE_RIDGE);
|
||||
|
||||
// Calculating the bias gradients
|
||||
_bias -= learning_rate * mlpp_cost.hinge_loss_derivwv(y_hat, current_output_batch_entry, _c)->sum_elements() / _n;
|
||||
_bias -= learning_rate * mlpp_cost.hinge_loss_derivwv(y_hat, current_output_batch_entry, _c)->sum_elements() / n;
|
||||
|
||||
forward_pass();
|
||||
|
||||
@ -214,84 +245,70 @@ void MLPPSVC::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, boo
|
||||
}
|
||||
|
||||
real_t MLPPSVC::score() {
|
||||
ERR_FAIL_COND_V(!_initialized, 0);
|
||||
ERR_FAIL_COND_V(needs_init(), 0);
|
||||
|
||||
MLPPUtilities util;
|
||||
return util.performance_vec(_y_hat, _output_set);
|
||||
}
|
||||
|
||||
void MLPPSVC::save(const String &file_name) {
|
||||
ERR_FAIL_COND(!_initialized);
|
||||
bool MLPPSVC::needs_init() const {
|
||||
if (!_input_set.is_valid()) {
|
||||
return true;
|
||||
}
|
||||
|
||||
MLPPUtilities util;
|
||||
if (!_output_set.is_valid()) {
|
||||
return true;
|
||||
}
|
||||
|
||||
//util.saveParameters(_file_name, _weights, _bias);
|
||||
}
|
||||
int n = _input_set->size().y;
|
||||
int k = _input_set->size().x;
|
||||
|
||||
bool MLPPSVC::is_initialized() {
|
||||
return _initialized;
|
||||
if (_y_hat->size() != n) {
|
||||
return true;
|
||||
}
|
||||
|
||||
if (_weights->size() != k) {
|
||||
return true;
|
||||
}
|
||||
|
||||
return false;
|
||||
}
|
||||
void MLPPSVC::initialize() {
|
||||
if (_initialized) {
|
||||
return;
|
||||
}
|
||||
|
||||
ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
|
||||
|
||||
_n = _input_set->size().y;
|
||||
_k = _input_set->size().x;
|
||||
int n = _input_set->size().y;
|
||||
int k = _input_set->size().x;
|
||||
|
||||
if (!_y_hat.is_valid()) {
|
||||
_y_hat.instance();
|
||||
}
|
||||
|
||||
_y_hat->resize(_n);
|
||||
_y_hat->resize(n);
|
||||
|
||||
MLPPUtilities util;
|
||||
|
||||
if (!_weights.is_valid()) {
|
||||
_weights.instance();
|
||||
}
|
||||
|
||||
_weights->resize(_k);
|
||||
_weights->resize(k);
|
||||
|
||||
util.weight_initializationv(_weights);
|
||||
_bias = util.bias_initializationr();
|
||||
|
||||
_initialized = true;
|
||||
}
|
||||
|
||||
MLPPSVC::MLPPSVC(const Ref<MLPPMatrix> &input_set, const Ref<MLPPVector> &output_set, real_t c) {
|
||||
_input_set = input_set;
|
||||
_output_set = output_set;
|
||||
|
||||
_n = _input_set->size().y;
|
||||
_k = _input_set->size().x;
|
||||
_c = c;
|
||||
|
||||
_z.instance();
|
||||
_y_hat.instance();
|
||||
|
||||
_y_hat->resize(_n);
|
||||
|
||||
MLPPUtilities util;
|
||||
|
||||
_weights.instance();
|
||||
_weights->resize(_k);
|
||||
util.weight_initializationv(_weights);
|
||||
_bias = util.bias_initializationr();
|
||||
_bias = 0;
|
||||
|
||||
_initialized = true;
|
||||
initialize();
|
||||
}
|
||||
|
||||
MLPPSVC::MLPPSVC() {
|
||||
_c = 0;
|
||||
|
||||
_z.instance();
|
||||
_y_hat.instance();
|
||||
_weights.instance();
|
||||
|
||||
_c = 0;
|
||||
_n = 0;
|
||||
_k = 0;
|
||||
|
||||
_initialized = false;
|
||||
_bias = 0;
|
||||
}
|
||||
MLPPSVC::~MLPPSVC() {
|
||||
}
|
||||
@ -343,17 +360,31 @@ void MLPPSVC::_bind_methods() {
|
||||
ClassDB::bind_method(D_METHOD("set_c", "val"), &MLPPSVC::set_c);
|
||||
ADD_PROPERTY(PropertyInfo(Variant::REAL, "c"), "set_c", "get_c");
|
||||
|
||||
ClassDB::bind_method(D_METHOD("data_z_get"), &MLPPSVC::data_z_get);
|
||||
ClassDB::bind_method(D_METHOD("data_z_set", "val"), &MLPPSVC::set_output_set);
|
||||
ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_z", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "data_z_set", "data_z_get");
|
||||
|
||||
ClassDB::bind_method(D_METHOD("data_y_hat_get"), &MLPPSVC::data_y_hat_get);
|
||||
ClassDB::bind_method(D_METHOD("data_y_hat_set", "val"), &MLPPSVC::data_y_hat_set);
|
||||
ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_y_hat", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "data_y_hat_set", "data_y_hat_get");
|
||||
|
||||
ClassDB::bind_method(D_METHOD("data_weights_get"), &MLPPSVC::data_weights_get);
|
||||
ClassDB::bind_method(D_METHOD("data_weights_set", "val"), &MLPPSVC::data_weights_set);
|
||||
ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "data_weights", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "data_weights_set", "data_weights_get");
|
||||
|
||||
ClassDB::bind_method(D_METHOD("data_bias_get"), &MLPPSVC::data_bias_get);
|
||||
ClassDB::bind_method(D_METHOD("data_bias_set", "val"), &MLPPSVC::data_bias_set);
|
||||
ADD_PROPERTY(PropertyInfo(Variant::REAL, "data_bias"), "data_bias_set", "data_bias_get");
|
||||
|
||||
ClassDB::bind_method(D_METHOD("model_set_test", "X"), &MLPPSVC::model_set_test);
|
||||
ClassDB::bind_method(D_METHOD("model_test", "x"), &MLPPSVC::model_test);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPSVC::gradient_descent, false);
|
||||
ClassDB::bind_method(D_METHOD("sgd", "learning_rate", "max_epoch", "ui"), &MLPPSVC::sgd, false);
|
||||
ClassDB::bind_method(D_METHOD("mbgd", "learning_rate", "max_epoch", "mini_batch_size", "ui"), &MLPPSVC::mbgd, false);
|
||||
ClassDB::bind_method(D_METHOD("train_gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPSVC::train_gradient_descent, false);
|
||||
ClassDB::bind_method(D_METHOD("train_sgd", "learning_rate", "max_epoch", "ui"), &MLPPSVC::train_sgd, false);
|
||||
ClassDB::bind_method(D_METHOD("train_mbgd", "learning_rate", "max_epoch", "mini_batch_size", "ui"), &MLPPSVC::train_mbgd, false);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("score"), &MLPPSVC::score);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("save", "file_name"), &MLPPSVC::save);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("is_initialized"), &MLPPSVC::is_initialized);
|
||||
ClassDB::bind_method(D_METHOD("needs_init"), &MLPPSVC::needs_init);
|
||||
ClassDB::bind_method(D_METHOD("initialize"), &MLPPSVC::initialize);
|
||||
}
|
||||
|
||||
@ -13,38 +13,48 @@
|
||||
|
||||
#include "core/math/math_defs.h"
|
||||
|
||||
#include "core/object/reference.h"
|
||||
#include "core/object/resource.h"
|
||||
|
||||
#include "../lin_alg/mlpp_matrix.h"
|
||||
#include "../lin_alg/mlpp_vector.h"
|
||||
|
||||
#include "../regularization/reg.h"
|
||||
|
||||
class MLPPSVC : public Reference {
|
||||
GDCLASS(MLPPSVC, Reference);
|
||||
class MLPPSVC : public Resource {
|
||||
GDCLASS(MLPPSVC, Resource);
|
||||
|
||||
public:
|
||||
Ref<MLPPMatrix> get_input_set();
|
||||
Ref<MLPPMatrix> get_input_set() const;
|
||||
void set_input_set(const Ref<MLPPMatrix> &val);
|
||||
|
||||
Ref<MLPPVector> get_output_set();
|
||||
Ref<MLPPVector> get_output_set() const;
|
||||
void set_output_set(const Ref<MLPPMatrix> &val);
|
||||
|
||||
real_t get_c();
|
||||
real_t get_c() const;
|
||||
void set_c(const real_t val);
|
||||
|
||||
Ref<MLPPVector> data_z_get() const;
|
||||
void data_z_set(const Ref<MLPPVector> &val);
|
||||
|
||||
Ref<MLPPVector> data_y_hat_get() const;
|
||||
void data_y_hat_set(const Ref<MLPPVector> &val);
|
||||
|
||||
Ref<MLPPVector> data_weights_get() const;
|
||||
void data_weights_set(const Ref<MLPPVector> &val);
|
||||
|
||||
real_t data_bias_get() const;
|
||||
void data_bias_set(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, bool ui = false);
|
||||
void sgd(real_t learning_rate, int max_epoch, bool ui = false);
|
||||
void mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);
|
||||
void train_gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
|
||||
void train_sgd(real_t learning_rate, int max_epoch, bool ui = false);
|
||||
void train_mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);
|
||||
|
||||
real_t score();
|
||||
|
||||
void save(const String &file_name);
|
||||
|
||||
bool is_initialized();
|
||||
bool needs_init() const;
|
||||
void initialize();
|
||||
|
||||
MLPPSVC(const Ref<MLPPMatrix> &input_set, const Ref<MLPPVector> &output_set, real_t c);
|
||||
@ -67,17 +77,12 @@ protected:
|
||||
|
||||
Ref<MLPPMatrix> _input_set;
|
||||
Ref<MLPPVector> _output_set;
|
||||
real_t _c;
|
||||
|
||||
Ref<MLPPVector> _z;
|
||||
Ref<MLPPVector> _y_hat;
|
||||
Ref<MLPPVector> _weights;
|
||||
real_t _bias;
|
||||
|
||||
real_t _c;
|
||||
int _n;
|
||||
int _k;
|
||||
|
||||
bool _initialized;
|
||||
};
|
||||
|
||||
#endif /* SVC_hpp */
|
||||
|
||||
@ -361,7 +361,7 @@ void MLPPTests::test_support_vector_classification(bool ui) {
|
||||
Ref<MLPPDataSimple> dt = data.load_breast_cancer_svc(_breast_cancer_svm_data_path);
|
||||
|
||||
MLPPSVC model(dt->get_input(), dt->get_output(), ui);
|
||||
model.sgd(0.00001, 100000, ui);
|
||||
model.train_sgd(0.00001, 100000, ui);
|
||||
PLOG_MSG((model.model_set_test(dt->get_input())->to_string()));
|
||||
PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%");
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user