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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 @@
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#include <random>
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Ref<MLPPMatrix> MLPPSVC::get_input_set() {
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Ref<MLPPMatrix> MLPPSVC::get_input_set() const {
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return _input_set;
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}
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void MLPPSVC::set_input_set(const Ref<MLPPMatrix> &val) {
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_input_set = val;
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_initialized = false;
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}
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Ref<MLPPVector> MLPPSVC::get_output_set() {
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Ref<MLPPVector> MLPPSVC::get_output_set() const {
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return _output_set;
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}
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void MLPPSVC::set_output_set(const Ref<MLPPMatrix> &val) {
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_output_set = val;
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_initialized = false;
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}
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real_t MLPPSVC::get_c() {
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real_t MLPPSVC::get_c() const {
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return _c;
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}
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void MLPPSVC::set_c(const real_t val) {
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_c = val;
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}
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_initialized = false;
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Ref<MLPPVector> MLPPSVC::data_z_get() const {
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return _z;
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}
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void MLPPSVC::data_z_set(const Ref<MLPPVector> &val) {
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_z = val;
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}
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Ref<MLPPVector> MLPPSVC::data_y_hat_get() const {
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return _y_hat;
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}
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void MLPPSVC::data_y_hat_set(const Ref<MLPPVector> &val) {
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_y_hat = val;
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}
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Ref<MLPPVector> MLPPSVC::data_weights_get() const {
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return _weights;
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}
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void MLPPSVC::data_weights_set(const Ref<MLPPVector> &val) {
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_weights = val;
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}
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real_t MLPPSVC::data_bias_get() const {
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return _bias;
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}
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void MLPPSVC::data_bias_set(const real_t val) {
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_bias = val;
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}
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Ref<MLPPVector> MLPPSVC::model_set_test(const Ref<MLPPMatrix> &X) {
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ERR_FAIL_COND_V(!_initialized, Ref<MLPPVector>());
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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 MLPPSVC::model_test(const Ref<MLPPVector> &x) {
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ERR_FAIL_COND_V(!_initialized, 0);
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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 MLPPSVC::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_initialized);
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void MLPPSVC::train_gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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ERR_FAIL_COND(needs_init());
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int n = _input_set->size().y;
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MLPPCost mlpp_cost;
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MLPPActivation avn;
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@ -68,11 +93,11 @@ void MLPPSVC::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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while (true) {
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cost_prev = cost(_y_hat, _output_set, _weights, _c);
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_weights->sub(_input_set->transposen()->mult_vec(mlpp_cost.hinge_loss_derivwv(_z, _output_set, _c))->scalar_multiplyn(learning_rate / _n));
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_weights = regularization.reg_weightsv(_weights, learning_rate / _n, 0, MLPPReg::REGULARIZATION_TYPE_RIDGE);
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_weights->sub(_input_set->transposen()->mult_vec(mlpp_cost.hinge_loss_derivwv(_z, _output_set, _c))->scalar_multiplyn(learning_rate / n));
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_weights = regularization.reg_weightsv(_weights, learning_rate / n, 0, MLPPReg::REGULARIZATION_TYPE_RIDGE);
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// Calculating the bias gradients
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_bias += learning_rate * mlpp_cost.hinge_loss_derivwv(_y_hat, _output_set, _c)->sum_elements() / _n;
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_bias += learning_rate * mlpp_cost.hinge_loss_derivwv(_y_hat, _output_set, _c)->sum_elements() / n;
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forward_pass();
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@ -90,8 +115,11 @@ void MLPPSVC::gradient_descent(real_t learning_rate, int max_epoch, bool ui) {
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}
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}
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void MLPPSVC::sgd(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_initialized);
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void MLPPSVC::train_sgd(real_t learning_rate, int max_epoch, bool ui) {
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ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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ERR_FAIL_COND(needs_init());
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int n = _input_set->size().y;
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MLPPCost mlpp_cost;
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MLPPActivation avn;
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@ -99,7 +127,7 @@ void MLPPSVC::sgd(real_t learning_rate, int max_epoch, bool ui) {
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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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std::uniform_int_distribution<int> distribution(0, int(n - 1));
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Ref<MLPPVector> input_set_row_tmp;
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input_set_row_tmp.instance();
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@ -161,8 +189,11 @@ void MLPPSVC::sgd(real_t learning_rate, int max_epoch, bool ui) {
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forward_pass();
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}
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void MLPPSVC::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui) {
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ERR_FAIL_COND(!_initialized);
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void MLPPSVC::train_mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui) {
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ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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ERR_FAIL_COND(needs_init());
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int n = _input_set->size().y;
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MLPPCost mlpp_cost;
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MLPPActivation avn;
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@ -172,7 +203,7 @@ void MLPPSVC::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, boo
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int epoch = 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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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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forward_pass();
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@ -187,11 +218,11 @@ void MLPPSVC::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, boo
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cost_prev = cost(z, current_output_batch_entry, _weights, _c);
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// Calculating the weight gradients
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_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));
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_weights = regularization.reg_weightsv(_weights, learning_rate / _n, 0, MLPPReg::REGULARIZATION_TYPE_RIDGE);
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_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));
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_weights = regularization.reg_weightsv(_weights, learning_rate / n, 0, MLPPReg::REGULARIZATION_TYPE_RIDGE);
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// Calculating the bias gradients
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_bias -= learning_rate * mlpp_cost.hinge_loss_derivwv(y_hat, current_output_batch_entry, _c)->sum_elements() / _n;
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_bias -= learning_rate * mlpp_cost.hinge_loss_derivwv(y_hat, current_output_batch_entry, _c)->sum_elements() / n;
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forward_pass();
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@ -214,84 +245,70 @@ void MLPPSVC::mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, boo
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}
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real_t MLPPSVC::score() {
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ERR_FAIL_COND_V(!_initialized, 0);
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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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void MLPPSVC::save(const String &file_name) {
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ERR_FAIL_COND(!_initialized);
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bool MLPPSVC::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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MLPPUtilities util;
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if (!_output_set.is_valid()) {
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return true;
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}
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//util.saveParameters(_file_name, _weights, _bias);
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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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bool MLPPSVC::is_initialized() {
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return _initialized;
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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 MLPPSVC::initialize() {
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if (_initialized) {
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return;
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}
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ERR_FAIL_COND(!_input_set.is_valid() || !_output_set.is_valid());
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_n = _input_set->size().y;
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_k = _input_set->size().x;
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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.is_valid()) {
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_y_hat.instance();
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}
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_y_hat->resize(_n);
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_y_hat->resize(n);
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MLPPUtilities util;
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if (!_weights.is_valid()) {
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_weights.instance();
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}
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_weights->resize(_k);
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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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_initialized = true;
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}
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MLPPSVC::MLPPSVC(const Ref<MLPPMatrix> &input_set, const Ref<MLPPVector> &output_set, real_t c) {
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_input_set = input_set;
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_output_set = output_set;
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_n = _input_set->size().y;
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_k = _input_set->size().x;
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_c = c;
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_z.instance();
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_y_hat.instance();
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_y_hat->resize(_n);
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MLPPUtilities util;
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_weights.instance();
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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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_bias = 0;
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_initialized = true;
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initialize();
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}
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MLPPSVC::MLPPSVC() {
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_c = 0;
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_z.instance();
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_y_hat.instance();
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_weights.instance();
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_c = 0;
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_n = 0;
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_k = 0;
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_initialized = false;
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_bias = 0;
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}
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MLPPSVC::~MLPPSVC() {
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}
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@ -343,17 +360,31 @@ void MLPPSVC::_bind_methods() {
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ClassDB::bind_method(D_METHOD("set_c", "val"), &MLPPSVC::set_c);
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ADD_PROPERTY(PropertyInfo(Variant::REAL, "c"), "set_c", "get_c");
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ClassDB::bind_method(D_METHOD("data_z_get"), &MLPPSVC::data_z_get);
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ClassDB::bind_method(D_METHOD("data_z_set", "val"), &MLPPSVC::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"), &MLPPSVC::data_y_hat_get);
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ClassDB::bind_method(D_METHOD("data_y_hat_set", "val"), &MLPPSVC::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"), &MLPPSVC::data_weights_get);
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ClassDB::bind_method(D_METHOD("data_weights_set", "val"), &MLPPSVC::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"), &MLPPSVC::data_bias_get);
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ClassDB::bind_method(D_METHOD("data_bias_set", "val"), &MLPPSVC::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"), &MLPPSVC::model_set_test);
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ClassDB::bind_method(D_METHOD("model_test", "x"), &MLPPSVC::model_test);
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ClassDB::bind_method(D_METHOD("gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPSVC::gradient_descent, false);
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ClassDB::bind_method(D_METHOD("sgd", "learning_rate", "max_epoch", "ui"), &MLPPSVC::sgd, false);
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ClassDB::bind_method(D_METHOD("mbgd", "learning_rate", "max_epoch", "mini_batch_size", "ui"), &MLPPSVC::mbgd, false);
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ClassDB::bind_method(D_METHOD("train_gradient_descent", "learning_rate", "max_epoch", "ui"), &MLPPSVC::train_gradient_descent, false);
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ClassDB::bind_method(D_METHOD("train_sgd", "learning_rate", "max_epoch", "ui"), &MLPPSVC::train_sgd, false);
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ClassDB::bind_method(D_METHOD("train_mbgd", "learning_rate", "max_epoch", "mini_batch_size", "ui"), &MLPPSVC::train_mbgd, false);
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ClassDB::bind_method(D_METHOD("score"), &MLPPSVC::score);
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ClassDB::bind_method(D_METHOD("save", "file_name"), &MLPPSVC::save);
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ClassDB::bind_method(D_METHOD("is_initialized"), &MLPPSVC::is_initialized);
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ClassDB::bind_method(D_METHOD("needs_init"), &MLPPSVC::needs_init);
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ClassDB::bind_method(D_METHOD("initialize"), &MLPPSVC::initialize);
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}
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@ -13,38 +13,48 @@
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#include "core/math/math_defs.h"
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#include "core/object/reference.h"
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#include "core/object/resource.h"
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#include "../lin_alg/mlpp_matrix.h"
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#include "../lin_alg/mlpp_vector.h"
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#include "../regularization/reg.h"
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class MLPPSVC : public Reference {
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GDCLASS(MLPPSVC, Reference);
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class MLPPSVC : public Resource {
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GDCLASS(MLPPSVC, Resource);
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public:
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Ref<MLPPMatrix> get_input_set();
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Ref<MLPPMatrix> get_input_set() const;
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void set_input_set(const Ref<MLPPMatrix> &val);
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Ref<MLPPVector> get_output_set();
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Ref<MLPPVector> get_output_set() const;
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void set_output_set(const Ref<MLPPMatrix> &val);
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real_t get_c();
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real_t get_c() const;
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void set_c(const real_t val);
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Ref<MLPPVector> data_z_get() const;
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void data_z_set(const Ref<MLPPVector> &val);
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Ref<MLPPVector> data_y_hat_get() const;
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void data_y_hat_set(const Ref<MLPPVector> &val);
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Ref<MLPPVector> data_weights_get() const;
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void data_weights_set(const Ref<MLPPVector> &val);
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real_t data_bias_get() const;
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void data_bias_set(const real_t val);
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Ref<MLPPVector> model_set_test(const Ref<MLPPMatrix> &X);
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real_t model_test(const Ref<MLPPVector> &x);
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void gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
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void sgd(real_t learning_rate, int max_epoch, bool ui = false);
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void mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);
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void train_gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
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void train_sgd(real_t learning_rate, int max_epoch, bool ui = false);
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void train_mbgd(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);
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real_t score();
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void save(const String &file_name);
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bool is_initialized();
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bool needs_init() const;
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void initialize();
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MLPPSVC(const Ref<MLPPMatrix> &input_set, const Ref<MLPPVector> &output_set, real_t c);
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@ -67,17 +77,12 @@ protected:
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Ref<MLPPMatrix> _input_set;
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Ref<MLPPVector> _output_set;
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real_t _c;
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Ref<MLPPVector> _z;
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Ref<MLPPVector> _y_hat;
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Ref<MLPPVector> _weights;
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real_t _bias;
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real_t _c;
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int _n;
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int _k;
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bool _initialized;
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};
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#endif /* SVC_hpp */
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@ -361,7 +361,7 @@ void MLPPTests::test_support_vector_classification(bool ui) {
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Ref<MLPPDataSimple> dt = data.load_breast_cancer_svc(_breast_cancer_svm_data_path);
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MLPPSVC model(dt->get_input(), dt->get_output(), ui);
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model.sgd(0.00001, 100000, ui);
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model.train_sgd(0.00001, 100000, ui);
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PLOG_MSG((model.model_set_test(dt->get_input())->to_string()));
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PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%");
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}
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