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Cleaned up SVC.
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320
mlpp/svc/svc.cpp
320
mlpp/svc/svc.cpp
@ -5,48 +5,84 @@
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//
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#include "svc.h"
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#include "../activation/activation.h"
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#include "../cost/cost.h"
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#include "../lin_alg/lin_alg.h"
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#include "../regularization/reg.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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std::vector<real_t> MLPPSVC::modelSetTest(std::vector<std::vector<real_t>> X) {
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return Evaluate(X);
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Ref<MLPPMatrix> MLPPSVC::get_input_set() {
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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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real_t MLPPSVC::modelTest(std::vector<real_t> x) {
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return Evaluate(x);
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Ref<MLPPVector> MLPPSVC::get_output_set() {
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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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void MLPPSVC::gradientDescent(real_t learning_rate, int max_epoch, bool UI) {
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class MLPPCost cost;
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real_t MLPPSVC::get_c() {
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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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_initialized = false;
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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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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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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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MLPPCost mlpp_cost;
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MLPPActivation avn;
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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int epoch = 1;
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forwardPass();
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forward_pass();
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while (true) {
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cost_prev = Cost(y_hat, outputSet, weights, C);
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cost_prev = cost(_y_hat, _output_set, _weights, _c);
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weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate / n, alg.mat_vec_mult(alg.transpose(inputSet), cost.HingeLossDeriv(z, outputSet, C))));
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weights = regularization.regWeights(weights, learning_rate / n, 0, "Ridge");
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_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate / _n, alg.mat_vec_multv(alg.transposem(_input_set), mlpp_cost.hinge_loss_derivwv(_z, _output_set, _c))));
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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 * alg.sum_elements(cost.HingeLossDeriv(y_hat, outputSet, C)) / n;
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_bias += learning_rate * alg.sum_elementsv(mlpp_cost.hinge_loss_derivwv(_y_hat, _output_set, _c)) / _n;
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forwardPass();
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forward_pass();
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// UI PORTION
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(y_hat, outputSet, weights, C));
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MLPPUtilities::UI(weights, bias);
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if (ui) {
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MLPPUtilities::cost_info(epoch, cost_prev, cost(_y_hat, _output_set, _weights, _c));
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MLPPUtilities::print_ui_vb(_weights, _bias);
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}
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epoch++;
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if (epoch > max_epoch) {
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@ -55,39 +91,66 @@ void MLPPSVC::gradientDescent(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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class MLPPCost cost;
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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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MLPPCost mlpp_cost;
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MLPPActivation avn;
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MLPPLinAlg alg;
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MLPPReg regularization;
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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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Ref<MLPPVector> input_set_row_tmp;
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input_set_row_tmp.instance();
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input_set_row_tmp->resize(_input_set->size().x);
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Ref<MLPPVector> output_set_row_tmp;
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output_set_row_tmp.instance();
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output_set_row_tmp->resize(1);
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Ref<MLPPVector> z_row_tmp;
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z_row_tmp.instance();
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z_row_tmp->resize(1);
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real_t cost_prev = 0;
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int epoch = 1;
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forward_pass();
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while (true) {
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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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int outputIndex = distribution(generator);
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int output_index = distribution(generator);
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//real_t y_hat = Evaluate(inputSet[outputIndex]);
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real_t z = propagate(inputSet[outputIndex]);
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cost_prev = Cost({ z }, { outputSet[outputIndex] }, weights, C);
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_input_set->get_row_into_mlpp_vector(output_index, input_set_row_tmp);
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real_t costDeriv = cost.HingeLossDeriv(std::vector<real_t>({ z }), std::vector<real_t>({ outputSet[outputIndex] }), C)[0]; // Explicit conversion to avoid ambiguity with overloaded function. Error occured on Ubuntu.
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real_t output_set_indx = _output_set->get_element(output_index);
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output_set_row_tmp->set_element(0, output_set_indx);
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//real_t y_hat = Evaluate(input_set_row_tmp);
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real_t z = propagatev(input_set_row_tmp);
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z_row_tmp->set_element(0, z);
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cost_prev = cost(z_row_tmp, output_set_row_tmp, _weights, _c);
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Ref<MLPPVector> cost_deriv_vec = mlpp_cost.hinge_loss_derivwv(z_row_tmp, output_set_row_tmp, _c);
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real_t cost_deriv = cost_deriv_vec->get_element(0);
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// Weight Updation
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weights = alg.subtraction(weights, alg.scalarMultiply(learning_rate * costDeriv, inputSet[outputIndex]));
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weights = regularization.regWeights(weights, learning_rate, 0, "Ridge");
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_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate * cost_deriv, input_set_row_tmp));
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_weights = regularization.reg_weightsv(_weights, learning_rate, 0, MLPPReg::REGULARIZATION_TYPE_RIDGE);
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// Bias updation
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bias -= learning_rate * costDeriv;
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_bias -= learning_rate * cost_deriv;
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//y_hat = Evaluate({ inputSet[outputIndex] });
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//y_hat = Evaluate({ _input_set[output_index] });
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost({ z }, { outputSet[outputIndex] }, weights, C));
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MLPPUtilities::UI(weights, bias);
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if (ui) {
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MLPPUtilities::cost_info(epoch, cost_prev, cost(z_row_tmp, output_set_row_tmp, _weights, _c));
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MLPPUtilities::print_ui_vb(_weights, _bias);
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}
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epoch++;
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@ -96,108 +159,207 @@ void MLPPSVC::SGD(real_t learning_rate, int max_epoch, bool UI) {
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break;
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}
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}
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forwardPass();
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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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class MLPPCost cost;
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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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MLPPCost mlpp_cost;
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MLPPActivation avn;
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MLPPLinAlg alg;
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MLPPReg regularization;
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real_t cost_prev = 0;
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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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auto batches = MLPPUtilities::createMiniBatches(inputSet, outputSet, n_mini_batch);
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auto inputMiniBatches = std::get<0>(batches);
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auto outputMiniBatches = std::get<1>(batches);
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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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while (true) {
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for (int i = 0; i < n_mini_batch; i++) {
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std::vector<real_t> y_hat = Evaluate(inputMiniBatches[i]);
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std::vector<real_t> z = propagate(inputMiniBatches[i]);
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cost_prev = Cost(z, outputMiniBatches[i], weights, C);
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Ref<MLPPMatrix> current_input_batch_entry = batches.input_sets[i];
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Ref<MLPPVector> current_output_batch_entry = batches.output_sets[i];
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Ref<MLPPVector> y_hat = evaluatem(current_input_batch_entry);
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Ref<MLPPVector> z = propagatem(current_input_batch_entry);
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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 = alg.subtraction(weights, alg.scalarMultiply(learning_rate / n, alg.mat_vec_mult(alg.transpose(inputMiniBatches[i]), cost.HingeLossDeriv(z, outputMiniBatches[i], C))));
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weights = regularization.regWeights(weights, learning_rate / n, 0, "Ridge");
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_weights = alg.subtractionnv(_weights, alg.scalar_multiplynv(learning_rate / _n, alg.mat_vec_multv(alg.transposem(current_input_batch_entry), mlpp_cost.hinge_loss_derivwv(z, current_output_batch_entry, _c))));
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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 * alg.sum_elements(cost.HingeLossDeriv(y_hat, outputMiniBatches[i], C)) / n;
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_bias -= learning_rate * alg.sum_elementsv(mlpp_cost.hinge_loss_derivwv(y_hat, current_output_batch_entry, _c)) / _n;
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forwardPass();
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forward_pass();
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y_hat = Evaluate(inputMiniBatches[i]);
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y_hat = evaluatem(current_input_batch_entry);
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if (UI) {
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MLPPUtilities::CostInfo(epoch, cost_prev, Cost(z, outputMiniBatches[i], weights, C));
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MLPPUtilities::UI(weights, bias);
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if (ui) {
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MLPPUtilities::cost_info(epoch, cost_prev, cost(z, current_output_batch_entry, _weights, _c));
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MLPPUtilities::print_ui_vb(_weights, _bias);
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}
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}
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epoch++;
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if (epoch > max_epoch) {
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break;
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}
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}
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forwardPass();
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forward_pass();
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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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MLPPUtilities util;
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return util.performance(y_hat, outputSet);
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return util.performance_vec(_y_hat, _output_set);
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}
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void MLPPSVC::save(std::string fileName) {
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void MLPPSVC::save(const String &file_name) {
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ERR_FAIL_COND(!_initialized);
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MLPPUtilities util;
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util.saveParameters(fileName, weights, bias);
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//util.saveParameters(_file_name, _weights, _bias);
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}
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MLPPSVC::MLPPSVC(std::vector<std::vector<real_t>> p_inputSet, std::vector<real_t> p_outputSet, real_t p_C) {
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inputSet = p_inputSet;
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outputSet = p_outputSet;
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n = inputSet.size();
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k = inputSet[0].size();
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C = p_C;
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y_hat.resize(n);
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weights = MLPPUtilities::weightInitialization(k);
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bias = MLPPUtilities::biasInitialization();
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bool MLPPSVC::is_initialized() {
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return _initialized;
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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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real_t MLPPSVC::Cost(std::vector<real_t> z, std::vector<real_t> y, std::vector<real_t> weights, real_t C) {
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class MLPPCost cost;
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return cost.HingeLoss(z, y, weights, C);
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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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if (!_y_hat.is_valid()) {
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_y_hat.instance();
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}
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std::vector<real_t> MLPPSVC::Evaluate(std::vector<std::vector<real_t>> X) {
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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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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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_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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_initialized = true;
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}
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MLPPSVC::MLPPSVC() {
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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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}
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MLPPSVC::~MLPPSVC() {
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}
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real_t MLPPSVC::cost(const Ref<MLPPVector> &z, const Ref<MLPPVector> &y, const Ref<MLPPVector> &weights, real_t c) {
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MLPPCost mlpp_cost;
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return mlpp_cost.hinge_losswv(z, y, weights, c);
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}
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Ref<MLPPVector> MLPPSVC::evaluatem(const Ref<MLPPMatrix> &X) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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return avn.sign(alg.scalarAdd(bias, alg.mat_vec_mult(X, weights)));
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return avn.sign_normv(alg.scalar_addnv(_bias, alg.mat_vec_multv(X, _weights)));
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}
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std::vector<real_t> MLPPSVC::propagate(std::vector<std::vector<real_t>> X) {
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Ref<MLPPVector> MLPPSVC::propagatem(const Ref<MLPPMatrix> &X) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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return alg.scalarAdd(bias, alg.mat_vec_mult(X, weights));
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return alg.scalar_addnv(_bias, alg.mat_vec_multv(X, _weights));
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}
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real_t MLPPSVC::Evaluate(std::vector<real_t> x) {
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real_t MLPPSVC::evaluatev(const Ref<MLPPVector> &x) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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return avn.sign(alg.dot(weights, x) + bias);
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return avn.sign_normr(alg.dotv(_weights, x) + _bias);
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}
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real_t MLPPSVC::propagate(std::vector<real_t> x) {
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real_t MLPPSVC::propagatev(const Ref<MLPPVector> &x) {
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MLPPLinAlg alg;
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MLPPActivation avn;
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return alg.dot(weights, x) + bias;
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return alg.dotv(_weights, x) + _bias;
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}
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// sign ( wTx + b )
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void MLPPSVC::forwardPass() {
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void MLPPSVC::forward_pass() {
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MLPPActivation avn;
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z = propagate(inputSet);
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y_hat = avn.sign(z);
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_z = propagatem(_input_set);
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_y_hat = avn.sign_normv(_z);
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}
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void MLPPSVC::_bind_methods() {
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ClassDB::bind_method(D_METHOD("get_input_set"), &MLPPSVC::get_input_set);
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ClassDB::bind_method(D_METHOD("set_input_set", "val"), &MLPPSVC::set_input_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "input_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPMatrix"), "set_input_set", "get_input_set");
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ClassDB::bind_method(D_METHOD("get_output_set"), &MLPPSVC::get_output_set);
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ClassDB::bind_method(D_METHOD("set_output_set", "val"), &MLPPSVC::set_output_set);
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ADD_PROPERTY(PropertyInfo(Variant::OBJECT, "output_set", PROPERTY_HINT_RESOURCE_TYPE, "MLPPVector"), "set_output_set", "get_output_set");
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ClassDB::bind_method(D_METHOD("get_c"), &MLPPSVC::get_c);
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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("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);
|
||||
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);
|
||||
|
||||
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("initialize"), &MLPPSVC::initialize);
|
||||
}
|
||||
|
||||
@ -13,43 +13,71 @@
|
||||
|
||||
#include "core/math/math_defs.h"
|
||||
|
||||
#include <string>
|
||||
#include <vector>
|
||||
#include "core/object/reference.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:
|
||||
std::vector<real_t> modelSetTest(std::vector<std::vector<real_t>> X);
|
||||
real_t modelTest(std::vector<real_t> x);
|
||||
void gradientDescent(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);
|
||||
Ref<MLPPMatrix> get_input_set();
|
||||
void set_input_set(const Ref<MLPPMatrix> &val);
|
||||
|
||||
Ref<MLPPVector> get_output_set();
|
||||
void set_output_set(const Ref<MLPPMatrix> &val);
|
||||
|
||||
real_t get_c();
|
||||
void set_c(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);
|
||||
|
||||
real_t score();
|
||||
void save(std::string fileName);
|
||||
|
||||
MLPPSVC(std::vector<std::vector<real_t>> inputSet, std::vector<real_t> outputSet, real_t C);
|
||||
void save(const String &file_name);
|
||||
|
||||
private:
|
||||
real_t Cost(std::vector<real_t> y_hat, std::vector<real_t> y, std::vector<real_t> weights, real_t C);
|
||||
bool is_initialized();
|
||||
void initialize();
|
||||
|
||||
std::vector<real_t> Evaluate(std::vector<std::vector<real_t>> X);
|
||||
std::vector<real_t> propagate(std::vector<std::vector<real_t>> X);
|
||||
real_t Evaluate(std::vector<real_t> x);
|
||||
real_t propagate(std::vector<real_t> x);
|
||||
void forwardPass();
|
||||
MLPPSVC(const Ref<MLPPMatrix> &input_set, const Ref<MLPPVector> &output_set, real_t c);
|
||||
|
||||
std::vector<std::vector<real_t>> inputSet;
|
||||
std::vector<real_t> outputSet;
|
||||
std::vector<real_t> z;
|
||||
std::vector<real_t> y_hat;
|
||||
std::vector<real_t> weights;
|
||||
real_t bias;
|
||||
MLPPSVC();
|
||||
~MLPPSVC();
|
||||
|
||||
real_t C;
|
||||
int n;
|
||||
int k;
|
||||
protected:
|
||||
real_t cost(const Ref<MLPPVector> &z, const Ref<MLPPVector> &y, const Ref<MLPPVector> &weights, real_t c);
|
||||
|
||||
// UI Portion
|
||||
void UI(int epoch, real_t cost_prev);
|
||||
Ref<MLPPVector> evaluatem(const Ref<MLPPMatrix> &X);
|
||||
Ref<MLPPVector> propagatem(const Ref<MLPPMatrix> &X);
|
||||
|
||||
real_t evaluatev(const Ref<MLPPVector> &x);
|
||||
real_t propagatev(const Ref<MLPPVector> &x);
|
||||
|
||||
void forward_pass();
|
||||
|
||||
static void _bind_methods();
|
||||
|
||||
Ref<MLPPMatrix> _input_set;
|
||||
Ref<MLPPVector> _output_set;
|
||||
|
||||
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 */
|
||||
|
||||
@ -43,6 +43,7 @@ SOFTWARE.
|
||||
#include "mlpp/uni_lin_reg/uni_lin_reg.h"
|
||||
#include "mlpp/wgan/wgan.h"
|
||||
#include "mlpp/probit_reg/probit_reg.h"
|
||||
#include "mlpp/svc/svc.h"
|
||||
|
||||
#include "mlpp/mlp/mlp.h"
|
||||
|
||||
@ -71,6 +72,7 @@ void register_pmlpp_types(ModuleRegistrationLevel p_level) {
|
||||
ClassDB::register_class<MLPPUniLinReg>();
|
||||
ClassDB::register_class<MLPPOutlierFinder>();
|
||||
ClassDB::register_class<MLPPProbitReg>();
|
||||
ClassDB::register_class<MLPPSVC>();
|
||||
|
||||
ClassDB::register_class<MLPPDataESimple>();
|
||||
ClassDB::register_class<MLPPDataSimple>();
|
||||
|
||||
@ -51,9 +51,9 @@
|
||||
#include "../mlpp/outlier_finder/outlier_finder_old.h"
|
||||
#include "../mlpp/pca/pca_old.h"
|
||||
#include "../mlpp/probit_reg/probit_reg_old.h"
|
||||
#include "../mlpp/svc/svc_old.h"
|
||||
#include "../mlpp/uni_lin_reg/uni_lin_reg_old.h"
|
||||
#include "../mlpp/wgan/wgan_old.h"
|
||||
#include "../mlpp/svc/svc_old.h"
|
||||
|
||||
Vector<real_t> dstd_vec_to_vec(const std::vector<real_t> &in) {
|
||||
Vector<real_t> r;
|
||||
@ -414,10 +414,16 @@ void MLPPTests::test_support_vector_classification(bool ui) {
|
||||
|
||||
// SUPPORT VECTOR CLASSIFICATION
|
||||
Ref<MLPPDataSimple> dt = data.load_breast_cancer_svc(_breast_cancer_svm_data_path);
|
||||
|
||||
MLPPSVCOld model_old(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector(), ui);
|
||||
model_old.SGD(0.00001, 100000, ui);
|
||||
alg.printVector(model_old.modelSetTest(dt->get_input()->to_std_vector()));
|
||||
std::cout << "ACCURACY: " << 100 * model_old.score() << "%" << std::endl;
|
||||
std::cout << "ACCURACY (old): " << 100 * model_old.score() << "%" << std::endl;
|
||||
|
||||
MLPPSVC model(dt->get_input(), dt->get_output(), ui);
|
||||
model.sgd(0.00001, 100000, ui);
|
||||
PLOG_MSG((model.model_set_test(dt->get_input())->to_string()));
|
||||
PLOG_MSG("ACCURACY: " + String::num(100 * model.score()) + "%");
|
||||
}
|
||||
|
||||
void MLPPTests::test_mlp(bool ui) {
|
||||
|
||||
Loading…
Reference in New Issue
Block a user