diff --git a/mlpp/gan/gan.cpp b/mlpp/gan/gan.cpp index e2d9d3e..624d3a9 100644 --- a/mlpp/gan/gan.cpp +++ b/mlpp/gan/gan.cpp @@ -7,7 +7,6 @@ #include "gan.h" #include "../activation/activation.h" #include "../cost/cost.h" -#include "../lin_alg/lin_alg.h" #include "../regularization/reg.h" #include "../utilities/utilities.h" @@ -40,53 +39,53 @@ void MLPPGAN::set_k(const int val) { */ Ref MLPPGAN::generate_example(int n) { - MLPPLinAlg alg; - - return model_set_test_generator(alg.gaussian_noise(n, _k)); + return model_set_test_generator(MLPPMatrix::create_gaussian_noise(n, _k)); } void MLPPGAN::gradient_descent(real_t learning_rate, int max_epoch, bool ui) { MLPPCost mlpp_cost; - MLPPLinAlg alg; real_t cost_prev = 0; int epoch = 1; forward_pass(); while (true) { - cost_prev = cost(_y_hat, alg.onevecnv(_n)); + cost_prev = cost(_y_hat, MLPPVector::create_vec_one(_n)); // Training of the discriminator. - Ref generator_input_set = alg.gaussian_noise(_n, _k); + Ref generator_input_set = MLPPMatrix::create_gaussian_noise(_n, _k); Ref discriminator_input_set = model_set_test_generator(generator_input_set); discriminator_input_set->rows_add_mlpp_matrix(_output_set); // Fake + real inputs. Ref y_hat = model_set_test_discriminator(discriminator_input_set); - Ref output_set = alg.zerovecnv(_n); - Ref output_set_real = alg.onevecnv(_n); + Ref output_set = MLPPVector::create_vec_zero(_n); + Ref output_set_real = MLPPVector::create_vec_one(_n); output_set->append_mlpp_vector(output_set_real); // Fake + real output scores. ComputeDiscriminatorGradientsResult dgrads = compute_discriminator_gradients(y_hat, _output_set); - dgrads.cumulative_hidden_layer_w_grad = alg.scalar_multiplynvt(learning_rate / _n, dgrads.cumulative_hidden_layer_w_grad); - dgrads.output_w_grad = alg.scalar_multiplynv(learning_rate / _n, dgrads.output_w_grad); + dgrads.cumulative_hidden_layer_w_grad->scalar_multiply(learning_rate / _n); + dgrads.output_w_grad->scalar_multiply(learning_rate / _n); + update_discriminator_parameters(dgrads.cumulative_hidden_layer_w_grad, dgrads.output_w_grad, learning_rate); // Training of the generator. - generator_input_set = alg.gaussian_noise(_n, _k); + generator_input_set = MLPPMatrix::create_gaussian_noise(_n, _k); discriminator_input_set = model_set_test_generator(generator_input_set); y_hat = model_set_test_discriminator(discriminator_input_set); - _output_set = alg.onevecnv(_n); + _output_set = MLPPVector::create_vec_one(_n); + + Ref cumulative_generator_hidden_layer_w_grad = compute_generator_gradients(y_hat, _output_set); + + cumulative_generator_hidden_layer_w_grad->scalar_multiply(learning_rate / _n); - Vector> cumulative_generator_hidden_layer_w_grad = compute_generator_gradients(y_hat, _output_set); - cumulative_generator_hidden_layer_w_grad = alg.scalar_multiplynvt(learning_rate / _n, cumulative_generator_hidden_layer_w_grad); update_generator_parameters(cumulative_generator_hidden_layer_w_grad, learning_rate); forward_pass(); if (ui) { - print_ui(epoch, cost_prev, _y_hat, alg.onevecnv(_n)); + print_ui(epoch, cost_prev, _y_hat, MLPPVector::create_vec_one(_n)); } epoch++; @@ -98,12 +97,11 @@ void MLPPGAN::gradient_descent(real_t learning_rate, int max_epoch, bool ui) { } real_t MLPPGAN::score() { - MLPPLinAlg alg; MLPPUtilities util; forward_pass(); - return util.performance_vec(_y_hat, alg.onevecnv(_n)); + return util.performance_vec(_y_hat, MLPPVector::create_vec_one(_n)); } void MLPPGAN::save(const String &file_name) { @@ -122,9 +120,8 @@ void MLPPGAN::save(const String &file_name) { } void MLPPGAN::add_layer(int n_hidden, MLPPActivation::ActivationFunction activation, MLPPUtilities::WeightDistributionType weight_init, MLPPReg::RegularizationType reg, real_t lambda, real_t alpha) { - MLPPLinAlg alg; if (_network.empty()) { - Ref layer = Ref(memnew(MLPPHiddenLayer(n_hidden, activation, alg.gaussian_noise(_n, _k), weight_init, reg, lambda, alpha))); + Ref layer = Ref(memnew(MLPPHiddenLayer(n_hidden, activation, MLPPMatrix::create_gaussian_noise(_n, _k), weight_init, reg, lambda, alpha))); _network.push_back(layer); @@ -139,12 +136,10 @@ void MLPPGAN::add_layer(int n_hidden, MLPPActivation::ActivationFunction activat } void MLPPGAN::add_output_layer(MLPPUtilities::WeightDistributionType weight_init, MLPPReg::RegularizationType reg, real_t lambda, real_t alpha) { - MLPPLinAlg alg; - if (!_network.empty()) { _output_layer = Ref(memnew(MLPPOutputLayer(_network.write[_network.size() - 1]->get_n_hidden(), MLPPActivation::ACTIVATION_FUNCTION_SIGMOID, MLPPCost::COST_TYPE_LOGISTIC_LOSS, _network.write[_network.size() - 1]->get_a(), weight_init, reg, lambda, alpha))); } else { - _output_layer = Ref(memnew(MLPPOutputLayer(_k, MLPPActivation::ACTIVATION_FUNCTION_SIGMOID, MLPPCost::COST_TYPE_LOGISTIC_LOSS, alg.gaussian_noise(_n, _k), weight_init, reg, lambda, alpha))); + _output_layer = Ref(memnew(MLPPOutputLayer(_k, MLPPActivation::ACTIVATION_FUNCTION_SIGMOID, MLPPCost::COST_TYPE_LOGISTIC_LOSS, MLPPMatrix::create_gaussian_noise(_n, _k), weight_init, reg, lambda, alpha))); } } @@ -209,10 +204,8 @@ real_t MLPPGAN::cost(const Ref &y_hat, const Ref &y) { } void MLPPGAN::forward_pass() { - MLPPLinAlg alg; - if (!_network.empty()) { - _network.write[0]->set_input(alg.gaussian_noise(_n, _k)); + _network.write[0]->set_input(MLPPMatrix::create_gaussian_noise(_n, _k)); _network.write[0]->forward_pass(); for (int i = 1; i < _network.size(); i++) { @@ -221,47 +214,55 @@ void MLPPGAN::forward_pass() { } _output_layer->set_input(_network.write[_network.size() - 1]->get_a()); } else { // Should never happen, though. - _output_layer->set_input(alg.gaussian_noise(_n, _k)); + _output_layer->set_input(MLPPMatrix::create_gaussian_noise(_n, _k)); } _output_layer->forward_pass(); _y_hat = _output_layer->get_a(); } -void MLPPGAN::update_discriminator_parameters(const Vector> &hidden_layer_updations, const Ref &output_layer_updation, real_t learning_rate) { - MLPPLinAlg alg; - - _output_layer->set_weights(alg.subtractionnv(_output_layer->get_weights(), output_layer_updation)); +void MLPPGAN::update_discriminator_parameters(const Ref &hidden_layer_updations, const Ref &output_layer_updation, real_t learning_rate) { + _output_layer->set_weights(_output_layer->get_weights()->subn(output_layer_updation)); real_t output_layer_bias = _output_layer->get_bias(); - output_layer_bias -= learning_rate * alg.sum_elementsv(_output_layer->get_delta()) / _n; + output_layer_bias -= learning_rate * _output_layer->get_delta()->sum_elements() / _n; _output_layer->set_bias(output_layer_bias); + Ref slice; + slice.instance(); + if (!_network.empty()) { Ref layer = _network[_network.size() - 1]; - layer->set_weights(alg.subtractionnm(layer->get_weights(), hidden_layer_updations[0])); - layer->set_bias(alg.subtract_matrix_rowsnv(layer->get_bias(), alg.scalar_multiplynm(learning_rate / _n, layer->get_delta()))); + hidden_layer_updations->z_slice_get_into_mlpp_matrix(0, slice); + + layer->set_weights(layer->get_weights()->subn(slice)); + layer->set_bias(layer->get_bias()->subtract_matrix_rowsn(layer->get_delta()->scalar_multiplyn(learning_rate / _n))); for (int i = _network.size() - 2; i > _network.size() / 2; i--) { layer = _network[i]; - layer->set_weights(alg.subtractionnm(layer->get_weights(), hidden_layer_updations[(_network.size() - 2) - i + 1])); - layer->set_bias(alg.subtract_matrix_rowsnv(layer->get_bias(), alg.scalar_multiplynm(learning_rate / _n, layer->get_delta()))); + hidden_layer_updations->z_slice_get_into_mlpp_matrix((_network.size() - 2) - i + 1, slice); + + layer->set_weights(layer->get_weights()->subn(slice)); + layer->set_bias(layer->get_bias()->subtract_matrix_rowsn(layer->get_delta()->scalar_multiplyn(learning_rate / _n))); } } } -void MLPPGAN::update_generator_parameters(const Vector> &hidden_layer_updations, real_t learning_rate) { - MLPPLinAlg alg; - +void MLPPGAN::update_generator_parameters(const Ref &hidden_layer_updations, real_t learning_rate) { if (!_network.empty()) { + Ref slice; + slice.instance(); + for (int i = _network.size() / 2; i >= 0; i--) { Ref layer = _network[i]; + hidden_layer_updations->z_slice_get_into_mlpp_matrix((_network.size() - 2) - i + 1, slice); + //std::cout << network[i].weights.size() << "x" << network[i].weights[0].size() << std::endl; //std::cout << hidden_layer_updations[(network.size() - 2) - i + 1].size() << "x" << hidden_layer_updations[(network.size() - 2) - i + 1][0].size() << std::endl; - layer->set_weights(alg.subtractionnm(layer->get_weights(), hidden_layer_updations[(_network.size() - 2) - i + 1])); - layer->set_bias(alg.subtract_matrix_rowsnv(layer->get_bias(), alg.scalar_multiplynm(learning_rate / _n, layer->get_delta()))); + layer->set_weights(layer->get_weights()->subn(slice)); + layer->set_bias(layer->get_bias()->subtract_matrix_rowsn(layer->get_delta()->scalar_multiplyn(learning_rate / _n))); } } } @@ -269,7 +270,6 @@ void MLPPGAN::update_generator_parameters(const Vector> &hidden_ MLPPGAN::ComputeDiscriminatorGradientsResult MLPPGAN::compute_discriminator_gradients(const Ref &y_hat, const Ref &output_set) { MLPPCost mlpp_cost; MLPPActivation avn; - MLPPLinAlg alg; MLPPReg regularization; ComputeDiscriminatorGradientsResult res; @@ -277,20 +277,22 @@ MLPPGAN::ComputeDiscriminatorGradientsResult MLPPGAN::compute_discriminator_grad Ref cost_deriv = mlpp_cost.run_cost_deriv_vector(_output_layer->get_cost(), y_hat, _output_set); Ref activ_deriv = avn.run_activation_deriv_vector(_output_layer->get_activation(), _output_layer->get_z()); - _output_layer->set_delta(alg.hadamard_productnv(cost_deriv, activ_deriv)); + _output_layer->set_delta(cost_deriv->hadamard_productn(activ_deriv)); - res.output_w_grad = alg.mat_vec_multnv(alg.transposenm(_output_layer->get_input()), _output_layer->get_delta()); - res.output_w_grad = alg.additionnv(res.output_w_grad, regularization.reg_deriv_termv(_output_layer->get_weights(), _output_layer->get_lambda(), _output_layer->get_alpha(), _output_layer->get_reg())); + res.output_w_grad = _output_layer->get_input()->transposen()->mult_vec(_output_layer->get_delta()); + res.output_w_grad->add(regularization.reg_deriv_termv(_output_layer->get_weights(), _output_layer->get_lambda(), _output_layer->get_alpha(), _output_layer->get_reg())); if (!_network.empty()) { Ref layer = _network[_network.size() - 1]; Ref hidden_layer_activ_deriv = avn.run_activation_deriv_vector(layer->get_activation(), layer->get_z()); - layer->set_delta(alg.hadamard_productnm(alg.outer_product(_output_layer->get_delta(), _output_layer->get_weights()), hidden_layer_activ_deriv)); - Ref hidden_layer_w_grad = alg.matmultnm(alg.transposenm(layer->get_input()), layer->get_delta()); + layer->set_delta(_output_layer->get_delta()->outer_product(_output_layer->get_weights())->hadamard_productn(hidden_layer_activ_deriv)); - res.cumulative_hidden_layer_w_grad.push_back(alg.additionnm(hidden_layer_w_grad, regularization.reg_deriv_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg()))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well. + Ref hidden_layer_w_grad = layer->get_input()->transposen()->multn(layer->get_delta()); + + hidden_layer_w_grad->add(regularization.reg_deriv_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg())); + res.cumulative_hidden_layer_w_grad->z_slice_add_mlpp_matrix(hidden_layer_w_grad); // Adding to our cumulative hidden layer grads. Maintain reg terms as well. for (int i = static_cast(_network.size()) - 2; i > static_cast(_network.size()) / 2; i--) { layer = _network[i]; @@ -298,41 +300,44 @@ MLPPGAN::ComputeDiscriminatorGradientsResult MLPPGAN::compute_discriminator_grad hidden_layer_activ_deriv = avn.run_activation_deriv_vector(layer->get_activation(), layer->get_z()); - layer->set_delta(alg.hadamard_productnm(alg.matmultnm(next_layer->get_delta(), alg.transposenm(next_layer->get_weights())), hidden_layer_activ_deriv)); - hidden_layer_w_grad = alg.matmultnm(alg.transposenm(layer->get_input()), layer->get_delta()); + layer->set_delta(next_layer->get_delta()->multn(next_layer->get_weights()->transposen())->hadamard_productn(hidden_layer_activ_deriv)); - res.cumulative_hidden_layer_w_grad.push_back(alg.additionnm(hidden_layer_w_grad, regularization.reg_deriv_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg()))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well. + hidden_layer_w_grad = layer->get_input()->transposen()->multn(layer->get_delta()); + + res.cumulative_hidden_layer_w_grad->z_slice_add_mlpp_matrix(hidden_layer_w_grad->addn(regularization.reg_deriv_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg()))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well. } } return res; } -Vector> MLPPGAN::compute_generator_gradients(const Ref &y_hat, const Ref &output_set) { +Ref MLPPGAN::compute_generator_gradients(const Ref &y_hat, const Ref &output_set) { MLPPCost mlpp_cost; MLPPActivation avn; - MLPPLinAlg alg; MLPPReg regularization; - Vector> cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads. + Ref cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads. Ref cost_deriv = mlpp_cost.run_cost_deriv_vector(_output_layer->get_cost(), y_hat, _output_set); Ref activ_deriv = avn.run_activation_deriv_vector(_output_layer->get_activation(), _output_layer->get_z()); - _output_layer->set_delta(alg.hadamard_productnv(cost_deriv, activ_deriv)); + _output_layer->set_delta(cost_deriv->hadamard_productn(activ_deriv)); - Ref output_w_grad = alg.mat_vec_multnv(alg.transposenm(_output_layer->get_input()), _output_layer->get_delta()); - output_w_grad = alg.additionnv(output_w_grad, regularization.reg_deriv_termv(_output_layer->get_weights(), _output_layer->get_lambda(), _output_layer->get_alpha(), _output_layer->get_reg())); + Ref output_w_grad = _output_layer->get_input()->transposen()->mult_vec(_output_layer->get_delta()); + + output_w_grad->add(regularization.reg_deriv_termv(_output_layer->get_weights(), _output_layer->get_lambda(), _output_layer->get_alpha(), _output_layer->get_reg())); if (!_network.empty()) { Ref layer = _network[_network.size() - 1]; Ref hidden_layer_activ_deriv = avn.run_activation_deriv_vector(layer->get_activation(), layer->get_z()); - layer->set_delta(alg.hadamard_productnv(alg.outer_product(_output_layer->get_delta(), _output_layer->get_weights()), hidden_layer_activ_deriv)); + layer->set_delta(_output_layer->get_delta()->outer_product(_output_layer->get_weights())->hadamard_productn(hidden_layer_activ_deriv)); - Ref hidden_layer_w_grad = alg.matmultnm(alg.transposenm(layer->get_input()), layer->get_delta()); - cumulative_hidden_layer_w_grad.push_back(alg.additionnm(hidden_layer_w_grad, regularization.reg_deriv_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg()))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well. + Ref hidden_layer_w_grad = layer->get_input()->transposen()->multn(layer->get_delta()); + hidden_layer_w_grad->add(regularization.reg_deriv_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg())); + + cumulative_hidden_layer_w_grad->z_slice_add_mlpp_matrix(hidden_layer_w_grad); // Adding to our cumulative hidden layer grads. Maintain reg terms as well. for (int i = _network.size() - 2; i >= 0; i--) { layer = _network[i]; @@ -340,10 +345,12 @@ Vector> MLPPGAN::compute_generator_gradients(const Refget_activation(), layer->get_z()); - layer->set_delta(alg.hadamard_productnm(alg.matmultnm(next_layer->get_delta(), alg.transposenm(next_layer->get_weights())), hidden_layer_activ_deriv)); + layer->set_delta(next_layer->get_delta()->multn(next_layer->get_weights()->transposen())->hadamard_productn(hidden_layer_activ_deriv)); - hidden_layer_w_grad = alg.matmultnm(alg.transposenm(layer->get_input()), layer->get_delta()); - cumulative_hidden_layer_w_grad.push_back(alg.additionnm(hidden_layer_w_grad, regularization.reg_deriv_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg()))); // Adding to our cumulative hidden layer grads. Maintain reg terms as well. + hidden_layer_w_grad = layer->get_input()->transposen()->multn(layer->get_delta()); + hidden_layer_w_grad->add(regularization.reg_deriv_termm(layer->get_weights(), layer->get_lambda(), layer->get_alpha(), layer->get_reg())); + + cumulative_hidden_layer_w_grad->z_slice_add_mlpp_matrix(hidden_layer_w_grad); // Adding to our cumulative hidden layer grads. Maintain reg terms as well. } } diff --git a/mlpp/gan/gan.h b/mlpp/gan/gan.h index 431da98..e4b8744 100644 --- a/mlpp/gan/gan.h +++ b/mlpp/gan/gan.h @@ -15,6 +15,8 @@ #include "../hidden_layer/hidden_layer.h" #include "../output_layer/output_layer.h" +#include "../lin_alg/mlpp_tensor3.h" + #include "../activation/activation.h" #include "../utilities/utilities.h" @@ -57,16 +59,21 @@ protected: void forward_pass(); - void update_discriminator_parameters(const Vector> &hidden_layer_updations, const Ref &output_layer_updation, real_t learning_rate); - void update_generator_parameters(const Vector> &hidden_layer_updations, real_t learning_rate); + void update_discriminator_parameters(const Ref &hidden_layer_updations, const Ref &output_layer_updation, real_t learning_rate); + void update_generator_parameters(const Ref &hidden_layer_updations, real_t learning_rate); struct ComputeDiscriminatorGradientsResult { - Vector> cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads. + Ref cumulative_hidden_layer_w_grad; // Tensor containing ALL hidden grads. Ref output_w_grad; + + ComputeDiscriminatorGradientsResult() { + cumulative_hidden_layer_w_grad.instance(); + output_w_grad.instance(); + } }; ComputeDiscriminatorGradientsResult compute_discriminator_gradients(const Ref &y_hat, const Ref &output_set); - Vector> compute_generator_gradients(const Ref &y_hat, const Ref &output_set); + Ref compute_generator_gradients(const Ref &y_hat, const Ref &output_set); void print_ui(int epoch, real_t cost_prev, const Ref &y_hat, const Ref &output_set); diff --git a/mlpp/gaussian_nb/gaussian_nb.cpp b/mlpp/gaussian_nb/gaussian_nb.cpp index bdd0141..4f81245 100644 --- a/mlpp/gaussian_nb/gaussian_nb.cpp +++ b/mlpp/gaussian_nb/gaussian_nb.cpp @@ -8,7 +8,6 @@ #include "core/math/math_defs.h" -#include "../lin_alg/lin_alg.h" #include "../stat/stat.h" #include "../utilities/utilities.h" @@ -126,7 +125,6 @@ MLPPGaussianNB::~MLPPGaussianNB() { void MLPPGaussianNB::evaluate() { MLPPStat stat; - MLPPLinAlg alg; // Computing mu_k_y and sigma_k_y _mu->resize(_class_num); @@ -160,7 +158,7 @@ void MLPPGaussianNB::evaluate() { _priors->element_set(indx, _priors->element_get(indx)); } - _priors = alg.scalar_multiplynv(real_t(1) / real_t(_output_set->size()), _priors); + _priors->scalar_multiply(real_t(1) / real_t(_output_set->size())); for (int i = 0; i < _output_set->size(); i++) { LocalVector score; diff --git a/mlpp/hidden_layer/hidden_layer.cpp b/mlpp/hidden_layer/hidden_layer.cpp index 5a85410..02154a6 100644 --- a/mlpp/hidden_layer/hidden_layer.cpp +++ b/mlpp/hidden_layer/hidden_layer.cpp @@ -6,7 +6,6 @@ #include "hidden_layer.h" #include "../activation/activation.h" -#include "../lin_alg/lin_alg.h" #include #include diff --git a/mlpp/kmeans/kmeans.cpp b/mlpp/kmeans/kmeans.cpp index d1392a2..a567350 100644 --- a/mlpp/kmeans/kmeans.cpp +++ b/mlpp/kmeans/kmeans.cpp @@ -5,7 +5,6 @@ // #include "kmeans.h" -#include "../lin_alg/lin_alg.h" #include "../utilities/utilities.h" #include "core/math/random_pcg.h" @@ -54,8 +53,6 @@ Ref MLPPKMeans::model_set_test(const Ref &X) { ERR_FAIL_COND_V(!X.is_valid(), Ref()); ERR_FAIL_COND_V(!_initialized, Ref()); - MLPPLinAlg alg; - int input_set_size_y = _input_set->size().y; Ref closest_centroids; @@ -83,7 +80,7 @@ Ref MLPPKMeans::model_set_test(const Ref &X) { for (int j = 0; j < r0_size; ++j) { _mu->row_get_into_mlpp_vector(j, tmp_mujv); - bool is_centroid_closer = alg.euclidean_distance(tmp_xiv, tmp_mujv) < alg.euclidean_distance(tmp_xiv, closest_centroid); + bool is_centroid_closer = tmp_xiv->euclidean_distance(tmp_mujv) < tmp_xiv->euclidean_distance(closest_centroid); if (is_centroid_closer) { closest_centroid->set_from_mlpp_vector(tmp_mujv); @@ -99,8 +96,6 @@ Ref MLPPKMeans::model_test(const Ref &x) { ERR_FAIL_COND_V(!x.is_valid(), Ref()); ERR_FAIL_COND_V(!_initialized, Ref()); - MLPPLinAlg alg; - Ref closest_centroid; closest_centroid.instance(); closest_centroid->resize(_mu->size().x); @@ -116,7 +111,7 @@ Ref MLPPKMeans::model_test(const Ref &x) { for (int j = 0; j < mu_size_y; ++j) { _mu->row_get_into_mlpp_vector(j, tmp_mujv); - if (alg.euclidean_distance(x, tmp_mujv) < alg.euclidean_distance(x, closest_centroid)) { + if (x->euclidean_distance(tmp_mujv) < x->euclidean_distance(closest_centroid)) { closest_centroid->set_from_mlpp_vector(tmp_mujv); } } @@ -168,8 +163,6 @@ real_t MLPPKMeans::score() { Ref MLPPKMeans::silhouette_scores() { ERR_FAIL_COND_V(!_initialized, Ref()); - MLPPLinAlg alg; - Ref closest_centroids = model_set_test(_input_set); ERR_FAIL_COND_V(!closest_centroids.is_valid(), Ref()); @@ -233,7 +226,7 @@ Ref MLPPKMeans::silhouette_scores() { if (r_i_tempv->is_equal_approx(r_j_tempv)) { _input_set->row_get_into_mlpp_vector(j, input_set_j_tempv); - a += alg.euclidean_distance(input_set_i_tempv, input_set_j_tempv); + a += input_set_i_tempv->euclidean_distance(input_set_j_tempv); } } @@ -252,7 +245,7 @@ Ref MLPPKMeans::silhouette_scores() { for (int k = 0; k < input_set_size_y; ++k) { _input_set->row_get_into_mlpp_vector(k, input_set_k_tempv); - sum += alg.euclidean_distance(input_set_i_tempv, input_set_k_tempv); + sum += input_set_i_tempv->euclidean_distance(input_set_k_tempv); } // NORMALIZE b[i] @@ -305,8 +298,6 @@ MLPPKMeans::~MLPPKMeans() { void MLPPKMeans::_evaluate() { ERR_FAIL_COND(!_initialized); - MLPPLinAlg alg; - if (_r->size() != Size2i(_k, _input_set->size().y)) { _r->resize(Size2i(_k, _input_set->size().y)); } @@ -335,16 +326,16 @@ void MLPPKMeans::_evaluate() { _mu->row_get_into_mlpp_vector(0, closest_centroid); _input_set->row_get_into_mlpp_vector(i, input_set_i_tempv); - closest_centroid_current_dist = alg.euclidean_distance(input_set_i_tempv, closest_centroid); + closest_centroid_current_dist = input_set_i_tempv->euclidean_distance(closest_centroid); for (int j = 0; j < r_size_x; ++j) { _mu->row_get_into_mlpp_vector(j, mu_j_tempv); - bool is_centroid_closer = alg.euclidean_distance(input_set_i_tempv, mu_j_tempv) < closest_centroid_current_dist; + bool is_centroid_closer = input_set_i_tempv->euclidean_distance(mu_j_tempv) < closest_centroid_current_dist; if (is_centroid_closer) { _mu->row_get_into_mlpp_vector(j, closest_centroid); - closest_centroid_current_dist = alg.euclidean_distance(input_set_i_tempv, closest_centroid); + closest_centroid_current_dist = input_set_i_tempv->euclidean_distance(closest_centroid); closest_centroid_index = j; } } @@ -355,8 +346,6 @@ void MLPPKMeans::_evaluate() { // This simply computes or re-computes mu_k void MLPPKMeans::_compute_mu() { - MLPPLinAlg alg; - int mu_size_y = _mu->size().y; int r_size_y = _r->size().y; @@ -385,13 +374,13 @@ void MLPPKMeans::_compute_mu() { real_t r_j_i = _r->element_get(j, i); - alg.scalar_multiplyv(_r->element_get(j, i), input_set_j_tempv, mat_tempv); - alg.additionv(num, mat_tempv, num); + mat_tempv->scalar_multiplyb(_r->element_get(j, i), input_set_j_tempv); + num->add(mat_tempv); den += r_j_i; } - alg.scalar_multiplyv(real_t(1) / real_t(den), num, mu_tempv); + mu_tempv->scalar_multiplyb(real_t(1) / real_t(den), num); _mu->row_set_mlpp_vector(i, mu_tempv); } @@ -422,8 +411,6 @@ void MLPPKMeans::_centroid_initialization() { } void MLPPKMeans::_kmeanspp_initialization() { - MLPPLinAlg alg; - RandomPCG rand; rand.randomize(); @@ -461,7 +448,7 @@ void MLPPKMeans::_kmeanspp_initialization() { for (int k = 0; k < i; k++) { _mu->row_get_into_mlpp_vector(k, mu_tempv); - sum += alg.euclidean_distance(input_set_j_tempv, mu_tempv); + sum += input_set_j_tempv->euclidean_distance(mu_tempv); } if (sum * sum > max_dist) { @@ -476,8 +463,6 @@ void MLPPKMeans::_kmeanspp_initialization() { real_t MLPPKMeans::_cost() { ERR_FAIL_COND_V(!_initialized, 0); - MLPPLinAlg alg; - Ref input_set_i_tempv; input_set_i_tempv.instance(); input_set_i_tempv->resize(_input_set->size().x); @@ -500,8 +485,8 @@ real_t MLPPKMeans::_cost() { for (int j = 0; j < r_size_x; j++) { _mu->row_get_into_mlpp_vector(j, mu_j_tempv); - alg.subtractionv(input_set_i_tempv, mu_j_tempv, sub_tempv); - sum += _r->element_get(i, j) * alg.norm_sqv(sub_tempv); + sub_tempv->subb(input_set_i_tempv, mu_j_tempv); + sum += _r->element_get(i, j) * sub_tempv->norm_sq(); } } diff --git a/mlpp/knn/knn.cpp b/mlpp/knn/knn.cpp index ba84a03..69b2f96 100644 --- a/mlpp/knn/knn.cpp +++ b/mlpp/knn/knn.cpp @@ -5,7 +5,6 @@ // #include "knn.h" -#include "../lin_alg/lin_alg.h" #include "../utilities/utilities.h" #include "core/containers/hash_map.h" @@ -72,7 +71,6 @@ MLPPKNN::~MLPPKNN() { PoolIntArray MLPPKNN::nearest_neighbors(const Ref &x) { ERR_FAIL_COND_V(!_input_set.is_valid(), PoolIntArray()); - MLPPLinAlg alg; // The nearest neighbors PoolIntArray knn; @@ -97,7 +95,7 @@ PoolIntArray MLPPKNN::nearest_neighbors(const Ref &x) { _input_set->row_get_into_mlpp_vector(j, tmpv1); _input_set->row_get_into_mlpp_vector(neighbor, tmpv2); - bool is_neighbor_nearer = alg.euclidean_distance(x, tmpv1) < alg.euclidean_distance(x, tmpv2); + bool is_neighbor_nearer = x->euclidean_distance(tmpv1) < x->euclidean_distance(tmpv2); if (is_neighbor_nearer) { neighbor = j;