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Removed unneeded file.
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#include "mlpp_tests_old.h"
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#include "core/math/math_funcs.h"
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#include "core/log/logger.h"
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//TODO remove
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#include <cmath>
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#include <ctime>
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#include <iostream>
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#include <vector>
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#include "../mlpp/data/data.h"
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#include "../mlpp/activation/activation_old.h"
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#include "../mlpp/ann/ann_old.h"
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#include "../mlpp/auto_encoder/auto_encoder_old.h"
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#include "../mlpp/bernoulli_nb/bernoulli_nb_old.h"
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#include "../mlpp/c_log_log_reg/c_log_log_reg_old.h"
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#include "../mlpp/convolutions/convolutions_old.h"
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#include "../mlpp/cost/cost_old.h"
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#include "../mlpp/data/data_old.h"
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#include "../mlpp/dual_svc/dual_svc_old.h"
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#include "../mlpp/exp_reg/exp_reg_old.h"
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#include "../mlpp/gan/gan_old.h"
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#include "../mlpp/gaussian_nb/gaussian_nb_old.h"
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#include "../mlpp/hidden_layer/hidden_layer_old.h"
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#include "../mlpp/lin_alg/lin_alg_old.h"
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#include "../mlpp/lin_reg/lin_reg_old.h"
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#include "../mlpp/log_reg/log_reg_old.h"
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#include "../mlpp/mann/mann_old.h"
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#include "../mlpp/mlp/mlp_old.h"
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#include "../mlpp/multi_output_layer/multi_output_layer_old.h"
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#include "../mlpp/multinomial_nb/multinomial_nb_old.h"
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#include "../mlpp/numerical_analysis/numerical_analysis_old.h"
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#include "../mlpp/outlier_finder/outlier_finder_old.h"
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#include "../mlpp/output_layer/output_layer_old.h"
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#include "../mlpp/pca/pca_old.h"
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#include "../mlpp/probit_reg/probit_reg_old.h"
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#include "../mlpp/softmax_net/softmax_net_old.h"
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#include "../mlpp/softmax_reg/softmax_reg_old.h"
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#include "../mlpp/stat/stat_old.h"
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#include "../mlpp/svc/svc_old.h"
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#include "../mlpp/tanh_reg/tanh_reg_old.h"
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#include "../mlpp/transforms/transforms_old.h"
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#include "../mlpp/uni_lin_reg/uni_lin_reg_old.h"
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#include "../mlpp/wgan/wgan_old.h"
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Vector<real_t> dstd_vec_to_vec_old(const std::vector<real_t> &in) {
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Vector<real_t> r;
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r.resize(static_cast<int>(in.size()));
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real_t *darr = r.ptrw();
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for (uint32_t i = 0; i < in.size(); ++i) {
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darr[i] = in[i];
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}
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return r;
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}
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Vector<Vector<real_t>> dstd_mat_to_mat_old(const std::vector<std::vector<real_t>> &in) {
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Vector<Vector<real_t>> r;
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for (uint32_t i = 0; i < in.size(); ++i) {
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r.push_back(dstd_vec_to_vec_old(in[i]));
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}
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return r;
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}
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void MLPPTestsOld::test_statistics() {
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}
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void MLPPTestsOld::test_linear_algebra() {
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}
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void MLPPTestsOld::test_univariate_linear_regression() {
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}
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void MLPPTestsOld::test_multivariate_linear_regression_gradient_descent(bool ui) {
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MLPPData data;
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MLPPLinAlgOld alg;
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Ref<MLPPDataSimple> ds = data.load_california_housing(_california_housing_data_path);
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MLPPLinRegOld model_old(ds->get_input()->to_std_vector(), ds->get_output()->to_std_vector()); // Can use Lasso, Ridge, ElasticNet Reg
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model_old.SGD(0.00000001, 300000, ui);
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alg.printVector(model_old.modelSetTest(ds->get_input()->to_std_vector()));
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//void Momentum(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool UI = false);
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//void NAG(real_t learning_rate, int max_epoch, int mini_batch_size, real_t gamma, bool UI = false);
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//void Adagrad(real_t learning_rate, int max_epoch, int mini_batch_size, real_t e, bool UI = false);
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//void Adadelta(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t e, bool UI = false);
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//void Adamax(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool UI = false);
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//void Nadam(real_t learning_rate, int max_epoch, int mini_batch_size, real_t b1, real_t b2, real_t e, bool UI = false);
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}
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void MLPPTestsOld::test_multivariate_linear_regression_sgd(bool ui) {
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}
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void MLPPTestsOld::test_multivariate_linear_regression_mbgd(bool ui) {
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}
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void MLPPTestsOld::test_multivariate_linear_regression_normal_equation(bool ui) {
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}
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void MLPPTestsOld::test_multivariate_linear_regression_adam() {
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}
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void MLPPTestsOld::test_multivariate_linear_regression_score_sgd_adam(bool ui) {
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}
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void MLPPTestsOld::test_multivariate_linear_regression_epochs_gradient_descent(bool ui) {
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}
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void MLPPTestsOld::test_multivariate_linear_regression_newton_raphson(bool ui) {
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}
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void MLPPTestsOld::test_logistic_regression(bool ui) {
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}
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void MLPPTestsOld::test_probit_regression(bool ui) {
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}
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void MLPPTestsOld::test_c_log_log_regression(bool ui) {
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}
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void MLPPTestsOld::test_exp_reg_regression(bool ui) {
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}
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void MLPPTestsOld::test_tanh_regression(bool ui) {
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}
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void MLPPTestsOld::test_softmax_regression(bool ui) {
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}
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void MLPPTestsOld::test_support_vector_classification(bool ui) {
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}
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void MLPPTestsOld::test_mlp(bool ui) {
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}
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void MLPPTestsOld::test_soft_max_network(bool ui) {
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}
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void MLPPTestsOld::test_autoencoder(bool ui) {
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}
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void MLPPTestsOld::test_dynamically_sized_ann(bool ui) {
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}
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void MLPPTestsOld::test_wgan_old(bool ui) {
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//MLPPStat stat;
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MLPPLinAlgOld alg;
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//MLPPActivation avn;
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//MLPPCost cost;
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//MLPPData data;
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//MLPPConvolutions conv;
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std::vector<std::vector<real_t>> outputSet = {
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{ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 },
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{ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40 }
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};
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MLPPWGANOld gan_old(2, alg.transpose(outputSet)); // our gan is a wasserstein gan (wgan)
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gan_old.addLayer(5, "Sigmoid");
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gan_old.addLayer(2, "RELU");
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gan_old.addLayer(5, "Sigmoid");
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gan_old.addOutputLayer(); // User can specify weight init- if necessary.
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gan_old.gradientDescent(0.1, 55000, ui);
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std::cout << "GENERATED INPUT: (Gaussian-sampled noise):" << std::endl;
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alg.printMatrix(gan_old.generateExample(100));
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}
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void MLPPTestsOld::test_wgan(bool ui) {
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}
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void MLPPTestsOld::test_ann(bool ui) {
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MLPPLinAlgOld alg;
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std::vector<std::vector<real_t>> inputSet = { { 0, 0 }, { 0, 1 }, { 1, 0 }, { 1, 1 } }; // XOR
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std::vector<real_t> outputSet = { 0, 1, 1, 0 };
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MLPPANNOld ann_old(inputSet, outputSet);
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ann_old.addLayer(5, "Sigmoid");
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ann_old.addLayer(8, "Sigmoid"); // Add more layers as needed.
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ann_old.addOutputLayer("Sigmoid", "LogLoss");
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ann_old.gradientDescent(1, 20000, ui);
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std::vector<real_t> predictions_old = ann_old.modelSetTest(inputSet);
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alg.printVector(predictions_old); // Testing out the model's preds for train set.
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std::cout << "ACCURACY: " << 100 * ann_old.score() << "%" << std::endl; // Accuracy.
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}
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void MLPPTestsOld::test_dynamically_sized_mann(bool ui) {
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MLPPLinAlgOld alg;
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MLPPData data;
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// DYNAMICALLY SIZED MANN (Multidimensional Output ANN)
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std::vector<std::vector<real_t>> inputSet = { { 1, 2, 3 }, { 2, 4, 6 }, { 3, 6, 9 }, { 4, 8, 12 } };
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std::vector<std::vector<real_t>> outputSet = { { 1, 5 }, { 2, 10 }, { 3, 15 }, { 4, 20 } };
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MLPPMANNOld mann_old(inputSet, outputSet);
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mann_old.addOutputLayer("Linear", "MSE");
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mann_old.gradientDescent(0.001, 80000, false);
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alg.printMatrix(mann_old.modelSetTest(inputSet));
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std::cout << "ACCURACY (old): " << 100 * mann_old.score() << "%" << std::endl;
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}
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void MLPPTestsOld::test_train_test_split_mann(bool ui) {
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MLPPLinAlgOld alg;
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MLPPData data;
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// TRAIN TEST SPLIT CHECK
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std::vector<std::vector<real_t>> inputSet1 = { { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }, { 3, 5, 9, 12, 15, 18, 21, 24, 27, 30 } };
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std::vector<std::vector<real_t>> outputSet1 = { { 2, 4, 6, 8, 10, 12, 14, 16, 18, 20 } };
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Ref<MLPPMatrix> input_set_1;
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input_set_1.instance();
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input_set_1->set_from_std_vectors(inputSet1);
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Ref<MLPPMatrix> output_set_1;
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output_set_1.instance();
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output_set_1->set_from_std_vectors(outputSet1);
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Ref<MLPPDataComplex> d;
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d.instance();
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d->set_input(input_set_1->transposen());
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d->set_output(output_set_1->transposen());
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MLPPData::SplitComplexData split_data = data.train_test_split(d, 0.2);
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PLOG_MSG(split_data.train->get_input()->to_string());
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PLOG_MSG(split_data.train->get_output()->to_string());
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PLOG_MSG(split_data.test->get_input()->to_string());
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PLOG_MSG(split_data.test->get_output()->to_string());
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MLPPMANNOld mann_old(split_data.train->get_input()->to_std_vector(), split_data.train->get_output()->to_std_vector());
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mann_old.addLayer(100, "RELU", "XavierNormal");
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mann_old.addOutputLayer("Softmax", "CrossEntropy", "XavierNormal");
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mann_old.gradientDescent(0.1, 80000, ui);
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alg.printMatrix(mann_old.modelSetTest(split_data.test->get_input()->to_std_vector()));
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std::cout << "ACCURACY (old): " << 100 * mann_old.score() << "%" << std::endl;
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}
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void MLPPTestsOld::test_naive_bayes() {
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MLPPLinAlgOld alg;
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// NAIVE BAYES
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std::vector<std::vector<real_t>> inputSet = { { 1, 1, 1, 1, 1 }, { 0, 0, 1, 1, 1 }, { 0, 0, 1, 0, 1 } };
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std::vector<real_t> outputSet = { 0, 1, 0, 1, 1 };
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MLPPMultinomialNBOld MNB_old(alg.transpose(inputSet), outputSet, 2);
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alg.printVector(MNB_old.modelSetTest(alg.transpose(inputSet)));
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Ref<MLPPMatrix> input_set;
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input_set.instance();
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input_set->set_from_std_vectors(alg.transpose(inputSet));
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Ref<MLPPVector> output_set;
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output_set.instance();
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output_set->set_from_std_vector(outputSet);
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MLPPBernoulliNBOld BNBOld(alg.transpose(inputSet), outputSet);
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alg.printVector(BNBOld.modelSetTest(alg.transpose(inputSet)));
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MLPPGaussianNBOld GNBOld(alg.transpose(inputSet), outputSet, 2);
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alg.printVector(GNBOld.modelSetTest(alg.transpose(inputSet)));
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}
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void MLPPTestsOld::test_k_means(bool ui) {
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}
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void MLPPTestsOld::test_knn(bool ui) {
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}
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void MLPPTestsOld::test_convolution_tensors_etc() {
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MLPPLinAlgOld alg;
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MLPPData data;
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MLPPConvolutionsOld conv;
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// CONVOLUTION, POOLING, ETC..
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std::vector<std::vector<real_t>> input = {
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{ 1 },
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};
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std::vector<std::vector<std::vector<real_t>>> tensorSet;
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tensorSet.push_back(input);
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tensorSet.push_back(input);
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tensorSet.push_back(input);
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alg.printTensor(data.rgb2xyz(tensorSet));
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std::vector<std::vector<real_t>> input2 = {
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{ 62, 55, 55, 54, 49, 48, 47, 55 },
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{ 62, 57, 54, 52, 48, 47, 48, 53 },
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{ 61, 60, 52, 49, 48, 47, 49, 54 },
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{ 63, 61, 60, 60, 63, 65, 68, 65 },
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{ 67, 67, 70, 74, 79, 85, 91, 92 },
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{ 82, 95, 101, 106, 114, 115, 112, 117 },
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{ 96, 111, 115, 119, 128, 128, 130, 127 },
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{ 109, 121, 127, 133, 139, 141, 140, 133 },
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};
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MLPPTransformsOld trans;
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alg.printMatrix(trans.discreteCosineTransform(input2));
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alg.printMatrix(conv.convolve_2d(input2, conv.get_prewitt_vertical(), 1)); // Can use padding
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alg.printMatrix(conv.pool_2d(input2, 4, 4, "Max")); // Can use Max, Min, or Average pooling.
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std::vector<std::vector<std::vector<real_t>>> tensorSet2;
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tensorSet2.push_back(input2);
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tensorSet2.push_back(input2);
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alg.printVector(conv.global_pool_3d(tensorSet2, "Average")); // Can use Max, Min, or Average global pooling.
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std::vector<std::vector<real_t>> laplacian = { { 1, 1, 1 }, { 1, -4, 1 }, { 1, 1, 1 } };
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alg.printMatrix(conv.convolve_2d(conv.gaussian_filter_2d(5, 1), laplacian, 1));
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}
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void MLPPTestsOld::test_pca_svd_eigenvalues_eigenvectors(bool ui) {
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MLPPLinAlgOld alg;
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// PCA, SVD, eigenvalues & eigenvectors
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std::vector<std::vector<real_t>> inputSet = { { 1, 1 }, { 1, 1 } };
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MLPPLinAlgOld::EigenResultOld eigen = alg.eigen_old(inputSet);
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std::cout << "Eigenvectors:" << std::endl;
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alg.printMatrix(eigen.eigen_vectors);
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std::cout << std::endl;
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std::cout << "Eigenvalues:" << std::endl;
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alg.printMatrix(eigen.eigen_values);
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std::cout << "SVD OLD START" << std::endl;
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MLPPLinAlgOld::SVDResultOld svd_old = alg.SVD(inputSet);
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std::cout << "U:" << std::endl;
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alg.printMatrix(svd_old.U);
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std::cout << "S:" << std::endl;
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alg.printMatrix(svd_old.S);
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std::cout << "Vt:" << std::endl;
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alg.printMatrix(svd_old.Vt);
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std::cout << "SVD OLD FIN" << std::endl;
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Ref<MLPPMatrix> input_set;
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input_set.instance();
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input_set->set_from_std_vectors(inputSet);
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/*
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String str_svd = "SVD\n";
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MLPPLinAlgOld::SVDResult svd = alg.svd(input_set);
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str_svd += "U:\n";
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str_svd += svd.U->to_string();
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str_svd += "\nS:\n";
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str_svd += svd.S->to_string();
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str_svd += "\nVt:\n";
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str_svd += svd.Vt->to_string();
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str_svd += "\n";
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PLOG_MSG(str_svd);
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*/
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std::cout << "PCA" << std::endl;
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// PCA done using Jacobi's method to approximate eigenvalues and eigenvectors.
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MLPPPCAOld dr_old(inputSet, 1); // 1 dimensional representation.
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std::cout << std::endl;
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std::cout << "OLD Dimensionally reduced representation:" << std::endl;
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alg.printMatrix(dr_old.principalComponents());
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std::cout << "SCORE: " << dr_old.score() << std::endl;
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}
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void MLPPTestsOld::test_nlp_and_data(bool ui) {
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MLPPLinAlgOld alg;
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MLPPDataOld data;
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// NLP/DATA
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std::string verbText = "I am appearing and thinking, as well as conducting.";
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std::cout << "Stemming Example:" << std::endl;
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std::cout << data.stemming(verbText) << std::endl;
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std::cout << std::endl;
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std::vector<std::string> sentences = { "He is a good boy", "She is a good girl", "The boy and girl are good" };
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std::cout << "Bag of Words Example:" << std::endl;
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alg.printMatrix(data.BOW(sentences, "Default"));
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std::cout << std::endl;
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std::cout << "TFIDF Example:" << std::endl;
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alg.printMatrix(data.TFIDF(sentences));
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std::cout << std::endl;
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std::cout << "Tokenization:" << std::endl;
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alg.printVector(data.tokenize(verbText));
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std::cout << std::endl;
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std::cout << "Word2Vec:" << std::endl;
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std::string textArchive = { "He is a good boy. She is a good girl. The boy and girl are good." };
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std::vector<std::string> corpus = data.splitSentences(textArchive);
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|
||||
std::tuple<std::vector<std::vector<real_t>>, std::vector<std::string>> wtvres = data.word2Vec(corpus, "CBOW", 2, 2, 0.1, 10000); // Can use either CBOW or Skip-n-gram.
|
||||
|
||||
alg.printMatrix(std::get<0>(wtvres));
|
||||
std::cout << std::endl;
|
||||
|
||||
std::vector<std::string> textArchive2 = { "pizza", "pizza hamburger cookie", "hamburger", "ramen", "sushi", "ramen sushi" };
|
||||
|
||||
alg.printMatrix(data.LSA(textArchive2, 2));
|
||||
alg.printMatrix(data.BOW(textArchive2, "Default"));
|
||||
std::cout << std::endl;
|
||||
|
||||
std::vector<std::vector<real_t>> inputSet = { { 1, 2 }, { 2, 3 }, { 3, 4 }, { 4, 5 }, { 5, 6 } };
|
||||
std::cout << "Feature Scaling Example:" << std::endl;
|
||||
alg.printMatrix(data.featureScaling(inputSet));
|
||||
std::cout << std::endl;
|
||||
|
||||
std::cout << "Mean Centering Example:" << std::endl;
|
||||
alg.printMatrix(data.meanCentering(inputSet));
|
||||
std::cout << std::endl;
|
||||
|
||||
std::cout << "Mean Normalization Example:" << std::endl;
|
||||
alg.printMatrix(data.meanNormalization(inputSet));
|
||||
std::cout << std::endl;
|
||||
}
|
||||
void MLPPTestsOld::test_outlier_finder(bool ui) {
|
||||
MLPPLinAlgOld alg;
|
||||
|
||||
// Outlier Finder
|
||||
std::vector<real_t> inputSet = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 23554332 };
|
||||
MLPPOutlierFinderOld outlierFinderOld(2); // Any datapoint outside of 2 stds from the mean is marked as an outlier.
|
||||
alg.printVector(outlierFinderOld.modelTest(inputSet));
|
||||
}
|
||||
void MLPPTestsOld::test_new_math_functions() {
|
||||
MLPPLinAlgOld alg;
|
||||
MLPPActivationOld avn;
|
||||
MLPPData data;
|
||||
|
||||
// Testing new Functions
|
||||
real_t z_s = 0.001;
|
||||
std::cout << avn.logit(z_s) << std::endl;
|
||||
std::cout << avn.logit(z_s, true) << std::endl;
|
||||
|
||||
std::vector<real_t> z_v = { 0.001 };
|
||||
alg.printVector(avn.logit(z_v));
|
||||
alg.printVector(avn.logit(z_v, true));
|
||||
|
||||
std::vector<std::vector<real_t>> Z_m = { { 0.001 } };
|
||||
alg.printMatrix(avn.logit(Z_m));
|
||||
alg.printMatrix(avn.logit(Z_m, true));
|
||||
|
||||
std::cout << alg.trace({ { 1, 2 }, { 3, 4 } }) << std::endl;
|
||||
alg.printMatrix(alg.pinverse({ { 1, 2 }, { 3, 4 } }));
|
||||
alg.printMatrix(alg.diag({ 1, 2, 3, 4, 5 }));
|
||||
alg.printMatrix(alg.kronecker_product({ { 1, 2, 3, 4, 5 } }, { { 6, 7, 8, 9, 10 } }));
|
||||
alg.printMatrix(alg.matrixPower({ { 5, 5 }, { 5, 5 } }, 2));
|
||||
alg.printVector(alg.solve({ { 1, 1 }, { 1.5, 4.0 } }, { 2200, 5050 }));
|
||||
|
||||
std::vector<std::vector<real_t>> matrixOfCubes = { { 1, 2, 64, 27 } };
|
||||
std::vector<real_t> vectorOfCubes = { 1, 2, 64, 27 };
|
||||
alg.printMatrix(alg.cbrt(matrixOfCubes));
|
||||
alg.printVector(alg.cbrt(vectorOfCubes));
|
||||
std::cout << alg.max({ { 1, 2, 3, 4, 5 }, { 6, 5, 3, 4, 1 }, { 9, 9, 9, 9, 9 } }) << std::endl;
|
||||
std::cout << alg.min({ { 1, 2, 3, 4, 5 }, { 6, 5, 3, 4, 1 }, { 9, 9, 9, 9, 9 } }) << std::endl;
|
||||
|
||||
//std::vector<real_t> chicken;
|
||||
//data.getImage("../../Data/apple.jpeg", chicken);
|
||||
//alg.printVector(chicken);
|
||||
|
||||
std::vector<std::vector<real_t>> P = { { 12, -51, 4 }, { 6, 167, -68 }, { -4, 24, -41 } };
|
||||
alg.printMatrix(P);
|
||||
alg.printMatrix(alg.gramSchmidtProcess(P));
|
||||
|
||||
MLPPLinAlgOld::QRDResult qrd_result = alg.qrd(P); // It works!
|
||||
alg.printMatrix(qrd_result.Q);
|
||||
alg.printMatrix(qrd_result.R);
|
||||
}
|
||||
void MLPPTestsOld::test_positive_definiteness_checker() {
|
||||
//MLPPStat stat;
|
||||
MLPPLinAlgOld alg;
|
||||
//MLPPActivation avn;
|
||||
//MLPPCost cost;
|
||||
//MLPPData data;
|
||||
//MLPPConvolutions conv;
|
||||
|
||||
// Checking positive-definiteness checker. For Cholesky Decomp.
|
||||
std::vector<std::vector<real_t>> A = {
|
||||
{ 1, -1, -1, -1 },
|
||||
{ -1, 2, 2, 2 },
|
||||
{ -1, 2, 3, 1 },
|
||||
{ -1, 2, 1, 4 }
|
||||
};
|
||||
|
||||
std::cout << std::boolalpha << alg.positiveDefiniteChecker(A) << std::endl;
|
||||
MLPPLinAlgOld::CholeskyResult chres = alg.cholesky(A); // works.
|
||||
alg.printMatrix(chres.L);
|
||||
alg.printMatrix(chres.Lt);
|
||||
}
|
||||
|
||||
// real_t f(real_t x){
|
||||
// return x*x*x + 2*x - 2;
|
||||
// }
|
||||
|
||||
real_t f_old(real_t x) {
|
||||
return sin(x);
|
||||
}
|
||||
|
||||
real_t f_prime_old(real_t x) {
|
||||
return 2 * x;
|
||||
}
|
||||
|
||||
real_t f_prime_2var_old(std::vector<real_t> x) {
|
||||
return 2 * x[0] + x[1];
|
||||
}
|
||||
/*
|
||||
y = x^3 + 2x - 2
|
||||
y' = 3x^2 + 2
|
||||
y'' = 6x
|
||||
y''(2) = 12
|
||||
*/
|
||||
|
||||
// real_t f_mv(std::vector<real_t> x){
|
||||
// return x[0] * x[0] + x[0] * x[1] * x[1] + x[1] + 5;
|
||||
// }
|
||||
|
||||
/*
|
||||
Where x, y = x[0], x[1], this function is defined as:
|
||||
f(x, y) = x^2 + xy^2 + y + 5
|
||||
∂f/∂x = 2x + 2y
|
||||
∂^2f/∂x∂y = 2
|
||||
*/
|
||||
|
||||
real_t f_mv_old(std::vector<real_t> x) {
|
||||
return x[0] * x[0] * x[0] + x[0] + x[1] * x[1] * x[1] * x[0] + x[2] * x[2] * x[1];
|
||||
}
|
||||
|
||||
/*
|
||||
Where x, y = x[0], x[1], this function is defined as:
|
||||
f(x, y) = x^3 + x + xy^3 + yz^2
|
||||
|
||||
fy = 3xy^2 + 2yz
|
||||
fyy = 6xy + 2z
|
||||
fyyz = 2
|
||||
|
||||
∂^2f/∂y^2 = 6xy + 2z
|
||||
∂^3f/∂y^3 = 6x
|
||||
|
||||
∂f/∂z = 2zy
|
||||
∂^2f/∂z^2 = 2y
|
||||
∂^3f/∂z^3 = 0
|
||||
|
||||
∂f/∂x = 3x^2 + 1 + y^3
|
||||
∂^2f/∂x^2 = 6x
|
||||
∂^3f/∂x^3 = 6
|
||||
|
||||
∂f/∂z = 2zy
|
||||
∂^2f/∂z^2 = 2z
|
||||
|
||||
∂f/∂y = 3xy^2
|
||||
∂^2f/∂y∂x = 3y^2
|
||||
|
||||
*/
|
||||
|
||||
void MLPPTestsOld::test_numerical_analysis() {
|
||||
MLPPLinAlgOld alg;
|
||||
MLPPConvolutionsOld conv;
|
||||
|
||||
// Checks for numerical analysis class.
|
||||
MLPPNumericalAnalysisOld numAn;
|
||||
|
||||
std::cout << numAn.quadraticApproximation(f_old, 0, 1) << std::endl;
|
||||
|
||||
std::cout << numAn.cubicApproximation(f_old, 0, 1.001) << std::endl;
|
||||
|
||||
std::cout << f_old(1.001) << std::endl;
|
||||
|
||||
std::cout << numAn.quadraticApproximation(f_mv_old, { 0, 0, 0 }, { 1, 1, 1 }) << std::endl;
|
||||
|
||||
std::cout << numAn.numDiff(&f_old, 1) << std::endl;
|
||||
std::cout << numAn.newtonRaphsonMethod(&f_old, 1, 1000) << std::endl;
|
||||
std::cout << numAn.invQuadraticInterpolation(&f_old, { 100, 2, 1.5 }, 10) << std::endl;
|
||||
|
||||
std::cout << numAn.numDiff(&f_mv_old, { 1, 1 }, 1) << std::endl; // Derivative w.r.t. x.
|
||||
|
||||
alg.printVector(numAn.jacobian(&f_mv_old, { 1, 1 }));
|
||||
|
||||
std::cout << numAn.numDiff_2(&f_old, 2) << std::endl;
|
||||
|
||||
std::cout << numAn.numDiff_3(&f_old, 2) << std::endl;
|
||||
|
||||
std::cout << numAn.numDiff_2(&f_mv_old, { 2, 2, 500 }, 2, 2) << std::endl;
|
||||
std::cout << numAn.numDiff_3(&f_mv_old, { 2, 1000, 130 }, 0, 0, 0) << std::endl;
|
||||
|
||||
alg.printTensor(numAn.thirdOrderTensor(&f_mv_old, { 1, 1, 1 }));
|
||||
std::cout << "Our Hessian." << std::endl;
|
||||
alg.printMatrix(numAn.hessian(&f_mv_old, { 2, 2, 500 }));
|
||||
|
||||
std::cout << numAn.laplacian(f_mv_old, { 1, 1, 1 }) << std::endl;
|
||||
|
||||
std::vector<std::vector<std::vector<real_t>>> tensor;
|
||||
tensor.push_back({ { 1, 2 }, { 1, 2 }, { 1, 2 } });
|
||||
tensor.push_back({ { 1, 2 }, { 1, 2 }, { 1, 2 } });
|
||||
|
||||
alg.printTensor(tensor);
|
||||
|
||||
alg.printMatrix(alg.tensor_vec_mult(tensor, { 1, 2 }));
|
||||
|
||||
std::cout << numAn.cubicApproximation(f_mv_old, { 0, 0, 0 }, { 1, 1, 1 }) << std::endl;
|
||||
std::cout << numAn.eulerianMethod(f_prime_old, { 1, 1 }, 1.5, 0.000001) << std::endl;
|
||||
std::cout << numAn.eulerianMethod(f_prime_2var_old, { 2, 3 }, 2.5, 0.00000001) << std::endl;
|
||||
|
||||
std::vector<std::vector<real_t>> A = {
|
||||
{ 1, 0, 0, 0 },
|
||||
{ 0, 0, 0, 0 },
|
||||
{ 0, 0, 0, 0 },
|
||||
{ 0, 0, 0, 1 }
|
||||
};
|
||||
|
||||
alg.printMatrix(conv.dx(A));
|
||||
alg.printMatrix(conv.dy(A));
|
||||
|
||||
alg.printMatrix(conv.grad_orientation(A));
|
||||
|
||||
std::vector<std::vector<std::string>> h = conv.harris_corner_detection(A);
|
||||
|
||||
for (uint32_t i = 0; i < h.size(); i++) {
|
||||
for (uint32_t j = 0; j < h[i].size(); j++) {
|
||||
std::cout << h[i][j] << " ";
|
||||
}
|
||||
std::cout << std::endl;
|
||||
} // Harris detector works. Life is good!
|
||||
|
||||
std::vector<real_t> a = { 3, 4, 4 };
|
||||
std::vector<real_t> b = { 4, 4, 4 };
|
||||
alg.printVector(alg.cross(a, b));
|
||||
}
|
||||
void MLPPTestsOld::test_support_vector_classification_kernel(bool ui) {
|
||||
MLPPLinAlgOld alg;
|
||||
MLPPData data;
|
||||
|
||||
//SUPPORT VECTOR CLASSIFICATION (kernel method)
|
||||
Ref<MLPPDataSimple> dt = data.load_breast_cancer_svc(_breast_cancer_svm_data_path);
|
||||
|
||||
MLPPDualSVCOld kernelSVMOld(dt->get_input()->to_std_vector(), dt->get_output()->to_std_vector(), 1000);
|
||||
kernelSVMOld.gradientDescent(0.0001, 20, ui);
|
||||
std::cout << "SCORE: " << kernelSVMOld.score() << std::endl;
|
||||
|
||||
std::vector<std::vector<real_t>> linearlyIndependentMat = {
|
||||
{ 1, 2, 3, 4 },
|
||||
{ 2345384, 4444, 6111, 55 }
|
||||
};
|
||||
|
||||
std::cout << "True of false: linearly independent?: " << std::boolalpha << alg.linearIndependenceChecker(linearlyIndependentMat) << std::endl;
|
||||
}
|
||||
|
||||
void MLPPTestsOld::test_mlpp_vector() {
|
||||
std::vector<real_t> a = { 4, 3, 1, 3 };
|
||||
|
||||
Ref<MLPPVector> rv;
|
||||
rv.instance();
|
||||
rv->set_from_std_vector(a);
|
||||
|
||||
Ref<MLPPVector> rv2;
|
||||
rv2.instance();
|
||||
rv2->set_from_std_vector(a);
|
||||
|
||||
is_approx_equals_vec(rv, rv2, "set_from_std_vectors test.");
|
||||
|
||||
rv2->set_from_std_vector(a);
|
||||
|
||||
is_approx_equals_vec(rv, rv2, "re-set_from_std_vectors test.");
|
||||
}
|
||||
|
||||
void MLPPTestsOld::is_approx_equalsd(real_t a, real_t b, const String &str) {
|
||||
if (!Math::is_equal_approx(a, b)) {
|
||||
PLOG_ERR("TEST FAILED: " + str + " Got: " + String::num(a) + " Should be: " + String::num(b));
|
||||
} else {
|
||||
PLOG_MSG("TEST PASSED: " + str);
|
||||
}
|
||||
}
|
||||
|
||||
void MLPPTestsOld::is_approx_equals_dvec(const Vector<real_t> &a, const Vector<real_t> &b, const String &str) {
|
||||
if (a.size() != b.size()) {
|
||||
goto IAEDVEC_FAILED;
|
||||
}
|
||||
|
||||
for (int i = 0; i < a.size(); ++i) {
|
||||
if (!Math::is_equal_approx(a[i], b[i])) {
|
||||
goto IAEDVEC_FAILED;
|
||||
}
|
||||
}
|
||||
|
||||
PLOG_MSG("TEST PASSED: " + str);
|
||||
|
||||
return;
|
||||
|
||||
IAEDVEC_FAILED:
|
||||
|
||||
String fail_str = "TEST FAILED: ";
|
||||
fail_str += str;
|
||||
fail_str += " Got: [ ";
|
||||
|
||||
for (int i = 0; i < a.size(); ++i) {
|
||||
fail_str += String::num(a[i]);
|
||||
fail_str += " ";
|
||||
}
|
||||
|
||||
fail_str += "] Should be: [ ";
|
||||
|
||||
for (int i = 0; i < b.size(); ++i) {
|
||||
fail_str += String::num(b[i]);
|
||||
fail_str += " ";
|
||||
}
|
||||
|
||||
fail_str += "].";
|
||||
|
||||
PLOG_ERR(fail_str);
|
||||
}
|
||||
|
||||
String vmat_to_str_old(const Vector<Vector<real_t>> &a) {
|
||||
String str;
|
||||
|
||||
str += "[ \n";
|
||||
|
||||
for (int i = 0; i < a.size(); ++i) {
|
||||
str += " [ ";
|
||||
|
||||
const Vector<real_t> &aa = a[i];
|
||||
|
||||
for (int j = 0; j < aa.size(); ++j) {
|
||||
str += String::num(aa[j]);
|
||||
str += " ";
|
||||
}
|
||||
|
||||
str += "]\n";
|
||||
}
|
||||
|
||||
str += "]\n";
|
||||
|
||||
return str;
|
||||
}
|
||||
|
||||
void MLPPTestsOld::is_approx_equals_dmat(const Vector<Vector<real_t>> &a, const Vector<Vector<real_t>> &b, const String &str) {
|
||||
if (a.size() != b.size()) {
|
||||
goto IAEDMAT_FAILED;
|
||||
}
|
||||
|
||||
for (int i = 0; i < a.size(); ++i) {
|
||||
const Vector<real_t> &aa = a[i];
|
||||
const Vector<real_t> &bb = b[i];
|
||||
|
||||
if (aa.size() != bb.size()) {
|
||||
goto IAEDMAT_FAILED;
|
||||
}
|
||||
|
||||
for (int j = 0; j < aa.size(); ++j) {
|
||||
if (!Math::is_equal_approx(aa[j], bb[j])) {
|
||||
goto IAEDMAT_FAILED;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
PLOG_MSG("TEST PASSED: " + str);
|
||||
|
||||
return;
|
||||
|
||||
IAEDMAT_FAILED:
|
||||
|
||||
String fail_str = "TEST FAILED: ";
|
||||
fail_str += str;
|
||||
fail_str += "\nGot:\n";
|
||||
fail_str += vmat_to_str_old(a);
|
||||
fail_str += "Should be:\n";
|
||||
fail_str += vmat_to_str_old(b);
|
||||
|
||||
PLOG_ERR(fail_str);
|
||||
}
|
||||
|
||||
void MLPPTestsOld::is_approx_equals_mat(Ref<MLPPMatrix> a, Ref<MLPPMatrix> b, const String &str) {
|
||||
ERR_FAIL_COND(!a.is_valid());
|
||||
ERR_FAIL_COND(!b.is_valid());
|
||||
|
||||
int ds = a->data_size();
|
||||
|
||||
const real_t *aa = a->ptr();
|
||||
const real_t *bb = b->ptr();
|
||||
|
||||
if (a->size() != b->size()) {
|
||||
goto IAEMAT_FAILED;
|
||||
}
|
||||
|
||||
ERR_FAIL_COND(!aa);
|
||||
ERR_FAIL_COND(!bb);
|
||||
|
||||
for (int i = 0; i < ds; ++i) {
|
||||
if (!Math::is_equal_approx(aa[i], bb[i])) {
|
||||
goto IAEMAT_FAILED;
|
||||
}
|
||||
}
|
||||
|
||||
PLOG_MSG("TEST PASSED: " + str);
|
||||
|
||||
return;
|
||||
|
||||
IAEMAT_FAILED:
|
||||
|
||||
String fail_str = "TEST FAILED: ";
|
||||
fail_str += str;
|
||||
fail_str += "\nGot:\n";
|
||||
fail_str += a->to_string();
|
||||
fail_str += "\nShould be:\n";
|
||||
fail_str += b->to_string();
|
||||
|
||||
PLOG_ERR(fail_str);
|
||||
}
|
||||
void MLPPTestsOld::is_approx_equals_vec(Ref<MLPPVector> a, Ref<MLPPVector> b, const String &str) {
|
||||
ERR_FAIL_COND(!a.is_valid());
|
||||
ERR_FAIL_COND(!b.is_valid());
|
||||
|
||||
if (a->size() != b->size()) {
|
||||
goto IAEDVEC_FAILED;
|
||||
}
|
||||
|
||||
for (int i = 0; i < a->size(); ++i) {
|
||||
if (!Math::is_equal_approx(a->element_get(i), b->element_get(i))) {
|
||||
goto IAEDVEC_FAILED;
|
||||
}
|
||||
}
|
||||
|
||||
PLOG_MSG("TEST PASSED: " + str);
|
||||
|
||||
return;
|
||||
|
||||
IAEDVEC_FAILED:
|
||||
|
||||
String fail_str = "TEST FAILED: ";
|
||||
fail_str += str;
|
||||
fail_str += "\nGot:\n";
|
||||
fail_str += a->to_string();
|
||||
fail_str += "\nShould be:\n";
|
||||
fail_str += b->to_string();
|
||||
fail_str += "\n.";
|
||||
|
||||
PLOG_ERR(fail_str);
|
||||
}
|
||||
|
||||
MLPPTestsOld::MLPPTestsOld() {
|
||||
_breast_cancer_data_path = "res://datasets/BreastCancer.csv";
|
||||
_breast_cancer_svm_data_path = "res://datasets/BreastCancerSVM.csv";
|
||||
_california_housing_data_path = "res://datasets/CaliforniaHousing.csv";
|
||||
_fires_and_crime_data_path = "res://datasets/FiresAndCrime.csv";
|
||||
_iris_data_path = "res://datasets/Iris.csv";
|
||||
_mnist_test_data_path = "res://datasets/MnistTest.csv";
|
||||
_mnist_train_data_path = "res://datasets/MnistTrain.csv";
|
||||
_wine_data_path = "res://datasets/Wine.csv";
|
||||
}
|
||||
|
||||
MLPPTestsOld::~MLPPTestsOld() {
|
||||
}
|
||||
|
||||
void MLPPTestsOld::_bind_methods() {
|
||||
ClassDB::bind_method(D_METHOD("test_statistics"), &MLPPTestsOld::test_statistics);
|
||||
ClassDB::bind_method(D_METHOD("test_linear_algebra"), &MLPPTestsOld::test_linear_algebra);
|
||||
ClassDB::bind_method(D_METHOD("test_univariate_linear_regression"), &MLPPTestsOld::test_univariate_linear_regression);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_gradient_descent", "ui"), &MLPPTestsOld::test_multivariate_linear_regression_gradient_descent, false);
|
||||
ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_sgd", "ui"), &MLPPTestsOld::test_multivariate_linear_regression_sgd, false);
|
||||
ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_mbgd", "ui"), &MLPPTestsOld::test_multivariate_linear_regression_mbgd, false);
|
||||
ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_normal_equation", "ui"), &MLPPTestsOld::test_multivariate_linear_regression_normal_equation, false);
|
||||
ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_adam"), &MLPPTestsOld::test_multivariate_linear_regression_adam);
|
||||
ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_score_sgd_adam", "ui"), &MLPPTestsOld::test_multivariate_linear_regression_score_sgd_adam, false);
|
||||
ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_epochs_gradient_descent", "ui"), &MLPPTestsOld::test_multivariate_linear_regression_epochs_gradient_descent, false);
|
||||
ClassDB::bind_method(D_METHOD("test_multivariate_linear_regression_newton_raphson", "ui"), &MLPPTestsOld::test_multivariate_linear_regression_newton_raphson, false);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("test_logistic_regression", "ui"), &MLPPTestsOld::test_logistic_regression, false);
|
||||
ClassDB::bind_method(D_METHOD("test_probit_regression", "ui"), &MLPPTestsOld::test_probit_regression, false);
|
||||
ClassDB::bind_method(D_METHOD("test_c_log_log_regression", "ui"), &MLPPTestsOld::test_c_log_log_regression, false);
|
||||
ClassDB::bind_method(D_METHOD("test_exp_reg_regression", "ui"), &MLPPTestsOld::test_exp_reg_regression, false);
|
||||
ClassDB::bind_method(D_METHOD("test_tanh_regression", "ui"), &MLPPTestsOld::test_tanh_regression, false);
|
||||
ClassDB::bind_method(D_METHOD("test_softmax_regression", "ui"), &MLPPTestsOld::test_softmax_regression, false);
|
||||
ClassDB::bind_method(D_METHOD("test_support_vector_classification", "ui"), &MLPPTestsOld::test_support_vector_classification, false);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("test_mlp", "ui"), &MLPPTestsOld::test_mlp, false);
|
||||
ClassDB::bind_method(D_METHOD("test_soft_max_network", "ui"), &MLPPTestsOld::test_soft_max_network, false);
|
||||
ClassDB::bind_method(D_METHOD("test_autoencoder", "ui"), &MLPPTestsOld::test_autoencoder, false);
|
||||
ClassDB::bind_method(D_METHOD("test_dynamically_sized_ann", "ui"), &MLPPTestsOld::test_dynamically_sized_ann, false);
|
||||
ClassDB::bind_method(D_METHOD("test_wgan_old", "ui"), &MLPPTestsOld::test_wgan_old, false);
|
||||
ClassDB::bind_method(D_METHOD("test_wgan", "ui"), &MLPPTestsOld::test_wgan, false);
|
||||
ClassDB::bind_method(D_METHOD("test_ann", "ui"), &MLPPTestsOld::test_ann, false);
|
||||
ClassDB::bind_method(D_METHOD("test_dynamically_sized_mann", "ui"), &MLPPTestsOld::test_dynamically_sized_mann, false);
|
||||
ClassDB::bind_method(D_METHOD("test_train_test_split_mann", "ui"), &MLPPTestsOld::test_train_test_split_mann, false);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("test_naive_bayes"), &MLPPTestsOld::test_naive_bayes);
|
||||
ClassDB::bind_method(D_METHOD("test_k_means", "ui"), &MLPPTestsOld::test_k_means, false);
|
||||
ClassDB::bind_method(D_METHOD("test_knn", "ui"), &MLPPTestsOld::test_knn, false);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("test_convolution_tensors_etc"), &MLPPTestsOld::test_convolution_tensors_etc);
|
||||
ClassDB::bind_method(D_METHOD("test_pca_svd_eigenvalues_eigenvectors", "ui"), &MLPPTestsOld::test_pca_svd_eigenvalues_eigenvectors, false);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("test_nlp_and_data", "ui"), &MLPPTestsOld::test_nlp_and_data, false);
|
||||
ClassDB::bind_method(D_METHOD("test_outlier_finder", "ui"), &MLPPTestsOld::test_outlier_finder, false);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("test_new_math_functions"), &MLPPTestsOld::test_new_math_functions);
|
||||
ClassDB::bind_method(D_METHOD("test_positive_definiteness_checker"), &MLPPTestsOld::test_positive_definiteness_checker);
|
||||
ClassDB::bind_method(D_METHOD("test_numerical_analysis"), &MLPPTestsOld::test_numerical_analysis);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("test_support_vector_classification_kernel", "ui"), &MLPPTestsOld::test_support_vector_classification_kernel, false);
|
||||
|
||||
ClassDB::bind_method(D_METHOD("test_mlpp_vector"), &MLPPTestsOld::test_mlpp_vector);
|
||||
}
|
||||
@ -1,92 +0,0 @@
|
||||
#ifndef MLPP_TESTS_OLD_H
|
||||
#define MLPP_TESTS_OLD_H
|
||||
|
||||
// TODO port this class to use the test module once it's working
|
||||
// Also don't forget to remove it's bindings
|
||||
|
||||
#include "core/math/math_defs.h"
|
||||
|
||||
#include "core/containers/vector.h"
|
||||
|
||||
#include "core/object/reference.h"
|
||||
|
||||
#include "core/string/ustring.h"
|
||||
|
||||
class MLPPMatrix;
|
||||
class MLPPVector;
|
||||
|
||||
class MLPPTestsOld : public Reference {
|
||||
GDCLASS(MLPPTestsOld, Reference);
|
||||
|
||||
public:
|
||||
void test_statistics();
|
||||
void test_linear_algebra();
|
||||
void test_univariate_linear_regression();
|
||||
|
||||
void test_multivariate_linear_regression_gradient_descent(bool ui = false);
|
||||
void test_multivariate_linear_regression_sgd(bool ui = false);
|
||||
void test_multivariate_linear_regression_mbgd(bool ui = false);
|
||||
void test_multivariate_linear_regression_normal_equation(bool ui = false);
|
||||
void test_multivariate_linear_regression_adam();
|
||||
void test_multivariate_linear_regression_score_sgd_adam(bool ui = false);
|
||||
void test_multivariate_linear_regression_epochs_gradient_descent(bool ui = false);
|
||||
void test_multivariate_linear_regression_newton_raphson(bool ui = false);
|
||||
|
||||
void test_logistic_regression(bool ui = false);
|
||||
void test_probit_regression(bool ui = false);
|
||||
void test_c_log_log_regression(bool ui = false);
|
||||
void test_exp_reg_regression(bool ui = false);
|
||||
void test_tanh_regression(bool ui = false);
|
||||
void test_softmax_regression(bool ui = false);
|
||||
void test_support_vector_classification(bool ui = false);
|
||||
|
||||
void test_mlp(bool ui = false);
|
||||
void test_soft_max_network(bool ui = false);
|
||||
void test_autoencoder(bool ui = false);
|
||||
void test_dynamically_sized_ann(bool ui = false);
|
||||
void test_wgan_old(bool ui = false);
|
||||
void test_wgan(bool ui = false);
|
||||
void test_ann(bool ui = false);
|
||||
void test_dynamically_sized_mann(bool ui = false);
|
||||
void test_train_test_split_mann(bool ui = false);
|
||||
|
||||
void test_naive_bayes();
|
||||
void test_k_means(bool ui = false);
|
||||
void test_knn(bool ui = false);
|
||||
|
||||
void test_convolution_tensors_etc();
|
||||
void test_pca_svd_eigenvalues_eigenvectors(bool ui = false);
|
||||
|
||||
void test_nlp_and_data(bool ui = false);
|
||||
void test_outlier_finder(bool ui = false);
|
||||
void test_new_math_functions();
|
||||
void test_positive_definiteness_checker();
|
||||
void test_numerical_analysis();
|
||||
void test_support_vector_classification_kernel(bool ui = false);
|
||||
|
||||
void test_mlpp_vector();
|
||||
|
||||
void is_approx_equalsd(real_t a, real_t b, const String &str);
|
||||
void is_approx_equals_dvec(const Vector<real_t> &a, const Vector<real_t> &b, const String &str);
|
||||
void is_approx_equals_dmat(const Vector<Vector<real_t>> &a, const Vector<Vector<real_t>> &b, const String &str);
|
||||
|
||||
void is_approx_equals_mat(Ref<MLPPMatrix> a, Ref<MLPPMatrix> b, const String &str);
|
||||
void is_approx_equals_vec(Ref<MLPPVector> a, Ref<MLPPVector> b, const String &str);
|
||||
|
||||
MLPPTestsOld();
|
||||
~MLPPTestsOld();
|
||||
|
||||
protected:
|
||||
static void _bind_methods();
|
||||
|
||||
String _breast_cancer_data_path;
|
||||
String _breast_cancer_svm_data_path;
|
||||
String _california_housing_data_path;
|
||||
String _fires_and_crime_data_path;
|
||||
String _iris_data_path;
|
||||
String _mnist_test_data_path;
|
||||
String _mnist_train_data_path;
|
||||
String _wine_data_path;
|
||||
};
|
||||
|
||||
#endif
|
||||
Loading…
Reference in New Issue
Block a user