mirror of https://github.com/Relintai/pmlpp.git
123 lines
5.2 KiB
C++
123 lines
5.2 KiB
C++
#ifndef MLPP_TESTS_H
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#define MLPP_TESTS_H
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/*************************************************************************/
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/* mlpp_tests.h */
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/*************************************************************************/
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/* This file is part of: */
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/* PMLPP Machine Learning Library */
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/* https://github.com/Relintai/pmlpp */
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/*************************************************************************/
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/* Copyright (c) 2023-present Péter Magyar. */
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/* Copyright (c) 2022-2023 Marc Melikyan */
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/* */
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/* Permission is hereby granted, free of charge, to any person obtaining */
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/* a copy of this software and associated documentation files (the */
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/* "Software"), to deal in the Software without restriction, including */
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/* without limitation the rights to use, copy, modify, merge, publish, */
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/* distribute, sublicense, and/or sell copies of the Software, and to */
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/* permit persons to whom the Software is furnished to do so, subject to */
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/* the following conditions: */
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/* */
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/* The above copyright notice and this permission notice shall be */
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/* included in all copies or substantial portions of the Software. */
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/* */
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/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, */
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/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF */
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/* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.*/
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/* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY */
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/* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, */
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/* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE */
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/* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */
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/*************************************************************************/
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#include "core/math/math_defs.h"
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#include "core/containers/vector.h"
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#include "core/object/reference.h"
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#include "core/string/ustring.h"
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// TODO port this class to use the test module once it's working
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// Also don't forget to remove it's bindings
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class MLPPMatrix;
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class MLPPVector;
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class MLPPTests : public Reference {
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GDCLASS(MLPPTests, Reference);
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public:
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void test_statistics();
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void test_linear_algebra();
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void test_univariate_linear_regression();
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void test_multivariate_linear_regression_gradient_descent(bool ui = false);
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void test_multivariate_linear_regression_sgd(bool ui = false);
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void test_multivariate_linear_regression_mbgd(bool ui = false);
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void test_multivariate_linear_regression_normal_equation(bool ui = false);
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void test_multivariate_linear_regression_adam(bool ui = false);
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void test_multivariate_linear_regression_score_sgd_adam(bool ui = false);
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void test_multivariate_linear_regression_epochs_gradient_descent(bool ui = false);
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void test_multivariate_linear_regression_newton_raphson(bool ui = false);
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void test_logistic_regression(bool ui = false);
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void test_probit_regression(bool ui = false);
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void test_c_log_log_regression(bool ui = false);
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void test_exp_reg_regression(bool ui = false);
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void test_tanh_regression(bool ui = false);
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void test_softmax_regression(bool ui = false);
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void test_support_vector_classification(bool ui = false);
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void test_mlp(bool ui = false);
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void test_soft_max_network(bool ui = false);
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void test_autoencoder(bool ui = false);
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void test_dynamically_sized_ann(bool ui = false);
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void test_wgan_old(bool ui = false);
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void test_wgan(bool ui = false);
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void test_ann(bool ui = false);
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void test_dynamically_sized_mann(bool ui = false);
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void test_train_test_split_mann(bool ui = false);
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void test_naive_bayes();
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void test_k_means(bool ui = false);
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void test_knn(bool ui = false);
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void test_convolution_tensors_etc();
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void test_pca_svd_eigenvalues_eigenvectors(bool ui = false);
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void test_nlp_and_data(bool ui = false);
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void test_outlier_finder(bool ui = false);
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void test_new_math_functions();
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void test_positive_definiteness_checker();
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void test_numerical_analysis();
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void test_support_vector_classification_kernel(bool ui = false);
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void test_mlpp_vector();
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void is_approx_equalsd(real_t a, real_t b, const String &str);
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void is_approx_equals_dvec(const Vector<real_t> &a, const Vector<real_t> &b, const String &str);
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void is_approx_equals_dmat(const Vector<Vector<real_t>> &a, const Vector<Vector<real_t>> &b, const String &str);
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void is_approx_equals_mat(Ref<MLPPMatrix> a, Ref<MLPPMatrix> b, const String &str);
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void is_approx_equals_vec(Ref<MLPPVector> a, Ref<MLPPVector> b, const String &str);
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MLPPTests();
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~MLPPTests();
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protected:
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static void _bind_methods();
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String _breast_cancer_data_path;
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String _breast_cancer_svm_data_path;
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String _california_housing_data_path;
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String _fires_and_crime_data_path;
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String _iris_data_path;
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String _mnist_test_data_path;
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String _mnist_train_data_path;
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String _wine_data_path;
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};
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#endif
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