mirror of https://github.com/Relintai/pmlpp.git
146 lines
5.8 KiB
C++
146 lines
5.8 KiB
C++
/*************************************************************************/
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/* register_types.cpp */
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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 "register_types.h"
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#include "data/data.h"
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#include "lin_alg/mlpp_matrix.h"
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#include "lin_alg/mlpp_tensor3.h"
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#include "lin_alg/mlpp_vector.h"
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#include "activation/activation.h"
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#include "convolutions/convolutions.h"
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#include "cost/cost.h"
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#include "gauss_markov_checker/gauss_markov_checker.h"
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#include "hypothesis_testing/hypothesis_testing.h"
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#include "lin_alg/lin_alg.h"
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#include "numerical_analysis/numerical_analysis.h"
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#include "regularization/reg.h"
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#include "stat/stat.h"
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#include "transforms/transforms.h"
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#include "utilities/utilities.h"
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#include "hidden_layer/hidden_layer.h"
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#include "multi_output_layer/multi_output_layer.h"
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#include "output_layer/output_layer.h"
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#include "ann/ann.h"
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#include "auto_encoder/auto_encoder.h"
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#include "bernoulli_nb/bernoulli_nb.h"
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#include "c_log_log_reg/c_log_log_reg.h"
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#include "dual_svc/dual_svc.h"
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#include "exp_reg/exp_reg.h"
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#include "gan/gan.h"
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#include "gaussian_nb/gaussian_nb.h"
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#include "kmeans/kmeans.h"
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#include "knn/knn.h"
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#include "lin_reg/lin_reg.h"
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#include "log_reg/log_reg.h"
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#include "mann/mann.h"
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#include "mlp/mlp.h"
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#include "multinomial_nb/multinomial_nb.h"
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#include "outlier_finder/outlier_finder.h"
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#include "pca/pca.h"
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#include "probit_reg/probit_reg.h"
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#include "softmax_net/softmax_net.h"
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#include "softmax_reg/softmax_reg.h"
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#include "svc/svc.h"
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#include "tanh_reg/tanh_reg.h"
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#include "uni_lin_reg/uni_lin_reg.h"
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#include "wgan/wgan.h"
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#ifdef TESTS_ENABLED
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#include "test/mlpp_matrix_tests.h"
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#include "test/mlpp_tests.h"
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#endif
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void register_pmlpp_types(ModuleRegistrationLevel p_level) {
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if (p_level == MODULE_REGISTRATION_LEVEL_SCENE) {
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ClassDB::register_class<MLPPVector>();
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ClassDB::register_class<MLPPMatrix>();
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ClassDB::register_class<MLPPTensor3>();
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ClassDB::register_class<MLPPUtilities>();
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ClassDB::register_class<MLPPReg>();
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ClassDB::register_class<MLPPActivation>();
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ClassDB::register_class<MLPPCost>();
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ClassDB::register_class<MLPPTransforms>();
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ClassDB::register_class<MLPPStat>();
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ClassDB::register_class<MLPPNumericalAnalysis>();
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ClassDB::register_class<MLPPHypothesisTesting>();
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ClassDB::register_class<MLPPGaussMarkovChecker>();
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ClassDB::register_class<MLPPConvolutions>();
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ClassDB::register_class<MLPPLinAlg>();
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ClassDB::register_class<MLPPHiddenLayer>();
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ClassDB::register_class<MLPPOutputLayer>();
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ClassDB::register_class<MLPPMultiOutputLayer>();
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ClassDB::register_class<MLPPKNN>();
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ClassDB::register_class<MLPPKMeans>();
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ClassDB::register_class<MLPPMLP>();
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ClassDB::register_class<MLPPWGAN>();
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ClassDB::register_class<MLPPPCA>();
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ClassDB::register_class<MLPPUniLinReg>();
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ClassDB::register_class<MLPPOutlierFinder>();
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ClassDB::register_class<MLPPProbitReg>();
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ClassDB::register_class<MLPPSVC>();
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ClassDB::register_class<MLPPSoftmaxReg>();
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ClassDB::register_class<MLPPAutoEncoder>();
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ClassDB::register_class<MLPPTanhReg>();
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ClassDB::register_class<MLPPSoftmaxNet>();
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ClassDB::register_class<MLPPMultinomialNB>();
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ClassDB::register_class<MLPPMANN>();
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ClassDB::register_class<MLPPLogReg>();
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ClassDB::register_class<MLPPLinReg>();
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ClassDB::register_class<MLPPGaussianNB>();
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ClassDB::register_class<MLPPGAN>();
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ClassDB::register_class<MLPPExpReg>();
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ClassDB::register_class<MLPPDualSVC>();
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ClassDB::register_class<MLPPCLogLogReg>();
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ClassDB::register_class<MLPPBernoulliNB>();
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ClassDB::register_class<MLPPANN>();
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ClassDB::register_class<MLPPDataESimple>();
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ClassDB::register_class<MLPPDataSimple>();
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ClassDB::register_class<MLPPDataComplex>();
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ClassDB::register_class<MLPPData>();
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#ifdef TESTS_ENABLED
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ClassDB::register_class<MLPPTests>();
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ClassDB::register_class<MLPPMatrixTests>();
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#endif
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}
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}
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void unregister_pmlpp_types(ModuleRegistrationLevel p_level) {
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}
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