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108 lines
4.1 KiB
C++
108 lines
4.1 KiB
C++
#ifndef MLPP_DUAL_SVC_H
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#define MLPP_DUAL_SVC_H
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/*************************************************************************/
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/* dual_svc.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) 2022-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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// http://disp.ee.ntu.edu.tw/~pujols/Support%20Vector%20Machine.pdf
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// http://ciml.info/dl/v0_99/ciml-v0_99-ch11.pdf
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// Were excellent for the practical intution behind the dual formulation.
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#include "core/math/math_defs.h"
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#include "core/object/reference.h"
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#include "../lin_alg/mlpp_matrix.h"
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#include "../lin_alg/mlpp_vector.h"
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class MLPPDualSVC : public Reference {
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GDCLASS(MLPPDualSVC, Reference);
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public:
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enum KernelMethod {
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KERNEL_METHOD_LINEAR = 0,
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};
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public:
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Ref<MLPPVector> model_set_test(const Ref<MLPPMatrix> &X);
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real_t model_test(const Ref<MLPPVector> &x);
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void gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
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//void SGD(real_t learning_rate, int max_epoch, bool ui = false);
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//void MBGD(real_t learning_rate, int max_epoch, int mini_batch_size, bool ui = false);
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real_t score();
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void save(const String &file_name);
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MLPPDualSVC(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &p_output_set, real_t p_C, KernelMethod p_kernel = KERNEL_METHOD_LINEAR);
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MLPPDualSVC();
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~MLPPDualSVC();
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protected:
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void init();
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real_t cost(const Ref<MLPPVector> &alpha, const Ref<MLPPMatrix> &X, const Ref<MLPPVector> &y);
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real_t evaluatev(const Ref<MLPPVector> &x);
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real_t propagatev(const Ref<MLPPVector> &x);
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Ref<MLPPVector> evaluatem(const Ref<MLPPMatrix> &X);
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Ref<MLPPVector> propagatem(const Ref<MLPPMatrix> &X);
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void forward_pass();
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void alpha_projection();
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real_t kernel_functionv(const Ref<MLPPVector> &v, const Ref<MLPPVector> &u, KernelMethod kernel);
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Ref<MLPPMatrix> kernel_functionm(const Ref<MLPPMatrix> &U, const Ref<MLPPMatrix> &V, KernelMethod kernel);
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static void _bind_methods();
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Ref<MLPPMatrix> _input_set;
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Ref<MLPPVector> _output_set;
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Ref<MLPPVector> _z;
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Ref<MLPPVector> _y_hat;
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real_t _bias;
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Ref<MLPPVector> _alpha;
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Ref<MLPPMatrix> _K;
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real_t _C;
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int _n;
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int _k;
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KernelMethod _kernel;
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real_t _p; // Poly
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real_t _c; // Poly
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};
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#endif /* DualSVC_hpp */
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