mirror of https://github.com/Relintai/pmlpp.git
84 lines
1.9 KiB
C++
84 lines
1.9 KiB
C++
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#ifndef MLPP_SVC_H
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#define MLPP_SVC_H
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//
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// SVC.hpp
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//
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// Created by Marc Melikyan on 10/2/20.
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//
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// https://towardsdatascience.com/svm-implementation-from-scratch-python-2db2fc52e5c2
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// Illustratd a practical definition of the Hinge Loss function and its gradient when optimizing with SGD.
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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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#include "../regularization/reg.h"
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class MLPPSVC : public Reference {
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GDCLASS(MLPPSVC, Reference);
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public:
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Ref<MLPPMatrix> get_input_set();
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void set_input_set(const Ref<MLPPMatrix> &val);
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Ref<MLPPVector> get_output_set();
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void set_output_set(const Ref<MLPPMatrix> &val);
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real_t get_c();
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void set_c(const real_t val);
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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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bool is_initialized();
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void initialize();
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MLPPSVC(const Ref<MLPPMatrix> &input_set, const Ref<MLPPVector> &output_set, real_t c);
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MLPPSVC();
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~MLPPSVC();
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protected:
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real_t cost(const Ref<MLPPVector> &z, const Ref<MLPPVector> &y, const Ref<MLPPVector> &weights, real_t c);
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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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real_t evaluatev(const Ref<MLPPVector> &x);
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real_t propagatev(const Ref<MLPPVector> &x);
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void forward_pass();
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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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Ref<MLPPVector> _weights;
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real_t _bias;
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real_t _c;
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int _n;
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int _k;
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bool _initialized;
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};
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#endif /* SVC_hpp */
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