mirror of https://github.com/Relintai/pmlpp.git
88 lines
2.3 KiB
C++
88 lines
2.3 KiB
C++
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#ifndef MLPP_SOFTMAX_REG_H
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#define MLPP_SOFTMAX_REG_H
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//
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// SoftmaxReg.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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#include "core/math/math_defs.h"
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#include "core/object/resource.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 MLPPSoftmaxReg : public Resource {
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GDCLASS(MLPPSoftmaxReg, Resource);
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public:
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Ref<MLPPMatrix> get_input_set() const;
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void set_input_set(const Ref<MLPPMatrix> &val);
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Ref<MLPPMatrix> get_output_set() const;
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void set_output_set(const Ref<MLPPMatrix> &val);
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MLPPReg::RegularizationType get_reg() const;
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void set_reg(const MLPPReg::RegularizationType val);
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real_t get_lambda() const;
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void set_lambda(const real_t val);
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real_t get_alpha() const;
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void set_alpha(const real_t val);
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Ref<MLPPMatrix> data_y_hat_get() const;
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void data_y_hat_set(const Ref<MLPPMatrix> &val);
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Ref<MLPPMatrix> data_weights_get() const;
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void data_weights_set(const Ref<MLPPMatrix> &val);
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Ref<MLPPVector> data_bias_get() const;
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void data_bias_set(const Ref<MLPPVector> &val);
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Ref<MLPPVector> model_test(const Ref<MLPPVector> &x);
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Ref<MLPPMatrix> model_set_test(const Ref<MLPPMatrix> &X);
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void train_gradient_descent(real_t learning_rate, int max_epoch, bool ui = false);
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void train_sgd(real_t learning_rate, int max_epoch, bool ui = false);
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void train_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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bool needs_init() const;
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void initialize();
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MLPPSoftmaxReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPMatrix> &p_output_set, MLPPReg::RegularizationType p_reg = MLPPReg::REGULARIZATION_TYPE_NONE, real_t p_lambda = 0.5, real_t p_alpha = 0.5);
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MLPPSoftmaxReg();
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~MLPPSoftmaxReg();
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protected:
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real_t cost(const Ref<MLPPMatrix> &y_hat, const Ref<MLPPMatrix> &y);
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Ref<MLPPVector> evaluatev(const Ref<MLPPVector> &x);
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Ref<MLPPMatrix> evaluatem(const Ref<MLPPMatrix> &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<MLPPMatrix> _output_set;
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// Regularization Params
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MLPPReg::RegularizationType _reg;
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real_t _lambda;
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real_t _alpha; /* This is the controlling param for Elastic Net*/
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Ref<MLPPMatrix> _y_hat;
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Ref<MLPPMatrix> _weights;
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Ref<MLPPVector> _bias;
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};
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#endif /* SoftmaxReg_hpp */
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