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72 lines
1.8 KiB
C++
72 lines
1.8 KiB
C++
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#ifndef MLPP_C_LOG_LOG_REG_H
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#define MLPP_C_LOG_LOG_REG_H
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//
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// CLogLogReg.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/reference.h"
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#include "../regularization/reg.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 MLPPCLogLogReg : public Reference {
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GDCLASS(MLPPCLogLogReg, Reference);
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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 mle(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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MLPPCLogLogReg(const Ref<MLPPMatrix> &p_input_set, const Ref<MLPPVector> &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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MLPPCLogLogReg();
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~MLPPCLogLogReg();
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protected:
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void weight_initialization(int k);
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void bias_initialization();
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real_t cost(const Ref<MLPPVector> &y_hat, 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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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> _y_hat;
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Ref<MLPPVector> _z;
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Ref<MLPPVector> _weights;
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real_t bias;
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int _n;
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int _k;
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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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};
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#endif /* CLogLogReg_hpp */
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