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Simplified evaluate_common.h.

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nodchip
2020-09-09 08:53:57 +09:00
parent 8d763fb503
commit cea17c92f9
1 changed files with 5 additions and 54 deletions
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src/eval/evaluate_common.h
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#ifndef _EVALUATE_COMMON_H_ #ifndef _EVALUATE_COMMON_H_
#define _EVALUATE_COMMON_H_ #define _EVALUATE_COMMON_H_
#if defined(EVAL_LEARN)
// A common header-like function for modern evaluation functions (EVAL_KPPT and EVAL_KPP_KKPT). // A common header-like function for modern evaluation functions (EVAL_KPPT and EVAL_KPP_KKPT).
#include <functional> #include <string>
// KK file name
#define KK_BIN "KK_synthesized.bin"
// KKP file name
#define KKP_BIN "KKP_synthesized.bin"
// KPP file name
#define KPP_BIN "KPP_synthesized.bin"
#include "../position.h"
namespace Eval namespace Eval
{ {
// An operator that applies the function f to each parameter of the evaluation function.
// Used for parameter analysis etc.
// type indicates the survey target.
// type = -1 :KK,KKP,KPP all
// type = 0: KK only
// type = 1: KKP only
// type = 2: KPP only
void foreach_eval_param(std::function<void(int32_t, int32_t)>f, int type = -1);
// -------------------------- // --------------------------
// for learning // for learning
// -------------------------- // --------------------------
#if defined(EVAL_LEARN)
// Initialize the gradient array during learning
// Pass the learning rate as an argument. If 0.0, the default value is used.
// The epoch of update_weights() gradually changes from eta to eta2 until eta_epoch.
// After eta2_epoch, gradually change from eta2 to eta3.
void init_grad(double eta1, uint64_t eta_epoch, double eta2, uint64_t eta2_epoch, double eta3);
// Add the gradient difference value to the gradient array for all features that appear in the current phase.
// freeze[0]: Flag that kk does not learn
// freeze[1]: Flag that kkp does not learn
// freeze[2]: Flag that kpp does not learn
// freeze[3]: Flag that kppp does not learn
void add_grad(Position& pos, Color rootColor, double delt_grad, const std::array<bool, 4>& freeze);
// Do SGD or AdaGrad or something based on the current gradient.
// epoch: Generation counter (starting from 0)
// freeze[0]: Flag that kk does not learn
// freeze[1]: Flag that kkp does not learn
// freeze[2]: Flag that kpp does not learn
// freeze[3]: Flag that kppp does not learn
void update_weights(uint64_t epoch, const std::array<bool, 4>& freeze);
// Save the evaluation function parameters to a file. // Save the evaluation function parameters to a file.
// You can specify the extension added to the end of the file. // You can specify the extension added to the end of the file.
void save_eval(std::string suffix); void save_eval(std::string suffix);
// Get the current eta. // Get the current eta.
double get_eta(); double get_eta();
// --learning related commands
// A function that normalizes KK. Note that it is not completely equivalent to the original evaluation function.
// By making the values ​​of kkp and kpp as close to zero as possible, the value of the feature factor (which is zero) that did not appear during learning
// The idea of ​​ensuring it is valid.
void regularize_kk();
#endif
} }
#endif // defined(EVAL_LEARN)
#endif // _EVALUATE_KPPT_COMMON_H_ #endif // _EVALUATE_KPPT_COMMON_H_