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83 lines
2.7 KiB
C++
83 lines
2.7 KiB
C++
#ifndef _EVALUATE_COMMON_H_
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#define _EVALUATE_COMMON_H_
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// A common header-like function for modern evaluation functions (EVAL_KPPT and EVAL_KPP_KKPT).
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#if defined(EVAL_NNUE) || defined(EVAL_LEARN)
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#include <functional>
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// KK file name
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#define KK_BIN "KK_synthesized.bin"
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// KKP file name
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#define KKP_BIN "KKP_synthesized.bin"
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// KPP file name
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#define KPP_BIN "KPP_synthesized.bin"
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namespace Eval
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{
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#if defined(USE_EVAL_HASH)
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// prefetch function
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void prefetch_evalhash(const Key key);
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#endif
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// An operator that applies the function f to each parameter of the evaluation function.
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// Used for parameter analysis etc.
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// type indicates the survey target.
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// type = -1 :KK,KKP,KPP all
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// type = 0: KK only
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// type = 1: KKP only
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// type = 2: KPP only
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void foreach_eval_param(std::function<void(int32_t, int32_t)>f, int type = -1);
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// --------------------------
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// for learning
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// --------------------------
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#if defined(EVAL_LEARN)
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// Initialize the gradient array during learning
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// Pass the learning rate as an argument. If 0.0, the default value is used.
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// The epoch of update_weights() gradually changes from eta to eta2 until eta_epoch.
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// After eta2_epoch, gradually change from eta2 to eta3.
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void init_grad(double eta1, uint64_t eta_epoch, double eta2, uint64_t eta2_epoch, double eta3);
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// Add the gradient difference value to the gradient array for all features that appear in the current phase.
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// freeze[0]: Flag that kk does not learn
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// freeze[1]: Flag that kkp does not learn
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// freeze[2]: Flag that kpp does not learn
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// freeze[3]: Flag that kppp does not learn
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void add_grad(Position& pos, Color rootColor, double delt_grad, const std::array<bool, 4>& freeze);
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// Do SGD or AdaGrad or something based on the current gradient.
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// epoch: Generation counter (starting from 0)
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// freeze[0]: Flag that kk does not learn
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// freeze[1]: Flag that kkp does not learn
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// freeze[2]: Flag that kpp does not learn
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// freeze[3]: Flag that kppp does not learn
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void update_weights(uint64_t epoch, const std::array<bool, 4>& freeze);
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// Save the evaluation function parameters to a file.
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// You can specify the extension added to the end of the file.
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void save_eval(std::string suffix);
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// Get the current eta.
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double get_eta();
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// --learning related commands
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// A function that normalizes KK. Note that it is not completely equivalent to the original evaluation function.
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// 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
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// The idea of ensuring it is valid.
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void regularize_kk();
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#endif
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}
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#endif // defined(EVAL_NNUE) || defined(EVAL_LEARN)
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#endif // _EVALUATE_KPPT_COMMON_H_
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