diff --git a/src/eval/evaluate_common.h b/src/eval/evaluate_common.h
index 927783cd..989169b3 100644
--- a/src/eval/evaluate_common.h
+++ b/src/eval/evaluate_common.h
@@ -1,75 +1,26 @@
 #ifndef _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).
 
-#include <functional>
-
-// 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"
+#include <string>
 
 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
 	// --------------------------
 
-#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.
 	// You can specify the extension added to the end of the file.
 	void save_eval(std::string suffix);
 
 	// Get the current 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_