BatchEvalContext Class Reference

#include <BatchEvalContext.h>

Inheritance diagram for BatchEvalContext:

Collaboration diagram for BatchEvalContext:

Public Member Functions
	BatchEvalContext (BatchEvaluator &batchEval)
float	evaluate (const Board &board, int ply=0) override
	Evaluates the board from the perspective of the side to move.
float	evaluate (const ChessInput &input) override
	Evaluates the position using pre-computed features.
Public Member Functions inherited from IEvaluator
virtual	~IEvaluator ()=default
virtual WDLConverter::WDL	evaluateWDL (const Board &board, int ply=0)
	Evaluates the board and returns Win/Draw/Loss probabilities.
virtual void	setAggression (float aggression)
	Sets the contempt or aggression factor for the evaluator.
virtual void	setEvalScale (int base, int weight)
	Sets the scaling parameters for the evaluation score.
virtual void	setEvalNormalization (bool enable)
	Enables or disables dynamic evaluation normalization.
virtual void	setIncrementalRebuildInterval (int interval)
	Sets the interval for forcing full feature rebuilds (to correct accumulation errors).
virtual uint64_t	getFullRebuilds () const
	Retrieves the number of full feature rebuilds performed (for profiling).

Private Attributes
BatchEvaluator &	batchEvaluator_

Constructor & Destructor Documentation

BatchEvalContext()

BatchEvalContext::BatchEvalContext ( BatchEvaluator & batchEval )

inlineexplicit

Member Function Documentation

evaluate() [1/2]

float BatchEvalContext::evaluate ( const Board & board, int ply = 0 )

inlineoverridevirtual

Evaluates the board from the perspective of the side to move.

Parameters

board	The current chess board state.
ply	The current depth from the root of the search (used for scaling).

Returns

Evaluation score in centipawns (e.g., 150 = +1.5 pawns advantage).

Implements IEvaluator.

evaluate() [2/2]

float BatchEvalContext::evaluate ( const ChessInput & input )

inlineoverridevirtual

Evaluates the position using pre-computed features.

This is an optional optimization path for evaluators that utilize incremental feature extraction (like MoE neural networks).

Parameters

input

The pre-extracted neural network features.

Returns

Evaluation score in centipawns.

Reimplemented from IEvaluator.

Member Data Documentation

batchEvaluator_

BatchEvaluator& BatchEvalContext::batchEvaluator_

private

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The documentation for this class was generated from the following file:

• engine/src/eval/BatchEvalContext.h