85 lines
3.3 KiB
C#
85 lines
3.3 KiB
C#
using JoshHeaps.Net.Models;
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using JoshHeaps.Net.Services.Interfaces;
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using Microsoft.Extensions.Options;
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namespace JoshHeaps.Net.Services.Implementations;
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/// <summary>
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/// Managed facade over the native learned-weights model (see <see cref="ILearnedWeightsStore"/>).
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/// On construction it points the native engine at the weights file; everything else delegates
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/// to the shared native ABI in <see cref="CustomChessEngine"/>.
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/// </summary>
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public sealed class LearnedWeightsStore : ILearnedWeightsStore
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{
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private const int Pieces = 6; // Pawn..King
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private const int Squares = 64;
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private const int Features = 8; // mobility N/B/R/Q, passed, isolated, doubled, king safety
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public string WeightsFilePath { get; }
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public LearnedWeightsStore(IHostEnvironment env, IOptions<ChessEngineOptions> options, ILogger<LearnedWeightsStore> logger)
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{
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// Prefer the configured path (production points this outside the deploy dir); fall
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// back to the content root for local dev.
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var configured = options.Value.WeightsPath;
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WeightsFilePath = string.IsNullOrWhiteSpace(configured)
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? Path.Combine(env.ContentRootPath, "chess-data", "learned-weights.txt")
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: configured;
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// A missing/unwritable/misconfigured path must not take down the whole app — the
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// learned engine just plays from a neutral table and can't persist training.
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try
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{
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Directory.CreateDirectory(Path.GetDirectoryName(WeightsFilePath)!);
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CustomChessEngine.NativeMethods.learned_load(WeightsFilePath);
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}
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catch (Exception ex)
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{
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logger.LogError(ex,
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"Could not initialize the learned-weights store at {Path}. The learned engine will " +
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"play from a neutral table and training will not persist. In production set " +
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"ChessEngine:WeightsPath (env ChessEngine__WeightsPath) to a service-writable directory.",
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WeightsFilePath);
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}
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}
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public nint CreateTrainer() => CustomChessEngine.NativeMethods.trainer_create();
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public void Record(nint trainer, string fen) =>
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CustomChessEngine.NativeMethods.trainer_record(trainer, fen);
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public void ApplyResult(nint trainer, PieceColor winner, double weight) =>
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CustomChessEngine.NativeMethods.trainer_apply(trainer, winner == PieceColor.White ? 0 : 1, weight);
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public void DestroyTrainer(nint trainer) =>
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CustomChessEngine.NativeMethods.trainer_destroy(trainer);
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public LearnedWeightsSnapshot Snapshot()
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{
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const int total = Pieces * Squares * 2 + Features;
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var buffer = new int[total];
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unsafe
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{
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fixed (int* p = buffer)
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CustomChessEngine.NativeMethods.weights_snapshot(p, total);
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}
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var mg = new int[Pieces][];
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var eg = new int[Pieces][];
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for (int piece = 0; piece < Pieces; piece++)
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{
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mg[piece] = new int[Squares];
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eg[piece] = new int[Squares];
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Array.Copy(buffer, piece * Squares, mg[piece], 0, Squares);
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Array.Copy(buffer, Pieces * Squares + piece * Squares, eg[piece], 0, Squares);
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}
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var features = new int[Features];
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Array.Copy(buffer, Pieces * Squares * 2, features, 0, Features);
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return new LearnedWeightsSnapshot(mg, eg, features);
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}
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}
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