Learn fractional win-rate advantage instead of unbounded plus/minus

Piece-square and feature weights now converge to a presence-weighted
fractional win-rate per cell (percent of games won with a piece there
minus percent lost), tracked via running visits/score sums instead of
an incrementally clamped nudge. The training signal is also scaled by
how much of a game's result is NOT already explained by the winner's
average material edge (via the standard centipawn win-probability
curve), so a piece no longer gets credit merely for existing on the
winning side of a material blowout in the endgame.

Co-Authored-By: Claude Sonnet 5 <[email protected]>
Claude-Session: https://claude.ai/code/session_018CmAti1baCjidbJM2HeJfk

native/chess_engine/include/chess_engine.h

@@ -103,9 +103,13 @@ CHESS_API void          CHESS_CALL trainer_destroy(TrainerHandle trainer);
 /* Record one played position (post-move FEN) into the accumulator. */
 CHESS_API void CHESS_CALL trainer_record(TrainerHandle trainer, const char* fen);
 
-/* Apply a finished game's outcome to the global weights and save: rewards the winner's
- * occupied squares / features and punishes the loser's, scaled by `weight` (e.g. 0.5 for a
- * material-imbalance draw). winner: 0 = white, 1 = black. */
+/* Apply a finished game's outcome to the global weights and save. Each touched cell (a piece
+ * on a square, or a feature) tracks a presence-weighted running average of the game's
+ * "advantage" — the game's outcome (scaled by `weight`, e.g. 0.5 for a material-imbalance
+ * draw) minus how much of that outcome the winner's average material edge already explains —
+ * so mg/eg/featW end up as a fractional win-rate (percent of games won with that piece there,
+ * minus percent lost), not an unbounded plus/minus tally, and a piece doesn't get credit
+ * merely for existing on the winning side of a material blowout. winner: 0 = white, 1 = black. */
 CHESS_API void CHESS_CALL trainer_apply(TrainerHandle trainer, int winner, double weight);
 
 #ifdef __cplusplus