big refactor

* use elo
* optionally train on only diagonal
* train-inference nonlinearity alignment

1 files changed, 4 insertions, 3 deletions

diff --git a/src/selfplay.c b/src/selfplay.c
index b2a5821..37f94fa 100644
--- a/src/selfplay.c
+++ b/src/selfplay.c
@@ -169,7 +169,8 @@ static int play_game(const uint8_t min_ply, const nn_function fn_1,
   // final outcome of the played game, from black's perspective
   const int8_t R = win_value(state->won);
 
-  fprintf(stdout, "{\"result\":%d,\"rows\":[", R);
+  fprintf(stdout, "{\"result\":%d,\"cand_A\":%u,\"cand_B\":%u,\"rows\":[", R,
+          CAND_A, CAND_B);
   for (uint32_t k = 0; k < n_rows; k++) {
     fprintf(stdout, "%s{\"cand\":%u,\"root\":{\"parity\":%d,", k ? "," : "",
             row_cand_id[k], row_mover_sign[k]);
@@ -188,8 +189,8 @@ const char *usage =
   "selfplay, one game of training data between candidates.\n\n\
 Usage: selfplay <size> <depth> <min_ply>\n\n\
 Plays a single game of <size>x<size>, with <min_ply> moves made randomly. Only the deterministic plies are written to stdout, one JSON record per game, from ply <min_ply> onwards:\n\n\
-\t{\"result\": R, \"rows\": [{\"cand\": id, \"root\": {\"parity\", \"head\", \"cells\"}, \"leaf\": {\"parity\", \"head\", \"cells\", \"terminal\", \"terminal_value\"}}]}\n\n\
-R is the outcome of the game from black's perspective. Each row records the net input of the root state s_t and the PV leaf selected at that ply; the leaf value Net(pv) is taken from the trained net. nn_train.py runs many of these in parallel to generate training data.\n\n\
+\t{\"result\": R, \"cand_A\": i, \"cand_B\": j, \"rows\": [{\"cand\": id, \"root\": {\"parity\", \"head\", \"cells\"}, \"leaf\": {\"parity\", \"head\", \"cells\", \"terminal\", \"terminal_value\"}}]}\n\n\
+R is the outcome of the game from black's perspective; cand_A and cand_B are the ids of the two candidates that played the game. Each row records the net input of the root state s_t and the PV leaf selected at that ply; the leaf value Net(pv) is taken from the trained net. nn_train.py runs many of these in parallel to generate training data.\n\n\
 Copyright (C) 2026, tslil clingman\n\n\
 This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.";