TD(lambda), and a big rework

NOTE: 6x6 weights are as-yet untrained

On the performance side:
* switch from linked-list chaining hash table to direct mapping
* cache check_win by hash
* persist transposition tables across plies (this was a big deal and difficult to puzzle out)
* add align directives to the network floats
* prepare_nn_input gained a small LUT to avoid branching (i suppose you might call this 1D embedding)
* apply mtune=native for the native builds

On the correctness side:
* Zobrist has now includes the ply parity
* the network now predicts something in [-1, 1] using tanh (approximated by polynomials)
* the training object is now MSE because we're learning a value estimate
* use .clangd instead of compile_commands.json

On the training side:
* completely forgo human-derived data set
* implement td(lambda), self-play, tournaments

1 files changed, 54 insertions, 0 deletions

diff --git a/include/nn_input.c b/include/nn_input.c
new file mode 100644
index 0000000..2fbd8da
--- /dev/null
+++ b/include/nn_input.c
@@ -0,0 +1,54 @@
+/*
+  This file is part of ctak.
+
+  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.
+
+  This program is distributed in the hope that it will be useful, but
+  WITHOUT ANY WARRANTY; without even the implied warranty of
+  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
+  General Public License for more details.
+
+  You should have received a copy of the GNU General Public License
+  along with ctak. If not, see <https://www.gnu.org/licenses/>.
+*/
+
+#include "nn.h"
+#include "tak.h"
+
+/*
+ * Encode a board position into the network's input vector:
+ *   [0]       ply parity (-1 / +1)
+ *   [1]       white flats remaining, as a fraction of the total
+ *   [2]       black flats remaining, as a fraction of the total
+ *   [3 ..]    one scalar per cell for the top stone:
+ *             0 empty, +-0.25 standing, +-0.5 flat, +-1 capstone,
+ *             + white, - black
+ *
+ * `cur_board' must have room for 3 + board_size*board_size floats.
+ */
+
+// The per-cell scalar depends only on the top stone's colour, variant
+// and occupancy, so it's a table lookup keyed on those three bits.
+static const float cell_scalar[16] = {
+    0.0f,  -0.5f, 0.0f, -0.25f, 0.0f, -1.0f, 0.0f, 0.0f, // black
+    0.0f,  +0.5f, 0.0f, +0.25f, 0.0f, +1.0f, 0.0f, 0.0f, // white
+};
+
+void nn_prepare_input(float *cur_board, tak_state_p state) {
+  const float inv_max_flats =
+      (state->board_size == 6) ? (1.0f / 30.0f) : (1.0f / 21.0f);
+  const unsigned int n_cells = state->board_size * state->board_size;
+  for (unsigned int loc = 0; loc < n_cells; loc++) {
+    const uint8_t d = state->celldat[loc];
+    cur_board[3 + loc] = cell_scalar[((state->colours[loc] & 1) << 3) |
+                                     ((d & STONE_MASK) << 1) |
+                                     ((d & NUM_MASK) != 0)];
+  }
+  // Add input of flat counts and ply parity
+  cur_board[0] = (state->ply & 1) ? 1 : -1;
+  cur_board[1] = (float)(state->white_count & 127) * inv_max_flats;
+  cur_board[2] = (float)(state->black_count & 127) * inv_max_flats;
+}