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/*
This file is part of ct.
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 ct. If not, see <https://www.gnu.org/licenses/>.
*/
#include "nn1986.h"
#include "tak.h"
#include "weights.h"
// ===================================================================
// Implementation of a small neural network
// ===================================================================
static float cur_board[INP_NUM];
static float dense1[DENSE_NUM];
static float output[2];
#define RELU(x) ((x) = ((x) < 0) ? 0 : (x))
float nn1986_evaluate_black_win(tak_state_p state) {
/* --------------- *
* Populate input *
* --------------- */
for (unsigned int y = 0; y < state->board_size; y++) {
for (unsigned int x = 0; x < state->board_size; x++) {
const unsigned int loc = x + y * state->board_size;
const unsigned int count = COUNT_AT(state, loc);
float lookup = 0;
if (count > 0) {
if (STONE_AT(state, loc) == STONE_STANDING) {
lookup = (state->colours[loc] & 1) ? +0.25 : -0.25;
} else if (STONE_AT(state, loc) == STONE_CAPSTONE) {
lookup = (state->colours[loc] & 1) ? +1.00 : -1.00;
} else {
lookup = (state->colours[loc] & 1) ? +0.50 : -0.50;
}
}
cur_board[3 + loc] = lookup;
}
}
/* ------------------ *
* Convolution layer *
* ------------------ */
// Add input of flat counts and ply parity
cur_board[0] = (state->ply & 1) ? 1 : -1;
cur_board[1] = (float)(state->white_count & 127) / 21.0;
cur_board[2] = (float)(state->black_count & 127) / 21.0;
/* ------------------ *
* First dense layer *
* ------------------ */
for (unsigned int d1 = 0; d1 < DENSE_NUM; d1++) {
dense1[d1] = dense1_biases[d1];
for (unsigned int fl = 0; fl < 3 + 5 * 5; fl++) {
dense1[d1] += cur_board[fl] * dense1_weights[d1][fl];
}
RELU(dense1[d1]);
}
/* ------------- *
* Output layer *
* ------------- */
for (uint8_t k = 0; k < 2; k++) {
output[k] = output_bias[k];
for (unsigned int d2 = 0; d2 < DENSE_NUM; d2++) {
output[k] += dense1[d2] * output_weights[k][d2];
}
RELU(output[k]);
}
const float norm = output[0] + output[1];
return (2 * output[0] / norm) - 1;
}
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