/*
  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/>.
*/

#ifndef NN_H
#define NN_H

#include <tak.h>
#include "weights_5.h"
#include "weights_6.h"

// ===================================================================
// Helpers
// ===================================================================

void nn_prepare_input(float *cur_board, tak_state_p state);

#define RELU(x) ((x) = ((x) < 0) ? 0 : (x))

/*
 * tanh via a degree-11 odd polynomial, least-squares fit of tanh on [-3, 3]
 * (max absolute error ~4e-3), saturating to +-1 outside that interval.
 */
static inline float fast_tanh(const float x) {
  if (x > 3.0f)
    return 1.0f;
  if (x < -3.0f)
    return -1.0f;
  const float x2 = x * x;
  return x * (0.997136032f + x2 * (-0.305215124f + x2 *
                                   (0.0832466371f + x2 *
                                    (-0.0139947924f + x2 *
                                     (0.00123106314f + x2 * -4.29748644e-05f)))));
}

// ===================================================================
// Implementation of small neural networks
// ===================================================================

static inline float eval_net(const float *board, const unsigned int n_inp,
                             const float *w, const float *biases,
                             const float *logit_w, const float logit_b,
                             const unsigned int n_dense) {
  float dense1[FIVE_DENSE_NUM]; // FIVE_DENSE_NUM == SIX_DENSE_NUM
  float z = logit_b;

  for (unsigned int d1 = 0; d1 < n_dense; d1++) {
    float h = biases[d1];
    const float *wd = w + d1 * n_inp;
    for (unsigned int fl = 0; fl < n_inp; fl++)
      h += board[fl] * wd[fl];
    RELU(h);
    dense1[d1] = h;
  }
  for (unsigned int d1 = 0; d1 < n_dense; d1++)
    z += dense1[d1] * logit_w[d1];

  return fast_tanh(z);
}

// ===================================================================
// Interfaces to the two built-in evaluators
// ===================================================================

static inline float five_nn_fn(tak_state_p state) {
  static float cur_board[FIVE_INP_NUM] NN_ALIGNED;

  nn_prepare_input(cur_board, state);
  return eval_net(cur_board, FIVE_INP_NUM, &five_dense_weights[0][0],
                  five_dense_biases, five_logit_weights, five_logit_bias,
                  FIVE_DENSE_NUM);
}

static inline float six_nn_fn(tak_state_p state) {
  static float cur_board[SIX_INP_NUM] NN_ALIGNED;

  nn_prepare_input(cur_board, state);
  return eval_net(cur_board, SIX_INP_NUM, &six_dense_weights[0][0],
                  six_dense_biases, six_logit_weights, six_logit_bias,
                  SIX_DENSE_NUM);
}

#endif