#ifndef NNTRAIN_INC_C

#define NNTRAIN_INC_C

#include "nnlayer.inc.c"

typedef struct NeuralTrainer {

  int sizeX, sizeY, count;

  double *x, *y;

} NeuralTrainer;

NeuralTrainer* ntNew(int sizeX, int sizeY, int count) {

  assert(sizeX > 0);

  assert(sizeY > 0);

  assert(count > 0);

  NeuralTrainer *nt = calloc(sizeof(NeuralTrainer), 1);

  nt->sizeX = sizeX;

  nt->sizeY = sizeY;

  nt->count = count;

  nt->x = calloc(sizeof(double)*(sizeX + sizeY)*count, 1);

  nt->y = nt->x + sizeX*count;

  return nt;

}

void ntFree(NeuralTrainer *nt) {

  free(nt->x);

  free(nt);

}

double ntTrain(NeuralTrainer *nt, NeuralLayer *nl, int successCount, int blockSize, double qmin) {

  assert(!nl->prev);

  assert(nt->sizeX == nl->size);

  assert(nt->sizeY == nlBack(nl)->size);

  assert(blockSize > 0 && qmin > 0);

  printf("training: %d, %lf\n", blockSize, qmin);

  double **blockXY = calloc(sizeof(double)*2, blockSize);

  double qmin2 = qmin*0.75;

  double qmin3 = qmin2*0.75;

  int success = 0;

  int total = 0;

  int repeats, blockRepeats;

  double qmax0, qsum0, qmax, qsum;

  for(int i = 0; i < 10000; ++i) {

    for(int i = 0; i < blockSize; ++i) {

      int index = rand() % nt->count;

      blockXY[i*2 + 0] = nt->x + nt->sizeX*index;

      blockXY[i*2 + 1] = nt->y + nt->sizeY*index;

    }

    repeats = blockRepeats = 0;

    qmax0 = qsum0 = 0;

    for(int i = 0; i < 1000; ++i) {

      double **xy = blockXY;

      qmax = 0, qsum = 0;

      for(int i = 0; i < blockSize; ++i, xy += 2) {

        double q0 = 0;

        for(int i = 0; i < 100; ++i) {

          double q = nlTrainPass(nl, xy[0], xy[1], qmin3);

          if (!i) q0 = q;

          ++repeats;

          if (q < qmin3) break;

        }

        qsum += q0;

        if (qmax < q0) qmax = q0;

      }

      if (!i) { qmax0 = qmax; qsum0 = qsum; }

      ++blockRepeats;

      if (qmax <= qmin2) break;

    }

    total += repeats;

    printf("  blocks %d (samples: %d, total: %d, repeats: %3d (%lf)): %lf -> %lf, %lf -> %lf\n",

      i+1, (i+1)*blockSize, total, blockRepeats-1, repeats/(double)(blockRepeats*blockSize) - 1, qmax0, qmax, qsum0/blockSize, qsum/blockSize);

    if (qmax0 <= qmin) {

      if (++success == successCount) break;

    } else {

      success = 0;

    }

  }

  free(blockXY);

  printf("done\n");

  return qmax0;

}

NeuralTrainer* ntNewSymbolMap(const char *filename, int sizeX, int sizeY) {

  FILE *f = fopen(filename, "rb");

  if (!f)

    return printf("cannot open file '%s' for read\n", filename), NULL;

  fseek(f, 0, SEEK_END);

  size_t fs = ftell(f);

  fseek(f, 0, SEEK_SET);

  size_t testSize = sizeX + 1;

  int count = fs/testSize;

  if (!count)

    return printf("file '%s' is lesser minimal size\n", filename), fclose(f), NULL;

  unsigned char *data = calloc(testSize, count);

  if (count != fread(data, testSize, count, f))

    return printf("cannot read from file '%s'\n", filename), fclose(f), NULL;

  fclose(f);

  NeuralTrainer *nt = ntNew(sizeX, sizeY, count);

  const unsigned char *d = data;

  double *x = nt->x, *y = nt->y, *ey = y + sizeY*count;

  const double delta = 0;

  for(double *p = y; p < ey; ++p) *p = delta;

  while(y < ey) {

    for(double *e = x + sizeX; x < e; ++x, ++d)

      *x = *d/255.0;

    assert(*d < sizeY);

    y[*d++] = 1 - delta;

    y += sizeY;

  }

  return nt;

}

void ntPrintSymbol(NeuralTrainer *nt, int index, int width) {

  assert(index >= 0 && index < nt->count);

  assert(width > 0);

  for(int i = 0; i < nt->sizeX; ++i) {

    if (i && !(i % width)) printf("\n");

    printf("%c", nt->x[nt->sizeX*index + i] > 0 ? '#' : '.');

  }

  printf("\n");

  for(int i = 0; i < nt->sizeY; ++i)

    printf(" %4.1lf", nt->y[nt->sizeY*index + i]);

  printf("\n");

}

#endif