#ifndef TRAIN_DIGIT_INC_CPP
#define TRAIN_DIGIT_INC_CPP


#include "train.inc.cpp"
#include "layer.simple.inc.cpp"


class TrainerDigit: public Trainer {
protected:
  std::vector<unsigned char> data;
  std::vector<unsigned int> shuffle;
  Layout ofl, obl;
  Layout::List oflist, oblist;
  int stride, count;

public:
  TrainerDigit(): stride(), count() { }

  bool loadSymbolMap(const char *filename) {
    data.clear();

    FILE *f = fopen(filename, "rb");
    if (!f)
      return printf("cannot open file for read: %s\n", filename), false;
    fseek(f, 0, SEEK_END);
    size_t fs = ftello(f);
    fseek(f, 0, SEEK_SET);

    data.resize(fs, 0);
    if (!fread(data.data(), fs, 1, f))
      return printf("cannot read from file: %s\n", filename), fclose(f), data.clear(), false;

    fclose(f);
    return true;
  }

protected:
  bool prepare() override {
    ofl = optimizeLayoutSimple(fl->layout);
    obl = optimizeLayoutSimple(bl->layout);
    ofl.split(oflist, threadsCount);
    obl.split(oblist, threadsCount);
    stride = ofl.getActiveCount() + 1;
    count = data.size()/stride;
    if (count <= 0) return false;
    shuffle.resize(count);
    for(int i = 0; i < count; ++i)
      shuffle[i] = i;
    return true;
  }


  bool prepareBlock() override {
    int cnt = itersPerBlock > count ? count : itersPerBlock;
    for(int i = 0; i < cnt; ++i) {
      int j = rand()%count;
      if (i != j) std::swap(shuffle[i], shuffle[j]);
    }
    return true;
  }


  void loadData(Barrier &barrier, int, int iter) override {
    struct I: public Iter {
      typedef const unsigned char* DataType;
      static inline void iter4(Neuron &n, DataType d, DataAccumType&) { n.v = *d/(NeuronReal)255; }
    };
    const unsigned char *id = data.data() + shuffle[iter%count]*stride;
    iterateNeurons2<I>(oflist[barrier.tid], ofl, fl->neurons, id);
  }


  AccumReal verifyDataMain(int, int iter) override {
    struct I: public Iter {
      typedef int DataType;
      struct DataAccumType { int ri, mi; NeuronReal m; };
      static inline void iter4(Neuron &n, DataType d, DataAccumType &a) {
        NeuronReal v1 = d == a.ri;
        NeuronReal v0 = n.v;
        n.d *= v1 - v0;
        if (a.m < v0) { a.m = v0; a.mi = d; }
      }
    };
    
    I::DataAccumType a = { data[ (shuffle[iter%count] + 1)*stride - 1 ], 0, 0 };
    iterateNeurons2<I>(obl, obl, bl->neurons, 0, 1, &a);
    
    return a.mi != a.ri;
  }
};


#endif