//creating the structure of the nn in a graph that will help in performing backpropagation and forward propagation
#pragma once

#include <cstdlib>
#include "neuralClasses.h"
#include <Eigen/Dense>

namespace nplm
{

template <class X>
class Node {
 public:
  X * param; //what parameter is this
  //vector <void *> children;
  //vector <void *> parents;
  Eigen::Matrix<user_data_t,Eigen::Dynamic,Eigen::Dynamic> fProp_matrix;
  Eigen::Matrix<user_data_t,Eigen::Dynamic,Eigen::Dynamic> bProp_matrix;
  int minibatch_size;

 public:
  Node() : param(NULL), minibatch_size(0) { }

  Node(X *input_param, int minibatch_size)
      : param(input_param),
        minibatch_size(minibatch_size)
  {
    resize(minibatch_size);
  }

  void resize(int minibatch_size)
  {
    this->minibatch_size = minibatch_size;
    if (param->n_outputs() != -1)
    {
      fProp_matrix.setZero(param->n_outputs(), minibatch_size);
    }
    if (param->n_inputs() != -1)
    {
      bProp_matrix.setZero(param->n_inputs(), minibatch_size);
    }
  }

  void resize() { resize(minibatch_size); }

  /*
    void Fprop(Matrix<user_data_t,Dynamic,Dynamic> & input,int n_cols)
    {
    param->fProp(input,fProp_matrix,0,0,n_cols);
    }
    void Fprop(Matrix<user_data_t,1,Dynamic> & input,int n_cols)
    {
    param->fProp(input,fProp_matrix,0,0,n_cols);
    }
  */
  //for f prop, just call the fProp node of the particular parameter.

};

} // namespace nplm