#ifndef MLP_H
#define MLP_H

  #include <stdlib.h>
  #include <unistd.h>
  #include <algorithm>
  #include <vector>
  #include <ctime>
  #include <iterator>
  #include <cmath>

  #define sigmoid(value)  (1/(1+exp(-value)));
  #define dersigmoid(value) (value*(1-value));


  //Macro's

  #define weightItoH(input,hidden) weightsIH.at(nInput*hidden+input)
  #define weightHtoO(hidden,output) weightsHO.at(nHidden*output+hidden)

  class MLP {

    public:
      std::vector<int> inputNeurons;
      std::vector<float> hiddenNeurons;
      std::vector<float> outputNeurons;
      std::vector<float> weightsIH;
      std::vector<float> weightsHO;
      std::vector< std::vector<int> > inputPatterns;
      std::vector<int> desiredOutput;
      std::vector<float> outputDelta;
      std::vector<float> hiddenDelta;

      int nInput;
      int nOutput;
      int nHidden;
      int nInputPatterns;
      int inPutPatterns;

      MLP (int nInput, int nHidden, int nOutput);
      ~MLP();

      void initWeights();
      void printWeights(int i);
      void feedforward();
      void initInput(int i, int j);
      void backPropagation(int i);
      void trainNetwork();

  };



#endif