#include "ml.h"
#include "cxcore.h"
#include "cv.h"
#include "stdio.h"

int main(){
  //XOR input
  double in[]={ 0 ,0,
                1, 0,
                0, 1,
                1, 1};
  double out[]={ 0,
                 1,
                 1,
                 0};

  const int IVS = 2; // Input Vector Size
  const int OVS = 1; // Output Vector Size
  const int HN = 10; // Number of Hidden nodes
  const int NV= 4;   //Number of Training Vector
  
  int layer[] = { IVS, HN, OVS};

  CvMat *input =cvCreateMat( NV, IVS, CV_64FC1);
  CvMat *output =cvCreateMat( NV, OVS, CV_64FC1);
  CvMat *p_output =cvCreateMat( NV, OVS, CV_64FC1);
  CvMat *layersize =cvCreateMat( 1 , 3 , CV_32SC1);
  
  cvInitMatHeader(input, NV, IVS, CV_64FC1, in);
  cvInitMatHeader(output, NV, OVS, CV_64FC1, out);
  cvInitMatHeader(layersize, 1, 3, CV_32SC1, layer);
  
  CvANN_MLP train_model(layersize, CvANN_MLP::SIGMOID_SYM,1,1);
  std::cout<< " =========== =========== =========== =========== ==========="<<std::endl;
  std::cout<< "  * First Iteration with initialzation of weights"<<std::endl;
  std::cout<< " =========== =========== =========== =========== ==========="<<std::endl;
  int iter = train_model.train(  input,
                                 output, 
                                 NULL, 
                                 0,
                                 CvANN_MLP_TrainParams( cvTermCriteria ( CV_TERMCRIT_ITER |
                                                                         CV_TERMCRIT_EPS,
                                                                         5000,0.001),
                                   CvANN_MLP_TrainParams::RPROP,
                                   0.1,0.1),
                                 0
                                 //+ CvANN_MLP::NO_OUTPUT_SCALE
                              );

  std::cout << " * iteration :"<<iter<<std::endl;
  train_model.predict( input, p_output );
  for(int i=0; i<NV;i++){
    std::cout<< CV_MAT_ELEM(*input,double,i,0) << " ," << CV_MAT_ELEM(*input,double,i,1)
             << " : " << CV_MAT_ELEM(*p_output,double,i,0) <<std::endl;
          
  }
  train_model.save( "/scratch/eunyokim/src/Regina_TempCode/OpenCV_XOR_TEST/firstModel.xml");
  std::cout<< " =========== =========== =========== =========== ==========="<<std::endl;
  std::cout<< "  * Second Iteration without initialzation of weights"<<std::endl;
  std::cout<< " =========== =========== =========== =========== ==========="<<std::endl;

  int iter2 = train_model.train(  input,
                                 output, 
                                 NULL, 
                                 0,
                                 CvANN_MLP_TrainParams( cvTermCriteria ( CV_TERMCRIT_ITER |
                                                                         CV_TERMCRIT_EPS,
                                                                         5000,0.001),
                                   CvANN_MLP_TrainParams::RPROP,
                                   0.1,0.1),
                                 0
                                 +CvANN_MLP::UPDATE_WEIGHTS
                                 //+ CvANN_MLP::NO_OUTPUT_SCALE
                              );
  std::cout << " * iteration :"<<iter2<<std::endl;
  train_model.save( "/scratch/eunyokim/src/Regina_TempCode/OpenCV_XOR_TEST/secondModel.xml");

  train_model.predict( input, p_output );
  for(int i=0; i<NV;i++){
    std::cout<< CV_MAT_ELEM(*input,double,i,0) << " ," << CV_MAT_ELEM(*input,double,i,1)
             << " : " << CV_MAT_ELEM(*p_output,double,i,0) <<std::endl;
          
  }


}