// file: $isip/doc/examples/class/stat/stat_example_01/example.cc
// version: $Id: example.cc 6164 2001-01-12 00:06:30Z peng $
//

// isip include files
//
#include <GaussianModel.h>
#include <UniformModel.h>
#include <MixtureModel.h>

// main program starts here:
//  this example demonstrates how to use MixtureModel to combining several
//  statistical models for creation of mixture distributions
//
int main() {

  // declare the MixtureModel object
  //
  MixtureModel model;
  
  // declare GaussianModel and UniformModel objects
  //
  GaussianModel gauss;
  UniformModel uniform;
  
  // set the GaussianModel object
  //
  VectorFloat mean(L"1, 2, 3");
  MatrixFloat covar(3, 3, L"4, 5, 6", Integral::DIAGONAL);
  gauss.setMean(mean);
  gauss.setCovariance(covar);
  
  // set the UniformModel object
  //
  VectorFloat min(L"-1, -2, -3");
  VectorFloat max(L"1, 2, 3");
  uniform.setMin(min);
  uniform.setMax(max);
  
  // add model objects into the MixtureModel object
  //
  model.add(gauss);
  model.add(uniform);

  // set the weights for two model objects
  //
  VectorFloat weights(L"0.3, 0.7");
  model.setWeights(weights);
  
  // declare the input vector 
  //
  VectorFloat input;
  input.assign(L"-0.9, -1.2, -2.3");
  
  // score the input vector
  //
  Float score = model.getLikelihood(input);  
  Float gauss_score = gauss.getLikelihood(input);
  Float uniform_score = uniform.getLikelihood(input); 

  // output the result to the console output
  //
  gauss_score.debug(L"First model's score:");
  uniform_score.debug(L"Second model's score:");
  weights.debug(L"Weights for models:");
  score.debug(L"Final score:");
 
  // exit gracefully
  //
  Integral::exit();
}