// file: $isip/doc/examples/class/stat/stat_example_00/example.cc
// version: $Id: example.cc 6146 2001-01-09 23:50:42Z peng $
//

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

// main program starts here:
//  this example demonstrates the several simple steps to compute the
//  Mahalanobis distance for a input vector from a GaussianModel
//
int main() {

  // declare the GaussianModel object
  //
  GaussianModel gauss;
  
  // declare mean and covariance matrix to define gaussian
  //
  VectorFloat mean(L"1.0, 1.0, 1.0");
  MatrixFloat cov(3, 3, L"0.1, 0.1, 0.1", Integral::DIAGONAL);
  
  // set gaussian
  //
  gauss.setMean(mean);
  gauss.setCovariance(cov);

  // declare the input vector 
  //
  VectorFloat input;
  input.assign(L"-1, -1, -1");
  
  // get the distance
  //
  Float distance = gauss.getLogLikelihood(input);  

  // output the result to the console output
  //
  distance.debug(L"Mahalanobis Distance:");
 
  // exit gracefully
  //
  Integral::exit();
}