Пример #1
0
/**
 * The optimization loop.
 */
int main(int, char**) {
  CMAES<double> evo;
  double *arFunvals, *const*pop, *xfinal;

  // Initialize everything
  const int dim = 22;
  double xstart[dim];
  for(int i=0; i<dim; i++) xstart[i] = 0.5;
  double stddev[dim];
  for(int i=0; i<dim; i++) stddev[i] = 0.3;
  Parameters<double> parameters;
  // TODO Adjust parameters here
  parameters.init(dim, xstart, stddev);
  arFunvals = evo.init(parameters);

  std::cout << evo.sayHello() << std::endl;

  // Iterate until stop criterion holds
  while(!evo.testForTermination())
  {
    // Generate lambda new search points, sample population
    pop = evo.samplePopulation(); // Do not change content of pop

    /* Here you may resample each solution point pop[i] until it
       becomes feasible, e.g. for box constraints (variable
       boundaries). function is_feasible(...) needs to be
       user-defined.  
       Assumptions: the feasible domain is convex, the optimum is
       not on (or very close to) the domain boundary, initialX is
       feasible and initialStandardDeviations are sufficiently small
       to prevent quasi-infinite looping.
    */
    /* for (i = 0; i < evo.get(CMAES<double>::PopSize); ++i)
         while (!is_feasible(pop[i]))
           evo.reSampleSingle(i);
    */

    // evaluate the new search points using fitfun from above
    for (int i = 0; i < evo.get(CMAES<double>::Lambda); ++i)
      arFunvals[i] = fitfun(pop[i], (int) evo.get(CMAES<double>::Dimension));

    // update the search distribution used for sampleDistribution()
    evo.updateDistribution(arFunvals);
  }
  std::cout << "Stop:" << std::endl << evo.getStopMessage();
  evo.writeToFile(CMAES<double>::WKResume, "resumeevo1.dat"); // write resumable state of CMA-ES

  // get best estimator for the optimum, xmean
  xfinal = evo.getNew(CMAES<double>::XMean); // "XBestEver" might be used as well

  // do something with final solution and finally release memory
  delete[] xfinal;

  return 0;
}
Point2f GoalPostDetector::findTheShift ()
{

	CannyThreshold();
	createPaddedImg();
	getPointsAlongTheGoalBar();

	 CMAES<float> evo;


		    float *arFunvals, *xfinal, *const*pop;
		    // Initialize everything
		    const int dim = 2;
		    float xstart[dim];


		    for(int i=0; i<dim; i++) xstart[i] = 0.0;
		    float stddev[dim];
		    for(int i=0; i<dim; i++) stddev[i] = 0.05;
		    Parameters<float> parameters;

		    parameters.init(dim, xstart, stddev);
		    arFunvals = evo.init(parameters);

		    // Iterate until stop criterion holds
		    while(!evo.testForTermination())
		    {
		      // Generate lambda new search points, sample population
		      pop = evo.samplePopulation();

		      // condition: solution dx and dy should not be bigger than squareSize
		       for (int i = 0; i < evo.get(CMAES<float>::PopSize); ++i)
		           while (abs(pop[i][0])*scale>=windowSearchSize||abs(pop[i][1])*scale>=windowSearchSize)
		            evo.reSampleSingle(i);

		      // evaluate the new search points using objectiveFunction from above
		      for (int i = 0; i < evo.get(CMAES<float>::Lambda); ++i)
		        {
		          arFunvals[i]=  objectiveFunction(pop[i]);
		        }
		      evo.updateDistribution(arFunvals);

		    }

	       return Point2f(evo.getNew(CMAES<float>::XMean)[0]*scale,
	    		          evo.getNew(CMAES<float>::XMean)[1]*scale);

}