public RegressionEvoOptimization(ExampleSet exampleSet,Kernel kernel,double c,double epsilon,int initType,int maxIterations,int generationsWithoutImprovement,int popSize,int selectionType,double tournamentFraction,boolean keepBest,int mutationType,double crossoverProb,boolean showConvergencePlot,boolean showPopulationPlot,RandomGenerator random,LoggingHandler logging,Operator executingOperator){
  super(EvoSVM.createBoundArray(0.0d,2 * exampleSet.size()),EvoSVM.determineMax(c,kernel,exampleSet,selectionType,2 * exampleSet.size()),popSize,2 * exampleSet.size(),initType,maxIterations,generationsWithoutImprovement,selectionType,tournamentFraction,keepBest,mutationType,Double.NaN,crossoverProb,showConvergencePlot,showPopulationPlot,random,logging,executingOperator);
  this.exampleSet=exampleSet;
  this.kernel=kernel;
  this.ys=new double[exampleSet.size()];
  Iterator<Example> reader=exampleSet.iterator();
  int index=0;
  while (reader.hasNext()) {
    Example example=reader.next();
    ys[index++]=example.getLabel();
  }
  this.optimizationFunction=new RegressionOptimizationFunction(epsilon);
}

public RegressionEvoOptimization ( ExampleSet exampleSet , Kernel kernel , double c , double epsilon , int initType , int maxIterations , int generationsWithoutImprovement , int popSize , int selectionType , double tournamentFraction , boolean keepBest , int mutationType , double crossoverProb , boolean showConvergencePlot , boolean showPopulationPlot , RandomGenerator random , LoggingHandler logging , Operator executingOperator ) { super ( EvoSVM . createBoundArray ( 0.0d , 2 * exampleSet . size ( ) ) , EvoSVM . determineMax ( c , kernel , exampleSet , selectionType , 2 * exampleSet . size ( ) ) , popSize , 2 * exampleSet . size ( ) , initType , maxIterations , generationsWithoutImprovement , selectionType , tournamentFraction , keepBest , mutationType , Double . NaN , crossoverProb , showConvergencePlot , showPopulationPlot , random , logging , executingOperator ) ; this . exampleSet = exampleSet ; this . kernel = kernel ; this . ys = new double [ exampleSet . size ( ) ] ; Iterator < Example > reader = exampleSet . iterator ( ) ; int index = 0 ; while ( reader . hasNext ( ) ) { Example example = reader . next ( ) ; ys [ index ++ ] = example . getLabel ( ) ; } this . optimizationFunction = new RegressionOptimizationFunction ( epsilon ) ; }
creates a new evolutionary svm optimization .
