public FloatRegFitPredictionIndividual(PushGP inSolutionGA, int[] inSamples) { _sampleIndices = new int[_sampleSize]; for (int i = 0; i < _sampleSize; i++) { _sampleIndices[i] = inSamples[i]; } _solutionGA = inSolutionGA; }
// Note: Oldest trainer has the lowest index; newest trainer has the highest // index. // The solution population and genetic algorithm. /// <summary> /// Customizes GA.GAWithParameters to allow the inclusion of the solution GA, /// which is required for the initialization of the prediction GA. /// </summary> /// <param name="ceFloatSymbolicRegression"/> /// <param name="getPredictorParameters"/> /// <returns/> /// <exception cref="System.Exception"></exception> public static PredictionGA PredictionGAWithParameters(PushGP inSolutionGA, Dictionary <string, string> inParams) { Type cls = Type.GetType(inParams["problem-class"]); object gaObject = System.Activator.CreateInstance(cls); if (!(gaObject is PredictionGA)) { throw (new Exception("Predictor problem-class must inherit from" + " class PredictorGA")); } PredictionGA ga = (PredictionGA)gaObject; // Must set the solution GA before InitFromParameters, since the latter // uses _solutionGA while creating the predictor population. ga.SetSolutionGA(inSolutionGA); ga.SetParams(inParams); ga.InitFromParameters(); return(ga); }
public FloatRegFitPredictionIndividual(PushGP inSolutionGA) { _sampleIndices = new int[_sampleSize]; _solutionGA = inSolutionGA; }
public FloatRegFitPredictionIndividual() { // The sample test cases used for fitness prediction. _sampleIndices = new int[_sampleSize]; _solutionGA = null; }
protected internal virtual void SetSolutionGA(PushGP inGA) { _solutionGA = inGA; }
public GenericPredictionIndividual() { _solutionGA = null; }
public GenericPredictionIndividual(Program inProgram, PushGP inSolutionGA) { _program = inProgram; _solutionGA = inSolutionGA; }