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;
}
