/// <summary>
/// Construct a function regression evaluator with the provided parameter sampling info and function to regress.
/// </summary>
public FnRegressionEvaluator(IFunction fn, ParamSamplingInfo paramSamplingInfo, double gradientMseWeight, IBlackBoxProbe blackBoxProbe)
{
_paramSamplingInfo = paramSamplingInfo;
_gradientMseWeight = gradientMseWeight;
_yMseWeight = 1.0 - gradientMseWeight;
_blackBoxProbe = blackBoxProbe;
// Predetermine target responses.
int sampleCount = _paramSamplingInfo._sampleCount;
_yArrTarget = new double[sampleCount];
_gradientArrTarget = new double[sampleCount];
FunctionProbe fnProbe = new FunctionProbe(paramSamplingInfo);
fnProbe.Probe(fn, _yArrTarget);
FnRegressionUtils.CalcGradients(paramSamplingInfo, _yArrTarget, _gradientArrTarget);
}
/// <summary>
/// Evaluate the provided IBlackBox against the XOR problem domain and return its fitness score.
/// </summary>
public FitnessInfo Evaluate(IBlackBox box)
{
int sampleCount = _paramSamplingInfo._sampleCount;
// TODO: We can avoid a memory allocation here by allocating at construction time, but this requires modification of
// ParallelGenomeListEvaluator to utilise multiple evaluators (one per thread).
double[] yArr = new double[sampleCount];
double[] gradientArr = new double[sampleCount];
// Probe the black box over the full range of the input parameter.
_blackBoxProbe.Probe(box, yArr);
// Calc gradients.
FnRegressionUtils.CalcGradients(_paramSamplingInfo, yArr, gradientArr);
// Calc y position mean squared error (MSE), and apply weighting.
double yMse = FnRegressionUtils.CalcMeanSquaredError(yArr, _yArrTarget);
yMse *= _yMseWeight;
// Calc gradient mean squared error.
double gradientMse = FnRegressionUtils.CalcMeanSquaredError(gradientArr, _gradientArrTarget);
gradientMse *= _gradientMseWeight;
// Calc fitness as the inverse of MSE (higher value is fitter).
// Add a constant to avoid divide by zero, and to constrain the fitness range between bad and good solutions;
// this allows the selection strategy to select solutions that are mediocre and therefore helps preserve diversity.
double fitness = 20.0 / (yMse + gradientMse + 0.02);
// Test for stopping condition (near perfect response).
if (fitness >= 100000.0)
{
_stopConditionSatisfied = true;
}
_evalCount++;
return(new FitnessInfo(fitness, fitness));
}
