/// <summary>
/// Constructs with the details of the function regression problem to be visualized.
/// </summary>
/// <param name="fn">The function being regressed.</param>
/// <param name="generativeMode">Indicates that blacbox has no inputs; it will generate a waveform as a function of time.</param>
/// <param name="paramSamplingInfo">Parameter sampling info.</param>
/// <param name="genomeDecoder">Genome decoder.</param>
public FnRegressionView2D(IFunction fn, ParamSamplingInfo paramSamplingInfo, bool generativeMode, IGenomeDecoder <NeatGenome, IBlackBox> genomeDecoder)
{
InitializeComponent();
InitGraph(string.Empty, string.Empty, string.Empty);
_fn = fn;
_paramSamplingInfo = paramSamplingInfo;
_generativeMode = generativeMode;
_genomeDecoder = genomeDecoder;
// Determine the mid output value of the function (over the specified sample points) and a scaling factor
// to apply the to neural netwkrk response for it to be able to recreate the function (because the neural net
// output range is [0,1] when using the logistic function as the neurn activation function).
double mid, scale;
FnRegressionUtils.CalcFunctionMidAndScale(fn, paramSamplingInfo, out mid, out scale);
if (generativeMode)
{
_blackBoxProbe = new GenerativeBlackBoxProbe(paramSamplingInfo, mid, scale);
}
else
{
_blackBoxProbe = new BlackBoxProbe(paramSamplingInfo, mid, scale);
}
_yArrTarget = new double[paramSamplingInfo._sampleCount];
// Pre-build plot point objects.
_plotPointListTarget = new PointPairList();
_plotPointListResponse = new PointPairList();
double[] xArr = paramSamplingInfo._xArr;
for (int i = 0; i < xArr.Length; i++)
{
double x = xArr[i];
_plotPointListTarget.Add(x, _fn.GetValue(x));
_plotPointListResponse.Add(x, 0.0);
}
// Bind plot points to graph.
zed.GraphPane.AddCurve("Target", _plotPointListTarget, Color.Black, SymbolType.None);
zed.GraphPane.AddCurve("Network Response", _plotPointListResponse, Color.Red, SymbolType.None);
}
/// <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));
}
