/// <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>
/// 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 blackbox 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 network 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 neuron 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>
/// Construct a new instance.
/// </summary>
/// <param name="fn">The target function.</param>
/// <param name="paramSamplingInfo">Sampling (defines the x range and sampling density).</param>
/// <param name="gradientMseWeight">The fitness weighting to assign to the gradient mean squared error (MSE) score.</param>
public FuncRegressionEvaluationScheme(
Func <double, double> fn,
ParamSamplingInfo paramSamplingInfo,
double gradientMseWeight)
{
_paramSamplingInfo = paramSamplingInfo;
_gradientMseWeight = gradientMseWeight;
// Alloc arrays.
int sampleCount = _paramSamplingInfo.SampleResolution;
_yArrTarget = new double[sampleCount];
_gradientArrTarget = new double[sampleCount];
// Predetermine target responses.
FuncRegressionUtils.Probe(fn, paramSamplingInfo, _yArrTarget);
FuncRegressionUtils.CalcGradients(paramSamplingInfo, _yArrTarget, _gradientArrTarget);
// Create blackbox probe.
_blackBoxProbe = CreateBlackBoxProbe(fn, paramSamplingInfo);
}
/// <summary>
/// Construct a new instance.
/// </summary>
/// <param name="paramSamplingInfo">Parameter sampling info.</param>
/// <param name="gradientMseWeight">Fitness weighting to apply to the gradient fitness score.</param>
/// <param name="yArrTarget">Array of target y values (function output values).</param>
/// <param name="gradientArrTarget">Array of target gradient values.</param>
/// <param name="blackBoxProbe">Black box probe. For obtaining the y value response array from an instance of <see cref="IBlackBox{T}"/>.</param>
internal FuncRegressionEvaluator(
ParamSamplingInfo paramSamplingInfo,
double gradientMseWeight,
double[] yArrTarget,
double[] gradientArrTarget,
IBlackBoxProbe blackBoxProbe)
{
_paramSamplingInfo = paramSamplingInfo;
_gradientMseWeight = gradientMseWeight;
_yMseWeight = 1.0 - gradientMseWeight;
_yArrTarget = yArrTarget;
_gradientArrTarget = gradientArrTarget;
// Alloc working arrays for receiving black box outputs.
int sampleCount = _paramSamplingInfo.SampleResolution;
_yArr = new double[sampleCount];
_gradientArr = new double[sampleCount];
_blackBoxProbe = blackBoxProbe;
}
