private void PaintView(IBlackBox box)
{
if (_initializing)
{
return;
}
Graphics g = Graphics.FromImage(_image);
g.FillRectangle(_brushBackground, 0, 0, _image.Width, _image.Height);
g.SmoothingMode = SmoothingMode.AntiAlias;
// Get control width and height.
int width = Width;
int height = Height;
// Determine smallest dimension. Use that as the edge length of the square grid.
width = height = Math.Min(width, height);
// Pixel size is calculated using integer division to produce cleaner lines when drawing.
// The inherent rounding down may produce a grid 1 pixel smaller then the determined edge length.
// Also make room for a combo box above the grid. This will be used allow the user to change the grid resolution.
int visualFieldPixelSize = (height - GridTop) / _visualFieldResolution;
width = height = visualFieldPixelSize * _visualFieldResolution;
// Paint pixel outline grid.
// Vertical lines.
int x = GridLeft;
for (int i = 0; i <= _visualFieldResolution; i++, x += visualFieldPixelSize)
{
g.DrawLine(__penGrey, x, GridTop, x, GridTop + height);
}
// Horizontal lines.
int y = GridTop;
for (int i = 0; i <= _visualFieldResolution; i++, y += visualFieldPixelSize)
{
g.DrawLine(__penGrey, GridLeft, y, GridLeft + width, y);
}
// Apply test case to black box's visual field sensor inputs.
BoxesVisualDiscriminationEvaluator.ApplyVisualFieldToBlackBox(_testCaseField, box, _visualFieldResolution, _visualOriginPixelXY, _visualPixelSize);
// Activate the black box.
box.ResetState();
box.Activate();
// Read output responses and paint them to the pixel grid.
// Also, we determine the range of values so that we can normalize the range of pixel shades used.
double low, high;
low = high = box.OutputSignalArray[0];
for (int i = 1; i < box.OutputSignalArray.Length; i++)
{
double val = box.OutputSignalArray[i];
if (val > high)
{
high = val;
}
else if (val < low)
{
low = val;
}
}
double colorScaleFactor;
if (0.0 == (high - low))
{
colorScaleFactor = 1.0;
}
else
{
colorScaleFactor = 1.0 / (high - low);
}
IntPoint maxActivationPoint = new IntPoint(0, 0);
double maxActivation = -1.0;
int outputIdx = 0;
y = GridTop;
for (int i = 0; i < _visualFieldResolution; i++, y += visualFieldPixelSize)
{
x = GridLeft;
for (int j = 0; j < _visualFieldResolution; j++, x += visualFieldPixelSize, outputIdx++)
{
double output = box.OutputSignalArray[outputIdx];
if (output > maxActivation)
{
maxActivation = output;
maxActivationPoint._x = j;
maxActivationPoint._y = i;
}
Color color = GetResponseColor((output - low) * colorScaleFactor);
Brush brush = new SolidBrush(color);
g.FillRectangle(brush, x + 1, y + 1, visualFieldPixelSize - 2, visualFieldPixelSize - 2);
}
}
// Paint lines around the small and large test case boxes to highlight them.
// Small box.
int testFieldPixelSize = (_visualFieldResolution / TestCaseField.TestFieldResolution) * visualFieldPixelSize;
g.DrawRectangle(__penBoxOutline,
GridLeft + (_testCaseField.SmallBoxTopLeft._x * testFieldPixelSize),
GridTop + (_testCaseField.SmallBoxTopLeft._y * testFieldPixelSize),
testFieldPixelSize, testFieldPixelSize);
// Large box.
g.DrawRectangle(__penBoxOutline,
GridLeft + (_testCaseField.LargeBoxTopLeft._x * testFieldPixelSize),
GridTop + (_testCaseField.LargeBoxTopLeft._y * testFieldPixelSize),
testFieldPixelSize * 3, testFieldPixelSize * 3);
// Draw red line around pixel with highest activation.
g.DrawRectangle(__penSelectedPixel,
GridLeft + (maxActivationPoint._x * visualFieldPixelSize),
GridTop + (maxActivationPoint._y * visualFieldPixelSize),
visualFieldPixelSize, visualFieldPixelSize);
Refresh();
}
/// <summary>
/// Create and return a GenerationalNeatEvolutionAlgorithm object ready for running the NEAT algorithm/search. Various sub-parts
/// of the algorithm are also constructed and connected up.
/// This overload accepts a pre-built genome population and their associated/parent genome factory.
/// </summary>
public INeatEvolutionAlgorithm<NeatGenome> CreateEvolutionAlgorithm(IGenomeFactory<NeatGenome> genomeFactory, List<NeatGenome> genomeList)
{
// Create distance metric. Mismatched genes have a fixed distance of 10; for matched genes the distance is their weigth difference.
IDistanceMetric distanceMetric = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
ISpeciationStrategy<NeatGenome> speciationStrategy = new ParallelKMeansClusteringStrategy<NeatGenome>(distanceMetric, _parallelOptions);
// Create complexity regulation strategy.
IComplexityRegulationStrategy complexityRegulationStrategy = ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
// Create the evolution algorithm.
GenerationalNeatEvolutionAlgorithm<NeatGenome> ea = new GenerationalNeatEvolutionAlgorithm<NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);
// Create IBlackBox evaluator.
BoxesVisualDiscriminationEvaluator evaluator = new BoxesVisualDiscriminationEvaluator(_visualFieldResolution);
// Create genome decoder. Decodes to a neural network packaged with an activation scheme that defines a fixed number of activations per evaluation.
IGenomeDecoder<NeatGenome,IBlackBox> genomeDecoder = CreateGenomeDecoder(_visualFieldResolution, _lengthCppnInput);
// Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
IGenomeEvaluator<NeatGenome> innerFitnessEvaluator = new ParallelGenomeFitnessEvaluator<NeatGenome, IBlackBox>(genomeDecoder, evaluator, _parallelOptions);
// Wrap the list evaluator in a 'selective' evaulator that will only evaluate new genomes. That is, we skip re-evaluating any genomes
// that were in the population in previous generations (elite genomes). This is determiend by examining each genome's evaluation info object.
IGenomeEvaluator<NeatGenome> selectiveFitnessEvaluator = new SelectiveGenomeFitnessEvaluator<NeatGenome>(
innerFitnessEvaluator,
SelectiveGenomeFitnessEvaluator<NeatGenome>.CreatePredicate_OnceOnly());
// Initialize the evolution algorithm.
ea.Initialize(selectiveFitnessEvaluator, genomeFactory, genomeList);
// Finished. Return the evolution algorithm
return ea;
}
