private static float performEpoch(UnifiedCameraNeuralNetwork ann,
int numOfSamples, float[] samples, int numOfTests, float[] tests,
DrawingGroup drawingGroup)
{
int failedTests = 0;
using (DrawingContext dc = drawingGroup.Open())
{
// Draw a transparent background to set the render size
float RenderWidth = 640f;
float RenderHeight = 480f;
dc.DrawRectangle(Brushes.Black, null, new System.Windows.Rect(0.0, 0.0, RenderWidth, RenderHeight));
drawingGroup.ClipGeometry = new RectangleGeometry(new System.Windows.Rect(0.0, 0.0, RenderWidth, RenderHeight));
for (int i = 0; i < numOfSamples; i++)
{
float x = samples[i];
float[] input = new float[1];
float[] expected = new float[1];
input[0] = (float)x;
expected[0] = normalizeFunc(x); // Normalize
ann.train(input, expected);
if (i % 10000 == 9999)
{
Console.WriteLine((i + 1) + " samples trained..");
}
}
if (ann.Stochastic)
{
ann.forceWeightUpdates();
}
for (int i = 0; i < numOfTests; i++)
{
float x = tests[i];
float[] input = new float[1];
input[0] = (float)x;
float result = approximationFunc(x);
float prediction = ann.feedForward(input)[0, 0];
if (float.IsNaN(prediction))
{
throw new InvalidOperationException("ANN returned a NaN output.");
}
prediction = denormalizeFunc(prediction); // Denormalize
// Draw
int brushSize = 2;
float xPlot = (x / (maxRange - minRange)) * RenderWidth;
float funcPlot = RenderHeight - normalizeFunc(x) * RenderHeight;
float predictPlot = RenderHeight - normalize(prediction) * RenderHeight;
dc.DrawEllipse(groundTruthBrush, null, new System.Windows.Point(xPlot, funcPlot), brushSize, brushSize);
dc.DrawEllipse(predictionBrush, null, new System.Windows.Point(xPlot, predictPlot), brushSize, brushSize);
float diff = Math.Abs(result - prediction);
if (diff > 0.0001)
{
failedTests++;
}
}
}
Console.WriteLine("Total failed tests: " + failedTests);
float successRate = 1.0f - (float)((float)failedTests / (float)numOfTests);
Console.WriteLine("Success rate: " + successRate);
return(successRate);
}
