public static Image <Rgba32> GenerateImage(List <List <Node> > network, Dictionary <string, bool> state, int size)
{
var img2 = new Image <Rgba32>(size, size);
for (int x = 0; x < size; x++)
{
for (int y = 0; y < size; y++)
{
double outx = x / (double)size * 2.0 - 1.0;
double outy = y / (double)size * 2.0 - 1.0;
var input = MLHelper.ConstructInput(state, outx, outy);
var lastNode = NetworkBuilder.ForwardProp(network, input);
//byte r = (byte)(255 * (1 - lastNode));
//byte g = 0;
//byte b = 0;
//=MAX((1-((A2+1)/2) / 2 * 3) *255; 0)
byte r = (byte)Math.Max((1 - ((lastNode + 1) / 2.0) / 2.0 * 3.0) * 255.0, 0);
//=MAX((1-((A2+1)/2) / 2 * 6) *128; 0)
byte g = (byte)Math.Max((1 - ((lastNode + 1) / 2.0) / 2.0 * 6.0) * 128.0, 0);
//=MIN(MAX((((A2+1 - 0,6666)/2 / 2 *3) ) *255; 0); 255)
byte b = (byte)Math.Min(Math.Max((((lastNode + 1 - (2.0 / 3.0)) / 2.0 / 2.0 * 3.0)) * 255.0, 0), 255);
img2[x, y] = new Rgba32(r, g, b);
//var pntX = ((pnt.X + 1.0) / 2.0) * size;
//var pntY = ((pnt.Y + 1.0) / 2.0) * size;
}
}
return(img2);
}
public static double GetLoss(List <List <Node> > network, Dictionary <string, bool> state, List <Puntje> dataPoints)
{
double loss = 0;
for (var i = 0; i < dataPoints.Count; i++)
{
var dataPoint = dataPoints[i];
var input = MLHelper.ConstructInput(state, dataPoint.X, dataPoint.Y);
var output = NetworkBuilder.ForwardProp(network, input);
loss += Errors.SQUARE.Error(output, dataPoint.Label);
}
return(loss / dataPoints.Count);
}