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); }