public override double Forecast(double[] vector)
{
double result = 0;
if (TrainedModel != null && vector != null)
{
Node[] node = new Node[vector.Length];
for (int i = 0; i < vector.Length; i++)
{
node[i] = new Node();
node[i].Index = i + 1;
node[i].Value = vector[i];
}
node = mRange.Transform(node);
result = Prediction.Predict(TrainedModel, node);
}
return(result);
}
/// <summary>
/// Scales a problem using the provided range. This will not affect the parameter.
/// </summary>
/// <param name="prob">The problem to scale</param>
/// <param name="range">The Range transform to use in scaling</param>
/// <returns>The Scaled problem</returns>
public static svm_problem Scale(this RangeTransform range, svm_problem prob)
{
svm_problem scaledProblem = new svm_problem()
{
l = prob.l, y = new double[prob.l], x = new svm_node[prob.l][]
};
for (int i = 0; i < scaledProblem.l; i++)
{
scaledProblem.x[i] = new svm_node[prob.x[i].Length];
for (int j = 0; j < scaledProblem.x[i].Length; j++)
{
scaledProblem.x[i][j] = new svm_node()
{
index = prob.x[i][j].index, value = range.Transform(prob.x[i][j].value, prob.x[i][j].index)
}
}
;
scaledProblem.y[i] = prob.y[i];
}
return(scaledProblem);
}
}
void classify(object args)
{
Problem train = new Problem
{
X = _data.Select(o => new Node[] { new Node(1, o.Position.X), new Node(2, o.Position.Y) }).ToArray(),
Y = _data.Select(o => o.Label).ToArray(),
Count = _data.Count,
MaxIndex = 2
};
Parameter param = args as Parameter;
RangeTransform transform = RangeTransform.Compute(train);
train = transform.Scale(train);
Model model = Training.Train(train, param);
int width = (int)plot.ActualWidth;
int height = (int)plot.ActualHeight;
byte[] pixels = new byte[width * height * 3];
int cWidth = (width >> SCALE) + 1;
int cHeight = (height >> SCALE) + 1;
int[,] labels = new int[cHeight, cWidth];
for (int r = 0, i = 0; r < cHeight; r++)
{
for (int c = 0; c < cWidth; c++, i++)
{
int rr = r << SCALE;
int cc = c << SCALE;
Node[] datum = new Node[] { new Node(1, cc), new Node(2, rr) };
datum = transform.Transform(datum);
labels[r, c] = (int)model.Predict(datum);
classifyPB.Dispatcher.Invoke(() => classifyPB.Value = (i * 100) / (cHeight * cWidth));
}
}
PixelFormat format = PixelFormats.Rgb24;
for (int i = 0, r = 0; r < height; r++)
{
for (int c = 0; c < width; c++)
{
int label = labels[r >> SCALE, c >> SCALE];
Color color = _colors[label];
pixels[i++] = color.R;
pixels[i++] = color.G;
pixels[i++] = color.B;
}
}
plot.Dispatcher.Invoke(() =>
{
ImageBrush brush = new ImageBrush(BitmapSource.Create(width, height, 96, 96, format, null, pixels, width * 3));
brush.Stretch = Stretch.None;
brush.AlignmentX = 0;
brush.AlignmentY = 0;
plot.Background = brush;
});
classifyPB.Dispatcher.Invoke(() => classifyPB.Value = 0);
}
