public static void Points(Pixbuf pixbuf, ManagedNN network, NeuralNetworkOptions opts, double threshold, ManagedArray x, int width, int height, int f1 = 0, int f2 = 0)
{
var m = Rows(x);
minx = Double.MaxValue;
maxx = Double.MinValue;
miny = Double.MaxValue;
maxy = Double.MinValue;
f1 = f1 >= 0 && f1 < Cols(x) ? f1 : 0;
f2 = f2 >= 0 && f2 < Cols(x) ? f2 : 0;
for (var j = 0; j < m; j++)
{
minx = Math.Min(x[f1, j], minx);
maxx = Math.Max(x[f1, j], maxx);
miny = Math.Min(x[f2, j], miny);
maxy = Math.Max(x[f2, j], maxy);
}
deltax = (maxx - minx) / width;
deltay = (maxy - miny) / height;
minx = minx - 8 * deltax;
maxx = maxx + 8 * deltax;
miny = miny - 8 * deltay;
maxy = maxy + 8 * deltay;
deltax = (maxx - minx) / width;
deltay = (maxy - miny) / height;
var colors = Common.Palette2();
colors.Shuffle();
var PlotOptions = opts;
PlotOptions.Items = Rows(x);
var classification = network.Classify(x, PlotOptions, threshold);
Points(pixbuf, x, classification, colors, f1, f2);
// Plot bounding box
var cw = pixbuf.Width - 1;
var ch = pixbuf.Height;
var border = new Color(128, 128, 128);
Common.Line(pixbuf, 0, 1, cw, 1, border);
Common.Line(pixbuf, cw, 1, cw, ch, border);
Common.Line(pixbuf, 0, ch, cw, ch, border);
Common.Line(pixbuf, 0, 1, 0, ch, border);
ManagedOps.Free(classification);
}
protected void Classify()
{
var test = DataTestSet.Buffer.Text.Trim();
if (string.IsNullOrEmpty(test))
{
return;
}
if (NetworkSetuped && SetupTestData(test))
{
var TestOptions = Options;
TestOptions.Items = TestData.y;
var classification = Network.Classify(TestData, TestOptions, Threshold.Value / 100);
Classification.Buffer.Clear();
string text = "";
for (var i = 0; i < classification.x; i++)
{
text += Convert.ToString(classification[i], ci);
if (i < classification.x - 1)
{
text += "\n";
}
}
Classification.Buffer.Text = text;
classification.Free();
}
DataTestSet.Buffer.Text = test;
}