public static Pixbuf Points(ManagedNN network, NeuralNetworkOptions opts, double threshold, ManagedArray x, int width, int height, int f1 = 0, int f2 = 0)
{
var pixbuf = Common.Pixbuf(width, height, new Color(255, 255, 255));
Points(pixbuf, network, opts, threshold, x, width, height, f1, f2);
return(pixbuf);
}
public static string Serialize(ManagedNN network)
{
var model = Convert(network);
string output = JsonConvert.SerializeObject(model);
return(output);
}
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);
}
public static ManagedNNJSON Convert(ManagedNN network)
{
var model = new ManagedNNJSON()
{
Wji = Convert2D(network.Wji),
Wkj = Convert2D(network.Wkj)
};
model.Normalization.Add(network.Min);
model.Normalization.Add(network.Max);
return(model);
}
public static ManagedNN Deserialize(string json)
{
var model = JsonConvert.DeserializeObject <ManagedNNJSON>(json);
var network = new ManagedNN();
if (model.Wji != null && model.Wkj != null)
{
network.Wji = Set(model.Wji);
network.Wkj = Set(model.Wkj);
}
if (model.Normalization != null && model.Normalization.Count > 1)
{
network.Min = model.Normalization[0];
network.Max = model.Normalization[1];
}
return(network);
}
public static Pixbuf Contour(ManagedNN network, NeuralNetworkOptions opts, double threshold, ManagedArray x, int width, int height, int f1 = 0, int f2 = 0)
{
InitializeContour(11, width, height);
var m = Rows(x);
var xplot = new double[width];
var yplot = new double[height];
var data = new double[height, width];
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;
// For predict
for (var i = 0; i < width; i++)
{
xplot[i] = minx + i * deltax;
}
for (var i = 0; i < height; i++)
{
yplot[i] = miny + i * deltay;
}
var xx = new ManagedArray(2, height);
for (var i = 0; i < width; i++)
{
for (var j = 0; j < height; j++)
{
xx[f1, j] = xplot[i];
xx[f2, j] = yplot[j];
}
var p = network.Predict(xx, opts);
for (var j = 0; j < height; j++)
{
data[i, j] = p[j];
}
ManagedOps.Free(p);
}
var z = new double[] { 0.6, 0.8, 1 };
Conrec.Contour(data, xplot, yplot, z, ContourLine);
Points(ContourGraph, network, opts, threshold, x, width, height, f1, f2);
ManagedOps.Free(xx);
var border = new Color(128, 128, 128);
// Plot bounding box
var cw = ContourGraph.Width - 1;
var ch = ContourGraph.Height;
Common.Line(ContourGraph, 0, 1, cw, 1, border);
Common.Line(ContourGraph, cw, 1, cw, ch, border);
Common.Line(ContourGraph, 0, ch, cw, ch, border);
Common.Line(ContourGraph, 0, 1, 0, ch, border);
return(ContourGraph);
}
