private void Form1_Load(object sender, EventArgs e)
{
nn = new NeuralNetworksLib.NetworkModels.NeuralNetwork(0.9, 0, 0, new int[] { 3, 3, 1 });
ttpEpochs.SetToolTip(prgEpochs, "Not Running");
ttpEpochs.Active = true;
UIThread = Dispatcher.CurrentDispatcher;
treeView1.TopNode = nn.ToTreeNode();
gfx = nn.CreateDrawing(pictureBox1, 400, 100, 1.5f);
}
public void Initialize(NeuralNetwork network, RectangleF pbxBounds)
{
LayerIndex = -1;
LayerWidths = new float[network.Count];
float maxWidth = 0;
float height = (network.Count * LayerDistance) + NeuronDiameter;
for (int i = 0; i < network.Count; ++i)
{
var layer = network[i];
var width = (layer.Count * NeuronDistance) + NeuronDiameter;
if (width > maxWidth)
{
maxWidth = width;
}
LayerWidths[i] = width;
}
bool pbxWidthWise = pbxBounds.Width > pbxBounds.Height;
bool netWidthWise = maxWidth > height;
if (pbxWidthWise != netWidthWise)
{
Rotated = true;
NextLayer = () =>
{
++LayerIndex;
CurrentY = (LayerWidths[LayerIndex] / ZoomFactor) + 10;
CurrentX += LayerDistance;
};
NextNeuron = () =>
{
CurrentY += NeuronDistance;
CurrentNeuronConnectorOut = new PointF(CurrentX + NeuronDiameter, CurrentY + CenterModifier);
CurrentNeuronConnectorIn = new PointF(CurrentX, CurrentY + CenterModifier);
};
Reset = () => CurrentX = 10;
}
else
{
Rotated = false;
NextLayer = () =>
{
++LayerIndex;
CurrentX = (LayerWidths[LayerIndex] / ZoomFactor) + 10;
CurrentY += LayerDistance;
};
NextNeuron = () =>
{
CurrentX += NeuronDistance;
CurrentNeuronConnectorOut = new PointF(CurrentX + CenterModifier, CurrentY + NeuronDiameter);
CurrentNeuronConnectorIn = new PointF(CurrentX + CenterModifier, CurrentY);
};
Reset = () => CurrentY = 10;
}
float zoomFactor = (
pbxWidthWise ? pbxBounds.Width : pbxBounds.Height
) / (
(netWidthWise ? maxWidth : height) + 10
);
InitializeDefault(zoomFactor);
}
