public void PaintNetwork(Graphics g,
NetworkModel nm,
float zoomFactor,
Point viewportOrigin,
Size viewportSize,
double connectionWeightMin,
double connectionWeightRange)
{
// Store painting parameters.
this.zoomFactor = zoomFactor;
this.viewportOrigin = viewportOrigin;
this.viewportSize = viewportSize;
this.connectionWeightMin = connectionWeightMin;
this.connectionWeightRange = connectionWeightRange;
// Some pre-calculated values.
this.viewportBound = new Point(viewportOrigin.X + viewportSize.Width,
viewportOrigin.Y + viewportSize.Height);
neuronDiameter = (int)(NEURON_DIAMETER_BASE * zoomFactor);
neuronDiameterHalfed = (int)((NEURON_DIAMETER_BASE * zoomFactor) / 2.0F);
backConnectionLegLength = (NEURON_DIAMETER_BASE * zoomFactor * 1.6F);
connectionWidthFactor = (float)(connectionWeightRange / 2.0);
fontNeuronId = new Font("Microsoft Sans Serif", (float)Math.Min(Math.Max(0.2, 7.0F * zoomFactor), Single.MaxValue));
// Assign a ConnectionPoints object to each neuron.
int neuronCount = nm.MasterNeuronList.Count;
for (int neuronIdx = 0; neuronIdx < neuronCount; neuronIdx++)
{
nm.MasterNeuronList[neuronIdx].AuxPaintingData = new ConnectionPoints();
}
//----- Painting code.
// Paint all connections first. The neurons are painted over the top to
// cover up any loose ends.
for (int neuronIdx = 0; neuronIdx < neuronCount; neuronIdx++)
{
ModelNeuron neuron = nm.MasterNeuronList[neuronIdx];
// Incoming connections.
int connectionCount = neuron.InConnectionList.Count;
for (int connectionIdx = 0; connectionIdx < connectionCount; connectionIdx++)
{
PaintConnection(g, neuron.InConnectionList[connectionIdx]);
}
}
foreach (ModelNeuron neuron in nm.MasterNeuronList)
{
PaintNeuron(g, neuron);
}
}
public void Layout(NetworkModel nm, Size areaSize)
{
// Use this to keep track of the coordinate bounds of the network (maxX, maxY).
bounds = new Size(0, 0);
//----- Determine how many layers we are going arrange the neurons into.
int inputCount = nm.InputNeuronList.Count;
int outputCount = nm.OutputNeuronList.Count;
int hiddenCount = nm.MasterNeuronList.Count - (inputCount + outputCount);
float heightWidthRatio = (float)areaSize.Height / (float)areaSize.Width;
// Default to 2 (input and output layers).
int numLayers = 2;
int numHiddenLayers = 0;
if (hiddenCount > 0)
{
// Arrange as if there no input/output layers.
double sqrtHidden = Math.Sqrt(hiddenCount);
numHiddenLayers = (int)Math.Floor(sqrtHidden * heightWidthRatio);
// Now factor in the input/output layers.
numHiddenLayers = Math.Max(1, numHiddenLayers - 2);
numLayers = 2 + numHiddenLayers;
}
//----- Arrange the neurons.
int heightLayers = areaSize.Height - 2 * MARGIN_Y;
int yIncrement = areaSize.Height / (numLayers + 1);
int yCurrent = MARGIN_Y + yIncrement;
double yIncrementHalfed = yIncrement / 2.0;
// Input layer. Place all input neurons in one layer.
int widthLayer = areaSize.Width - 2 * MARGIN_X;
int xIncrement = widthLayer / (inputCount + 1);
int xCurrent = MARGIN_X + xIncrement;
foreach (ModelNeuron modelNeuron in nm.InputNeuronList)
{
modelNeuron.Position = new Point(xCurrent, yCurrent);
modelNeuron.HasPositionInfo = true;
UpdateModelBounds(modelNeuron);
xCurrent += xIncrement;
}
// Increment yCurrent, ready for the next layer.
yCurrent += yIncrement;
// Hidden layers.
if (numLayers > 0)
{
// Keep track of which neuron is being positioned.
int neuronIdx = 0;
// calculate the max number of neurons in any hidden layer.
int layerNeuronsMax = (int)Math.Ceiling((float)hiddenCount / (float)numHiddenLayers);
// Keep track of how many neurons remain to be positioned.
int neuronsRemaining = hiddenCount;
for (int hiddenLayer = 0; hiddenLayer < numHiddenLayers; hiddenLayer++)
{
// Calculate the number of neurons in this layer.
int layerNeuronCount = Math.Min(neuronsRemaining, layerNeuronsMax);
// Position neurons in this layer.
xIncrement = widthLayer / (layerNeuronCount + 1);
xCurrent = MARGIN_X + xIncrement;
for (int i = 0; i < layerNeuronCount; i++)
{
ModelNeuron modelNeuron = nm.HiddenNeuronList[neuronIdx++];
modelNeuron.Position = new Point(
xCurrent,
yCurrent);
//yCurrent + (int)(((random.NextDouble()*(double)yIncrement)-yIncrementHalfed)*JIGGLE_PROPORTION));
modelNeuron.HasPositionInfo = true;
UpdateModelBounds(modelNeuron);
xCurrent += xIncrement;
}
// Increment yCurrent, ready for the next layer.
yCurrent += yIncrement;
neuronsRemaining -= layerNeuronCount;
}
}
// Output layer.
xIncrement = widthLayer / (outputCount + 1);
xCurrent = MARGIN_X + xIncrement;
foreach (ModelNeuron modelNeuron in nm.OutputNeuronList)
{
modelNeuron.Position = new Point(
xCurrent,
yCurrent);
//yCurrent + (int)(((random.NextDouble()*(double)yIncrement)-yIncrementHalfed)*JIGGLE_PROPORTION));
modelNeuron.HasPositionInfo = true;
UpdateModelBounds(modelNeuron);
xCurrent += xIncrement;
}
nm.Bounds = bounds;
}
