public void RunDemo()
{
Console.WriteLine("### BUILDING BLOCK DEMO ###");
//Initialize the pattern matching building block
//for a 2x2 grid and 2 output neurons.
Grid2DControler gpm = new Grid2DControler(2,2,2);
gpm.BuildNetwork(true);
//Add 4 patterns to the pattern manager.
//note that the input array is in row by row order,
//and the output numers the (single!) output neuron to train for.
gpm.Patterns.Add("first",0,new bool[] {true,false,true,false});
gpm.Patterns.Add("second", 1, new bool[] { true, true, false, false });
gpm.Patterns.Add("third", 1, new bool[] { false, true, false, false });
gpm.Patterns.Add("fourth", 0, new bool[] { false, true, true, false });
//Some configuration ...
gpm.TrainingConfiguration.AutoTrainingEpochs.Value = 500;
//Calculate all patterns. The CalculateCurrentNetwork method
//returns the output neuron index that matches best.
//First select a pattern, then calculate it:
Console.WriteLine("# Initial Status");
for(int i=0;i<gpm.PatternCount;i++)
{
gpm.SelectPattern(i);
Console.Write(gpm.CalculateCurrentNetwork() + ": ");
App.PrintArray(gpm.NeuralNetwork.CollectOutput());
}
Console.WriteLine("Successful Patterns: " + gpm.CountSuccessfulPatterns());
//Train the third pattern one time:
Console.WriteLine("# Third Pattern Training");
gpm.SelectPattern(2);
gpm.TrainCurrentNetwork();
for(int i=0;i<gpm.PatternCount;i++)
{
gpm.SelectPattern(i);
Console.Write(gpm.CalculateCurrentNetwork() + ": ");
App.PrintArray(gpm.NeuralNetwork.CollectOutput());
}
Console.WriteLine("Successful Patterns: " + gpm.CountSuccessfulPatterns());
//Autotrain all Patterns:
Console.WriteLine("# Auto Training:");
Console.WriteLine(gpm.AutoTrainNetwork() ? "successful" : "failed");
for(int i=0;i<gpm.PatternCount;i++)
{
gpm.SelectPattern(i);
Console.Write(gpm.CalculateCurrentNetwork() + ": ");
App.PrintArray(gpm.NeuralNetwork.CollectOutput());
}
Console.WriteLine("Successful Patterns: " + gpm.CountSuccessfulPatterns());
Console.WriteLine("=== COMPLETE ===");
Console.WriteLine();
}
public void Init(Grid2DControler pm)
{
Layer layer = pm.NeuralNetwork.LastLayer;
layer.NeuronAdded += layer_OnNeuronAdded;
layer.NeuronRemoved += layer_OnNeuronRemoved;
pm.PatternSelectionChanged += pm_OnPatternSelectionChanged;
pm.NetworkCalculated += pm_OnNetworkCalculated;
if(items != null)
for(int i=0;i<items.Count;i++)
if(this[i] != null)
Controls.Remove(this[i]);
items = new ArrayList(layer.Count + 15);
for(int i=0;i<layer.Count;i++)
AddOutputNeuron(layer[i]);
}