/// <summary>
/// Construct the outstar trainer.
/// </summary>
///
/// <param name="theNetwork">The network to train.</param>
/// <param name="theTraining">The training data, must provide ideal outputs.</param>
/// <param name="theLearningRate">The learning rate.</param>
public TrainOutstar(CPNNetwork theNetwork, IMLDataSet theTraining,
double theLearningRate) : base(TrainingImplementationType.Iterative)
{
_mustInit = true;
_network = theNetwork;
_training = theTraining;
_learningRate = theLearningRate;
}
/// <summary>
/// Construct the instar training object.
/// </summary>
///
/// <param name="theNetwork">The network to be trained.</param>
/// <param name="theTraining">The training data.</param>
/// <param name="theLearningRate">The learning rate.</param>
/// <param name="theInitWeights">training elements as instar neurons.</param>
public TrainInstar(CPNNetwork theNetwork, IMLDataSet theTraining,
double theLearningRate, bool theInitWeights) : base(TrainingImplementationType.Iterative)
{
_network = theNetwork;
_training = theTraining;
_learningRate = theLearningRate;
_mustInit = theInitWeights;
}
public void TrainOutstar(CPNNetwork network, IMLDataSet training)
{
int epoch = 1;
IMLTrain train = new TrainOutstar(network, training, 0.1);
for (int i = 0; i < 1000; i++)
{
train.Iteration();
app.WriteLine("Training outstar, Epoch #" + epoch);
epoch++;
}
}
public void Test(CPNNetwork network, String[][] pattern, double[][] input)
{
for (int i = 0; i < pattern.Length; i++)
{
IMLData inputData = new BasicMLData(input[i]);
IMLData outputData = network.Compute(inputData);
double angle = DetermineAngle(outputData);
// display image
for (int j = 0; j < HEIGHT; j++)
{
if (j == HEIGHT - 1)
{
app.WriteLine("[" + pattern[i][j] + "] -> " + ((int)angle) + " deg");
}
else
{
app.WriteLine("[" + pattern[i][j] + "]");
}
}
Console.WriteLine();
}
}
private CPNNetwork Create()
{
CPNNetwork result = new CPNNetwork(5, 4, 3, 2);
return(result);
}
