/// <summary>
/// Trains a network.
/// </summary>
/// <param name="network">The network to train.</param>
/// <param name="maxRunCount">The maximum number of runs.</param>
/// <param name="maxIterationCout">The maximum number of iterations.</param>
/// <param name="maxTolerableNetworkError">The maximum tolerable network error.</param>
public TrainingLog Train(INetwork network, int maxRunCount, int maxIterationCout, double maxTolerableNetworkError)
{
// The backpropagation parameters.
int networkErrorUpdateInterval = 100;
bool accumulatedLearning = true;
// Setup:
// Decorate the network as backpropagation network.
BackpropagationNetwork backpropagationNetwork = new BackpropagationNetwork(network);
int bestIterationCount = 0;
double bestNetworkError = backpropagationNetwork.CalculateError(trainingSet);
double[] bestWeights = backpropagationNetwork.GetWeights();
int runCount = 0;
while ((runCount < maxRunCount) && (bestNetworkError > maxTolerableNetworkError))
{
int iterationCount;
double networkError;
Train(backpropagationNetwork, maxIterationCout, out iterationCount, maxTolerableNetworkError, out networkError, networkErrorUpdateInterval, accumulatedLearning);
if (networkError < bestNetworkError)
{
bestIterationCount = iterationCount;
bestNetworkError = networkError;
bestWeights = backpropagationNetwork.GetWeights();
}
runCount++;
// DEBUG
Console.Write(".");
}
// DEBUG
Console.WriteLine();
backpropagationNetwork.SetWeights(bestWeights);
// Teardown:
// Undecorate the backpropagation network as network.
network = backpropagationNetwork.GetDecoratedNetwork();
// LOGGING
// -------
// Create the training log and log the training data.
TrainingLog trainingLog = new TrainingLog(runCount, bestIterationCount, bestNetworkError);
// Log the network statistics.
LogNetworkStatistics(trainingLog, network);
return(trainingLog);
}
