/// <summary>
/// Test a teacher.
/// </summary>
/// <param name="testNumber">The number of the test.</param>
/// <param name="testDescription">The description of the test.</param>
/// <param name="teacher">The teacher to test.</param>
/// <param name="network">The network to train.</param>
/// <param name="maxIterationCount">The maximum number of iterations.</param>
/// <param name="maxTolerableNetworkError">The maximum tolerable network error.</param>
private static void Test(int testNumber, string testDescription, ITeacher teacher, INetwork network, int maxIterationCount, double maxTolerableNetworkError)
{
// Print the number of the test and its description.
Console.WriteLine("Test " + testNumber + " : " + testDescription);
// Run the teacher (i.e. train the network).
DateTime startTime = DateTime.Now;
TrainingLog trainingLog = teacher.Train(network, maxIterationCount, maxTolerableNetworkError);
DateTime endTime = DateTime.Now;
// Print the results.
Console.WriteLine("Test " + testNumber + " : Duration : " + (endTime - startTime));
Console.WriteLine("Test " + testNumber + " : Number of iterations taken : " + trainingLog.IterationCount);
Console.WriteLine("Test " + testNumber + " : Network error achieved : " + trainingLog.NetworkError);
foreach (TrainingPattern trainingPattern in teacher.TrainingSet.TrainingPatterns)
{
double[] outputVector = network.Evaluate(trainingPattern.InputVector);
Console.WriteLine(
TrainingPattern.VectorToString(trainingPattern.InputVector) +
" -> " +
TrainingPattern.VectorToString(outputVector)
);
}
}
static void Main(string[] args)
{
Console.WriteLine("{0:.10}, {1}", "Hello", "World");
// Step 1 : Alternative A : Building a training set manually
// ---------------------------------------------------------
int inputVectorLength = 2;
int outputVectorLength = 1;
TrainingSet trainingSet = new TrainingSet(inputVectorLength, outputVectorLength);
TrainingPattern trainingPattern = new TrainingPattern(new double[2] {
0.0, 0.0
}, new double[1] {
0.0
});
trainingSet.Add(trainingPattern);
trainingPattern = new TrainingPattern(new double[2] {
0.0, 1.0
}, new double[1] {
1.0
});
trainingSet.Add(trainingPattern);
trainingPattern = new TrainingPattern(new double[2] {
1.0, 0.0
}, new double[1] {
1.0
});
trainingSet.Add(trainingPattern);
trainingPattern = new TrainingPattern(new double[2] {
1.0, 1.0
}, new double[1] {
0.0
});
trainingSet.Add(trainingPattern);
// Step 2 : Building a blueprint of a network
// ------------------------------------------
LayerBlueprint inputLayerBlueprint = new LayerBlueprint(inputVectorLength);
ActivationLayerBlueprint[] hiddenLayerBlueprints = new ActivationLayerBlueprint[1];
hiddenLayerBlueprints[0] = new ActivationLayerBlueprint(2, new LogisticActivationFunction());
ActivationLayerBlueprint outputLayerBlueprint = new ActivationLayerBlueprint(outputVectorLength, new LogisticActivationFunction());
NetworkBlueprint networkBlueprint = new NetworkBlueprint(inputLayerBlueprint, hiddenLayerBlueprints, outputLayerBlueprint);
// Step 3 : Building a network
// ---------------------------
Network network = new Network(networkBlueprint);
Console.WriteLine(network.ToString());
// Step 4 : Building a teacher
// ---------------------------
ITeacher teacher = new AntColonyOptimizationTeacher(trainingSet, null, null);
// Step 5 : Training the network
// -----------------------------
int maxIterationCount = 10000;
double maxTolerableNetworkError = 1e-3;
TrainingLog trainingLog = teacher.Train(network, maxIterationCount, maxTolerableNetworkError);
Console.WriteLine("Number of runs used : " + trainingLog.RunCount);
Console.WriteLine("Number of iterations used : " + trainingLog.IterationCount);
Console.WriteLine("Minimum network error achieved : " + trainingLog.NetworkError);
// Step 6 : Using the trained network
// ----------------------------------
foreach (TrainingPattern tp in trainingSet.TrainingPatterns)
{
double[] inputVector = tp.InputVector;
double[] outputVector = network.Evaluate(inputVector);
Console.WriteLine(tp.ToString() + " -> " + TrainingPattern.VectorToString(outputVector));
}
}