public void TestFileConsistency()
{
var training = EncoderTrainingFactory.generateTraining(4, false);
var network = (BasicNetwork)EncogDirectoryPersistence.LoadResourceObject("Encog.Resources.xor-nn.eg");
var e = network.CalculateError(training);
Assert.AreEqual(0.046796914913558987, e, 0.00001);
}
public void Execute(IExampleInterface app)
{
Console.WriteLine("Average iterations needed (lower is better)");
IMLDataSet training = EncoderTrainingFactory.GenerateTraining(INPUT_OUTPUT, false, -1, 1);
evaluateNetwork(createTANH(), training);
evaluateNetwork(createElliott(), training);
EncogFramework.Instance.Shutdown();
}
public void TestRPROPConsistency()
{
IMLDataSet training = EncoderTrainingFactory.generateTraining(4, false);
var network = EncogUtility.SimpleFeedForward(4, 2, 0, 4, true);
(new ConsistentRandomizer(-1, 1, 50)).Randomize(network);
var rprop = new ResilientPropagation(network, training);
for (var i = 0; i < 5; i++)
{
rprop.Iteration();
}
Assert.IsTrue(CompareArray.Compare(ExpectedWeights1, network.Flat.Weights, 0.00001));
for (var i = 0; i < 5; i++)
{
rprop.Iteration();
}
Assert.IsTrue(CompareArray.Compare(ExpectedWeights2, network.Flat.Weights, 0.00001));
var e = network.CalculateError(training);
Assert.AreEqual(0.0767386807494191, e, 0.00001);
}
