static Logger _log = new Logger("ABuilder", InternalTraceLevel.Default, TextWriter.Null); // LogManager.GetLogger("ABuilder");
public static ANFIS Build(double[][] input, double[][] output, IRuleExtractor RuleExtractor, ITraining trainer, int MaxIterations)
{
_log.Info("Start...");
_log.Info($"Constructing initial rule set with [{RuleExtractor.GetType().Name}]");
var ruleBase = RuleSetFactory <R> .Build(input, output, RuleExtractor).Select(z => z as IRule).ToList();
_log.Info($"Get {ruleBase.Count} initial rules.");
int epoch = 0;
double trnError = 0.0;
Console.WriteLine();
Console.WriteLine();
do
{
trnError = trainer.Iteration(input, output, ruleBase);
_log.Info($"Epoch {epoch}, training error {trnError}");
if (double.IsNaN(trnError))
{
_log.Info("Failure! Training error is NAN.");
throw new Exception("Failure! Bad system design.");
}
} while (!trainer.isTrainingstoped() && epoch++ < MaxIterations);
ANFIS fis = new ANFIS(ruleBase);
_log.Info("Done");
return(fis);
}
private static void subTestOptimization1(ITraining bprop, double[][] x, double[][] y, double[][] tx, double[][] ty)
{
GaussianRule2[] terms = new GaussianRule2[] { new GaussianRule2() };
terms[0].Init(
new double[] { 0.5, 0.3 },
new double[] { 0 },
new double[] { 0.0, 0.0 });
int epoch = 0;
int maxit = 1000;
double trnError = 0.0;
double tstError = 0.0;
do
{
trnError = bprop.Iteration(x, y, terms);
tstError = bprop.Error(tx, ty, terms);
} while (!bprop.isTrainingstoped() && epoch++ < maxit);
Trace.WriteLine(string.Format("Epochs {0} - Error {1}/{2}", epoch, trnError, tstError), "training");
Assert.IsFalse(tstError > 1e-2);
Assert.IsFalse(trnError > 1e-2);
Assert.AreEqual(terms[0].Z[0], 1.0, 1e-2);
}