public static ANFIS Build(double[][] input, double[][] output, IRuleExtractor RuleExtractor, ITraining trainer, int MaxIterations)
{
InMemoryLogger.PrintMessage("Start...");
InMemoryLogger.PrintMessage($"Constructing initial rule set with [{RuleExtractor.GetType().Name}]");
var ruleBase = RuleSetFactory <R> .Build(input, output, RuleExtractor).Select(z => z as IRule).ToList();
InMemoryLogger.PrintMessage($"Get {ruleBase.Count} initial rules.");
int epoch = 0;
double trnError = 0.0;
Console.WriteLine();
Console.WriteLine();
do
{
trnError = trainer.Iteration(input, output, ruleBase);
InMemoryLogger.PrintMessage($"Epoch {epoch}, training error {trnError}");
if (double.IsNaN(trnError))
{
InMemoryLogger.PrintMessage("Failure! Training error is NAN.");
throw new Exception("Failure! Bad system design.");
}
} while (!trainer.isTrainingstoped() && epoch++ < MaxIterations);
ANFIS fis = new ANFIS(ruleBase);
InMemoryLogger.PrintMessage("Done");
return(fis);
}
public void TestRulesetGeneration()
{
int trainingSamples = 100;
double[][] x = new double[trainingSamples][];
double[][] y = new double[trainingSamples][];
Random rnd = new Random();
for (int i = 0; i < trainingSamples; i++)
{
bool isRigth = i % 2 == 0;
double valx = (isRigth ? 1 : -1) + (0.5 - rnd.NextDouble());
x[i] = new double[] { valx, valx };
y[i] = new double[] { isRigth ? 1 : 0, isRigth ? 0 : 1 };
}
KMEANSExtractorIO extractor = new KMEANSExtractorIO(2);
List <GaussianRule> ruleBase = RuleSetFactory <GaussianRule> .Build(x, y, extractor);
if (ruleBase[0].Z[0] > 0.5)
{
Assert.AreEqual(ruleBase[0].Z[0], 1, 1e-2);
Assert.AreEqual(ruleBase[0].Z[1], 0, 1e-2);
Assert.AreEqual(ruleBase[1].Z[1], 1, 1e-2);
Assert.AreEqual(ruleBase[1].Z[0], 0, 1e-2);
Assert.AreEqual(ruleBase[0].Parameters[0], 1, 1e-1);
Assert.AreEqual(ruleBase[0].Parameters[1], 1, 1e-1);
Assert.AreEqual(ruleBase[1].Parameters[0], -1, 1e-1);
Assert.AreEqual(ruleBase[1].Parameters[1], -1, 1e-1);
}
else
{
Assert.AreEqual(ruleBase[0].Z[1], 1, 1e-2);
Assert.AreEqual(ruleBase[0].Z[0], 0, 1e-2);
Assert.AreEqual(ruleBase[1].Z[0], 1, 1e-2);
Assert.AreEqual(ruleBase[1].Z[1], 0, 1e-2);
Assert.AreEqual(ruleBase[1].Parameters[0], 1, 1e-1);
Assert.AreEqual(ruleBase[1].Parameters[1], 1, 1e-1);
Assert.AreEqual(ruleBase[0].Parameters[0], -1, 1e-1);
Assert.AreEqual(ruleBase[0].Parameters[1], -1, 1e-1);
}
}
public static ANFIS Build(double[][] input, double[][] output, IRuleExtractor RuleExtractor, ITraining trainer, int MaxIterations)
{
_log.Info("Start...");
_log.Info($"Constructing initial rule set with [{RuleExtractor.GetType().Name}]");
var ruleBase = RuleSetFactory <R> .Build(input, output, RuleExtractor).Select(z => z as IRule).ToList();
_log.Info($"Get {ruleBase.Count} initial rules.");
int epoch = 0;
double trnError = 0.0;
Console.WriteLine();
Console.WriteLine("Старт обучения");
do
{
trnError = trainer.Iteration(input, output, ruleBase);
_log.Info($"Epoch {epoch}, training error {trnError}");
if (double.IsNaN(trnError))
{
_log.Info("Failure! Training error is NAN.");
throw new Exception("Failure! Bad system design.");
}
if (epoch % 100 == 0)
{
Console.WriteLine("Обучение: " + epoch.ToString() + " итерация. Ошибка: " + trnError);
}
} while (!trainer.isTrainingstoped() && epoch++ < MaxIterations);
ANFIS fis = new ANFIS(ruleBase);
Console.WriteLine("Завершение обучения");
_log.Info("Done");
return(fis);
}