private static void subtestIris(double[][] x, double[][] y, double[][] tx, double[][] ty, ITraining bprop)
{
KMEANSExtractorI extractor = new KMEANSExtractorI(15);
var timer = Stopwatch.StartNew();
ANFIS fis = ANFISBuilder <GaussianRule2> .Build(x, y, extractor, bprop, 1000);
timer.Stop();
double err = bprop.Error(tx, ty, fis.RuleBase);
double correctClass = 0;
for (int i = 0; i < tx.Length; i++)
{
double[] o = fis.Inference(tx[i]);
for (int j = 0; j < ty[i].Length; j++)
{
if (ty[i][j] == 1.0 && o[j] == o.Max())
{
correctClass++;
}
}
}
Trace.WriteLine(string.Format("[{1}]\tIris Dataset Error {0} Classification Error {4}\tElapsed {2}\tRuleBase {3}", err, bprop.GetType().Name, timer.Elapsed, fis.RuleBase.Length, 1.0 - correctClass / ty.Length), "training");
Assert.IsFalse(ty.Length - correctClass > 2);
}
private static void subtestLogisticsMap <T>(double[][] x, double[][] y, double[][] tx, double[][] ty, ITraining bprop) where T : IRule, new()
{
KMEANSExtractorIO extractor = new KMEANSExtractorIO(10);
var timer = Stopwatch.StartNew();
ANFIS fis = ANFISBuilder <T> .Build(x, y, extractor, bprop, 1000);
timer.Stop();
double err = bprop.Error(tx, ty, fis.RuleBase);
Trace.WriteLine(string.Format("[{1} - {4}]\tLogistic map Error {0}\tElapsed {2}\tRuleBase {3}", err, bprop.GetType().Name, timer.Elapsed, fis.RuleBase.Length, typeof(T).Name), "training");
Assert.IsFalse(err > 1e-2);
}
private void Solve(double[][] x, double[][] y, double[][] tx, double[][] ty, ITraining bprop)
{
KMEANSExtractorI extractor = new KMEANSExtractorI(int.Parse(txtbxRulesCount.Text));
var timer = Stopwatch.StartNew();
var fis = ANFISBuilder <GaussianRule2> .Build(x, y, extractor, bprop, int.Parse(txtbxMaxIterCount.Text));
timer.Stop();
double err = bprop.Error(tx, ty, fis.RuleBase);
string line = "";
double correctClass = 0;
for (int i = 0; i < tx.Length; i++)
{
double[] o = fis.Inference(tx[i]);
if (tx[i].Length == 4 && o.Length == 3)
{
line = $"input: [{tx[i][0]}, {tx[i][1]}, {tx[i][2]}, {tx[i][3]}] output:[{o[0].ToString("F2")}, {o[1].ToString("F2")}, {o[2].ToString("F2")}] expected output: [{ty[i][0]}, {ty[i][1]}, {ty[i][2]}]";
}
for (int j = 0; j < ty[i].Length; j++)
{
if (ty[i][j] == 1.0 && o[j] == o.Max())
{
correctClass++;
line += " OK";
}
}
if (tx[i].Length == 4 && o.Length == 3)
{
InMemoryLogger.PrintMessage(line);
}
}
InMemoryLogger.PrintMessage(string.Format("Correct answers {5}\tClassification Error {4}\tElapsed {2}\tRuleBase {3}", err, bprop.GetType().Name, timer.Elapsed, fis.RuleBase.Length, 1.0 - correctClass / ty.Length, correctClass));
}
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);
}
