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);
}
public void ANFIS_OutputFromDataSet()
{
var sampleSize = RobotArmDataSet.Input.Length - 1;
//StochasticBatch sprop = new StochasticBatch(sampleSize, 1e-5);
//sprop.UnknownCaseFaced += AddRule<GaussianRule2>;
//var sprop = new Backprop(1e-1);
var sprop = new StochasticQprop(sampleSize);
var extractor = new KMEANSExtractorIO(25);
//ANFIS fis = ANFISBuilder<GaussianRule2>.Build(RobotArmDataSet.Input, RobotArmDataSet.OutputTheta1, extractor, sprop, 150);
ANFIS fis = ANFISBuilder <GaussianRule> .Build(RobotArmDataSet.Input, RobotArmDataSet.OutputTheta1, extractor, sprop, 150);
var output1 = fis.Inference(new[] { 1.10413546487088, 2.81104319371924 }).FirstOrDefault(); // 1.1
var output2 = fis.Inference(new[] { 2.31665592712393, 1.9375717475909 }).FirstOrDefault(); // 0.6
var output3 = fis.Inference(new[] { 2.88944142930409, 16.7526454098038 }).FirstOrDefault(); // 1.4
}
void Awake()
{
//Check if there is already an instance of SoundManager
if (instance == null)
{
//if not, set it to this.
instance = this;
}
//If instance already exists:
else if (instance != this)
{
//Destroy this, this enforces our singleton pattern so there can only be one instance of SoundManager.
Destroy(gameObject);
}
//Set SoundManager to DontDestroyOnLoad so that it won't be destroyed when reloading our scene.
DontDestroyOnLoad(gameObject);
Backprop bprop = new Backprop(1e-2);
KMEANSExtractorIO extractor = new KMEANSExtractorIO(5);
fis = ANFISBuilder <GaussianRule> .Build(gameParams, musicParams, extractor, bprop, 5);
}
/// <summary>
///
/// </summary>
/// <param name="ruleNumber"></param>
/// <param name="maxIterations"></param>
/// <returns></returns>
public Task <bool> TrainANFIS(int ruleNumber, int maxIterations, bool useAnalicitalOutcomeForTraining = false)
{
return(Task.Run(() => {
if (!IsDataSetCalculated)
{
throw new ApplicationException("DataSet is not calculated or provided.");
}
var sampleSize = Positions.Count() - 1;
var dynamicObj = useAnalicitalOutcomeForTraining ? Positions.Select(x => new { Point = x, KinematicOutCome = CalculateArmJoint(x).GetAwaiter().GetResult().FirstOrDefault() }) : null;
var input = useAnalicitalOutcomeForTraining
? dynamicObj.Select(x => x.Point).ConvertToANFISParameter()
: Positions.ConvertToANFISParameter();
var theta1ANFIS = Task.Run(() => {
var sPropTheta1 = new StochasticQprop(sampleSize);
var extractorForTheta1 = new KMEANSExtractorIO(ruleNumber);
var expectedOutcome = useAnalicitalOutcomeForTraining
? dynamicObj.Select(x => new[] { x.KinematicOutCome.Theta1.ConvertRadiansToDegrees() }).ToArray()
: AnglesGrid.First().ConvertToANFISParameter();
Theta1ANFIS = ANFISBuilder <GaussianRule> .Build(input,
expectedOutcome, extractorForTheta1, sPropTheta1, maxIterations);
});
var theta2ANFIS = Task.Run(() => {
var sPropTheta2 = new StochasticQprop(sampleSize);
var extractorForTheta2 = new KMEANSExtractorIO(ruleNumber);
var expectedOutcome2 = useAnalicitalOutcomeForTraining
? dynamicObj.Select(x => new[] { x.KinematicOutCome.Theta2.ConvertRadiansToDegrees() }).ToArray()
: AnglesGrid.Last().ConvertToANFISParameter();
Theta2ANFIS = ANFISBuilder <GaussianRule> .Build(input,
expectedOutcome2, extractorForTheta2, sPropTheta2, maxIterations);
});
Task.WaitAll(theta1ANFIS, theta2ANFIS);
IsANFISTrained = true;
return true;
}));
}
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));
}