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 }
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); } }
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; })); }