public static CreateModel3 ( double &sequences2, int &labels2 ) : HiddenMarkovClassifier |
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sequences2 | double | |
labels2 | int | |
return | HiddenMarkovClassifier |
public void LogBackwardTest2() { double[][][] observations; int[] labels; var hmm = IndependentMarkovFunctionTest.CreateModel3(out observations, out labels); MarkovMultivariateFunction function = new MarkovMultivariateFunction(hmm); foreach (double[][] x in observations) { foreach (int y in labels) { double[,] actual = new double[x.Length, 5]; Accord.Statistics.Models.Fields. ForwardBackwardAlgorithm.LogBackward(function.Factors[y], x, y, actual); double[,] expected = new double[x.Length, 5]; Accord.Statistics.Models.Markov. ForwardBackwardAlgorithm.LogBackward(hmm.Models[y], x, expected); for (int i = 0; i < actual.GetLength(0); i++) { for (int j = 0; j < actual.GetLength(1); j++) { Assert.AreEqual(expected[i, j], actual[i, j], 1e-10); Assert.IsFalse(Double.IsNaN(actual[i, j])); } } } } }
public void ForwardTest2() { double[][][] observations; int[] labels; var hmm = IndependentMarkovFunctionTest.CreateModel3(out observations, out labels); var function = new MarkovMultivariateFunction(hmm, includePriors: false); foreach (double[][] x in observations) { foreach (int y in labels) { double[] scaling1; double logLikelihood1; double[,] actual = Accord.Statistics.Models.Fields. ForwardBackwardAlgorithm.Forward(function.Factors[y], x, y, out scaling1, out logLikelihood1); double[] scaling2; double logLikelihood2; double[,] expected = Accord.Statistics.Models.Markov. ForwardBackwardAlgorithm.Forward(hmm.Models[y], x, out scaling2, out logLikelihood2); for (int i = 0; i < actual.GetLength(0); i++) { for (int j = 0; j < actual.GetLength(1); j++) { Assert.AreEqual(expected[i, j], actual[i, j], 1e-10); Assert.IsFalse(Double.IsNaN(actual[i, j])); } } Assert.AreEqual(logLikelihood1, logLikelihood2, 1e-10); for (int i = 0; i < scaling1.Length; i++) { Assert.IsTrue(scaling1[i].IsRelativelyEqual(scaling2[i], 1e-10)); Assert.IsFalse(Double.IsNaN(scaling1[i])); Assert.IsFalse(Double.IsNaN(scaling2[i])); } } } }