public void FitTest()
{
double[] coefficients = { 0.50, 0.50 };
NormalDistribution[] components = new NormalDistribution[2];
components[0] = new NormalDistribution(2, 1);
components[1] = new NormalDistribution(5, 1);
var target = new Mixture<NormalDistribution>(coefficients, components);
double[] values = { 0, 1, 1, 0, 1, 6, 6, 5, 7, 5 };
double[] part1 = values.Submatrix(0, 4);
double[] part2 = values.Submatrix(5, 9);
MixtureOptions options = new MixtureOptions() { Threshold = 1e-10 };
target.Fit(values, options);
var actual = target;
var mean1 = Accord.Statistics.Tools.Mean(part1);
var var1 = Accord.Statistics.Tools.Variance(part1);
Assert.AreEqual(mean1, actual.Components[0].Mean, 1e-6);
Assert.AreEqual(var1, actual.Components[0].Variance, 1e-6);
var mean2 = Accord.Statistics.Tools.Mean(part2);
var var2 = Accord.Statistics.Tools.Variance(part2);
Assert.AreEqual(mean2, actual.Components[1].Mean, 1e-6);
Assert.AreEqual(var2, actual.Components[1].Variance, 1e-5);
var expectedMean = Accord.Statistics.Tools.Mean(values);
var actualMean = actual.Mean;
Assert.AreEqual(expectedMean, actualMean, 1e-7);
var expectedVar = Accord.Statistics.Tools.Variance(values, false);
var actualVar = actual.Variance;
Assert.AreEqual(expectedVar, actualVar, 0.15);
}
public void FitTest2()
{
double[] coefficients = { 0.50, 0.50 };
NormalDistribution[] components = new NormalDistribution[2];
components[0] = new NormalDistribution(2, 1);
components[1] = new NormalDistribution(5, 1);
var target = new Mixture<NormalDistribution>(coefficients, components);
double[] values = { 12512, 1, 1, 0, 1, 6, 6, 5, 7, 5 };
double[] weights = { 0, 1, 1, 1, 1, 1, 1, 1, 1, 1 };
weights = weights.Divide(weights.Sum());
double[] part1 = values.Submatrix(1, 4);
double[] part2 = values.Submatrix(5, 9);
MixtureOptions opt = new MixtureOptions()
{
Threshold = 0.000001
};
target.Fit(values, weights, opt);
var mean1 = Accord.Statistics.Tools.Mean(part1);
var var1 = Accord.Statistics.Tools.Variance(part1);
Assert.AreEqual(mean1, target.Components[0].Mean, 1e-5);
Assert.AreEqual(var1, target.Components[0].Variance, 1e-5);
var mean2 = Accord.Statistics.Tools.Mean(part2);
var var2 = Accord.Statistics.Tools.Variance(part2);
Assert.AreEqual(mean2, target.Components[1].Mean, 1e-5);
Assert.AreEqual(var2, target.Components[1].Variance, 1e-5);
var expectedMean = Accord.Statistics.Tools.WeightedMean(values, weights);
var actualMean = target.Mean;
Assert.AreEqual(expectedMean, actualMean, 1e-5);
}
/// <summary>
/// Creates a Baum-Welch with default configurations for
/// hidden Markov models with normal mixture densities.
/// </summary>
///
public static BaumWelchLearning<MultivariateMixture<MultivariateNormalDistribution>> FromMixtureModel(
HiddenMarkovModel<MultivariateMixture<MultivariateNormalDistribution>> model, NormalOptions options)
{
MixtureOptions mixOptions = new MixtureOptions()
{
Iterations = 1,
InnerOptions = options
};
return new BaumWelchLearning<MultivariateMixture<MultivariateNormalDistribution>>(model)
{
FittingOptions = mixOptions
};
}