public class ExponentialFamilyMixture extends Mixture
Mixture.Component
Modifier and Type | Field and Description |
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double |
bic
The BIC score when the distribution is fit on a sample data.
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double |
L
The log-likelihood when the distribution is fit on a sample data.
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components
Constructor and Description |
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ExponentialFamilyMixture(Mixture.Component... components)
Constructor.
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Modifier and Type | Method and Description |
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static ExponentialFamilyMixture |
fit(double[] x,
Mixture.Component... components)
Fits the mixture model with the EM algorithm.
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static ExponentialFamilyMixture |
fit(double[] x,
Mixture.Component[] components,
double gamma,
int maxIter,
double tol)
Fits the mixture model with the EM algorithm.
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bic, cdf, entropy, length, logp, map, mean, p, posteriori, quantile, rand, size, toString, variance
inverseTransformSampling, quantile, quantile, rejection
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait
likelihood, logLikelihood, rand, sd
public final double L
public final double bic
public ExponentialFamilyMixture(Mixture.Component... components)
components
- a list of exponential family distributions.public static ExponentialFamilyMixture fit(double[] x, Mixture.Component... components)
components
- the initial configuration of mixture. Components may have
different distribution form.x
- the training data.public static ExponentialFamilyMixture fit(double[] x, Mixture.Component[] components, double gamma, int maxIter, double tol)
components
- the initial configuration.x
- the training data.gamma
- the regularization parameter. Although regularization works
well for high dimensional data, it often reduces the model
to too few components. For one-dimensional data, gamma should
be 0 in general.maxIter
- the maximum number of iterations.tol
- the tolerance of convergence test.