public static class MixtureOfGaussians.PDF extends MultivariateMixtureDensityModel.PDF<MultivariateGaussian>
MultivariateMixtureDensityModel.PDF<DistributionType extends ClosedFormComputableDistribution<Vector>>distributions, priorWeights| Constructor and Description |
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PDF(java.util.Collection<? extends MultivariateGaussian> distributions)
Creates a new instance of MixtureOfGaussians
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PDF(java.util.Collection<? extends MultivariateGaussian> distributions,
double[] priorWeights)
Creates a new instance of LinearMixtureModel
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PDF(MixtureOfGaussians.PDF other)
Copy Constructor
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PDF(MultivariateGaussian... distributions)
Creates a new instance of MixtureOfGaussians
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| Modifier and Type | Method and Description |
|---|---|
MixtureOfGaussians.PDF |
clone()
This makes public the clone method on the
Object class and
removes the exception that it throws. |
double |
computeWeightedZSquared(Vector input)
Computes the weighted z-value (deviate) of the given input.
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MultivariateGaussian.PDF |
fitSingleGaussian()
Fits a single MultivariateGaussian to the given MixtureOfGaussians
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int |
getDimensionality()
Gets the dimensionality of the MultivariateGaussian in the mixture
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MixtureOfGaussians.PDF |
getProbabilityFunction()
Gets the distribution function associated with this Distribution,
either the PDF or PMF.
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computeRandomVariableLikelihoods, computeRandomVariableProbabilities, evaluate, getMostLikelyRandomVariable, logEvaluateconvertFromVector, convertToVector, getMeangetDistributionCount, getDistributions, getPriorWeights, getPriorWeightSum, sample, sampleInto, setDistributions, setPriorWeights, toStringsampleequals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, waitgetMeanconvertFromVector, convertToVectorsample, sample, sampleIntopublic PDF(MultivariateGaussian... distributions)
distributions - Underlying distributions from which we samplepublic PDF(java.util.Collection<? extends MultivariateGaussian> distributions)
distributions - Underlying distributions from which we samplepublic PDF(java.util.Collection<? extends MultivariateGaussian> distributions, double[] priorWeights)
distributions - Underlying distributions from which we samplepriorWeights - Weights proportionate by which the distributions are sampledpublic PDF(MixtureOfGaussians.PDF other)
other - MixtureOfGaussians to copypublic MixtureOfGaussians.PDF clone()
AbstractCloneableSerializableObject class and
removes the exception that it throws. Its default behavior is to
automatically create a clone of the exact type of object that the
clone is called on and to copy all primitives but to keep all references,
which means it is a shallow copy.
Extensions of this class may want to override this method (but call
super.clone() to implement a "smart copy". That is, to target
the most common use case for creating a copy of the object. Because of
the default behavior being a shallow copy, extending classes only need
to handle fields that need to have a deeper copy (or those that need to
be reset). Some of the methods in ObjectUtil may be helpful in
implementing a custom clone method.
Note: The contract of this method is that you must use
super.clone() as the basis for your implementation.clone in interface Vectorizableclone in interface CloneableSerializableclone in class MultivariateMixtureDensityModel<MultivariateGaussian>public MixtureOfGaussians.PDF getProbabilityFunction()
ComputableDistributiongetProbabilityFunction in interface ComputableDistribution<Vector>getProbabilityFunction in interface ProbabilityDensityFunction<Vector>getProbabilityFunction in class MultivariateMixtureDensityModel.PDF<MultivariateGaussian>public int getDimensionality()
public MultivariateGaussian.PDF fitSingleGaussian()
public double computeWeightedZSquared(Vector input)
input - Input about which to compute the z-value.