public static class MultivariateGaussian.MaximumLikelihoodEstimator extends AbstractCloneableSerializable implements DistributionEstimator<Vector,MultivariateGaussian.PDF>
Modifier and Type | Field and Description |
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static double |
DEFAULT_COVARIANCE
Default covariance used in estimation, 1.0E-5.
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Constructor and Description |
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MaximumLikelihoodEstimator()
Default constructor;
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MaximumLikelihoodEstimator(double defaultCovariance)
Creates a new instance of MaximumLikelihoodEstimator
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Modifier and Type | Method and Description |
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MultivariateGaussian.PDF |
learn(java.util.Collection<? extends Vector> data)
Computes the Gaussian that estimates the maximum likelihood of
generating the given set of samples.
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static MultivariateGaussian.PDF |
learn(java.util.Collection<? extends Vector> data,
double defaultCovariance)
Computes the Gaussian that estimates the maximum likelihood of
generating the given set of samples.
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clone
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
clone
public static final double DEFAULT_COVARIANCE
public MaximumLikelihoodEstimator()
public MaximumLikelihoodEstimator(double defaultCovariance)
defaultCovariance
- Amount to add to the diagonal of the
covariance matrixpublic static MultivariateGaussian.PDF learn(java.util.Collection<? extends Vector> data, double defaultCovariance)
defaultCovariance
- amount to add to the diagonals of the
covariance matrix, typically on the order of 1e-4, can be 0.0.data
- The samples to calculate the Gaussian from throws
IllegalArgumentException if samples has 1 or fewer samples.public MultivariateGaussian.PDF learn(java.util.Collection<? extends Vector> data)
learn
in interface BatchLearner<java.util.Collection<? extends Vector>,MultivariateGaussian.PDF>
data
- The samples to calculate the Gaussian from throws
IllegalArgumentException if samples has 1 or fewer samples.