gov.sandia.cognition.statistics.distribution
Class MultivariateGaussian.WeightedMaximumLikelihoodEstimator

java.lang.Object
  extended by gov.sandia.cognition.util.AbstractCloneableSerializable
      extended by gov.sandia.cognition.statistics.distribution.MultivariateGaussian.WeightedMaximumLikelihoodEstimator
All Implemented Interfaces:
BatchLearner<Collection<? extends WeightedValue<? extends Vector>>,MultivariateGaussian.PDF>, DistributionWeightedEstimator<Vector,MultivariateGaussian.PDF>, CloneableSerializable, Serializable, Cloneable
Enclosing class:
MultivariateGaussian

public static class MultivariateGaussian.WeightedMaximumLikelihoodEstimator
extends AbstractCloneableSerializable
implements DistributionWeightedEstimator<Vector,MultivariateGaussian.PDF>

Computes the Weighted Maximum Likelihood Estimate of the MultivariateGaussian given a weighted set of Vectors

See Also:
Serialized Form

Field Summary
static double DEFAULT_COVARIANCE
          Default covariance used in estimation, 1.0E-5.
 
Constructor Summary
MultivariateGaussian.WeightedMaximumLikelihoodEstimator()
          Default constructor.
MultivariateGaussian.WeightedMaximumLikelihoodEstimator(double defaultCovariance)
          Creates a new instance of WeightedMaximumLikelihoodEstimator
 
Method Summary
 MultivariateGaussian.PDF learn(Collection<? extends WeightedValue<? extends Vector>> data)
          Computes the Gaussian that estimates the maximum likelihood of generating the given set of weighted samples.
static MultivariateGaussian.PDF learn(Collection<? extends WeightedValue<? extends Vector>> data, double defaultCovariance)
          Computes the Gaussian that estimates the maximum likelihood of generating the given set of weighted samples.
 
Methods inherited from class gov.sandia.cognition.util.AbstractCloneableSerializable
clone
 
Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 
Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable
clone
 

Field Detail

DEFAULT_COVARIANCE

public static final double DEFAULT_COVARIANCE
Default covariance used in estimation, 1.0E-5.

See Also:
Constant Field Values
Constructor Detail

MultivariateGaussian.WeightedMaximumLikelihoodEstimator

public MultivariateGaussian.WeightedMaximumLikelihoodEstimator()
Default constructor.


MultivariateGaussian.WeightedMaximumLikelihoodEstimator

public MultivariateGaussian.WeightedMaximumLikelihoodEstimator(double defaultCovariance)
Creates a new instance of WeightedMaximumLikelihoodEstimator

Parameters:
defaultCovariance - Amount to add to the diagonal of the covariance matrix
Method Detail

learn

public MultivariateGaussian.PDF learn(Collection<? extends WeightedValue<? extends Vector>> data)
Computes the Gaussian that estimates the maximum likelihood of generating the given set of weighted samples.

Specified by:
learn in interface BatchLearner<Collection<? extends WeightedValue<? extends Vector>>,MultivariateGaussian.PDF>
Parameters:
data - The weighted samples to calculate the Gaussian from throws IllegalArgumentException if samples has 1 or fewer samples.
Returns:
The Gaussian that estimates the maximum likelihood of generating the given weighted data.

learn

public static MultivariateGaussian.PDF learn(Collection<? extends WeightedValue<? extends Vector>> data,
                                             double defaultCovariance)
Computes the Gaussian that estimates the maximum likelihood of generating the given set of weighted samples.

Parameters:
defaultCovariance - Amount to add to the diagonal of the covariance matrix
data - The weighted samples to calculate the Gaussian from throws IllegalArgumentException if samples has 1 or fewer samples.
Returns:
The Gaussian that estimates the maximum likelihood of generating the given weighted data.