gov.sandia.cognition.statistics.distribution
Class GeometricDistribution.MaximumLikelihoodEstimator

java.lang.Object
  extended by gov.sandia.cognition.util.AbstractCloneableSerializable
      extended by gov.sandia.cognition.statistics.distribution.GeometricDistribution.MaximumLikelihoodEstimator
All Implemented Interfaces:
BatchLearner<Collection<? extends Number>,GeometricDistribution>, DistributionEstimator<Number,GeometricDistribution>, CloneableSerializable, Serializable, Cloneable
Enclosing class:
GeometricDistribution

public static class GeometricDistribution.MaximumLikelihoodEstimator
extends AbstractCloneableSerializable
implements DistributionEstimator<Number,GeometricDistribution>

Maximum likelihood estimator of the distribution

See Also:
Serialized Form

Constructor Summary
GeometricDistribution.MaximumLikelihoodEstimator()
          Default constructor
 
Method Summary
 GeometricDistribution.PMF learn(Collection<? extends Number> data)
          The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.
 
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
 

Constructor Detail

GeometricDistribution.MaximumLikelihoodEstimator

public GeometricDistribution.MaximumLikelihoodEstimator()
Default constructor

Method Detail

learn

public GeometricDistribution.PMF learn(Collection<? extends Number> data)
Description copied from interface: BatchLearner
The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.

Specified by:
learn in interface BatchLearner<Collection<? extends Number>,GeometricDistribution>
Parameters:
data - The data that the learning algorithm will use to create an object of ResultType.
Returns:
The object that is created based on the given data using the learning algorithm.