gov.sandia.cognition.statistics.distribution

Class LogNormalDistribution.WeightedMaximumLikelihoodEstimator

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.statistics.distribution.LogNormalDistribution.WeightedMaximumLikelihoodEstimator

All Implemented Interfaces:

BatchLearner<java.util.Collection<? extends WeightedValue<? extends java.lang.Double>>,LogNormalDistribution>, DistributionWeightedEstimator<java.lang.Double,LogNormalDistribution>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Enclosing class:

LogNormalDistribution

public static class LogNormalDistribution.WeightedMaximumLikelihoodEstimator
extends AbstractCloneableSerializable
implements DistributionWeightedEstimator<java.lang.Double,LogNormalDistribution>

Maximum Likelihood Estimator from weighted data

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
WeightedMaximumLikelihoodEstimator() Default constructor

Method Summary

Modifier and Type	Method and Description
LogNormalDistribution.PDF	learn(java.util.Collection<? extends WeightedValue<? extends java.lang.Double>> 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

WeightedMaximumLikelihoodEstimator

public WeightedMaximumLikelihoodEstimator()

Default constructor

Method Detail

learn

public LogNormalDistribution.PDF learn(java.util.Collection<? extends WeightedValue<? extends java.lang.Double>> 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<java.util.Collection<? extends WeightedValue<? extends java.lang.Double>>,LogNormalDistribution>

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.