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

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

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

Maximum Likelihood Estimator from weighted data

See Also:
Serialized Form

Constructor Summary
LogNormalDistribution.WeightedMaximumLikelihoodEstimator()
          Default constructor
 
Method Summary
 LogNormalDistribution.PDF learn(Collection<? extends WeightedValue<? extends 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

LogNormalDistribution.WeightedMaximumLikelihoodEstimator

public LogNormalDistribution.WeightedMaximumLikelihoodEstimator()
Default constructor

Method Detail

learn

public LogNormalDistribution.PDF learn(Collection<? extends WeightedValue<? extends 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<Collection<? extends WeightedValue<? extends 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.