DataType- The type of data generated by the Distribution.
SufficientStatisticsType- The type of the sufficient statistics for the distribution.
DistributionType- The type of Distribution this is the sufficient statistics of.
public abstract class AbstractIncrementalEstimator<DataType,DistributionType extends Distribution<? extends DataType>,SufficientStatisticsType extends SufficientStatistic<DataType,DistributionType>> extends AbstractBatchAndIncrementalLearner<DataType,SufficientStatisticsType> implements IncrementalEstimator<DataType,DistributionType,SufficientStatisticsType>
|Constructor and Description|
Creates a new instance of AbstractIncrementalEstimator
|Modifier and Type||Method and Description|
This makes public the clone method on the
learn, learn, update
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
public AbstractIncrementalEstimator<DataType,DistributionType,SufficientStatisticsType> clone()
Objectclass and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call
super.clone()to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in
ObjectUtilmay be helpful in implementing a custom clone method. Note: The contract of this method is that you must use
super.clone()as the basis for your implementation.
public void update(SufficientStatisticsType target, DataType data)
updatemethod updates an object of
ResultTypeusing the given new data of type
DataType, using some form of "learning" algorithm.