ObservationType
- Type of observationParameterType
- Type of parameters to infer@PublicationReference(author="Wikipedia", title="Importance Sampling", type=WebPage, year=2009, url="http://en.wikipedia.org/wiki/Importance_sampling") public class ImportanceSampling<ObservationType,ParameterType> extends AbstractRandomized implements BayesianEstimator<ObservationType,ParameterType,DataDistribution<ParameterType>>
Modifier and Type | Class and Description |
---|---|
static class |
ImportanceSampling.DefaultUpdater<ObservationType,ParameterType>
Default ImportanceSampling Updater that uses a BayesianParameter
to compute the quantities of interest.
|
static interface |
ImportanceSampling.Updater<ObservationType,ParameterType>
Updater for ImportanceSampling
|
Modifier and Type | Field and Description |
---|---|
static int |
DEFAULT_NUM_SAMPLES
Default maximum number of samples, 1000.
|
protected ImportanceSampling.Updater<ObservationType,ParameterType> |
updater
Updater for the ImportanceSampling algorithm.
|
random
Constructor and Description |
---|
ImportanceSampling()
Creates a new instance of ImportanceSampling
|
Modifier and Type | Method and Description |
---|---|
ImportanceSampling<ObservationType,ParameterType> |
clone()
This makes public the clone method on the
Object class and
removes the exception that it throws. |
int |
getNumSamples()
Getter for numSamples
|
ImportanceSampling.Updater<ObservationType,ParameterType> |
getUpdater()
Getter for updater
|
DataDistribution<ParameterType> |
learn(java.util.Collection<? extends ObservationType> data)
The
learn method creates an object of ResultType using
data of type DataType , using some form of "learning" algorithm. |
void |
setNumSamples(int numSamples)
Setter for numSamples
|
void |
setUpdater(ImportanceSampling.Updater<ObservationType,ParameterType> updater)
Setter for updater
|
getRandom, setRandom
public static final int DEFAULT_NUM_SAMPLES
protected ImportanceSampling.Updater<ObservationType,ParameterType> updater
public ImportanceSampling()
public ImportanceSampling<ObservationType,ParameterType> clone()
AbstractCloneableSerializable
Object
class 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 ObjectUtil
may 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.clone
in interface CloneableSerializable
clone
in class AbstractRandomized
public DataDistribution<ParameterType> learn(java.util.Collection<? extends ObservationType> data)
BatchLearner
learn
method creates an object of ResultType
using
data of type DataType
, using some form of "learning" algorithm.learn
in interface BatchLearner<java.util.Collection<? extends ObservationType>,DataDistribution<ParameterType>>
data
- The data that the learning algorithm will use to create an
object of ResultType
.public ImportanceSampling.Updater<ObservationType,ParameterType> getUpdater()
public void setUpdater(ImportanceSampling.Updater<ObservationType,ParameterType> updater)
updater
- Updater for the ImportanceSampling algorithm.public int getNumSamples()
public void setNumSamples(int numSamples)
numSamples
- Number of samples.