gov.sandia.cognition.learning.function.categorization

Class MaximumAPosterioriCategorizer<ObservationType,CategoryType>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.statistics.AbstractDistribution<ObservationType>

gov.sandia.cognition.learning.function.categorization.MaximumAPosterioriCategorizer<ObservationType,CategoryType>

Type Parameters:

ObservationType - Type of observations

CategoryType - Type of categories

All Implemented Interfaces:

Evaluator<ObservationType,CategoryType>, Categorizer<ObservationType,CategoryType>, DiscriminantCategorizer<ObservationType,CategoryType,java.lang.Double>, Distribution<ObservationType>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

@PublicationReference(author="Wikipedia",
                      title="Maximum a posteriori estimation",
                      type=WebPage,
                      year=2010,
                      url="http://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation")
public class MaximumAPosterioriCategorizer<ObservationType,CategoryType>
extends AbstractDistribution<ObservationType>
implements DiscriminantCategorizer<ObservationType,CategoryType,java.lang.Double>

Categorizer that returns the category with the highest posterior likelihood for a given observation. This is known as a MAP categorizer, where the posterior is proportionate to the category's conditional likelihood for a given observation times the prior probability of the category.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	MaximumAPosterioriCategorizer.Learner<ObservationType,CategoryType> Learner for the MAP categorizer

Constructor Summary

Constructors

Constructor and Description
MaximumAPosterioriCategorizer() Creates a new instance of MaximumAPosterioriCategorizer

Method Summary

Modifier and Type	Method and Description
void	addCategory(CategoryType category, double mass, ProbabilityFunction<ObservationType> conditional) Adds the given category with the given mass (which is divided by the masses of all categories to determine the prior probability weight) and the distribution function
MaximumAPosterioriCategorizer<ObservationType,CategoryType>	clone() This makes public the clone method on the Object class and removes the exception that it throws.
double	computePosterior(ObservationType observation, CategoryType category) Computes the posterior of the observation given the category.
CategoryType	evaluate(ObservationType input) Evaluates the function on the given input and returns the output.
DefaultWeightedValueDiscriminant<CategoryType>	evaluateWithDiscriminant(ObservationType input) Evaluate the categorizer on the given input to produce the expected category plus a discriminant for later producing an ordering of how well items fit into that category.
java.util.Set<? extends CategoryType>	getCategories() Gets the list of possible categories that the categorizer can produce.
WeightedValue<ProbabilityFunction<ObservationType>>	getCategory(CategoryType category) Gets the prior probability weight and conditional distribution for the given category.
ObservationType	getMean() Gets the mean category, if it is a number or ring.
void	sampleInto(java.util.Random random, int numSamples, java.util.Collection<? super ObservationType> output) Draws multiple random samples from the distribution and puts the result into the given collection.

Methods inherited from class gov.sandia.cognition.statistics.AbstractDistribution

sample, sample

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

MaximumAPosterioriCategorizer

public MaximumAPosterioriCategorizer()

Creates a new instance of MaximumAPosterioriCategorizer

Method Detail

clone

public MaximumAPosterioriCategorizer<ObservationType,CategoryType> clone()

Description copied from class: AbstractCloneableSerializable

This makes public the clone method on the 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.

Specified by:

clone in interface CloneableSerializable

Overrides:

clone in class AbstractCloneableSerializable

Returns:

A clone of this object.

addCategory

public void addCategory(CategoryType category,
                        double mass,
                        ProbabilityFunction<ObservationType> conditional)

Adds the given category with the given mass (which is divided by the masses of all categories to determine the prior probability weight) and the distribution function

Parameters:

category - Category to add

mass - Mass of the category

conditional - Conditional probability function of observations for the category

getCategory

public WeightedValue<ProbabilityFunction<ObservationType>> getCategory(CategoryType category)

Gets the prior probability weight and conditional distribution for the given category.

Parameters:

category - Category to consider

Returns:

Prior probability weight and conditional distribution for the given category.

getCategories

public java.util.Set<? extends CategoryType> getCategories()

Description copied from interface: Categorizer

Gets the list of possible categories that the categorizer can produce.

Specified by:

getCategories in interface Categorizer<ObservationType,CategoryType>

Returns:

The list of possible categories.

evaluate

public CategoryType evaluate(ObservationType input)

Description copied from interface: Evaluator

Evaluates the function on the given input and returns the output.

Specified by:

evaluate in interface Evaluator<ObservationType,CategoryType>

Parameters:

input - The input to evaluate.

Returns:

The output produced by evaluating the input.

evaluateWithDiscriminant

public DefaultWeightedValueDiscriminant<CategoryType> evaluateWithDiscriminant(ObservationType input)

Description copied from interface: DiscriminantCategorizer

Evaluate the categorizer on the given input to produce the expected category plus a discriminant for later producing an ordering of how well items fit into that category.

Specified by:

evaluateWithDiscriminant in interface DiscriminantCategorizer<ObservationType,CategoryType,java.lang.Double>

Parameters:

input - The input value to categorize with a discriminate

Returns:

A pair containing the value and the discriminant value used for ordering results belonging to the same category.

computePosterior

public double computePosterior(ObservationType observation,
                               CategoryType category)

Computes the posterior of the observation given the category. Actually, this is the conjunctive likelihood since we've not normalizing by the likelihood of the observation over all categories. Since we're only interested in finding the MAP category, we're doing the standard thing and not normalizing.

Parameters:

observation - Observation to consider

category - Category to consider

Returns:

Posterior likelihood of the observation given the category.

getMean

public ObservationType getMean()

Gets the mean category, if it is a number or ring.

Returns:

The mean.

sampleInto

public void sampleInto(java.util.Random random,
                       int numSamples,
                       java.util.Collection<? super ObservationType> output)

Description copied from interface: Distribution

Draws multiple random samples from the distribution and puts the result into the given collection.

Specified by:

sampleInto in interface Distribution<ObservationType>

Parameters:

random - Random number generator to use.

numSamples - The number of samples to draw. Cannot be negative.

output - The collection to add the samples into.