VectorNaiveBayesCategorizer (Cognitive Foundry)

java.lang.Object
- gov.sandia.cognition.util.AbstractCloneableSerializable
- - gov.sandia.cognition.learning.algorithm.bayes.VectorNaiveBayesCategorizer<CategoryType,DistributionType>

Type Parameters:

CategoryType - The output category type for the categorizer. Must implement equals and hash code.

DistributionType - The type of the distributions used to compute the conditionals for each dimension.

All Implemented Interfaces:

Evaluator<Vectorizable,CategoryType>, Categorizer<Vectorizable,CategoryType>, DiscriminantCategorizer<Vectorizable,CategoryType,java.lang.Double>, VectorInputEvaluator<Vectorizable,CategoryType>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable
```
public class VectorNaiveBayesCategorizer<CategoryType,DistributionType extends UnivariateProbabilityDensityFunction>
extends AbstractCloneableSerializable
implements Categorizer<Vectorizable,CategoryType>, VectorInputEvaluator<Vectorizable,CategoryType>, DiscriminantCategorizer<Vectorizable,CategoryType,java.lang.Double>
```
A naive Bayesian categorizer that takes an input vector and applies an independent scalar probability density function to each one.

Since:

3.1

Author:

Justin Basilico

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type	Class and Description
`static class`	`VectorNaiveBayesCategorizer.BatchGaussianLearner<CategoryType>` A supervised batch distributionLearner for a vector Naive Bayes categorizer that fits a Gaussian.
`static class`	`VectorNaiveBayesCategorizer.Learner<CategoryType,DistributionType extends UnivariateProbabilityDensityFunction>` A supervised batch distributionLearner for a vector Naive Bayes categorizer.
`static class`	`VectorNaiveBayesCategorizer.OnlineLearner<CategoryType,DistributionType extends UnivariateProbabilityDensityFunction>` An online (incremental) distributionLearner for the Naive Bayes categorizer that uses an incremental distribution learner for the distribution representing each dimension for each category.

Field Summary

Fields
Modifier and Type	Field and Description
`protected java.util.Map<CategoryType,java.util.List<DistributionType>>`	`conditionals` The mapping of category to the conditional distribution for the category with one probability density function for each dimension.
`protected DataDistribution<CategoryType>`	`priors` The prior distribution for the categorizer.

Constructor Summary

Constructors
Constructor and Description
`VectorNaiveBayesCategorizer()` Creates a new `VectorNaiveBayesCategorizer` with an empty prior and conditionals.
`VectorNaiveBayesCategorizer(DataDistribution<CategoryType> priors, java.util.Map<CategoryType,java.util.List<DistributionType>> conditionals)` Creates a new `VectorNaiveBayesCategorizer` with the given prior and conditionals.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`VectorNaiveBayesCategorizer<CategoryType,DistributionType>`	`clone()` This makes public the clone method on the `Object` class and removes the exception that it throws.
`double`	`computeLogPosterior(Vector input, CategoryType category)` Computes the log-posterior probability that the input belongs to the given category.
`double`	`computePosterior(Vector input, CategoryType category)` Computes the posterior probability that the input belongs to the given category.
`CategoryType`	`evaluate(Vectorizable input)` Evaluates the function on the given input and returns the output.
`DefaultWeightedValueDiscriminant<CategoryType>`	`evaluateWithDiscriminant(Vectorizable 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<CategoryType>`	`getCategories()` Gets the list of possible categories that the categorizer can produce.
`java.util.Map<CategoryType,java.util.List<DistributionType>>`	`getConditionals()` Gets the conditional distributions, which is a mapping of category to the list of probability density functions, one for each dimension of the vector.
`int`	`getInputDimensionality()` Gets the expected dimensionality of the input vector to the evaluator, if it is known.
`DataDistribution<CategoryType>`	`getPriors()` Gets the prior distribution over the categories.
`void`	`setConditionals(java.util.Map<CategoryType,java.util.List<DistributionType>> conditionals)` Sets the conditional distributions, which is a mapping of category to the list of probability density functions, one for each dimension of the vector.
`void`	`setPriors(DataDistribution<CategoryType> priors)` Sets the prior distribution over the categories.

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - priors
```
protected DataDistribution<CategoryType> priors
```
    The prior distribution for the categorizer.
  - conditionals
```
protected java.util.Map<CategoryType,java.util.List<DistributionType extends UnivariateProbabilityDensityFunction>> conditionals
```
    The mapping of category to the conditional distribution for the category with one probability density function for each dimension.
- Constructor Detail
  - VectorNaiveBayesCategorizer
```
public VectorNaiveBayesCategorizer()
```
    Creates a new VectorNaiveBayesCategorizer with an empty prior and conditionals.
  - VectorNaiveBayesCategorizer
```
public VectorNaiveBayesCategorizer(DataDistribution<CategoryType> priors,
                                   java.util.Map<CategoryType,java.util.List<DistributionType>> conditionals)
```
    Creates a new VectorNaiveBayesCategorizer with the given prior and conditionals.
    
    Parameters:
    
    priors - The prior distribution.
    
    conditionals - The conditional distribution.
- Method Detail
  - clone
```
public VectorNaiveBayesCategorizer<CategoryType,DistributionType> 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.
  - evaluate
```
public CategoryType evaluate(Vectorizable input)
```
    Description copied from interface: Evaluator
    
    Evaluates the function on the given input and returns the output.
    
    Specified by:
    
    evaluate in interface Evaluator<Vectorizable,CategoryType>
    
    Parameters:
    
    input - The input to evaluate.
    
    Returns:
    
    The output produced by evaluating the input.
  - evaluateWithDiscriminant
```
public DefaultWeightedValueDiscriminant<CategoryType> evaluateWithDiscriminant(Vectorizable 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<Vectorizable,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(Vector input,
                               CategoryType category)
```
    Computes the posterior probability that the input belongs to the given category.
    
    Parameters:
    
    input - The input vector.
    
    category - The category to compute the posterior for.
    
    Returns:
    
    The posterior probability that the input is part of the given category. Between 0.0 and 1.0.
  - computeLogPosterior
```
public double computeLogPosterior(Vector input,
                                  CategoryType category)
```
    Computes the log-posterior probability that the input belongs to the given category.
    
    Parameters:
    
    input - The input vector.
    
    category - The category to compute the posterior for.
    
    Returns:
    
    The log-posterior probability.
  - getCategories
```
public java.util.Set<CategoryType> getCategories()
```
    Description copied from interface: Categorizer
    
    Gets the list of possible categories that the categorizer can produce.
    
    Specified by:
    
    getCategories in interface Categorizer<Vectorizable,CategoryType>
    
    Returns:
    
    The list of possible categories.
  - getInputDimensionality
```
public int getInputDimensionality()
```
    Description copied from interface: VectorInputEvaluator
    
    Gets the expected dimensionality of the input vector to the evaluator, if it is known. If it is not known, -1 is returned.
    
    Specified by:
    
    getInputDimensionality in interface VectorInputEvaluator<Vectorizable,CategoryType>
    
    Returns:
    
    The expected dimensionality of the input vector to the evaluator, or -1 if it is not known.
  - getPriors
```
public DataDistribution<CategoryType> getPriors()
```
    Gets the prior distribution over the categories.
    
    Returns:
    
    The prior distribution over the categories.
  - setPriors
```
public void setPriors(DataDistribution<CategoryType> priors)
```
    Sets the prior distribution over the categories.
    
    Parameters:
    
    priors - The prior distribution over the categories.
  - getConditionals
```
public java.util.Map<CategoryType,java.util.List<DistributionType>> getConditionals()
```
    Gets the conditional distributions, which is a mapping of category to the list of probability density functions, one for each dimension of the vector.
    
    Returns:
    
    The conditional distributions for each category.
  - setConditionals
```
public void setConditionals(java.util.Map<CategoryType,java.util.List<DistributionType>> conditionals)
```
    Sets the conditional distributions, which is a mapping of category to the list of probability density functions, one for each dimension of the vector.
    
    Parameters:
    
    conditionals - The conditional distributions for each category.

Class VectorNaiveBayesCategorizer<CategoryType,DistributionType extends UnivariateProbabilityDensityFunction>

Nested Class Summary

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

priors

conditionals

Constructor Detail

VectorNaiveBayesCategorizer

VectorNaiveBayesCategorizer

Method Detail

clone

evaluate

evaluateWithDiscriminant

computePosterior

computeLogPosterior

getCategories

getInputDimensionality

getPriors

setPriors

getConditionals

setConditionals