OnlineMultiPerceptron (Cognitive Foundry)

java.lang.Object
- gov.sandia.cognition.util.AbstractCloneableSerializable
- - gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner<InputOutputPair<? extends Vectorizable,CategoryType>,LinearMultiCategorizer<CategoryType>>
  - - gov.sandia.cognition.learning.algorithm.perceptron.OnlineMultiPerceptron<CategoryType>

Type Parameters:

CategoryType - The type of output categories. Can be any type that has a valid equals and hashCode method.

All Implemented Interfaces:

BatchAndIncrementalLearner<InputOutputPair<? extends Vectorizable,CategoryType>,LinearMultiCategorizer<CategoryType>>, BatchLearner<java.util.Collection<? extends InputOutputPair<? extends Vectorizable,CategoryType>>,LinearMultiCategorizer<CategoryType>>, IncrementalLearner<InputOutputPair<? extends Vectorizable,CategoryType>,LinearMultiCategorizer<CategoryType>>, VectorFactoryContainer, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

OnlineMultiPerceptron.ProportionalUpdate, OnlineMultiPerceptron.UniformUpdate
```
public class OnlineMultiPerceptron<CategoryType>
extends AbstractBatchAndIncrementalLearner<InputOutputPair<? extends Vectorizable,CategoryType>,LinearMultiCategorizer<CategoryType>>
implements VectorFactoryContainer
```
An online, multiple category version of the Perceptron algorithm. It learns a separate linear binary categorizer for each category.

Since:

3.3.0

Author:

Justin Basilico

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type	Class and Description
`static class`	`OnlineMultiPerceptron.ProportionalUpdate<CategoryType>` Variant of a multi-category Perceptron that performs a proportional weight update on all categories that are scored higher than the true category such that the weights sum to 1.0 and are proportional how much larger the score was for each incorrect category than the true category.
`static class`	`OnlineMultiPerceptron.UniformUpdate<CategoryType>` Variant of a multi-category Perceptron that performs a uniform weight update on all categories that are scored higher than the true category such that the weights are equal and sum to -1.

Field Summary

Fields
Modifier and Type	Field and Description
`static double`	`DEFAULT_MIN_MARGIN` The default minimum margin is 0.0.
`protected double`	`minMargin` The minimum margin to enforce.
`protected VectorFactory<?>`	`vectorFactory` The factory to create weight vectors.

Constructor Summary

Constructors
Constructor and Description
`OnlineMultiPerceptron()` Creates a new `OnlineMultiPerceptron`.
`OnlineMultiPerceptron(double minMargin)` Creates a new `OnlineMultiPerceptron` with the given minimum margin.
`OnlineMultiPerceptron(double minMargin, VectorFactory<?> vectorFactory)` Creates a new `OnlineMultiPerceptron` with the given minimum margin and backing vector factory.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`LinearMultiCategorizer<CategoryType>`	`createInitialLearnedObject()` Creates a new initial learned object, before any data is given.
`double`	`getMinMargin()` Gets the minimum margin to enforce.
`VectorFactory<?>`	`getVectorFactory()` Gets the VectorFactory used to create the weight vector.
`void`	`setMinMargin(double minMargin)` Gets the minimum margin to enforce.
`void`	`setVectorFactory(VectorFactory<?> vectorFactory)` Sets the VectorFactory used to create the weight vector.
`void`	`update(LinearMultiCategorizer<CategoryType> target, InputOutputPair<? extends Vectorizable,CategoryType> example)` The `update` method updates an object of `ResultType` using the given new data of type `DataType`, using some form of "learning" algorithm.

Methods inherited from class gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner
clone, learn, learn, update

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

- Field Detail
  - DEFAULT_MIN_MARGIN
```
public static final double DEFAULT_MIN_MARGIN
```
    The default minimum margin is 0.0.
    
    See Also:
    
    Constant Field Values
  - minMargin
```
protected double minMargin
```
    The minimum margin to enforce. Must be non-negative.
  - vectorFactory
```
protected VectorFactory<?> vectorFactory
```
    The factory to create weight vectors.
- Constructor Detail
  - OnlineMultiPerceptron
```
public OnlineMultiPerceptron()
```
    Creates a new OnlineMultiPerceptron.
  - OnlineMultiPerceptron
```
public OnlineMultiPerceptron(double minMargin)
```
    Creates a new OnlineMultiPerceptron with the given minimum margin.
    
    Parameters:
    
    minMargin - The minimum margin to consider an example correct.
  - OnlineMultiPerceptron
```
public OnlineMultiPerceptron(double minMargin,
                             VectorFactory<?> vectorFactory)
```
    Creates a new OnlineMultiPerceptron with the given minimum margin and backing vector factory.
    
    Parameters:
    
    minMargin - The minimum margin to consider an example correct.
    
    vectorFactory - The vector factory used to create the weight vectors.
- Method Detail
  - createInitialLearnedObject
```
public LinearMultiCategorizer<CategoryType> createInitialLearnedObject()
```
    Description copied from interface: IncrementalLearner
    
    Creates a new initial learned object, before any data is given.
    
    Specified by:
    
    createInitialLearnedObject in interface IncrementalLearner<InputOutputPair<? extends Vectorizable,CategoryType>,LinearMultiCategorizer<CategoryType>>
    
    Returns:
    
    The initial learned object.
  - update
```
public void update(LinearMultiCategorizer<CategoryType> target,
                   InputOutputPair<? extends Vectorizable,CategoryType> example)
```
    Description copied from interface: IncrementalLearner
    
    The update method updates an object of ResultType using the given new data of type DataType, using some form of "learning" algorithm.
    
    Specified by:
    
    update in interface IncrementalLearner<InputOutputPair<? extends Vectorizable,CategoryType>,LinearMultiCategorizer<CategoryType>>
    
    Parameters:
    
    target - The object to update.
    
    example - The new data for the learning algorithm to use to update the object.
  - getMinMargin
```
public double getMinMargin()
```
    Gets the minimum margin to enforce. Any value less than or equal to this is considered an error for the algorithm.
    
    Returns:
    
    The minimum margin. Cannot be negative.
  - setMinMargin
```
public void setMinMargin(double minMargin)
```
    Gets the minimum margin to enforce. Any value less than or equal to this is considered an error for the algorithm.
    
    Parameters:
    
    minMargin - The minimum margin. Cannot be negative.
  - getVectorFactory
```
public VectorFactory<?> getVectorFactory()
```
    Gets the VectorFactory used to create the weight vector.
    
    Specified by:
    
    getVectorFactory in interface VectorFactoryContainer
    
    Returns:
    
    The VectorFactory used to create the weight vector.
  - setVectorFactory
```
public void setVectorFactory(VectorFactory<?> vectorFactory)
```
    Sets the VectorFactory used to create the weight vector.
    
    Parameters:
    
    vectorFactory - The VectorFactory used to create the weight vector.

Class OnlineMultiPerceptron<CategoryType>

Nested Class Summary

Field Summary

Constructor Summary

Method Summary

Methods inherited from class gov.sandia.cognition.learning.algorithm.AbstractBatchAndIncrementalLearner

Methods inherited from class java.lang.Object

Field Detail

DEFAULT_MIN_MARGIN

minMargin

vectorFactory

Constructor Detail

OnlineMultiPerceptron

OnlineMultiPerceptron

OnlineMultiPerceptron

Method Detail

createInitialLearnedObject

update

getMinMargin

setMinMargin

getVectorFactory

setVectorFactory