Projectron (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.BaseUpdateableClassifier
- - jsat.classifiers.linear.kernelized.Projectron

All Implemented Interfaces:

Serializable, Cloneable, BinaryScoreClassifier, Classifier, UpdateableClassifier, Parameterized
```
public class Projectron
extends BaseUpdateableClassifier
implements BinaryScoreClassifier, Parameterized
```
An implementation of the Projectron and Projectrion++ algorithms. These are a kernelized extensions of the Perceptron that bound the number of support vectors used, with the latter incorporating some similarities from PassiveAggressive.
Unlike many other bounded kernel learners, the number of support vectors is not specified by the user. This value is controlled by a sparsity parameter setEta(double).

See:
- Orabona, F., Keshet, J.,&Caputo, B. (2008). The Projectron: a bounded kernel-based Perceptron. Proceedings of the 25th international conference on Machine learning - ICML ’08 (pp. 720–727). New York, New York, USA: ACM Press. doi:10.1145/1390156.1390247
- Orabona, F., Keshet, J.,&Caputo, B. (2009). Bounded Kernel-Based Online Learning. The Journal of Machine Learning Research, 10, 2643–2666.
Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Modifier	Constructor and Description
	`Projectron(KernelTrick k)` Creates a new Projectron++ learner
	`Projectron(KernelTrick k, double eta)` Creates a new Projectron++ learner
	`Projectron(KernelTrick k, double eta, boolean useMarginUpdates)` Creates a new Projectron learner
`protected`	`Projectron(Projectron toCopy)` Copy constructor

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`CategoricalResults`	`classify(DataPoint data)` Performs classification on the given data point.
`Projectron`	`clone()`
`double`	`getEta()` Returns the sparsity parameter value
`KernelTrick`	`getKernel()` Returns the kernel trick in use
`Parameter`	`getParameter(String paramName)` Returns the parameter with the given name.
`List<Parameter>`	`getParameters()` Returns the list of parameters that can be altered for this learner.
`double`	`getScore(DataPoint dp)` Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.
`boolean`	`isUseMarginUpdates()` Returns `true` if margin errors can cause updates, `false` if not.
`void`	`setEta(double eta)` Sets the η parameter which controls the sparsity of the Projection solution.
`void`	`setKernel(KernelTrick k)` Sets the kernel trick to be used
`void`	`setUp(CategoricalData[] categoricalAttributes, int numericAttributes, CategoricalData predicting)` Prepares the classifier to begin learning from its `UpdateableClassifier.update(jsat.classifiers.DataPoint, int)` method.
`void`	`setUseMarginUpdates(boolean useMarginUpdates)` Sets whether or not projection updates will be performed for margin errors.
`boolean`	`supportsWeightedData()` Indicates whether the model knows how to train using weighted data points.
`void`	`update(DataPoint dataPoint, int targetClass)` Updates the classifier by giving it a new data point to learn from.

Methods inherited from class jsat.classifiers.BaseUpdateableClassifier
getEpochs, setEpochs, trainC, trainC, trainEpochs

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

Methods inherited from interface jsat.classifiers.Classifier
trainC, trainC

- Constructor Detail
  - Projectron
```
public Projectron(KernelTrick k)
```
    Creates a new Projectron++ learner
    
    Parameters:
    
    k - the kernel to use
  - Projectron
```
public Projectron(KernelTrick k,
                  double eta)
```
    Creates a new Projectron++ learner
    
    Parameters:
    
    k - the kernel to use
    
    eta - the sparsity parameter
  - Projectron
```
public Projectron(KernelTrick k,
                  double eta,
                  boolean useMarginUpdates)
```
    Creates a new Projectron learner
    
    Parameters:
    
    k - the kernel to use
    
    eta - the sparsity parameter
    
    useMarginUpdates - whether or not to perform projection updates on margin errors
  - Projectron
```
protected Projectron(Projectron toCopy)
```
    Copy constructor
    
    Parameters:
    
    toCopy - the object to copy
- Method Detail
  - setKernel
```
public void setKernel(KernelTrick k)
```
    Sets the kernel trick to be used
    
    Parameters:
    
    k - the kernel trick to be use
  - getKernel
```
public KernelTrick getKernel()
```
    Returns the kernel trick in use
    
    Returns:
    
    the kernel trick in use
  - setEta
```
public void setEta(double eta)
```
    Sets the η parameter which controls the sparsity of the Projection solution. Larger values result in greater sparsity, at the potential loss of accuracy. If set to 0 and setUseMarginUpdates(boolean) is false, the Projectron degenerates into the standard kernelized Perceptron.
    
    Parameters:
    
    eta - the sparsity parameter in [0, Infinity)
  - getEta
```
public double getEta()
```
    Returns the sparsity parameter value
    
    Returns:
    
    the sparsity parameter value
  - setUseMarginUpdates
```
public void setUseMarginUpdates(boolean useMarginUpdates)
```
    Sets whether or not projection updates will be performed for margin errors. If true, this behaves as the Projectrion++ algorithm. If false, the behavior is equal to the standard Projectron.
    
    Parameters:
    
    useMarginUpdates - true to perform updates on margin errors
  - isUseMarginUpdates
```
public boolean isUseMarginUpdates()
```
    Returns true if margin errors can cause updates, false if not.
    
    Returns:
    
    true if margin errors can cause updates, false if not.
  - clone
```
public Projectron clone()
```
    Specified by:
    
    clone in interface BinaryScoreClassifier
    
    Specified by:
    
    clone in interface Classifier
    
    Specified by:
    
    clone in interface UpdateableClassifier
    
    Specified by:
    
    clone in class BaseUpdateableClassifier
  - setUp
```
public void setUp(CategoricalData[] categoricalAttributes,
                  int numericAttributes,
                  CategoricalData predicting)
```
    Description copied from interface: UpdateableClassifier
    
    Prepares the classifier to begin learning from its UpdateableClassifier.update(jsat.classifiers.DataPoint, int) method.
    
    Specified by:
    
    setUp in interface UpdateableClassifier
    
    Parameters:
    
    categoricalAttributes - an array containing the categorical attributes that will be in each data point
    
    numericAttributes - the number of numeric attributes that will be in each data point
    
    predicting - the information for the target class that will be predicted
  - update
```
public void update(DataPoint dataPoint,
                   int targetClass)
```
    Description copied from interface: UpdateableClassifier
    
    Updates the classifier by giving it a new data point to learn from.
    
    Specified by:
    
    update in interface UpdateableClassifier
    
    Parameters:
    
    dataPoint - the data point to learn
    
    targetClass - the target class of the data point
  - classify
```
public CategoricalResults classify(DataPoint data)
```
    Description copied from interface: Classifier
    
    Performs classification on the given data point.
    
    Specified by:
    
    classify in interface Classifier
    
    Parameters:
    
    data - the data point to classify
    
    Returns:
    
    the results of the classification.
  - supportsWeightedData
```
public boolean supportsWeightedData()
```
    Description copied from interface: Classifier
    
    Indicates whether the model knows how to train using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.
    
    Specified by:
    
    supportsWeightedData in interface Classifier
    
    Returns:
    
    true if the model supports weighted data, false otherwise
  - getScore
```
public double getScore(DataPoint dp)
```
    Description copied from interface: BinaryScoreClassifier
    
    Returns the numeric score for predicting a class of a given data point, where the sign of the value indicates which class the data point is predicted to belong to.
    
    Specified by:
    
    getScore in interface BinaryScoreClassifier
    
    Parameters:
    
    dp - the data point to predict the class label of
    
    Returns:
    
    the score for the given data point
  - getParameters
```
public List<Parameter> getParameters()
```
    Description copied from interface: Parameterized
    
    Returns the list of parameters that can be altered for this learner.
    
    Specified by:
    
    getParameters in interface Parameterized
    
    Returns:
    
    the list of parameters that can be altered for this learner.
  - getParameter
```
public Parameter getParameter(String paramName)
```
    Description copied from interface: Parameterized
    
    Returns the parameter with the given name. Two different strings may map to a single Parameter object. An ASCII only string, and a Unicode style string.
    
    Specified by:
    
    getParameter in interface Parameterized
    
    Parameters:
    
    paramName - the name of the parameter to obtain
    
    Returns:
    
    the Parameter in question, or null if no such named Parameter exists.

Class Projectron

Constructor Summary

Method Summary

Methods inherited from class jsat.classifiers.BaseUpdateableClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface jsat.classifiers.Classifier

Constructor Detail

Projectron

Projectron

Projectron

Projectron

Method Detail

setKernel

getKernel

setEta

getEta

setUseMarginUpdates

isUseMarginUpdates

clone

setUp

update

classify

supportsWeightedData

getScore

getParameters

getParameter