OneVSOne (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.OneVSOne

All Implemented Interfaces:

Serializable, Cloneable, Classifier, Parameterized

Direct Known Subclasses:

DDAG
```
public class OneVSOne
extends Object
implements Classifier, Parameterized
```
A One VS One classifier extends binary decision classifiers into multi-class decision classifiers. This is done by creating a binary-classification problem for every possible pair of classes, and then classifying by taking the result of all possible combinations and choosing the class that got the most results. This allows for a soft decision result, however, it often proves to be a meaningless soft boundary.

Author:

Edward Raff

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected Classifier`	`baseClassifier` Main binary classifier
`protected Classifier[][]`	`oneVone` Uper-diagonal matrix of classifiers sans the first index since a classifier vs itself is useless.
`protected CategoricalData`	`predicting`

Constructor Summary

Constructors
Constructor and Description
`OneVSOne(Classifier baseClassifier)` Creates a new One-vs-One classifier
`OneVSOne(Classifier baseClassifier, boolean concurrentTrain)` Creates a new One-vs-One classifier

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.
`OneVSOne`	`clone()`
`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.
`boolean`	`isConcurrentTraining()`
`void`	`setConcurrentTraining(boolean concurrentTrain)` Controls whether or not training of the several classifiers occurs concurrently or sequentually.
`boolean`	`supportsWeightedData()` Indicates whether the model knows how to train using weighted data points.
`void`	`trainC(ClassificationDataSet dataSet)` Trains the classifier and constructs a model for classification using the given data set.
`void`	`trainC(ClassificationDataSet dataSet, ExecutorService threadPool)` Trains the classifier and constructs a model for classification using the given data set.

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

- Field Detail
  - baseClassifier
```
protected Classifier baseClassifier
```
    Main binary classifier
  - oneVone
```
protected Classifier[][] oneVone
```
    Uper-diagonal matrix of classifiers sans the first index since a classifier vs itself is useless. First index is the first dimensions is the first class, 2nd index + the value of the first + 1 is the opponent class
  - predicting
```
protected CategoricalData predicting
```
- Constructor Detail
  - OneVSOne
```
public OneVSOne(Classifier baseClassifier)
```
    Creates a new One-vs-One classifier
    
    Parameters:
    
    baseClassifier - the binary classifier to extend
  - OneVSOne
```
public OneVSOne(Classifier baseClassifier,
                boolean concurrentTrain)
```
    Creates a new One-vs-One classifier
    
    Parameters:
    
    baseClassifier - the binary classifier to extend
    
    concurrentTrain - true to have training of individual classifiers occur in parallel, false to have them use their native parallel training method.
- Method Detail
  - setConcurrentTraining
```
public void setConcurrentTraining(boolean concurrentTrain)
```
    Controls whether or not training of the several classifiers occurs concurrently or sequentually.
    
    Parameters:
    
    concurrentTrain - true to have training of individual classifiers occur in parallel, false to have them use their native parallel training method.
  - isConcurrentTraining
```
public boolean isConcurrentTraining()
```
    Returns:
    
    true if training of individual classifiers occur in parallel, false they use their native parallel training method.
  - 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.
  - trainC
```
public void trainC(ClassificationDataSet dataSet,
                   ExecutorService threadPool)
```
    Description copied from interface: Classifier
    
    Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.
    
    Specified by:
    
    trainC in interface Classifier
    
    Parameters:
    
    dataSet - the data set to train on
    
    threadPool - the source of threads to use.
  - trainC
```
public void trainC(ClassificationDataSet dataSet)
```
    Description copied from interface: Classifier
    
    Trains the classifier and constructs a model for classification using the given data set.
    
    Specified by:
    
    trainC in interface Classifier
    
    Parameters:
    
    dataSet - the data set to train on
  - 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
  - clone
```
public OneVSOne clone()
```
    Specified by:
    
    clone in interface Classifier
    
    Overrides:
    
    clone in class Object
  - 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 OneVSOne

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

baseClassifier

oneVone

predicting

Constructor Detail

OneVSOne

OneVSOne

Method Detail

setConcurrentTraining

isConcurrentTraining

classify

trainC

trainC

supportsWeightedData

clone

getParameters

getParameter