ROMMA (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.BaseUpdateableClassifier
- - jsat.classifiers.linear.ROMMA

All Implemented Interfaces:

Serializable, Cloneable, BinaryScoreClassifier, Classifier, UpdateableClassifier, SimpleWeightVectorModel, SingleWeightVectorModel
```
public class ROMMA
extends BaseUpdateableClassifier
implements BinaryScoreClassifier, SingleWeightVectorModel
```
Provides an implementation of the linear Relaxed online Maximum Margin algorithm, which finds a similar solution to SVMs. By default, the aggressive variant with an implicit bias term is used, which is the suggested form from the paper. It is a binary classifier.

See: Li, Y.,&Long, P. M. (2002). The Relaxed Online Maximum Margin Algorithm. Machine Learning, 46(1-3), 361–387. doi:10.1023/A:1012435301888

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Modifier	Constructor and Description
	`ROMMA()` Creates a new aggressive ROMMA classifier
	`ROMMA(boolean aggressive)` Creates a new ROMMA classifier
`protected`	`ROMMA(ROMMA other)` 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.
`ROMMA`	`clone()`
`double`	`getBias()` Returns the bias term used for the model, or 0 of the model does not support or was not trained with a bias term.
`double`	`getBias(int index)` Returns the bias term used with the weight vector for the given class index.
`Vec`	`getRawWeight()` Returns the only weight vector used for the model
`Vec`	`getRawWeight(int index)` Returns the raw weight vector associated with the given class index.
`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.
`Vec`	`getWeightVec()` Returns the weight vector used to compute results via a dot product.
`boolean`	`isAggressive()` Returns whether or not the aggressive variant of ROMMA is used
`boolean`	`isUseBias()` Returns whether or not an implicit bias term is in use
`int`	`numWeightsVecs()` Returns the number of weight vectors that can be returned.
`void`	`setAggressive(boolean aggressive)` Determines whether the normal or aggressive ROMMA algorithm will 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`	`setUseBias(boolean useBias)` Sets whether or not an implicit bias term will be added to the data set
`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
  - ROMMA
```
public ROMMA()
```
    Creates a new aggressive ROMMA classifier
  - ROMMA
```
public ROMMA(boolean aggressive)
```
    Creates a new ROMMA classifier
    
    Parameters:
    
    aggressive - whether or not to use the aggressive variant
  - ROMMA
```
protected ROMMA(ROMMA other)
```
    Copy constructor
    
    Parameters:
    
    other - the ROMMA object to copy
- Method Detail
  - clone
```
public ROMMA 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
  - setAggressive
```
public void setAggressive(boolean aggressive)
```
    Determines whether the normal or aggressive ROMMA algorithm will be used.
    
    Parameters:
    
    aggressive - true to use the aggressive variant
  - isAggressive
```
public boolean isAggressive()
```
    Returns whether or not the aggressive variant of ROMMA is used
    
    Returns:
    
    true if the aggressive variant of ROMMA is used
  - setUseBias
```
public void setUseBias(boolean useBias)
```
    Sets whether or not an implicit bias term will be added to the data set
    
    Parameters:
    
    useBias - true to add an implicit bias term
  - isUseBias
```
public boolean isUseBias()
```
    Returns whether or not an implicit bias term is in use
    
    Returns:
    
    true if a bias term is in use
  - getWeightVec
```
public Vec getWeightVec()
```
    Returns the weight vector used to compute results via a dot product.
    Do not modify this value, or you will alter the results returned.
    
    Returns:
    
    the learned weight vector for prediction
  - getRawWeight
```
public Vec getRawWeight()
```
    Description copied from interface: SingleWeightVectorModel
    
    Returns the only weight vector used for the model
    
    Specified by:
    
    getRawWeight in interface SingleWeightVectorModel
    
    Returns:
    
    the only weight vector used for the model
  - getBias
```
public double getBias()
```
    Description copied from interface: SingleWeightVectorModel
    
    Returns the bias term used for the model, or 0 of the model does not support or was not trained with a bias term.
    
    Specified by:
    
    getBias in interface SingleWeightVectorModel
    
    Returns:
    
    the bias term for the model
  - getRawWeight
```
public Vec getRawWeight(int index)
```
    Description copied from interface: SimpleWeightVectorModel
    
    Returns the raw weight vector associated with the given class index. If the given class is an implicit zero vector, a ConstantVector object may be returned.
    Do not alter the returned weight vector, as it will change the model's values.
    
    If a regression problem, only index = 0 should be used
    
    Specified by:
    
    getRawWeight in interface SimpleWeightVectorModel
    
    Parameters:
    
    index - the class index to get the weight vector for
    
    Returns:
    
    the weight vector used for the specified class
  - getBias
```
public double getBias(int index)
```
    Description copied from interface: SimpleWeightVectorModel
    
    Returns the bias term used with the weight vector for the given class index. If the model does not support or was not trained with bias weights, 0 will be returned.
    
    If a regression problem, only index = 0 should be used
    
    Specified by:
    
    getBias in interface SimpleWeightVectorModel
    
    Parameters:
    
    index - the class index to get the weight vector for
    
    Returns:
    
    the bias term for the specified class
  - numWeightsVecs
```
public int numWeightsVecs()
```
    Description copied from interface: SimpleWeightVectorModel
    
    Returns the number of weight vectors that can be returned. For binary classification problems the value may be 1 if only a single weight vector's sign is used to determine the class. For multi-class problems, the weight vector count includes the implicit zero vector (if one is being used).
    
    Specified by:
    
    numWeightsVecs in interface SimpleWeightVectorModel
    
    Returns:
    
    the number of weight vectors for which SimpleWeightVectorModel.getRawWeight(int) can be called.
  - 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.
  - 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
  - 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

Class ROMMA

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

ROMMA

ROMMA

ROMMA

Method Detail

clone

setAggressive

isAggressive

setUseBias

isUseBias

getWeightVec

getRawWeight

getBias

getRawWeight

getBias

numWeightsVecs

setUp

update

classify

getScore

supportsWeightedData