AROW (Java Statistical Analysis Tool 0.0.8 API)

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

All Implemented Interfaces:

Serializable, Cloneable, BinaryScoreClassifier, Classifier, UpdateableClassifier, Parameterized, SimpleWeightVectorModel, SingleWeightVectorModel
```
public class AROW
extends BaseUpdateableClassifier
implements BinaryScoreClassifier, Parameterized, SingleWeightVectorModel
```
An implementation of Adaptive Regularization of Weight Vectors (AROW), which uses second order information to learn a large margin binary classifier. As such, updates can occur on correctly classified instances if they are not far enough from the margin. Unlike many margin algorithms, it handles noise well.
NOTE: This implementation does not add an implicit bias term, so the solution goes through the origin

See: Crammer, K., Kulesza, A.,&Dredze, M. (2013). Adaptive regularization of weight vectors. Machine Learning, 91(2), 155–187. doi:10.1007/s10994-013-5327-x

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Modifier	Constructor and Description
	`AROW()` Creates a new AROW learner
`protected`	`AROW(AROW other)` Copy constructor
	`AROW(double r, boolean diagonalOnly)` Creates a new AROW learner

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`CategoricalResults`	`classify(DataPoint data)` Performs classification on the given data point.
`AROW`	`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.
`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`	`getR()` Returns the regularization parameter
`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.
`static Distribution`	`guessR(DataSet d)` Guess the distribution to use for the regularization term `r` .
`boolean`	`isDiagonalOnly()` Returns `true` if the covariance matrix is restricted to its diagonal entries
`int`	`numWeightsVecs()` Returns the number of weight vectors that can be returned.
`void`	`setDiagonalOnly(boolean diagonalOnly)` Using the full covariance matrix requires O(d²) work on mistakes, where d is the dimension of the data.
`void`	`setR(double r)` Sets the r parameter of AROW, which controls the regularization.
`void`	`setUp(CategoricalData[] categoricalAttributes, int numericAttributes, CategoricalData predicting)` Prepares the classifier to begin learning from its `UpdateableClassifier.update(jsat.classifiers.DataPoint, int)` method.
`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
  - AROW
```
public AROW()
```
    Creates a new AROW learner
  - AROW
```
public AROW(double r,
            boolean diagonalOnly)
```
    Creates a new AROW learner
    
    Parameters:
    
    r - the regularization parameter
    
    diagonalOnly - whether or not to use only the diagonal of the covariance
    
    See Also:
    
    setR(double), setDiagonalOnly(boolean)
  - AROW
```
protected AROW(AROW other)
```
    Copy constructor
    
    Parameters:
    
    other - object to copy
- Method Detail
  - setDiagonalOnly
```
public void setDiagonalOnly(boolean diagonalOnly)
```
    Using the full covariance matrix requires O(d²) work on mistakes, where d is the dimension of the data. Runtime can be reduced by using only the diagonal of the matrix to perform updates in O(s) time, where s ≤ d is the number of non-zero values in the input
    
    Parameters:
    
    diagonalOnly - true to use only the diagonal of the covariance
  - isDiagonalOnly
```
public boolean isDiagonalOnly()
```
    Returns true if the covariance matrix is restricted to its diagonal entries
    
    Returns:
    
    true if the covariance matrix is restricted to its diagonal entries
  - setR
```
public void setR(double r)
```
    Sets the r parameter of AROW, which controls the regularization. Larger values reduce the change in the model on each update.
    
    Parameters:
    
    r - the regularization parameter in (0, Inf)
  - getR
```
public double getR()
```
    Returns the regularization parameter
    
    Returns:
    
    the regularization parameter
  - 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
  - clone
```
public AROW 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.
  - 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
  - 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.
  - 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.
  - guessR
```
public static Distribution guessR(DataSet d)
```
    Guess the distribution to use for the regularization term r .
    
    Parameters:
    
    d - the data set to get the guess for
    
    Returns:
    
    the guess for the r parameter

Class AROW

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

AROW

AROW

AROW

Method Detail

setDiagonalOnly

isDiagonalOnly

setR

getR

getWeightVec

clone

setUp

update

classify

getScore

supportsWeightedData

getParameters

getParameter

getRawWeight

getBias

getRawWeight

getBias

numWeightsVecs

guessR