PassiveAggressive (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.linear.PassiveAggressive

All Implemented Interfaces:

Serializable, Cloneable, BinaryScoreClassifier, Classifier, UpdateableClassifier, Parameterized, Regressor, UpdateableRegressor, SimpleWeightVectorModel, SingleWeightVectorModel
```
public class PassiveAggressive
extends Object
implements UpdateableClassifier, BinaryScoreClassifier, UpdateableRegressor, Parameterized, SingleWeightVectorModel
```
An implementations of the 3 versions of the Passive Aggressive algorithm for binary classification and regression. Its a type of online algorithm that performs the minimal update necessary to correct for a mistake.

See:
Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S.,&Singer, Y. (2006). Online passive-aggressive algorithms. Journal of Machine Learning Research, 7, 551–585.

Author:

Edward Raff

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class PassiveAggressive.Mode
Controls which version of the Passive Aggressive update is used

Nested Classes
Modifier and Type	Class and Description
`static class`	`PassiveAggressive.Mode` Controls which version of the Passive Aggressive update is used

Constructor Summary

Constructors
Constructor and Description
`PassiveAggressive()` Creates a new Passive Aggressive learner that does 10 epochs and uses `PassiveAggressive.Mode.PA1`
`PassiveAggressive(int epochs, PassiveAggressive.Mode mode)` Creates a new Passive Aggressive 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.
`PassiveAggressive`	`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.
`double`	`getC()` Returns the aggressiveness parameter
`int`	`getEpochs()` Returns the number of epochs used for training
`double`	`getEps()` Returns the maximum acceptable difference in prediction and truth
`PassiveAggressive.Mode`	`getMode()` Returns which version of the PA update is used
`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.
`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.
`static Distribution`	`guessC(DataSet d)` Guess the distribution to use for the regularization term `C` in PassiveAggressive.
`int`	`numWeightsVecs()` Returns the number of weight vectors that can be returned.
`double`	`regress(DataPoint data)`
`void`	`setC(double C)` Set the aggressiveness parameter.
`void`	`setEpochs(int epochs)` Sets the number of whole iterations through the training set that will be performed for training
`void`	`setEps(double eps)` Sets the range for numerical prediction.
`void`	`setMode(PassiveAggressive.Mode mode)` Sets which version of the PA update is used.
`void`	`setUp(CategoricalData[] categoricalAttributes, int numericAttributes)` Prepares the classifier to begin learning from its `UpdateableRegressor.update(jsat.classifiers.DataPoint, double)` method.
`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`	`train(RegressionDataSet dataSet)`
`void`	`train(RegressionDataSet dataSet, ExecutorService threadPool)`
`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.
`void`	`update(DataPoint dataPoint, double targetValue)` Updates the classifier by giving it a new data point to learn from.
`void`	`update(DataPoint dataPoint, int targetClass)` Updates the classifier by giving it a new data point to learn from.

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

- Constructor Detail
  - PassiveAggressive
```
public PassiveAggressive()
```
    Creates a new Passive Aggressive learner that does 10 epochs and uses PassiveAggressive.Mode.PA1
  - PassiveAggressive
```
public PassiveAggressive(int epochs,
                         PassiveAggressive.Mode mode)
```
    Creates a new Passive Aggressive learner
    
    Parameters:
    
    epochs - the number of training epochs to use during batch training
    
    mode - which version of the update to perform
- Method Detail
  - setC
```
public void setC(double C)
```
    Set the aggressiveness parameter. Increasing the value of this parameter increases the aggressiveness of the algorithm. It must be a positive value. This parameter essentially performs a type of regularization on the updates
    An infinitely large value is equivalent to being completely aggressive, and is performed when the mode is set to PassiveAggressive.Mode.PA.
    
    Parameters:
    
    C - the positive aggressiveness parameter
  - getC
```
public double getC()
```
    Returns the aggressiveness parameter
    
    Returns:
    
    the aggressiveness parameter
  - setMode
```
public void setMode(PassiveAggressive.Mode mode)
```
    Sets which version of the PA update is used.
    
    Parameters:
    
    mode - which PA update style to perform
  - getMode
```
public PassiveAggressive.Mode getMode()
```
    Returns which version of the PA update is used
    
    Returns:
    
    which PA update style is used
  - setEps
```
public void setEps(double eps)
```
    Sets the range for numerical prediction. If it is within range of the given value, no error will be incurred.
    
    Parameters:
    
    eps - the maximum acceptable difference in prediction and truth
  - getEps
```
public double getEps()
```
    Returns the maximum acceptable difference in prediction and truth
    
    Returns:
    
    the maximum acceptable difference in prediction and truth
  - setEpochs
```
public void setEpochs(int epochs)
```
    Sets the number of whole iterations through the training set that will be performed for training
    
    Parameters:
    
    epochs - the number of whole iterations through the data set
  - getEpochs
```
public int getEpochs()
```
    Returns the number of epochs used for training
    
    Returns:
    
    the number of epochs used for training
  - 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.
  - 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
  - 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
    
    Specified by:
    
    supportsWeightedData in interface Regressor
    
    Returns:
    
    true if the model supports weighted data, false otherwise
  - 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
  - setUp
```
public void setUp(CategoricalData[] categoricalAttributes,
                  int numericAttributes)
```
    Description copied from interface: UpdateableRegressor
    
    Prepares the classifier to begin learning from its UpdateableRegressor.update(jsat.classifiers.DataPoint, double) method.
    
    Specified by:
    
    setUp in interface UpdateableRegressor
    
    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
  - 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
  - update
```
public void update(DataPoint dataPoint,
                   double targetValue)
```
    Description copied from interface: UpdateableRegressor
    
    Updates the classifier by giving it a new data point to learn from.
    
    Specified by:
    
    update in interface UpdateableRegressor
    
    Parameters:
    
    dataPoint - the data point to learn
    
    targetValue - the target value of the data point
  - regress
```
public double regress(DataPoint data)
```
    Specified by:
    
    regress in interface Regressor
  - train
```
public void train(RegressionDataSet dataSet,
                  ExecutorService threadPool)
```
    Specified by:
    
    train in interface Regressor
  - train
```
public void train(RegressionDataSet dataSet)
```
    Specified by:
    
    train in interface Regressor
  - clone
```
public PassiveAggressive clone()
```
    Specified by:
    
    clone in interface BinaryScoreClassifier
    
    Specified by:
    
    clone in interface Classifier
    
    Specified by:
    
    clone in interface UpdateableClassifier
    
    Specified by:
    
    clone in interface Regressor
    
    Specified by:
    
    clone in interface UpdateableRegressor
    
    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.
  - guessC
```
public static Distribution guessC(DataSet d)
```
    Guess the distribution to use for the regularization term C in PassiveAggressive.
    
    Parameters:
    
    d - the data set to get the guess for
    
    Returns:
    
    the guess for the C parameter

Class PassiveAggressive

Nested Class Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

PassiveAggressive

PassiveAggressive

Method Detail

setC

getC

setMode

getMode

setEps

getEps

setEpochs

getEpochs

getRawWeight

getBias

getRawWeight

getBias

numWeightsVecs

classify

getScore

trainC

trainC

supportsWeightedData

setUp

setUp

update

update

regress

train

train

clone

getParameters

getParameter

guessC