SPA (Java Statistical Analysis Tool 0.0.8 API)

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

All Implemented Interfaces:

Serializable, Cloneable, Classifier, UpdateableClassifier, Parameterized, SimpleWeightVectorModel
```
public class SPA
extends BaseUpdateableClassifier
implements Parameterized, SimpleWeightVectorModel
```
Support class Passive Aggressive (SPA) is a multi class generalization of PassiveAggressive. It works in the same philosophy, and can obtain better multi class accuracy then PA used with a meta learner.
SPA is more sensitive to small values for the aggressiveness parameter.
If working with a binary classification problem, SPA reduces to PA, and the original PA implementation should be used instead.
By default, the biast term is not used.

See:
Matsushima, S., Shimizu, N., Yoshida, K., Ninomiya, T.,&Nakagawa, H. (2010). Exact Passive-Aggressive Algorithm for Multiclass Classification Using Support Class. SIAM International Conference on Data Mining - SDM (pp. 303–314). Retrieved from here

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description
`SPA()` Creates a new Passive Aggressive learner that does 10 epochs and uses PA2.
`SPA(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.
`SPA`	`clone()`
`double`	`getBias(int index)` Returns the bias term used with the weight vector for the given class index.
`double`	`getC()` Returns the aggressiveness parameter
`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(int index)` Returns the raw weight vector associated with the given class index.
`static Distribution`	`guessC(DataSet d)` Guess the distribution to use for the regularization term `C` in Support PassiveAggressive.
`boolean`	`isUseBias()` Returns true if an implicit bias term will be added, false otherwise
`int`	`numWeightsVecs()` Returns the number of weight vectors that can be returned.
`void`	`setC(double C)` Set the aggressiveness parameter.
`void`	`setMode(PassiveAggressive.Mode mode)` Sets which version of the PA update is 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 the implementation will use an implicit bias term appended to the inputs or not.
`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

- Constructor Detail
  - SPA
```
public SPA()
```
    Creates a new Passive Aggressive learner that does 10 epochs and uses PA2.
  - SPA
```
public SPA(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
  - setUseBias
```
public void setUseBias(boolean useBias)
```
    Sets whether or not the implementation will use an implicit bias term appended to the inputs or not.
    
    Parameters:
    
    useBias - true to add an implicit bias term, false to use the data as given
  - isUseBias
```
public boolean isUseBias()
```
    Returns true if an implicit bias term will be added, false otherwise
    
    Returns:
    
    true if an implicit bias term will be added, false otherwise
  - 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
  - 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.
  - clone
```
public SPA clone()
```
    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
  - 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 Support PassiveAggressive.
    
    Parameters:
    
    d - the data set to get the guess for
    
    Returns:
    
    the guess for the C parameter

Class SPA

Constructor Summary

Method Summary

Methods inherited from class jsat.classifiers.BaseUpdateableClassifier

Methods inherited from class java.lang.Object

Constructor Detail

SPA

SPA

Method Detail

setUseBias

isUseBias

setC

getC

setMode

getMode

getRawWeight

getBias

numWeightsVecs

clone

setUp

update

classify

supportsWeightedData

getParameters

getParameter

guessC