SCW (Java Statistical Analysis Tool 0.0.8 API)

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

All Implemented Interfaces:

Serializable, Cloneable, BinaryScoreClassifier, Classifier, UpdateableClassifier, Parameterized, SimpleWeightVectorModel, SingleWeightVectorModel
```
public class SCW
extends BaseUpdateableClassifier
implements BinaryScoreClassifier, Parameterized, SingleWeightVectorModel
```
Provides an Implementation of Confidence-Weighted (CW) learning and Soft Confidence-Weighted (SCW), both of which are binary linear classifiers inspired by PassiveAggressive. The SCW mode handles noisy and nonlinearly separable datasets better.
NOTE: Unlike other online second order methods, when using the full covariance matrix, all new inputs cost O(d²) time to process, even if update is needed.
NOTE: This implementation does not add an implicit bias term, so the solution goes through the origin

See:
- Crammer, K., Fern, M.,&Pereira, O. (2008). Exact Convex Confidence-Weighted Learning. In Advances in Neural Information Processing Systems 22 (pp. 345–352). Retrieved from here
- Wang, J., Zhao, P.,&Hoi, S. C. H. (2012). Exact Soft Confidence-Weighted Learning. ICML. Learning. Retrieved from here
Author:

Edward Raff

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class SCW.Mode
Which version of the algorithms shuld be used

Nested Classes
Modifier and Type	Class and Description
`static class`	`SCW.Mode` Which version of the algorithms shuld be used

Constructor Summary

Constructors
Modifier	Constructor and Description
	`SCW()` Creates a new SCW learner
	`SCW(double eta, SCW.Mode mode, boolean diagonalOnly)` Creates a new SCW learner
`protected`	`SCW(SCW other)` Copy constructor

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.
`SCW`	`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
`double`	`getEta()` Returns the target correction confidence
`SCW.Mode`	`getMode()` Returns which algorithm 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.
`Vec`	`getWeightVec()` Returns the weight vector used to compute results via a dot product.
`static Distribution`	`guessC(DataSet d)` Guess the distribution to use for the regularization term `C` .
`static Distribution`	`guessEta(DataSet d)` Guess the distribution to use for the regularization term `η` .
`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`	`setC(double C)` Set the aggressiveness parameter.
`void`	`setDiagonalOnly(boolean diagonalOnly)` Using the full covariance matrix requires O(d²) work on updates, where d is the dimension of the data.
`void`	`setEta(double eta)` SCW uses a probabilistic version of the margin and attempts to make a correction so that the confidence with correct label would be of a certain threshold, which is set by eta.
`void`	`setMode(SCW.Mode mode)` Controls which version of the algorithm 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.
`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
  - SCW
```
public SCW()
```
    Creates a new SCW learner
  - SCW
```
public SCW(double eta,
           SCW.Mode mode,
           boolean diagonalOnly)
```
    Creates a new SCW learner
    
    Parameters:
    
    eta - the margin confidence parameter in [0.5, 1]
    
    mode - mode controlling which algorithm to use
    
    diagonalOnly - whether or not to use only the diagonal of the covariance matrix
    
    See Also:
    
    setEta(double), setMode(jsat.classifiers.linear.SCW.Mode), setDiagonalOnly(boolean)
  - SCW
```
protected SCW(SCW other)
```
    Copy constructor
    
    Parameters:
    
    other - object to copy
- Method Detail
  - setEta
```
public void setEta(double eta)
```
    SCW uses a probabilistic version of the margin and attempts to make a correction so that the confidence with correct label would be of a certain threshold, which is set by eta. So the threshold must be in [0.5, 1.0]. Values in the range [0.8, 0.9] often work well on a wide range of problems
    
    Parameters:
    
    eta - the confidence to correct to
  - getEta
```
public double getEta()
```
    Returns the target correction confidence
    
    Returns:
    
    the target correction confidence
  - 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
    The aggressiveness parameter is only used by SCW.Mode.SCWI and SCW.Mode.SCWII
    
    Parameters:
    
    C - the positive aggressiveness parameter
  - getC
```
public double getC()
```
    Returns the aggressiveness parameter
    
    Returns:
    
    the aggressiveness parameter
  - setMode
```
public void setMode(SCW.Mode mode)
```
    Controls which version of the algorithm is used
    
    Parameters:
    
    mode - which algorithm to use
  - getMode
```
public SCW.Mode getMode()
```
    Returns which algorithm is used
    
    Returns:
    
    which algorithm is used
  - setDiagonalOnly
```
public void setDiagonalOnly(boolean diagonalOnly)
```
    Using the full covariance matrix requires O(d²) work on updates, 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
  - 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.
  - clone
```
public SCW 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.
  - guessC
```
public static Distribution guessC(DataSet d)
```
    Guess the distribution to use for the regularization term C .
    
    Parameters:
    
    d - the data set to get the guess for
    
    Returns:
    
    the guess for the C parameter
  - guessEta
```
public static Distribution guessEta(DataSet d)
```
    Guess the distribution to use for the regularization term η .
    
    Parameters:
    
    d - the data set to get the guess for
    
    Returns:
    
    the guess for the C parameter

Class SCW

Nested Class Summary

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

SCW

SCW

SCW

Method Detail

setEta

getEta

setC

getC

setMode

getMode

setDiagonalOnly

isDiagonalOnly

getWeightVec

getRawWeight

getBias

getRawWeight

getBias

numWeightsVecs

clone

setUp

update

classify

getScore

supportsWeightedData

getParameters

getParameter

guessC

guessEta