Wagging (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.boosting.Wagging

All Implemented Interfaces:

Serializable, Cloneable, Classifier, Parameterized, Regressor

Direct Known Subclasses:

WaggingNormal
```
public class Wagging
extends Object
implements Classifier, Regressor, Parameterized
```
Wagging is a meta-classifier that is related to Bagging. Instead training on re-sampled data sets, it trains on randomly re-weighted data sets. The weight of each point is selected at random from a specified distribution, and set to zero if negative.

See: Bauer, E.,&Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine learning, 38(1998), 1–38.

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Modifier	Constructor and Description
	`Wagging(ContinuousDistribution dist, Classifier weakL, int iterations)` Creates a new Wagging classifier
	`Wagging(ContinuousDistribution dist, Regressor weakR, int iterations)` Creates a new Wagging regressor
`protected`	`Wagging(Wagging clone)` 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.
`Wagging`	`clone()`
`ContinuousDistribution`	`getDistribution()` Returns the distribution used for weight sampling
`int`	`getIterations()` Returns the number of iterations to create weak learners
`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.
`Classifier`	`getWeakClassifier()` Returns the weak learner used for classification.
`Regressor`	`getWeakRegressor()` Returns the weak learner used for regression
`double`	`regress(DataPoint data)`
`void`	`setDistribution(ContinuousDistribution dist)` Sets the distribution to select the random weights from
`void`	`setIterations(int iterations)` Sets the number of iterations to create weak learners
`void`	`setWeakLearner(Classifier weakL)` Sets the weak learner used for classification.
`void`	`setWeakLearner(Regressor weakR)` Sets the weak learner used for regressions .
`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.

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

- Constructor Detail
  - Wagging
```
public Wagging(ContinuousDistribution dist,
               Classifier weakL,
               int iterations)
```
    Creates a new Wagging classifier
    
    Parameters:
    
    dist - the distribution to select weights from
    
    weakL - the weak learner to use
    
    iterations - the number of iterations to perform
  - Wagging
```
public Wagging(ContinuousDistribution dist,
               Regressor weakR,
               int iterations)
```
    Creates a new Wagging regressor
    
    Parameters:
    
    dist - the distribution to select weights from
    
    weakR - the weak learner to use
    
    iterations - the number of iterations to perform
  - Wagging
```
protected Wagging(Wagging clone)
```
    Copy constructor
    
    Parameters:
    
    clone - the one to clone
- Method Detail
  - setWeakLearner
```
public void setWeakLearner(Classifier weakL)
```
    Sets the weak learner used for classification. If it also supports regressions that will be set as well.
    
    Parameters:
    
    weakL - the weak learner to use
  - getWeakClassifier
```
public Classifier getWeakClassifier()
```
    Returns the weak learner used for classification.
    
    Returns:
    
    the weak learner used for classification.
  - setWeakLearner
```
public void setWeakLearner(Regressor weakR)
```
    Sets the weak learner used for regressions . If it also supports classification that will be set as well.
    
    Parameters:
    
    weakR - the weak learner to use
  - getWeakRegressor
```
public Regressor getWeakRegressor()
```
    Returns the weak learner used for regression
    
    Returns:
    
    the weak learner used for regression
  - setIterations
```
public void setIterations(int iterations)
```
    Sets the number of iterations to create weak learners
    
    Parameters:
    
    iterations - the number of iterations to perform
  - getIterations
```
public int getIterations()
```
    Returns the number of iterations to create weak learners
    
    Returns:
    
    the number of iterations to perform
  - setDistribution
```
public void setDistribution(ContinuousDistribution dist)
```
    Sets the distribution to select the random weights from
    
    Parameters:
    
    dist - the distribution to use
  - getDistribution
```
public ContinuousDistribution getDistribution()
```
    Returns the distribution used for weight sampling
    
    Returns:
    
    the distribution used
  - 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.
  - 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
  - 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 Wagging clone()
```
    Specified by:
    
    clone in interface Classifier
    
    Specified by:
    
    clone in interface Regressor
    
    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.

Class Wagging

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

Wagging

Wagging

Wagging

Method Detail

setWeakLearner

getWeakClassifier

setWeakLearner

getWeakRegressor

setIterations

getIterations

setDistribution

getDistribution

classify

trainC

trainC

supportsWeightedData

regress

train

train

clone

getParameters

getParameter