Pegasos (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.svm.Pegasos

All Implemented Interfaces:

Serializable, Cloneable, BinaryScoreClassifier, Classifier, Parameterized, SimpleWeightVectorModel, SingleWeightVectorModel
```
public class Pegasos
extends Object
implements BinaryScoreClassifier, Parameterized, SingleWeightVectorModel
```
Implements the linear kernel mini-batch version of the Pegasos SVM classifier. It performs updates stochastically and is very fast.
Because Pegasos updates the primal directly, there are no support vectors saved from the training set.

See: Shalev-Shwartz, S., Singer, Y.,&Srebro, N. (2007). Pegasos : Primal Estimated sub-GrAdient SOlver for SVM. 24th international conference on Machine learning (pp. 807–814). New York, NY: ACM. doi:10.1145/1273496.1273598

Author:

Edward Raff

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`DEFAULT_BATCH_SIZE` The default batch size is 1
`static int`	`DEFAULT_EPOCHS` The default number of epochs is 5
`static double`	`DEFAULT_REG` The default regularization value is 1.0E-4

Constructor Summary

Constructors
Constructor and Description
`Pegasos()` Creates a new Pegasos SVM classifier using default values.
`Pegasos(int epochs, double reg, int batchSize)` Creates a new Pegasos SVM classifier
`Pegasos(Pegasos toCopy)` 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.
`Pegasos`	`clone()`
`int`	`getBatchSize()` Returns the number of points used in each iteration
`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`	`getEpochs()` Returns the number of iterations of updating that will be done
`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`	`getRegularization()` Returns the amount of regularization to used in training
`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`	`guessRegularization(DataSet d)` Guess the distribution to use for the regularization term `setRegularization(double)` in Pegasos.
`boolean`	`isProjectionStep()` Returns whether or not the projection step is in use after each iteration
`int`	`numWeightsVecs()` Returns the number of weight vectors that can be returned.
`void`	`setBatchSize(int batchSize)` Sets the batch size used during training.
`void`	`setEpochs(int epochs)` Sets the number of iterations through the training set that will be performed.
`void`	`setProjectionStep(boolean projectionStep)` Sets whether or not to use the projection step after each update per iteration
`void`	`setRegularization(double reg)` Sets the regularization constant used for learning.
`boolean`	`supportsWeightedData()` Indicates whether the model knows how to train using weighted data points.
`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

- Field Detail
  - DEFAULT_EPOCHS
```
public static final int DEFAULT_EPOCHS
```
    The default number of epochs is 5
    
    See Also:
    
    Constant Field Values
  - DEFAULT_REG
```
public static final double DEFAULT_REG
```
    The default regularization value is 1.0E-4
    
    See Also:
    
    Constant Field Values
  - DEFAULT_BATCH_SIZE
```
public static final int DEFAULT_BATCH_SIZE
```
    The default batch size is 1
    
    See Also:
    
    Constant Field Values
- Constructor Detail
  - Pegasos
```
public Pegasos()
```
    Creates a new Pegasos SVM classifier using default values.
  - Pegasos
```
public Pegasos(int epochs,
               double reg,
               int batchSize)
```
    Creates a new Pegasos SVM classifier
    
    Parameters:
    
    epochs - the number of training iterations
    
    reg - the regularization term
    
    batchSize - the batch size
  - Pegasos
```
public Pegasos(Pegasos toCopy)
```
    Copy constructor
    
    Parameters:
    
    toCopy - the object to copy
- Method Detail
  - setBatchSize
```
public void setBatchSize(int batchSize)
```
    Sets the batch size used during training. At each epoch, a batch of randomly selected data points will be used to update.
    
    Parameters:
    
    batchSize - the number of data points to use when updating
  - getBatchSize
```
public int getBatchSize()
```
    Returns the number of points used in each iteration
    
    Returns:
    
    the number of points used in each iteration
  - setEpochs
```
public void setEpochs(int epochs)
```
    Sets the number of iterations through the training set that will be performed.
    
    Parameters:
    
    epochs - the number of iterations
  - getEpochs
```
public double getEpochs()
```
    Returns the number of iterations of updating that will be done
    
    Returns:
    
    the number of iterations
  - setProjectionStep
```
public void setProjectionStep(boolean projectionStep)
```
    Sets whether or not to use the projection step after each update per iteration
    
    Parameters:
    
    projectionStep - whether or not to use the projection step
  - isProjectionStep
```
public boolean isProjectionStep()
```
    Returns whether or not the projection step is in use after each iteration
    
    Returns:
    
    true if the projection step will be performed
  - setRegularization
```
public void setRegularization(double reg)
```
    Sets the regularization constant used for learning. The regularization must be positive, and the learning rate is proportional to the regularization value. This means regularizations very near zero will take a long time to converge.
    
    Parameters:
    
    reg - the regularization to apply
  - getRegularization
```
public double getRegularization()
```
    Returns the amount of regularization to used in training
    
    Returns:
    
    the regularization parameter.
  - 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 Pegasos clone()
```
    Specified by:
    
    clone in interface BinaryScoreClassifier
    
    Specified by:
    
    clone in interface Classifier
    
    Overrides:
    
    clone in class Object
  - 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
    
    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.
  - guessRegularization
```
public static Distribution guessRegularization(DataSet d)
```
    Guess the distribution to use for the regularization term setRegularization(double) in Pegasos.
    
    Parameters:
    
    d - the data set to get the guess for
    
    Returns:
    
    the guess for the λ parameter

Class Pegasos

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

DEFAULT_EPOCHS

DEFAULT_REG

DEFAULT_BATCH_SIZE

Constructor Detail

Pegasos

Pegasos

Pegasos

Method Detail

setBatchSize

getBatchSize

setEpochs

getEpochs

setProjectionStep

isProjectionStep

setRegularization

getRegularization

getRawWeight

getBias

getRawWeight

getBias

numWeightsVecs

clone

classify

getScore

trainC

trainC

supportsWeightedData

getParameters

getParameter

guessRegularization