PegasosLearningAlgorithm (kelp-full 2.2.3 API)

java.lang.Object
- it.uniroma2.sag.kelp.learningalgorithm.classification.pegasos.PegasosLearningAlgorithm

All Implemented Interfaces:

BinaryLearningAlgorithm, ClassificationLearningAlgorithm, LearningAlgorithm, LinearMethod
```
public class PegasosLearningAlgorithm
extends Object
implements LinearMethod, ClassificationLearningAlgorithm, BinaryLearningAlgorithm
```
It implements the Primal Estimated sub-GrAdient SOlver (PEGASOS) for SVM. It is a learning algorithm for binary linear classification Support Vector Machines. It operates in an explicit feature space (i.e. it does not relies on any kernel). Further details can be found in:
[SingerICML2007] Y. Singer and N. Srebro. Pegasos: Primal estimated sub-gradient solver for SVM. In Proceeding of ICML 2007.

Author:

Simone Filice

Constructor Summary

Constructors
Constructor and Description
`PegasosLearningAlgorithm()`
`PegasosLearningAlgorithm(int k, float lambda, int T, String Representation, Label label)`

Method Summary

Methods
Modifier and Type	Method and Description
`PegasosLearningAlgorithm`	`duplicate()` Creates a new instance of the LearningAlgorithm initialized with the same parameters of the learningAlgorithm to be duplicated.
`int`	`getIterations()` Returns the number of iterations
`int`	`getK()` Returns the number of examples k that Pegasos exploits in its mini-batch learning approach
`Label`	`getLabel()`
`List<Label>`	`getLabels()` Returns the labels representing the concept to be learned.
`float`	`getLambda()` Returns the regularization coefficient
`BinaryLinearClassifier`	`getPredictionFunction()` Returns the classifier learned during the training process
`String`	`getRepresentation()` Returns the representation this learning algorithm exploits
`void`	`learn(Dataset dataset)` It starts the training process exploiting the provided `dataset`
`void`	`reset()` Resets all the learning process, returning to the default state.
`void`	`setIterations(int T)` Sets the number of iterations
`void`	`setK(int k)` Sets the number of examples k that Pegasos exploits in its mini-batch learning approach
`void`	`setLabel(Label label)`
`void`	`setLabels(List<Label> labels)` Sets the labels representing the concept to be learned.
`void`	`setLambda(float lambda)` Sets the regularization coefficient
`void`	`setPredictionFunction(PredictionFunction predictionFunction)` Sets the predictionFunction learned during the training process.
`void`	`setRepresentation(String representation)` Sets the representation this learning algorithm will exploit

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

- Constructor Detail
  - PegasosLearningAlgorithm
```
public PegasosLearningAlgorithm()
```
  - PegasosLearningAlgorithm
```
public PegasosLearningAlgorithm(int k,
                        float lambda,
                        int T,
                        String Representation,
                        Label label)
```
- Method Detail
  - getK
```
public int getK()
```
    Returns the number of examples k that Pegasos exploits in its mini-batch learning approach
    
    Returns:
    k
  - setK
```
public void setK(int k)
```
    Sets the number of examples k that Pegasos exploits in its mini-batch learning approach
    
    Parameters:
    k - the k to set
  - getIterations
```
public int getIterations()
```
    Returns the number of iterations
    
    Returns:
    the number of iterations
  - setIterations
```
public void setIterations(int T)
```
    Sets the number of iterations
    
    Parameters:
    T - the number of iterations to set
  - getLambda
```
public float getLambda()
```
    Returns the regularization coefficient
    
    Returns:
    the lambda
  - setLambda
```
public void setLambda(float lambda)
```
    Sets the regularization coefficient
    
    Parameters:
    lambda - the lambda to set
  - getRepresentation
```
public String getRepresentation()
```
    Description copied from interface: LinearMethod
    
    Returns the representation this learning algorithm exploits
    
    Specified by:
    
    getRepresentation in interface LinearMethod
    
    Returns:
    the representation
  - setRepresentation
```
public void setRepresentation(String representation)
```
    Description copied from interface: LinearMethod
    
    Sets the representation this learning algorithm will exploit
    
    Specified by:
    
    setRepresentation in interface LinearMethod
    
    Parameters:
    representation - the representation to set
  - learn
```
public void learn(Dataset dataset)
```
    Description copied from interface: LearningAlgorithm
    
    It starts the training process exploiting the provided dataset
    
    Specified by:
    
    learn in interface LearningAlgorithm
    
    Parameters:
    dataset - the training data
  - duplicate
```
public PegasosLearningAlgorithm duplicate()
```
    Description copied from interface: LearningAlgorithm
    
    Creates a new instance of the LearningAlgorithm initialized with the same parameters of the learningAlgorithm to be duplicated. A shallow copy of the learning parameters is performed, while the state parameters are initialized to their default value. The labels are not initialized neither copied
    
    Specified by:
    
    duplicate in interface LearningAlgorithm
  - reset
```
public void reset()
```
    Description copied from interface: LearningAlgorithm
    
    Resets all the learning process, returning to the default state. All the learned process is forgotten
    
    Specified by:
    
    reset in interface LearningAlgorithm
  - getPredictionFunction
```
public BinaryLinearClassifier getPredictionFunction()
```
    Description copied from interface: ClassificationLearningAlgorithm
    
    Returns the classifier learned during the training process
    
    Specified by:
    
    getPredictionFunction in interface ClassificationLearningAlgorithm
    
    Specified by:
    
    getPredictionFunction in interface LearningAlgorithm
    
    Returns:
    the predictionFunction learned during the training process
  - setLabels
```
public void setLabels(List<Label> labels)
```
    Description copied from interface: LearningAlgorithm
    
    Sets the labels representing the concept to be learned.
    
    Specified by:
    
    setLabels in interface BinaryLearningAlgorithm
    
    Specified by:
    
    setLabels in interface LearningAlgorithm
    
    Parameters:
    labels - the labels representing the concept to be learned
  - getLabels
```
public List<Label> getLabels()
```
    Description copied from interface: LearningAlgorithm
    
    Returns the labels representing the concept to be learned.
    
    Specified by:
    
    getLabels in interface BinaryLearningAlgorithm
    
    Specified by:
    
    getLabels in interface LearningAlgorithm
    
    Returns:
    the labels representing the concept to be learned
  - getLabel
```
public Label getLabel()
```
    Specified by:
    
    getLabel in interface BinaryLearningAlgorithm
    
    Returns:
    the label associated to the positive class
  - setLabel
```
public void setLabel(Label label)
```
    Specified by:
    
    setLabel in interface BinaryLearningAlgorithm
  - setPredictionFunction
```
public void setPredictionFunction(PredictionFunction predictionFunction)
```
    Description copied from interface: LearningAlgorithm
    
    Sets the predictionFunction learned during the training process. It can be used to change the default prediction function, or to set an already trained one.
    
    Specified by:
    
    setPredictionFunction in interface LearningAlgorithm

Class PegasosLearningAlgorithm

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

PegasosLearningAlgorithm

PegasosLearningAlgorithm

Method Detail

getK

setK

getIterations

setIterations

getLambda

setLambda

getRepresentation

setRepresentation

learn

duplicate

reset

getPredictionFunction

setLabels

getLabels

getLabel

setLabel

setPredictionFunction