DCDLearningAlgorithm (kelp-full 2.2.3 API)

Constructor Summary

Constructors
Constructor and Description
`DCDLearningAlgorithm()`
`DCDLearningAlgorithm(double cp, double cn, DCDLoss dcdLoss, boolean useBias, int maxIterations, String representationName)`
`DCDLearningAlgorithm(double cp, double cn, int maxIterations, String representationName)` This constructor uses the L2 loss and ignores the bias of the hyper-plane
`DCDLearningAlgorithm(Label label, double cp, double cn, DCDLoss dcdLoss, boolean useBias, int maxIterations, String representationName)`

Method Summary

Methods
Modifier and Type	Method and Description
`DCDLearningAlgorithm`	`duplicate()` Creates a new instance of the LearningAlgorithm initialized with the same parameters of the learningAlgorithm to be duplicated.
`double`	`getCn()`
`double`	`getCp()`
`DCDLoss`	`getDcdLoss()`
`Label`	`getLabel()`
`List<Label>`	`getLabels()` Returns the labels representing the concept to be learned.
`int`	`getMaxIterations()`
`BinaryLinearClassifier`	`getPredictionFunction()` Returns the classifier learned during the training process
`String`	`getRepresentation()` Returns the representation this learning algorithm exploits
`long`	`getSeed()`
`boolean`	`isFairness()`
`boolean`	`isUseBias()`
`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`	`setCn(double cn)`
`void`	`setCp(double cp)`
`void`	`setDcdLoss(DCDLoss dcdLoss)`
`void`	`setFairness(boolean fairness)`
`void`	`setLabel(Label label)`
`void`	`setLabels(List<Label> labels)` Sets the labels representing the concept to be learned.
`void`	`setMaxIterations(int maxIterations)`
`void`	`setPredictionFunction(PredictionFunction predictionFunction)` Sets the predictionFunction learned during the training process.
`void`	`setRepresentation(String representation)` Sets the representation this learning algorithm will exploit
`void`	`setSeed(long seed)`
`void`	`setUseBias(boolean useBias)`

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

- Constructor Detail
  - DCDLearningAlgorithm
```
public DCDLearningAlgorithm()
```
  - DCDLearningAlgorithm
```
public DCDLearningAlgorithm(Label label,
                    double cp,
                    double cn,
                    DCDLoss dcdLoss,
                    boolean useBias,
                    int maxIterations,
                    String representationName)
```
    Parameters:
    label - The label to be learned
    cp - The regularization parameter for positive examples
    cn - The regularization parameter for negative examples
    dcdLoss - The Loss function
    useBias - Set the use of bias
    maxIterations - The maximum number of iterations
    representationName - The identifier of the representation to be considered for the training step
  - DCDLearningAlgorithm
```
public DCDLearningAlgorithm(double cp,
                    double cn,
                    DCDLoss dcdLoss,
                    boolean useBias,
                    int maxIterations,
                    String representationName)
```
    Parameters:
    label - The label to be learned
    cp - The regularization parameter for positive examples
    cn - The regularization parameter for negative examples
    dcdLoss - The Loss function
    useBias - Set the use of bias
    maxIterations - The maximum number of iterations
    representationName - The identifier of the representation to be considered for the training step
  - DCDLearningAlgorithm
```
public DCDLearningAlgorithm(double cp,
                    double cn,
                    int maxIterations,
                    String representationName)
```
    This constructor uses the L2 loss and ignores the bias of the hyper-plane
    
    Parameters:
    label - The label to be learned
    cp - The regularization parameter for positive examples
    cn - The regularization parameter for negative examples
    maxIterations - The maximum number of iterations
    representationName - The identifier of the representation to be considered for the training step
- Method Detail
  - duplicate
```
public DCDLearningAlgorithm 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
  - getCn
```
public double getCn()
```
  - getCp
```
public double getCp()
```
  - getDcdLoss
```
public DCDLoss getDcdLoss()
```
  - getLabel
```
public Label getLabel()
```
    Specified by:
    
    getLabel in interface BinaryLearningAlgorithm
    
    Returns:
    the label associated to the positive class
  - 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
  - getMaxIterations
```
public int getMaxIterations()
```
  - 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
  - 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
  - getSeed
```
public long getSeed()
```
  - isFairness
```
public boolean isFairness()
```
  - isUseBias
```
public boolean isUseBias()
```
  - 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
  - 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
  - setCn
```
public void setCn(double cn)
```
  - setCp
```
public void setCp(double cp)
```
  - setDcdLoss
```
public void setDcdLoss(DCDLoss dcdLoss)
```
  - setFairness
```
public void setFairness(boolean fairness)
```
  - setLabel
```
public void setLabel(Label label)
```
    Specified by:
    
    setLabel in interface BinaryLearningAlgorithm
  - 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
  - setMaxIterations
```
public void setMaxIterations(int maxIterations)
```
  - 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
  - setSeed
```
public void setSeed(long seed)
```
  - setUseBias
```
public void setUseBias(boolean useBias)
```
  - 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 DCDLearningAlgorithm

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

DCDLearningAlgorithm

DCDLearningAlgorithm

DCDLearningAlgorithm

DCDLearningAlgorithm

Method Detail

duplicate

getCn

getCp

getDcdLoss

getLabel

getLabels

getMaxIterations

getPredictionFunction

getRepresentation

getSeed

isFairness

isUseBias

learn

reset

setCn

setCp

setDcdLoss

setFairness

setLabel

setLabels

setMaxIterations

setRepresentation

setSeed

setUseBias

setPredictionFunction