SCD (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.linear.SCD

All Implemented Interfaces:

Serializable, Cloneable, Classifier, Parameterized, Regressor, SimpleWeightVectorModel, SingleWeightVectorModel
```
public class SCD
extends Object
implements Classifier, Regressor, Parameterized, SingleWeightVectorModel
```
Implementation of Stochastic Coordinate Descent for L1 regularized classification and regression. Which one is supported is controlled by the LossFunc used. To be used the loss function must be twice differentiable with a finite maximal second derivative value. LogisticLoss for classification and SquaredLoss for regression are the ones used in the original paper.

Because the SCD needs column major data for efficient implementation, a second copy of data will be created in memory during training.

See: Shalev-Shwartz, S.,&Tewari, A. (2009). Stochastic Methods for L₁-regularized Loss Minimization. In 26th International Conference on Machine Learning (Vol. 12, pp. 929–936). Retrieved from here

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description

SCD(LossFunc loss, double regularization, int iterations)
Creates anew SCD learner

SCD(SCD toCopy)
Copy constructor

Constructors
Constructor and Description
`SCD(LossFunc loss, double regularization, int iterations)` Creates anew SCD learner
`SCD(SCD toCopy)` 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.
`SCD`	`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.
`int`	`getIterations()` Returns the number of iterations 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`	`getRegularization()` Returns the regularization parameter value used for learning.
`int`	`numWeightsVecs()` Returns the number of weight vectors that can be returned.
`double`	`regress(DataPoint data)`
`void`	`setIterations(int iterations)` Sets the number of iterations that will be used.
`void`	`setRegularization(double regularization)` Sets the regularization constant used for learning.
`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
  - SCD
```
public SCD(LossFunc loss,
           double regularization,
           int iterations)
```
    Creates anew SCD learner
    
    Parameters:
    
    loss - the loss function to use
    
    regularization - the regularization term to used
    
    iterations - the number of iterations to perform
  - SCD
```
public SCD(SCD toCopy)
```
    Copy constructor
    
    Parameters:
    
    toCopy - the object to copy
- Method Detail
  - setIterations
```
public void setIterations(int iterations)
```
    Sets the number of iterations that will be used.
    
    Parameters:
    
    iterations - the number of training iterations
  - getIterations
```
public int getIterations()
```
    Returns the number of iterations used
    
    Returns:
    
    the number of iterations used
  - setRegularization
```
public void setRegularization(double regularization)
```
    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:
    
    regularization - the regularization to apply in (0, Infinity)
  - getRegularization
```
public double getRegularization()
```
    Returns the regularization parameter value used for learning.
    
    Returns:
    
    the regularization parameter value used for learning.
  - 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.
  - 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 SCD 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 SCD

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

SCD

SCD

Method Detail

setIterations

getIterations

setRegularization

getRegularization

getRawWeight

getBias

getRawWeight

getBias

numWeightsVecs

classify

trainC

trainC

supportsWeightedData

regress

train

train

clone

getParameters

getParameter