STGD (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.BaseUpdateableClassifier
- - jsat.classifiers.linear.STGD

All Implemented Interfaces:

Serializable, Cloneable, BinaryScoreClassifier, Classifier, UpdateableClassifier, Parameterized, Regressor, UpdateableRegressor, SimpleWeightVectorModel, SingleWeightVectorModel
```
public class STGD
extends BaseUpdateableClassifier
implements UpdateableRegressor, BinaryScoreClassifier, Parameterized, SingleWeightVectorModel
```
This provides an implementation of Sparse Truncated Gradient Descent for L₁ regularized linear classification and regression on sparse data sets.

Unlike normal L₁ regression, regularization is controlled by the gravity parameter, but other parameters contribute to the level of sparsity.

See: Langford, J., Li, L.,&Zhang, T. (2009). Sparse online learning via truncated gradient. The Journal of Machine Learning Research, 10, 777–801. Retrieved from here

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Modifier	Constructor and Description
	`STGD(int K, double learningRate, double threshold, double gravity)` Creates a new STGD learner
`protected`	`STGD(STGD 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.
`STGD`	`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.
`double`	`getGravity()` Returns the regularization parameter
`int`	`getK()` Returns the frequency of regularization application
`double`	`getLearningRate()` Returns the learning rate to use
`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`	`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.
`double`	`getThreshold()` Returns the coefficient threshold parameter
`int`	`numWeightsVecs()` Returns the number of weight vectors that can be returned.
`double`	`regress(DataPoint data)`
`void`	`setGravity(double gravity)` Sets the gravity regularization parameter that "weighs down" the coefficient values.
`void`	`setK(int K)` Sets the frequency of applying the `gravity` parameter to the weight vector.
`void`	`setLearningRate(double learningRate)` Sets the learning rate to use
`void`	`setThreshold(double threshold)` Sets the threshold for a coefficient value to avoid regularization.
`void`	`setUp(CategoricalData[] categoricalAttributes, int numericAttributes)` Prepares the classifier to begin learning from its `UpdateableRegressor.update(jsat.classifiers.DataPoint, double)` method.
`void`	`setUp(CategoricalData[] categoricalAttributes, int numericAttributes, CategoricalData predicting)` Prepares the classifier to begin learning from its `UpdateableClassifier.update(jsat.classifiers.DataPoint, int)` method.
`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`	`update(DataPoint dataPoint, double y)` Updates the classifier by giving it a new data point to learn from.
`void`	`update(DataPoint dataPoint, int targetClass)` Updates the classifier by giving it a new data point to learn from.

Methods inherited from class jsat.classifiers.BaseUpdateableClassifier
getEpochs, setEpochs, trainC, trainC, trainEpochs

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

Methods inherited from interface jsat.classifiers.Classifier
trainC, trainC

- Constructor Detail
  - STGD
```
public STGD(int K,
            double learningRate,
            double threshold,
            double gravity)
```
    Creates a new STGD learner
    
    Parameters:
    
    K - the regularization frequency
    
    learningRate - the learning rate to use
    
    threshold - the regularization threshold
    
    gravity - the regularization parameter
  - STGD
```
protected STGD(STGD toCopy)
```
    Copy constructor
    
    Parameters:
    
    toCopy - the object to copy
- Method Detail
  - setK
```
public void setK(int K)
```
    Sets the frequency of applying the gravity parameter to the weight vector. This value must be positive, and the gravity will be applied every K updates. Increasing this value encourages greater sparsity.
    
    Parameters:
    
    K - the frequency to apply regularization in [1, Infinity )
  - getK
```
public int getK()
```
    Returns the frequency of regularization application
    
    Returns:
    
    the frequency of regularization application
  - setLearningRate
```
public void setLearningRate(double learningRate)
```
    Sets the learning rate to use
    
    Parameters:
    
    learningRate - the learning rate > 0.
  - getLearningRate
```
public double getLearningRate()
```
    Returns the learning rate to use
    
    Returns:
    
    the learning rate to use
  - setThreshold
```
public void setThreshold(double threshold)
```
    Sets the threshold for a coefficient value to avoid regularization. While a coefficient reaches this magnitude, regularization will not be applied.
    
    Parameters:
    
    threshold - the coefficient regularization threshold in ( 0, Infinity ]
  - getThreshold
```
public double getThreshold()
```
    Returns the coefficient threshold parameter
    
    Returns:
    
    the coefficient threshold parameter
  - setGravity
```
public void setGravity(double gravity)
```
    Sets the gravity regularization parameter that "weighs down" the coefficient values. Larger gravity values impose stronger regularization, and encourage greater sparsity.
    
    Parameters:
    
    gravity - the regularization parameter in ( 0, Infinity )
  - getGravity
```
public double getGravity()
```
    Returns the regularization parameter
    
    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 STGD clone()
```
    Specified by:
    
    clone in interface BinaryScoreClassifier
    
    Specified by:
    
    clone in interface Classifier
    
    Specified by:
    
    clone in interface UpdateableClassifier
    
    Specified by:
    
    clone in interface Regressor
    
    Specified by:
    
    clone in interface UpdateableRegressor
    
    Specified by:
    
    clone in class BaseUpdateableClassifier
  - setUp
```
public void setUp(CategoricalData[] categoricalAttributes,
                  int numericAttributes,
                  CategoricalData predicting)
```
    Description copied from interface: UpdateableClassifier
    
    Prepares the classifier to begin learning from its UpdateableClassifier.update(jsat.classifiers.DataPoint, int) method.
    
    Specified by:
    
    setUp in interface UpdateableClassifier
    
    Parameters:
    
    categoricalAttributes - an array containing the categorical attributes that will be in each data point
    
    numericAttributes - the number of numeric attributes that will be in each data point
    
    predicting - the information for the target class that will be predicted
  - setUp
```
public void setUp(CategoricalData[] categoricalAttributes,
                  int numericAttributes)
```
    Description copied from interface: UpdateableRegressor
    
    Prepares the classifier to begin learning from its UpdateableRegressor.update(jsat.classifiers.DataPoint, double) method.
    
    Specified by:
    
    setUp in interface UpdateableRegressor
    
    Parameters:
    
    categoricalAttributes - an array containing the categorical attributes that will be in each data point
    
    numericAttributes - the number of numeric attributes that will be in each data point
  - 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
  - update
```
public void update(DataPoint dataPoint,
                   int targetClass)
```
    Description copied from interface: UpdateableClassifier
    
    Updates the classifier by giving it a new data point to learn from.
    
    Specified by:
    
    update in interface UpdateableClassifier
    
    Parameters:
    
    dataPoint - the data point to learn
    
    targetClass - the target class of the data point
  - update
```
public void update(DataPoint dataPoint,
                   double y)
```
    Description copied from interface: UpdateableRegressor
    
    Updates the classifier by giving it a new data point to learn from.
    
    Specified by:
    
    update in interface UpdateableRegressor
    
    Parameters:
    
    dataPoint - the data point to learn
    
    y - the target value of the data point
  - 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.
  - regress
```
public double regress(DataPoint data)
```
    Specified by:
    
    regress in interface Regressor
  - 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
  - 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
  - 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 STGD

Constructor Summary

Method Summary

Methods inherited from class jsat.classifiers.BaseUpdateableClassifier

Methods inherited from class java.lang.Object

Methods inherited from interface jsat.classifiers.Classifier

Constructor Detail

STGD

STGD

Method Detail

setK

getK

setLearningRate

getLearningRate

setThreshold

getThreshold

setGravity

getGravity

getRawWeight

getBias

getRawWeight

getBias

numWeightsVecs

clone

setUp

setUp

train

train

update

update

classify

regress

supportsWeightedData

getScore

getParameters

getParameter