BOGD (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.BaseUpdateableClassifier
- - jsat.classifiers.linear.kernelized.BOGD

All Implemented Interfaces:

Serializable, Cloneable, BinaryScoreClassifier, Classifier, UpdateableClassifier, Parameterized
```
public class BOGD
extends BaseUpdateableClassifier
implements BinaryScoreClassifier, Parameterized
```
Bounded Online Gradient Descent (BOGD) is a kernel learning algorithm that uses a bounded number of support vectors. Once the maximum number of support vectors is reached, old vectors are dropped either in a uniform random fashion, or weighted by the kernel function and the current coefficient for the vector. The later is the default method and is referred to as BOGD++.

See: Zhao, P., Wang, J., Wu, P., Jin, R.,&Hoi, S. C. H. (2012). Fast Bounded Online Gradient Descent Algorithms for Scalable Kernel-Based Online Learning. In Proceedings of the 29th International Conference on Machine Learning (pp. 169–176). Learning; Machine Learning. Retrieved from here

Author:

Edward Raff

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description
`BOGD(BOGD toCopy)` Copy constructor
`BOGD(KernelTrick k, int budget, double eta, double reg, double maxCoeff)` Creates a new BOGD++ learner using the `HingeLoss`
`BOGD(KernelTrick k, int budget, double eta, double reg, double maxCoeff, LossC lossC)` Creates a new BOGD++ learner

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.
`BOGD`	`clone()`
`int`	`getBudget()` Returns the maximum number of allowed support vectors
`double`	`getEta()` Returns the learning rate in use
`KernelTrick`	`getKernel()` Returns the kernel to use
`double`	`getMaxCoeff()` Returns the maximum allowed value for any support vector
`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.
`double`	`getRegularization()` Returns the regularization parameter used
`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`	`guessEta(DataSet d)` Guesses the distribution to use for the η parameter
`static Distribution`	`guessMaxCoeff(DataSet d)` Guesses the distribution to use for the MaxCoeff parameter
`static Distribution`	`guessRegularization(DataSet d)` Guesses the distribution to use for the Regularization parameter
`boolean`	`isUniformSampling()` Returns `true` is uniform sampling is in use, or `false` if the BOGD++ sampling procedure is in use
`void`	`setBudget(int budget)` Sets the budget for support vectors
`void`	`setEta(double eta)` Sets the learning rate to use for training.
`void`	`setKernel(KernelTrick k)` Sets the kernel to use
`void`	`setMaxCoeff(double maxCoeff)` Sets the maximum allowed value for any support vector allowed.
`void`	`setRegularization(double regularization)` Sets the regularization parameter used for training.
`void`	`setUniformSampling(boolean uniformSampling)` Sets whether or not support vectors should be removed by uniform sampling or not.
`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`	`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
  - BOGD
```
public BOGD(KernelTrick k,
            int budget,
            double eta,
            double reg,
            double maxCoeff)
```
    Creates a new BOGD++ learner using the HingeLoss
    
    Parameters:
    
    k - the kernel trick to use
    
    budget - the budget for support vectors to allow
    
    eta - the learning rate to use
    
    reg - the regularization parameter
    
    maxCoeff - the maximum support vector coefficient to allow
  - BOGD
```
public BOGD(KernelTrick k,
            int budget,
            double eta,
            double reg,
            double maxCoeff,
            LossC lossC)
```
    Creates a new BOGD++ learner
    
    Parameters:
    
    k - the kernel trick to use
    
    budget - the budget for support vectors to allow
    
    eta - the learning rate to use
    
    reg - the regularization parameter
    
    maxCoeff - the maximum support vector coefficient to allow
    
    lossC - the loss function to use
  - BOGD
```
public BOGD(BOGD toCopy)
```
    Copy constructor
    
    Parameters:
    
    toCopy - the object to make a copy of
- Method Detail
  - setRegularization
```
public void setRegularization(double regularization)
```
    Sets the regularization parameter used for training. The original paper suggests values in the range 2^x/T² for x ∈ {-3, -2, -1, 0, 1, 2, 3} where T is the number of data instances that will be trained on
    
    Parameters:
    
    regularization - the positive regularization parameter to use.
  - getRegularization
```
public double getRegularization()
```
    Returns the regularization parameter used
    
    Returns:
    
    the regularization parameter used
  - setEta
```
public void setEta(double eta)
```
    Sets the learning rate to use for training. The original paper suggests values in the range 2^x for x ∈ {-3, -2, -1, 0, 1, 2, 3}
    
    Parameters:
    
    eta - the positive learning rate to use
  - getEta
```
public double getEta()
```
    Returns the learning rate in use
    
    Returns:
    
    the learning rate in use
  - setMaxCoeff
```
public void setMaxCoeff(double maxCoeff)
```
    Sets the maximum allowed value for any support vector allowed. The original paper suggests values in the range 2^x for x ∈ {0, 1, 2, 3, 4}
    
    Parameters:
    
    maxCoeff - the maximum value for any support vector
  - getMaxCoeff
```
public double getMaxCoeff()
```
    Returns the maximum allowed value for any support vector
    
    Returns:
    
    the maximum allowed value for any support vector
  - setBudget
```
public void setBudget(int budget)
```
    Sets the budget for support vectors
    
    Parameters:
    
    budget - the allowed budget for support vectors
  - getBudget
```
public int getBudget()
```
    Returns the maximum number of allowed support vectors
    
    Returns:
    
    the maximum number of allowed support vectors
  - setKernel
```
public void setKernel(KernelTrick k)
```
    Sets the kernel to use
    
    Parameters:
    
    k - the kernel to use
  - getKernel
```
public KernelTrick getKernel()
```
    Returns the kernel to use
    
    Returns:
    
    the kernel to use
  - setUniformSampling
```
public void setUniformSampling(boolean uniformSampling)
```
    Sets whether or not support vectors should be removed by uniform sampling or not. The default is false, which corresponds to BOGD++.
    
    Parameters:
    
    uniformSampling - true to use uniform sampling, false otherwise.
  - isUniformSampling
```
public boolean isUniformSampling()
```
    Returns true is uniform sampling is in use, or false if the BOGD++ sampling procedure is in use
    
    Returns:
    
    true is uniform sampling is in use, or false if the BOGD++ sampling procedure is in use
  - clone
```
public BOGD clone()
```
    Specified by:
    
    clone in interface BinaryScoreClassifier
    
    Specified by:
    
    clone in interface Classifier
    
    Specified by:
    
    clone in interface UpdateableClassifier
    
    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
  - 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
  - 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
  - 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.
  - 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
  - guessRegularization
```
public static Distribution guessRegularization(DataSet d)
```
    Guesses the distribution to use for the Regularization parameter
    
    Parameters:
    
    d - the dataset to get the guess for
    
    Returns:
    
    the guess for the Regularization parameter
    
    See Also:
    
    setRegularization(double)
  - guessEta
```
public static Distribution guessEta(DataSet d)
```
    Guesses the distribution to use for the η parameter
    
    Parameters:
    
    d - the dataset to get the guess for
    
    Returns:
    
    the guess for the η parameter
    
    See Also:
    
    setEta(double)
  - guessMaxCoeff
```
public static Distribution guessMaxCoeff(DataSet d)
```
    Guesses the distribution to use for the MaxCoeff parameter
    
    Parameters:
    
    d - the dataset to get the guess for
    
    Returns:
    
    the guess for the MaxCoeff parameter
    
    See Also:
    
    (double)
  - 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 BOGD

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

BOGD

BOGD

BOGD

Method Detail

setRegularization

getRegularization

setEta

getEta

setMaxCoeff

getMaxCoeff

setBudget

getBudget

setKernel

getKernel

setUniformSampling

isUniformSampling

clone

setUp

getScore

update

classify

supportsWeightedData

guessRegularization

guessEta

guessMaxCoeff

getParameters

getParameter