OnlineAMM (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.BaseUpdateableClassifier
- - jsat.classifiers.svm.extended.OnlineAMM

All Implemented Interfaces:

Serializable, Cloneable, Classifier, UpdateableClassifier, Parameterized

Direct Known Subclasses:

AMM
```
public class OnlineAMM
extends BaseUpdateableClassifier
implements Parameterized
```
This is the Online variant of the Adaptive Multi-Hyperplane Machine (AMM) algorithm. It is related to linear SVMs where instead of having only a single weight vector, it is extended to multi-class problems by giving each class its own weight vector. It is further extended by allowing each class to dynamically add new weight vectors to increase the non-linearity of the solution.
This algorithm works best for problems with a very large number of data points where traditional kernelized SVMs are prohibitively expensive to train due to computational cost.

Unlike the batch variant, the online AMM algorithm has no convergence guarantees. However it still produces good results.
See:
- Wang, Z., Djuric, N., Crammer, K., & Vucetic, S. (2011). Trading representability for scalability Adaptive Multi-Hyperplane Machine for nonlinear Classification. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’11 (p. 24). New York, New York, USA: ACM Press. doi:10.1145/2020408.2020420
- Djuric, N., Lan, L., Vucetic, S., & Wang, Z. (2014). BudgetedSVM: A Toolbox for Scalable SVM Approximations. Journal of Machine Learning Research, 14, 3813–3817. Retrieved from here
Author:

Edward Raff

See Also:

Serialized Form

Field Summary

Fields
Modifier and Type	Field and Description
`protected double`	`c`
`protected int`	`classBudget`
`static int`	`DEFAULT_CLASS_BUDGET` The default `class budget` is 50.
`static double`	`DEFAULT_PRUNE_CONSTANT` The default `pruning constant` is 10.0.
`static int`	`DEFAULT_PRUNE_FREQUENCY` The default `frequency for pruning` is 10000.
`static double`	`DEFAULT_REGULARIZER` The default `regularization value` is 0.01.
`protected int`	`k`
`protected double`	`lambda`
`protected int[]`	`nextID`
`protected int`	`time`
`protected List<Map<Integer,Vec>>`	`weightMatrix`

Constructor Summary

Constructors
Constructor and Description
`OnlineAMM()` Creates a new online AMM learner
`OnlineAMM(double lambda)` Creates a new online AMM learner
`OnlineAMM(double lambda, int classBudget)` Creates a new online AMM learner
`OnlineAMM(OnlineAMM toCopy)` Copy constructor

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.
`OnlineAMM`	`clone()`
`double`	`getC()` Returns the pruning constant
`int`	`getClassBudget()` Returns the maximum number of hyperplanes allowed per class
`double`	`getLambda()` Returns the regularization parameter
`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.
`int`	`getPruneFrequency()` Returns the number of iterations between each pruning
`static Distribution`	`guessLambda(DataSet d)` Guess the distribution to use for the regularization term `λ` in AMM.
`void`	`setC(double c)` Sets the pruning constant which controls how powerful pruning is when pruning occurs.
`void`	`setClassBudget(int classBudget)` When given bad parameters there is the possibility for unbounded growth in the number of hyperplanes used.
`void`	`setLambda(double lambda)` Sets the regularization parameter for this algorithm.
`void`	`setPruneFrequency(int frequency)` Sets the frequency at which the weight vectors are pruned.
`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 y_t)` Updates the classifier by giving it a new data point to learn from.
`protected int`	`update(DataPoint dataPoint, int y_t, int z_t)` Performs the work for an update.

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

- Field Detail
  - weightMatrix
```
protected List<Map<Integer,Vec>> weightMatrix
```
  - nextID
```
protected int[] nextID
```
  - lambda
```
protected double lambda
```
  - k
```
protected int k
```
  - c
```
protected double c
```
  - time
```
protected int time
```
  - classBudget
```
protected int classBudget
```
  - DEFAULT_PRUNE_FREQUENCY
```
public static final int DEFAULT_PRUNE_FREQUENCY
```
    The default frequency for pruning is 10000.
    
    See Also:
    
    Constant Field Values
  - DEFAULT_PRUNE_CONSTANT
```
public static final double DEFAULT_PRUNE_CONSTANT
```
    The default pruning constant is 10.0.
    
    See Also:
    
    Constant Field Values
  - DEFAULT_CLASS_BUDGET
```
public static final int DEFAULT_CLASS_BUDGET
```
    The default class budget is 50.
    
    See Also:
    
    Constant Field Values
  - DEFAULT_REGULARIZER
```
public static final double DEFAULT_REGULARIZER
```
    The default regularization value is 0.01.
    
    See Also:
    
    Constant Field Values
- Constructor Detail
  - OnlineAMM
```
public OnlineAMM()
```
    Creates a new online AMM learner
  - OnlineAMM
```
public OnlineAMM(double lambda)
```
    Creates a new online AMM learner
    
    Parameters:
    
    lambda - the regularization value to use
  - OnlineAMM
```
public OnlineAMM(double lambda,
                 int classBudget)
```
    Creates a new online AMM learner
    
    Parameters:
    
    lambda - the regularization value to use
    
    classBudget - the maximum number of weight vectors for each class
  - OnlineAMM
```
public OnlineAMM(OnlineAMM toCopy)
```
    Copy constructor
    
    Parameters:
    
    toCopy - the object to copy
- Method Detail
  - clone
```
public OnlineAMM clone()
```
    Specified by:
    
    clone in interface Classifier
    
    Specified by:
    
    clone in interface UpdateableClassifier
    
    Specified by:
    
    clone in class BaseUpdateableClassifier
  - setLambda
```
public void setLambda(double lambda)
```
    Sets the regularization parameter for this algorithm. The original paper suggests trying values 10^-2, 10^-3, ..., 10^-6, 10^-7.
    
    Parameters:
    
    lambda - the positive regularization parameter in (0, ∞)
  - getLambda
```
public double getLambda()
```
    Returns the regularization parameter
    
    Returns:
    
    the regularization parameter
  - setPruneFrequency
```
public void setPruneFrequency(int frequency)
```
    Sets the frequency at which the weight vectors are pruned. Increasing the frequency increases the aggressiveness of pruning.
    
    Parameters:
    
    frequency - the number of iterations between each pruning
  - getPruneFrequency
```
public int getPruneFrequency()
```
    Returns the number of iterations between each pruning
    
    Returns:
    
    the number of iterations between each pruning
  - setC
```
public void setC(double c)
```
    Sets the pruning constant which controls how powerful pruning is when pruning occurs. Increasing C increases how many weights will be pruned. Changes to the scaling of feature vectors may require a change in the value of C
    NOTE: This parameter is not the same as the standard C parameter associated with SVMs.
    
    Parameters:
    
    c - the positive pruning constant to use in (0, ∞)
  - getC
```
public double getC()
```
    Returns the pruning constant
    
    Returns:
    
    the pruning constant
  - setClassBudget
```
public void setClassBudget(int classBudget)
```
    When given bad parameters there is the possibility for unbounded growth in the number of hyperplanes used. By setting this value to a reasonable upperbound catastrophic memory and CPU use can be avoided.
    
    Parameters:
    
    classBudget - the maximum number of hyperplanes allowed per class
  - getClassBudget
```
public int getClassBudget()
```
    Returns the maximum number of hyperplanes allowed per class
    
    Returns:
    
    the maximum number of hyperplanes allowed per class
  - 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
  - update
```
public void update(DataPoint dataPoint,
                   int y_t)
```
    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
    
    y_t - the target class of the data point
  - update
```
protected int update(DataPoint dataPoint,
                     int y_t,
                     int z_t)
```
    Performs the work for an update. It can be used for regular online learning, or in the batch scenario where the assignments should not be updated with each online update.
    The output will change under certain circumstances.
    NOTE: this method may change in the future, dont rely on it
    
    Parameters:
    
    dataPoint - the data point to use in the update
    
    y_t - the true label of the data point
    
    z_t - the hyperplane of the true class with the maximum response, or Integer.MIN_VALUE if it should be calculated
    
    Returns:
    
    the index of the hyperplane of the true class with that maximum response
  - 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
  - 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.
  - guessLambda
```
public static Distribution guessLambda(DataSet d)
```
    Guess the distribution to use for the regularization term λ in AMM.
    
    Parameters:
    
    d - the data set to get the guess for
    
    Returns:
    
    the guess for the λ parameter

Class OnlineAMM

Field Summary

Constructor Summary

Method Summary

Methods inherited from class jsat.classifiers.BaseUpdateableClassifier

Methods inherited from class java.lang.Object

Field Detail

weightMatrix

nextID

lambda

k

c

time

classBudget

DEFAULT_PRUNE_FREQUENCY

DEFAULT_PRUNE_CONSTANT

DEFAULT_CLASS_BUDGET

DEFAULT_REGULARIZER

Constructor Detail

OnlineAMM

OnlineAMM

OnlineAMM

OnlineAMM

Method Detail

clone

setLambda

getLambda

setPruneFrequency

getPruneFrequency

setC

getC

setClassBudget

getClassBudget

setUp

update

update

classify

supportsWeightedData

getParameters

getParameter

guessLambda