NHERD (Java Statistical Analysis Tool 0.0.8 API)

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

All Implemented Interfaces:

Serializable, Cloneable, BinaryScoreClassifier, Classifier, UpdateableClassifier, Parameterized, SimpleWeightVectorModel, SingleWeightVectorModel
```
public class NHERD
extends BaseUpdateableClassifier
implements BinaryScoreClassifier, Parameterized, SingleWeightVectorModel
```
Implementation of the Normal Herd (NHERD) algorithm for learning a linear binary classifier. It is related to both AROW and the PA-II variant of PassiveAggressive.
Unlike similar algorithms, several methods of using only the diagonal values of the covariance are available.
NOTE: This implementation does not add an implicit bias term, so the solution goes through the origin

See:
Crammer, K.,&Lee, D. D. (2010). Learning via Gaussian Herding. Pre-proceeding of NIPS (pp. 451–459). Retrieved from here

Author:

Edward Raff

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class NHERD.CovMode
Sets what form of covariance matrix to use

Nested Classes
Modifier and Type	Class and Description
`static class`	`NHERD.CovMode` Sets what form of covariance matrix to use

Constructor Summary

Constructors
Modifier Constructor and Description

NHERD(double C, NHERD.CovMode covMode)
Creates a new NHERD learner

protected NHERD(NHERD other)
Copy constructor

Constructors
Modifier	Constructor and Description
	`NHERD(double C, NHERD.CovMode covMode)` Creates a new NHERD learner
`protected`	`NHERD(NHERD other)` 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.
`NHERD`	`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`	`getC()` Returns the aggressiveness parameter
`NHERD.CovMode`	`getCovMode()` Returns the mode for forming the covariance
`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.
`Vec`	`getWeightVec()` Returns the weight vector used to compute results via a dot product.
`static Distribution`	`guessC(DataSet d)` Guess the distribution to use for the regularization term `C` .
`int`	`numWeightsVecs()` Returns the number of weight vectors that can be returned.
`void`	`setC(double C)` Set the aggressiveness parameter.
`void`	`setCovMode(NHERD.CovMode covMode)` Sets the way in which the covariance matrix is formed.
`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
  - NHERD
```
public NHERD(double C,
             NHERD.CovMode covMode)
```
    Creates a new NHERD learner
    
    Parameters:
    
    C - the aggressiveness parameter
    
    covMode - how to form the covariance matrix
    
    See Also:
    
    setC(double), setCovMode(jsat.classifiers.linear.NHERD.CovMode)
  - NHERD
```
protected NHERD(NHERD other)
```
    Copy constructor
    
    Parameters:
    
    other - the object to copy
- Method Detail
  - setC
```
public void setC(double C)
```
    Set the aggressiveness parameter. Increasing the value of this parameter increases the aggressiveness of the algorithm. It must be a positive value. This parameter essentially performs a type of regularization on the updates
    
    Parameters:
    
    C - the positive aggressiveness parameter
  - getC
```
public double getC()
```
    Returns the aggressiveness parameter
    
    Returns:
    
    the aggressiveness parameter
  - setCovMode
```
public void setCovMode(NHERD.CovMode covMode)
```
    Sets the way in which the covariance matrix is formed. If using the full covariance matrix, rank-1 updates mean updates to the model take O(d²) time, where d is the dimension of the input. Runtime can be reduced by using only the diagonal of the matrix to perform updates in O(s) time, where s ≤ d is the number of non-zero values in the input
    
    Parameters:
    
    covMode - the way to form the covariance matrix
  - getCovMode
```
public NHERD.CovMode getCovMode()
```
    Returns the mode for forming the covariance
    
    Returns:
    
    the mode for forming the covariance
  - getWeightVec
```
public Vec getWeightVec()
```
    Returns the weight vector used to compute results via a dot product.
    Do not modify this value, or you will alter the results returned.
    
    Returns:
    
    the learned weight vector for prediction
  - 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 NHERD 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
  - 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.
  - 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
  - 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.
  - guessC
```
public static Distribution guessC(DataSet d)
```
    Guess the distribution to use for the regularization term C .
    
    Parameters:
    
    d - the data set to get the guess for
    
    Returns:
    
    the guess for the C parameter

Class NHERD

Nested Class Summary

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

NHERD

NHERD

Method Detail

setC

getC

setCovMode

getCovMode

getWeightVec

getRawWeight

getBias

getRawWeight

getBias

numWeightsVecs

clone

setUp

update

classify

getScore

supportsWeightedData

getParameters

getParameter

guessC