BalancedWinnow (Mallet 2 API)

java.lang.Object
- cc.mallet.classify.Classifier
- - cc.mallet.classify.BalancedWinnow

All Implemented Interfaces:

AlphabetCarrying, java.io.Serializable
```
public class BalancedWinnow
extends Classifier
implements java.io.Serializable
```
Classification methods of BalancedWinnow algorithm.

Author:

Gary Huang ghuang@cs.umass.edu

See Also:

BalancedWinnowTrainer, Serialized Form

Field Summary
- Fields inherited from class cc.mallet.classify.Classifier
  instancePipe

Constructor Summary

Constructors
Constructor Description

BalancedWinnow(Pipe dataPipe, double[][] weights)
Passes along data pipe and weights from BalancedWinnowTrainer

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`Classification`	`classify(Instance instance)`	Classifies an instance using BalancedWinnow's weights
`double[][]`	`getWeights()`

Methods inherited from class cc.mallet.classify.Classifier
alphabetsMatch, classify, classify, classify, getAccuracy, getAlphabet, getAlphabets, getAverageRank, getF1, getF1, getF1, getFeatureSelection, getInstancePipe, getLabelAlphabet, getPerClassFeatureSelection, getPrecision, getPrecision, getPrecision, getRecall, getRecall, getRecall, print, print

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

- Constructor Detail
  - BalancedWinnow
```
public BalancedWinnow(Pipe dataPipe,
                      double[][] weights)
```
    Passes along data pipe and weights from BalancedWinnowTrainer
    
    Parameters:
    
    dataPipe - needed for dictionary, labels, feature vectors, etc
    
    weights - weights calculated during training phase
- Method Detail
  - getWeights
```
public double[][] getWeights()
```
    Returns:
    
    a copy of the weight vectors
  - classify
```
public Classification classify(Instance instance)
```
    Classifies an instance using BalancedWinnow's weights
    Returns a Classification containing the normalized dot products between class weight vectors and the instance feature vector.
    One can obtain the confidence of the classification by calculating weight(j')/weight(j), where j' is the highest weight prediction and j is the 2nd-highest. Another possibility is to calculate
    e^{dot(w_j', x} / sum_j[e^{dot(w_j, x)}]
    
    Specified by:
    
    classify in class Classifier