edu.stanford.nlp.classify

Class KNNClassifierFactory<K,V>

java.lang.Object

edu.stanford.nlp.classify.KNNClassifierFactory<K,V>

public class KNNClassifierFactory<K,V>
extends java.lang.Object

This constructs trained KNNClassifier objects, given sets of RVFDatums, or Counters (dimensions are identified by the keys).

Constructor Summary

Constructors

Constructor and Description
KNNClassifierFactory(int k, boolean weightedVotes, boolean l2NormalizeVectors) Creates a new factory that generates K-NN classifiers with the given k-value, and if the votes are weighted by their similarity score, or unit value.

Method Summary

Modifier and Type	Method and Description
KNNClassifier<K,V>	train(java.util.Collection<Counter<V>> vectors, java.util.Map<V,K> labelMap) Given a set of vectors, and a mapping from each vector to its class label, generates the sets of instances used to perform classifications and returns the corresponding K-NN classifier.
KNNClassifier<K,V>	train(java.util.Collection<RVFDatum<K,V>> instances) Given a set of labeled RVFDatums, treats each as an instance vector of that label and adds it to the examples used for classification.
KNNClassifier<K,V>	train(CollectionValuedMap<K,Counter<V>> vecBag) Given a CollectionValued Map of vectors, treats outer key as label for each set of inner vectors.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

KNNClassifierFactory

public KNNClassifierFactory(int k,
                            boolean weightedVotes,
                            boolean l2NormalizeVectors)

Creates a new factory that generates K-NN classifiers with the given k-value, and if the votes are weighted by their similarity score, or unit value.

Method Detail

train

public KNNClassifier<K,V> train(java.util.Collection<RVFDatum<K,V>> instances)

Given a set of labeled RVFDatums, treats each as an instance vector of that label and adds it to the examples used for classification. NOTE: l2NormalizeVectors is NOT applied here.

train

public KNNClassifier<K,V> train(java.util.Collection<Counter<V>> vectors,
                                java.util.Map<V,K> labelMap)

Given a set of vectors, and a mapping from each vector to its class label, generates the sets of instances used to perform classifications and returns the corresponding K-NN classifier. NOTE: if l2NormalizeVectors is T, creates a copy and applies L2Normalize to it.

train

public KNNClassifier<K,V> train(CollectionValuedMap<K,Counter<V>> vecBag)

Given a CollectionValued Map of vectors, treats outer key as label for each set of inner vectors. NOTE: if l2NormalizeVectors is T, creates a copy of each vector and applies l2Normalize to it.