bijnum
Class BIJknn

java.lang.Object
  bijnum.BIJknn

public class BIJknn
extends java.lang.Object

k Nearest Neighborhood (brute-force) implementation. You can create a kNN object, add datasets to it, and classify vectors. Copyright (c) 1999-2003, Michael Abramoff. All rights reserved.

Field Summary

float[]	classset The corresponding classification for each vector in the dataset, a 1xM vector.
float[][]	dataset The dataset, a MxN matrix of N-dimensional vectors.
static int	inserts
int	n The number of different classes in classset.

Constructor Summary

BIJknn(float[][] dataset, float[] classset, int n)

BIJknn(int n)

Method Summary

void	add(float[][] extradataset, float[] extraclassset) Add extra data and classification to this knn.
float[]	classify(float[][] unknown, int k) Find the classification of M (N dimensional) vectors unknown in the dataset.
void	prune(int k) Cleans the dataset using Wilson's pruning algorithm.
void	search(int[] kIndices, float[] kDistances, float[] v, int k) Find the k-nearest neighbors of vector v (N dimensional) in dataset and put indices in kIndices, distances in kDistances.

Methods inherited from class java.lang.Object

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

Field Detail

dataset

public float[][] dataset

The dataset, a MxN matrix of N-dimensional vectors.

classset

public float[] classset

The corresponding classification for each vector in the dataset, a 1xM vector.

n

public int n

The number of different classes in classset.

inserts

public static int inserts

Constructor Detail

BIJknn

public BIJknn(int n)

BIJknn

public BIJknn(float[][] dataset,
              float[] classset,
              int n)

Method Detail

add

public void add(float[][] extradataset,
                float[] extraclassset)
         throws java.lang.Exception

Add extra data and classification to this knn.

Parameters:

extradataset - an extra dataset

extraclassset - an extra class set.

Throws:

java.lang.Exception

classify

public float[] classify(float[][] unknown,
                        int k)

Find the classification of M (N dimensional) vectors unknown in the dataset.

Parameters:

unknown - a MxN matrix of M vectors to be classified in the dataset.

k - the number of neighbors to use for classification

Returns:

classification a vector that will contain the nearest neighbor classificiations for the corresponding vectors in unknown.

search

public void search(int[] kIndices,
                   float[] kDistances,
                   float[] v,
                   int k)

Find the k-nearest neighbors of vector v (N dimensional) in dataset and put indices in kIndices, distances in kDistances.

Parameters:

kIndices - a int[] of length k that will contain the indices to the k closest neighbors of v

kDistances - a float[] of length k that will contain the distances to the k closest neighbors of v.

v - the vector to classify

k - the number of neighbors to use for classification

prune

public void prune(int k)

Cleans the dataset using Wilson's pruning algorithm. Classifies each point in the dataset (k=3) and removes it if its class is not the same as the classification by its k neighbors.