bijnum
Class BIJknn

java.lang.Object
  |
  +--bijnum.BIJknn

public class BIJknn

extends java.lang.Object

k Nearest Neighborhood (brute-force) implementation. Copyright (c) 1999-2003, Michael Abramoff. All rights reserved.

Field Summary

float[]	classset The classification for each element in the dataset, a 1xM vector.
float[][]	dataset The dataset, a MxN matrix of N-dimensional vectors.
static int	inserts
protected float[]	kDistances Some variables that I do not want to allocate for every element.
protected int[]	kIndices
int	n The numbe rof different classes in classset.

Constructor Summary

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

Method Summary

void	classify(float[] classification, float[][] unknown, int k) Find the classification of all M (N dimensional) vectors unknown in the dataset.
void	clean() Cleans the dataset using Wilson's pruning algorithm.
protected static float	distance(float[] a, float[] b) Return the distance according to some Minkowski metric.
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

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

Field Detail

inserts

public static int inserts

dataset

public float[][] dataset

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

classset

public float[] classset

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

n

public int n

The numbe rof different classes in classset.

kDistances

protected float[] kDistances

Some variables that I do not want to allocate for every element.

kIndices

protected int[] kIndices

Constructor Detail

BIJknn

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

Method Detail

classify

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

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

Parameters:

classification - a float[][] array that will contain the nearest neighbor classificiations for all elements in unknown.

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

k - the number of neighbors to use for classification modifies classification

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

clean

public void clean()

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.

distance

protected static float distance(float[] a,
                                float[] b)

Return the distance according to some Minkowski metric. This implements Euclidean distance. To save time, the POWER(1/k) is not performed.

Parameters:

a - a float[] vector containing a point

b - a float[] vector containing another point.

Returns:

the Minkowski distance between a and b.