001/*
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004*/
005/**
006 * Copyright (c) 2011, The University of Southampton and the individual contributors.
007 * All rights reserved.
008 *
009 * Redistribution and use in source and binary forms, with or without modification,
010 * are permitted provided that the following conditions are met:
011 *
012 *   *  Redistributions of source code must retain the above copyright notice,
013 *      this list of conditions and the following disclaimer.
014 *
015 *   *  Redistributions in binary form must reproduce the above copyright notice,
016 *      this list of conditions and the following disclaimer in the documentation
017 *      and/or other materials provided with the distribution.
018 *
019 *   *  Neither the name of the University of Southampton nor the names of its
020 *      contributors may be used to endorse or promote products derived from this
021 *      software without specific prior written permission.
022 *
023 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
024 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
025 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
026 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
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028 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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030 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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033 */
034package org.openimaj.ml.clustering.assignment.soft;
035
036import org.openimaj.feature.ShortFVComparison;
037import org.openimaj.knn.ShortNearestNeighbours;
038import org.openimaj.knn.ShortNearestNeighboursExact;
039import org.openimaj.knn.ShortNearestNeighboursProvider;
040import org.openimaj.knn.approximate.ShortNearestNeighboursKDTree;
041import org.openimaj.ml.clustering.assignment.SoftAssigner;
042import org.openimaj.ml.clustering.CentroidsProvider;
043import org.openimaj.util.pair.IndependentPair;
044
045/**
046 * A {@link SoftAssigner} that picks a fixed number of nearest neighbours.
047 * Weights returned are actually the distances to the centroids.
048 * 
049 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
050 *
051 */
052public class ShortKNNAssigner implements SoftAssigner<short[], float[]> {
053        protected ShortNearestNeighbours nn;
054        protected int numNeighbours;
055
056        /**
057         * Construct the assigner using the given cluster data. The assigner
058         * is backed by either a {@link ShortNearestNeighboursExact} or 
059         * {@link ShortNearestNeighboursKDTree}, depending on whether the exact
060         * parameter is true or false. If the parameter is true, then the 
061         * resultant {@link ShortNearestNeighboursExact} will use Euclidean
062         * distance.
063         * 
064         * @param provider the cluster data provider
065         * @param exact if true, then use exact mode; false implies approximate mode.
066         * @param numNeighbours the number of nearest neighbours to select.
067         */
068        public ShortKNNAssigner(CentroidsProvider<short[]> provider, boolean exact, int numNeighbours) {
069                this.numNeighbours = numNeighbours;
070                
071                if (exact) {
072                        nn = new ShortNearestNeighboursExact(provider.getCentroids());
073                } else {
074                        if (provider instanceof ShortNearestNeighboursProvider) {
075                                ShortNearestNeighbours internal = ((ShortNearestNeighboursProvider)provider).getNearestNeighbours();
076
077                                if (internal != null && internal instanceof ShortNearestNeighboursKDTree) {
078                                        nn = (ShortNearestNeighboursKDTree) internal;
079                                        return;
080                                }
081                        }
082
083                        nn = new ShortNearestNeighboursKDTree(provider.getCentroids(), ShortNearestNeighboursKDTree.DEFAULT_NTREES, ShortNearestNeighboursKDTree.DEFAULT_NCHECKS);
084                }
085        }
086        
087        /**
088         * Construct the assigner using the given cluster data. The assigner
089         * is backed by either a {@link ShortNearestNeighboursExact} or 
090         * {@link ShortNearestNeighboursKDTree}, depending on whether the exact
091         * parameter is true or false. If the parameter is true, then the 
092         * resultant {@link ShortNearestNeighboursExact} will use Euclidean
093         * distance.
094         * 
095         * @param data the cluster data
096         * @param exact if true, then use exact mode; false implies approximate mode.
097         * @param numNeighbours the number of nearest neighbours to select.
098         */
099        public ShortKNNAssigner(short[][] data, boolean exact, int numNeighbours) {
100                this.numNeighbours = numNeighbours;
101                
102                if (exact) {
103                        nn = new ShortNearestNeighboursExact(data);
104                } else {
105                        nn = new ShortNearestNeighboursKDTree(data, ShortNearestNeighboursKDTree.DEFAULT_NTREES, ShortNearestNeighboursKDTree.DEFAULT_NCHECKS);
106                }
107        }
108        
109        /**
110         * Construct the assigner using the given cluster data and 
111         * distance function. The assigner will operate in exact mode,
112         * using a {@link ShortNearestNeighboursExact}.
113         * 
114         * @param provider the cluster data provider
115         * @param comparison the distance function
116         * @param numNeighbours the number of nearest neighbours to select.
117         */
118        public ShortKNNAssigner(CentroidsProvider<short[]> provider, ShortFVComparison comparison, int numNeighbours) {
119                this.numNeighbours = numNeighbours;
120                
121                nn = new ShortNearestNeighboursExact(provider.getCentroids(), comparison);
122        }
123        
124        /**
125         * Construct the assigner using the given cluster data and 
126         * distance function. The assigner will operate in exact mode,
127         * using a {@link ShortNearestNeighboursExact}.
128         * 
129         * @param data the cluster data
130         * @param comparison the distance function
131         * @param numNeighbours the number of nearest neighbours to select.
132         */
133        public ShortKNNAssigner(short[][] data, ShortFVComparison comparison, int numNeighbours) {
134                this.numNeighbours = numNeighbours;
135                
136                nn = new ShortNearestNeighboursExact(data, comparison);
137        }
138
139        @Override
140        public int[][] assign(short[][] data) {
141                int [][] indices = new int [data.length][numNeighbours];
142                float [][] distances = new float [data.length][numNeighbours];
143                
144                nn.searchKNN(data, numNeighbours, indices, distances);
145                
146                return indices;
147        }
148
149        @Override
150        public int[] assign(short[] data) {
151                return assign(new short[][] { data })[0];
152        }
153
154        @Override
155        public void assignWeighted(short[][] data, int[][] assignments, float[][] weights) {
156                nn.searchKNN(data, numNeighbours, assignments, weights);
157        }
158
159        @Override
160        public IndependentPair<int[], float[]> assignWeighted(short[] data) {
161                int [][] indices = new int [data.length][numNeighbours];
162                float [][] distances = new float [data.length][numNeighbours];
163                
164                nn.searchKNN(new short[][] { data }, numNeighbours, indices, distances);
165                
166                return new IndependentPair<int[], float[]>(indices[0], distances[0]);
167        }
168        
169        @Override
170        public int numDimensions() {
171            return nn.numDimensions();
172        }
173        
174        @Override
175        public int size() {
176            return nn.size();
177        }
178}