1 /**
2 * Copyright (c) 2011, The University of Southampton and the individual contributors.
3 * All rights reserved.
4 *
5 * Redistribution and use in source and binary forms, with or without modification,
6 * are permitted provided that the following conditions are met:
7 *
8 * * Redistributions of source code must retain the above copyright notice,
9 * this list of conditions and the following disclaimer.
10 *
11 * * Redistributions in binary form must reproduce the above copyright notice,
12 * this list of conditions and the following disclaimer in the documentation
13 * and/or other materials provided with the distribution.
14 *
15 * * Neither the name of the University of Southampton nor the names of its
16 * contributors may be used to endorse or promote products derived from this
17 * software without specific prior written permission.
18 *
19 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
20 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
21 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
22 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
23 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
24 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
25 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
26 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
27 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
28 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
29 */
30 package org.openimaj.ml.clustering.spectral;
31
32 import org.apache.log4j.Logger;
33 import org.openimaj.feature.DoubleFV;
34 import org.openimaj.feature.DoubleFVComparison;
35 import org.openimaj.feature.FeatureExtractor;
36 import org.openimaj.ml.clustering.SimilarityClusterer;
37
38 import ch.akuhn.matrix.SparseMatrix;
39
40 /**
41 * Wraps the functionality of a {@link SimilarityClusterer} around a dataset
42 *
43 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
44 *
45 * @param <T>
46 */
47 public class NormalisedSimilarityDoubleClustererWrapper<T> extends DoubleFVSimilarityFunction<T> {
48
49 private double eps;
50
51 /**
52 *
53 * @param extractor
54 * @param eps
55 */
56 public NormalisedSimilarityDoubleClustererWrapper(FeatureExtractor<DoubleFV, T> extractor, double eps) {
57 super(extractor);
58 this.eps = eps;
59 }
60
61 Logger logger = Logger.getLogger(NormalisedSimilarityDoubleClustererWrapper.class);
62
63 @Override
64 protected SparseMatrix similarity() {
65 final SparseMatrix mat = new SparseMatrix(feats.length, feats.length);
66 final DoubleFVComparison dist = DoubleFVComparison.EUCLIDEAN;
67 double maxD = 0;
68 for (int i = 0; i < feats.length; i++) {
69 for (int j = i; j < feats.length; j++) {
70 double d = dist.compare(feats[i], feats[j]);
71 if (d > eps)
72 d = Double.NaN;
73 else {
74 maxD = Math.max(d, maxD);
75 }
76 mat.put(i, j, d);
77 mat.put(j, i, d);
78 }
79 }
80 final SparseMatrix mat_norm = new SparseMatrix(feats.length, feats.length);
81 for (int i = 0; i < feats.length; i++) {
82 for (int j = i; j < feats.length; j++) {
83 double d = mat.get(i, j);
84 if (Double.isNaN(d)) {
85 continue;
86 }
87 else {
88 d /= maxD;
89 }
90 mat_norm.put(i, j, 1 - d);
91 mat_norm.put(j, i, 1 - d);
92 }
93 }
94 return mat_norm;
95 }
96
97 }