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
33 import org.apache.commons.math.stat.descriptive.moment.Variance;
34 import org.apache.log4j.Logger;
35 import org.openimaj.feature.DoubleFV;
36 import org.openimaj.feature.FeatureExtractor;
37
38 import ch.akuhn.matrix.DenseMatrix;
39 import ch.akuhn.matrix.Matrix;
40 import ch.akuhn.matrix.SparseMatrix;
41
42 /**
43 * Construct a similarity matrix using a Radial Basis Function
44 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
45 *
46 * @param <T>
47 */
48 public class RBFSimilarityDoubleClustererWrapper<T> extends DoubleFVSimilarityFunction<T> {
49
50 private double[] var;
51 Logger logger = Logger.getLogger(RBFSimilarityDoubleClustererWrapper.class);
52
53 /**
54 * @param extractor
55 */
56 public RBFSimilarityDoubleClustererWrapper(FeatureExtractor<DoubleFV,T> extractor) {
57 super(extractor);
58 }
59
60 private void prepareVariance() {
61 this.var = new double[this.feats[0].length];
62 Matrix m = new DenseMatrix(feats);
63 double[] colArr = new double[this.feats.length];
64 Variance v = new Variance();
65 for (int i = 0; i < this.var.length; i++) {
66 m.column(i).storeOn(colArr, 0);
67 this.var[i] = v.evaluate(colArr);
68 }
69 }
70
71 @Override
72 protected SparseMatrix similarity() {
73 prepareVariance();
74 int N = feats.length;
75 SparseMatrix sim = new SparseMatrix(N,N);
76 for (int i = 0; i < N; i++) {
77 double[] di = feats[i];
78 sim.put(i,i,1);
79 for (int j = i+1; j < N; j++) {
80 double[] dj = feats[j];
81 double expInner = 0;
82 // -1*sum((data(i,:)-data(j,:)).^2./(2*my_var))
83 for (int k = 0; k < dj.length; k++) {
84 double kv = di[k] - dj[k];
85 expInner += (kv * kv) / (2 * this.var[k]);
86 }
87
88 double v = Math.exp(-1 * expInner);
89 sim.put(i, j, v);
90 sim.put(j, i, v);
91 }
92 }
93 return sim;
94 }
95
96
97
98 }