source: src/main/java/agents/anac/y2015/Phoenix/GP/CovSEard.java@ 346

Last change on this file since 346 was 1, checked in by Wouter Pasman, 6 years ago

Initial import : Genius 9.0.0
File size: 7.6 KB
Line 
1package agents.anac.y2015.Phoenix.GP;/* This file is part of the jgpml Project.
2 * http://github.com/renzodenardi/jgpml
3 *
4 * Copyright (c) 2011 Renzo De Nardi and Hugo Gravato-Marques
5 *
6 * Permission is hereby granted, free of charge, to any person
7 * obtaining a copy of this software and associated documentation
8 * files (the "Software"), to deal in the Software without
9 * restriction, including without limitation the rights to use,
10 * copy, modify, merge, publish, distribute, sublicense, and/or sell
11 * copies of the Software, and to permit persons to whom the
12 * Software is furnished to do so, subject to the following
13 * conditions:
14 *
15 * The above copyright notice and this permission notice shall be
16 * included in all copies or substantial portions of the Software.
17 *
18 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
19 * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
20 * OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
21 * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
22 * HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
23 * WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
24 * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
25 * OTHER DEALINGS IN THE SOFTWARE.
26 */
27
28import static agents.anac.y2015.Phoenix.GP.MatrixOperations.*;
29
30import java.util.Arrays;
31
32import agents.Jama.Matrix;
33
34
35/** Squared Exponential covariance function with Automatic Relevance Detemination
36 * (ARD) distance measure. The covariance function is parameterized as:
37 * <p>
38 * k(x^p,x^q) = sf2 * exp(-(x^p - x^q)'*inv(P)*(x^p - x^q)/2)
39 * <p>
40 * where the P matrix is diagonal with ARD parameters ell_1^2,...,ell_D^2, where
41 * D is the dimension of the input space and sf2 is the signal variance. The
42 * hyperparameters are:
43 * <p>
44 * [ log(ell_1)
45 * log(ell_2)
46 * .
47 * log(ell_D)
48 * log(sqrt(sf2))]
49 */
50public class CovSEard implements CovarianceFunction {
51
52 private int D;
53 private int numParameters;
54 private Matrix K=null;
55
56 /**
57 * Creates a new <code>PhoenixAlpha.CovSEard PhoenixAlpha.CovarianceFunction<code>
58 * @param inputDimension muber of dimension of the input
59 */
60 public CovSEard(int inputDimension){
61 this.D = inputDimension;
62 numParameters = D+1;
63 }
64
65 /**
66 * Returns the number of hyperparameters of <code>PhoenixAlpha.CovSEard</code>
67 * @return number of hyperparameters
68 */
69 public int numParameters() {
70 return numParameters;
71 }
72
73 /**
74 * Compute covariance matrix of a dataset X
75 * @param loghyper column <code>Matrix</code> of hyperparameters
76 * @param X input dataset
77 * @return K covariance <code>Matrix</code>
78 */
79 public Matrix compute(Matrix loghyper, Matrix X) {
80
81 if(X.getColumnDimension()!=D)
82 throw new IllegalArgumentException("The number of dimensions specified on the covariance function "+D+" must agree with the size of the input vector"+X.getColumnDimension());
83 if(loghyper.getColumnDimension()!=1 || loghyper.getRowDimension()!=numParameters)
84 throw new IllegalArgumentException("Wrong number of hyperparameters, "+loghyper.getRowDimension()+" instead of "+numParameters);
85
86 final Matrix ell = exp(loghyper.getMatrix(0,D-1,0,0)); // characteristic length scales
87 final double sf2 = Math.exp(2*loghyper.get(D,0)); // signal variance
88
89 Matrix diag = new Matrix(D,D);
90 for(int i=0; i<D; i++)
91 diag.set(i,i,1/ell.get(i,0));
92
93 K = exp(squareDist(diag.times(X.transpose())).times(-0.5)).times(sf2); // SE covariance
94
95 return K;
96 }
97
98 /**
99 * Compute compute test set covariances
100 * @param loghyper column <code>Matrix</code> of hyperparameters
101 * @param X input dataset
102 * @param Xstar test set
103 * @return [K(Xstar,Xstar) K(X,Xstar)]
104 */
105 public Matrix[] compute(Matrix loghyper, Matrix X, Matrix Xstar) {
106
107 if(X.getColumnDimension()!=D)
108 throw new IllegalArgumentException("The number of dimensions specified on the covariance function "+D+" must agree with the size of the input vector"+X.getColumnDimension());
109 if(loghyper.getColumnDimension()!=1 || loghyper.getRowDimension()!=numParameters)
110 throw new IllegalArgumentException("Wrong number of hyperparameters, "+loghyper.getRowDimension()+" instead of "+numParameters);
111
112 final Matrix ell = exp(loghyper.getMatrix(0,D-1,0,0)); // characteristic length scales
113 final double sf2 = Math.exp(2*loghyper.get(D,0)); // signal variance
114
115 double[] a = new double[Xstar.getRowDimension()];
116 Arrays.fill(a,sf2);
117 Matrix A = new Matrix(a,Xstar.getRowDimension());
118
119 Matrix diag = new Matrix(D,D);
120 for(int i=0; i<D; i++)
121 diag.set(i,i,1/ell.get(i,0));
122
123 Matrix B = exp(squareDist(diag.times(X.transpose()),diag.times(Xstar.transpose())).times(-0.5)).times(sf2);
124
125 return new Matrix[]{A,B};
126 }
127
128
129 /**
130 * Coompute the derivatives of this <code>PhoenixAlpha.CovarianceFunction</code> with respect
131 * to the hyperparameter with index <code>idx</code>
132 *
133 * @param loghyper hyperparameters
134 * @param X input dataset
135 * @param index hyperparameter index
136 * @return <code>Matrix</code> of derivatives
137 */
138 public Matrix computeDerivatives(Matrix loghyper, Matrix X, int index){
139
140 if(X.getColumnDimension()!=D)
141 throw new IllegalArgumentException("The number of dimensions specified on the covariance function "+D+" must agree with the size of the input vector"+X.getColumnDimension());
142 if(loghyper.getColumnDimension()!=1 || loghyper.getRowDimension()!=numParameters)
143 throw new IllegalArgumentException("Wrong number of hyperparameters, "+loghyper.getRowDimension()+" instead of "+numParameters);
144 if(index>numParameters()-1)
145 throw new IllegalArgumentException("Wrong hyperparameters index "+index+" it should be smaller or equal to "+(numParameters()-1));
146
147 Matrix A=null;
148
149 final Matrix ell = exp(loghyper.getMatrix(0,D-1,0,0)); // characteristic length scales
150 final double sf2 = Math.exp(2*loghyper.get(D,0)); // signal variance
151 // noise variance
152
153 if(K.getRowDimension()!=X.getRowDimension() || K.getColumnDimension()!=X.getRowDimension()){
154 Matrix diag = new Matrix(D,D);
155 for(int i=0; i<D; i++)
156 diag.set(i,i,1/ell.get(i,0));
157
158 K = exp(squareDist(diag.times(X.transpose())).times(-0.5)).times(sf2); // SE covariance
159 }
160
161 if(index<D){ //length scale parameters
162 Matrix col = squareDist(X.getMatrix(0,X.getRowDimension()-1,index,index).transpose().times(1/ell.get(index,0)));
163
164 A = K.arrayTimes(col);
165 } else { // magnitude parameter
166 A=K.times(2);
167 K = null;
168 }
169
170 return A;
171 }
172
173 private static Matrix squareDist(Matrix a){
174 return squareDist(a,a);
175 }
176
177 private static Matrix squareDist(Matrix a, Matrix b){
178 Matrix C = new Matrix(a.getColumnDimension(),b.getColumnDimension());
179 final int m = a.getColumnDimension();
180 final int n = b.getColumnDimension();
181 final int d = a.getRowDimension();
182
183 for (int i=0; i<m; i++){
184 for (int j=0; j<n; j++) {
185 double z = 0.0;
186 for (int k=0; k<d; k++) { double t = a.get(k,i) - b.get(k,j); z += t*t; }
187 C.set(i,j,z);
188 }
189 }
190
191 return C;
192 }
193
194}
Note: See TracBrowser for help on using the repository browser.