package agents.anac.y2015.Phoenix.GP;/* This file is part of the jgpml Project. * http://github.com/renzodenardi/jgpml * * Copyright (c) 2011 Renzo De Nardi and Hugo Gravato-Marques * * Permission is hereby granted, free of charge, to any person * obtaining a copy of this software and associated documentation * files (the "Software"), to deal in the Software without * restriction, including without limitation the rights to use, * copy, modify, merge, publish, distribute, sublicense, and/or sell * copies of the Software, and to permit persons to whom the * Software is furnished to do so, subject to the following * conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES * OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT * HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, * WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR * OTHER DEALINGS IN THE SOFTWARE. */ import static agents.anac.y2015.Phoenix.GP.MatrixOperations.*; import java.util.Arrays; import agents.Jama.Matrix; /** * Squared Exponential covariance function with isotropic distance measure. The * covariance function is parameterized as: *
* [ log(ell)
* log(sqrt(sf2)) ]
*/
public class CovSEiso implements CovarianceFunction{
public CovSEiso(){}
/**
* Returns the number of hyperparameters of thisPhoenixAlpha.CovarianceFunction
*
* @return number of hyperparameters
*/
public int numParameters() {
return 2;
}
/**
* Compute covariance matrix of a dataset X
*
* @param loghyper column Matrix
of hyperparameters
* @param X input dataset
* @return K covariance Matrix
*/
public Matrix compute(Matrix loghyper, Matrix X) {
if(loghyper.getColumnDimension()!=1 || loghyper.getRowDimension()!=numParameters())
throw new IllegalArgumentException("Wrong number of hyperparameters, "+loghyper.getRowDimension()+" instead of "+numParameters());
double ell = Math.exp(loghyper.get(0,0));
double sf2 = Math.exp(2*loghyper.get(1,0));
Matrix K = exp(squareDist(X.transpose().times(1/ell)).times(-0.5)).times(sf2);
return K;
}
/**
* Compute compute test set covariances
*
* @param loghyper column Matrix
of hyperparameters
* @param X input dataset
* @param Xstar test set
* @return [K(Xstar,Xstar) K(X,Xstar)]
*/
public Matrix[] compute(Matrix loghyper, Matrix X, Matrix Xstar) {
if(loghyper.getColumnDimension()!=1 || loghyper.getRowDimension()!=numParameters())
throw new IllegalArgumentException("Wrong number of hyperparameters, "+loghyper.getRowDimension()+" instead of "+numParameters());
double ell = Math.exp(loghyper.get(0,0));
double sf2 = Math.exp(2*loghyper.get(1,0));
double[] a = new double[Xstar.getRowDimension()];
Arrays.fill(a,sf2);
Matrix A = new Matrix(a,a.length);
Matrix B = exp(squareDist(X.transpose().times(1/ell),Xstar.transpose().times(1/ell)).times(-0.5)).times(sf2);
return new Matrix[]{A,B};
}
/**
* Coompute the derivatives of this PhoenixAlpha.CovarianceFunction
with respect
* to the hyperparameter with index idx
*
* @param loghyper hyperparameters
* @param X input dataset
* @param index hyperparameter index
* @return Matrix
of derivatives
*/
public Matrix computeDerivatives(Matrix loghyper, Matrix X, int index) {
if(loghyper.getColumnDimension()!=1 || loghyper.getRowDimension()!=numParameters())
throw new IllegalArgumentException("Wrong number of hyperparameters, "+loghyper.getRowDimension()+" instead of "+numParameters());
if(index>numParameters()-1)
throw new IllegalArgumentException("Wrong hyperparameters index "+index+" it should be smaller or equal to "+(numParameters()-1));
double ell = Math.exp(loghyper.get(0,0));
double sf2 = Math.exp(2*loghyper.get(1,0));
Matrix tmp = squareDist(X.transpose().times(1/ell));
Matrix A = null;
if(index==0){
A = exp(tmp.times(-0.5)).arrayTimes(tmp).times(sf2);
} else {
A = exp(tmp.times(-0.5)).times(2*sf2);
}
return A;
}
private static Matrix squareDist(Matrix a){
return squareDist(a,a);
}
private static Matrix squareDist(Matrix a, Matrix b){
Matrix C = new Matrix(a.getColumnDimension(),b.getColumnDimension());
final int m = a.getColumnDimension();
final int n = b.getColumnDimension();
final int d = a.getRowDimension();
for (int i=0; i