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 agents.Jama.Matrix; /** Linear covariance function with a single hyperparameter. The covariance * function is parameterized as: *
* k(x^p,x^q) = x^p'*inv(P)*x^q + 1./t2; *
* where the P matrix is t2 times the unit matrix. The second term plays the * role of the bias. The hyperparameter is: *
* [ log(sqrt(t2)) ]
*/
public class CovLINone implements CovarianceFunction{
public CovLINone(){}
/**
* Returns the number of hyperparameters of thisPhoenixAlpha.CovarianceFunction
*
* @return number of hyperparameters
*/
public int numParameters() {
return 1;
}
/**
* 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());
final double it2 = Math.exp(-2*loghyper.get(0,0));
Matrix A = X.times(X.transpose());
return addValue(A,1).times(it2);
}
/**
* 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());
final double it2 = Math.exp(-2*loghyper.get(0,0));
Matrix A = sumRows(Xstar.arrayTimes(Xstar));
A= addValue(A,1).times(it2);
Matrix B = X.times(Xstar.transpose());
B = addValue(B,1).times(it2);
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));
final double it2 = Math.exp(-2*loghyper.get(0,0));
Matrix A = X.times(X.transpose());
return addValue(A,1).times(-2*it2);
}
public static void main(String[] args) {
CovLINone cf = new CovLINone();
Matrix X = Matrix.identity(6,6);
Matrix logtheta = new Matrix(new double[][]{{0.1}});
Matrix z = new Matrix(new double[][]{{1,2,3,4,5,6},{1,2,3,4,5,6}});
System.out.println("");
Matrix K = cf.compute(logtheta,X);
K.print(K.getColumnDimension(), 8);
Matrix[] res = cf.compute(logtheta,X,z);
res[0].print(res[0].getColumnDimension(), 8);
res[1].print(res[1].getColumnDimension(), 8);
Matrix d = cf.computeDerivatives(logtheta,X,0);
d.print(d.getColumnDimension(), 8);
}
}