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 agents.Jama.Matrix; /** * Neural network covariance function with a single parameter for the distance * measure. The covariance function is parameterized as: *
* k(x^p,x^q) = sf2 * asin(x^p'*P*x^q / sqrt[(1+x^p'*P*x^p)*(1+x^q'*P*x^q)]) *
* where the x^p and x^q vectors on the right hand side have an added extra bias * entry with unit value. P is ell^-2 times the unit matrix and sf2 controls the * signal variance. The hyperparameters are: *
* [ log(ell)
* log(sqrt(sf2) ]
*/
public class CovNNone implements CovarianceFunction{
double[][] k;
double[][] q;
public CovNNone(){}
/**
* 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());
final double ell = Math.exp(loghyper.get(0,0));
final double em2 = 1/(ell*ell);
final double oneplusem2 = 1+em2;
final double sf2 = Math.exp(2*loghyper.get(1,0));
final int m = X.getRowDimension();
final int n = X.getColumnDimension();
double[][] x= X.getArray();
// Matrix Xc= X.times(1/ell);
//
// Q = Xc.times(Xc.transpose());
// System.out.print("Q=");Q.print(Q.getColumnDimension(), 8);
// Q = new Matrix(m,m);
// double[][] q = Q.getArray();
q = new double[m][m];
for(int i=0;iidx
*
* @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 ell = Math.exp(loghyper.get(0,0));
final double em2 = 1/(ell*ell);
final double oneplusem2 = 1+em2;
final double twosf2 = 2*Math.exp(2*loghyper.get(1,0));
final int m = X.getRowDimension();
final int n = X.getColumnDimension();
double[][] x= X.getArray();
// Matrix X = XX.times(1/ell);
if(q==null || q.length!=m || q[0].length!=m) {
q = new double[m][m];
for(int i=0;i