code: /* 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.
   */
  
  package jgpml.covariancefunctions;
  
  import Jama.Matrix;
  
  
  public interface CovarianceFunction {
  
      /**
       * Returns the number of hyperparameters of this<code>CovarianceFunction</code>
       * @return number of hyperparameters
       */
      public int numParameters();
  
      /**
       * Compute covariance matrix of a dataset X
       * @param loghyper column <code>Matrix</code> of hyperparameters
       * @param X  input dataset
       * @return  K covariance <code>Matrix</code>
       */
      public Matrix compute(Matrix loghyper, Matrix X);
  
      /**
       * Compute compute test set covariances
       * @param loghyper column <code>Matrix</code> 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);
  
      /**
       * Coompute the derivatives of this <code>CovarianceFunction</code> with respect
       * to the hyperparameter with index <code>idx</code>
       *
       * @param loghyper hyperparameters
       * @param X input dataset
       * @param index hyperparameter index
       * @return  <code>Matrix</code> of derivatives
       */
      public Matrix computeDerivatives(Matrix loghyper, Matrix X, int index);
  
  }
  