/**
* Copyright (C) 2001-2017 by RapidMiner and the contributors
*
* Complete list of developers available at our web site:
*
* http://rapidminer.com
*
* This program is free software: you can redistribute it and/or modify it under the terms of the
* GNU Affero General Public License as published by the Free Software Foundation, either version 3
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without
* even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Affero General Public License for more details.
*
* You should have received a copy of the GNU Affero General Public License along with this program.
* If not, see http://www.gnu.org/licenses/.
*/
package com.rapidminer.operator.learner.functions.kernel.gaussianprocess;
import com.rapidminer.operator.learner.functions.kernel.rvm.kernel.Kernel;
/**
* Holds the data defining the regression / classification problem to be learned. - All input
* vectors are assumed to have the same dimension. - All target vectors are assumed to have the same
* dimension
*
* @author Piotr Kasprzak
*
*/
public abstract class Problem {
private double[][] x; // Input vectors
private Kernel kernel;
/* The variance^2 of the gaussian noise */
public double sigma_0_2 = 0.09;
/** Problem types */
/** Constructor */
public Problem(double[][] x, Kernel kernel) {
this.x = x;
this.kernel = kernel;
}
/** Getters */
public int getProblemSize() {
return x.length;
}
public int getInputDimension() {
return x[0].length;
}
public double[][] getInputVectors() {
return x;
}
abstract public int getTargetDimension();
public Kernel getKernel() {
return kernel;
}
}