Java tutorial
/* * This program is free software: you can redistribute it and/or modify * it under the terms of the GNU 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 General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program. If not, see <http://www.gnu.org/licenses/>. */ /* * Prediction.java * Copyright (C) 2002-2012 University of Waikato, Hamilton, New Zealand * */ package weka.classifiers.evaluation; /** * Encapsulates a single evaluatable prediction: the predicted value plus the * actual class value. * * @author Len Trigg (len@reeltwo.com) * @version $Revision$ */ public interface Prediction { /** * Constant representing a missing value. This should have the same value * as weka.core.Instance.MISSING_VALUE */ double MISSING_VALUE = weka.core.Utils.missingValue(); /** * Gets the weight assigned to this prediction. This is typically the weight * of the test instance the prediction was made for. * * @return the weight assigned to this prediction. */ double weight(); /** * Gets the actual class value. * * @return the actual class value, or MISSING_VALUE if no * prediction was made. */ double actual(); /** * Gets the predicted class value. * * @return the predicted class value, or MISSING_VALUE if no * prediction was made. */ double predicted(); }