JTrainer.java

/*
 *
 * YAQP - Yet Another QSAR Project:
 * Machine Learning algorithms designed for the prediction of toxicological
 * features of chemical compounds become available on the Web. Yaqp is developed
 * under OpenTox (http://opentox.org) which is an FP7-funded EU research project.
 * This project was developed at the Automatic Control Lab in the Chemical Engineering
 * School of National Technical University of Athens. Please read README for more
 * information.
 *
 * Copyright (C) 2009-2010 Pantelis Sopasakis & Charalampos Chomenides
 *
 * 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/>.
 *
 * Contact:
 * Pantelis Sopasakis
 * chvng@mail.ntua.gr
 * Address: Iroon Politechniou St. 9, Zografou, Athens Greece
 * tel. +30 210 7723236
 */

package org.opentox.qsar.interfaces;

import org.opentox.core.interfaces.JProcessor;
import org.opentox.qsar.exceptions.QSARException;

/**
 * @author Pantelis Sopasakis
 * @author Charalampos Chomenides
 *
 * @param <Input> Input type to the trainer.
 * @param <Output> Output from the trainer
 */
public interface JTrainer<Input, Output> extends JProcessor<Input, Output>{

    /**
     *
     * This methods is called to perform training.
     * @param training_data The input given to the trainer (Training data)
     * @return Training Result (e.g. a success message or the ID of the
     * generated model)
     * @throws QSARException in case something goes wrong with the training
     */
    Output train(Input training_data) throws QSARException;

}