/**
 * Copyright (c) 2011, The University of Southampton and the individual contributors.
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without modification,
 * are permitted provided that the following conditions are met:
 *
 *   *  Redistributions of source code must retain the above copyright notice,
 *      this list of conditions and the following disclaimer.
 *
 *   *  Redistributions in binary form must reproduce the above copyright notice,
 *      this list of conditions and the following disclaimer in the documentation
 *      and/or other materials provided with the distribution.
 *
 *   *  Neither the name of the University of Southampton nor the names of its
 *      contributors may be used to endorse or promote products derived from this
 *      software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
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 */
package org.openimaj.ml.training;

import java.util.List;

/**
 * Interface describing objects capable of performing
 * training in "batch" mode; all training examples are 
 * presented at once. Calling the {@link #train(List)} 
 * method more than once will cause
 * the internal model to be re-initialised using the new
 * data. If you want to implement an object that 
 * can be updated, implement the {@link IncrementalTrainer}
 * interface instead. 
 * 
 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
 *
 * @param <T> Type of object being trained upon
 */
public interface BatchTrainer<T> {
        /**
         * Train the object with the given data. 
         * {@link BatchTrainer}s should reset the underlying
         * model everytime this method is called.
         * 
         * @param data the training data
         */
        public abstract void train(List<? extends T> data);
}