/**
 * 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
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package org.openimaj.experiment.validation;

import org.openimaj.data.RandomData;
import org.openimaj.data.dataset.ListBackedDataset;
import org.openimaj.data.dataset.ListDataset;
import org.openimaj.experiment.dataset.util.DatasetAdaptors;
import org.openimaj.util.list.AcceptingListView;
import org.openimaj.util.list.SkippingListView;

/**
 * Hold-Out validation that selects a percentage of the original
 * data to use for training, and the remainder to use for validation. 
 * 
 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
 *
 * @param <INSTANCE> Type of the instances in the dataset
 */
public class RandomisedPercentageHoldOut<INSTANCE> extends DefaultValidationData<ListDataset<INSTANCE>> {
        
        /**
         * Construct with the given dataset and percentage of training
         * data (0..1).
         * 
         * @param percentageTraining percentage of the dataset to use for training
         * @param dataset the dataset
         */
        public RandomisedPercentageHoldOut(double percentageTraining, ListDataset<INSTANCE> dataset) {
                if (percentageTraining < 0 || percentageTraining > 1)
                        throw new IllegalArgumentException("percentage of training instances must be between 0 and 1");

                if (percentageTraining < 0.5) {
                        int nTraining = (int)Math.round(percentageTraining * dataset.size());
                        int [] trainKeys = RandomData.getUniqueRandomInts(nTraining, 0, dataset.size());
                        
                        training = new ListBackedDataset<INSTANCE>(new AcceptingListView<INSTANCE>(DatasetAdaptors.asList(dataset), trainKeys));
                        validation = new ListBackedDataset<INSTANCE>(new SkippingListView<INSTANCE>(DatasetAdaptors.asList(dataset), trainKeys));
                } else {
                        int nValidation = (int)Math.round((1.0 - percentageTraining) * dataset.size());
                        int [] validationKeys = RandomData.getUniqueRandomInts(nValidation, 0, dataset.size());
                        
                        training = new ListBackedDataset<INSTANCE>(new SkippingListView<INSTANCE>(DatasetAdaptors.asList(dataset), validationKeys));
                        validation = new ListBackedDataset<INSTANCE>(new AcceptingListView<INSTANCE>(DatasetAdaptors.asList(dataset), validationKeys));
                }
        }
}