/*
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*/
/**
 * 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.
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 *   *  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
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package org.openimaj.ml.clustering.random;

import java.util.Arrays;
import java.util.Random;

import org.openimaj.data.DataSource;
import org.openimaj.data.RandomData;
import org.openimaj.ml.clustering.DoubleCentroidsResult;

/**
 * A similar strategy to {@link RandomSetDoubleClusterer} however it is 
 * guaranteed that the same training vector will not be sampled more than once. 
 * 
 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
 */
public class RandomSetDoubleClusterer extends RandomDoubleClusterer {
        /**
         * Creates a new random byte cluster used to create K centroids with data containing M elements.
         *
         * @param M number of elements in each data vector 
         */
        public RandomSetDoubleClusterer(int M) {
                super(M);
        }
        
        /**
         * Creates a new random byte cluster used to create centroids with data containing M elements. The 
         * number of clusters will be equal to the number of data points provided in training.
         *
         * @param M number of elements in each data vector
         * @param K number of centroids to be created
         */
        public RandomSetDoubleClusterer(int M, int K) {
                super(M, K);
        }
        
        /**
         * Selects K elements from the provided data as the centroids of the clusters. If K is -1 all provided
         * data points will be selected. It is guaranteed that the same data point will not be selected
         * many times, though this is not the case if two seperate entries provided are identical.
         * 
         * @params data source of centroids
         * @return the selected centroids
         */
    @Override
        public DoubleCentroidsResult cluster(double[][] data) {
                DoubleCentroidsResult result = new DoubleCentroidsResult();
                
                if (K == -1) {
                        result.centroids = data;
                } else {
                        if (data.length < K) {
                                throw new IllegalArgumentException("Not enough data");
                        }
                        
                        result.centroids = new double[K][];
                        
                        int[] indices;
                        if(this.seed >= 0)
                                indices = RandomData.getUniqueRandomInts(this.K, 0, data.length, new Random(this.seed));
                        else
                                indices = RandomData.getUniqueRandomInts(this.K, 0, data.length);
                        
                        for (int i = 0 ; i < indices.length; i++) {
                                int dIndex = indices[i];
                                
                                result.centroids[i] = Arrays.copyOf(data[dIndex ], data[dIndex ].length);
                        }
                }
                
                return result;
        }
        
        /**
         * Selects K elements from the provided {@link DataSource} as the centroids of the clusters. 
         * If K is -1 all provided data points will be selected. It is guaranteed that the same data 
         * point will not be selected many times, though this is not the case if two seperate entries 
         * provided are identical.
         * 
         * @params data a data source object
         * @return the selected centroids
         */
        @Override
        public DoubleCentroidsResult cluster(DataSource<double[]> data) {
                DoubleCentroidsResult result = new DoubleCentroidsResult();
                
                if(K == -1) {
                        final int nc = data.size();
                        result.centroids = new double[nc][data.numDimensions()];
                } else {
                        result.centroids = new double[K][data.numDimensions()];
                }
                
                data.getRandomRows(result.centroids);
                
                return result;
        }
}