/*
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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.
 *
 *   *  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
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 * 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.ml.clustering.IntCentroidsResult;
import org.openimaj.ml.clustering.IndexClusters;
import org.openimaj.ml.clustering.SpatialClusterer;

/**
 * A simple (yet apparently quite effective in high dimensions) 
 * clustering technique trained used randomly sampled data points. A 
 * configurable number of data points are selected from the training data to
 * represent the centroids.
 *
 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
 *
 */
public class RandomIntClusterer implements SpatialClusterer<IntCentroidsResult, int[]> {
        protected int M;
        protected int K;
        protected Random random;
        protected long seed;
        
        /**
         * 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
         * @param K number of centroids to be created
         */
        public RandomIntClusterer(int M, int K) {
                this.M = M;
                this.K = K;
                this.random = new Random();
                this.seed = -1;
        }
        
        /**
         * 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
         */
        public RandomIntClusterer(int M) {
                this(M, -1);
        }
                
        /**
         * @param seed the seed for the {@link Random} object used to select random data points.
         */
        public void setSeed(long seed) {
                this.random = new Random();
                random.setSeed(seed);
                this.seed = seed;
        }
        
        /***
         * 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 not guaranteed that the same data point will not be selected
         * many times.
         * 
         * @params data source of centroids
         * @return the selected centroids
         */
        @Override
        public IntCentroidsResult cluster(int[][] data) {
                int nc = this.K;
                
                if (nc == -1) {
                        nc = data.length;
                }
                
                IntCentroidsResult result = new IntCentroidsResult();
                result.centroids = new int[nc][];
                
                for (int i = 0; i < nc; i++) {
                        int dIndex = this.random.nextInt(data.length);
                        
                        result.centroids[i] = Arrays.copyOf(data[dIndex], data[dIndex].length);
                }
                
                return result;
        }
        
        @Override
        public int[][] performClustering(int[][] data) {
                IntCentroidsResult res = this.cluster(data);
                return new IndexClusters(res.defaultHardAssigner().assign(data)).clusters();
        }
        
        /**
         * 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 not guaranteed that the same data 
         * point will not be selected many times.
         * 
         * @params data a data source object
         * @return the selected centroids
         */
        @Override
        public IntCentroidsResult cluster(DataSource<int[]> data) {
                int nc = this.K;
                
                if (nc == -1) {
                        nc = data.size();
                }
                
                IntCentroidsResult result = new IntCentroidsResult();
                result.centroids = new int[nc][M];
                int[][] dataRow = new int[1][];
                
                for (int i = 0; i < nc; i++) {
                        int dIndex = this.random.nextInt(data.size());
                        dataRow[0] = result.centroids[i];
                        
                        data.getData(dIndex, dIndex+1, dataRow);
                }
                
                return result;
        }
}