/**
 * 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
 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */
package org.openimaj.ml.clustering.kdtree;


import org.apache.commons.math.stat.descriptive.rank.Max;
import org.openimaj.data.DataSource;
import org.openimaj.math.matrix.MatrixUtils;
import org.openimaj.ml.clustering.SpatialClusterer;
import org.openimaj.util.array.IntArrayView;
import org.openimaj.util.pair.IntDoublePair;
import org.openimaj.util.tree.DoubleKDTree;
import org.openimaj.util.tree.DoubleKDTree.SplitChooser;

import Jama.Matrix;

/**
 *
 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
 */
public class DoubleKDTreeClusterer implements SpatialClusterer<KDTreeClusters, double[]>{
        private static final int DEFAULT_MINPTS = 4;
        private double DEFAULT_VARIANCE_PROP = 0.1;
        
        double varprop = DEFAULT_VARIANCE_PROP ;
        int minpts = DEFAULT_MINPTS;
        int ndims = -1;
        private int startindex = -1;
        private SplitDetectionMode detectionMode = new SplitDetectionMode.VARIABLE_MEDIAN();
//      private SplitDetectionMode detectionMode = new SplitDetectionMode.MEAN();
        class CappedVarianceSplitChooser implements SplitChooser{
                
                
                Matrix firstVariances = null;
                Max max = new Max();
                
                public CappedVarianceSplitChooser() {
                }


                @Override
                public IntDoublePair chooseSplit(double[][] pnts, IntArrayView inds, int depth, double[] minBounds, double[] maxBounds) {
                         
                        if(inds.size() < minpts) return null;
                        
                        double[][] subarr = new double[inds.size()][ndims];
                        for (int i = 0; i < subarr.length; i++) {
                                double[] pnti = pnts[inds.get(i)];
                                for (int dim = startindex; dim < startindex + ndims; dim++) {                                   
                                        subarr[i][dim-startindex] = pnti[dim];
                                }
                        }
                        
                        Matrix mat = new Matrix(subarr);
                        Matrix mean = MatrixUtils.sumCols(mat).times(1./inds.size());
                        Matrix var = MatrixUtils.sumCols(mat.arrayTimes(mat)).times(1./inds.size()).minus(mean.arrayTimes(mean));
                        
                        if(firstVariances == null){
                                firstVariances = var;
                                double[] variances = var.getArray()[0];
                                if(max.evaluate(variances) == 0){
                                        return null; // special case, if the first variance is null we've been handed ALL the same, screw it
                                }
                        }
                        
                        Matrix propchange = var.arrayRightDivide(firstVariances);
                        if(max.evaluate(propchange.getArray()[0]) < varprop){
                                return null;
                        }
                        else{
                                IntDoublePair maxDim = maxDim(MatrixUtils.abs(var).getArray()[0]);
                                double[] col = mat.getMatrix(0, inds.size()-1, maxDim.first, maxDim.first).transpose().getArray()[0];
                                double mid = detectionMode.detect(col);
                                return IntDoublePair.pair(maxDim.first+startindex,mid);
                        }
                }


                private IntDoublePair maxDim(double[] ds) {
                        double maxv = -Double.MAX_VALUE;
                        int maxi = -1;
                        for (int i = 0; i < ds.length; i++) {
                                if(maxv < ds[i]){
                                        maxi = i;
                                        maxv = ds[i];
                                }
                        }
                        return IntDoublePair.pair(maxi, maxv);
                }
                
        }
        
        /**
         * calls: {@link #DoubleKDTreeClusterer()} with 0.01
         */
        public DoubleKDTreeClusterer() {
                
        }
        
        /**
         * @param varprop the proportion of variance change from the root variance before splitting stops
         * @param startindex the index to start from
         * @param ndims the number of dimensions to split on
         */
        public DoubleKDTreeClusterer(double varprop, int startindex, int ndims) {
                this.varprop = varprop;
                this.startindex = startindex;
                this.ndims = ndims;
        }
        
        /**
         * @param detectionMode The {@link SplitDetectionMode} given the feature of highest variance
         * @param varprop The minimum proportional variance (compared to the first variance)
         * @param startindex the feature start index
         * @param ndims the number of features to use
         * 
         */
        public DoubleKDTreeClusterer(SplitDetectionMode detectionMode, double varprop, int startindex, int ndims) {
                this.detectionMode = detectionMode;
                this.varprop = varprop;
                this.startindex = startindex;
                this.ndims = ndims;
        }
        
        /**
         * @param varprop the proportion of variance change from the root variance before splitting stops
         */
        public DoubleKDTreeClusterer(double varprop) {
                this.varprop = varprop;
        }

        @Override
        public int[][] performClustering(double[][] data) {
                return cluster(data).clusters();
        }

        @Override
        public KDTreeClusters cluster(double[][] data) {
                if(ndims == -1)
                {
                        this.startindex = 0;
                        ndims = data[0].length;
                }
                
                DoubleKDTree tree = new DoubleKDTree(data, new CappedVarianceSplitChooser());
                return new KDTreeClusters(tree, ndims);
        }

        @Override
        public KDTreeClusters cluster(DataSource<double[]> data) {
                double[][] arrdata = new double[data.size()][];
                for (int i = 0; i < arrdata.length; i++) {
                        arrdata[i] = data.getData(i);
                }
                return cluster(arrdata);
        }

}