/*
        AUTOMATICALLY GENERATED BY jTemp FROM
        /Users/jsh2/Work/openimaj/target/checkout/machine-learning/clustering/src/main/jtemp/org/openimaj/ml/clustering/assignment/hard/Hierarchical#T#HardAssigner.jtemp
*/
/**
 * 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.assignment.hard;

import org.openimaj.ml.clustering.assignment.HardAssigner;
import org.openimaj.ml.clustering.assignment.soft.HierarchicalLongPathAssigner;
import org.openimaj.ml.clustering.kmeans.HierarchicalLongKMeansResult;
import org.openimaj.util.pair.IndependentPair;
import org.openimaj.util.pair.IntDoublePair;

/**
 * The {@link HierarchicalLongHardAssigner} is a {@link HardAssigner} for
 * {@link HierarchicalLongKMeansResult} instances. The assigner
 * produces the index of the assigned leaf node as if the clusters were
 * actually flat. 
 * 
 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
 */
public class HierarchicalLongHardAssigner implements HardAssigner<long[], double[], IntDoublePair> {
        /**
         * The {@link ScoringScheme} determines how the distance
         * to a cluster is estimated from the hierarchy of k-means
         * generated clusters.
         * 
         * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
         */
        public enum ScoringScheme {
                /**
                 * Sum distances down the tree.
                 * 
                 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
                 */
                SUM {
                        @Override
                        public double computeScore(double[] weights) {
                                double sum = 0;
                                for (double w : weights) {
                                        if (w < 0) break;
                                        sum += w;
                                }
                                
                                return sum;
                        }
                },
                /**
                 * Product of distances down the tree.
                 * 
                 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
                 */
                PRODUCT {
                        @Override
                        public double computeScore(double[] weights) {
                                double prod = 1;
                                for (double w : weights) {
                                        if (w < 0) break;
                                        prod *= w;
                                }
                                
                                return prod;
                        }
                },
                /**
                 * The distance in the root cluster 
                 * 
                 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
                 */
                FIRST {
                        @Override
                        public double computeScore(double[] weights) {
                                return weights[0];
                        }
                },
                /**
                 * The distance in the leaf cluster
                 * 
                 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
                 */
                LAST {
                        @Override
                        public double computeScore(double[] weights) {
                                double last = -1;
                                
                                for (double w : weights) {
                                        if (w < 0) break;
                                        last = w;
                                }
                                
                                return last;
                        }
                },
                /**
                 * The mean distance down the tree
                 * 
                 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
                 */
                MEAN {
                        @Override
                        public double computeScore(double[] weights) {
                                double sum = 0;
                                int count = 0;
                                
                                for (double w : weights) {
                                        if (w < 0) break;
                                        sum += w;
                                        count++;
                                }
                                
                                return sum / (double)count;
                        }
                }
                ;
                
                protected abstract double computeScore(double[] weights); 
        }
        
        protected HierarchicalLongKMeansResult result;
        protected HierarchicalLongPathAssigner path;
        protected ScoringScheme scorer;
        
        /**
         * Construct with the given hierarchical KMeans clusterer
         * and scoring scheme.
         *
         * @param result the hierarchical KMeans clusterer
         * @param scorer the scoring scheme
         */
        public HierarchicalLongHardAssigner(HierarchicalLongKMeansResult result, ScoringScheme scorer) {
                this.result = result;
                this.scorer = scorer;
                this.path = new HierarchicalLongPathAssigner(result);
        }

        /**
         * Construct with the given Hierarchical KMeans clusterer
         * and the SUM scoring scheme.
         *
         * @param result the hierarchical KMeans clusterer
         */
        public HierarchicalLongHardAssigner(HierarchicalLongKMeansResult result) {
                this(result, ScoringScheme.SUM);
        }
        
        @Override
        public int[] assign(long[][] data) {
                int [] asgn = new int[data.length];

                for (int i=0; i<data.length; i++) {
                        asgn[i] = result.getIndex(path.assign(data[i]));
                }

                return asgn;
        }

        @Override
        public int assign(long[] data) {
                return result.getIndex(path.assign(data));
        }

        @Override
        public void assignDistance(long[][] data, int[] indices, double[] distances) {
                int depth = result.getDepth();
                long [][] d = new long[1][];
                int [][] p = new int[1][depth];
                double [][] w = new double[1][depth];
                
                for (int i=0; i<data.length; i++) {
                        d[0] = data[i];
                        
                        path.assignWeighted(d, p, w);
                        
                        indices[i] = result.getIndex(p[0]);
                        distances[i] = scorer.computeScore(w[0]);
                }
        }

        @Override
        public IntDoublePair assignDistance(long[] data) {
                IndependentPair<int[], double[]> pw = path.assignWeighted(data);
                
                int index = result.getIndex(pw.firstObject());
                double score = scorer.computeScore(pw.secondObject());
                
                return new IntDoublePair(index, score);
        }
        
        @Override
        public int size() {
            return result.countLeafs();
        }
        
        @Override
        public int numDimensions() {
            return result.numDimensions();
        }
}