/*
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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
 * 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;
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package org.openimaj.ml.clustering.assignment.soft;

import org.openimaj.feature.IntFVComparison;
import org.openimaj.knn.IntNearestNeighbours;
import org.openimaj.knn.IntNearestNeighboursExact;
import org.openimaj.knn.IntNearestNeighboursProvider;
import org.openimaj.knn.approximate.IntNearestNeighboursKDTree;
import org.openimaj.ml.clustering.assignment.SoftAssigner;
import org.openimaj.ml.clustering.CentroidsProvider;
import org.openimaj.util.pair.IndependentPair;

/**
 * A {@link SoftAssigner} that picks a fixed number of nearest neighbours.
 * Weights returned are actually the distances to the centroids.
 * 
 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
 *
 */
public class IntKNNAssigner implements SoftAssigner<int[], float[]> {
        protected IntNearestNeighbours nn;
        protected int numNeighbours;

        /**
         * Construct the assigner using the given cluster data. The assigner
         * is backed by either a {@link IntNearestNeighboursExact} or 
         * {@link IntNearestNeighboursKDTree}, depending on whether the exact
         * parameter is true or false. If the parameter is true, then the 
         * resultant {@link IntNearestNeighboursExact} will use Euclidean
         * distance.
         * 
         * @param provider the cluster data provider
         * @param exact if true, then use exact mode; false implies approximate mode.
         * @param numNeighbours the number of nearest neighbours to select.
         */
        public IntKNNAssigner(CentroidsProvider<int[]> provider, boolean exact, int numNeighbours) {
                this.numNeighbours = numNeighbours;
                
                if (exact) {
                        nn = new IntNearestNeighboursExact(provider.getCentroids());
                } else {
                        if (provider instanceof IntNearestNeighboursProvider) {
                                IntNearestNeighbours internal = ((IntNearestNeighboursProvider)provider).getNearestNeighbours();

                                if (internal != null && internal instanceof IntNearestNeighboursKDTree) {
                                        nn = (IntNearestNeighboursKDTree) internal;
                                        return;
                                }
                        }

                        nn = new IntNearestNeighboursKDTree(provider.getCentroids(), IntNearestNeighboursKDTree.DEFAULT_NTREES, IntNearestNeighboursKDTree.DEFAULT_NCHECKS);
                }
        }
        
        /**
         * Construct the assigner using the given cluster data. The assigner
         * is backed by either a {@link IntNearestNeighboursExact} or 
         * {@link IntNearestNeighboursKDTree}, depending on whether the exact
         * parameter is true or false. If the parameter is true, then the 
         * resultant {@link IntNearestNeighboursExact} will use Euclidean
         * distance.
         * 
         * @param data the cluster data
         * @param exact if true, then use exact mode; false implies approximate mode.
         * @param numNeighbours the number of nearest neighbours to select.
         */
        public IntKNNAssigner(int[][] data, boolean exact, int numNeighbours) {
                this.numNeighbours = numNeighbours;
                
                if (exact) {
                        nn = new IntNearestNeighboursExact(data);
                } else {
                        nn = new IntNearestNeighboursKDTree(data, IntNearestNeighboursKDTree.DEFAULT_NTREES, IntNearestNeighboursKDTree.DEFAULT_NCHECKS);
                }
        }
        
        /**
         * Construct the assigner using the given cluster data and 
         * distance function. The assigner will operate in exact mode,
         * using a {@link IntNearestNeighboursExact}.
         * 
         * @param provider the cluster data provider
         * @param comparison the distance function
         * @param numNeighbours the number of nearest neighbours to select.
         */
        public IntKNNAssigner(CentroidsProvider<int[]> provider, IntFVComparison comparison, int numNeighbours) {
                this.numNeighbours = numNeighbours;
                
                nn = new IntNearestNeighboursExact(provider.getCentroids(), comparison);
        }
        
        /**
         * Construct the assigner using the given cluster data and 
         * distance function. The assigner will operate in exact mode,
         * using a {@link IntNearestNeighboursExact}.
         * 
         * @param data the cluster data
         * @param comparison the distance function
         * @param numNeighbours the number of nearest neighbours to select.
         */
        public IntKNNAssigner(int[][] data, IntFVComparison comparison, int numNeighbours) {
                this.numNeighbours = numNeighbours;
                
                nn = new IntNearestNeighboursExact(data, comparison);
        }

        @Override
        public int[][] assign(int[][] data) {
                int [][] indices = new int [data.length][numNeighbours];
                float [][] distances = new float [data.length][numNeighbours];
                
                nn.searchKNN(data, numNeighbours, indices, distances);
                
                return indices;
        }

        @Override
        public int[] assign(int[] data) {
                return assign(new int[][] { data })[0];
        }

        @Override
        public void assignWeighted(int[][] data, int[][] assignments, float[][] weights) {
                nn.searchKNN(data, numNeighbours, assignments, weights);
        }

        @Override
        public IndependentPair<int[], float[]> assignWeighted(int[] data) {
                int [][] indices = new int [data.length][numNeighbours];
                float [][] distances = new float [data.length][numNeighbours];
                
                nn.searchKNN(new int[][] { data }, numNeighbours, indices, distances);
                
                return new IndependentPair<int[], float[]>(indices[0], distances[0]);
        }
        
        @Override
        public int numDimensions() {
            return nn.numDimensions();
        }
        
        @Override
        public int size() {
            return nn.size();
        }
}