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Spark algorithms for building k-nn graphs
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/*
 * The MIT License
 *
 * Copyright 2015 Thibault Debatty.
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to deal
 * in the Software without restriction, including without limitation the rights
 * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 * copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in
 * all copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
 * THE SOFTWARE.
 */
package info.debatty.spark.knngraphs;

import info.debatty.java.graphs.Neighbor;
import info.debatty.java.graphs.NeighborList;
import info.debatty.java.graphs.Node;
import info.debatty.java.graphs.SimilarityInterface;
import java.io.Serializable;
import java.util.ArrayList;
import java.util.Iterator;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.function.FlatMapFunction;
import scala.Tuple2;

/**
 *
 * @author Thibault Debatty
 * @param 
 */
public class ExhaustiveSearch implements Serializable {
    private final JavaPairRDD, NeighborList> graph;
    private final SimilarityInterface similarity;
    
    public ExhaustiveSearch(JavaPairRDD, NeighborList> graph, SimilarityInterface similarity) {
        this.graph = graph;
        this.similarity = similarity;
    }

    public NeighborList search(final Node query, final int k) {
        JavaRDD candidates_neighborlists = graph.mapPartitions( new FlatMapFunction, NeighborList>>, NeighborList>() {

            public Iterable call(Iterator, NeighborList>> tuples_iterator) throws Exception {
                NeighborList local_nl = new NeighborList(k);
                while (tuples_iterator.hasNext()) {
                    Node next = tuples_iterator.next()._1;
                    local_nl.add(new Neighbor(
                            next,
                            similarity.similarity(query.value, next.value)));
                }

                ArrayList result = new ArrayList(1);
                result.add(local_nl);
                return result;

            }
        });

        NeighborList final_neighborlist = new NeighborList(k);
        for (NeighborList nl : candidates_neighborlists.collect()) {
            final_neighborlist.addAll(nl);
        }

        return final_neighborlist;
    }
}