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Algorithms that build k-nearest neighbors graph (k-nn graph): Brute-force, NN-Descent,...
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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.java.graphs.examples;

import info.debatty.java.graphs.CallbackInterface;
import info.debatty.java.graphs.Graph;
import info.debatty.java.graphs.NeighborList;
import info.debatty.java.graphs.SimilarityInterface;
import info.debatty.java.graphs.build.Brute;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.LinkedList;
import java.util.Random;

public class BruteExample {

    public static void main(String[] args) {

        // Generate some random nodes
        Random r = new Random();
        int count = 1000;

        LinkedList nodes = new LinkedList();
        for (int i = 0; i < count; i++) {
            // The value of our nodes will be an int
            nodes.add(r.nextInt(10 * count));
        }

        // Instantiate and configure the brute-force graph building algorithm
        // The minimum is to define k (number of edges per node)
        // and a similarity metric between nodes
        Brute builder = new Brute();
        builder.setK(10);
        builder.setSimilarity(new SimilarityInterface() {

            public double similarity(Integer value1, Integer value2) {
                return 1.0 / (1.0 + Math.abs(value1 - value2));
            }
        });

        // Optionaly, we can define a callback, to get some feedback...
        builder.setCallback(new CallbackInterface() {

            @Override
            public void call(HashMap data) {
                System.out.println(data);
            }

        });

        // Run the algorithm, and get the resulting neighbor lists
        Graph graph = builder.computeGraph(nodes);

        // Display the computed neighbor lists
        for (Integer n : nodes) {
            NeighborList nl = graph.getNeighbors(n);
            System.out.print(n);
            System.out.println(nl);
        }

        graph.prune(0.15);
        ArrayList> connectedComponents = graph.connectedComponents();
        System.out.println(connectedComponents.size());
        System.out.println(connectedComponents.get(0));
    }
}