knnbucketsize.cpp

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/*

Copyright (c) 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland
You can contact the author at <stephane at magnenat dot net>

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 <organization> 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 ETH-ASL 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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*/

#include "nabo/nabo.h"
#include "helpers.h"
#include <iostream>
#include <fstream>
#include <stdexcept>

using namespace std;
using namespace Nabo;

template<typename T>
void doTestEpsilon(const char *fileName, const int K, const int method, const int searchCount)
{
        typedef Nabo::NearestNeighbourSearch<T> NNS;
        typedef typename NNS::Matrix Matrix;
        typedef typename NNS::IndexMatrix IndexMatrix;
        
        // check if file is ok
        const Matrix d(load<T>(fileName));
        if (K >= d.cols())
        {
                cerr << "Requested more nearest neighbour than points in the data set" << endl;
                exit(2);
        }
        
        // check methods together
        const int itCount(method >= 0 ? method : d.cols() * 2);
        
        // create big query
        const Matrix q(createQuery<T>(d, itCount, method));
        IndexMatrix indexes_bf(K, q.cols());
        Matrix dists2_bf(K, q.cols());
        
        for (unsigned bucketSize = 2; bucketSize < 40; ++bucketSize)
        {
                Parameters additionalParameters("bucketSize", unsigned(bucketSize));
                NNS* nns = NNS::createKDTreeLinearHeap(d, std::numeric_limits<typename NNS::Index>::max(), 0, additionalParameters);
        
                double duration(0);
                for (int s = 0; s < searchCount; ++s)
                {
                        boost::timer t;
                        nns->knn(q, indexes_bf, dists2_bf, K, 0, NNS::ALLOW_SELF_MATCH);
                        duration += t.elapsed();
                }
                cout << bucketSize << " " << duration/double(searchCount) << endl;
                
                delete nns;
        }
}


int main(int argc, char* argv[])
{
        if (argc != 5)
        {
                cerr << "Usage " << argv[0] << " DATA K METHOD SEARCH_COUNT" << endl;
                return 1;
        }
        
        const int K(atoi(argv[2]));
        const int method(atoi(argv[3]));
        const int searchCount(atoi(argv[4]));
        
        cout << "bucketSize average_duration\n";
        doTestEpsilon<double>(argv[1], K, method, searchCount);
        
        return 0;
}