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324 lines
11 KiB
324 lines
11 KiB
/***********************************************************************
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* Software License Agreement (BSD License)
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*
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* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
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* Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
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*
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* THE BSD LICENSE
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* 1. Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* 2. Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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*
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* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
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* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
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* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
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* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
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* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*************************************************************************/
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#ifndef FLANN_INDEX_TESTING_H_
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#define FLANN_INDEX_TESTING_H_
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#include <cstring>
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#include <cassert>
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#include <cmath>
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#include "FLANN/util/matrix.h"
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#include "FLANN/algorithms/nn_index.h"
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#include "FLANN/util/result_set.h"
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#include "FLANN/util/logger.h"
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#include "FLANN/util/timer.h"
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namespace flann
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{
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inline int countCorrectMatches(size_t* neighbors, size_t* groundTruth, int n)
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{
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int count = 0;
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for (int i=0; i<n; ++i) {
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for (int k=0; k<n; ++k) {
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if (neighbors[i]==groundTruth[k]) {
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count++;
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break;
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}
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}
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}
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return count;
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}
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template <typename Distance>
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typename Distance::ResultType computeDistanceRaport(const Matrix<typename Distance::ElementType>& inputData, typename Distance::ElementType* target,
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size_t* neighbors, size_t* groundTruth, int veclen, int n, const Distance& distance)
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{
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typedef typename Distance::ResultType DistanceType;
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DistanceType ret = 0;
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for (int i=0; i<n; ++i) {
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DistanceType den = distance(inputData[groundTruth[i]], target, veclen);
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DistanceType num = distance(inputData[neighbors[i]], target, veclen);
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if ((den==0)&&(num==0)) {
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ret += 1;
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}
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else {
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ret += num/den;
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}
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}
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return ret;
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}
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template <typename Index, typename Distance>
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float search_with_ground_truth(Index& index, const Matrix<typename Distance::ElementType>& inputData,
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const Matrix<typename Distance::ElementType>& testData, const Matrix<size_t>& matches, int nn, int checks,
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float& time, typename Distance::ResultType& dist, const Distance& distance, int skipMatches)
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{
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typedef typename Distance::ElementType ElementType;
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typedef typename Distance::ResultType DistanceType;
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if (matches.cols<size_t(nn)) {
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Logger::info("matches.cols=%d, nn=%d\n",matches.cols,nn);
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throw FLANNException("Ground truth is not computed for as many neighbors as requested");
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}
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SearchParams searchParams(checks);
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size_t* indices = new size_t[nn+skipMatches];
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DistanceType* dists = new DistanceType[nn+skipMatches];
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Matrix<size_t> indices_mat(indices, 1, nn+skipMatches);
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Matrix<DistanceType> dists_mat(dists, 1, nn+skipMatches);
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size_t* neighbors = indices + skipMatches;
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int correct = 0;
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DistanceType distR = 0;
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StartStopTimer t;
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int repeats = 0;
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while (t.value<0.2) {
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repeats++;
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t.start();
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correct = 0;
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distR = 0;
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for (size_t i = 0; i < testData.rows; i++) {
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index.knnSearch(Matrix<ElementType>(testData[i], 1, testData.cols), indices_mat, dists_mat, nn+skipMatches, searchParams);
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correct += countCorrectMatches(neighbors,matches[i], nn);
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distR += computeDistanceRaport<Distance>(inputData, testData[i], neighbors, matches[i], testData.cols, nn, distance);
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}
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t.stop();
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}
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time = float(t.value/repeats);
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delete[] indices;
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delete[] dists;
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float precicion = (float)correct/(nn*testData.rows);
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dist = distR/(testData.rows*nn);
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Logger::info("%8d %10.4g %10.5g %10.5g %10.5g\n",
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checks, precicion, time, 1000.0 * time / testData.rows, dist);
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return precicion;
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}
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template <typename Index, typename Distance>
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float test_index_checks(Index& index, const Matrix<typename Distance::ElementType>& inputData,
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const Matrix<typename Distance::ElementType>& testData, const Matrix<size_t>& matches,
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int checks, float& precision, const Distance& distance, int nn = 1, int skipMatches = 0)
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{
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typedef typename Distance::ResultType DistanceType;
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Logger::info(" Nodes Precision(%) Time(s) Time/vec(ms) Mean dist\n");
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Logger::info("---------------------------------------------------------\n");
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float time = 0;
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DistanceType dist = 0;
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precision = search_with_ground_truth(index, inputData, testData, matches, nn, checks, time, dist, distance, skipMatches);
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return time;
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}
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template <typename Index, typename Distance>
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float test_index_precision(Index& index, const Matrix<typename Distance::ElementType>& inputData,
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const Matrix<typename Distance::ElementType>& testData, const Matrix<size_t>& matches,
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float precision, int& checks, const Distance& distance, int nn = 1, int skipMatches = 0)
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{
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typedef typename Distance::ResultType DistanceType;
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const float SEARCH_EPS = 0.001f;
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Logger::info(" Nodes Precision(%) Time(s) Time/vec(ms) Mean dist\n");
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Logger::info("---------------------------------------------------------\n");
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int c2 = 1;
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float p2;
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int c1 = 1;
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// float p1;
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float time;
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DistanceType dist;
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p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches);
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if (p2>precision) {
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Logger::info("Got as close as I can\n");
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checks = c2;
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return time;
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}
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while (p2<precision) {
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c1 = c2;
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// p1 = p2;
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c2 *=2;
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p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches);
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}
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int cx;
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float realPrecision;
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if (fabs(p2-precision)>SEARCH_EPS) {
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Logger::info("Start linear estimation\n");
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// after we got to values in the vecinity of the desired precision
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// use linear approximation get a better estimation
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cx = (c1+c2)/2;
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realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches);
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while (fabs(realPrecision-precision)>SEARCH_EPS) {
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if (realPrecision<precision) {
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c1 = cx;
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}
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else {
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c2 = cx;
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}
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cx = (c1+c2)/2;
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if (cx==c1) {
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Logger::info("Got as close as I can\n");
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break;
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}
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realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches);
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}
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c2 = cx;
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p2 = realPrecision;
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}
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else {
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Logger::info("No need for linear estimation\n");
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cx = c2;
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realPrecision = p2;
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}
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checks = cx;
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return time;
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}
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template <typename Index, typename Distance>
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void test_index_precisions(Index& index, const Matrix<typename Distance::ElementType>& inputData,
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const Matrix<typename Distance::ElementType>& testData, const Matrix<int>& matches,
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float* precisions, int precisions_length, const Distance& distance, int nn = 1, int skipMatches = 0, float maxTime = 0)
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{
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typedef typename Distance::ResultType DistanceType;
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const float SEARCH_EPS = 0.001;
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// make sure precisions array is sorted
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std::sort(precisions, precisions+precisions_length);
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int pindex = 0;
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float precision = precisions[pindex];
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Logger::info(" Nodes Precision(%) Time(s) Time/vec(ms) Mean dist\n");
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Logger::info("---------------------------------------------------------\n");
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int c2 = 1;
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float p2;
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int c1 = 1;
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float p1;
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float time;
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DistanceType dist;
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p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches);
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// if precision for 1 run down the tree is already
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// better then some of the requested precisions, then
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// skip those
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while (precisions[pindex]<p2 && pindex<precisions_length) {
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pindex++;
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}
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if (pindex==precisions_length) {
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Logger::info("Got as close as I can\n");
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return;
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}
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for (int i=pindex; i<precisions_length; ++i) {
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precision = precisions[i];
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while (p2<precision) {
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c1 = c2;
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p1 = p2;
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c2 *=2;
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p2 = search_with_ground_truth(index, inputData, testData, matches, nn, c2, time, dist, distance, skipMatches);
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if ((maxTime> 0)&&(time > maxTime)&&(p2<precision)) return;
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}
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int cx;
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float realPrecision;
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if (fabs(p2-precision)>SEARCH_EPS) {
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Logger::info("Start linear estimation\n");
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// after we got to values in the vecinity of the desired precision
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// use linear approximation get a better estimation
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cx = (c1+c2)/2;
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realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches);
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while (fabs(realPrecision-precision)>SEARCH_EPS) {
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if (realPrecision<precision) {
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c1 = cx;
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}
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else {
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c2 = cx;
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}
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cx = (c1+c2)/2;
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if (cx==c1) {
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Logger::info("Got as close as I can\n");
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break;
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}
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realPrecision = search_with_ground_truth(index, inputData, testData, matches, nn, cx, time, dist, distance, skipMatches);
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}
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c2 = cx;
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p2 = realPrecision;
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}
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else {
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Logger::info("No need for linear estimation\n");
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cx = c2;
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realPrecision = p2;
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}
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}
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}
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}
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#endif //FLANN_INDEX_TESTING_H_
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