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Diffstat (limited to 'src/Gudhi_stat/include/gudhi/bootstrap.h')
| -rw-r--r-- | src/Gudhi_stat/include/gudhi/bootstrap.h | 174 |
1 files changed, 0 insertions, 174 deletions
diff --git a/src/Gudhi_stat/include/gudhi/bootstrap.h b/src/Gudhi_stat/include/gudhi/bootstrap.h deleted file mode 100644 index c3cd80c7..00000000 --- a/src/Gudhi_stat/include/gudhi/bootstrap.h +++ /dev/null @@ -1,174 +0,0 @@ -/* Thicharacteristic_of_all_pointss file is part of the Gudhi Library. The Gudhi library - * (Geometric Understanding in Higher Dimensions) is a generic C++ - * library for computational topology. - * - * Author(s): Pawel Dlotko - * - * Copyright (C) 2015 INRIA (France) - * - * This program is free software: you can redistribute it and/or modify - * it under the terms of the GNU General Public License as published by - * the Free Software Foundation, either version 3 of the License, or - * (at your option) any later version. - * - * This program is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the - * GNU General Public License for more details. - * - * You should have received a copy of the GNU General Public License - * along with this program. If not, see <http://www.gnu.org/licenses/>. - */ - -#ifndef BOOTSTRAP_H -#define BOOTSTRAP_H - - -#ifdef GUDHI_USE_TBB -#include <tbb/parallel_sort.h> -#include <tbb/task_scheduler_init.h> -#endif - -#include <vector> -#include <algorithm> -#include <random> -#include <ctime> - -namespace Gudhi -{ -namespace Gudhi_stat -{ - -/** -* This is a generic function to perform bootstrap. -* In this function we assume that there is a class to compute characteristic of collection of points (PointCloudCharacteristics) and that it stores coordinates of all points. It allows to compute the characteristic -* of the whole point cloud (by using CharacteristicFunction) or of it proper subset of the whole point cloud (given the list of numers of points in the subset). -* Both functionalities will be used in this implementation. -* The characteristic of point cloud, may be the poit cloud itself, its persistence diagram in a fixed dimension, or anything else. We only assume that space of points characteristics is a metric space -* and that we can compute a distance between two characteristics of collections of points by using DistanceBetweenPointsCharacteristics function. -**/ - - - -template < typename PointCloudCharacteristics , typename CharacteristicFunction , typename DistanceBetweenPointsCharacteristics > -double bootstrap( size_t number_of_points , CharacteristicFunction f , DistanceBetweenPointsCharacteristics distance , size_t number_of_repetitions , size_t size_of_subsample , double quantile = 0.95 , size_t maximal_number_of_threads_in_TBB = std::numeric_limits<size_t>::max() ) -{ - bool dbg = false; - - #ifdef GUDHI_USE_TBB - tbb::task_scheduler_init init(maximal_number_of_threads_in_TBB == std::numeric_limits<size_t>::max() ? tbb::task_scheduler_init::automatic : maximal_number_of_threads_in_TBB); - #endif - - if ( size_of_subsample >= number_of_points ) - { - std::cerr << "Size of subsample is greater or equal to the number of points. The bootstrap procedure do not make sense in this case. \n"; - return 0; - } - - //initialization of a random number generator: - std::srand ( unsigned ( std::time(0) ) ); - - //we will shuffle the vector of numbers 0,1,2,...,points.size()-1 in order to pick a subset of a size size_of_subsample - std::vector<size_t> numbers_to_sample_(number_of_points) ; //create vector of size_t of a size number_of_points - std::iota (std::begin(numbers_to_sample_), std::end(numbers_to_sample_), 0);//populate it with 1 2 3 ... number_of_points. - - //now we compute the characteristic od all the points: - PointCloudCharacteristics characteristic_of_all_points = f( numbers_to_sample_ ); - - //vector to keep the distances between characteristic_of_points and characteristic_of_subsample: - std::vector< double > vector_of_distances( number_of_repetitions , 0 ); - - - -//TODO- at the moment, the operations I am doing over here do not seems to be threat safe. When using TBB, I am getting wrong results. -//It is quite likelly because I am not using a method to compute persistence which is threat safe. VERIFY this as soon as I merge with -//the new metod to compute persistence. - -// #ifdef GUDHI_USE_TBB -// tbb::parallel_for ( tbb::blocked_range<size_t>(0, number_of_repetitions), [&](const tbb::blocked_range<size_t>& range) -// { -// for ( size_t it_no = range.begin() ; it_no != range.end() ; ++it_no ) -// #else - for ( size_t it_no = 0 ; it_no < number_of_repetitions ; ++it_no ) -// #endif - { - if ( dbg ) - { - std::cout << "Still : " << number_of_repetitions-it_no << " tests to go. \n The subsampled vector consist of points number : "; - std::cout << "it_no : " << it_no << std::endl; - std::cout << "number_of_points : " << number_of_points << std::endl; - } - //do a random shuffle of vector_of_characteristics_of_poits - std::vector<size_t> numbers_to_sample(number_of_points) ; //create vector of size_t of a size number_of_points - std::iota (std::begin(numbers_to_sample), std::end(numbers_to_sample), 0);//populate it with 1 2 3 ... number_of_points. - //TODO: consider doing it in a smarter/faster way. - std::random_shuffle( numbers_to_sample.begin() , numbers_to_sample.end() ); - - //construct a vector< PointType > of a size size_of_subsample: - std::vector< size_t > subsampled_points; - subsampled_points.reserve( size_of_subsample ); - for ( size_t i = 0 ; i != size_of_subsample ; ++i ) - { - subsampled_points.push_back( numbers_to_sample[i] ); - if ( dbg )std::cout << numbers_to_sample[i] << " , "; - } - - - //now we can compute characteristic of subsampled_points: - PointCloudCharacteristics characteristic_of_subsampled_points = f( subsampled_points ); - if ( dbg )std::cout << std::endl << "Characteristic of subsampled points computed.\n"; - - //and now we compute distance between characteristic_of_points and characteristic_of_subsample. Note that subsampled points go first, and this is neded, since sometimes all points are not needed. - double dist = distance( characteristic_of_subsampled_points , characteristic_of_all_points ); - - if ( dbg ) - { - std::cout << "The distance between characteristic of all points and the characteristic of subsample is : " << dist << std::endl; - getchar(); - } - - vector_of_distances[it_no] = dist; - } -// #ifdef GUDHI_USE_TBB -// } -// ); -// #endif - - size_t position_of_quantile = floor(quantile*vector_of_distances.size()); - if ( position_of_quantile ) --position_of_quantile; - - - - if ( dbg ) - { - std::cerr << "quantile : " << quantile << std::endl; - std::cerr << "position_of_quantile : " << position_of_quantile << std::endl; - - std::sort( vector_of_distances.begin() , vector_of_distances.end() ); - //std::cout << "position_of_quantile : " << position_of_quantile << ", and here is the array : " << std::endl; - for ( size_t i = 0 ; i != vector_of_distances.size() ; ++i ) - { - std::cout << vector_of_distances[i] << " " ; - } - std::cout << std::endl; - } - - //now we need to sort the vector_of_distances and find the quantile: - std::nth_element (vector_of_distances.begin(), vector_of_distances.begin()+position_of_quantile, vector_of_distances.end()); - - - //for Hausdorff bootrstra I have to multily it by 2. - //In case of other bootsraps, I do not have to do it. We need a special variable saying if Ineed this multiplication or not.//This should be done outside the bootstrap, since the fact hat we need it do not come from bootstrab, but from geometry of bottleneck distance - - if ( dbg )std::cout << "Result : " << vector_of_distances[ position_of_quantile ] << std::endl; - - return vector_of_distances[ position_of_quantile ]; - -}//bootstrap - - - -}//namespace Gudhi_stat -}//namespace Gudhi - -#endif |