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Diffstat (limited to 'src/Gudhi_stat/include/gudhi/multiplicative_bootstrap.h')
| -rw-r--r-- | src/Gudhi_stat/include/gudhi/multiplicative_bootstrap.h | 179 |
1 files changed, 0 insertions, 179 deletions
diff --git a/src/Gudhi_stat/include/gudhi/multiplicative_bootstrap.h b/src/Gudhi_stat/include/gudhi/multiplicative_bootstrap.h deleted file mode 100644 index a19e6d60..00000000 --- a/src/Gudhi_stat/include/gudhi/multiplicative_bootstrap.h +++ /dev/null @@ -1,179 +0,0 @@ -/* This 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 - -//concretizations -#include <gudhi/persistence_vectors.h> -#include <gudhi/Persistence_landscape.h> -#include <gudhi/Persistence_landscape_on_grid.h> -#include <gudhi/Persistence_heat_maps.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 -{ - -template < typename TopologicalObject > -class difference_of_objects -{ -public: - TopologicalObject operator()( const TopologicalObject& first, const TopologicalObject& second )const - { - return first-second; - } -}; - -template < typename TopologicalObject > -class norm_of_objects -{ -public: - norm_of_objects():power(1){} - norm_of_objects( double power_ ):power(power_){} - double operator()( const TopologicalObject& obj )const - { - TopologicalObject empty; - double dist = empty.distance( obj , power ); - //std::cerr << "dist : " << dist << std::endl;getchar(); - return dist; - } -private: - double power; -}; - - - -/** -* This is a generic function to perform multiplicative bootstrap. -**/ -template < typename TopologicalObject , typename OperationOnObjects , typename NormOnObjects > -double multiplicative_bootstrap( const std::vector< TopologicalObject* >& topological_objects , size_t number_of_bootstrap_operations , const OperationOnObjects& oper , const NormOnObjects& norm , 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 - - //initialization of a random number generator: - std::random_device rd; - std::mt19937 generator( time(NULL) ); - std::normal_distribution<> norm_distribution(0.,1.); - - - //first compute an average of topological_objects - TopologicalObject average; - average.compute_average( topological_objects ); - - std::vector< double > vector_of_intermediate_characteristics( number_of_bootstrap_operations , 0 ); - - - #ifdef GUDHI_USE_TBB - tbb::parallel_for ( tbb::blocked_range<size_t>(0, number_of_bootstrap_operations), [&](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_bootstrap_operations ; ++it_no ) - #endif - { - if ( dbg ) - { - std::cout << "Still : " << number_of_bootstrap_operations-it_no << " tests to go. \n The subsampled vector consist of points number : "; - } - - - //and compute the intermediate characteristic: - TopologicalObject result; - for ( size_t i = 0 ; i != topological_objects.size() ; ++i ) - { - double rand_variable = norm_distribution( generator ); - result = result + rand_variable*oper(*(topological_objects[i]) , average); - } - if ( dbg ) - { - std::cerr << "Result 1 : " << result << std::endl; - getchar(); - } - //HERE THE NORM SEEMS TO BE MISSING!! - result = result.abs(); - if ( dbg ) - { - std::cerr << "Result 2 : " << result << std::endl; - getchar(); - } - result = result*(1.0/sqrt( topological_objects.size() )); - if ( dbg ) - { - std::cerr << "Result 3 : " << result << std::endl; - getchar(); - } - //NEED TO TAKE MAX - if ( dbg ) - { - std::cerr << "Result 4 : " << norm(result) << std::endl; - getchar(); - } - vector_of_intermediate_characteristics[it_no] = norm(result); - } - #ifdef GUDHI_USE_TBB - } - ); - #endif - - - - size_t position_of_quantile = floor(quantile*vector_of_intermediate_characteristics.size()); - if ( position_of_quantile ) --position_of_quantile; - if ( dbg ) - { - std::cout << "position_of_quantile : " << position_of_quantile << ", and here is the array : " << std::endl; - for ( size_t i = 0 ; i != vector_of_intermediate_characteristics.size() ; ++i ) - { - std::cout << vector_of_intermediate_characteristics[i] << std::endl; - } - std::cout << std::endl; - } - - //now we need to sort the vector_of_distances and find the quantile: - std::nth_element (vector_of_intermediate_characteristics.begin(), vector_of_intermediate_characteristics.begin()+position_of_quantile, vector_of_intermediate_characteristics.end()); - double result = vector_of_intermediate_characteristics[ position_of_quantile ]/(sqrt( topological_objects.size() )); - if ( dbg )std::cout << "Result : " << result << std::endl; - - return result; - -}//multiplicative_bootstrap - -}//namespace Gudhi_stat -}//namespace Gudhi - -#endif |