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Diffstat (limited to 'cython/cython/subsampling.pyx')
| -rw-r--r-- | cython/cython/subsampling.pyx | 142 |
1 files changed, 0 insertions, 142 deletions
diff --git a/cython/cython/subsampling.pyx b/cython/cython/subsampling.pyx deleted file mode 100644 index e9d61a37..00000000 --- a/cython/cython/subsampling.pyx +++ /dev/null @@ -1,142 +0,0 @@ -from cython cimport numeric -from libcpp.vector cimport vector -from libcpp.string cimport string -from libcpp cimport bool -import os - -"""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): Vincent Rouvreau - - Copyright (C) 2016 Inria - - 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/>. -""" - -__author__ = "Vincent Rouvreau" -__copyright__ = "Copyright (C) 2016 Inria" -__license__ = "GPL v3" - -cdef extern from "Subsampling_interface.h" namespace "Gudhi::subsampling": - vector[vector[double]] subsampling_n_farthest_points(vector[vector[double]] points, unsigned nb_points) - vector[vector[double]] subsampling_n_farthest_points(vector[vector[double]] points, unsigned nb_points, unsigned starting_point) - vector[vector[double]] subsampling_n_farthest_points_from_file(string off_file, unsigned nb_points) - vector[vector[double]] subsampling_n_farthest_points_from_file(string off_file, unsigned nb_points, unsigned starting_point) - vector[vector[double]] subsampling_n_random_points(vector[vector[double]] points, unsigned nb_points) - vector[vector[double]] subsampling_n_random_points_from_file(string off_file, unsigned nb_points) - vector[vector[double]] subsampling_sparsify_points(vector[vector[double]] points, double min_squared_dist) - vector[vector[double]] subsampling_sparsify_points_from_file(string off_file, double min_squared_dist) - -def choose_n_farthest_points(points=None, off_file='', nb_points=0, starting_point = ''): - """Subsample by a greedy strategy of iteratively adding the farthest point - from the current chosen point set to the subsampling. - The iteration starts with the landmark `starting point`. - - :param points: The input point set. - :type points: vector[vector[double]]. - - Or - - :param off_file: An OFF file style name. - :type off_file: string - - :param nb_points: Number of points of the subsample. - :type nb_points: unsigned. - :param starting_point: The iteration starts with the landmark `starting \ - point`,which is the index of the poit to start with. If not set, this \ - index is choosen randomly. - :type starting_point: unsigned. - :returns: The subsample point set. - :rtype: vector[vector[double]] - """ - if off_file is not '': - if os.path.isfile(off_file): - if starting_point is '': - return subsampling_n_farthest_points_from_file(str.encode(off_file), - nb_points) - else: - return subsampling_n_farthest_points_from_file(str.encode(off_file), - nb_points, - starting_point) - else: - print("file " + off_file + " not found.") - else: - if points is None: - # Empty points - points=[] - if starting_point is '': - return subsampling_n_farthest_points(points, nb_points) - else: - return subsampling_n_farthest_points(points, nb_points, - starting_point) - -def pick_n_random_points(points=None, off_file='', nb_points=0): - """Subsample a point set by picking random vertices. - - :param points: The input point set. - :type points: vector[vector[double]]. - - Or - - :param off_file: An OFF file style name. - :type off_file: string - - :param nb_points: Number of points of the subsample. - :type nb_points: unsigned. - :returns: The subsample point set. - :rtype: vector[vector[double]] - """ - if off_file is not '': - if os.path.isfile(off_file): - return subsampling_n_random_points_from_file(str.encode(off_file), - nb_points) - else: - print("file " + off_file + " not found.") - else: - if points is None: - # Empty points - points=[] - return subsampling_n_random_points(points, nb_points) - -def sparsify_point_set(points=None, off_file='', min_squared_dist=0.0): - """Outputs a subset of the input points so that the squared distance - between any two points is greater than or equal to min_squared_dist. - - :param points: The input point set. - :type points: vector[vector[double]]. - - Or - - :param off_file: An OFF file style name. - :type off_file: string - - :param min_squared_dist: Minimum squared distance separating the output \ - points. - :type min_squared_dist: float. - :returns: The subsample point set. - :rtype: vector[vector[double]] - """ - if off_file is not '': - if os.path.isfile(off_file): - return subsampling_sparsify_points_from_file(str.encode(off_file), - min_squared_dist) - else: - print("file " + off_file + " not found.") - else: - if points is None: - # Empty points - points=[] - return subsampling_sparsify_points(points, min_squared_dist) |