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# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
# Author(s): Vincent Rouvreau
#
# Copyright (C) 2016 Inria
#
# Modification(s):
# - YYYY/MM Author: Description of the modification
from cython cimport numeric
from libcpp.vector cimport vector
from libcpp.string cimport string
from libcpp cimport bool
import os
__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 point to start with. If not set, this \
index is chosen randomly.
:type starting_point: unsigned.
:returns: The subsample point set.
:rtype: vector[vector[double]]
"""
if off_file:
if os.path.isfile(off_file):
if starting_point == '':
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 == '':
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:
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:
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)
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