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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) 2021 Inria
#
# Modification(s):
# - YYYY/MM Author: Description of the modification
from .. import CubicalComplex
from sklearn.base import BaseEstimator, TransformerMixin
# joblib is required by scikit-learn
from joblib import Parallel, delayed
class CubicalPersistence(BaseEstimator, TransformerMixin):
# Fast way to find primes and should be enough
_available_primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97]
"""
This is a class for computing the persistence diagrams from a cubical complex.
"""
def __init__(self, dimensions=None, persistence_dim=0, min_persistence=0, n_jobs=None):
"""
Constructor for the CubicalPersistence class.
Parameters:
dimensions (list of int): A list of number of top dimensional cells if cells filtration values will require
to be reshaped (cf. :func:`~gudhi.sklearn.cubical_persistence.CubicalPersistence.transform`)
persistence_dim (int): The returned persistence diagrams dimension. Default value is `0`.
min_persistence (float): The minimum persistence value to take into account (strictly greater than
`min_persistence`). Default value is `0.0`. Sets `min_persistence` to `-1.0` to see all values.
n_jobs (int): cf. https://joblib.readthedocs.io/en/latest/generated/joblib.Parallel.html
"""
self.dimensions = dimensions
self.persistence_dim = persistence_dim
self.homology_coeff_field_ = None
for dim in self._available_primes:
if dim > persistence_dim + 1:
self.homology_coeff_field_ = dim
break
if self.homology_coeff_field_ == None:
raise ValueError("persistence_dim must be less than 96")
self.min_persistence = min_persistence
self.n_jobs = n_jobs
def fit(self, X, Y=None):
"""
Nothing to be done, but useful when included in a scikit-learn Pipeline.
"""
return self
def __transform(self, cells):
cubical_complex = CubicalComplex(top_dimensional_cells=cells, dimensions=self.dimensions)
cubical_complex.compute_persistence(
homology_coeff_field=self.homology_coeff_field_, min_persistence=self.min_persistence
)
diagrams = cubical_complex.persistence_intervals_in_dimension(self.persistence_dim)
return diagrams
def transform(self, X, Y=None):
"""
Compute all the cubical complexes and their associated persistence diagrams.
Parameters:
X (list of list of double OR list of numpy.ndarray): List of cells filtration values that can be flatten if
dimensions is set in the constructor, or already with the correct shape in a numpy.ndarray (and
dimensions must not be set).
Returns:
Persistence diagrams
"""
# threads is preferred as cubical construction and persistence computation releases the GIL
return Parallel(n_jobs=self.n_jobs, prefer="threads")(delayed(self.__transform)(cells) for cells in X)
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