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from .. import CubicalComplex
from sklearn.base import TransformerMixin
class CubicalPersistence(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):
"""
Constructor for the CubicalPersistence class.
Parameters:
dimensions (list of int): A list of number of top dimensional cells.
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.
"""
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
def transform(self, X):
"""
Compute all the cubical complexes and their persistence diagrams.
Parameters:
X (list of double OR numpy.ndarray): Cells filtration values.
Returns:
Persistence diagrams
"""
cubical_complex = CubicalComplex(top_dimensional_cells = X,
dimensions = self.dimensions_)
cubical_complex.compute_persistence(homology_coeff_field = self.homology_coeff_field_,
min_persistence = self.min_persistence_)
self.diagrams_ = cubical_complex.persistence_intervals_in_dimension(self.persistence_dim_)
if self.persistence_dim_ == 0:
# return all but the last, always [ 0., inf]
self.diagrams_ = self.diagrams_[:-1]
return self.diagrams_
def fit_transform(self, X):
"""
Compute all the cubical complexes and their persistence diagrams.
Parameters:
X (list of double OR numpy.ndarray): Cells filtration values.
Returns:
Persistence diagrams
"""
self.transform(X)
return self.diagrams_
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