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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 sklearn.base import BaseEstimator, TransformerMixin
# joblib is required by scikit-learn
from joblib import Parallel, delayed
# Mermaid sequence diagram - https://mermaid-js.github.io/mermaid-live-editor/
# sequenceDiagram
# USER->>DimensionSelector: fit_transform(<br/>[[array( H0(X0) ), array( H1(X0) ), ...],<br/> [array( H0(X1) ), array( H1(X1) ), ...],<br/> ...])
# DimensionSelector->>thread1: _transform([array( H0(X0) ), array( H1(X0) )], ...)
# DimensionSelector->>thread2: _transform([array( H0(X1) ), array( H1(X1) )], ...)
# Note right of DimensionSelector: ...
# thread1->>DimensionSelector: array( Hn(X0) )
# thread2->>DimensionSelector: array( Hn(X1) )
# Note right of DimensionSelector: ...
# DimensionSelector->>USER: [array( Hn(X0) ), <br/> array( Hn(X1) ), <br/> ...]
class DimensionSelector(BaseEstimator, TransformerMixin):
"""
This is a class to select persistence diagrams in a specific dimension.
"""
def __init__(self, persistence_dimension=0, n_jobs=None):
"""
Constructor for the DimensionSelector class.
Parameters:
persistence_dimension (int): The returned persistence diagrams dimension. Default value is `0`.
"""
self.persistence_dimension = persistence_dimension
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, X, Y=None):
"""
Select persistence diagrams from its dimension.
Parameters:
X (list of list of pairs): List of list of persistence pairs, i.e.
`[[array( H0(X0) ), array( H1(X0) ), ...], [array( H0(X1) ), array( H1(X1) ), ...], ...]`
Returns:
Persistence diagrams in a specific dimension, i.e.
`[array( Hn(X0) ), array( Hn(X1), ...]`
"""
return [persistence[self.persistence_dimension] for persistence in X]
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