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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 gudhi.sklearn.cubical_persistence import CubicalPersistence
import numpy as np
from sklearn import datasets
CUBICAL_PERSISTENCE_H0_IMG0 = np.array([[0.0, 6.0], [0.0, 8.0], [0.0, np.inf]])
def test_simple_constructor_from_top_cells():
cells = datasets.load_digits().images[0]
cp = CubicalPersistence(homology_dimensions=0)
np.testing.assert_array_equal(cp._CubicalPersistence__transform_only_this_dim(cells), CUBICAL_PERSISTENCE_H0_IMG0)
cp = CubicalPersistence(homology_dimensions=[0, 2])
diags = cp._CubicalPersistence__transform(cells)
assert len(diags) == 2
np.testing.assert_array_equal(diags[0], CUBICAL_PERSISTENCE_H0_IMG0)
def test_simple_constructor_from_top_cells_list():
digits = datasets.load_digits().images[:10]
cp = CubicalPersistence(homology_dimensions=0, n_jobs=-2)
diags = cp.fit_transform(digits)
assert len(diags) == 10
np.testing.assert_array_equal(diags[0], CUBICAL_PERSISTENCE_H0_IMG0)
cp = CubicalPersistence(homology_dimensions=[0, 1], n_jobs=-1)
diagsH0H1 = cp.fit_transform(digits)
assert len(diagsH0H1) == 10
for idx in range(10):
np.testing.assert_array_equal(diags[idx], diagsH0H1[idx][0])
def test_simple_constructor_from_flattened_cells():
cells = datasets.load_digits().images[0]
# Not squared (extended) flatten cells
flat_cells = np.hstack((cells, np.zeros((cells.shape[0], 2)))).flatten()
cp = CubicalPersistence(homology_dimensions=0, newshape=[-1, 8, 10])
diags = cp.fit_transform([flat_cells])
np.testing.assert_array_equal(diags[0], CUBICAL_PERSISTENCE_H0_IMG0)
# Not squared (extended) non-flatten cells
cells = np.hstack((cells, np.zeros((cells.shape[0], 2))))
# The aim of this second part of the test is to resize even if not mandatory
cp = CubicalPersistence(homology_dimensions=0, newshape=[-1, 8, 10])
diags = cp.fit_transform([cells])
np.testing.assert_array_equal(diags[0], CUBICAL_PERSISTENCE_H0_IMG0)
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