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import os
import sys
import matplotlib.pyplot as plt
import numpy as np
import pytest
def test_representations_examples():
# Disable graphics for testing purposes
plt.show = lambda: None
here = os.path.dirname(os.path.realpath(__file__))
sys.path.append(here + "/../example")
import diagram_vectorizations_distances_kernels
return None
from gudhi.representations.metrics import *
from gudhi.representations.kernel_methods import *
def _n_diags(n):
l = []
for _ in range(n):
a = np.random.rand(50, 2)
a[:, 1] += a[:, 0] # So that y >= x
l.append(a)
return l
def test_multiple():
l1 = _n_diags(9)
l2 = _n_diags(11)
l1b = l1.copy()
d1 = pairwise_persistence_diagram_distances(l1, e=0.00001, n_jobs=4)
d2 = BottleneckDistance(epsilon=0.00001).fit_transform(l1)
d3 = pairwise_persistence_diagram_distances(l1, l1b, e=0.00001, n_jobs=4)
assert d1 == pytest.approx(d2)
assert d3 == pytest.approx(d2, abs=1e-5) # Because of 0 entries (on the diagonal)
d1 = pairwise_persistence_diagram_distances(l1, l2, metric="wasserstein", order=2, internal_p=2)
d2 = WassersteinDistance(order=2, internal_p=2, n_jobs=4).fit(l2).transform(l1)
print(d1.shape, d2.shape)
assert d1 == pytest.approx(d2, rel=.02)
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