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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): Marc Glisse
Copyright (C) 2020 Inria
Modification(s):
- YYYY/MM Author: Description of the modification
"""
from gudhi.point_cloud.knn import KNN
import numpy as np
import pytest
def test_knn_explicit():
base = np.array([[1.0, 1], [1, 2], [4, 2], [4, 3]])
query = np.array([[1.0, 1], [2, 2], [4, 4]])
knn = KNN(2, metric="manhattan", return_distance=True, return_index=True)
knn.fit(base)
r = knn.transform(query)
assert r[0] == pytest.approx(np.array([[0, 1], [1, 0], [3, 2]]))
assert r[1] == pytest.approx(np.array([[0.0, 1], [1, 2], [1, 2]]))
knn = KNN(2, metric="chebyshev", return_distance=True, return_index=False)
knn.fit(base)
r = knn.transform(query)
assert r == pytest.approx(np.array([[0.0, 1], [1, 1], [1, 2]]))
r = (
KNN(2, metric="chebyshev", return_distance=True, return_index=False, implementation="keops")
.fit(base)
.transform(query)
)
assert r == pytest.approx(np.array([[0.0, 1], [1, 1], [1, 2]]))
knn = KNN(2, metric="minkowski", p=3, return_distance=False, return_index=True)
knn.fit(base)
r = knn.transform(query)
assert np.array_equal(r, [[0, 1], [1, 0], [3, 2]])
r = (
KNN(2, metric="minkowski", p=3, return_distance=False, return_index=True, implementation="keops")
.fit(base)
.transform(query)
)
assert np.array_equal(r, [[0, 1], [1, 0], [3, 2]])
dist = np.array([[0.0, 3, 8], [1, 0, 5], [1, 2, 0]])
knn = KNN(2, metric="precomputed", return_index=True, return_distance=False)
r = knn.fit_transform(dist)
assert np.array_equal(r, [[0, 1], [1, 0], [2, 0]])
knn = KNN(2, metric="precomputed", return_index=True, return_distance=True)
r = knn.fit_transform(dist)
assert np.array_equal(r[0], [[0, 1], [1, 0], [2, 0]])
assert np.array_equal(r[1], [[0, 3], [0, 1], [0, 1]])
def test_knn_compare():
base = np.array([[1.0, 1], [1, 2], [4, 2], [4, 3]])
query = np.array([[1.0, 1], [2, 2], [4, 4]])
r0 = KNN(2, implementation="ckdtree", return_index=True, return_distance=False).fit(base).transform(query)
r1 = KNN(2, implementation="sklearn", return_index=True, return_distance=False).fit(base).transform(query)
r2 = KNN(2, implementation="hnsw", return_index=True, return_distance=False).fit(base).transform(query)
r3 = KNN(2, implementation="keops", return_index=True, return_distance=False).fit(base).transform(query)
assert np.array_equal(r0, r1) and np.array_equal(r0, r2) and np.array_equal(r0, r3)
r0 = KNN(2, implementation="ckdtree", return_index=True, return_distance=True).fit(base).transform(query)
r1 = KNN(2, implementation="sklearn", return_index=True, return_distance=True).fit(base).transform(query)
r2 = KNN(2, implementation="hnsw", return_index=True, return_distance=True).fit(base).transform(query)
r3 = KNN(2, implementation="keops", return_index=True, return_distance=True).fit(base).transform(query)
assert np.array_equal(r0[0], r1[0]) and np.array_equal(r0[0], r2[0]) and np.array_equal(r0[0], r3[0])
d0 = pytest.approx(r0[1])
assert r1[1] == d0 and r2[1] == d0 and r3[1] == d0
def test_knn_nop():
# This doesn't look super useful...
p = np.array([[0.0]])
assert None is KNN(k=1, return_index=False, return_distance=False, implementation="sklearn").fit_transform(p)
assert None is KNN(k=1, return_index=False, return_distance=False, implementation="ckdtree").fit_transform(p)
assert None is KNN(k=1, return_index=False, return_distance=False, implementation="hnsw", ef=5).fit_transform(p)
assert None is KNN(k=1, return_index=False, return_distance=False, implementation="keops").fit_transform(p)
assert None is KNN(k=1, return_index=False, return_distance=False, metric="precomputed").fit_transform(p)
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