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author | MathieuCarriere <mathieu.carriere3@gmail.com> | 2020-05-20 18:10:58 -0400 |
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committer | MathieuCarriere <mathieu.carriere3@gmail.com> | 2020-05-20 18:10:58 -0400 |
commit | 96c3ce4adb7bd56b1bd8f1f62f1b7850ba640c48 (patch) | |
tree | ab3a859dfbb99572516d168a131f4e705ed90f7f /src/python/test/test_dtm.py | |
parent | 4101fbf1472ba769a96a6010622d0f054e6f34c0 (diff) | |
parent | b6f798f0df407440dbaaa5f0dc9f5995e52b076e (diff) |
Merge branch 'master' of https://github.com/GUDHI/gudhi-devel
Diffstat (limited to 'src/python/test/test_dtm.py')
-rwxr-xr-x | src/python/test/test_dtm.py | 23 |
1 files changed, 22 insertions, 1 deletions
diff --git a/src/python/test/test_dtm.py b/src/python/test/test_dtm.py index bff4c267..0a52279e 100755 --- a/src/python/test/test_dtm.py +++ b/src/python/test/test_dtm.py @@ -8,10 +8,11 @@ - YYYY/MM Author: Description of the modification """ -from gudhi.point_cloud.dtm import DistanceToMeasure +from gudhi.point_cloud.dtm import DistanceToMeasure, DTMDensity import numpy import pytest import torch +import math def test_dtm_compare_euclidean(): @@ -66,3 +67,23 @@ def test_dtm_precomputed(): dtm = DistanceToMeasure(2, q=2, metric="neighbors") r = dtm.fit_transform(dist) assert r == pytest.approx([2.0, 0.707, 3.5355], rel=0.01) + + +def test_density_normalized(): + sample = numpy.random.normal(0, 1, (1000000, 2)) + queries = numpy.array([[0.0, 0.0], [-0.5, 0.7], [0.4, 1.7]]) + expected = numpy.exp(-(queries ** 2).sum(-1) / 2) / (2 * math.pi) + estimated = DTMDensity(k=150, normalize=True).fit(sample).transform(queries) + assert estimated == pytest.approx(expected, rel=0.4) + + +def test_density(): + distances = [[0, 1, 10], [2, 0, 30], [1, 3, 5]] + density = DTMDensity(k=2, metric="neighbors", dim=1).fit_transform(distances) + expected = numpy.array([2.0, 1.0, 0.5]) + assert density == pytest.approx(expected) + distances = [[0, 1], [2, 0], [1, 3]] + density = DTMDensity(metric="neighbors", dim=1).fit_transform(distances) + assert density == pytest.approx(expected) + density = DTMDensity(weights=[0.5, 0.5], metric="neighbors", dim=1).fit_transform(distances) + assert density == pytest.approx(expected) |