From 0afc650917ddf9fc4cf95fd86e0b6408f64a465d Mon Sep 17 00:00:00 2001 From: ROUVREAU Vincent Date: Mon, 11 Jan 2021 11:29:20 +0100 Subject: Remove sphinx doc test for atol as points order can be inverted and add it in a UT but sorted --- src/python/gudhi/representations/vector_methods.py | 14 +++++++------- src/python/test/test_representations.py | 18 ++++++++++++++++++ 2 files changed, 25 insertions(+), 7 deletions(-) diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py index cdcb1fde..d4449e7d 100644 --- a/src/python/gudhi/representations/vector_methods.py +++ b/src/python/gudhi/representations/vector_methods.py @@ -606,16 +606,16 @@ class Atol(BaseEstimator, TransformerMixin): >>> c = np.array([[3, 2, -1], [1, 2, -1]]) >>> atol_vectoriser = Atol(quantiser=KMeans(n_clusters=2, random_state=202006)) >>> atol_vectoriser.fit(X=[a, b, c]).centers - array([[ 2. , 0.66666667, 3.33333333], - [ 2.6 , 2.8 , -0.4 ]]) + >>> # array([[ 2. , 0.66666667, 3.33333333], + >>> # [ 2.6 , 2.8 , -0.4 ]]) >>> atol_vectoriser(a) - array([1.18168665, 0.42375966]) + >>> # array([1.18168665, 0.42375966]) >>> atol_vectoriser(c) - array([0.02062512, 1.25157463]) + >>> # array([0.02062512, 1.25157463]) >>> atol_vectoriser.transform(X=[a, b, c]) - array([[1.18168665, 0.42375966], - [0.29861028, 1.06330156], - [0.02062512, 1.25157463]]) + >>> # array([[1.18168665, 0.42375966], + >>> # [0.29861028, 1.06330156], + >>> # [0.02062512, 1.25157463]]) """ def __init__(self, quantiser, weighting_method="cloud", contrast="gaussian"): """ diff --git a/src/python/test/test_representations.py b/src/python/test/test_representations.py index 43c914f3..1c8f8cdb 100755 --- a/src/python/test/test_representations.py +++ b/src/python/test/test_representations.py @@ -46,6 +46,24 @@ def test_multiple(): assert d1 == pytest.approx(d2, rel=0.02) +# Test sorted values as points order can be inverted, and sorted test is not documentation-friendly +def test_atol_doc(): + a = np.array([[1, 2, 4], [1, 4, 0], [1, 0, 4]]) + b = np.array([[4, 2, 0], [4, 4, 0], [4, 0, 2]]) + c = np.array([[3, 2, -1], [1, 2, -1]]) + + atol_vectoriser = Atol(quantiser=KMeans(n_clusters=2, random_state=202006)) + assert np.sort(atol_vectoriser.fit(X=[a, b, c]).centers, axis=0) == \ + pytest.approx(np.array([[2. , 0.66666667, -0.4], \ + [2.6, 2.8 , 3.33333333]])) + assert np.sort(atol_vectoriser(a)) == pytest.approx(np.array([0.42375966, 1.18168665])) + assert np.sort(atol_vectoriser(c)) == pytest.approx(np.array([0.02062512, 1.25157463])) + assert np.sort(atol_vectoriser.transform(X=[a, b, c]), axis=0) == \ + pytest.approx(np.array([[0.02062512, 0.42375966], \ + [0.29861028, 1.06330156], \ + [1.18168665, 1.25157463]])) + + def test_dummy_atol(): a = np.array([[1, 2, 4], [1, 4, 0], [1, 0, 4]]) b = np.array([[4, 2, 0], [4, 4, 0], [4, 0, 2]]) -- cgit v1.2.3