diff options
Diffstat (limited to 'src/python')
| -rw-r--r-- | src/python/gudhi/representations/vector_methods.py | 19 | ||||
| -rw-r--r-- | src/python/gudhi/rips_complex.pyx | 8 | ||||
| -rwxr-xr-x | src/python/test/test_representations.py | 26 |
3 files changed, 40 insertions, 13 deletions
diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py index cdcb1fde..84bc99a2 100644 --- a/src/python/gudhi/representations/vector_methods.py +++ b/src/python/gudhi/representations/vector_methods.py @@ -605,18 +605,19 @@ class Atol(BaseEstimator, TransformerMixin): >>> 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)) - >>> atol_vectoriser.fit(X=[a, b, c]).centers - array([[ 2. , 0.66666667, 3.33333333], - [ 2.6 , 2.8 , -0.4 ]]) + >>> atol_vectoriser.fit(X=[a, b, c]).centers # doctest: +SKIP + >>> # 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]) # doctest: +SKIP >>> atol_vectoriser(c) - 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([0.02062512, 1.25157463]) # doctest: +SKIP + >>> atol_vectoriser.transform(X=[a, b, c]) # doctest: +SKIP + >>> # array([[1.18168665, 0.42375966], + >>> # [0.29861028, 1.06330156], + >>> # [0.02062512, 1.25157463]]) """ + # Note the example above must be up to date with the one in tests called test_atol_doc def __init__(self, quantiser, weighting_method="cloud", contrast="gaussian"): """ Constructor for the Atol measure vectorisation class. diff --git a/src/python/gudhi/rips_complex.pyx b/src/python/gudhi/rips_complex.pyx index 72e82c79..c3470292 100644 --- a/src/python/gudhi/rips_complex.pyx +++ b/src/python/gudhi/rips_complex.pyx @@ -49,13 +49,13 @@ cdef class RipsComplex: :type max_edge_length: float :param points: A list of points in d-Dimension. - :type points: list of list of double + :type points: list of list of float Or :param distance_matrix: A distance matrix (full square or lower triangular). - :type points: list of list of double + :type points: list of list of float And in both cases @@ -89,10 +89,10 @@ cdef class RipsComplex: def create_simplex_tree(self, max_dimension=1): """ - :param max_dimension: graph expansion for rips until this given maximal + :param max_dimension: graph expansion for Rips until this given maximal dimension. :type max_dimension: int - :returns: A simplex tree created from the Delaunay Triangulation. + :returns: A simplex tree encoding the Vietoris–Rips filtration. :rtype: SimplexTree """ stree = SimplexTree() diff --git a/src/python/test/test_representations.py b/src/python/test/test_representations.py index 43c914f3..cda1a15b 100755 --- a/src/python/test/test_representations.py +++ b/src/python/test/test_representations.py @@ -46,6 +46,32 @@ 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 +# Note the test below must be up to date with the Atol class documentation +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)) + # Atol will do + # X = np.concatenate([a,b,c]) + # kmeans = KMeans(n_clusters=2, random_state=202006).fit(X) + # kmeans.labels_ will be : array([1, 0, 1, 0, 0, 1, 0, 0]) + first_cluster = np.asarray([a[0], a[2], b[2]]) + second_cluster = np.asarray([a[1], b[0], b[2], c[0], c[1]]) + + # Check the center of the first_cluster and second_cluster are in Atol centers + centers = atol_vectoriser.fit(X=[a, b, c]).centers + np.isclose(centers, first_cluster.mean(axis=0)).all(1).any() + np.isclose(centers, second_cluster.mean(axis=0)).all(1).any() + + vectorization = atol_vectoriser.transform(X=[a, b, c]) + assert np.allclose(vectorization[0], atol_vectoriser(a)) + assert np.allclose(vectorization[1], atol_vectoriser(b)) + assert np.allclose(vectorization[2], atol_vectoriser(c)) + + 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]]) |