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author | Marc Glisse <marc.glisse@inria.fr> | 2020-11-13 11:57:17 +0100 |
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committer | GitHub <noreply@github.com> | 2020-11-13 11:57:17 +0100 |
commit | a6fe8d15a755c4843b2981cf6e1ba00b6eccd81b (patch) | |
tree | 0a40aa6922e78c87d6b109a31e2c85791daeccf4 | |
parent | 6811a26e8b45ba4fde5ad3268d0eb07d3070a349 (diff) | |
parent | 0022442a303f297ac773e262abd2661d2ce0a614 (diff) |
Merge pull request #423 from mglisse/betti
More numpy in BettiCurve
-rw-r--r-- | src/python/gudhi/representations/vector_methods.py | 17 | ||||
-rwxr-xr-x | src/python/test/test_representations.py | 20 |
2 files changed, 22 insertions, 15 deletions
diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py index 5ca127f6..cdcb1fde 100644 --- a/src/python/gudhi/representations/vector_methods.py +++ b/src/python/gudhi/representations/vector_methods.py @@ -323,22 +323,15 @@ class BettiCurve(BaseEstimator, TransformerMixin): Returns: numpy array with shape (number of diagrams) x (**resolution**): output Betti curves. """ - num_diag, Xfit = len(X), [] + Xfit = [] x_values = np.linspace(self.sample_range[0], self.sample_range[1], self.resolution) step_x = x_values[1] - x_values[0] - for i in range(num_diag): - - diagram, num_pts_in_diag = X[i], X[i].shape[0] - + for diagram in X: + diagram_int = np.clip(np.ceil((diagram[:,:2] - self.sample_range[0]) / step_x), 0, self.resolution).astype(int) bc = np.zeros(self.resolution) - for j in range(num_pts_in_diag): - [px,py] = diagram[j,:2] - min_idx = np.clip(np.ceil((px - self.sample_range[0]) / step_x).astype(int), 0, self.resolution) - max_idx = np.clip(np.ceil((py - self.sample_range[0]) / step_x).astype(int), 0, self.resolution) - for k in range(min_idx, max_idx): - bc[k] += 1 - + for interval in diagram_int: + bc[interval[0]:interval[1]] += 1 Xfit.append(np.reshape(bc,[1,-1])) Xfit = np.concatenate(Xfit, 0) diff --git a/src/python/test/test_representations.py b/src/python/test/test_representations.py index e5c211a0..43c914f3 100755 --- a/src/python/test/test_representations.py +++ b/src/python/test/test_representations.py @@ -39,11 +39,11 @@ def test_multiple(): 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) + 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) + assert d1 == pytest.approx(d2, rel=0.02) def test_dummy_atol(): @@ -53,8 +53,22 @@ def test_dummy_atol(): for weighting_method in ["cloud", "iidproba"]: for contrast in ["gaussian", "laplacian", "indicator"]: - atol_vectoriser = Atol(quantiser=KMeans(n_clusters=1, random_state=202006), weighting_method=weighting_method, contrast=contrast) + atol_vectoriser = Atol( + quantiser=KMeans(n_clusters=1, random_state=202006), + weighting_method=weighting_method, + contrast=contrast, + ) atol_vectoriser.fit([a, b, c]) atol_vectoriser(a) atol_vectoriser.transform(X=[a, b, c]) + +from gudhi.representations.vector_methods import BettiCurve + + +def test_infinity(): + a = np.array([[1.0, 8.0], [2.0, np.inf], [3.0, 4.0]]) + c = BettiCurve(20, [0.0, 10.0])(a) + assert c[1] == 0 + assert c[7] == 3 + assert c[9] == 2 |