1 files changed, 49 insertions, 1 deletions
diff --git a/src/python/test/test_representations.py b/src/python/test/test_representations.py
index dba7f952..e5c211a0 100755
--- a/src/python/test/test_representations.py
+++ b/src/python/test/test_representations.py
@@ -1,12 +1,60 @@
 import os
 import sys
 import matplotlib.pyplot as plt
+import numpy as np
+import pytest
+
+from sklearn.cluster import KMeans
+
 
 def test_representations_examples():
     # Disable graphics for testing purposes
-    plt.show = lambda:None
+    plt.show = lambda: None
     here = os.path.dirname(os.path.realpath(__file__))
     sys.path.append(here + "/../example")
     import diagram_vectorizations_distances_kernels
 
     return None
+
+
+from gudhi.representations.vector_methods import Atol
+from gudhi.representations.metrics import *
+from gudhi.representations.kernel_methods import *
+
+
+def _n_diags(n):
+    l = []
+    for _ in range(n):
+        a = np.random.rand(50, 2)
+        a[:, 1] += a[:, 0]  # So that y >= x
+        l.append(a)
+    return l
+
+
+def test_multiple():
+    l1 = _n_diags(9)
+    l2 = _n_diags(11)
+    l1b = l1.copy()
+    d1 = pairwise_persistence_diagram_distances(l1, e=0.00001, n_jobs=4)
+    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)
+    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)
+
+
+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]])
+    c = np.array([[3, 2, -1], [1, 2, -1]])
+
+    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.fit([a, b, c])
+            atol_vectoriser(a)
+            atol_vectoriser.transform(X=[a, b, c])
+