Add empty diags tests

1 files changed, 39 insertions, 21 deletions

diff --git a/src/python/test/test_representations.py b/src/python/test/test_representations.py
index c1f4df12..b888b7f1 100755
--- a/src/python/test/test_representations.py
+++ b/src/python/test/test_representations.py
@@ -6,9 +6,16 @@ import pytest
 
 from sklearn.cluster import KMeans
 
-from gudhi.representations import (DiagramSelector, Clamping, Landscape, Silhouette, BettiCurve, ComplexPolynomial,\
-  TopologicalVector, DiagramScaler, BirthPersistenceTransform,\
-  PersistenceImage, PersistenceWeightedGaussianKernel, Entropy, \
+# Vectorization
+from gudhi.representations import (Landscape, Silhouette, BettiCurve, ComplexPolynomial,\
+  TopologicalVector, PersistenceImage, Entropy)
+
+# Preprocessing
+from gudhi.representations import (BirthPersistenceTransform, Clamping, DiagramScaler, Padding, ProminentPoints, \
+  DiagramSelector)
+
+# Kernel
+from gudhi.representations import (PersistenceWeightedGaussianKernel, \
   PersistenceScaleSpaceKernel, SlicedWassersteinDistance,\
   SlicedWassersteinKernel, PersistenceFisherKernel, WassersteinDistance)
 
@@ -105,33 +112,44 @@ def test_infinity():
     assert c[7] == 3
     assert c[9] == 2
 
+
+def test_preprocessing_empty_diagrams():
+    empty_diag = np.empty(shape = [0, 2])
+    assert not np.any(BirthPersistenceTransform()(empty_diag))
+    assert not np.any(Clamping().fit_transform(empty_diag))
+    assert not np.any(DiagramScaler()(empty_diag))
+    assert not np.any(Padding()(empty_diag))
+    assert not np.any(ProminentPoints()(empty_diag))
+    assert not np.any(DiagramSelector()(empty_diag))
+
 def pow(n):
   return lambda x: np.power(x[1]-x[0],n)
 
 def test_vectorization_empty_diagrams():
     empty_diag = np.empty(shape = [0, 2])
-    Landscape(resolution=1000)(empty_diag)
-    Silhouette(resolution=1000, weight=pow(2))(empty_diag)
-    BettiCurve(resolution=1000)(empty_diag)
-    ComplexPolynomial(threshold=-1, polynomial_type="T")(empty_diag)
-    TopologicalVector(threshold=-1)(empty_diag)
-    PersistenceImage(bandwidth=.1, weight=lambda x: x[1], im_range=[0,1,0,1], resolution=[100,100])(empty_diag)
-    #Entropy(mode="scalar")(empty_diag)
-    #Entropy(mode="vector", normalized=False)(empty_diag)
-
-#def arctan(C,p):
-#  return lambda x: C*np.arctan(np.power(x[1], p))
+    assert not np.any(Landscape(resolution=1000)(empty_diag))
+    assert not np.any(Silhouette(resolution=1000, weight=pow(2))(empty_diag))
+    assert not np.any(BettiCurve(resolution=1000)(empty_diag))
+    assert not np.any(ComplexPolynomial(threshold=-1, polynomial_type="T")(empty_diag))
+    assert not np.any(TopologicalVector(threshold=-1)(empty_diag))
+    assert not np.any(PersistenceImage(bandwidth=.1, weight=lambda x: x[1], im_range=[0,1,0,1], resolution=[100,100])(empty_diag))
+    assert not np.any(Entropy(mode="scalar")(empty_diag))
+    assert not np.any(Entropy(mode="vector", normalized=False)(empty_diag))
+
+def arctan(C,p):
+  return lambda x: C*np.arctan(np.power(x[1], p))
 #
-#def test_kernel_empty_diagrams():
-#    empty_diag = np.empty(shape = [0, 2])
+def test_kernel_empty_diagrams():
+    empty_diag = np.empty(shape = [0, 2])
 #    PersistenceWeightedGaussianKernel(bandwidth=1., kernel_approx=None, weight=arctan(1.,1.))(empty_diag, empty_diag)
 #    PersistenceWeightedGaussianKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])), weight=arctan(1.,1.))(empty_diag, empty_diag)
 #    PersistenceScaleSpaceKernel(bandwidth=1.)(empty_diag, empty_diag)
 #    PersistenceScaleSpaceKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))(empty_diag, empty_diag)
-#    SlicedWassersteinDistance(num_directions=100)(empty_diag, empty_diag)
-#    SlicedWassersteinKernel(num_directions=100, bandwidth=1.)(empty_diag, empty_diag)
-#    WassersteinDistance(order=2, internal_p=2, mode="pot")(empty_diag, empty_diag)
-#    WassersteinDistance(order=2, internal_p=2, mode="hera", delta=0.0001)(empty_diag, empty_diag)
-#    BottleneckDistance(epsilon=.001)(empty_diag, empty_diag)
+    assert SlicedWassersteinDistance(num_directions=100)(empty_diag, empty_diag) == 0.
+    assert SlicedWassersteinKernel(num_directions=100, bandwidth=1.)(empty_diag, empty_diag) == 1.
+    assert WassersteinDistance(mode="hera", delta=0.0001)(empty_diag, empty_diag) == 0.
+    assert WassersteinDistance(mode="pot")(empty_diag, empty_diag) == 0.
+    assert BottleneckDistance(epsilon=.001)(empty_diag, empty_diag) == 0.
+    assert BottleneckDistance()(empty_diag, empty_diag) == 0.
 #    PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1.)(empty_diag, empty_diag)
 #    PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1., kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))(empty_diag, empty_diag)