Replaced for loop with vectorised mapping on ClusterOverlap regularization layer

tests/test_model_utils.py

@@ -13,6 +13,8 @@ from hypothesis import HealthCheck
 from hypothesis import settings
 from hypothesis import strategies as st
 from hypothesis.extra.numpy import arrays
+from scipy.stats import entropy
+from sklearn.neighbors import NearestNeighbors
 import deepof.models
 import deepof.model_utils
 import numpy as np
@@ -28,6 +30,37 @@ tfpl = tfp.layers
 tfd = tfp.distributions
 
 
+@settings(deadline=None, suppress_health_check=[HealthCheck.too_slow])
+@given(
+    tensor=arrays(
+        shape=[10],
+        dtype=int,
+        unique=False,
+        elements=st.integers(min_value=0, max_value=10),
+    )
+)
+def test_compute_shannon_entropy(tensor):
+    deepof_tensor_entropy = deepof.model_utils.compute_shannon_entropy(tensor).numpy()
+    assert np.allclose(
+        np.round(deepof_tensor_entropy, 4), entropy(np.bincount(tensor)), rtol=1e-3
+    )
+
+
+@settings(deadline=None, suppress_health_check=[HealthCheck.too_slow])
+@given(
+    tensor=arrays(
+        shape=[100, 10],
+        dtype=float,
+        unique=False,
+        elements=st.floats(min_value=0.0, max_value=10.0),
+    )
+)
+def test_k_nearest_neighbors(tensor, k):
+    deepof_knn = deepof.model_utils.get_k_nearest_neighbors(tensor, k, 0)
+    sklearn_knn = NearestNeighbors().fit(tensor)
+    assert np.allclose(deepof_knn, sklearn_knn.kneighbors())
+
+
 @settings(deadline=None, suppress_health_check=[HealthCheck.too_slow])
 @given(
     tensor=arrays(