Fix some tests

mfs/Metrics.py

@@ -47,12 +47,12 @@ class Metrics:
         return d[:, -1]  # returns the distance to the kth nearest neighbor
 
     @staticmethod
-    def differential_entropy(X, k=1):
+    def differential_entropy(x, k=1):
 
         """Returns the entropy of the X.
         Parameters
         ===========
-        X : array-like, shape (n_samples, n_features)
+        x : array-like, shape (n_samples, n_features)
             The data the entropy of which is computed
         k : int, optional
             number of nearest neighbors for density estimation
@@ -66,11 +66,11 @@ class Metrics:
         Kraskov A, Stogbauer H, Grassberger P. (2004). Estimating mutual
         information. Phys Rev E 69(6 Pt 2):066138.
         """
-        if X.ndim == 1:
-            X = X.reshape(-1, 1)
+        if x.ndim == 1:
+            x = x.reshape(-1, 1)
         # Distance to kth nearest neighbor
-        r = Metrics._nearest_distances(X, k)  # squared distances
-        n, d = X.shape
+        r = Metrics._nearest_distances(x, k)  # squared distances
+        n, d = x.shape
         volume_unit_ball = (np.pi ** (0.5 * d)) / gamma(0.5 * d + 1)
         """
         F. Perez-Cruz, (2008). Estimation of Information Theoretic Measures
@@ -79,7 +79,7 @@ class Metrics:
         return d*mean(log(r))+log(volume_unit_ball)+log(n-1)-log(k)
         """
         return (
-            d * np.mean(np.log(r + np.finfo(X.dtype).eps))
+            d * np.mean(np.log(r + np.finfo(x.dtype).eps))
             + np.log(volume_unit_ball)
             + psi(n)
             - psi(k)