final changes to entropy_hist

hoi/core/entropies.py

@@ -318,9 +318,7 @@ def entropy_hist(x: jnp.array, base: float = 2, n_bins: int = 8) -> jnp.array:
         Entropy of x (in bits)
     """
 
-    x_binned, bin_size = digitize_hist(
-        x, n_bins, axis=1
-    )
+    x_binned, bin_size = digitize_hist(x, n_bins, axis=1)
 
     n_features, n_samples = x_binned.shape
 
@@ -339,6 +337,7 @@ def entropy_hist(x: jnp.array, base: float = 2, n_bins: int = 8) -> jnp.array:
 
     return -jax.scipy.special.rel_entr(probs, bin_s).sum() / jnp.log(base)
 
+
 ###############################################################################
 ###############################################################################
 #                                    KNN

hoi/utils/stats.py

@@ -123,6 +123,7 @@ def landscape(
 
     return lscp
 
+
 def digitize_1d(x, n_bins):
     """One dimensional digitization."""
     assert x.ndim == 1
@@ -182,6 +183,7 @@ def digitize(x, n_bins, axis=0, use_sklearn=False, **kwargs):
 
 partial(jax.jit, static_argnums=(1, 2))
 
+
 def digitize_1d_hist(x, n_bins):
     """One dimensional digitization."""
     assert x.ndim == 1
@@ -214,7 +216,7 @@ def digitize_hist(x, n_bins, axis=0, **kwargs):
     x_binned : array_like
         Digitized array with the same shape as x
     b_size : float
-        Size of the bin used 
+        Size of the bin used
     """
     # In case use_sklearn = False, all bins have the same size. In this case,
     # in order to allow the histogram estimator, also the size of the bins is