changed siz -> size

hoi/core/entropies.py

@@ -251,7 +251,7 @@ def entropy_gauss(x: jnp.array) -> jnp.array:
 
 @partial(jax.jit, static_argnums=(1,))
 def entropy_bin(
-    x: jnp.array, base: int = 2, bin_siz: float = None
+    x: jnp.array, base: int = 2, bin_size: float = None
 ) -> jnp.array:
     """Entropy using binning.
 
@@ -262,7 +262,7 @@ def entropy_bin(
         be discretize
     base : int | 2
         The logarithmic base to use. Default is base 2.
-    bin_siz : float | None
+    bin_size : float | None
         The size of all the bins. Will be taken in consideration only if all
         bins have the same size, for histogram estimator.
 
@@ -283,8 +283,8 @@ def entropy_bin(
     )[1]
     probs = counts / n_samples
 
-    if bin_siz is not None:
-        bins = jnp.where(probs != 0, bin_siz, 0)
+    if bin_size is not None:
+        bins = jnp.where(probs != 0, bin_size, 0)
         return -jax.scipy.special.rel_entr(probs, bins).sum() / jnp.log(base)
     else:
         return (jax.scipy.special.entr(probs)).sum() / jnp.log(base)

hoi/utils/stats.py

@@ -176,9 +176,9 @@ def digitize(x, n_bins, axis=0, use_sklearn=False, **kwargs):
     # in order to allow the histogram estimator, also the size of the bins is
     # returned.
     bins_arr = (x.max(axis=axis) - x.min(axis=axis)) / n_bins
-    b_siz = jnp.prod(bins_arr)
+    b_size = jnp.prod(bins_arr)
     if not use_sklearn:
-        return np.apply_along_axis(digitize_1d, axis, x, n_bins), b_siz
+        return np.apply_along_axis(digitize_1d, axis, x, n_bins), b_size
     else:
         kwargs["n_bins"] = n_bins
         kwargs["encode"] = "ordinal"