fixing indentation for #36

Author: gnopik

src/simdec/decomposition.py

@@ -167,34 +167,45 @@ def statistic_(inputs):
     sorted_inputs = np.sort(inputs, axis=0)
     bin_edges = []
     for i, states_ in enumerate(states):
-            col = inputs[:, i]
-    uniq = np.unique(col)
+        col = inputs[:, i]
+        uniq = np.unique(col)
 
     # If this input has only a few unique numeric values (categorical-like),
     # build bin edges around unique values so we don't get empty states.
-    if uniq.size <= 5:
-        uniq = np.sort(uniq).astype(float)
-        if uniq.size == 1:
-            bin_edges_ = np.array([uniq[0] - 0.5, uniq[0] + 0.5], dtype=float)
-        else:
-            gaps = np.diff(uniq)
-            margin = 0.1 * np.min(gaps)  # fixing boundaries for categorical
-
-            # edges length = n_unique + 1
-            bin_edges_ = np.concatenate(
-                ([uniq[0] - margin], uniq[:-1] + margin, [uniq[-1] + margin])
-            ).astype(float)
-
-        bin_edges.append(bin_edges_)
-        continue
-      
-        splits = np.array_split(sorted_inputs[:, i], states_)
-        
-        bin_edges_ = [splits_[0] for splits_ in splits]
-        bin_edges_.append(splits[-1][-1])  # last point to close the edges
-        bin_edges_ = np.array(bin_edges_, dtype=float)
-        bin_edges_ += 1e-10 * np.linspace(0, 1, len(bin_edges_))
-        bin_edges.append(bin_edges_)
+    sorted_inputs = np.sort(inputs, axis=0)
+    bin_edges = []
+    
+    for i, states_ in enumerate(states):
+        col = inputs[:, i]
+        uniq = np.unique(col)
+    
+        # Categorical-like numeric inputs
+        if uniq.size <= 5 and states_ == uniq.size:
+            uniq = np.sort(uniq).astype(float)
+    
+            if uniq.size == 1:
+                bin_edges_ = np.array(
+                    [uniq[0] - 0.5, uniq[0] + 0.5], dtype=float
+                )
+            else:
+                gaps = np.diff(uniq)
+                margin = 0.1 * np.min(gaps)
+    
+                bin_edges_ = np.concatenate(
+                    ([uniq[0] - margin], uniq[:-1] + margin, [uniq[-1] + margin])
+                ).astype(float)
+    
+            bin_edges.append(bin_edges_)
+            continue
+
+    # Default: equal-number-of-samples bins
+    splits = np.array_split(sorted_inputs[:, i], states_)
+    bin_edges_ = [splits_[0] for splits_ in splits]
+    bin_edges_.append(splits[-1][-1])  # last point to close the edges
+    bin_edges_ = np.array(bin_edges_, dtype=float)
+    bin_edges_ += 1e-10 * np.linspace(0, 1, len(bin_edges_))
+    bin_edges.append(bin_edges_)
+
 
     res = stats.binned_statistic_dd(
         inputs, values=output, statistic=statistic_, bins=bin_edges