Bug fixes and linting for the activation functions addition.

manim_ml/decision_tree/decision_tree_surface.py

@@ -3,6 +3,7 @@ import numpy as np
 from collections import deque
 from sklearn.tree import _tree as ctree
 
+
 class AABB:
     """Axis-aligned bounding box"""
 
@@ -19,6 +20,7 @@ class AABB:
 
         return left, right
 
+
 def tree_bounds(tree, n_features=None):
     """Compute final decision rule for each node in tree"""
     if n_features is None:
@@ -34,6 +36,7 @@ def tree_bounds(tree, n_features=None):
             queue.extend([l, r])
     return aabbs
 
+
 def compute_decision_areas(tree_classifier, maxrange, x=0, y=1, n_features=None):
     """Extract decision areas.
 
@@ -70,6 +73,7 @@ def compute_decision_areas(tree_classifier, maxrange, x=0, y=1, n_features=None)
     rectangles[:, [1, 3]] = np.minimum(rectangles[:, [1, 3]], maxrange[1::2])
     return rectangles
 
+
 def plot_areas(rectangles):
     for rect in rectangles:
         color = ["b", "r"][int(rect[4])]
@@ -83,6 +87,7 @@ def plot_areas(rectangles):
         )
         plt.gca().add_artist(rp)
 
+
 def merge_overlapping_polygons(all_polygons, colors=[BLUE, GREEN, ORANGE]):
     # get all polygons of each color
     polygon_dict = {
@@ -156,6 +161,7 @@ def merge_overlapping_polygons(all_polygons, colors=[BLUE, GREEN, ORANGE]):
             return_polygons.append(polygon)
     return return_polygons
 
+
 class IrisDatasetPlot(VGroup):
     def __init__(self, iris):
         points = iris.data[:, 0:2]
@@ -269,7 +275,6 @@ class IrisDatasetPlot(VGroup):
 
 
 class DecisionTreeSurface(VGroup):
-    
     def __init__(self, tree_clf, data, axes, class_colors=[BLUE, ORANGE, GREEN]):
         # take the tree and construct the surface from it
         self.tree_clf = tree_clf
@@ -346,8 +351,8 @@ class DecisionTreeSurface(VGroup):
 
     def make_split_to_animation_map(self):
         """
-            Returns a dictionary mapping a given split 
-            node to an animation to be played
+        Returns a dictionary mapping a given split
+        node to an animation to be played
         """
         # Create an initial decision tree surface
         # Go through each split node