add type hints

src/machine_learning/data_set.py

@@ -1,3 +1,4 @@
+from typing import List
 from itertools import product
 
 visibility = ('bad', 'medium', 'good')
@@ -9,7 +10,7 @@ pressure_gt_two = (True, False)
 attributes = ['visibility', 'stability', 'ground', 'mine_type', 'armed', 'pressure_gt_two']
 
 
-def generate_data_set():
+def generate_data_set() -> List[dict]:
     data_list = list(product(visibility, stability, ground, mine_type, armed, pressure_gt_two))
     data = [
         {

src/machine_learning/decision_tree.py

@@ -60,7 +60,7 @@ def tree_learn(examples: List[Dict], attributes: List[str], default_class: str)
     return root
 
 
-def get_decision(data, root):
+def get_decision(data: dict, root: Node) -> str:
     while root.children:
         for children in root.children:
             if data[root.name] == children.edge:

src/tile.py

@@ -2,13 +2,20 @@ from typing import Union
 
 from ap_mine import APMine
 from at_mine import ATMine
+from adm_mine import ADMMine
 
 
 class Tile:
-	def __init__(self, number: int, weight: int, visibility, stability, ground, mine: Union[None, APMine, ATMine] = None):
-		self.number = number
-		self.mine = mine
-		self.weight = weight
-		self.visibility = visibility
-		self.stability = stability
-		self.ground = ground
+    def __init__(self,
+                 number: int,
+                 weight: int,
+                 visibility: str,
+                 stability: str,
+                 ground: str,
+                 mine: Union[None, APMine, ATMine, ADMMine] = None):
+        self.number = number
+        self.mine = mine
+        self.weight = weight
+        self.visibility = visibility
+        self.stability = stability
+        self.ground = ground