refactor(ai): add Optional

fix

fix

src/ai/fitness/fitness.py

@@ -13,10 +13,6 @@ def calculate_fitness(game: Game) -> float:
     return fitness
 
 
-def get_holes(field: np.ndarray) -> int:
-    pass
-
-
 def get_wells(field: np.ndarray) -> int:
     pass
 

src/ai/fitness/peaks.py

@@ -1,3 +1,5 @@
+from typing import Optional
+
 import numpy as np
 
 from ai.log import log
@@ -8,9 +10,21 @@ def get_peaks(field: np.ndarray) -> np.ndarray:
     return peaks.max(axis=0)
 
 
-def get_peaks_max(field: np.ndarray) -> int:
+def get_peaks_max(
-    return int(np.max(get_peaks(field)))
+    peaks: Optional[np.ndarray], field: Optional[np.ndarray] = None
+) -> int:
+    if peaks is None and field is None:
+        raise ValueError("peaks and field cannot both be None")
+    elif peaks is None:
+        peaks = get_peaks(field)
+    return int(np.max(peaks))
 
 
-def get_peaks_sum(field: np.ndarray) -> int:
+def get_peaks_sum(
-    return np.sum(get_peaks(field))
+    peaks: Optional[np.ndarray], field: Optional[np.ndarray] = None
+) -> int:
+    if peaks is None and field is None:
+        raise ValueError("peaks and field cannot both be None")
+    elif peaks is None:
+        peaks = get_peaks(field)
+    return np.sum(peaks)

src/ai/fitness/transitions.py

@@ -1,10 +1,13 @@
+from typing import Optional
+
 import numpy as np
 
 from .peaks import get_peaks, get_peaks_max
 
 
-def get_row_transition(field: np.ndarray) -> int:
+def get_row_transition(field: np.ndarray, highest_peak: Optional[int] = None) -> int:
-    highest_peak = get_peaks_max(field)
+    if highest_peak is None:
+        highest_peak = get_peaks_max(None, field)
 
     rows_to_check = slice(int(field.shape[0] - highest_peak), field.shape[0])
     transitions = np.sum(field[rows_to_check, 1:] != field[rows_to_check, :-1])
@@ -12,10 +15,12 @@ def get_row_transition(field: np.ndarray) -> int:
     return int(transitions)
 
 
-def get_col_transition(field: np.ndarray) -> int:
+def get_col_transition(field: np.ndarray, peaks: Optional[np.ndarray] = None) -> int:
-    transitions_sum = 0
+    if peaks is None:
         peaks = get_peaks(field)
 
+    transitions_sum = 0
+
     for col in range(field.shape[1]):
         if peaks[col] <= 1:
             continue

tests/ai/test_fitness.py

@@ -32,7 +32,7 @@ class TestFitness(unittest.TestCase):
     def test_get_peaks_sum(self) -> None:
         answers: tuple[int] = (11, 0, 2)
         for field, answer in zip(self.fields, answers):
-            self.assertEqual(get_peaks_sum(field), answer)
+            self.assertEqual(get_peaks_sum(None, field), answer)
 
     def test_get_row_transistions(self):
         answers = (8, 0, 2)
@@ -45,6 +45,6 @@ class TestFitness(unittest.TestCase):
             self.assertEqual(get_col_transition(field), answer)
 
     def test_get_bumpiness(self):
-        answers = (8, 0, 2)
+        answers = (5, 0, 4)
         for field, answer in zip(self.fields, answers):
-            self.assertEqual(get_bumpiness(field), answer)
+            self.assertEqual(get_bumpiness(None, field), answer)