Adjusted assignment 1 and 2

01_sudoku_hillclimber.py

@@ -25,16 +25,17 @@ last_fitness = -np.sum([len(set(board[:, i])) != 9 for i in range(9)]) # -9
 
 print("Working...")
 while -np.sum([len(set(board[:, i])) != 9 for i in range(9)]):
-    for row in range(board_size):
-        for col in range(board_size):
-            # swap col in row with random other col
-            rand_col = random.randrange(board_size)
-            board[row, col], board[row, rand_col] = board[row, rand_col], board[row, col]
-            current_fitness = -np.sum([len(set(board[:, i])) != 9 for i in range(9)])
-            if  current_fitness >= last_fitness:
-                last_fitness = current_fitness
-            else:
-                board[row, rand_col], board[row, col], = board[row, col], board[row, rand_col] # swap back
+    # swap col in row with random other col
+    rand_row = random.randrange(board_size)
+    rand_col = random.randrange(board_size)
+    swap_col = (rand_col + random.randrange(1, board_size)) % board_size
+    
+    board[rand_row, rand_col], board[rand_row, swap_col] = board[rand_row, swap_col], board[rand_row, rand_col]
+    current_fitness = -np.sum([len(set(board[:, i])) != 9 for i in range(9)])
+    if  current_fitness >= last_fitness:
+        last_fitness = current_fitness
+    else:
+        board[rand_row, swap_col], board[rand_row, rand_col] = board[rand_row, rand_col], board[rand_row, swap_col]
     print(last_fitness) # debugging
 
 print(board)        

02_sudoku_sa.py

@@ -9,20 +9,6 @@ import numpy as np
 import random
 import math
 
-board = np.array([
-    [1, 2, 3, 4, 5, 6, 7, 8, 9],
-    [1, 2, 3, 4, 5, 6, 7, 8, 9],
-    [1, 2, 3, 4, 5, 6, 7, 8, 9],
-    [1, 2, 3, 4, 5, 6, 7, 8, 9],
-    [1, 2, 3, 4, 5, 6, 7, 8, 9],
-    [1, 2, 3, 4, 5, 6, 7, 8, 9],
-    [1, 2, 3, 4, 5, 6, 7, 8, 9],
-    [1, 2, 3, 4, 5, 6, 7, 8, 9],
-    [1, 2, 3, 4, 5, 6, 7, 8, 9]
-])
-
-board_size = len(board) # Board is always quadratic
-
 def calculate_fitness(board):
     # Previous fitness
     column_violations = np.sum([len(set(board[:, i])) != 9 for i in range(9)])
@@ -38,28 +24,42 @@ def calculate_fitness(board):
     
     return -(column_violations + grid_violations)  # Negative because we want to maximize
 
+board = np.array([
+    [1, 2, 3, 4, 5, 6, 7, 8, 9],
+    [1, 2, 3, 4, 5, 6, 7, 8, 9],
+    [1, 2, 3, 4, 5, 6, 7, 8, 9],
+    [1, 2, 3, 4, 5, 6, 7, 8, 9],
+    [1, 2, 3, 4, 5, 6, 7, 8, 9],
+    [1, 2, 3, 4, 5, 6, 7, 8, 9],
+    [1, 2, 3, 4, 5, 6, 7, 8, 9],
+    [1, 2, 3, 4, 5, 6, 7, 8, 9],
+    [1, 2, 3, 4, 5, 6, 7, 8, 9]
+])
+
+board_size = len(board) # Board is always quadratic
 last_fitness = calculate_fitness(board)
 T = 10
 
 print("Working...")
-while calculate_fitness(board) < 0:  # Continue until no violations
-    for row in range(board_size):
-        for col in range(board_size):
-            # swap col in row with random other col
-            rand_col = random.randrange(board_size)
-            board[row, col], board[row, rand_col] = board[row, rand_col], board[row, col]
-            current_fitness = calculate_fitness(board)
-            
-            if current_fitness >= last_fitness:
-                last_fitness = current_fitness
-            else:
-                p = math.e ** (-(last_fitness - current_fitness) / T) # adjusted formula
-                # print(p) # debugging
-                if p > random.random(): # if probability occurs
-                    last_fitness = current_fitness
-                else:
-                    board[row, rand_col], board[row, col] = board[row, col], board[row, rand_col] # swap back
-            T = max(T - 0.1, 0.1)  # Decrease T more slowly and don't let it reach 0
+while calculate_fitness(board) < 0:
+    # swap col in row with random other col
+    rand_row = random.randrange(board_size)
+    rand_col = random.randrange(board_size)
+    swap_col = (rand_col + random.randrange(1, board_size)) % board_size
+    
+    board[rand_row, rand_col], board[rand_row, swap_col] = board[rand_row, swap_col], board[rand_row, rand_col]
+    current_fitness = calculate_fitness(board)
+    
+    if current_fitness >= last_fitness:
+        last_fitness = current_fitness
+    else:
+        p = math.e ** (-(last_fitness - current_fitness) / T) # adjusted formula
+        # print(p) # debugging
+        if p > random.random(): # if probability occurs
+            last_fitness = current_fitness
+        else:
+            board[rand_row, swap_col], board[rand_row, rand_col] = board[rand_row, rand_col], board[rand_row, swap_col]
+    T = max(T - 0.1, 0.1)  # Decrease T more slowly and don't let it reach 0
     
     print(last_fitness) # debugging
 

03_euler_gen_alg.py

@@ -0,0 +1,2 @@
+import numpy as np
+import matplotlib.pyplot as plt