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Finished Hillclimber algorithm

master
Ruben Seitz 2025-10-05 14:40:38 +02:00
parent c96abad68f
commit b68e2fcfc3
2 changed files with 26 additions and 18 deletions
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17
01_sudoku_hillclimber.py
View File

@ -6,6 +6,7 @@ erfüllen.
"""
import numpy as np
import random
board = np.array([
[1, 2, 3, 4, 5, 6, 7, 8, 9],
@ -20,15 +21,19 @@ board = np.array([
])
board_size = len(board) # Board is always quadratic
last_fitness = -np.sum([len(set(board[:, i])) != 9 for i in range(9)]) # -9
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):
# Create array of column values excluding current row
column_without_current = np.concatenate([board[:row, col], board[row + 1:, col]])
if board[row, col] in column_without_current:
board[row, col], board[row, (col + 1) % board_size] = board[row, (col + 1) % board_size], board[row, col]
break
# print(-np.sum([len(set(board[:, i])) != 9 for i in range(9)])) # debugging
# 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
# print(last_fitness) # debugging
print(board)

27
02_sudoku_sa.py
View File

@ -1,13 +1,12 @@
"""
Erweitere das SUDOKU aus Aufgabe 1 so, dass auch die 9
üblichen 3x3 Quadrate alle Zahlen von 1-9 enthalten. Außerdem
soll der Hill-Climber in einen Simulated-Annealing-Algorithmus
abgewandelt werden. Wichtig ist, dass dabei die Wahrscheinlichkeit
berechnet und ausgegeben wird, falls ein Schritt zu einer kleineren
Fitness auftreten würde.
Implementiere einen Hill-Climbing-Algorithmus, der die Zahlen in
einem SUDOKU-Feld durch vertauschen innerhalb einer Zeile so
umsortiert, dass sie die SUDOKU-Bedingung auch für die Spalten
erfüllen.
"""
import numpy as np
import random
board = np.array([
[1, 2, 3, 4, 5, 6, 7, 8, 9],
@ -22,15 +21,19 @@ board = np.array([
])
board_size = len(board) # Board is always quadratic
last_fitness = -np.sum([len(set(board[:, i])) != 9 for i in range(9)]) # -9
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):
# Create array of column values excluding current row
column_without_current = np.concatenate([board[:row, col], board[row + 1:, col]])
if board[row, col] in column_without_current:
board[row, col], board[row, (col + 1) % board_size] = board[row, (col + 1) % board_size], board[row, col]
break
# print(-np.sum([len(set(board[:, i])) != 9 for i in range(9)])) # debugging
# 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
# print(last_fitness) # debugging
print(board)