"""
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
import math

def calculate_fitness(board):
    # Previous fitness
    column_violations = np.sum([len(set(board[:, i])) != 9 for i in range(9)])
    
    # plus checking 3x3 sub-grids
    grid_violations = 0
    for block_row in range(0, 9, 3):
        for block_col in range(0, 9, 3):
            # Extract the 3x3 block
            block = board[block_row:block_row + 3, block_col:block_col + 3].flatten()
            if len(set(block)) != 9:
                grid_violations += 1
    
    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 = 100

print("Working...")
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 - 0.01, 0.1)  # Decrease T more slowly and don't let it reach 0
    
    # print(last_fitness) # debugging

print(board)