mini_ceo_2

import pandas as pd
import random

def is_balanced(board):
    n = len(board)
    for i in range(n-1):
        for j in range(n-1):
            square = {board[i][j], board[i][j+1], board[i+1][j], board[i+1][j+1]}
            if len(square) < 3:
                return False
    return True

def dominant_lines_pandas(board, k):
    df = pd.DataFrame(board)
    row_reds = (df == 'R').sum(axis=1)
    col_reds = (df == 'R').sum(axis=0)
    dominant_rows = (row_reds >= k).sum()
    dominant_cols = (col_reds >= k).sum()
    return dominant_rows + dominant_cols

def generate_shifted_board(n, reds_per_row, shift):
    board = [['G' for _ in range(n)] for _ in range(n)]
    for i in range(n):
        for j in range(reds_per_row):
            board[i][(j + shift * i) % n] = 'R'
    # Оставшиеся закрашиваем в синий
    for i in range(n):
        for j in range(n):
            if board[i][j] == 'G':
                board[i][j] = 'B'
    return board

def random_fill_board(n, reds_per_row):
    board = []
    for i in range(n):
        row = ['B'] * n
        red_indices = random.sample(range(n), reds_per_row)
        for idx in red_indices:
            row[idx] = 'R'
        for j in range(n):
            if row[j] != 'R':
                row[j] = random.choice(['G', 'B'])
        board.append(row)
    return board

def run_experiments(n, k, num_attempts=1000):
    max_dom_lines = 0
    best_board = None

    # Циклические сдвиги (жадно)
    for shift in range(n):
        board = generate_shifted_board(n, k, shift)
        if is_balanced(board):
            dom_lines = dominant_lines_pandas(board, k)
            if dom_lines > max_dom_lines:
                max_dom_lines = dom_lines
                best_board = [row[:] for row in board]

    # Рандомные генерации
    for _ in range(num_attempts):
        board = random_fill_board(n, k)
        if is_balanced(board):
            dom_lines = dominant_lines_pandas(board, k)
            if dom_lines > max_dom_lines:
                max_dom_lines = dom_lines
                best_board = [row[:] for row in board]

    return max_dom_lines, best_board

# Параметры
n = 6
k = 4

max_dom_lines, best_board = run_experiments(n, k, num_attempts=2000)
print("Максимум доминирующих по красному линий:", max_dom_lines)
print(pd.DataFrame(best_board))