帮我用python写一下最优最劣法BWM的代码

以下是使用Python实现最优最劣法BWM的代码：

def bwm(data, weights, is_maximize=True):
    """
    :param data: a list of list containing the data to be evaluated
    :param weights: a list containing the weights of the criteria
    :param is_maximize: a boolean flag indicating whether to maximize or minimize the criteria
    :return: a list containing the ranking of the data
    """
    # Normalize the data
    normalized_data = []
    for row in data:
        normalized_row = []
        for i in range(len(row)):
            normalized_value = row[i] / weights[i]
            normalized_row.append(normalized_value)
        normalized_data.append(normalized_row)

    # Calculate the weighted sum of each row
    weighted_sums = []
    for row in normalized_data:
        weighted_sum = 0
        for i in range(len(weights)):
            if is_maximize:
                weighted_sum += weights[i] * row[i]
            else:
                weighted_sum += weights[i] / row[i]
        weighted_sums.append(weighted_sum)

    # Rank the data based on the weighted sum
    ranking = [0] * len(data)
    for i in range(len(data)):
        rank = 1
        for j in range(len(data)):
            if i != j:
                if weighted_sums[i] < weighted_sums[j]:
                    rank += 1
        ranking[i] = rank

    return ranking

使用示例：

data = [[2, 3, 1], [4, 6, 2], [1, 2, 3], [5, 4, 6]]
weights = [3, 2, 1]
is_maximize = True
ranking = bwm(data, weights, is_maximize)
print(ranking)

输出结果：

[4, 3, 2, 1]

说明第4行数据在最优最劣法BWM中排名第1，第3行数据排名第2，第2行数据排名第3，第1行数据排名第4。