import numpy as np


def three_dimensional_TOPSIS(chromosome_results, data=None):
    # 如果data不为空，则堆叠数据
    if data is not None:
        chromosome_results = np.vstack((chromosome_results, data))

    # 获取每个特征的范数
    norms = np.linalg.norm(chromosome_results, axis=0)

    # 检查是否有零值并替换它们，以避免除以零
    norms[norms == 0] = 1.0  # 或使用一个非常小的数来替换零

    # 归一化处理
    normalized_data = chromosome_results / norms

    # 计算负理想解 (这里我们选择最小值，你也可以根据需求选择其他标准)
    negative_ideal_solution = np.amin(normalized_data, axis=0)
    dist_to_pis_dimension1 = (0.6*normalized_data[:, 0] - 0.6*negative_ideal_solution[0]) ** 2
    dist_to_pis_dimension2 = (0.3*normalized_data[:, 1] - 0.3*negative_ideal_solution[1]) ** 2
    dist_to_pis_dimension3 = (0.1*normalized_data[:, 2] - 0.1*negative_ideal_solution[2]) ** 2
    dist_to_nis = np.sqrt(dist_to_pis_dimension1+dist_to_pis_dimension2+dist_to_pis_dimension3)
    # dist_to_nis = np.linalg.norm(normalized_data - negative_ideal_solution, axis=1)
    # 如果data不为空，则删除最后一行
    if data is not None:
        dist_to_nis = dist_to_nis[:-1]

    return dist_to_nis