python新陈代谢GM代码，请以便于修改的方式呈现。

以下是一个简单的 Python 实现的新陈代谢 GM 模型代码示例：

import numpy as np

def cmgm(data, max_iteration=500, pop_size=50, beta=0.98, alpha=0.3, gamma=0.1, delta=0.1):
    '''
    新陈代谢 GM 模型
    :param data: 输入数据，每一行为一个样本
    :param max_iteration: 最大迭代次数
    :param pop_size: 种群大小
    :param beta: 代谢速率系数
    :param alpha: 新陈代谢速率系数
    :param gamma: 产生新个体速率系数
    :param delta: 消亡速率系数
    :return: 返回模型训练的结果
    '''

    def calc_fit(pop):
        '''
        计算适应度函数
        :param pop: 种群
        :return: 返回种群的适应度函数值
        '''
        u = np.mean(pop, axis=0)  # 计算均值
        diff = pop - u  # 计算离差矩阵
        cov = np.dot(diff.T, diff) / (pop.shape[0] - 1)  # 计算协方差矩阵
        try:
            np.linalg.cholesky(cov)
        except:
            return 1e100
        else:
            cov_inv = np.linalg.inv(cov)  # 计算协方差矩阵的逆矩阵
            fit = np.sum(np.dot(diff, cov_inv) * diff, axis=1)  # 计算适应度函数值
            return fit

    # 初始化种群
    n, m = data.shape
    pop = np.random.rand(pop_size, m)
    pop_fit = calc_fit(pop)

    # 迭代更新种群
    for t in range(max_iteration):
        # 计算代谢速率
        metab_rate = beta * np.exp(-alpha * t / max_iteration)

        # 计算新陈代谢速率
        metap_rate = 1 - metab_rate

        # 计算产生新个体的速率
        birth_rate = gamma * np.exp(-delta * t / max_iteration)

        # 计算消亡速率
        death_rate = 1 - birth_rate

        # 产生新个体
        new_pop = np.zeros((pop_size, m))
        for i in range(pop_size):
            if np.random.rand() < birth_rate:
                j, k = np.random.choice(pop_size, 2, replace=False)
                new_pop[i] = pop[j] * metab_rate + pop[k] * metap_rate
            else:
                new_pop[i] = pop[i]

        # 更新种群
        new_pop_fit = calc_fit(new_pop)
        for i in range(pop_size):
            if np.random.rand() < death_rate:
                j = np.argmax(new_pop_fit)
                new_pop[i] = new_pop[j]
                new_pop_fit[i] = new_pop_fit[j]

        pop = new_pop
        pop_fit = new_pop_fit

    # 计算结果
    u = np.mean(pop, axis=0)  # 计算均值
    diff = pop - u  # 计算离差矩阵
    cov = np.dot(diff.T, diff) / (pop.shape[0] - 1)  # 计算协方差矩阵
    try:
        np.linalg.cholesky(cov)
    except:
        return None
    else:
        eigen_values, eigen_vectors = np.linalg.eig(cov)
        idx = np.argsort(-eigen_values)
        eigen_values = eigen_values[idx]
        eigen_vectors = eigen_vectors[:, idx]
        for i in range(m):
            if eigen_values[i] < 0:
                eigen_values[i] = 0
        return u, eigen_values, eigen_vectors

此代码使用 NumPy 实现了新陈代谢 GM 模型，并包含了参数调节和结果计算等部分。您可以根据您的需求修改此代码，并使用您自己的数据进行训练。