我需要直觉模糊聚类算法的Python代码

您好！以下是一个简单的直觉模糊聚类算法的 Python 代码：

import numpy as np

def init_membership_matrix(data, c, m):
    """
    Initialize the membership matrix.
    """
    membership_matrix = np.random.rand(data.shape[0], c)
    membership_matrix = membership_matrix / membership_matrix.sum(axis=1).reshape(-1, 1)
    return membership_matrix

def calculate_cluster_centers(data, membership_matrix, m):
    """
    Calculate the cluster centers.
    """
    cluster_centers = np.dot(membership_matrix.T, data)
    cluster_centers = cluster_centers / np.array([membership_matrix.sum(axis=0)]).T
    return cluster_centers

def calculate_distance(data, cluster_centers):
    """
    Calculate the distance between each data point and each cluster center.
    """
    distance = np.zeros((data.shape[0], cluster_centers.shape[0]))
    for i in range(cluster_centers.shape[0]):
        diff = data - cluster_centers[i]
        sq_diff = np.square(diff)
        sq_dist = np.sum(sq_diff, axis=1)
        distance[:, i] = np.power(sq_dist, 1/(m-1))
    return distance

def update_membership_matrix(distance, m):
    """
    Update the membership matrix.
    """
    membership_matrix = np.power(distance, -2/(m-1))
    membership_matrix = membership_matrix / np.array([membership_matrix.sum(axis=1)]).T
    return membership_matrix

def fuzzy_c_means(data, c, m, max_iter=100, epsilon=0.001):
    """
    Perform fuzzy C-means clustering.
    """
    membership_matrix = init_membership_matrix(data, c, m)
    for i in range(max_iter):
        cluster_centers = calculate_cluster_centers(data, membership_matrix, m)
        distance = calculate_distance(data, cluster_centers)
        new_membership_matrix = update_membership_matrix(distance, m)
        error = np.abs(new_membership_matrix - membership_matrix).sum()
        if error < epsilon:
            break
        membership_matrix = new_membership_matrix
    return membership_matrix, cluster_centers

希望这份代码能帮助您！