AP聚类算法python

AP聚类算法（Affinity Propagation）是一种基于数据点之间的消息传递机制的聚类算法。该算法不需要指定聚类数目，且适用于不同形状、密度和大小的聚类。下面是AP聚类算法的Python实现：

import numpy as np

def apcluster(data, max_iter, conv_iter):
    n = data.shape[0]  # 样本数目
    s = np.zeros((n, n))  # 相似度矩阵
    r = np.zeros((n, n))  # 负责度矩阵
    a = np.zeros((n, n))  # 归属度矩阵
    e = np.zeros((n, max_iter))  # 每一次迭代的能量值

    # 计算相似度矩阵
    for i in range(n):
        for j in range(n):
            s[i, j] = -np.sum((data[i] - data[j]) ** 2)

    # 初始化消息
    tmp = np.zeros((n, n))
    for i in range(n):
        tmp[i] = s[i] - np.max(s[i])
    r = tmp.copy()
    for i in range(n):
        r[i, np.argmax(tmp[i])] = s[i, np.argmax(tmp[i])]

    # 迭代
    for it in range(max_iter):
        # 更新归属度矩阵
        tmp = s + r
        a_new = np.zeros((n, n))
        for i in range(n):
            a_new[i, np.argmax(tmp[:, i])] = 1
        tmp2 = np.sum(a_new, axis=0)
        for i in range(n):
            a_new[i, i] = 1 if tmp2[i] > 0 else 0
        a = 0.5 * a + 0.5 * a_new

        # 更新负责度矩阵
        tmp = np.maximum(0, a)
        tmp2 = np.sum(tmp, axis=0) - tmp
        r = np.minimum(0, tmp2)

        # 计算能量值
        e_new = np.sum(a * (s + r))
        e[:, it] = e_new

        # 判断收敛
        if it > conv_iter and np.sum(e[:, it - conv_iter] - e[:, it]) < 1e-6:
            break

    # 聚类
    c = np.argmax(a + r, axis=1)

    return c

其中，`data`是样本数据的矩阵，每一行代表一个样本，`max_iter`是最大迭代次数，`conv_iter`是判断收敛的迭代次数。该函数返回样本的聚类结果。