diana算法python

Diana算法是一种聚类算法，用于将数据集中的数据分为多个组或簇。下面是使用Python实现Diana算法的示例代码：

import numpy as np
from scipy.spatial.distance import cdist

def diana(X, k):
    n, m = X.shape
    labels = np.zeros(n)
    clusters = [np.arange(n)]

    while len(clusters) < k:
        # find the cluster with the largest diameter
        diameters = np.zeros(len(clusters))
        for i, cluster in enumerate(clusters):
            centroid = X[cluster].mean(axis=0)
            diameters[i] = cdist(X[cluster], [centroid]).max()

        largest_cluster_idx = np.argmax(diameters)
        largest_cluster = clusters[largest_cluster_idx]

        # split the largest cluster using k-means
        centroid1, centroid2 = kmeans(X[largest_cluster], 2)
        cluster1 = largest_cluster[labels[largest_cluster] == 0]
        cluster2 = largest_cluster[labels[largest_cluster] == 1]
        new_clusters = [cluster1, cluster2]

        # update labels and clusters
        for i, new_cluster in enumerate(new_clusters):
            labels[new_cluster] = i
        clusters.pop(largest_cluster_idx)
        clusters.extend(new_clusters)

    return labels

def kmeans(X, k, max_iter=100):
    n, m = X.shape
    centroids = X[np.random.choice(n, k, replace=False)]
    for _ in range(max_iter):
        labels = cdist(X, centroids).argmin(axis=1)
        new_centroids = np.zeros((k, m))
        for i in range(k):
            new_centroids[i] = X[labels == i].mean(axis=0)
        if np.all(centroids == new_centroids):
            break
        centroids = new_centroids
    return centroids[0], centroids[1]

在上面的代码中，我们使用了Scipy库中的cdist函数来计算数据点之间的欧几里得距离。我们还实现了一个kmeans函数，用于Diana算法中的分裂步骤。在主函数diana中，我们首先将所有数据点归为一个簇，然后在每个迭代中找到直径最大的簇并将其分裂成两个新的簇。最终，我们得到k个簇，并返回每个数据点所属的簇标签。