Python 最短距离法聚类实现代码

以下是Python最短距离法聚类的实现代码：

import numpy as np

def euclidean_distance(x1, x2):
    return np.sqrt(np.sum((x1 - x2) ** 2))

class MyAgglomerativeClustering:
    def __init__(self, n_clusters=2):
        self.n_clusters = n_clusters
        self.labels = None

    def fit(self, X):
        n_samples, n_features = X.shape
        self.labels = np.zeros(n_samples, dtype=np.int)

        # Initialize each point to its own cluster
        clusters = [[i] for i in range(n_samples)]

        # Keep merging clusters until we have the desired number
        # of clusters
        while len(clusters) > self.n_clusters:
            # Find the closest pair of clusters
            min_distance = np.inf
            for i in range(len(clusters)):
                for j in range(i + 1, len(clusters)):
                    for index_i in clusters[i]:
                        for index_j in clusters[j]:
                            distance = euclidean_distance(X[index_i], X[index_j])
                            if distance < min_distance:
                                min_distance = distance
                                merge_i = i
                                merge_j = j
            # Merge the closest pair of clusters
            clusters[merge_i] += clusters[merge_j]
            del clusters[merge_j]

        # Assign labels
        for i, cluster in enumerate(clusters):
            for index in cluster:
                self.labels[index] = i
        return self.labels

这里我们定义了一个名为MyAgglomerativeClustering的类，其中fit()方法实现了最短距离法聚类。该算法的基本思想是：开始时将每个点看作一个独立的簇，然后不断地合并距离最近的两个簇，直到达到预定的簇的数目。

该实现使用了欧氏距离作为相似度度量。可通过调整n_clusters参数来设置需要得到的簇的数目。