用python写一个实现近邻聚类算法的程序

很高兴能够回答你的问题。Python实现近邻聚类算法的程序非常简单，你可以使用sklearn库中的KMeans算法，只需要定义聚类的数量以及数据格式即可。你也可以使用scipy库中的cluster模块中的KMeans函数来调用近邻聚类算法。

相关问题

用python实现近邻聚类算法

很高兴回答您的问题，用python实现近邻聚类算法可以使用scikit-learn库中的KMeans算法，该算法将数据点聚类为k个不同的簇，每一个簇中的点更加相似，它基于距离度量的迭代算法，具有易于实现的优点。

用python写一个近邻聚类法

以下是一个使用Python编写的近邻聚类算法示例：

import numpy as np

class NearestNeighborClustering:
    def __init__(self, n_clusters=2, similarity='euclidean'):
        self.n_clusters = n_clusters
        self.similarity = similarity
        
    def _calc_similarity(self, X):
        if self.similarity == 'euclidean':
            similarity_matrix = np.zeros((X.shape[0], X.shape[0]))
            for i in range(X.shape[0]):
                for j in range(X.shape[0]):
                    similarity_matrix[i,j] = np.sqrt(np.sum((X[i] - X[j])**2))
            return similarity_matrix
        else:
            raise ValueError("Similarity metric not recognized.")
        
    def fit(self, X):
        similarity_matrix = self._calc_similarity(X)
        cluster_assignments = np.arange(X.shape[0])
        
        while len(np.unique(cluster_assignments)) > self.n_clusters:
            min_similarity = np.inf
            for i in range(X.shape[0]):
                for j in range(X.shape[0]):
                    if similarity_matrix[i,j] < min_similarity and cluster_assignments[i] != cluster_assignments[j]:
                        min_similarity = similarity_matrix[i,j]
                        merge_pair = (i,j)
                        
            new_cluster_assignments = np.copy(cluster_assignments)
            new_cluster_assignments[new_cluster_assignments == cluster_assignments[merge_pair[1]]] = cluster_assignments[merge_pair[0]]
            cluster_assignments = new_cluster_assignments
            
        self.cluster_assignments_ = cluster_assignments
        
    def predict(self, X):
        similarity_matrix = self._calc_similarity(X)
        cluster_assignments = np.arange(X.shape[0])
        
        while len(np.unique(cluster_assignments)) > self.n_clusters:
            min_similarity = np.inf
            for i in range(X.shape[0]):
                for j in range(X.shape[0]):
                    if similarity_matrix[i,j] < min_similarity and cluster_assignments[i] != cluster_assignments[j]:
                        min_similarity = similarity_matrix[i,j]
                        merge_pair = (i,j)
                        
            new_cluster_assignments = np.copy(cluster_assignments)
            new_cluster_assignments[new_cluster_assignments == cluster_assignments[merge_pair[1]]] = cluster_assignments[merge_pair[0]]
            cluster_assignments = new_cluster_assignments
            
        return cluster_assignments

上述代码实现了一个类`NearestNeighborClustering`，它具有以下方法：

- `__init__(self, n_clusters=2, similarity='euclidean')`：初始化函数，可以指定聚类数和相似度度量方法。
- `_calc_similarity(self, X)`：计算相似度矩阵。
- `fit(self, X)`：拟合模型，对输入数据进行聚类。
- `predict(self, X)`：对输入数据进行聚类。

使用示例：

import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs

X, y = make_blobs(n_samples=100, centers=3, random_state=42)

model = NearestNeighborClustering(n_clusters=3)
model.fit(X)

plt.scatter(X[:,0], X[:,1], c=model.cluster_assignments_)
plt.show()

输出结果：


可以看到，该算法将数据分为了3类，并且聚类效果较好。