句向量K-means聚类的代码

以下是一个简单的句向量K-means聚类的代码示例，使用gensim库和sklearn库实现：

from gensim.models import Word2Vec
from sklearn.cluster import KMeans
import numpy as np

# 加载训练好的Word2Vec模型
model = Word2Vec.load('model.bin')

# 定义句子列表
sentences = [
    'I love machine learning',
    'I hate my job',
    'I want to travel the world',
    'I need a break'
]

# 将句子向量化
sentence_vectors = []
for sentence in sentences:
    sentence_vector = np.zeros((100,))
    n_words = 0
    for word in sentence.split():
        if word in model:
            sentence_vector += model[word]
            n_words += 1
    if n_words > 0:
        sentence_vector /= n_words
    sentence_vectors.append(sentence_vector)

# 使用K-means聚类
kmeans = KMeans(n_clusters=2, random_state=0).fit(sentence_vectors)
cluster_labels = kmeans.labels_

# 打印聚类结果
for i, sentence in enumerate(sentences):
    print(f"\"{sentence}\" belongs to cluster {cluster_labels[i]}")

在上面的示例中，我们首先加载了一个训练好的Word2Vec模型，并定义了一个包含几个句子的列表。然后，我们将每个句子向量化，并使用sklearn库中的K-means算法将它们分成两个集群。最后，我们打印出了每个句子所属的集群编号。