Coding-kmeans聚类+T-SEN可视化

• 摘要
• main.py

摘要

kmeans聚类,T-SEN可视化

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.manifold import TSNE

# 加载数据
data = np.load('test.npy')

# 假设 data 的特征在第一维
features = data[:, 0, :]  # 根据实际数据结构选择特征

# 聚类
num_clusters = 3  # 根据需要设置聚类的数量
kmeans = KMeans(n_clusters=num_clusters, random_state=0)
kmeans.fit(features)
clusters = kmeans.labels_  # 获取每个样本的聚类标签

# 使用 t-SNE 降维
tsne = TSNE(n_components=2, random_state=10)
feature_tsne = tsne.fit_transform(features)

# 绘制 t-SNE 散点图
plt.figure(figsize=(10, 8))
scatter = plt.scatter(feature_tsne[:, 0], feature_tsne[:, 1], c=clusters, cmap='viridis', marker='o')

# 创建颜色条
legend1 = plt.legend(*scatter.legend_elements(), title="Clusters")
plt.gca().add_artist(legend1)

plt.title('t-SNE Visualization of Clusters')
plt.xlabel('t-SNE Component 1')
plt.ylabel('t-SNE Component 2')
plt.grid()
plt.show()