这个好像没有用pytorch建网络，但使用了kmeans_pytorch包，不知道如何实现的。
一，代码
import torch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from kmeans_pytorch import kmeans
from torch.autograd import Variable
import torch.nn.functional as F
from torch.utils.data import DataLoader, TensorDataset
import time
if torch.cuda.is_available():
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
plant = pd.read_csv('./聚类分析/plant.csv')
plant_d = plant[['Sepal_Length', 'Sepal_Width','Petal_Length', 'Petal_Width']]
plant['target'] = plant['Species']
x = torch.from_numpy(np.array(plant_d))
y = torch.from_numpy(np.array(plant.target))
num_clusters = 3
cluster_ids_x, cluster_centers = kmeans(
X=x,
num_clusters=num_clusters,
distance='euclidean',
device=device
)
print(cluster_ids_x)
print(cluster_centers)
plt.figure(figsize=(4, 3), dpi=160)
plt.scatter(x[:, 0], x[:, 1], c=cluster_ids_x, cmap='cool', marker='D')
plt.scatter(
cluster_centers[:, 0], cluster_centers[:, 1],
c='white',
alpha=0.6,
edgecolors='black',
linewidths=2
)
plt.tight_layout()
plt.show()
二，截图
2024-08-21 11_05_33-ch2 – 333.py.png
