超图神经网络代码实现
时间: 2023-11-14 11:12:52 浏览: 425
DHNN代表动态超图神经网络,它结合了KNN和KMeans算法,以实现动态构建超图和进行超图卷积操作。以下是超图神经网络的代码实现步骤:
1. 导入必要的Python库和模块,例如numpy、scikit-learn、pytorch等。
2. 定义超图的节点和边,以及节点和边的特征。
3. 使用KNN和KMeans算法动态构建超图。
4. 定义超图卷积层,以实现信息传播和特征提取。
5. 定义损失函数和优化器,以进行模型训练。
6. 训练模型并进行预测。
以下是一个简单的DHNN代码实现示例:
```
import numpy as np
from sklearn.neighbors import NearestNeighbors
from sklearn.cluster import KMeans
import torch
import torch.nn as nn
import torch.optim as optim
# 定义超图节点和边
nodes = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8]])
edges = np.array([[0, 1], [1, 2], [0, 2], [3, 4], [4, 5], [3, 5], [0, 3], [1, 4], [2, 5]])
# 定义节点和边的特征
node_features = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12], [13, 14, 15], [16, 17, 18], [19, 20, 21], [22, 23, 24], [25, 26, 27]])
edge_features = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12], [13, 14], [15, 16], [17, 18]])
# 使用KNN算法动态构建超图
knn = NearestNeighbors(n_neighbors=2)
knn.fit(nodes)
adj = knn.kneighbors_graph(nodes).toarray()
# 使用KMeans算法动态构建超图
kmeans = KMeans(n_clusters=3)
kmeans.fit(node_features)
clusters = kmeans.labels_
adj = np.zeros((3, 3))
for i in range(len(edges)):
u, v = edges[i]
if clusters[u] == clusters[v]:
adj[clusters[u], clusters[v]] = 1
# 定义超图卷积层
class GraphConv(nn.Module):
def __init__(self, in_features, out_features):
super(GraphConv, self).__init__()
self.linear = nn.Linear(in_features, out_features)
def forward(self, x, adj):
x = torch.matmul(adj, x)
x = self.linear(x)
return x
# 定义模型
model = nn.Sequential(
GraphConv(3, 16),
nn.ReLU(),
GraphConv(16, 1)
)
# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.01)
# 训练模型
for epoch in range(100):
optimizer.zero_grad()
output = model(torch.Tensor(node_features), torch.Tensor(adj))
loss = criterion(output, torch.Tensor(edge_features))
loss.backward()
optimizer.step()
# 进行预测
with torch.no_grad():
output = model(torch.Tensor(node_features), torch.Tensor(adj))
print(output)
```
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