时空图卷积 python

时空图卷积（Spatio-temporal Graph Convolutional, STGCN）是一种用于处理时空数据的图卷积网络（Graph Convolutional Network, GCN）的变体。它主要用于建模和预测时空数据中的关联性和时空变化。

STGCN的python实现主要使用了一些常见的机器学习和深度学习库，如numpy、pytorch或tensorflow等。下面是一个简单的STGCN的python代码示例：

import numpy as np
import torch
import torch.nn as nn

class GraphConvolution(nn.Module):
    def __init__(self, in_features, out_features, adjacency):
        super(GraphConvolution, self).__init__()
        self.adjacency = adjacency
        self.weight = nn.Parameter(torch.Tensor(in_features, out_features))
        self.bias = nn.Parameter(torch.Tensor(out_features))

    def forward(self, x):
        x = torch.matmul(self.adjacency, x)
        x = torch.matmul(x, self.weight)
        x = x + self.bias
        return x

class STGCN(nn.Module):
    def __init__(self, in_channels, num_nodes, num_timesteps, num_features, num_classes):
        super(STGCN, self).__init__()
        self.num_nodes = num_nodes
        self.num_timesteps = num_timesteps
        self.num_features = num_features

        self.temporal_conv1 = nn.Conv2d(in_channels, 64, kernel_size=(1, 3))
        self.temporal_conv2 = nn.Conv2d(64, 64, kernel_size=(1, 3))
        self.spatial_conv1 = GraphConvolution(num_features, 16, adjacency)
        self.spatial_conv2 = GraphConvolution(16, num_classes, adjacency)
        self.relu = nn.ReLU()

    def forward(self, x):
        x = x.permute(0, 3, 1, 2)
        x = self.temporal_conv1(x)
        x = self.relu(x)
        x = self.temporal_conv2(x)
        x = self.relu(x)
        x = x.permute(0, 2, 3, 1)
        x = x.reshape(-1, self.num_nodes * self.num_timesteps, self.num_features)
        x = self.spatial_conv1(x)
        x = self.relu(x)
        x = self.spatial_conv2(x)
        x = x.reshape(-1, self.num_nodes, self.num_timesteps, self.num_classes)
        x = x.permute(0, 3, 2, 1)
        return x

# 构建输入
in_channels = 2  # 输入数据的通道数
num_nodes = 10  # 图中节点的数量
num_timesteps = 6  # 时间步的数量
num_features = 16  # 每个节点的特征数量
num_classes = 2  # 预测类别的数量
adjacency = np.random.rand(num_nodes, num_nodes)  # 图的邻接矩阵

# 创建网络实例
model = STGCN(in_channels, num_nodes, num_timesteps, num_features, num_classes)
input_data = torch.Tensor(np.random.rand(1, num_nodes, num_timesteps, in_channels))

# 运行前向传播
output = model(input_data)
print(output.shape)

这个例子展示了一个简单的STGCN的实现，它包括一个GraphConvolution层和两个卷积层。在使用时，可以根据实际情况调整网络的参数和输入数据的大小。