TrafficGCN / haversine_mapping_for_spatial_integration_in_graph_convolutional_networks Star 4 Code Issues Pull requests Calculating the nearest weather sensor for each traffic sensor and then merging the weather sensors' temporal data with the traffic sensors'. python weather data-science data neur...
A PyTorch implementation of T-GCN. Contribute to martinwhl/T-GCN-PyTorch development by creating an account on GitHub.
.github A3T-GCN AST-GCN data README.md acell.py main.py tgcn.py visualization.py Baselines HoT-GCN IDGCL KST-GCN STCGNN STGC-GNN Sep-GCL T-GCN data iGCL out .DS_Store .gitattributes README.md Table.png big picture.png big picture2.png Breadcrumbs T-GCN / AST-GCN/ Directory acti...
我们率先提出一种可用于交通数据预测的时间图卷积网络(T-GCN)框架,填补深度图卷积模型不能处理时空数据的空白:利用GCN获取图的拓扑结构,捕捉图的空间依赖关系;利用GRU模型学习节点属性的时间依赖关系。实际交通数据测试表明T-GCN模型用于交通预测任务能够获得较好的性能。 我们提出的T-GCN模型能够成功地从交通数据中学习...
解读了一下这篇论文github上关于T-GCN的代码,主要分为main文件与TGCN文件两部分,后续有空将会更新其他部分作为baseline代码的解读(鸽)。 1、main.py # -*- coding: utf-8 -*- import pickle as pkl import tensorflow as tf import pandas as pd import numpy as np import math import os import numpy....
.github/workflows Update stale.yml 4年前 A3T-GCN Add files via upload 4年前 AST-GCN Add files via upload 4年前 Baselines Create README.MD 5年前 T-GCN update requirements 4年前 data update 6年前 out Create README.MD 5年前 .DS_Store ...
54 changes: 7 additions & 47 deletions 54 A3T-GCN/A3T-GCN.py Original file line numberDiff line numberDiff line change @@ -8,7 +8,7 @@ import math import os import numpy.linalg as la from input_data import preprocess_data,preprocess_average_data,load_sz_data,load_los_data from in...
Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets. Our tensorflow implementation of the T-GCN is available at https://github.com/lehaifeng/T-GCN. ...
flags.DEFINE_string('model_name', 'tgcn', 'tgcn or GRU or GCN.') model_name = FLAGS.model_name data_name = FLAGS.dataset train_rate = FLAGS.train_rate seq_len = FLAGS.seq_len output_dim = pre_len = FLAGS.pre_len batch_size = FLAGS.batch_size lr = FLAGS.learning_rate training...
Explore All features Documentation GitHub Skills Blog Solutions By company size Enterprises Small and medium teams Startups Nonprofits By use case DevSecOps DevOps CI/CD View all use cases By industry Healthcare Financial services Manufacturing Government View all industries View all ...