STULIG

The implementation for STULIG

Towards Interpreting, Discriminating and Synthesizing Motion Traces via Deep Probabilistic Generative Models

Environment

• python 2.7
• Tensorflow 1.7 or ++ （updated now 2019.06)

Dataset

Here we choose gowalla dataset for training.

• Gowalla: http://snap.stanford.edu/data/loc-gowalla.html
• (remark) Please do not use these datasets for commercial purpose. For academic uses, please cite the paper. Thanks for their help.

Usage

• Training process: We choose the 201 users' sub-trajectories, split these to training data(about 50%) and test data (about 50%). The new code with tensorflow>=1.7, you can run it easily. and also some records will stored by the code (including model, train data and sample results)
• The format of total.dat : userid/locationid/time/longitude/latitude
• python STUL.py
• python STULIG.py

Reference

Hope such an implementation could help you on your projects. Any comments and feedback are appreciated.