This repo is the implementation of the paper [1] along with the competing methods listed in Table 1 in [1]. Please check Table 1 for details.
Install the ssm package to run comppeting methods. It is recommended to install Anaconda and create a new environment (python>=3.7) to run this code.
conda create --name snscov python=3.7
conda activate snscov
python setup.py install
Some simulation examples.
- Run Simulation 1. Variations of waveforms
cd examples/simulation1
python simulation1.py
Models are stored in folder ./results
and plots are stored in ./figures
. Specify the waveform by using --waveform
- Run Simulation 2. Comparison with other mehtods
cd examples/comparison1
python compare_all.py
Test different number of test subjects by running the follow bash file.
Select the waveform (sine, square, mixing) by changing --waveform
in compare.sh
cd examples/comparison1
bash compare.sh
- Run Simulation 3. Comparison with other mehtods (high-dimensional data)
cd examples/comparison2
python test_large_scale.py
Distributed under the MIT License. See LICENSE.txt
for more information.
[1] Tsai, K., Kolar, M., & Koyejo, O. (2022). A Nonconvex Framework for Structured Dynamic Covariance Recovery. Journal of Machine Learning Research, 23(200), 1-91.