Configuration model for correlation/covariance matrices
This package contains two algorithms.
- Algorithm DMCC solves a convex optimization problem and usually better than the other naive gradient descent algorithm. Therefore, we recommend DMCC over the naive gradient descent algorithm.
- DMCC, including the performance test, is documented in the accompanying DMCC-algorithm.pdf.
- DMCC is provided in Python only.
- The naive gradient descent algorithm is provided in Python and MATLAB.
pip install configcorr
Package configcorr has the following dependencies:
Python >= 3.4
setuptools >= 1.4
NumPy >= 1.8
CVXPy >= 1.0
These packages need to be installed manually.
Clone the configcorr git repository by running
git clone https://github.com/naokimas/config_corr.git
Then, navigate to the top-level of the cloned directory and run
python setup.py install
python3 test_dmcc.py
- test_dmcc.py calls max_ent_config_dmcc.py
python3 test_naive_gradient_descent.py
- test_naive_gradient_descent.py calls max_ent_config_naive_gradient_descent.py
test_naive_gradient_descent
- test_naive_gradient_descent.m calls max_ent_config_naive_gradient_descent.m
- The data set to be used should be specified within test_naive_gradient_descent.m
- Variable curr_dir should be modified according to where you place the data set.