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PaperCode Stricter tolerance threshold Sep 15, 2017
README.md Edited readme Jun 15, 2017
TVGL.py
exampleTVGL.py
inferGraphL1.py Fixed float error Jun 19, 2017
inferGraphL2.py
inferGraphLaplacian.py Fixed float error Jun 19, 2017
inferGraphLinf.py
inferGraphPN.py

README.md

TVGL

TVGL is a python solver for inferring dynamic networks from raw time series data. For implementation details, refer to the paper, available at: http://stanford.edu/~hallac/TVGL.pdf.


Download & Setup

Download the source code by running the following code in the terminal:

git clone https://github.com/davidhallac/TVGL.git

Usage

TVGL can be called through the following file:

TVGL.py

Parameters

data : a T-by-n numpy array with the raw data (each row is a new timestamp)

lengthOfSlice : Number of samples in each ``slice'', or timestamp

lamb : the lambda regularization parameter controlling the network sparsity (as described in the paper)

beta : the beta parameter controlling the temporal consistency (as described in the paper)

indexOfPenalty : The regularization penalty to use (1 = L1, 2 = L2, 3 = Laplacian, 4 = L_inf, 5 = perturbed node)

verbose = False : Whether or not to run ADMM in ``verbose'' mode (to print intermediate steps)

eps = 3e-3 : Threshold at which we treat output network weight as zero

epsAbs = 1e-3 : ADMM absolute tolerance threshold (see full details in http://stanford.edu/~boyd/papers/pdf/admm_distr_stats.pdf)

epsRel = 1e-3 : ADMM relative tolerance threshold (see http://stanford.edu/~boyd/papers/pdf/admm_distr_stats.pdf)

Example

Running the following script provides an example of how the TVGL solver can be used:

exampleTVGL.py
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