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|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.|
|Code to run experiments for ``Network Inference via the Time-Varying Graphical Lasso''.|
|Stay tuned for a full solver (coming soon!)|
|Download & Setup|
|Download the source code by running the following code in the terminal:|
|git clone https://github.com/davidhallac/TVGL.git|
|TVGL can be called through the following file:|
|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)|
|Running the following script provides an example of how the TVGL solver can be used:|