@@ -9,7 +9,7 @@ The file `sbm_core.py` is the main part of the code, which contains utility func
The class `optimize.py` contains code of three main algorithms. It uses the functions contained in `sbm_core.py`.
The estimated intensity functions, groups/clusters and change-points can be obtained from calling functions in `optimize.py`.
The file `utils.py` contains the utility code to read data, SMAWK utilities, creating networkx graph object etc.
The file `experiments.py` contains the function to run the simulations.
The file `experiments.py` contains required functions to run the simulations.
# Experimental files and Running the code
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@@ -49,12 +49,11 @@ From the file `synthetic_experiment_1.py` to `synthetic_experiment_5.py` contain
These files contain the code to generate synthetic data as well.
The files `likelihood_vs_H_bikes.py`,`likelihood_vs_H_bitcoin.py`,`likelihood_vs_H_dep2.py` return normalized likelihood values for
a set of given `H` levels. Ex: `current_h` = 1,2,3,4,5,6,7,8,...20;
a set of given `H` levels. eg: `current_h` = 1,2,3,4,5,6,7,8,...20;
To reproduce the results of the paper just run `synthetic_experiment_likelihood_vs_H_?.py`files.
Note that to find the `Normlaized likelihood`, in the bottom of each files we have a code snippet as follows.
(Normalized log-likelihood is the ratio between a particular likelihood and
the likelihoodvalue which corresponds to a single group and a single segment.)
Note that Normalized log-likelihood is the ratio between a particular likelihood and
the likelihoodvalue which corresponds to a single group and a single segment.
The file `bike_times_edges_large.py` returns the running time and edges for given a fraction of edges.
To reproduce the results of the paper, eg. set `_frac` = .4, .5, .8 or 1; one at a time. Note that running time can be dependent on the machine you run.
