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src/s/t/StochasticBlockmodel-HEAD/test.py   StochasticBlockmodel(Download)
 
        if params['fit_method'] == 'convex_opt':
            fit_model.fit_convex_opt(subnet)
        elif params['fit_method'] == 'logistic':
            fit_model.fit_logistic(subnet)

src/s/t/StochasticBlockmodel-HEAD/test_regular.py   StochasticBlockmodel(Download)
        subnet.generate(data_model, r, c, params['gibbs_cover'])
 
        fit_model.fit_convex_opt(subnet)
 
        results.record(sub_size, rep, subnet, data_model, fit_model)