diff options
| -rw-r--r-- | src/convex_optimization.py | 2 | ||||
| -rw-r--r-- | src/make_plots.py | 26 |
2 files changed, 14 insertions, 14 deletions
diff --git a/src/convex_optimization.py b/src/convex_optimization.py index d667c6e..39f7ee7 100644 --- a/src/convex_optimization.py +++ b/src/convex_optimization.py @@ -107,7 +107,7 @@ def diff_and_opt(M_val, w_val, f_x, f_xz): #Relaxing precision constraints cvxopt.solvers.options['feastol'] = 2e-5 cvxopt.solvers.options['abstol'] = 2e-5 - #cvxopt.solvers.options['maxiters'] = 100 + cvxopt.solvers.options['maxiters'] = 50 cvxopt.solvers.options['show_progress'] = False try: theta = cvxopt.solvers.cp(F, G, h)['x'] diff --git a/src/make_plots.py b/src/make_plots.py index 7da3e1e..d92b008 100644 --- a/src/make_plots.py +++ b/src/make_plots.py @@ -32,7 +32,7 @@ def watts_strogatz(n_cascades, lbda, passed_function): Test running time on different algorithms """ G = cascade_creation.InfluenceGraph(max_proba=.7, min_proba=.2) - G.import_from_file("../datasets/watts_strogatz_500_80_point3.txt") + G.import_from_file("../datasets/watts_strogatz_300_30_point3.txt") A = cascade_creation.generate_cascades(G, p_init=.05, n_cascades=n_cascades) if passed_function==algorithms.greedy_prediction: @@ -44,19 +44,19 @@ def watts_strogatz(n_cascades, lbda, passed_function): algorithms.correctness_measure(G, G_hat, print_values=True) -def test(): - """ - unit test - """ - G = cascade_creation.InfluenceGraph(max_proba=1, min_proba=.2) - G.erdos_init(n=50, p=.2) - A = cascade_creation.generate_cascades(G, p_init=.1, n_cascades=1000) - G_hat = algorithms.recovery_passed_function(G, A, - passed_function=convex_optimization.sparse_recovery, - floor_cstt=.1, lbda=.001, n_cascades=1000) - algorithms.correctness_measure(G, G_hat, print_values=True) +# def test(): +# """ +# unit test +# """ +# G = cascade_creation.InfluenceGraph(max_proba=1, min_proba=.2) +# G.erdos_init(n=50, p=.2) +# A = cascade_creation.generate_cascades(G, p_init=.1, n_cascades=1000) +# G_hat = algorithms.recovery_passed_function(G, A, +# passed_function=convex_optimization.sparse_recovery, +# floor_cstt=.1, lbda=.001, n_cascades=1000) +# algorithms.correctness_measure(G, G_hat, print_values=True) if __name__=="__main__": - watts_strogatz(n_cascades=1000, lbda=.001, passed_function= + watts_strogatz(n_cascades=500, lbda=.001, passed_function= convex_optimization.sparse_recovery) |