From 6470fd53a89102b365aa1c0c288acf0076115a97 Mon Sep 17 00:00:00 2001 From: jeanpouget-abadie Date: Sun, 7 Dec 2014 17:33:49 -0500 Subject: related works --- src/convex_optimization.py | 4 ++-- src/make_plots.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) (limited to 'src') diff --git a/src/convex_optimization.py b/src/convex_optimization.py index 530e3f5..96dc26f 100644 --- a/src/convex_optimization.py +++ b/src/convex_optimization.py @@ -6,7 +6,7 @@ import timeout import cvxopt -@timeout.timeout(10) +@timeout.timeout(20) def l1obj_l2constraint(M_val, w_val): """ Solves: @@ -61,7 +61,7 @@ def l1obj_l2constraint(M_val, w_val): return 1 - np.exp(theta), theta -@timeout.timeout(10) +@timeout.timeout(20) def l1obj_l2penalization(M_val, w_val, lbda): """ Solves: diff --git a/src/make_plots.py b/src/make_plots.py index 905c731..5a61c10 100644 --- a/src/make_plots.py +++ b/src/make_plots.py @@ -40,7 +40,7 @@ def compare_greedy_and_lagrange_cs284r(): """ G = cascade_creation.InfluenceGraph(max_proba = .8) G.import_from_file("../datasets/subset_facebook_SNAPnormalize.txt") - A = cascade_creation.generate_cascades(G, p_init=.05, n_cascades=100) + A = cascade_creation.generate_cascades(G, p_init=.05, n_cascades=1000) #Greedy G_hat = algorithms.greedy_prediction(G, A) -- cgit v1.2.3-70-g09d2