more changes

2 files changed, 6 insertions, 20 deletions

diff --git a/src/convex_optimization.py b/src/convex_optimization.py
index 5942c70..913fcb4 100644
--- a/src/convex_optimization.py
+++ b/src/convex_optimization.py
@@ -90,7 +90,7 @@ def diff_and_opt(M_val, w_val, f_x, f_xz):
 
     def F(x=None, z=None):
         if x is None:
-            return 0, cvxopt.matrix(-.001, (n,1))
+            return 0, cvxopt.matrix(-.1, (n,1))
         elif z is None:
             y, y_diff = f_x(x, M_val, w_val)
             return cvxopt.matrix(float(y), (1, 1)),\
@@ -105,10 +105,10 @@ def diff_and_opt(M_val, w_val, f_x, f_xz):
     h = cvxopt.matrix(0.0, (n,1))
 
     #Relaxing precision constraints
-    cvxopt.solvers.options['feastol'] = 2e-5
-    cvxopt.solvers.options['abstol'] = 2e-5
+    # cvxopt.solvers.options['feastol'] = 2e-5
+    # cvxopt.solvers.options['abstol'] = 2e-5
     # cvxopt.solvers.options['maxiters'] = 50
-    cvxopt.solvers.options['show_progress'] = True
+    cvxopt.solvers.options['show_progress'] = False
     try:
         theta = cvxopt.solvers.cp(F, G, h)['x']
     except ArithmeticError:
diff --git a/src/make_plots.py b/src/make_plots.py
index 201e375..947bf57 100644
--- a/src/make_plots.py
+++ b/src/make_plots.py
@@ -44,20 +44,6 @@ 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)
-
-
 if __name__=="__main__":
-    watts_strogatz(n_cascades=3000, lbda=.002, passed_function=
-        convex_optimization.sparse_recovery)
-        #algorithms.greedy_prediction)
\ No newline at end of file
+    watts_strogatz(n_cascades=500, lbda=.002, passed_function=
+        convex_optimization.sparse_recovery)
\ No newline at end of file