model section

1 files changed, 9 insertions, 7 deletions

diff --git a/src/algorithms.py b/src/algorithms.py
index 926d9bc..9ebccf1 100644
--- a/src/algorithms.py
+++ b/src/algorithms.py
@@ -31,7 +31,7 @@ def greedy_prediction(G, cascades):
     return G_hat
 
 
-def recovery_l1obj_l2constraint(G, cascades, floor_cstt, passed_function,
+def recovery_passed_function(G, cascades, floor_cstt, passed_function,
                                 *args, **kwargs):
     """
     Returns estimated graph from convex program specified by passed_function
@@ -40,11 +40,13 @@ def recovery_l1obj_l2constraint(G, cascades, floor_cstt, passed_function,
     G_hat = cascade_creation.InfluenceGraph(max_proba=None)
     G_hat.add_nodes_from(G.nodes()) 
 
+    f_x, f_xz = passed_function(*args, **kwargs)
+
     for node in G_hat.nodes():
         print(node)
         try:
             M, w = cascade_creation.icc_matrixvector_for_node(cascades, node)
-            p_node, __ = passed_function(M,w, *args, **kwargs)
+            p_node, _ = convex_optimization.diff_and_opt(M, w, f_x, f_xz)
             G_hat = cascade_creation.add_edges_from_proba_vector(G=G_hat,
                         p_node=p_node, node=node, floor_cstt=floor_cstt)
         except timeout.TimeoutError:
@@ -86,15 +88,15 @@ def test():
     unit test
     """
     G = cascade_creation.InfluenceGraph(max_proba = .8)
-    G.erdos_init(n = 10, p = .2)
+    G.erdos_init(n = 100, p = .2)
     import time
     t0 = time.time()
     A = cascade_creation.generate_cascades(G, .2, 1000)
-    if 1:
-        G_hat = greedy_prediction(G, A)
     if 0:
-        G_hat = recovery_l1obj_l2constraint(G, A,
-                passed_function=convex_optimization.l1obj_l2penalization,
+        G_hat = greedy_prediction(G, A)
+    if 1:
+        G_hat = recovery_passed_function(G, A,
+                passed_function=convex_optimization.type_lasso,
                 floor_cstt=.1, lbda=10)
     correctness_measure(G, G_hat, print_values=True)