convex_optimization first draft

1 files changed, 18 insertions, 1 deletions

diff --git a/jpa_test/algorithms.py b/jpa_test/algorithms.py
index 2a32f57..cf4ce50 100644
--- a/jpa_test/algorithms.py
+++ b/jpa_test/algorithms.py
@@ -2,13 +2,14 @@ import numpy as np
 import networkx as nx
 import cascade_creation
 from collections import Counter
-
 from itertools import izip
+import convex_optimization
 
 
 def greedy_prediction(G, cascades):
     """
     Returns estimated graph from Greedy algorithm in "Learning Epidemic ..."
+    TODO: write cleaner code?
     """
     G_hat = cascade_creation.InfluenceGraph(max_proba=None)
     G_hat.add_nodes_from(G.nodes())
@@ -33,6 +34,19 @@ def greedy_prediction(G, cascades):
     return G_hat
 
 
+def sparserecovery(G, cascades):
+    """
+    Returns estimated graph from following convex program:
+    min |theta_1| + lbda | exp(M theta) -(1- w)|
+    where theta = log (1 - p); w = 1_{infected}; lbda = lagrange cstt
+    """
+    G_hat = cascade_creation.InfluenceGraph(max_proba=None)
+    G_hat.add_nodes_from(G.nodes())
+    for node in G_hat.nodes():
+        M, w = cascade_creation.icc_matrixvector_for_node(cascades, node)
+        edges_node = convex_optimization.l1regls(M,w)
+
+
 def correctness_measure(G, G_hat):
     """
     Measures correctness of estimated graph G_hat to ground truth G
@@ -54,6 +68,9 @@ def test():
     import time
     t0 = time.time()
     A = cascade_creation.generate_cascades(G, .1, 100)
+
+    sparserecovery(G, A)
+
     G_hat = greedy_prediction(G, A)
     fp, fn, gp = correctness_measure(G, G_hat)
     print "False Positive: {}".format(len(fp))