fixed bug

1 files changed, 11 insertions, 10 deletions

diff --git a/src/convex_optimization.py b/src/convex_optimization.py
index 6a43705..1f34508 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 sparse_recovery(M_val, w_val, lbda):
     """
     Solves:
@@ -24,7 +24,7 @@ def sparse_recovery(M_val, w_val, lbda):
     if M_val.dtype == bool:
         M_val = M_val.astype('float32')
 
-    if type(lbda) == int:
+    if type(lbda) == int or type(lbda) == float:
         lbda = np.array(lbda)
 
     m, n = M_val.shape
@@ -37,8 +37,8 @@ def sparse_recovery(M_val, w_val, lbda):
     lbda = theano.shared(lbda.astype(theano.config.floatX))
 
     y =  lbda * theta_.norm(1) - 1./m*(
-            tensor.dot(1-w, tensor.log(1-tensor.exp(M.dot(theta_))))
-            + tensor.dot(w, tensor.dot(M, theta_))
+            (w).dot(tensor.log(1-tensor.exp(M.dot(theta_))))
+            + (1-w).dot(tensor.dot(M, theta_))
         )  
 
     return diff_and_opt(theta, theta_, M, M_val, w, lbda, y)
@@ -56,7 +56,7 @@ def type_lasso(M_val, w_val, lbda):
     if M_val.dtype == bool:
         M_val = M_val.astype('float32')
 
-    if type(lbda) == int:
+    if type(lbda) == int or type(lbda) == float:
         lbda = np.array(lbda)
 
     theta = tensor.row().T
@@ -104,11 +104,11 @@ def diff_and_opt(theta, theta_, M, M_val, w, lbda, y):
     #cvxopt.solvers.options['feastol'] = 2e-5
     #cvxopt.solvers.options['abstol'] = 2e-5
     #cvxopt.solvers.options['maxiters'] = 100
-    cvxopt.solvers.options['show_progress'] = True
+    cvxopt.solvers.options['show_progress'] = False
     try:
         theta = cvxopt.solvers.cp(F, G, h)['x']
     except ArithmeticError:
-        print("ArithmeticError thrown, change initial point"+\
+        print("ArithmeticError thrown, change initial point"
               " given to the solver")
 
     if cvxopt.solvers.options['show_progress']:
@@ -121,9 +121,9 @@ def test():
     """
     unit test
     """
-    lbda = 1
+    lbda = .001
     G = cascade_creation.InfluenceGraph(max_proba=.9)
-    G.erdos_init(n=10, p = .3)
+    G.erdos_init(n=100, p = .3)
     A = cascade_creation.generate_cascades(G, .1, 1000)
     M_val, w_val = cascade_creation.icc_matrixvector_for_node(A, 0)
     
@@ -131,12 +131,13 @@ def test():
     if 0:
         p_vec, theta = type_lasso(M_val, w_val, lbda)
         print(p_vec)
+        print(G.mat)
 
     #Sparse recovery
     if 1:
         p_vec, theta = sparse_recovery(M_val, w_val, lbda)
-        print(G.mat[0])
         print(p_vec)
+        print(G.mat)
 
 if __name__=="__main__":
     test()