remodeling results section

1 files changed, 13 insertions, 59 deletions

diff --git a/src/convex_optimization.py b/src/convex_optimization.py
index 96dc26f..2661e25 100644
--- a/src/convex_optimization.py
+++ b/src/convex_optimization.py
@@ -6,63 +6,9 @@ import timeout
 import cvxopt
 
 
-@timeout.timeout(20)
-def l1obj_l2constraint(M_val, w_val):
-    """
-    Solves:
-        min - sum_j theta_j
-        s.t theta_j <= 0
-            |e^{M*theta} - (1 - w)|_2 <= 1
-    """
-    assert len(M_val) == len(w_val)
-
-    if M_val.dtype == bool:
-        M_val = M_val.astype('float32')
-
-    m, n = M_val.shape
-    c = cvxopt.matrix(-1.0, (n,1))
-
-    theta = tensor.row().T
-    z = tensor.row().T
-    theta_ = theta.flatten()
-    z_ = z.flatten()
-    M = theano.shared(M_val.astype(theano.config.floatX))
-    w = theano.shared(w_val.astype(theano.config.floatX))
-    y = (tensor.exp(M.dot(theta_)) - (1 - w)).norm(2) - 1
-    y_diff = tensor.grad(y, theta_)
-    y_hess = z[0] * theano.gradient.hessian(y, theta_)
-    f_x = theano.function([theta], [y, y_diff], allow_input_downcast=True)
-    f_xz = theano.function([theta, z], [y, y_diff, y_hess],
-                            allow_input_downcast=True)
-
-    def F(x=None, z=None):
-        if x is None:
-            return 1, cvxopt.matrix(.0001, (n,1))
-        elif z is None:
-            y, y_diff = f_x(x)
-            return cvxopt.matrix(float(y), (1, 1)),\
-                    cvxopt.matrix(y_diff.astype("float64")).T
-        else:
-            y, y_diff, y_hess = f_xz(x, z)
-            return cvxopt.matrix(float(y), (1, 1)), \
-                    cvxopt.matrix(y_diff.astype("float64")).T, \
-                    cvxopt.matrix(y_hess.astype("float64"))
-
-    G = cvxopt.spdiag([1 for i in xrange(n)])
-    h = cvxopt.matrix(0.0, (n,1))
-
-    cvxopt.solvers.options['show_progress'] = False
-    try:
-        theta = cvxopt.solvers.cpl(c,F, G, h)['x']
-    except ArithmeticError:
-        print "ArithmeticError thrown, change initial point"+\
-              " given to the solver"
-
-    return 1 - np.exp(theta), theta
-
 
 @timeout.timeout(20)
-def l1obj_l2penalization(M_val, w_val, lbda):
+def sparse_recovery(M_val, w_val, lbda):
     """
     Solves:
         min - sum_j theta_j + lbda*|e^{M*theta} - (1 - w)|_2
@@ -76,17 +22,25 @@ def l1obj_l2penalization(M_val, w_val, lbda):
     if type(lbda) == int:
         lbda = np.array(lbda)
 
-    m, n = M_val.shape
-
     theta = tensor.row().T
-    z = tensor.row().T
     theta_ = theta.flatten()
-    z_ = z.flatten()
+
     M = theano.shared(M_val.astype(theano.config.floatX))
     w = theano.shared(w_val.astype(theano.config.floatX))
     lbda = theano.shared(lbda.astype(theano.config.floatX))
+
     y = (theta_).norm(1) + lbda * (
         tensor.exp(M.dot(theta_)) - (1 - w)).norm(2)
+
+    return diff_and_opt(theta, theta_, M, w, lbda, y)
+
+
+def diff_and_opt(theta, theta_, M, w, lbda, y):
+    z = tensor.row().T
+    z_ = z.flatten()
+
+    m, n = M_val.shape
+
     y_diff = tensor.grad(y, theta_)
     y_hess = z[0] * theano.gradient.hessian(y, theta_)
     f_x = theano.function([theta], [y, y_diff], allow_input_downcast=True)