small changes to optimization functions

1 files changed, 37 insertions, 2 deletions

diff --git a/src/convex_optimization.py b/src/convex_optimization.py
index 02787d3..1d84db2 100644
--- a/src/convex_optimization.py
+++ b/src/convex_optimization.py
@@ -6,11 +6,46 @@ import timeout
 import cvxopt
 
 
-
 @timeout.timeout(20)
 def sparse_recovery(M_val, w_val, lbda):
     """
     Solves:
+        min lbda * |theta|_1 - sum b^i log(exp(-<M[i], theta_>) -1) - M*theta
+        s.t theta_j <= 0
+    """
+    assert len(M_val) == len(w_val)
+
+    if M_val.dtype == bool:
+        M_val = M_val.astype('float32')
+
+    if type(lbda) == int:
+        lbda = np.array(lbda)
+
+    theta = tensor.row().T
+    theta_ = theta.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_tmp = lbda * (theta_).norm(1) - tensor.sum(tensor.dot(M, theta_
+        )) + w.dot(tensor.log(tensor.exp(-M.dot(theta_))-1))
+    y_diff = tensor.grad(y_tmp, theta_)
+    y_hess = theano.gradient.hessian(y_tmp, theta_)
+    f = function([theta_], y_hess)
+
+    print(f(-1*np.random.rand(M_val.shape[1]).astype("float32")))
+
+    y = lbda * (theta_).norm(1) - tensor.sum(tensor.dot(M, theta_
+        )) - w.dot(tensor.log(tensor.exp(-M.dot(theta_))-1))
+
+    return diff_and_opt(theta, theta_, M, M_val, w, lbda, y)
+
+
+@timeout.timeout(20)
+def type_lasso(M_val, w_val, lbda):
+    """
+    Solves:
         min - sum_j theta_j + lbda*|e^{M*theta} - (1 - w)|_2
         s.t theta_j <= 0
     """
@@ -63,7 +98,7 @@ def diff_and_opt(theta, theta_, M, M_val, w, lbda, y):
     G = cvxopt.spdiag([1 for i in range(n)])
     h = cvxopt.matrix(0.0, (n,1))
 
-    cvxopt.solvers.options['show_progress'] = False
+    cvxopt.solvers.options['show_progress'] = True
     try:
         theta = cvxopt.solvers.cp(F, G, h)['x']
     except ArithmeticError: