from cens_fit_data import *
import cvxpy as cvx

y_cens = cvx.Variable(K - M)
c = cvx.Variable(n)
X = X.squeeze()
select_cens_mat = np.block([[np.zeros((M, K-M))],
	                    [np.eye(K-M)]])
select_ucens_mat = np.block([[np.eye((M))],
	                       [np.zeros((K-M, M))]])
objective = cvx.Minimize(cvx.norm(np.dot(select_ucens_mat, y).flatten() + select_cens_mat*y_cens - X.T*c))

prob = cvx.Problem(objective, [y_cens >= D])
prob.solve()

print "Relative error: ", np.linalg.norm(c.value - c_true.flatten())/np.linalg.norm(c_true)

c2 = cvx.Variable(n)
objective2 = cvx.Minimize(cvx.norm(y.flatten() - X.T[:M] * c2))
prob2 = cvx.Problem(objective2)
prob2.solve()
print "Relative error: ", np.linalg.norm(c2.value - c_true.flatten())/np.linalg.norm(c_true)