diff options
Diffstat (limited to 'src/convex_optimization.py')
| -rw-r--r-- | src/convex_optimization.py | 48 |
1 files changed, 23 insertions, 25 deletions
diff --git a/src/convex_optimization.py b/src/convex_optimization.py index 1f34508..163b6d5 100644 --- a/src/convex_optimization.py +++ b/src/convex_optimization.py @@ -44,55 +44,52 @@ def sparse_recovery(M_val, w_val, lbda): return diff_and_opt(theta, theta_, M, M_val, w, lbda, y) -@timeout.timeout(20) -def type_lasso(M_val, w_val, lbda): +def type_lasso(lbda): """ Solves: min - sum_j theta_j + lbda*|e^{M*theta} - (1 - w)|_2 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 or type(lbda) == float: lbda = np.array(lbda) + lbda = theano.shared(lbda.astype(theano.config.floatX)) + 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)) + M = tensor.matrix(name='M', dtype=theano.config.floatX) + w = tensor.vector(name='w', dtype=theano.config.floatX) y = lbda * (theta_).norm(1) + ( tensor.exp(M.dot(theta_)) - (1 - w)).norm(2) - return diff_and_opt(theta, theta_, M, M_val, w, lbda, y) - - -def diff_and_opt(theta, theta_, M, M_val, 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) - f_xz = theano.function([theta, z], [y, y_diff, y_hess], - allow_input_downcast=True) + f_x = theano.function([theta, M, w], [y, y_diff], + allow_input_downcast=True) + f_xz = theano.function([theta, z, M, w], [y, y_diff, y_hess], + allow_input_downcast=True) + + return f_x, f_xz + +@timeout.timeout(8) +def diff_and_opt(M_val, w_val, f_x, f_xz): + + m, n = M_val.shape def F(x=None, z=None): if x is None: return 0, cvxopt.matrix(-.001, (n,1)) elif z is None: - y, y_diff = f_x(x) + y, y_diff = f_x(x, M_val, w_val) return cvxopt.matrix(float(y), (1, 1)),\ cvxopt.matrix(y_diff.astype("float64")).T else: - y, y_diff, y_hess = f_xz(x, z) + y, y_diff, y_hess = f_xz(x, z, M_val, w_val) return cvxopt.matrix(float(y), (1, 1)), \ cvxopt.matrix(y_diff.astype("float64")).T, \ cvxopt.matrix(y_hess.astype("float64")) @@ -104,7 +101,7 @@ 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'] = False + cvxopt.solvers.options['show_progress'] = True try: theta = cvxopt.solvers.cp(F, G, h)['x'] except ArithmeticError: @@ -128,13 +125,14 @@ def test(): M_val, w_val = cascade_creation.icc_matrixvector_for_node(A, 0) #Type lasso - if 0: - p_vec, theta = type_lasso(M_val, w_val, lbda) + if 1: + f_x, f_xz = type_lasso(lbda) + p_vec, _ = diff_and_opt(M_val, w_val, f_x, f_xz) print(p_vec) print(G.mat) #Sparse recovery - if 1: + if 0: p_vec, theta = sparse_recovery(M_val, w_val, lbda) print(p_vec) print(G.mat) |