solution = solver.problem.coefs l1_soln = np.fabs(D * solution).sum() tfocs_penalty = maxnorm(499, l=l1_soln) tfocs_loss = quadratic.affine(DT, -Y, l=0.5) tfocs_loss.coefs = np.zeros(499) tfocs_problem = tfocs_loss.add_seminorm(tfocs_penalty) tfocs_solver = FISTA(tfocs_problem) tfocs_solver.debug = True tfocs_solver.fit(max_its=1000, tol=1e-10) tfocs_dual_solution = tfocs_problem.coefs tfocs_primal_solution = Y - DT * tfocs_dual_solution import pylab pylab.scatter(np.arange(Y.shape[0]), Y, c='r') pylab.plot(solution, color='yellow', linewidth=5) pylab.plot(tfocs_primal_solution, color='black', linewidth=3) newl1 = l1norm(D, l=l1_soln) conjugate = quadratic.shift(Y, l=0.5) from regreg.constraint import constraint loss_constraint = constraint(conjugate, newl1) new_solver = FISTA(loss_constraint.dual_problem()) new_solver.debug = True new_solver.fit(max_its=1000, tol=1e-10) soln3 = loss_constraint.primal_from_dual(new_solver.problem.coefs) pylab.plot(soln3, color='gray', linewidth=1)
fused = l1norm(D, l=20) penalty = seminorm(sparsity,fused) Y = np.random.standard_normal(500); Y[100:150] += 7; Y[250:300] += 14 loss = l2normsq.shift(-Y, l=0.5) problem = loss.add_seminorm(penalty) solver = FISTA(problem) solver.fit(max_its=100, tol=1e-10) solution = solver.problem.coefs import pylab pylab.scatter(np.arange(Y.shape[0]), Y, c='r') pylab.plot(solution, color='yellow', linewidth=5) l1_fused = np.fabs(D * solution).sum() l1_sparsity = np.fabs(solution).sum() new_fused = l1norm(D, l=l1_fused) new_sparsity = l1norm(500, l=l1_sparsity) conjugate = l2normsq.shift(Y, l=0.5) from regreg.constraint import constraint loss_constraint = constraint(conjugate, new_fused, new_sparsity) constrained_solver = FISTA(loss_constraint.dual_problem()) constrained_solver.debug = True constrained_solver.fit(max_its=2000, tol=1e-10) constrained_solution = loss_constraint.primal_from_dual(constrained_solver.problem.coefs) pylab.plot(constrained_solution, color='black', linewidth=3)
solver = FISTA(problem) solver.fit(max_its=100, tol=1e-10) solution = solver.problem.coefs l1_soln = np.fabs(D * solution).sum() tfocs_penalty = maxnorm(499, l=l1_soln) tfocs_loss = l2normsq.affine(DT, -Y, l=0.5) tfocs_loss.coefs = np.zeros(499) tfocs_problem = tfocs_loss.add_seminorm(tfocs_penalty) tfocs_solver = FISTA(tfocs_problem) tfocs_solver.debug = True tfocs_solver.fit(max_its=1000, tol=1e-10) tfocs_dual_solution = tfocs_problem.coefs tfocs_primal_solution = Y - DT * tfocs_dual_solution import pylab pylab.scatter(np.arange(Y.shape[0]), Y, c='r') pylab.plot(solution, color='yellow', linewidth=5) pylab.plot(tfocs_primal_solution, color='black', linewidth=3) newl1 = l1norm(D, l=l1_soln) conjugate = l2normsq.shift(Y, l=0.5) from regreg.constraint import constraint loss_constraint = constraint(conjugate, newl1) new_solver = FISTA(loss_constraint.dual_problem()) new_solver.debug = True new_solver.fit(max_its=1000, tol=1e-10) soln3 = loss_constraint.primal_from_dual(new_solver.problem.coefs) pylab.plot(soln3, color='gray', linewidth=1)
