def xtest_adaptive_sparse_grid_aposteriori_error_based_refinement( self ):
num_dims = 1
quadrature_rule_1d = ClosedEquidistantQuadRule1D()
basis = ClosedPiecewisePolynomialBasis()
basis.degree( 1 )
tpqr = self.get_tensor_product_quadrature_rule_single(num_dims,
quadrature_rule_1d,
basis )
m = AdjointModel()
forward_solution_num_DOF, adjoint_solution_num_DOF = m.get_num_dof()
print 'forward ndof: %d\n adjoint ndof: %d' %(forward_solution_num_DOF,
adjoint_solution_num_DOF)
refinement_manager = AposterioriErrorRefinementManager()
refinement_manager.tolerance( 0.0 )
refinement_manager.maxPoints( 3 )
refinement_manager.maxLevel( 10 )
refinement_manager.forward_solution_num_DOF( int( forward_solution_num_DOF ) )
refinement_manager.adjoint_solution_num_DOF( int( adjoint_solution_num_DOF ) )
domain = numpy.array( [-1.,1.], numpy.double )
sg = AdaptiveSparseGrid()
from utilities.visualisation import plot_surface_from_function
sg.initialise( m, domain, tpqr, refinement_manager, 1 )
sg.build()
print 'sparse grid built successfully'
# need to set this before evaluate is called or
# quantities_of_interest will be whatever internal sg routines
# 3 have set it as. todo move this to the end of build
sg.quantities_of_interest( numpy.array( [0], numpy.int32 ) )
evaluate_enhanced_sparse_grid = lambda x: (sg.evaluate_set( x ) + \
refinement_manager.compute_error_estimates( x, sg, m )).squeeze()
grid = sg.get_coordinates()
rng = numpy.random.RandomState( 0 )
test_pts = rng.uniform( -1., 1., ( num_dims, 1000 ) )
test_vals = m.evaluate_set( test_pts )[:,0]
print 'sparse grid error: ', numpy.linalg.norm( test_vals -
sg.evaluate_set( test_pts ).squeeze() )
print 'enhanced sparse grid error: ', numpy.linalg.norm( test_vals -
evaluate_enhanced_sparse_grid( test_pts ) )
def adaptive_sparse_grid_aposteriori_error_based_refinement():
num_dims = 2
quadrature_rule_1d = ClosedEquidistantQuadRule1D()
basis = ClosedPiecewisePolynomialBasis()
basis.degree(1)
tpqr = get_tensor_product_quadrature_rule_single(num_dims, quadrature_rule_1d, basis)
path = "/home/jdjakem/software/matlab/packages/" + "a-posteriori-error-estimation/work_stochastic/"
# last / is needed if path called from c code directly
# path = ''
# initiate model and run in the background
# os.system( 'python adjoint_file_model.py &' )
# m = CppAdjointFileModel( path )
# m = AdjointMatlabModel()
m = MatlabModel()
m.add_matlab_paths(["/home/jdjakem/software/matlab/packages/" + "a-posteriori-error-estimation/ACES/", path])
# path needs to be specified last so that sample_input.m is the one
# in path and not one in the directores added earlier
# m.capture_matlab_buffer( 100 )
m.initialise()
# m.print_matlab_buffer()
print "Initialised model"
forward_solution_num_DOF, adjoint_solution_num_DOF = m.get_num_dof()
print "forward ndof: %d\n adjoint ndof: %d" % (forward_solution_num_DOF, adjoint_solution_num_DOF)
# refinement_manager = AposterioriErrorRefinementManager()
# refinement_manager.path( path );
# refinement_manager.forward_solution_num_DOF( int(forward_solution_num_DOF) )
# refinement_manager.adjoint_solution_num_DOF( int(adjoint_solution_num_DOF) )
refinement_manager = HierarchicalSurplusRefinementManager()
refinement_manager.tolerance(0.0)
refinement_manager.maxPoints(2000)
refinement_manager.maxLevel(20)
domain = numpy.array([0.0, 1.0, 0.0, 1], numpy.double)
sg = AdaptiveSparseGrid()
from utilities.visualisation import plot_surface_from_function
sg.initialise(m, domain, tpqr, refinement_manager, 0)
sg.build()
print "sparse grid built successfully"
# need to set this before evaluate is called or
# quantities_of_interest will be whatever internal sg routines
# 3 have set it as. todo move this to the end of build
sg.quantities_of_interest(numpy.array([0], numpy.int32))
# evaluate_enhanced_sparse_grid = lambda x: (sg.evaluate_set( x ) + \
# refinement_manager.compute_error_estimates( x, sg, m )).squeeze()
grid = sg.get_coordinates()
rng = numpy.random.RandomState(0)
test_pts = rng.uniform(0.0, 1.0, (num_dims, 100))
test_vals = m.evaluate_set(test_pts)[:, 0]
print "sparse grid error: ", numpy.linalg.norm(test_vals - sg.evaluate_set(test_pts).squeeze()) / numpy.sqrt(
test_pts.shape[1]
)
# print 'enhanced sparse grid error: ', numpy.linalg.norm( test_vals -
# evaluate_enhanced_sparse_grid( test_pts ) ) / numpy.sqrt( test_pts.shape[1] )
# plot_surface_from_function( evaluate_enhanced_sparse_grid, domain, 30 )
# m.terminate()
# plot_surface_from_function( sg.evaluate_set, domain, 30 )
import pylab
pylab.plot(grid[0, :], grid[1, :], "o")
pylab.xlim([0, 1])
pylab.ylim([0, 1])
pylab.show()
from sparse_grid_cpp import AposterioriGeneralisedSparseGrid