def build_sparse_grid_cpp( quadrature_rule_1d, basis, domain, f,
num_dims, max_level, max_num_points,
tolerance = 0., test_pts = None,
test_vals = None, breaks = None ):
tpqr = get_tensor_product_quadrature_rule_single( num_dims,
quadrature_rule_1d,
basis )
sg = GeneralisedSparseGrid()
sg.quantities_of_interest( numpy.array( [0], dtype = numpy.int32 ) )
sg.tolerance( 0.0 )
sg.max_num_points( max_num_points )
sg.max_level( max_level )
break_cnt = 0
if breaks is None:
breaks = numpy.logspace( 1, numpy.log10( max_num_points ), 10 )
if test_pts is not None and test_vals is None:
test_vals = f( test_pts ).squeeze()
error = []
num_pts = []
sg.initialise( f, domain, tpqr, 0 )
#sg.build()
sg.initialise_build();
while ( True ):
sg.prioritise();
if ( not sg.grid_warrants_refinement() ): break;
sg.refine();
if ( test_pts is not None and break_cnt < breaks.shape[0] and sg.num_points() > breaks[break_cnt] ):
pred_vals = sg.evaluate_set( test_pts ).squeeze()
l2_error = get_l2_error( test_vals, pred_vals )
error.append( l2_error )
num_pts.append( sg.num_points() )
#print 'e', error
#print 'n', sg.num_points()
break_cnt += 1
tmp = SubSpaceVector()
sg.get_subspaces( tmp )
if ( test_pts is not None ):
pred_vals = sg.evaluate_set( test_pts ).squeeze()
l2_error = get_l2_error( test_vals, pred_vals )
error.append( l2_error )
num_pts.append( sg.num_points() )
return sg, numpy.asarray( error ), numpy.asarray( num_pts )
