def fixed_level_quadrature_latin_hypercube( I, max_order ):
num_dims, num_pts = I.shape
I = I % max_order
I = unique_matrix_rows( I.T ).T
while ( I.shape[1] != num_pts ):
I = numpy.hstack( ( I, numpy.random.randint( 0, max_order,
( num_dims, num_pts - I.shape[1] ) ) ) )
I = unique_matrix_rows( I.T ).T
pts = numpy.empty( ( num_dims, num_pts ), numpy.double )
x, w = gauss_legendre_pts_wts_1D( max_order, 0., 1. )
for dim in xrange( num_dims ):
pts[dim,:] = x[I[dim,:]]
return pts
def test_index_generation( self ):
num_dims = 2
order = 3
indices = PolyIndexVector()
get_hyperbolic_indices( num_dims, order, 1., indices )
true_indices = [[0, 0],
[1, 0],
[2, 0],
[0, 1],
[1, 1],
[0, 2],
[1, 2],
[2, 1],
[3, 0],
[0, 3]]
indices_list = []
for i in xrange( indices.size() ):
indices_list.append( indices[i].uncompressed_data( num_dims ) )
indices = unique_matrix_rows( numpy.array( indices_list ))
true_indices = unique_matrix_rows( numpy.array( true_indices ) )
assert numpy.allclose( true_indices, indices )
num_dims = 3
order = 2
indices = PolyIndexVector()
get_hyperbolic_indices( num_dims, order, 1., indices )
true_indices = [[0, 0, 0],
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[2, 0, 0],
[1, 1, 0],
[0, 2, 0],
[1, 0, 1],
[0, 1, 1],
[0, 0, 2]]
indices_list = []
for i in xrange( indices.size() ):
indices_list.append( indices[i].uncompressed_data( num_dims ) )
indices = unique_matrix_rows( numpy.array( indices_list ))
true_indices = unique_matrix_rows( numpy.array( true_indices ) )
assert numpy.allclose( true_indices, indices )
num_dims = 2
order = 3
indices = PolyIndexVector()
get_hyperbolic_indices( num_dims, order, .5, indices )
true_indices = [[0, 0],
[1, 0],
[2, 0],
[3, 0],
[0, 1],
[0, 2],
[0, 3]]
indices_list = []
for i in xrange( indices.size() ):
indices_list.append( indices[i].uncompressed_data( num_dims ) )
indices = unique_matrix_rows( numpy.array( indices_list ))
true_indices = unique_matrix_rows( numpy.array( true_indices ) )
assert numpy.allclose( true_indices, indices )
num_dims = 2
order = 6
indices = PolyIndexVector()
get_hyperbolic_indices( num_dims, order, .5, indices )
true_indices = [[0, 0],
[1, 0],
[2, 0],
[3, 0],
[4, 0],
[5, 0],
[6, 0],
[1, 1],
[2, 1],
[1, 2],
[0, 1],
[0, 2],
[0, 3],
[0, 4],
[0, 5],
[0, 6]]
indices_list = []
for i in xrange( indices.size() ):
indices_list.append( indices[i].uncompressed_data( num_dims ) )
indices = unique_matrix_rows( numpy.array( indices_list ))
true_indices = unique_matrix_rows( numpy.array( true_indices ) )
assert numpy.allclose( true_indices, indices )
def test_index_generator( self ):
index_generator = IndexGenerator()
# num_dims = 2, max_level = 3 index_norm_order = 1
num_dims = 2
max_level = 3
index_generator.set_parameters( num_dims, max_level,
index_norm_order = 1,
priority_weight = 1. )
index_generator.build_isotropic_index_set()
indices = index_generator.get_all_indices()
true_indices = [[0, 0],
[1, 0],
[2, 0],
[0, 1],
[1, 1],
[0, 2],
[1, 2],
[2, 1],
[3, 0],
[0, 3]]
indices_list = []
for i, index in enumerate( indices ):
indices_list.append( index.uncompressed_data( num_dims ) )
indices = unique_matrix_rows( numpy.array( indices_list ))
true_indices = unique_matrix_rows( numpy.array( true_indices ) )
assert numpy.allclose( true_indices, indices )
# num_dims = 3, max_level = 2 index_norm_order = 1
num_dims = 3
max_level = 2
index_generator.set_parameters( num_dims, max_level,
index_norm_order = 1,
priority_weight = 1. )
index_generator.build_isotropic_index_set()
indices = index_generator.get_all_indices()
true_indices = [[0, 0, 0],
[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[2, 0, 0],
[1, 1, 0],
[0, 2, 0],
[1, 0, 1],
[0, 1, 1],
[0, 0, 2]]
indices_list = []
for i, index in enumerate( indices ):
indices_list.append( index.uncompressed_data( num_dims ) )
indices = unique_matrix_rows( numpy.array( indices_list ))
true_indices = unique_matrix_rows( numpy.array( true_indices ) )
assert numpy.allclose( true_indices, indices )
# num_dims = 2, max_level = 3 index_norm_order = 0.5
num_dims = 2
max_level = 3
index_generator = IndexGenerator()
index_generator.set_parameters( num_dims, max_level,
index_norm_order = 0.5,
priority_weight = 1. )
index_generator.build_isotropic_index_set()
indices = index_generator.get_all_indices()
true_indices = [[0, 0],
[1, 0],
[2, 0],
[3, 0],
[0, 1],
[0, 2],
[0, 3]]
indices_list = []
for i, index in enumerate( indices ):
indices_list.append( index.uncompressed_data( num_dims ) )
indices = unique_matrix_rows( numpy.array( indices_list ) )
true_indices = unique_matrix_rows( numpy.array( true_indices ) )
assert numpy.allclose( true_indices, indices )
# num_dims = 2, max_level = 6 index_norm_order = 0.5
num_dims = 2
max_level = 6
index_generator = IndexGenerator()
index_generator.set_parameters( num_dims, max_level,
index_norm_order = 0.5,
priority_weight = 1. )
index_generator.build_isotropic_index_set()
indices = index_generator.get_all_indices()
true_indices = [[0, 0],
[1, 0],
[2, 0],
[3, 0],
[4, 0],
[5, 0],
[6, 0],
[1, 1],
[2, 1],
[1, 2],
[0, 1],
[0, 2],
[0, 3],
[0, 4],
[0, 5],
[0, 6]]
indices_list = []
for i, index in enumerate( indices ):
indices_list.append( index.uncompressed_data( num_dims ) )
indices = unique_matrix_rows( numpy.array( indices_list ) )
true_indices = unique_matrix_rows( numpy.array( true_indices ) )
assert numpy.allclose( true_indices, indices )
from math_tools_cpp import compute_hyperbolic_level_subdim_indices as compute_hyperbolic_level_subdim_indices_cpp
print "b", compute_hyperbolic_level_subdim_indices_cpp(num_dims, level, 2, p)
num_dims = 10
level = 9
p = 0.4
import time
t0 = time.time()
indices = compute_hyperbolic_indices(num_dims, level, p)
numpy.set_printoptions(threshold=numpy.nan)
print indices.shape
from utilities.math_utils import unique_matrix_rows
print unique_matrix_rows(indices).shape
eps = 100 * numpy.finfo(numpy.double).eps
count = numpy.zeros(3)
count1 = numpy.zeros(2)
for i in xrange(indices.shape[0]):
if numpy.count_nonzero(indices[i, :]) == 1:
count[0] += 1
if numpy.count_nonzero(indices[i, :]) == 2:
count[1] += 1
if numpy.count_nonzero(indices[i, :]) == 3:
count[2] += 1
if indices[i, :].sum() == 2 and numpy.count_nonzero(indices[i, :]) == 2:
count1[0] += 1
if indices[i, :].sum() == 3 and numpy.count_nonzero(indices[i, :]) == 2:
count1[1] += 1
