def plot_tensor_product_lagrange_basis_2d(level, ii, jj, ax=None):
abscissa, tmp = clenshaw_curtis_pts_wts_1D(level)
abscissa_1d = [abscissa, abscissa]
barycentric_weights_1d = [compute_barycentric_weights_1d(abscissa_1d[0]),
compute_barycentric_weights_1d(abscissa_1d[1])]
training_samples = cartesian_product(abscissa_1d, 1)
fn_vals = np.zeros((training_samples.shape[1], 1))
idx = jj*abscissa_1d[1].shape[0]+ii
fn_vals[idx] = 1.
def f(samples): return multivariate_barycentric_lagrange_interpolation(
samples, abscissa_1d, barycentric_weights_1d, fn_vals,
np.array([0, 1]))
plot_limits = [-1, 1, -1, 1]
num_pts_1d = 101
X, Y, Z = get_meshgrid_function_data(f, plot_limits, num_pts_1d)
if ax is None:
ax = create_3d_axis()
cmap = mpl.cm.coolwarm
plot_surface(X, Y, Z, ax, axis_labels=None, limit_state=None,
alpha=0.3, cmap=mpl.cm.coolwarm, zorder=3, plot_axes=False)
num_contour_levels = 30
offset = -(Z.max()-Z.min())/2
cmap = mpl.cm.gray
ax.contourf(
X, Y, Z, zdir='z', offset=offset,
levels=np.linspace(Z.min(), Z.max(), num_contour_levels),
cmap=cmap, zorder=-1)
ax.plot(training_samples[0, :], training_samples[1, :],
offset*np.ones(training_samples.shape[1]), 'o',
zorder=100, color='b')
x = np.linspace(-1, 1, 100)
y = training_samples[1, idx]*np.ones((x.shape[0]))
z = f(np.vstack((x[np.newaxis, :], y[np.newaxis, :])))[:, 0]
ax.plot(x, Y.max()*np.ones((x.shape[0])), z, '-r')
ax.plot(abscissa_1d[0], Y.max()*np.ones(
(abscissa_1d[0].shape[0])), np.zeros(abscissa_1d[0].shape[0]), 'or')
y = np.linspace(-1, 1, 100)
x = training_samples[0, idx]*np.ones((y.shape[0]))
z = f(np.vstack((x[np.newaxis, :], y[np.newaxis, :])))[:, 0]
ax.plot(X.min()*np.ones((x.shape[0])), y, z, '-r')
ax.plot(X.min()*np.ones(
(abscissa_1d[1].shape[0])), abscissa_1d[1],
np.zeros(abscissa_1d[1].shape[0]), 'or')
def evaluate_sparse_grid_subspace(samples, subspace_index, subspace_values,
samples_1d, config_variables_idx, output):
if config_variables_idx is None:
config_variables_idx = samples.shape[0]
active_sample_vars = np.where(subspace_index[:config_variables_idx]>0)[0]
num_active_sample_vars = active_sample_vars.shape[0]
abscissa_1d = []
barycentric_weights_1d = []
for dd in range(num_active_sample_vars):
active_idx = active_sample_vars[dd]
abscissa_1d.append(samples_1d[active_idx][subspace_index[active_idx]])
interval_length = 2
if abscissa_1d[dd].shape[0] > 1:
interval_length = abscissa_1d[dd].max()-abscissa_1d[dd].min()
barycentric_weights_1d.append(
compute_barycentric_weights_1d(
abscissa_1d[dd], interval_length=interval_length))
if num_active_sample_vars==0:
return np.tile(subspace_values,(samples.shape[1],1))
poly_vals = multivariate_barycentric_lagrange_interpolation(
samples, abscissa_1d, barycentric_weights_1d, subspace_values,
active_sample_vars)
return poly_vals
def test_tensor_product_lagrange_interpolation(self):
nvars = 5
level = 10
x = gauss_hermite_pts_wts_1D(level + 1)[0]
# active_vars = np.arange(nvars)
active_vars = np.hstack([np.arange(2), np.arange(3, nvars)])
nactive_vars = active_vars.shape[0]
abscissa_1d = [x] * nactive_vars
power = x.shape[0] - 2
def fun(samples):
return np.sum(samples[active_vars, :]**power, axis=0)[:, None]
nsamples = 1000
validation_samples = np.random.normal(0, 1, (nvars, nsamples))
zz = abscissa_1d.copy()
zz.insert(2, np.zeros(1))
train_samples = cartesian_product(zz)
values = fun(train_samples)
approx_values = tensor_product_lagrange_interpolation(
validation_samples, abscissa_1d, active_vars, values)
barycentric_weights_1d = [
compute_barycentric_weights_1d(x) for x in abscissa_1d
]
poly_vals = multivariate_barycentric_lagrange_interpolation(
validation_samples, abscissa_1d, barycentric_weights_1d, values,
active_vars)
assert np.allclose(approx_values, fun(validation_samples))
assert np.allclose(poly_vals, fun(validation_samples))
def preconditioned_barycentric_weights():
nmasses = 20
xk = np.array(range(nmasses), dtype='float')
pk = np.ones(nmasses) / nmasses
var1 = float_rv_discrete(name='float_rv_discrete', values=(xk, pk))()
univariate_variables = [var1]
variable = IndependentMultivariateRandomVariable(univariate_variables)
var_trans = AffineRandomVariableTransformation(variable)
growth_rule = partial(constant_increment_growth_rule, 2)
quad_rule = get_univariate_leja_quadrature_rule(var1, growth_rule)
samples = quad_rule(3)[0]
num_samples = samples.shape[0]
poly = PolynomialChaosExpansion()
poly_opts = define_poly_options_from_variable_transformation(var_trans)
poly_opts['numerically_generated_poly_accuracy_tolerance'] = 1e-5
poly.configure(poly_opts)
poly.set_indices(np.arange(num_samples))
# precond_weights = np.sqrt(
# (poly.basis_matrix(samples[np.newaxis,:])**2).mean(axis=1))
precond_weights = np.ones(num_samples)
bary_weights = compute_barycentric_weights_1d(
samples, interval_length=samples.max() - samples.min())
def barysum(x, y, w, f):
x = x[:, np.newaxis]
y = y[np.newaxis, :]
temp = w * f / (x - y)
return np.sum(temp, axis=1)
def function(x):
return np.cos(2 * np.pi * x)
y = samples
print(samples)
w = precond_weights * bary_weights
# x = np.linspace(-3,3,301)
x = np.linspace(-1, 1, 301)
f = function(y) / precond_weights
# cannot interpolate on data
II = []
for ii, xx in enumerate(x):
if xx in samples:
II.append(ii)
x = np.delete(x, II)
r1 = barysum(x, y, w, f)
r2 = barysum(x, y, w, 1 / precond_weights)
interp_vals = r1 / r2
# import matplotlib.pyplot as plt
# plt.plot(x, interp_vals, 'k')
# plt.plot(samples, function(samples), 'ro')
# plt.plot(x, function(x), 'r--')
# plt.plot(samples,function(samples),'ro')
# print(num_samples)
# print(precond_weights)
print(np.linalg.norm(interp_vals - function(x)))
def convert_univariate_lagrange_basis_to_orthonormal_polynomials(
samples_1d, get_recursion_coefficients):
"""
Returns
-------
coeffs_1d : list [np.ndarray(num_terms_i,num_terms_i)]
The coefficients of the orthonormal polynomial representation of
each Lagrange basis. The columns are the coefficients of each
lagrange basis. The rows are the coefficient of the degree i
orthonormalbasis
"""
# Get the maximum number of terms in the orthonormal polynomial that
# are need to interpolate all the interpolation nodes in samples_1d
max_num_terms = samples_1d[-1].shape[0]
num_quad_points = max_num_terms + 1
# Get the recursion coefficients of the orthonormal basis
recursion_coeffs = get_recursion_coefficients(num_quad_points)
# compute the points and weights of the correct quadrature rule
x_quad, w_quad = gauss_quadrature(recursion_coeffs, num_quad_points)
# evaluate the orthonormal basis at the quadrature points. This can
# be computed once for all degrees up to the maximum degree
ortho_basis_matrix = evaluate_orthonormal_polynomial_1d(
x_quad, max_num_terms, recursion_coeffs)
# compute coefficients of orthonormal basis using pseudo spectral projection
coeffs_1d = []
w_quad = w_quad[:, np.newaxis]
for ll in range(len(samples_1d)):
num_terms = samples_1d[ll].shape[0]
# evaluate the lagrange basis at the quadrature points
barycentric_weights_1d = [
compute_barycentric_weights_1d(samples_1d[ll])
]
values = np.eye((num_terms), dtype=float)
# Sometimes the following function will cause the erro
# interpolation abscissa are not unique. This can be due to x_quad
# not abscissa. E.g. x_quad may have points far enough outside
# range of abscissa, e.g. abscissa are clenshaw curtis points and
# x_quad points are Gauss-Hermite quadrature points
lagrange_basis_vals = multivariate_barycentric_lagrange_interpolation(
x_quad[np.newaxis, :], samples_1d[ll][np.newaxis, :],
barycentric_weights_1d, values, np.zeros(1, dtype=int))
# compute fourier like coefficients
basis_coeffs = []
for ii in range(num_terms):
basis_coeffs.append(
np.dot(w_quad.T, lagrange_basis_vals *
ortho_basis_matrix[:, ii:ii + 1])[0, :])
coeffs_1d.append(np.asarray(basis_coeffs))
return coeffs_1d
def interpolate(self, mesh_values, eval_samples):
if eval_samples.ndim == 1:
eval_samples = eval_samples[None, :]
if mesh_values.ndim == 1:
mesh_values = mesh_values[:, None]
assert mesh_values.ndim == 2
num_dims = eval_samples.shape[0]
abscissa_1d = [self.mesh_pts_1d]*num_dims
weights_1d = [compute_barycentric_weights_1d(xx) for xx in abscissa_1d]
interp_vals = multivariate_barycentric_lagrange_interpolation(
eval_samples,
abscissa_1d,
weights_1d,
mesh_values,
np.arange(num_dims))
return interp_vals
def test_interpolation_gaussian_leja_sequence(self):
def f(x):
return np.exp(-np.sum(x**2, axis=0))
level = 30
# abscissa_leja, __ = gaussian_leja_quadrature_rule(
# level, return_weights_for_all_levels=False)
# abscissa = abscissa_leja
abscissa_gauss = gauss_hermite_pts_wts_1D(level + 1)[0]
abscissa = abscissa_gauss
# print(abscissa_leja.shape,abscissa_gauss.shape)
abscissa_1d = [abscissa]
barycentric_weights_1d = [
compute_barycentric_weights_1d(abscissa_1d[0])
]
# print(barycentric_weights_1d[0])
barycentric_weights_1d[0] /= barycentric_weights_1d[0].max()
# print(barycentric_weights_1d[0])
fn_vals = f(np.array(abscissa).reshape(1,
abscissa.shape[0]))[:,
np.newaxis]
# print(fn_vals.shape)
samples = np.random.normal(0, 1, (1, 1000))
poly_vals = multivariate_barycentric_lagrange_interpolation(
samples, abscissa_1d, barycentric_weights_1d, fn_vals,
np.array([0]))[:, 0]
l2_error = np.linalg.norm(poly_vals-f(samples)) / \
np.sqrt(samples.shape[1])
# print('l2_error',l2_error)
# pts = np.linspace(abscissa.min(),abscissa.max(),101).reshape(1,101)
# poly_vals = multivariate_barycentric_lagrange_interpolation(
# pts,abscissa_1d,barycentric_weights_1d,fn_vals,np.array([0]))
# import matplotlib.pyplot as plt
# plt.plot(pts[0,:],poly_vals.squeeze())
# plt.plot(abscissa_1d[0],fn_vals.squeeze(),'r*')
# plt.plot(abscissa_leja,abscissa_leja*0,'ro')
# plt.plot(abscissa_gauss,abscissa_gauss*0,'ks',ms=3)
# plt.ylim(-1,2)
# plt.show()
assert l2_error < 1e-2
def evaluate_sparse_grid_subspace_hierarchically(samples,values,subspace_index,
subspace_values_indices,
samples_1d,
subspace_poly_indices,
config_variables_idx):
if config_variables_idx is None:
config_variables_idx = samples.shape[0]
abscissa_1d = []
barycentric_weights_1d = []
hier_indices_1d = []
active_vars = np.where(subspace_index>0)[0]
num_active_vars = active_vars.shape[0]
subspace_values = values[subspace_values_indices,:]
if num_active_vars==0:
return subspace_values
for dd in range(num_active_vars):
subspace_level = subspace_index[active_vars[dd]]
if subspace_level>0:
idx1 = samples_1d[subspace_level-1].shape[0]
else:
idx1=0
idx2 = samples_1d[subspace_level].shape[0]
hier_indices_1d.append(np.arange(idx1,idx2))
abscissa_1d.append(samples_1d[subspace_level])
barycentric_weights_1d.append(
compute_barycentric_weights_1d(abscissa_1d[dd]))
hier_indices = get_hierarchical_sample_indices(
subspace_index,subspace_poly_indices,
samples_1d,config_variables_idx)
hier_subspace_values = subspace_values[hier_indices,:]
values = multivariate_hierarchical_barycentric_lagrange_interpolation(
samples,abscissa_1d,barycentric_weights_1d,hier_subspace_values,
active_vars,hier_indices_1d)
return values
def test_barycentric_weights_1d(self):
eps = 1e-12
# test barycentric weights for uniform points using direct calculation
abscissa = np.linspace(-1, 1., 5)
weights = compute_barycentric_weights_1d(abscissa,
normalize_weights=False)
n = abscissa.shape[0] - 1
h = 2. / n
true_weights = np.empty((n + 1), np.double)
for j in range(n + 1):
true_weights[j] = (-1.)**(n - j) * nchoosek(
n, j) / (h**n * factorial(n))
assert np.allclose(true_weights, weights, eps)
# test barycentric weights for uniform points using analytical formula
# and with scaling on
weights = compute_barycentric_weights_1d(abscissa,
interval_length=1,
normalize_weights=False)
weights_analytical = equidistant_barycentric_weights(5)
ratio = weights / weights_analytical
# assert the two weights array differ by only a constant factor
assert np.allclose(np.min(ratio), np.max(ratio))
# test barycentric weights for clenshaw curtis points
level = 7
abscissa, tmp = clenshaw_curtis_pts_wts_1D(level)
n = abscissa.shape[0]
weights = compute_barycentric_weights_1d(abscissa,
normalize_weights=False,
interval_length=2)
true_weights = np.empty((n), np.double)
true_weights[0] = true_weights[n - 1] = 0.5
true_weights[1:n - 1] = [(-1)**ii for ii in range(1, n - 1)]
factor = true_weights[1] / weights[1]
assert np.allclose(true_weights / factor, weights, atol=eps)
# check barycentric weights are correctly computed regardless of
# order of points. Eventually ordering can effect numerical stability
# but not until very high level
abscissa, tmp = clenshaw_curtis_in_polynomial_order(level)
II = np.argsort(abscissa)
n = abscissa.shape[0]
weights = compute_barycentric_weights_1d(
abscissa,
normalize_weights=False,
interval_length=abscissa.max() - abscissa.min())
true_weights = np.empty((n), np.double)
true_weights[0] = true_weights[n - 1] = 0.5
true_weights[1:n - 1] = [(-1)**ii for ii in range(1, n - 1)]
factor = true_weights[1] / weights[II][1]
assert np.allclose(true_weights / factor, weights[II], eps)
num_samples = 65
abscissa, tmp = gauss_hermite_pts_wts_1D(num_samples)
weights = compute_barycentric_weights_1d(
abscissa,
normalize_weights=False,
interval_length=abscissa.max() - abscissa.min())
print(weights)
print(np.absolute(weights).max(), np.absolute(weights).min())
print(np.absolute(weights).max() / np.absolute(weights).min())
def test_multivariate_barycentric_lagrange_interpolation(self):
def f(x):
return np.sum(x**2, axis=0)
eps = 1e-14
# test 1d barycentric lagrange interpolation
level = 5
# abscissa, __ = clenshaw_curtis_pts_wts_1D( level )
# barycentric_weights_1d = [clenshaw_curtis_barycentric_weights(level)]
abscissa, __ = clenshaw_curtis_in_polynomial_order(level, False)
abscissa_1d = [abscissa]
barycentric_weights_1d = [
compute_barycentric_weights_1d(abscissa_1d[0])
]
fn_vals = f(np.array(abscissa).reshape(1,
abscissa.shape[0]))[:,
np.newaxis]
pts = np.linspace(-1., 1., 3).reshape(1, 3)
poly_vals = multivariate_barycentric_lagrange_interpolation(
pts, abscissa_1d, barycentric_weights_1d, fn_vals, np.array([0]))
# import pylab
# print poly_vals.squeeze().shape
# pylab.plot(pts[0,:],poly_vals.squeeze())
# pylab.plot(abscissa_1d[0],fn_vals.squeeze(),'ro')
# print np.linalg.norm( poly_vals - f( pts ) )
# pylab.show()
assert np.allclose(poly_vals, f(pts)[:, np.newaxis], eps)
# test 2d barycentric lagrange interpolation
# with the same abscissa in each dimension
a = -3.0
b = 3.0
x = np.linspace(a, b, 21)
[X, Y] = np.meshgrid(x, x)
pts = np.vstack((X.reshape((1, X.shape[0] * X.shape[1])),
Y.reshape((1, Y.shape[0] * Y.shape[1]))))
num_abscissa = [10, 10]
abscissa_1d = [
np.linspace(a, b, num_abscissa[0]),
np.linspace(a, b, num_abscissa[1])
]
abscissa = cartesian_product(abscissa_1d, 1)
fn_vals = f(abscissa)
barycentric_weights_1d = [
compute_barycentric_weights_1d(abscissa_1d[0]),
compute_barycentric_weights_1d(abscissa_1d[1])
]
poly_vals = multivariate_barycentric_lagrange_interpolation(
pts, abscissa_1d, barycentric_weights_1d, fn_vals[:, np.newaxis],
np.array([0, 1]))
assert np.allclose(poly_vals, f(pts)[:, np.newaxis], eps)
# test 2d barycentric lagrange interpolation
# with different abscissa in each dimension
a = -1.0
b = 1.0
x = np.linspace(a, b, 21)
[X, Y] = np.meshgrid(x, x)
pts = np.vstack((X.reshape((1, X.shape[0] * X.shape[1])),
Y.reshape((1, Y.shape[0] * Y.shape[1]))))
level = [1, 2]
nodes_0, tmp = clenshaw_curtis_pts_wts_1D(level[0])
nodes_1, tmp = clenshaw_curtis_pts_wts_1D(level[1])
abscissa_1d = [nodes_0, nodes_1]
barycentric_weights_1d = [
clenshaw_curtis_barycentric_weights(level[0]),
clenshaw_curtis_barycentric_weights(level[1])
]
abscissa = cartesian_product(abscissa_1d, 1)
fn_vals = f(abscissa)
poly_vals = multivariate_barycentric_lagrange_interpolation(
pts, abscissa_1d, barycentric_weights_1d, fn_vals[:, np.newaxis],
np.array([0, 1]))
assert np.allclose(poly_vals, f(pts)[:, np.newaxis], eps)
# test 3d barycentric lagrange interpolation
# with different abscissa in each dimension
num_dims = 3
a = -1.0
b = 1.0
pts = np.random.uniform(-1., 1., (num_dims, 10))
level = [1, 1, 1]
nodes_0, tmp = clenshaw_curtis_pts_wts_1D(level[0])
nodes_1, tmp = clenshaw_curtis_pts_wts_1D(level[1])
nodes_2, tmp = clenshaw_curtis_pts_wts_1D(level[2])
abscissa_1d = [nodes_0, nodes_1, nodes_2]
barycentric_weights_1d = [
clenshaw_curtis_barycentric_weights(level[0]),
clenshaw_curtis_barycentric_weights(level[1]),
clenshaw_curtis_barycentric_weights(level[2])
]
abscissa = cartesian_product(abscissa_1d, 1)
fn_vals = f(abscissa)
poly_vals = multivariate_barycentric_lagrange_interpolation(
pts, abscissa_1d, barycentric_weights_1d, fn_vals[:, np.newaxis],
np.array([0, 1, 2]))
assert np.allclose(poly_vals, f(pts)[:, np.newaxis], eps)
# test 3d barycentric lagrange interpolation
# with different abscissa in each dimension
# and only two active dimensions (0 and 2)
num_dims = 3
a = -1.0
b = 1.0
pts = np.random.uniform(-1., 1., (num_dims, 5))
level = [2, 0, 1]
# to get fn_vals we must specify abscissa for all three dimensions
# but only the abscissa of the active dimensions should get passed
# to the interpolation function
nodes_0, tmp = clenshaw_curtis_pts_wts_1D(level[0])
nodes_1, tmp = clenshaw_curtis_pts_wts_1D(level[1])
nodes_2, tmp = clenshaw_curtis_pts_wts_1D(level[2])
abscissa_1d = [nodes_0, nodes_1, nodes_2]
abscissa = cartesian_product(abscissa_1d, 1)
abscissa_1d = [nodes_0, nodes_2]
barycentric_weights_1d = [
clenshaw_curtis_barycentric_weights(level[0]),
clenshaw_curtis_barycentric_weights(level[2])
]
fn_vals = f(abscissa)
poly_vals = multivariate_barycentric_lagrange_interpolation(
pts, abscissa_1d, barycentric_weights_1d, fn_vals[:, np.newaxis],
np.array([0, 2]))
pts[1, :] = 0.
assert np.allclose(poly_vals, f(pts)[:, np.newaxis], eps)
# test 3d barycentric lagrange interpolation
# with different abscissa in each dimension
# and only two active dimensions (0 and 1)
num_dims = 3
a = -1.0
b = 1.0
pts = np.random.uniform(-1., 1., (num_dims, 5))
level = [2, 3, 0]
# to get fn_vals we must specify abscissa for all three dimensions
# but only the abscissa of the active dimensions should get passed
# to the interpolation function
nodes_0, tmp = clenshaw_curtis_pts_wts_1D(level[0])
nodes_1, tmp = clenshaw_curtis_pts_wts_1D(level[1])
nodes_2, tmp = clenshaw_curtis_pts_wts_1D(level[2])
abscissa_1d = [nodes_0, nodes_1, nodes_2]
abscissa = cartesian_product(abscissa_1d, 1)
abscissa_1d = [nodes_0, nodes_1]
barycentric_weights_1d = [
clenshaw_curtis_barycentric_weights(level[0]),
clenshaw_curtis_barycentric_weights(level[1])
]
fn_vals = f(abscissa)
poly_vals = multivariate_barycentric_lagrange_interpolation(
pts, abscissa_1d, barycentric_weights_1d, fn_vals[:, np.newaxis],
np.array([0, 1]))
# The interpolant will only be correct on the plane involving
# the active dimensions so we must set the coordinate of the inactive
# dimension to the abscissa coordinate of the inactive dimension.
# The interpoolation algorithm is efficient in the sense that it
# ignores all dimensions involving only one point because the
# interpolant will be a constant in that direction
pts[2, :] = 0.
assert np.allclose(poly_vals, f(pts)[:, np.newaxis], eps)
# test 3d barycentric lagrange interpolation
# with different abscissa in each dimension
# and only two active dimensions (1 and 2)
num_dims = 3
a = -1.0
b = 1.0
pts = np.random.uniform(-1., 1., (num_dims, 5))
level = [0, 2, 4]
# to get fn_vals we must specify abscissa for all three dimensions
# but only the abscissa of the active dimensions should get passed
# to the interpolation function
nodes_0, tmp = clenshaw_curtis_pts_wts_1D(level[0])
nodes_1, tmp = clenshaw_curtis_pts_wts_1D(level[1])
nodes_2, tmp = clenshaw_curtis_pts_wts_1D(level[2])
abscissa_1d = [nodes_0, nodes_1, nodes_2]
abscissa = cartesian_product(abscissa_1d, 1)
abscissa_1d = [nodes_1, nodes_2]
barycentric_weights_1d = [
clenshaw_curtis_barycentric_weights(level[1]),
clenshaw_curtis_barycentric_weights(level[2])
]
fn_vals = f(abscissa)
poly_vals = multivariate_barycentric_lagrange_interpolation(
pts, abscissa_1d, barycentric_weights_1d, fn_vals[:, np.newaxis],
np.array([1, 2]))
pts[0, :] = 0.
assert np.allclose(poly_vals, f(pts)[:, np.newaxis], eps)
# test 2d barycentric lagrange interpolation
# with different abscissa in each dimension and only some of
# the coefficients of the basis terms being non-zero. This situation
# arises in hierarchical interpolation. In these cases we need
# to construct the basis functions on all abscissa but we only
# need to add the basis functions that are one at the hierachical
# nodes
a = -1.0
b = 1.0
# x = np.linspace( a, b, 21 )
x = np.linspace(a, b, 5)
[X, Y] = np.meshgrid(x, x)
pts = np.vstack((X.reshape((1, X.shape[0] * X.shape[1])),
Y.reshape((1, Y.shape[0] * Y.shape[1]))))
poly_vals = np.ones((pts.shape[1], 1), np.double) * \
f(np.array([[0.0, 0.0]]).T)[:, np.newaxis]
level = [1]
nodes_0, tmp = clenshaw_curtis_pts_wts_1D(level[0])
abscissa_1d = [nodes_0]
barycentric_weights_1d = [
compute_barycentric_weights_1d(abscissa_1d[0])
]
sets = copy.copy(abscissa_1d)
sets.append(np.array([0.0]))
abscissa = cartesian_product(sets, 1)
hier_indices = np.array([[0, 2]], np.int32)
abscissa = abscissa[:, hier_indices[0]]
fn_vals = f(abscissa)
poly_vals_increment = \
multivariate_hierarchical_barycentric_lagrange_interpolation(
pts, abscissa_1d, barycentric_weights_1d,
(fn_vals - np.ones((abscissa.shape[1]), np.double) * f(
np.array([0.0, 0.0])))[:, np.newaxis],
np.array([0]), hier_indices)
poly_vals += poly_vals_increment
level = [1]
nodes_0, tmp = clenshaw_curtis_pts_wts_1D(level[0])
abscissa_1d = [nodes_0]
# barycentric_weights_1d = [barycentric_weights( np.array( [0.0] ) ),
# barycentric_weights( abscissa_1d[0] )]
barycentric_weights_1d = [
compute_barycentric_weights_1d(abscissa_1d[0])
]
sets = [np.array([0.0])]
sets.append(nodes_0)
abscissa = cartesian_product(sets, 1)
hier_indices = np.array([[0, 2]], np.int32)
abscissa = abscissa[:, hier_indices[0]]
fn_vals = f(abscissa)
poly_vals += \
multivariate_hierarchical_barycentric_lagrange_interpolation(
pts, abscissa_1d, barycentric_weights_1d,
(fn_vals - np.ones((abscissa.shape[1]), np.double) * f(
np.array([[0.0, 0.0]]).T))[:, np.newaxis],
np.array([1]), hier_indices)
assert np.allclose(poly_vals, f(pts)[:, np.newaxis], eps)
