def __call__(self, N):
"""
Sample generator
Parameters
----------
N : int
Upper quadrature order
Returns
-------
samples : np.ndarray
The quadrature nodes with `samples.shape=(D,K)` where
`D=len(dist)` and `K` is the number of nodes.
"""
dim = len(self.dist)
if self.sparse == 0 or self.scheme in (3, 4, 5, 6):
X = self.segment(N)
if self.scheme in (1, 2):
X = combine((X, ) * dim)
out = self.trans(X)
else:
out = []
for i in xrange(be.terms(N - self.sparse, dim), be.terms(N, dim)):
I = be.multi_index(i, dim)
out.append(combine([self.segment(n) for n in I]))
out = self.trans(np.concatenate(out, 0))
return out.T
def __call__(self, N):
"""
Sample generator
Parameters
----------
N : int
Upper quadrature order
Returns
-------
samples : np.ndarray
The quadrature nodes with `samples.shape=(D,K)` where
`D=len(dist)` and `K` is the number of nodes.
"""
dim = len(self.dist)
if self.sparse==0 or self.scheme in (3,4,5,6):
X = self.segment(N)
if self.scheme in (1,2):
X = combine((X,)*dim)
out = self.trans(X)
else:
out = []
for i in xrange(be.terms(N-self.sparse, dim),
be.terms(N, dim)):
I = be.multi_index(i, dim)
out.append(combine([self.segment(n) for n in I]))
out = self.trans(np.concatenate(out, 0))
return out.T
def regular_grid_nested(dim, n):
"regular grid, nested"
x = np.arange(1, 2**n) * 1. / (2**n)
X = combine([x] * dim)
return X.T
def regular_grid(dim, n):
"regular grid"
x = np.arange(1, n + 1) / (n + 1.)
X = combine([x] * dim)
return X.T
def chebyshev_nested(dim, n):
"Chebyshev sampling, nested"
x = .5 * np.cos(np.arange(2**n - 1, 0, -1) * np.pi / (2**n)) + .5
X = combine([x] * dim)
return X.T
def chebyshev(dim, n):
"Chebyshev sampling"
x = .5 * np.cos(np.arange(n, 0, -1) * np.pi / (n + 1)) + .5
X = combine([x] * dim)
return X.T
def regular_grid_nested(dim, n):
"regular grid, nested"
x = np.arange(1,2**n)*1./(2**n)
X = combine([x]*dim)
return X.T
def regular_grid(dim, n):
"regular grid"
x = np.arange(1,n+1)/(n+1.)
X = combine([x]*dim)
return X.T
def chebyshev_nested(dim, n):
"Chebyshev sampling, nested"
x = .5*np.cos(np.arange(2**n-1,0,-1)*np.pi/(2**n)) + .5
X = combine([x]*dim)
return X.T
def chebyshev(dim, n):
"Chebyshev sampling"
x = .5*np.cos(np.arange(n,0,-1)*np.pi/(n+1)) + .5
X = combine([x]*dim)
return X.T
