/usr/include/openturns/swig/HyperbolicAnisotropicEnumerateFunction_doc.i is in libopenturns-dev 1.7-3.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | %feature("docstring") OT::HyperbolicAnisotropicEnumerateFunction
"Hyperbolic and anisotropic enumerate function.
Available constructors:
HyperbolicAnisotropicEnumerateFunction(*dim*)
HyperbolicAnisotropicEnumerateFunction(*dim, q*)
HyperbolicAnisotropicEnumerateFunction(*weight*)
HyperbolicAnisotropicEnumerateFunction(*weight, q*)
Parameters
----------
dim : integer
Dimension of the :class:`~openturns.EnumerateFunction`. *dim* must be equal
to the dimension of the :class:`~openturns.OrthogonalBasis`.
q : float
Correspond to the q-quasi norm parameter. If not precised, :math:`q = 0.4`.
weight : sequence of float
Weights of the indices in each dimension. If not precised, all weights are
equals to :math:`w_i = 1`.
See also
--------
EnumerateFunction, LinearEnumerateFunction
Notes
-----
The hyperbolic truncation strategy is inspired by the so-called sparsity-of-
effects principle, which states that most models are principally governed by
main effects and low-order interactions. Accordingly, one wishes to define an
enumeration strategy which first selects those multi-indices related to main
effects, i.e. with a reasonably small number of nonzero components, prior to
selecting those associated with higher-order interactions.
For any real number :math:`q \\\\in ]0, 1]`, one defines the anisotropic hyperbolic
norm of a multi-index :math:`a` by:
.. math::
\\\\| \\\\vect{a} \\\\|_{\\\\vect{w}, q} = \\\\left( \\\\sum_{i=1}^{n_X} w_i a^q \\\\right)^{1/q}
where the :math:`w_i`'s are real positive numbers. This would lead to first
select the basis polynomials depending on a specific subset of input variables.
Examples
--------
>>> import openturns as ot
>>> # 4-dimensional case
>>> enumerateFunction = ot.HyperbolicAnisotropicEnumerateFunction(2, 0.5)
>>> for i in range(10):
... print(enumerateFunction(i))
[0,0]
[1,0]
[0,1]
[2,0]
[0,2]
[3,0]
[0,3]
[1,1]
[4,0]
[0,4]
"
// ---------------------------------------------------------------------
%feature("docstring") OT::HyperbolicAnisotropicEnumerateFunction::getQ
"Accessor to the norm.
Returns
-------
q : float
q-quasi norm parameter."
// ---------------------------------------------------------------------
%feature("docstring") OT::HyperbolicAnisotropicEnumerateFunction::getWeight
"Accessor to the weights.
Returns
-------
w : :class:`~openturns.NumericalPoint`
Weights of the indices in each dimension."
// ---------------------------------------------------------------------
%feature("docstring") OT::HyperbolicAnisotropicEnumerateFunction::setQ
"Accessor to the norm.
Parameters
----------
q : float
q-quasi norm parameter."
// ---------------------------------------------------------------------
%feature("docstring") OT::HyperbolicAnisotropicEnumerateFunction::setWeight
"Accessor to the weights.
Parameters
----------
w : sequence of float
Weights of the indices in each dimension."
|