/usr/include/openturns/swig/LowDiscrepancyExperiment_doc.i is in libopenturns-dev 1.9-5.
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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | %feature("docstring") OT::LowDiscrepancyExperiment
"LowDiscrepancy experiment.
Available constructors:
LowDiscrepancyExperiment(*size, restart*)
LowDiscrepancyExperiment(*sequence, size, restart*)
LowDiscrepancyExperiment(*sequence, distribution, size, restart*)
Parameters
----------
size : positive int
Number :math:`N` of points of the sequence.
sequence : :class:`~openturns.LowDiscrepancySequence`
Sequence of points :math:`(u_1, \\\\cdots, u_N)` with low discrepancy.
If not specified, the sequence is a :class:`~openturns.SobolSequence`.
distribution :
Distribution :math:`\\\\mu` of dimension :math:`n` with an independent copula.
The low discrepancy sequence :math:`(u_1, \\\\cdots, u_N)` is uniformly
distributed over :math:`[0,1]^n`. We use the marginal transformation
:math:`\\\\Xi_i =F_i^{-1}(u_i)` to generate points :math:`(\\\\Xi_i)_{i\\\\in I}`
according to the distribution :math:`\\\\mu`. The weights are all equal to
:math:`1/N`.
restart : bool
Flag to tell if the low discrepancy sequence must be restarted from
its initial state at each change of distribution or not.
Default is *True*: the sequence is restarted at each change of
distribution.
Notes
-----
The :meth:`generate` method generates points :math:`(\\\\Xi_i)_{i \\\\in I}`
independently from the distribution :math:`\\\\mu`. When the :meth:`generate`
method is recalled, the generated sample changes.
See also
--------
WeightedExperiment
Examples
--------
>>> import openturns as ot
>>> distribution = ot.ComposedDistribution([ot.Uniform(0.0, 1.0)] * 2)
Generate the sample with a reinitialization of the sequence at each change
of distribution:
>>> myPlane = ot.LowDiscrepancyExperiment(ot.SobolSequence(), distribution, 5, True)
>>> print(myPlane.generate())
[ X0 X1 ]
0 : [ 0.5 0.5 ]
1 : [ 0.75 0.25 ]
2 : [ 0.25 0.75 ]
3 : [ 0.375 0.375 ]
4 : [ 0.875 0.875 ]
>>> print(myPlane.generate())
[ X0 X1 ]
0 : [ 0.625 0.125 ]
1 : [ 0.125 0.625 ]
2 : [ 0.1875 0.3125 ]
3 : [ 0.6875 0.8125 ]
4 : [ 0.9375 0.0625 ]
>>> myPlane.setDistribution(distribution)
>>> print(myPlane.generate())
[ X0 X1 ]
0 : [ 0.5 0.5 ]
1 : [ 0.75 0.25 ]
2 : [ 0.25 0.75 ]
3 : [ 0.375 0.375 ]
4 : [ 0.875 0.875 ]
Generate the sample keeping the previous state of the sequence at each change
of distribution:
>>> myPlane = ot.LowDiscrepancyExperiment(ot.SobolSequence(), distribution, 5, False)
>>> print(myPlane.generate())
[ X0 X1 ]
0 : [ 0.5 0.5 ]
1 : [ 0.75 0.25 ]
2 : [ 0.25 0.75 ]
3 : [ 0.375 0.375 ]
4 : [ 0.875 0.875 ]
>>> print(myPlane.generate())
[ X0 X1 ]
0 : [ 0.625 0.125 ]
1 : [ 0.125 0.625 ]
2 : [ 0.1875 0.3125 ]
3 : [ 0.6875 0.8125 ]
4 : [ 0.9375 0.0625 ]
>>> myPlane.setDistribution(distribution)
>>> print(myPlane.generate())
[ X0 X1 ]
0 : [ 0.4375 0.5625 ]
1 : [ 0.3125 0.1875 ]
2 : [ 0.8125 0.6875 ]
3 : [ 0.5625 0.4375 ]
4 : [ 0.0625 0.9375 ]
"
// ---------------------------------------------------------------------
%feature("docstring") OT::LowDiscrepancyExperiment::getSequence
"Return the sequence.
Returns
-------
sequence : :class:`~openturns.LowDiscrepancySequence`
Sequence of points :math:`(u_1, \\\\cdots, u_N)` with low discrepancy."
// ---------------------------------------------------------------------
%feature("docstring") OT::LowDiscrepancyExperiment::getRestart
"Return the value of the *restart* flag.
Returns
-------
restart : bool
The value of the *restart* flag."
// ---------------------------------------------------------------------
%feature("docstring") OT::LowDiscrepancyExperiment::setRestart
"Set the value of the *restart* flag.
Parameters
----------
restart : bool
The value of the *restart* flag. If equals to *True*, the low
discrepancy sequence is restarted at each change of distribution,
else it is changed only if the new distribution has a dimension
different from the current one.
"
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