/usr/include/openturns/swig/UserDefined_doc.i is in libopenturns-dev 1.7-3.
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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 | %feature("docstring") OT::UserDefined
"UserDefined distribution.
Available constructors:
UserDefined(*coll=ot.UserDefinedPairCollection(1)*)
UserDefined(*sample*)
UserDefined(*sample, weights*)
Parameters
----------
coll : :class:`~openturns.UserDefinedPairCollection`
`n` lists of `d` points :math:`x_{ij}, i = 1, \\\\ldots, n, j = 1, \\\\ldots, d`
associated with their weight :math:`p_i`.
If not :math:`\\\\sum_1^n p_i = 1.0`, the weights are normalized.
sample : 2-d sequence of float
`n` lists of `d` points :math:`x_{ij}, i = 1, \\\\ldots, n, j = 1, \\\\ldots, d`.
weights : :class:`~openturns.NumericalPoint`
List of `n` weights :math:`p_i, i = 1, \\\\ldots, n`.
If not :math:`\\\\sum_1^n p_i = 1.0`, the weights are normalized.
Notes
-----
Its probability density function is defined as:
.. math::
\\\\Prob{X = x_{ij}} = p_i, \\\\quad i = 1,\\\\ldots,n
where :math:`j =1, \\\\ldots, d`, `d` the distribution's dimension and
`n` the size of the multivariate d-dimensional distribution.
Its first moments are:
.. math::
:nowrap:
\\\\begin{eqnarray*}
\\\\Expect{X_j} & = & \\\\Tr{(\\\\sum_{i=1}^n x_i p_i)}\\\\\\\\
\\\\Var{X_j} & = & \\\\Expect{X_j} - (\\\\Expect{X_j})^2
\\\\end{eqnarray*}
with :math:`j =1, \\\\ldots, d` and `d` the distribution's dimension.
Examples
--------
Create a distribution:
>>> import openturns as ot
>>> sample = ot.NumericalSample(4, 3)
>>> for i in range(4):
... for j in range(3):
... sample[i, j] = 10 * (i + 1) + 0.1 * (j + 1)
>>> distribution = ot.UserDefined(sample, [0.3,0.2,0.25,0.25])
Draw a sample:
>>> sample = distribution.getSample(10)"
// ---------------------------------------------------------------------
%feature("docstring") OT::UserDefined::getPairCollection
"Accessor to the distribution's :math:`coll` parameter.
Returns
-------
coll : :class:`~openturns.UserDefinedPairCollection`
Collection of points with their associated weight."
// ---------------------------------------------------------------------
%feature("docstring") OT::UserDefined::setPairCollection
"Accessor to the distribution's :math:`coll` parameter.
Parameters
----------
coll : :class:`~openturns.UserDefinedPairCollection`
Collection of points with their associated weight."
// ---------------------------------------------------------------------
%feature("docstring") OT::UserDefined::compactSupport()
"Compact the support of the distribution.
Compact by concatenating points of distance less than :math:`\\\\varepsilon`
and adding their weights.
Notes
-----
The :math:`\\\\varepsilon` has a default value stored in the ResourceMap: to
change the default value the new value :math:`1.3e-3`, use the command:
>>> import openturns as ot
>>> ot.ResourceMap.SetAsNumericalScalar('DiscreteDistribution-SupportEpsilon', 1e-3)
The method is always used for any univariate distributions and for upper
dimensions, it is only used when the number of points defining the support is
less than a limit specified in the ResourceMap in the key
'UserDefined-SmallSize'. By default, the size limit is equal to :math:`1e4`.
"
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