/usr/include/openturns/swig/MixtureClassifier_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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 | %feature("docstring") OT::MixtureClassifier
"Particular classifier based on a mixture distribution.
Available constructors:
MixtureClassifier(*mixtDist*)
Parameters
----------
mixtDist : :class:`~openturns.Mixture`
A mixture distribution.
See also
--------
Classifier, ExpertMixture
Notes
-----
This implements a mixture classifier which is a particular classifier based on
a mixture distribution:
.. math::
p( \\\\vect{x} ) = \\\\sum_{i=1}^N w_i p_i ( \\\\vect{x} )
The classifier proposes :math:`N` classes. The rule to assign a point
:math:`\\\\vect{x}` to a class :math:`i` is defined as follows:
.. math::
i = \\\\argmax_k \\\\log w_k p_k( \\\\vect{x} )
See useful methods :meth:`classify` and :meth:`grade`."
// ---------------------------------------------------------------------
%feature("docstring") OT::MixtureClassifier::classify
"Classify points according to the classifier.
**Available usages**:
classify(*inputPoint*)
classify(*inputSample*)
Parameters
----------
inputPoint : sequence of float
A point to classify.
inputSample : 2-d a sequence of float
A set of point to classify.
Notes
-----
The classifier proposes :math:`N` classes where :math:`N` is the dimension of
the mixture distribution *mixtDist*. The rule to assign a point :math:`\\\\vect{x}`
to a class :math:`i` is defined as follows:
.. math::
i = \\\\argmax_k \\\\log w_k p_k( \\\\vect{x} )
In the first usage, it returns an integer which corresponds to the class where
*inputPoint* has been assigned.
In the second usage, it returns an :class:`~openturns.Indices` that collects the
class of each point of *inputSample*."
// ---------------------------------------------------------------------
%feature("docstring") OT::MixtureClassifier::grade
"Grade points according to the classifier.
**Available usages**:
grade(*inputPoint, k*)
grade(*inputSample, classList*)
Parameters
----------
inputPoint : sequence of float
A point to grade.
inputSample : 2-d a sequence of float
A set of point to grade.
k : integer
The class number.
classList : sequence of integer
The list of class number.
Notes
-----
The grade of :math:`\\\\vect{x}` with respect to the class *k* is
:math:`log w_k p_k ( \\\\vect{x} )`.
In the first usage, it returns a real that grades *inputPoint* with respect to
the class *k*. The greatest, the best.
In the second usage, it returns an :class:`~openturns.Indices` that collects the
grades of the :math:`i^{th}` point of *inputSample* with respect to the
:math:`i^{th}` class of *classList*."
// ---------------------------------------------------------------------
%feature("docstring") OT::MixtureClassifier::getMixture
"Accessor to the mixture distribution.
Returns
-------
mixtDist : :class:`~openturns.Mixture`
The mixture distribution."
// ---------------------------------------------------------------------
%feature("docstring") OT::MixtureClassifier::setMixture
"Accessor to the mixture distribution.
Parameters
----------
mixtDist : :class:`~openturns.Mixture`
The mixture distribution."
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