PythonRandomVector

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class PythonRandomVector(dim=0)

Allow to overload RandomVector from Python.

Parameters

dimpositive int

Vector dimension. Default is 0.

See also

RandomVector

Examples

>>> import openturns as ot
>>> ot.RandomGenerator.SetSeed(0)

Overload RandomVector from Python:

>>> class RVEC(ot.PythonRandomVector):
...    def __init__(self):
...        super(RVEC, self).__init__(2)
...        self.setDescription(['R', 'S'])
...
...    def getRealization(self):
...        X = [ot.RandomGenerator.Generate(), 2 + ot.RandomGenerator.Generate()]
...        return X
...
...    def getSample(self, size):
...        X = []
...        for i in range(size):
...            X.append([ot.RandomGenerator.Generate(), 2 + ot.RandomGenerator.Generate()])
...        return X
...
...    def getMean(self):
...        return [0.5, 2.5]
...
...    def getCovariance(self):
...        return [[0.0833333, 0.], [0., 0.0833333]]

Use the overloaded class:

>>> R = RVEC()
>>> # Instance creation
>>> myRV = ot.RandomVector(R)
>>> # Realization
>>> print(myRV.getRealization())
[0.629877,2.88281]
>>> # Sample
>>> print(myRV.getSample(5))
0 : [ 0.135276  2.0325    ]
1 : [ 0.347057  2.96942   ]
2 : [ 0.92068   2.50304   ]
3 : [ 0.0632061 2.29276   ]
4 : [ 0.714382  2.38336   ]
>>> # Mean
>>> print(myRV.getMean())
[0.5,2.5]
>>> # Covariance
>>> print(myRV.getCovariance())
[[ 0.0833333 0         ]
 [ 0         0.0833333 ]]

Random vectors can admit parameters.

In the following example, we define a RandomVector to sample from a normal multivariate normal distribution truncated to a ball. We implement the setParameter method to define the ball’s center.

>>> class NormalTruncatedToBall(ot.PythonRandomVector):
...    def __init__(self, dim, max_dist):
...        super().__init__(dim)
...        self._center = ot.Point(dim)
...        self._normal = ot.Normal(dim)
...        self._max_dist = max_dist
...        self.setParameter(ot.Point(dim))
...
...    def getRealization(self):
...        dist = ot.SpecFunc.MaxScalar
...        while dist>self._max_dist:
...            candidate = self._normal.getRealization()
...            dist = (candidate - self._center).norm()
...        return candidate
...
...    def setParameter(self, center): # the parameter influences sampling
...        self._center = center
...
...    def getParameter(self): # implemented for the sake of consistency
...        return self._center
...
...    def getParameterDescription(self): # optional
...        return ["center_{}".format(i) for i in range(self.getDimension())]

Define an instance of this RandomVector and set the parameter:

>>> myRV = ot.RandomVector(NormalTruncatedToBall(2, 1.5))
>>> myRV.setParameter([1.3, 0.6])

Get a sample and plot it:

>>> sample = myRV.getSample(100)
>>> graph = ot.Graph("Sample from a PythonRandomVector", "", "", True, '')
>>> cloud = ot.Cloud(sample)
>>> graph.add(cloud)
>>> from openturns.viewer import View
>>> view = View(graph)

Methods

getDescription()	Get the description.
getDimension()	Get the dimension.
setDescription(desc)	Set the description.

__init__(dim=0)

getDescription()

Get the description.

Returns

descDescription

desc describes the components of the RandomVector.

getDimension()

Get the dimension.

Returns

dimpositive int

Dimension of the RandomVector.

setDescription(desc)

Set the description.

Parameters

descsequence of str

desc describes the components of the RandomVector. Its size must be equal to the dimension of the RandomVector.