OpenTURNSPythonFunction

class OpenTURNSPythonFunction(n=0, p=0)

Override Function from Python.

Parameters:
inputDimpositive int

Dimension of the input vector

outputDimpositive int

Dimension of the output vector

Notes

You have to overload the function:

_exec(X): single evaluation, X is a sequence of float, returns a sequence of float

You can also optionally override these functions:

_exec_sample(X): multiple evaluations, X is a 2-d sequence of float, returns a 2-d sequence of float

_gradient(X): gradient, X is a sequence of float, returns a 2-d sequence of float

_hessian(X): hessian, X is a sequence of float, returns a 3-d sequence of float

Examples

>>> import openturns as ot
>>> class FUNC(OpenTURNSPythonFunction):
...     def __init__(self):
...         super(FUNC, self).__init__(2, 1)
...         self.setInputDescription(['R', 'S'])
...         self.setOutputDescription(['T'])
...     def _exec(self, X):
...         Y = [X[0] + X[1]]
...         return Y
>>> F = FUNC()

Create the associated Function:

>>> myFunc = Function(F)

Methods

__call__(X)

Call self as a function.

getInputDescription()

Input description accessor.

getInputDimension()

Input dimension accessor.

getOutputDescription()

Output description accessor.

getOutputDimension()

Output dimension accessor.

setInputDescription(descIn)

Input description accessor.

setOutputDescription(descOut)

Output description accessor.
__init__(n=0, p=0)
getInputDescription()

Input description accessor.

getInputDimension()

Input dimension accessor.

getOutputDescription()

Output description accessor.

getOutputDimension()

Output dimension accessor.

setInputDescription(descIn)

Input description accessor.

setOutputDescription(descOut)

Output description accessor.

Examples using the class

Customize your Metropolis-Hastings algorithm

Customize your Metropolis-Hastings algorithm