UniVariateFunction

class UniVariateFunction(*args)

Base class for univariate functions.

Methods

__call__(x)

Call self as a function.

draw(xMin, xMax, pointNumber)

Draw the function.

getClassName()

Accessor to the object’s name.

getId()

Accessor to the object’s id.

getImplementation()

Accessor to the underlying implementation.

getName()

Accessor to the object’s name.

gradient(x)

Compute the gradient at point x.

hessian(x)

Compute the hessian at point x.

setName(name)

Accessor to the object’s name.
__init__(*args)

Initialize self. See help(type(self)) for accurate signature.

draw(xMin, xMax, pointNumber)

Draw the function.

Parameters
x_minfloat, optional

The starting value that is used for meshing the x-axis.

x_maxfloat, optional, x_{\max} > x_{\min}

The ending value that is used for meshing the x-axis.

n_pointsint, optional

The number of points that is used for meshing the x-axis.

Examples

>>> import openturns as ot
>>> from openturns.viewer import View
>>> f = ot.UniVariatePolynomial([1.0, 2.0, -3.0, 5.0])
>>> View(f.draw(-10.0, 10.0, 100)).show()
getClassName()

Accessor to the object’s name.

Returns
class_namestr

The object class name (object.__class__.__name__).

getId()

Accessor to the object’s id.

Returns
idint

Internal unique identifier.

getImplementation()

Accessor to the underlying implementation.

Returns
implImplementation

The implementation class.

getName()

Accessor to the object’s name.

Returns
namestr

The name of the object.

gradient(x)

Compute the gradient at point x.

Returns
gradientfloat

The value of the function’s first-order derivative at point x.

Examples

>>> import openturns as ot
>>> P = ot.UniVariatePolynomial([1.0, 2.0, 3.0])
>>> print(P.gradient(1.0))
8.0
hessian(x)

Compute the hessian at point x.

Parameters
xfloat

Input value.

Returns
hessianfloat

The value of the function’s second-order derivative at point x.

setName(name)

Accessor to the object’s name.

Parameters
namestr

The name of the object.