Function¶

class Function(*args)¶

Function base class.

Notes

A function acts on points to produce points: $f: \Rset^d \rightarrow \Rset^{d'}$ .

A function enables to evaluate its gradient and its hessian when mathematically defined.

Examples

Create a Function from a list of analytical formulas and descriptions of the input vector and the output vector :

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x0', 'x1'],
...                         ['x0 + x1', 'x0 - x1'])
>>> print(f([1, 2]))
[3,-1]

Create a Function from strings:

>>> import openturns as ot
>>> f = ot.SymbolicFunction('x', '2.0*sqrt(x)')
>>> print(f(([16],[4])))
    [ y0 ]
0 : [ 8  ]
1 : [ 4  ]

Create a Function from a Python function:

>>> def a_function(X):
...     return [X[0] + X[1]]
>>> f = ot.PythonFunction(2, 1, a_function)
>>> print(f(((10, 5),(6, 7))))
    [ y0 ]
0 : [ 15 ]
1 : [ 13 ]

See PythonFunction for further details.

Create a Function from a Python class:

>>> 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()
>>> myFunc = Function(F)
>>> print(myFunc((1.0, 2.0)))
[3]

See PythonFunction for further details.

Create a Function from another Function:

>>> f = ot.SymbolicFunction(ot.Description.BuildDefault(4, 'x'),
...                         ['x0', 'x0 + x1', 'x0 + x2 + x3'])

Then create another function by setting x1=4 and x3=10:

>>> g = ot.ParametricFunction(f, [3, 1], [10.0, 4.0], True)
>>> print(g.getInputDescription())
[x0,x2]
>>> print(g((1, 2)))
[1,5,13]

Or by setting x0=6 and x2=5:

>>> g = ot.ParametricFunction(f, [3, 1], [6.0, 5.0], False)
>>> print(g.getInputDescription())
[x3,x1]
>>> print(g((1, 2)))
[6,8,12]

Create a Function from another Function and by using a comparison operator:

>>> analytical = ot.SymbolicFunction(['x0','x1'], ['x0 + x1'])
>>> indicator = ot.IndicatorFunction(ot.LevelSet(analytical, ot.Less(), 0.0))
>>> print(indicator([2, 3]))
[0]
>>> print(indicator([2, -3]))
[1]

Create a Function from a collection of functions:

>>> functions = list()
>>> functions.append(ot.SymbolicFunction(['x1', 'x2', 'x3'],
...                                      ['x1^2 + x2', 'x1 + x2 + x3']))
>>> functions.append(ot.SymbolicFunction(['x1', 'x2', 'x3'],
...                                      ['x1 + 2 * x2 + x3', 'x1 + x2 - x3']))
>>> myFunction = ot.AggregatedFunction(functions)
>>> print(myFunction([1.0, 2.0, 3.0]))
[3,6,8,0]

Create a Function which is the linear combination linComb of the functions defined in functionCollection with scalar weights defined in scalarCoefficientColl:

$functionCollection = (f_1, \hdots, f_N)$ where $\forall 1 \leq i \leq N, \, f_i: \Rset^n \rightarrow \Rset^{p}$ and $scalarCoefficientColl = (c_1, \hdots, c_N) \in \Rset^N$ then the linear combination is:

$linComb: \left|\begin{array}{rcl} \Rset^n & \rightarrow & \Rset^{p} \\ \vect{X} & \mapsto & \displaystyle \sum_i c_if_i (\vect{X}) \end{array}\right.$

>>> myFunction2 = ot.LinearCombinationFunction(functions, [2.0, 4.0])
>>> print(myFunction2([1.0, 2.0, 3.0]))
[38,12]

Create a Function which is the linear combination vectLinComb of the scalar functions defined in scalarFunctionCollection with vectorial weights defined in vectorCoefficientColl:

If $scalarFunctionCollection = (f_1, \hdots, f_N)$ where $\forall 1 \leq i \leq N, \, f_i: \Rset^n \rightarrow \Rset$ and $vectorCoefficientColl = (\vect{c}_1, \hdots, \vect{c}_N)$ where $\forall 1 \leq i \leq N, \, \vect{c}_i \in \Rset^p$

$vectLinComb: \left|\begin{array}{rcl} \Rset^n & \rightarrow & \Rset^{p} \\ \vect{X} & \mapsto & \displaystyle \sum_i \vect{c}_if_i (\vect{X}) \end{array}\right.$

>>> functions=list()
>>> functions.append(ot.SymbolicFunction(['x1', 'x2', 'x3'],
...                                      ['x1 + 2 * x2 + x3']))
>>> functions.append(ot.SymbolicFunction(['x1', 'x2', 'x3'],
...                                      ['x1^2 + x2']))
>>> myFunction2 = ot.DualLinearCombinationFunction(functions, [[2., 4.], [3., 1.]])
>>> print(myFunction2([1, 2, 3]))
[25,35]

Create a Function from values of the inputs and the outputs:

>>> inputSample = [[1.0, 1.0], [2.0, 2.0]]
>>> outputSample = [[4.0], [5.0]]
>>> database = ot.DatabaseFunction(inputSample, outputSample)
>>> x = [1.8]*database.getInputDimension()
>>> print(database(x))
[5]

Create a Function which is the composition function $f\circ g$ :

>>> g = ot.SymbolicFunction(['x1', 'x2'],
...                         ['x1 + x2','3 * x1 * x2'])
>>> f = ot.SymbolicFunction(['x1', 'x2'], ['2 * x1 - x2'])
>>> composed = ot.ComposedFunction(f, g)
>>> print(composed([3, 4]))
[-22]

Methods

`__call__`(*args)	Call self as a function.
`draw`(*args)	Draw the output of function as a `Graph`.
`getCallsNumber`()	Accessor to the number of times the function has been called.
`getClassName`()	Accessor to the object's name.
`getDescription`()	Accessor to the description of the inputs and outputs.
`getEvaluation`()	Accessor to the evaluation function.
`getEvaluationCallsNumber`()	Accessor to the number of times the function has been called.
`getGradient`()	Accessor to the gradient function.
`getGradientCallsNumber`()	Accessor to the number of times the gradient of the function has been called.
`getHessian`()	Accessor to the hessian function.
`getHessianCallsNumber`()	Accessor to the number of times the hessian of the function has been called.
`getId`()	Accessor to the object's id.
`getImplementation`()	Accessor to the underlying implementation.
`getInputDescription`()	Accessor to the description of the input vector.
`getInputDimension`()	Accessor to the dimension of the input vector.
`getMarginal`(*args)	Accessor to marginal.
`getName`()	Accessor to the object's name.
`getOutputDescription`()	Accessor to the description of the output vector.
`getOutputDimension`()	Accessor to the number of the outputs.
`getParameter`()	Accessor to the parameter values.
`getParameterDescription`()	Accessor to the parameter description.
`getParameterDimension`()	Accessor to the dimension of the parameter.
`gradient`(inP)	Return the Jacobian transposed matrix of the function at a point.
`hessian`(inP)	Return the hessian of the function at a point.
`isLinear`()	Accessor to the linearity of the function.
`isLinearlyDependent`(index)	Accessor to the linearity of the function with regard to a specific variable.
`parameterGradient`(inP)	Accessor to the gradient against the parameter.
`setDescription`(description)	Accessor to the description of the inputs and outputs.
`setEvaluation`(evaluation)	Accessor to the evaluation function.
`setGradient`(gradient)	Accessor to the gradient function.
`setHessian`(hessian)	Accessor to the hessian function.
`setInputDescription`(inputDescription)	Accessor to the description of the input vector.
`setName`(name)	Accessor to the object's name.
`setOutputDescription`(inputDescription)	Accessor to the description of the output vector.
`setParameter`(parameter)	Accessor to the parameter values.
`setParameterDescription`(description)	Accessor to the parameter description.

__init__(*args)¶

draw(*args)¶

Draw the output of function as a Graph.

Available usages:

draw(inputMarg, outputMarg, centralPoint, xiMin, xiMax, ptNb)

draw(firstInputMarg, secondInputMarg, outputMarg, centralPoint, xiMin_xjMin, xiMax_xjMax, ptNbs)

draw(xiMin, xiMax, ptNb)

draw(xiMin_xjMin, xiMax_xjMax, ptNbs)

Parameters:

outputMarg, inputMargint, $outputMarg, inputMarg \geq 0$: outputMarg is the index of the marginal to draw as a function of the marginal with index inputMarg.
firstInputMarg, secondInputMargint, $firstInputMarg, secondInputMarg \geq 0$: In the 2D case, the marginal outputMarg is drawn as a function of the two marginals with indexes firstInputMarg and secondInputMarg.
centralPointsequence of float: Central point with dimension equal to the input dimension of the function.
xiMin, xiMaxfloat: Define the interval where the curve is plotted.
xiMin_xjMin, xiMax_xjMaxsequence of float of dimension 2.: In the 2D case, define the intervals where the curves are plotted.
ptNbint $ptNb > 0$ or list of ints of dimension 2 $ptNb_k > 0, k=1,2$: The number of points to draw the curves.

Notes

We note $f: \Rset^n \rightarrow \Rset^p$ where $\vect{x} = (x_1, \dots, x_n)$ and $f(\vect{x}) = (f_1(\vect{x}), \dots, f_p(\vect{x}))$ , with $n\geq 1$ and $p\geq 1$ .

In the first usage:

Draws graph of the given 1D outputMarg marginal $f_k: \Rset^n \rightarrow \Rset$ as a function of the given 1D inputMarg marginal with respect to the variation of $x_i$ in the interval $[x_i^{min}, x_i^{max}]$ , when all the other components of $\vect{x}$ are fixed to the corresponding components of the centralPoint $\vect{c}$ . Then OpenTURNS draws the graph:

$y = f_k^{(i)}(s)$

for any $s \in [x_i^{min}, x_i^{max}]$ where $f_k^{(i)}(s)$ is defined by the equation:

$f_k^{(i)}(s) = f_k(c_1, \dots, c_{i-1}, s, c_{i+1} \dots, c_n).$

In the second usage:

Draws the iso-curves of the given outputMarg marginal $f_k$ as a function of the given 2D firstInputMarg and secondInputMarg marginals with respect to the variation of $(x_i, x_j)$ in the interval $[x_i^{min}, x_i^{max}] \times [x_j^{min}, x_j^{max}]$ , when all the other components of $\vect{x}$ are fixed to the corresponding components of the centralPoint $\vect{c}$ . Then OpenTURNS draws the graph:

$y = f_k^{(i,j)}(s, t)$

for any $(s, t) \in [x_i^{min}, x_i^{max}] \times [x_j^{min}, x_j^{max}]$ where $f_k^{(i,j)}$ is defined by the equation:

$f_k^{(i,j)}(s,t) = f_k(c_1, \dots, c_{i-1}, s, c_{i+1}, \dots, c_{j-1}, t, c_{j+1} \dots, c_n).$

In the third usage:

The same as the first usage but only for function $f: \Rset \rightarrow \Rset$ .

In the fourth usage:

The same as the second usage but only for function $f: \Rset^2 \rightarrow \Rset$ .

Examples

>>> import openturns as ot
>>> from openturns.viewer import View
>>> f = ot.SymbolicFunction('x', 'sin(2*pi_*x)*exp(-x^2/2)')
>>> graph = f.draw(-1.2, 1.2, 100)
>>> View(graph).show()

getCallsNumber()¶

Accessor to the number of times the function has been called.

Returns:

calls_numberint: Integer that counts the number of times the function has been called since its creation.

getClassName()¶

Accessor to the object’s name.

Returns:

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

getDescription()¶

Accessor to the description of the inputs and outputs.

Returns:

descriptionDescription: Description of the inputs and the outputs.

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                         ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getDescription())
[x1,x2,y0]

getEvaluation()¶

Accessor to the evaluation function.

Returns:

functionEvaluationImplementation: The evaluation function.

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                         ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getEvaluation())
[x1,x2]->[2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6]

getEvaluationCallsNumber()¶

Accessor to the number of times the function has been called.

Returns:

evaluation_calls_numberint: Integer that counts the number of times the function has been called since its creation.

getGradient()¶

Accessor to the gradient function.

Returns:

gradientGradientImplementation: The gradient function.

getGradientCallsNumber()¶

Accessor to the number of times the gradient of the function has been called.

Returns:

gradient_calls_numberint: Integer that counts the number of times the gradient of the Function has been called since its creation. Note that if the gradient is implemented by a finite difference method, the gradient calls number is equal to 0 and the different calls are counted in the evaluation calls number.

getHessian()¶

Accessor to the hessian function.

Returns:

hessianHessianImplementation: The hessian function.

getHessianCallsNumber()¶

Accessor to the number of times the hessian of the function has been called.

Returns:

hessian_calls_numberint: Integer that counts the number of times the hessian of the Function has been called since its creation. Note that if the hessian is implemented by a finite difference method, the hessian calls number is equal to 0 and the different calls are counted in the evaluation calls number.

getId()¶

Accessor to the object’s id.

Returns:

idint: Internal unique identifier.

getImplementation()¶

Accessor to the underlying implementation.

Returns:

implImplementation: A copy of the underlying implementation object.

getInputDescription()¶

Accessor to the description of the input vector.

Returns:

descriptionDescription: Description of the input vector.

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                          ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getInputDescription())
[x1,x2]

getInputDimension()¶

Accessor to the dimension of the input vector.

Returns:

inputDimint: Dimension of the input vector $d$ .

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                          ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getInputDimension())
2

getMarginal(*args)¶

Accessor to marginal.

Parameters:

indicesint or list of ints: Set of indices for which the marginal is extracted.

Returns:

marginalFunction: Function corresponding to either $f_i$ or $(f_i)_{i \in indices}$ , with $f:\Rset^n \rightarrow \Rset^p$ and $f=(f_0 , \dots, f_{p-1})$ .

getName()¶

Accessor to the object’s name.

Returns:

namestr: The name of the object.

getOutputDescription()¶

Accessor to the description of the output vector.

Returns:

descriptionDescription: Description of the output vector.

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                          ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getOutputDescription())
[y0]

getOutputDimension()¶

Accessor to the number of the outputs.

Returns:

number_outputsint: Dimension of the output vector $d'$ .

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                          ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getOutputDimension())
1

getParameter()¶

Accessor to the parameter values.

Returns:

parameterPoint: The parameter values.

getParameterDescription()¶

Accessor to the parameter description.

Returns:

parameterDescription: The parameter description.

getParameterDimension()¶

Accessor to the dimension of the parameter.

Returns:

parameterDimensionint: Dimension of the parameter.

gradient(inP)¶

Return the Jacobian transposed matrix of the function at a point.

Parameters:

pointsequence of float: Point where the Jacobian transposed matrix is calculated.

Returns:

gradientMatrix: The Jacobian transposed matrix of the function at point.

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6','x1 + x2'])
>>> print(f.gradient([3.14, 4]))
[[ 13.5345   1       ]
 [  4.00001  1       ]]

hessian(inP)¶

Return the hessian of the function at a point.

Parameters:

pointsequence of float: Point where the hessian of the function is calculated.

Returns:

hessianSymmetricTensor: Hessian of the function at point.

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6','x1 + x2'])
>>> print(f.hessian([3.14, 4]))
sheet #0
[[ 20          -0.00637061 ]
 [ -0.00637061  0          ]]
sheet #1
[[  0           0          ]
 [  0           0          ]]

isLinear()¶

Accessor to the linearity of the function.

Returns:

linearbool: True if the function is linear, False otherwise.

isLinearlyDependent(index)¶

Accessor to the linearity of the function with regard to a specific variable.

Parameters:

indexint: The index of the variable with regard to which linearity is evaluated.

Returns:

linearbool: True if the function is linearly dependent on the specified variable, False otherwise.

parameterGradient(inP)¶

Accessor to the gradient against the parameter.

Returns:

gradientMatrix: The gradient.

setDescription(description)¶

Accessor to the description of the inputs and outputs.

Parameters:

descriptionsequence of str: Description of the inputs and the outputs.

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                          ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> print(f.getDescription())
[x1,x2,y0]
>>> f.setDescription(['a','b','y'])
>>> print(f.getDescription())
[a,b,y]

setEvaluation(evaluation)¶

Accessor to the evaluation function.

Parameters:

functionEvaluationImplementation: The evaluation function.

setGradient(gradient)¶

Accessor to the gradient function.

Parameters:

gradient_functionGradientImplementation: The gradient function.

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                          ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> f.setGradient(ot.CenteredFiniteDifferenceGradient(
...  ot.ResourceMap.GetAsScalar('CenteredFiniteDifferenceGradient-DefaultEpsilon'),
...  f.getEvaluation()))

setHessian(hessian)¶

Accessor to the hessian function.

Parameters:

hessian_functionHessianImplementation: The hessian function.

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction(['x1', 'x2'],
...                         ['2 * x1^2 + x1 + 8 * x2 + 4 * cos(x1) * x2 + 6'])
>>> f.setHessian(ot.CenteredFiniteDifferenceHessian(
...  ot.ResourceMap.GetAsScalar('CenteredFiniteDifferenceHessian-DefaultEpsilon'),
...  f.getEvaluation()))