MemoizeFunction

class MemoizeFunction(*args)

Function which keep tracks of input and output.

When this function is evaluated, it calls the Function passed as argument, and store input and output Sample.

Available constructors:

MemoizeFunction(function, historyStrategy)

Parameters
functionFunction

Delegate function

historyStrategyHistoryStrategy (optional)

Strategy used to store points, default is Full.

Notes

When the function passed as argument is a MemoizeFunction, its input and output history are copied into current instance. This allows to retrieve this history from a Function object which is in fact a MemoizeFunction. Thus, if you create a MemoizeFunction from an unknown Function, it is better to call clearHistory().

Examples

>>> import openturns as ot
>>> f = ot.SymbolicFunction('x', 'x^2')
>>> inputSample = ot.Sample([[1], [2], [3], [4]])
>>> f = ot.MemoizeFunction(f)
>>> outputSample = f(inputSample)

Retrieve input sample:

>>> print(f.getInputHistory())
0 : [ 1 ]
1 : [ 2 ]
2 : [ 3 ]
3 : [ 4 ]

Retrieve output sample:

>>> print(f.getOutputHistory())
0 : [  1 ]
1 : [  4 ]
2 : [  9 ]
3 : [ 16 ]

Methods

__call__(self, \*args)

Call self as a function.

addCacheContent(self, inSample, outSample)

Add input numerical points and associated output to the cache.

clearCache(self)

Empty the content of the cache.

clearHistory(self)

Clear input and output history.

disableCache(self)

Disable the cache mechanism.

disableHistory(self)

Disable the history mechanism.

draw(self, \*args)

Draw the output of function as a Graph.

enableCache(self)

Enable the cache mechanism.

enableHistory(self)

Enable the history mechanism.

getCacheHits(self)

Accessor to the number of computations saved thanks to the cache mecanism.

getCacheInput(self)

Accessor to all the input numerical points stored in the cache mecanism.

getCacheOutput(self)

Accessor to all the output numerical points stored in the cache mecanism.

getCallsNumber(self)

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

getClassName(self)

Accessor to the object’s name.

getDescription(self)

Accessor to the description of the inputs and outputs.

getEvaluation(self)

Accessor to the evaluation function.

getEvaluationCallsNumber(self)

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

getGradient(self)

Accessor to the gradient function.

getGradientCallsNumber(self)

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

getHessian(self)

Accessor to the hessian function.

getHessianCallsNumber(self)

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

getId(self)

Accessor to the object’s id.

getInputDescription(self)

Accessor to the description of the input vector.

getInputDimension(self)

Accessor to the dimension of the input vector.

getInputHistory(self)

Get the input sample.

getMarginal(self, \*args)

Accessor to marginal.

getName(self)

Accessor to the object’s name.

getOutputDescription(self)

Accessor to the description of the output vector.

getOutputDimension(self)

Accessor to the number of the outputs.

getOutputHistory(self)

Get the output sample.

getParameter(self)

Accessor to the parameter values.

getParameterDescription(self)

Accessor to the parameter description.

getParameterDimension(self)

Accessor to the dimension of the parameter.

getShadowedId(self)

Accessor to the object’s shadowed id.

getVisibility(self)

Accessor to the object’s visibility state.

gradient(self, inP)

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

hasName(self)

Test if the object is named.

hasVisibleName(self)

Test if the object has a distinguishable name.

hessian(self, inP)

Return the hessian of the function at a point.

isCacheEnabled(self)

Test whether the cache mechanism is enabled or not.

isHistoryEnabled(self)

Test whether the history mechanism is enabled or not.

isLinearlyDependent(self, index)

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

parameterGradient(self, inP)

Accessor to the gradient against the parameter.

setDescription(self, description)

Accessor to the description of the inputs and outputs.

setEvaluation(self, evaluation)

Accessor to the evaluation function.

setGradient(self, gradient)

Accessor to the gradient function.

setHessian(self, hessian)

Accessor to the hessian function.

setInputDescription(self, inputDescription)

Accessor to the description of the input vector.

setName(self, name)

Accessor to the object’s name.

setOutputDescription(self, outputDescription)

Accessor to the description of the output vector.

setParameter(self, parameter)

Accessor to the parameter values.

setParameterDescription(self, description)

Accessor to the parameter description.

setShadowedId(self, id)

Accessor to the object’s shadowed id.

setVisibility(self, visible)

Accessor to the object’s visibility state.

isLinear

__init__(self, *args)

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

addCacheContent(self, inSample, outSample)

Add input numerical points and associated output to the cache.

Parameters
input_sample2-d sequence of float

Input numerical points to be added to the cache.

output_sample2-d sequence of float

Output numerical points associated with the input_sample to be added to the cache.

clearCache(self)

Empty the content of the cache.

clearHistory(self)

Clear input and output history.

disableCache(self)

Disable the cache mechanism.

disableHistory(self)

Disable the history mechanism.

draw(self, *args)

Draw the output of function as a Graph.

Available usages:

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

draw(firstInputMarg, secondInputMarg, outputMarg, CP, 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.

CPsequence of float

Central point.

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 ones of the central point CP. Then OpenTURNS draws the graph: t\in [x_i^{min}, x_i^{max}] \mapsto f_k(CP_1, \dots, CP_{i-1}, t,  CP_{i+1} \dots, CP_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 ones of the central point CP. Then OpenTURNS draws the graph: (t,u) \in [x_i^{min}, x_i^{max}] \times [x_j^{min}, x_j^{max}] \mapsto f_k(CP_1, \dots, CP_{i-1}, t, CP_{i+1}, \dots, CP_{j-1}, u,  CP_{j+1} \dots, CP_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()
enableCache(self)

Enable the cache mechanism.

enableHistory(self)

Enable the history mechanism.

getCacheHits(self)

Accessor to the number of computations saved thanks to the cache mecanism.

Returns
cacheHitsint

Integer that counts the number of computations saved thanks to the cache mecanism.

getCacheInput(self)

Accessor to all the input numerical points stored in the cache mecanism.

Returns
cacheInputSample

All the input numerical points stored in the cache mecanism.

getCacheOutput(self)

Accessor to all the output numerical points stored in the cache mecanism.

Returns
cacheInputSample

All the output numerical points stored in the cache mecanism.

getCallsNumber(self)

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(self)

Accessor to the object’s name.

Returns
class_namestr

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

getDescription(self)

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(self)

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(self)

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(self)

Accessor to the gradient function.

Returns
gradientGradientImplementation

The gradient function.

getGradientCallsNumber(self)

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(self)

Accessor to the hessian function.

Returns
hessianHessianImplementation

The hessian function.

getHessianCallsNumber(self)

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(self)

Accessor to the object’s id.

Returns
idint

Internal unique identifier.

getInputDescription(self)

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(self)

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
getInputHistory(self)

Get the input sample.

Returns
inputSampleSample

Input points which have been evaluated.

getMarginal(self, *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(self)

Accessor to the object’s name.

Returns
namestr

The name of the object.

getOutputDescription(self)

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(self)

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
getOutputHistory(self)

Get the output sample.

Returns
outputSampleSample

Output points which have been evaluated.

getParameter(self)

Accessor to the parameter values.

Returns
parameterPoint

The parameter values.

getParameterDescription(self)

Accessor to the parameter description.

Returns
parameterDescription

The parameter description.

getParameterDimension(self)

Accessor to the dimension of the parameter.

Returns
parameterDimensionint

Dimension of the parameter.

getShadowedId(self)

Accessor to the object’s shadowed id.

Returns
idint

Internal unique identifier.

getVisibility(self)

Accessor to the object’s visibility state.

Returns
visiblebool

Visibility flag.

gradient(self, 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       ]]
hasName(self)

Test if the object is named.

Returns
hasNamebool

True if the name is not empty.

hasVisibleName(self)

Test if the object has a distinguishable name.

Returns
hasVisibleNamebool

True if the name is not empty and not the default one.

hessian(self, 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          ]]
isCacheEnabled(self)

Test whether the cache mechanism is enabled or not.

Returns
isCacheEnabledbool

Flag telling whether the cache mechanism is enabled. It is disabled by default.

isHistoryEnabled(self)

Test whether the history mechanism is enabled or not.

Returns
isHistoryEnabledbool

Flag telling whether the history mechanism is enabled.

isLinearlyDependent(self, 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(self, inP)

Accessor to the gradient against the parameter.

Returns
gradientMatrix

The gradient.

setDescription(self, 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(self, evaluation)

Accessor to the evaluation function.

Parameters
functionEvaluationImplementation

The evaluation function.

setGradient(self, 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(self, 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()))
setInputDescription(self, inputDescription)

Accessor to the description of the input vector.

Parameters
descriptionDescription

Description of the input vector.

setName(self, name)

Accessor to the object’s name.

Parameters
namestr

The name of the object.

setOutputDescription(self, outputDescription)

Accessor to the description of the output vector.

Parameters
descriptionDescription

Description of the output vector.

setParameter(self, parameter)

Accessor to the parameter values.

Parameters
parametersequence of float

The parameter values.

setParameterDescription(self, description)

Accessor to the parameter description.

Parameters
parameterDescription

The parameter description.

setShadowedId(self, id)

Accessor to the object’s shadowed id.

Parameters
idint

Internal unique identifier.

setVisibility(self, visible)

Accessor to the object’s visibility state.

Parameters
visiblebool

Visibility flag.