MemoizeEvaluation¶

class MemoizeEvaluation(*args)¶

Evaluation which keeps tracks of input and output.

Usually we use MemoizeFunction (aa function provides both the evaluation, the gradient and the hessian). that relies on a Function passed as argument, and store input and output Sample. It also has a caching behavior, enabled by default.

Parameters:

functionEvaluation: Delegate evaluation
historyStrategyHistoryStrategy (optional): Strategy used to store points, default is Full.

See also

HistoryStrategy, Full, Last, Compact, Null

Examples

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

Retrieve input sample:

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

Retrieve output sample:

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

Methods

`__call__`(*args)	Call self as a function.
`addCacheContent`(inSample, outSample)	Add input and output points to the cache.
`clearCache`()	Empty the content of the cache.
`clearHistory`()	Clear input and output history.
`disableCache`()	Disable the cache.
`disableHistory`()	Disable the history.
`draw`(*args)	Draw the output of function as a `Graph`.
`enableCache`()	Enable the cache.
`enableHistory`()	Enable the history.
`getCacheHits`()	Accessor to the number of computations saved thanks to the cache.
`getCacheInput`()	Accessor to the input points stored in the cache.
`getCacheOutput`()	Accessor to the output points stored in the cache.
`getCallsNumber`()	Accessor to the number of times the function has been called.
`getCheckOutput`()	Accessor to the output verification flag.
`getClassName`()	Accessor to the object's name.
`getDescription`()	Accessor to the description of the inputs and outputs.
`getId`()	Accessor to the object's id.
`getInputDescription`()	Accessor to the description of the inputs.
`getInputDimension`()	Accessor to the number of the inputs.
`getInputHistory`()	Get the input sample history.
`getMarginal`(*args)	Accessor to marginal.
`getName`()	Accessor to the object's name.
`getOutputDescription`()	Accessor to the description of the outputs.
`getOutputDimension`()	Accessor to the number of the outputs.
`getOutputHistory`()	Get the output sample history.
`getParameter`()	Accessor to the parameter values.
`getParameterDescription`()	Accessor to the parameter description.
`getParameterDimension`()	Accessor to the dimension of the parameter.
`getShadowedId`()	Accessor to the object's shadowed id.
`getVisibility`()	Accessor to the object's visibility state.
`hasName`()	Test if the object is named.
`hasVisibleName`()	Test if the object has a distinguishable name.
`isActualImplementation`()	Accessor to the validity flag.
`isCacheEnabled`()	Test whether the cache is enabled or not.
`isHistoryEnabled`()	Test whether the history is enabled or not.
`isLinear`()	Accessor to the linearity of the evaluation.
`isLinearlyDependent`(index)	Accessor to the linearity of the evaluation with regard to a specific variable.
`parameterGradient`(inP)	Gradient against the parameters.
`setCheckOutput`(checkOutput)	Accessor to the output verification flag.
`setDescription`(description)	Accessor to the description of the inputs and outputs.
`setEvaluation`(evaluation)	Set the evaluation.
`setInputDescription`(inputDescription)	Accessor to the description of the inputs.
`setName`(name)	Accessor to the object's name.
`setOutputDescription`(outputDescription)	Accessor to the description of the outputs.
`setParameter`(parameters)	Accessor to the parameter values.
`setParameterDescription`(description)	Accessor to the parameter description.
`setShadowedId`(id)	Accessor to the object's shadowed id.
`setVisibility`(visible)	Accessor to the object's visibility state.

getEvaluation

__init__(*args)¶

addCacheContent(inSample, outSample)¶

Add input and output points to the cache.

Parameters:

input_sample2-d sequence of float: Input points to be added to the cache.
output_sample2-d sequence of float: Output points associated with the input_sample to be added to the cache.

clearCache()¶: Empty the content of the cache.

clearHistory()¶: Clear input and output history.

disableCache()¶: Disable the cache.

disableHistory()¶: Disable the history.

draw(*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 it 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 it 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()¶: Enable the cache.

enableHistory()¶: Enable the history.

getCacheHits()¶

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

Returns:

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

getCacheInput()¶

Accessor to the input points stored in the cache.

Returns:

cacheInputSample: The input points stored in the cache.

getCacheOutput()¶

Accessor to the output points stored in the cache.

Returns:

cacheInputSample: The output points stored in the cache.

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.

getCheckOutput()¶

Accessor to the output verification flag.

Returns:

check_outputbool: Whether to check return values for nan or inf.

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]

getId()¶

Accessor to the object’s id.

Returns:

idint: Internal unique identifier.

getInputDescription()¶

Accessor to the description of the inputs.

Returns:

descriptionDescription: Description of the inputs.

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 number of the inputs.

Returns:

number_inputsint: Number of inputs.

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()¶

Get the input sample history.

Returns:

inputSampleSample: Input points which have been evaluated.

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 outputs.

Returns:

descriptionDescription: Description of 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.getOutputDescription())
[y0]

getOutputDimension()¶

Accessor to the number of the outputs.

Returns:

number_outputsint: Number of outputs.

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()¶

Get the output sample history.

Returns:

outputSampleSample: Output points which have been evaluated.

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:

parameter_dimensionint: Dimension of the parameter.

getShadowedId()¶

Accessor to the object’s shadowed id.

Returns:

idint: Internal unique identifier.

getVisibility()¶

Accessor to the object’s visibility state.

Returns:

visiblebool: Visibility flag.

hasName()¶

Test if the object is named.

Returns:

hasNamebool: True if the name is not empty.

hasVisibleName()¶

Test if the object has a distinguishable name.

Returns:

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

isActualImplementation()¶

Accessor to the validity flag.

Returns:

is_implbool: Whether the implementation is valid.

isCacheEnabled()¶

Test whether the cache is enabled or not.

Returns:

isCacheEnabledbool: Flag telling whether the cache is enabled. It is enabled by default.

isHistoryEnabled()¶

Test whether the history is enabled or not.

Returns:

isHistoryEnabledbool: Flag telling whether the history is enabled.

isLinear()¶

Accessor to the linearity of the evaluation.

Returns:

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

isLinearlyDependent(index)¶

Accessor to the linearity of the evaluation 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 evaluation is linearly dependent on the specified variable, False otherwise.

parameterGradient(inP)¶

Gradient against the parameters.

Parameters:

xsequence of float: Input point

Returns:

parameter_gradientMatrix: The parameters gradient computed at x.

setCheckOutput(checkOutput)¶

Accessor to the output verification flag.

Parameters:

check_outputbool: Whether to check return values for nan or inf.

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)¶

Set the evaluation.

Parameters:

evaluationEvaluation: Evaluation.

Notes

This sets a new evaluation of interest. Cache and history are cleaned and the class will stores new one accounting for the given evaluation and store input and output Sample. It also has a caching behavior, enabled by default.

setInputDescription(inputDescription)¶

Accessor to the description of the inputs.

Returns:

descriptionDescription: Description of the inputs.

setName(name)¶

Accessor to the object’s name.

Parameters:

namestr: The name of the object.

setOutputDescription(outputDescription)¶

Accessor to the description of the outputs.

Returns:

descriptionDescription: Description of the outputs.

setParameter(parameters)¶

Accessor to the parameter values.

Parameters:

parametersequence of float: The parameter values.

setParameterDescription(description)¶

Accessor to the parameter description.

Parameters:

parameterDescription: The parameter description.

setShadowedId(id)¶

Accessor to the object’s shadowed id.

Parameters:

idint: Internal unique identifier.

setVisibility(visible)¶

Accessor to the object’s visibility state.

Parameters:

visiblebool: Visibility flag.

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