The SUMO toolbox
2018a
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Construct a `PolynomialModel' object. More...
Public Member Functions | |
function | PolynomialModel (var varargin) |
function | complexity () |
function | constructInModelSpace (var samples, var values) |
Build a polynomial model through the samples and values degrees: a structure returning the order in which degrees of freedom are selected. More... | |
function | evaluateInModelSpace (var points) |
Evaluate discrete points, given by the M times dim array points. More... | |
function | getDescription () |
Return a user friendly description of the model. More... | |
function | getExpressionInModelSpace (var outputIndex) |
Returns the closed formula (as a string) for this rational function. More... | |
function | getOrder () |
Return the order of the polynomial. More... | |
function | getNestedModel () |
function | basicPlotModel () |
This is a basic/default implementation called by plotModel.m, if subclasses want more fancy features they should override plotModel and not basicPlotModel. More... | |
function | init (var samples, var values) |
This base class method simply sets all the necessary fields (dimension, samples, values, id, ...) More... | |
function | construct (var samples, var values) |
Constructs the model given the samples and values in simulator space. More... | |
function | update (var samples, var values) |
Update the model with new data without fully retraining the model. More... | |
function | getDimensions () |
Return the input and output dimensions of the model. More... | |
function | getExpression (var outputIndex) |
This function first converts the inputs to model space [-1,1] range using the transformation functions and then passes on these functions to the model-space expression. More... | |
function | getId () |
Returns the id of this model. More... | |
function | getGrid () |
Returns a set of samples evaluated on a dense grid to be used for evaluation of the model. More... | |
function | equals (var m, var threshold) |
A grid based equality method. More... | |
function | evaluate (var points) |
Evaluation at a set of points. More... | |
function | evaluateDerivative (var points, var outputIndex) |
Approximate the derivative at the given points in simulator space. More... | |
function | evaluateBatch (var points, var batchSize) |
Evaluate a large set of samples in smaller batchs. More... | |
function | evaluateInModelSpaceBatch (var points, var batchSize) |
Evaluate a large set of samples in smaller batchs. More... | |
function | setTransformationValues (var transf) |
Set the simulator to model and model to simulator transformation functions for this model. More... | |
function | getTransformationFunctions () |
Get the simulator to model and model to simulator transformation functions for this model. More... | |
function | getTransformationValues () |
function | setMeasureScores (var scores) |
Sets scores of this model. More... | |
function | getMeasureScores () |
Returns the structure containing the detailed measure scores for each output. More... | |
function | getSamples () |
Returns the set of samples (in simulator space) on which this model was built. More... | |
function | getSamplesInModelSpace () |
Returns the set of samples on which this model was built (in model space! so in [-1 1]) More... | |
function | getValues () |
Returns the set of values in the samples on which this model was built. More... | |
function | setScore (var score) |
Set the global score for this model. More... | |
function | getScore () |
Return the global score of the model. More... | |
function | getBounds () |
Return the lower and upper bounds (in the original simulator space) of each model input. More... | |
function | getInputNames () |
Get the names of the input parameters. More... | |
function | getOutputNames () |
Get the names of the output parameters. More... | |
function | setMode (var mode) |
function | getMode () |
function | setTrainingTime (var trainingTime) |
function | getTrainingTime () |
function | getType () |
function | getVersionId () |
function | display () |
function | plotModel () |
Plots the model on the given domain (default: [-1 1]). More... | |
function | plotSobolIndices (var varargin) |
Plots the first order total Sobol indices in a pie chart. More... | |
function | evaluateDerivativeInModelSpace (var points, var outputIndex) |
Approximate the derivative at the given points in model space. More... | |
function | updateInModelSpace (var samples, var values) |
Update the model with new data without fully retraining the model. More... | |
function | getSensitivityIndices (var order, var type, var method) |
function | setInputNames (var names) |
Set the names of the input parameters. More... | |
function | setOutputNames (var names) |
Set the names of the output parameters. More... | |
Static Public Member Functions | |
static function | getPlotDefaults () |
static function | loadobj (var this) |
Construct a `PolynomialModel' object.
Simply fits a polynomial of degree 'degree' using (generalized) least squares
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This is a basic/default implementation called by plotModel.m, if subclasses want more fancy features they should override plotModel and not basicPlotModel.
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Constructs the model given the samples and values in simulator space.
This function scales the data to model space and calls constructInModelSpace()
function constructInModelSpace | ( | var | samples, |
var | values | ||
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Build a polynomial model through the samples and values degrees: a structure returning the order in which degrees of freedom are selected.
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A grid based equality method.
Evaluates 'm' and 'this' on a grid and returns true if the mean relative error is <= threshold
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Evaluation at a set of points.
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Evaluate a large set of samples in smaller batchs.
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Approximate the derivative at the given points in simulator space.
This function transforms the points to model space and calls evaluateDerivativeInModelSpace
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Approximate the derivative at the given points in model space.
It will return the gradient in each dimension. This is just a very simple implementation based on a simple symmetric approximation. Note that models can override this with more efficient implementations. For example Rational/Polynomial models would be able to provide exact derivatives. The same goes for Kriging models.
For a good tutorial/intro on gradients see: http://www-math.mit.edu/18.013A/HTML/chapter07/contents.html
function evaluateInModelSpace | ( | var | points | ) |
Evaluate discrete points, given by the M times dim array points.
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Evaluate a large set of samples in smaller batchs.
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Return the lower and upper bounds (in the original simulator space) of each model input.
function getDescription | ( | ) |
Return a user friendly description of the model.
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Return the input and output dimensions of the model.
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This function first converts the inputs to model space [-1,1] range using the transformation functions and then passes on these functions to the model-space expression.
function getExpressionInModelSpace | ( | var | outputIndex | ) |
Returns the closed formula (as a string) for this rational function.
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Returns a set of samples evaluated on a dense grid to be used for evaluation of the model.
Recycles previously evaluated grids.
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Returns the id of this model.
Id's are defined in the construct function, so that a model that is reconstructed gets a new unique id.
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Get the names of the input parameters.
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Returns the structure containing the detailed measure scores for each output.
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function getOrder | ( | ) |
Return the order of the polynomial.
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Get the names of the output parameters.
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Returns the set of samples (in simulator space) on which this model was built.
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Returns the set of samples on which this model was built (in model space! so in [-1 1])
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Return the global score of the model.
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Get the simulator to model and model to simulator transformation functions for this model.
inFunc | simulator -> model space |
outFunc | model -> simulator space |
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Returns the set of values in the samples on which this model was built.
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This base class method simply sets all the necessary fields (dimension, samples, values, id, ...)
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Plots the model on the given domain (default: [-1 1]).
Slice plots are made for dimensions larger than 2. Dimensions higher than 5 are clamped to 0. Subclasses may override this method if they wish to allow for model specific plotting. This implementation simply calls basicPlotModel. The default plot options can be found by calling the static member function getPlotDefaults().
Calling syntax: fighandle = plotModel(model,[output number],[options structure])
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Plots the first order total Sobol indices in a pie chart.
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Set the names of the input parameters.
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Sets scores of this model.
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Set the names of the output parameters.
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Set the global score for this model.
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Set the simulator to model and model to simulator transformation functions for this model.
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Update the model with new data without fully retraining the model.
This is useful for model types that support on-line learning This base class implementation simply calls construct. It is up to the model type to give a more efficient implementation
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inherited |
Update the model with new data without fully retraining the model.
This is useful for model types that support on-line learning This base class implementation simply calls constructInModelSpace. It is up to the model type to give a more efficient implementation