Solutions
DataDrivenDiffEq.DataDrivenSolution
— Typestruct DataDrivenSolution{L, A, O} <: DataDrivenDiffEq.AbstractDataDrivenSolution
The solution to a DataDrivenProblem
derived via a certain algorithm. The solution is represented via an AbstractBasis
, which makes it callable.
Fields
basis
The basis representation of the solution
parameters
Parameters of the solution
retcode
Returncode
alg
Algorithm
out
Original output of the solution algorithm
prob
Problem
l2_error
L₂ norm error
aic
AIC
rsquared
Coefficient of determinantion
Note
The L₂ norm error, AIC and coefficient of determinantion get only computed, if eval_expression is set to true or if the solution can be interpreted as a linear regression result.
Functions
DataDrivenDiffEq.result
— Functionresult(r)
Returns the Basis
of the result.
DataDrivenDiffEq.get_problem
— Functionget_problem(r)
Returns the original DataDrivenProblem
.
DataDrivenDiffEq.metrics
— Functionmetrics(r)
Returns all applicable metrics of the solution.
ModelingToolkit.parameters
— Functionparameters(r)
Returns the estimated parameters in form of an Vector
.
DataDrivenDiffEq.parameter_map
— Functionparameter_map(r)
Generate a mapping of the parameter values and symbolic representation useable to solve
and ODESystem
.
DataDrivenDiffEq.aic
— Functionaic(r)
Returns the AIC of the result.
DataDrivenDiffEq.l2error
— Functionl2error(r)
Returns the L₂ norm error of the result.
DataDrivenDiffEq.determination
— FunctionReturns the coefficient of determinantion of the result, if the result has been derived via a linear regression, e.g. sparse regression or koopman.
DataDrivenDiffEq.algorithm
— Functionalgorithm(r)
Returns the algorithm used to derive the solution.
DataDrivenDiffEq.output
— Functionoutput(r)
Returns the original output of the algorithm.