DOE algorithms¶
Warning
Some algorithms may require the installation of GEMSEO with all its features and some others may depend on plugins.
Note
All the features of the wrapped optimization libraries may not be exposed through GEMSEO.
CustomDOE¶
Module: gemseo.algos.doe.lib_custom
This samples are provided either as a file in text or csv format or as a sequence of sequences of numbers.
- Optional parameters
comments : str | Sequence[str] | None, optional
The characters or list of characters used to indicate the start of a comment. None implies no comments.
By default it is set to #.
delimiter : str | None, optional
The character used to separate values. If None, use whitespace.
By default it is set to ,.
doe_file : str | Path | TextIO | None, optional
Either a file path or the generator to read. If None, the samples are used and must be provided.
By default it is set to None.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
samples : ndarray | None, optional
The samples. If None, the doe_file is used and must be provided.
By default it is set to None.
skiprows : int, optional
The number of first lines to skip.
By default it is set to 0.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.
DiagonalDOE¶
Module: gemseo.algos.doe.lib_scalable
Diagonal design of experiments
- Optional parameters
eval_jac : bool, optional
Whether to evaluate the Jacobian.
By default it is set to False.
max_time : float, optional
The maximum runtime in seconds. If 0, no maximum runtime is set.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_samples : int, optional
The number of samples. The number of samples must be greater than or equal to 2.
By default it is set to 2.
reverse : Container[str] | None, optional
The dimensions or variables to sample from their upper bounds to their lower bounds. If None, every dimension will be sampled from its lower bound to its upper bound.
By default it is set to None.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
Additional arguments.
OT_AXIAL¶
Module: gemseo.algos.doe.lib_openturns
Axial design
More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.Axial.html.
- Optional parameters
annealing : bool, optional
If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.
By default it is set to True.
centers : Sequence[int] | None, optional
The centers for axial, factorial and composite designs. If None, centers = 0.5.
By default it is set to None.
criterion : str, optional
The space-filling criterion, either “C2”, “PhiP” or “MinDist”.
By default it is set to C2.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
levels : int | Sequence[int] | None, optional
The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.
By default it is set to None.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_replicates : int, optional
The number of Monte Carlo replicates to optimize LHS.
By default it is set to 1000.
n_samples : int | None, optional
The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument
levels.By default it is set to None.
seed : int, optional
The seed value.
By default it is set to 1.
temperature : str, optional
The temperature profile for simulated annealing, either “Geometric” or “Linear”.
By default it is set to Geometric.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.
OT_COMPOSITE¶
Module: gemseo.algos.doe.lib_openturns
Composite design
More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.Composite.html.
- Optional parameters
annealing : bool, optional
If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.
By default it is set to True.
centers : Sequence[int] | None, optional
The centers for axial, factorial and composite designs. If None, centers = 0.5.
By default it is set to None.
criterion : str, optional
The space-filling criterion, either “C2”, “PhiP” or “MinDist”.
By default it is set to C2.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
levels : int | Sequence[int] | None, optional
The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.
By default it is set to None.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_replicates : int, optional
The number of Monte Carlo replicates to optimize LHS.
By default it is set to 1000.
n_samples : int | None, optional
The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument
levels.By default it is set to None.
seed : int, optional
The seed value.
By default it is set to 1.
temperature : str, optional
The temperature profile for simulated annealing, either “Geometric” or “Linear”.
By default it is set to Geometric.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.
OT_FACTORIAL¶
Module: gemseo.algos.doe.lib_openturns
Factorial design
More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.Factorial.html.
- Optional parameters
annealing : bool, optional
If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.
By default it is set to True.
centers : Sequence[int] | None, optional
The centers for axial, factorial and composite designs. If None, centers = 0.5.
By default it is set to None.
criterion : str, optional
The space-filling criterion, either “C2”, “PhiP” or “MinDist”.
By default it is set to C2.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
levels : int | Sequence[int] | None, optional
The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.
By default it is set to None.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_replicates : int, optional
The number of Monte Carlo replicates to optimize LHS.
By default it is set to 1000.
n_samples : int | None, optional
The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument
levels.By default it is set to None.
seed : int, optional
The seed value.
By default it is set to 1.
temperature : str, optional
The temperature profile for simulated annealing, either “Geometric” or “Linear”.
By default it is set to Geometric.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.
OT_FAURE¶
Module: gemseo.algos.doe.lib_openturns
Faure sequence
More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.FaureSequence.html.
- Optional parameters
annealing : bool, optional
If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.
By default it is set to True.
centers : Sequence[int] | None, optional
The centers for axial, factorial and composite designs. If None, centers = 0.5.
By default it is set to None.
criterion : str, optional
The space-filling criterion, either “C2”, “PhiP” or “MinDist”.
By default it is set to C2.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
levels : int | Sequence[int] | None, optional
The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.
By default it is set to None.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_replicates : int, optional
The number of Monte Carlo replicates to optimize LHS.
By default it is set to 1000.
n_samples : int | None, optional
The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument
levels.By default it is set to None.
seed : int, optional
The seed value.
By default it is set to 1.
temperature : str, optional
The temperature profile for simulated annealing, either “Geometric” or “Linear”.
By default it is set to Geometric.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.
OT_FULLFACT¶
Module: gemseo.algos.doe.lib_openturns
Full factorial design
More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.Box.html.
- Optional parameters
annealing : bool, optional
If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.
By default it is set to True.
centers : Sequence[int] | None, optional
The centers for axial, factorial and composite designs. If None, centers = 0.5.
By default it is set to None.
criterion : str, optional
The space-filling criterion, either “C2”, “PhiP” or “MinDist”.
By default it is set to C2.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
levels : int | Sequence[int] | None, optional
The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.
By default it is set to None.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_replicates : int, optional
The number of Monte Carlo replicates to optimize LHS.
By default it is set to 1000.
n_samples : int | None, optional
The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument
levels.By default it is set to None.
seed : int, optional
The seed value.
By default it is set to 1.
temperature : str, optional
The temperature profile for simulated annealing, either “Geometric” or “Linear”.
By default it is set to Geometric.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.
OT_HALTON¶
Module: gemseo.algos.doe.lib_openturns
Halton sequence
More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.HaltonSequence.html.
- Optional parameters
annealing : bool, optional
If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.
By default it is set to True.
centers : Sequence[int] | None, optional
The centers for axial, factorial and composite designs. If None, centers = 0.5.
By default it is set to None.
criterion : str, optional
The space-filling criterion, either “C2”, “PhiP” or “MinDist”.
By default it is set to C2.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
levels : int | Sequence[int] | None, optional
The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.
By default it is set to None.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_replicates : int, optional
The number of Monte Carlo replicates to optimize LHS.
By default it is set to 1000.
n_samples : int | None, optional
The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument
levels.By default it is set to None.
seed : int, optional
The seed value.
By default it is set to 1.
temperature : str, optional
The temperature profile for simulated annealing, either “Geometric” or “Linear”.
By default it is set to Geometric.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.
OT_HASELGROVE¶
Module: gemseo.algos.doe.lib_openturns
Haselgrove sequence
More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.HaselgroveSequence.html.
- Optional parameters
annealing : bool, optional
If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.
By default it is set to True.
centers : Sequence[int] | None, optional
The centers for axial, factorial and composite designs. If None, centers = 0.5.
By default it is set to None.
criterion : str, optional
The space-filling criterion, either “C2”, “PhiP” or “MinDist”.
By default it is set to C2.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
levels : int | Sequence[int] | None, optional
The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.
By default it is set to None.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_replicates : int, optional
The number of Monte Carlo replicates to optimize LHS.
By default it is set to 1000.
n_samples : int | None, optional
The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument
levels.By default it is set to None.
seed : int, optional
The seed value.
By default it is set to 1.
temperature : str, optional
The temperature profile for simulated annealing, either “Geometric” or “Linear”.
By default it is set to Geometric.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.
OT_LHS¶
Module: gemseo.algos.doe.lib_openturns
Latin Hypercube Sampling
More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.LHS.html.
- Optional parameters
annealing : bool, optional
If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.
By default it is set to True.
centers : Sequence[int] | None, optional
The centers for axial, factorial and composite designs. If None, centers = 0.5.
By default it is set to None.
criterion : str, optional
The space-filling criterion, either “C2”, “PhiP” or “MinDist”.
By default it is set to C2.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
levels : int | Sequence[int] | None, optional
The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.
By default it is set to None.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_replicates : int, optional
The number of Monte Carlo replicates to optimize LHS.
By default it is set to 1000.
n_samples : int | None, optional
The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument
levels.By default it is set to None.
seed : int, optional
The seed value.
By default it is set to 1.
temperature : str, optional
The temperature profile for simulated annealing, either “Geometric” or “Linear”.
By default it is set to Geometric.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.
OT_LHSC¶
Module: gemseo.algos.doe.lib_openturns
Centered Latin Hypercube Sampling
More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.LHS.html.
- Optional parameters
annealing : bool, optional
If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.
By default it is set to True.
centers : Sequence[int] | None, optional
The centers for axial, factorial and composite designs. If None, centers = 0.5.
By default it is set to None.
criterion : str, optional
The space-filling criterion, either “C2”, “PhiP” or “MinDist”.
By default it is set to C2.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
levels : int | Sequence[int] | None, optional
The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.
By default it is set to None.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_replicates : int, optional
The number of Monte Carlo replicates to optimize LHS.
By default it is set to 1000.
n_samples : int | None, optional
The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument
levels.By default it is set to None.
seed : int, optional
The seed value.
By default it is set to 1.
temperature : str, optional
The temperature profile for simulated annealing, either “Geometric” or “Linear”.
By default it is set to Geometric.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.
OT_MONTE_CARLO¶
Module: gemseo.algos.doe.lib_openturns
Monte Carlo sequence
More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.RandomGenerator.html.html.
- Optional parameters
annealing : bool, optional
If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.
By default it is set to True.
centers : Sequence[int] | None, optional
The centers for axial, factorial and composite designs. If None, centers = 0.5.
By default it is set to None.
criterion : str, optional
The space-filling criterion, either “C2”, “PhiP” or “MinDist”.
By default it is set to C2.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
levels : int | Sequence[int] | None, optional
The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.
By default it is set to None.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_replicates : int, optional
The number of Monte Carlo replicates to optimize LHS.
By default it is set to 1000.
n_samples : int | None, optional
The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument
levels.By default it is set to None.
seed : int, optional
The seed value.
By default it is set to 1.
temperature : str, optional
The temperature profile for simulated annealing, either “Geometric” or “Linear”.
By default it is set to Geometric.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.
OT_OPT_LHS¶
Module: gemseo.algos.doe.lib_openturns
Optimal Latin Hypercube Sampling
More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.SimulatedAnnealingLHS.html.
- Optional parameters
annealing : bool, optional
If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.
By default it is set to True.
centers : Sequence[int] | None, optional
The centers for axial, factorial and composite designs. If None, centers = 0.5.
By default it is set to None.
criterion : str, optional
The space-filling criterion, either “C2”, “PhiP” or “MinDist”.
By default it is set to C2.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
levels : int | Sequence[int] | None, optional
The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.
By default it is set to None.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_replicates : int, optional
The number of Monte Carlo replicates to optimize LHS.
By default it is set to 1000.
n_samples : int | None, optional
The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument
levels.By default it is set to None.
seed : int, optional
The seed value.
By default it is set to 1.
temperature : str, optional
The temperature profile for simulated annealing, either “Geometric” or “Linear”.
By default it is set to Geometric.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.
OT_RANDOM¶
Module: gemseo.algos.doe.lib_openturns
Random sampling
More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.RandomGenerator.html.
- Optional parameters
annealing : bool, optional
If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.
By default it is set to True.
centers : Sequence[int] | None, optional
The centers for axial, factorial and composite designs. If None, centers = 0.5.
By default it is set to None.
criterion : str, optional
The space-filling criterion, either “C2”, “PhiP” or “MinDist”.
By default it is set to C2.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
levels : int | Sequence[int] | None, optional
The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.
By default it is set to None.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_replicates : int, optional
The number of Monte Carlo replicates to optimize LHS.
By default it is set to 1000.
n_samples : int | None, optional
The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument
levels.By default it is set to None.
seed : int, optional
The seed value.
By default it is set to 1.
temperature : str, optional
The temperature profile for simulated annealing, either “Geometric” or “Linear”.
By default it is set to Geometric.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.
OT_REVERSE_HALTON¶
Module: gemseo.algos.doe.lib_openturns
Reverse Halton
More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.ReverseHaltonSequence.html.
- Optional parameters
annealing : bool, optional
If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.
By default it is set to True.
centers : Sequence[int] | None, optional
The centers for axial, factorial and composite designs. If None, centers = 0.5.
By default it is set to None.
criterion : str, optional
The space-filling criterion, either “C2”, “PhiP” or “MinDist”.
By default it is set to C2.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
levels : int | Sequence[int] | None, optional
The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.
By default it is set to None.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_replicates : int, optional
The number of Monte Carlo replicates to optimize LHS.
By default it is set to 1000.
n_samples : int | None, optional
The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument
levels.By default it is set to None.
seed : int, optional
The seed value.
By default it is set to 1.
temperature : str, optional
The temperature profile for simulated annealing, either “Geometric” or “Linear”.
By default it is set to Geometric.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.
OT_SOBOL¶
Module: gemseo.algos.doe.lib_openturns
Sobol sequence
More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.SobolSequence.html.
- Optional parameters
annealing : bool, optional
If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.
By default it is set to True.
centers : Sequence[int] | None, optional
The centers for axial, factorial and composite designs. If None, centers = 0.5.
By default it is set to None.
criterion : str, optional
The space-filling criterion, either “C2”, “PhiP” or “MinDist”.
By default it is set to C2.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
levels : int | Sequence[int] | None, optional
The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.
By default it is set to None.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_replicates : int, optional
The number of Monte Carlo replicates to optimize LHS.
By default it is set to 1000.
n_samples : int | None, optional
The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument
levels.By default it is set to None.
seed : int, optional
The seed value.
By default it is set to 1.
temperature : str, optional
The temperature profile for simulated annealing, either “Geometric” or “Linear”.
By default it is set to Geometric.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.
OT_SOBOL_INDICES¶
Module: gemseo.algos.doe.lib_openturns
DOE for Sobol ‘indices
More details about the algorithm and its options on http://openturns.github.io/openturns/latest/user_manual/_generated/openturns.SobolIndicesAlgorithm.html.
- Optional parameters
annealing : bool, optional
If True, use simulated annealing to optimize LHS. Otherwise, use crude Monte Carlo.
By default it is set to True.
centers : Sequence[int] | None, optional
The centers for axial, factorial and composite designs. If None, centers = 0.5.
By default it is set to None.
criterion : str, optional
The space-filling criterion, either “C2”, “PhiP” or “MinDist”.
By default it is set to C2.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
levels : int | Sequence[int] | None, optional
The levels for axial, full-factorial (box), factorial and composite designs. If None, the number of samples is used in order to deduce the levels.
By default it is set to None.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_replicates : int, optional
The number of Monte Carlo replicates to optimize LHS.
By default it is set to 1000.
n_samples : int | None, optional
The number of samples. If None, the algorithm uses the number of levels per input dimension provided by the argument
levels.By default it is set to None.
seed : int, optional
The seed value.
By default it is set to 1.
temperature : str, optional
The temperature profile for simulated annealing, either “Geometric” or “Linear”.
By default it is set to Geometric.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.
bbdesign¶
Module: gemseo.algos.doe.lib_pydoe
Box-Behnken design implemented in pyDOE
More details about the algorithm and its options on https://pythonhosted.org/pyDOE/rsm.html#box-behnken.
- Optional parameters
alpha : str, optional
A parameter to describe how the variance is distributed. Either “orthogonal” or “rotatable”.
By default it is set to orthogonal.
center_bb : int | None, optional
The number of center points for the Box-Behnken design. If None, use a pre-determined number of points.
By default it is set to None.
center_cc : tuple[int, int] | None, optional
The 2-tuple of center points for the central composite design. If None, use (4, 4).
By default it is set to None.
criterion : str | None, optional
The criterion to use when sampling the points. If None, randomize the points within the intervals.
By default it is set to None.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
face : str, optional
The relation between the start points and the corner (factorial) points. Either “circumscribed”, “inscribed” or “faced”.
By default it is set to faced.
iterations : int, optional
The number of iterations in the correlation and maximin algorithms.
By default it is set to 5.
levels : Sequence[int] | None, optional
The level in each direction for the full-factorial design. If None, then the number of samples provided by the argument n_samples is used in order to deduce the levels.
By default it is set to None.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_samples : int | None, optional
The number of samples. If None, then use the number of levels per input dimension provided by the argument levels.
By default it is set to None.
seed : int, optional
The seed value.
By default it is set to 1.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.
ccdesign¶
Module: gemseo.algos.doe.lib_pydoe
Central Composite implemented in pyDOE
More details about the algorithm and its options on https://pythonhosted.org/pyDOE/rsm.html#central-composite.
- Optional parameters
alpha : str, optional
A parameter to describe how the variance is distributed. Either “orthogonal” or “rotatable”.
By default it is set to orthogonal.
center_bb : int | None, optional
The number of center points for the Box-Behnken design. If None, use a pre-determined number of points.
By default it is set to None.
center_cc : tuple[int, int] | None, optional
The 2-tuple of center points for the central composite design. If None, use (4, 4).
By default it is set to None.
criterion : str | None, optional
The criterion to use when sampling the points. If None, randomize the points within the intervals.
By default it is set to None.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
face : str, optional
The relation between the start points and the corner (factorial) points. Either “circumscribed”, “inscribed” or “faced”.
By default it is set to faced.
iterations : int, optional
The number of iterations in the correlation and maximin algorithms.
By default it is set to 5.
levels : Sequence[int] | None, optional
The level in each direction for the full-factorial design. If None, then the number of samples provided by the argument n_samples is used in order to deduce the levels.
By default it is set to None.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_samples : int | None, optional
The number of samples. If None, then use the number of levels per input dimension provided by the argument levels.
By default it is set to None.
seed : int, optional
The seed value.
By default it is set to 1.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.
ff2n¶
Module: gemseo.algos.doe.lib_pydoe
2-Level Full-Factorial implemented in pyDOE
More details about the algorithm and its options on https://pythonhosted.org/pyDOE/factorial.html#level-full-factorial.
- Optional parameters
alpha : str, optional
A parameter to describe how the variance is distributed. Either “orthogonal” or “rotatable”.
By default it is set to orthogonal.
center_bb : int | None, optional
The number of center points for the Box-Behnken design. If None, use a pre-determined number of points.
By default it is set to None.
center_cc : tuple[int, int] | None, optional
The 2-tuple of center points for the central composite design. If None, use (4, 4).
By default it is set to None.
criterion : str | None, optional
The criterion to use when sampling the points. If None, randomize the points within the intervals.
By default it is set to None.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
face : str, optional
The relation between the start points and the corner (factorial) points. Either “circumscribed”, “inscribed” or “faced”.
By default it is set to faced.
iterations : int, optional
The number of iterations in the correlation and maximin algorithms.
By default it is set to 5.
levels : Sequence[int] | None, optional
The level in each direction for the full-factorial design. If None, then the number of samples provided by the argument n_samples is used in order to deduce the levels.
By default it is set to None.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_samples : int | None, optional
The number of samples. If None, then use the number of levels per input dimension provided by the argument levels.
By default it is set to None.
seed : int, optional
The seed value.
By default it is set to 1.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.
fullfact¶
Module: gemseo.algos.doe.lib_pydoe
Full-Factorial implemented in pyDOE
More details about the algorithm and its options on https://pythonhosted.org/pyDOE/factorial.html#general-full-factorial.
- Optional parameters
alpha : str, optional
A parameter to describe how the variance is distributed. Either “orthogonal” or “rotatable”.
By default it is set to orthogonal.
center_bb : int | None, optional
The number of center points for the Box-Behnken design. If None, use a pre-determined number of points.
By default it is set to None.
center_cc : tuple[int, int] | None, optional
The 2-tuple of center points for the central composite design. If None, use (4, 4).
By default it is set to None.
criterion : str | None, optional
The criterion to use when sampling the points. If None, randomize the points within the intervals.
By default it is set to None.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
face : str, optional
The relation between the start points and the corner (factorial) points. Either “circumscribed”, “inscribed” or “faced”.
By default it is set to faced.
iterations : int, optional
The number of iterations in the correlation and maximin algorithms.
By default it is set to 5.
levels : Sequence[int] | None, optional
The level in each direction for the full-factorial design. If None, then the number of samples provided by the argument n_samples is used in order to deduce the levels.
By default it is set to None.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_samples : int | None, optional
The number of samples. If None, then use the number of levels per input dimension provided by the argument levels.
By default it is set to None.
seed : int, optional
The seed value.
By default it is set to 1.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.
lhs¶
Module: gemseo.algos.doe.lib_pydoe
Latin Hypercube Sampling implemented in pyDOE
More details about the algorithm and its options on https://pythonhosted.org/pyDOE/randomized.html#latin-hypercube.
- Optional parameters
alpha : str, optional
A parameter to describe how the variance is distributed. Either “orthogonal” or “rotatable”.
By default it is set to orthogonal.
center_bb : int | None, optional
The number of center points for the Box-Behnken design. If None, use a pre-determined number of points.
By default it is set to None.
center_cc : tuple[int, int] | None, optional
The 2-tuple of center points for the central composite design. If None, use (4, 4).
By default it is set to None.
criterion : str | None, optional
The criterion to use when sampling the points. If None, randomize the points within the intervals.
By default it is set to None.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
face : str, optional
The relation between the start points and the corner (factorial) points. Either “circumscribed”, “inscribed” or “faced”.
By default it is set to faced.
iterations : int, optional
The number of iterations in the correlation and maximin algorithms.
By default it is set to 5.
levels : Sequence[int] | None, optional
The level in each direction for the full-factorial design. If None, then the number of samples provided by the argument n_samples is used in order to deduce the levels.
By default it is set to None.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_samples : int | None, optional
The number of samples. If None, then use the number of levels per input dimension provided by the argument levels.
By default it is set to None.
seed : int, optional
The seed value.
By default it is set to 1.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.
pbdesign¶
Module: gemseo.algos.doe.lib_pydoe
Plackett-Burman design implemented in pyDOE
More details about the algorithm and its options on https://pythonhosted.org/pyDOE/factorial.html#plackett-burman.
- Optional parameters
alpha : str, optional
A parameter to describe how the variance is distributed. Either “orthogonal” or “rotatable”.
By default it is set to orthogonal.
center_bb : int | None, optional
The number of center points for the Box-Behnken design. If None, use a pre-determined number of points.
By default it is set to None.
center_cc : tuple[int, int] | None, optional
The 2-tuple of center points for the central composite design. If None, use (4, 4).
By default it is set to None.
criterion : str | None, optional
The criterion to use when sampling the points. If None, randomize the points within the intervals.
By default it is set to None.
eval_jac : bool, optional
Whether to evaluate the jacobian.
By default it is set to False.
face : str, optional
The relation between the start points and the corner (factorial) points. Either “circumscribed”, “inscribed” or “faced”.
By default it is set to faced.
iterations : int, optional
The number of iterations in the correlation and maximin algorithms.
By default it is set to 5.
levels : Sequence[int] | None, optional
The level in each direction for the full-factorial design. If None, then the number of samples provided by the argument n_samples is used in order to deduce the levels.
By default it is set to None.
max_time : float, optional
The maximum runtime in seconds, disabled if 0.
By default it is set to 0.
n_processes : int, optional
The maximum simultaneous number of processes used to parallelize the execution.
By default it is set to 1.
n_samples : int | None, optional
The number of samples. If None, then use the number of levels per input dimension provided by the argument levels.
By default it is set to None.
seed : int, optional
The seed value.
By default it is set to 1.
wait_time_between_samples : float, optional
The waiting time between two samples.
By default it is set to 0.0.
**kwargs : OptionType
The additional arguments.