.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/doe/plot_seed.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_examples_doe_plot_seed.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_doe_plot_seed.py:


Change the seed of a DOE
========================

`Latin hypercube sampling <https://en.wikipedia.org/wiki/Latin_hypercube_sampling>`__
is an example of stochastic DOE algorithm:
given an input dimension and a number of samples,
running the algorithm twice will give two different DOEs.

For the sake of reproducibility,
|g| uses a `random seed <https://en.wikipedia.org/wiki/Random_seed>`__:
given an input dimension, a number of samples and a random seed,
running the algorithm twice will give the same DOE.

In this example,
we will see how |g| uses the random seed
and how the user can change its value.

.. GENERATED FROM PYTHON SOURCE LINES 38-49

.. code-block:: Python


    from __future__ import annotations

    from gemseo import create_design_space
    from gemseo import create_discipline
    from gemseo import create_scenario
    from gemseo import execute_algo
    from gemseo.algos.doe.openturns.openturns import OpenTURNS
    from gemseo.algos.optimization_problem import OptimizationProblem
    from gemseo.core.mdo_functions.mdo_function import MDOFunction








.. GENERATED FROM PYTHON SOURCE LINES 50-60

At the scenario level
---------------------
First,
we illustrate the use of the random seed at the :class:`.DOEScenario` level
which is the appropriate level for most users.
Then,
we will illustrate this use at the :class:`.OptimizationProblem` level
which can be useful for developers.

Let us consider an :class:`.Discipline` representing the function :math:`y=x^2`:

.. GENERATED FROM PYTHON SOURCE LINES 60-62

.. code-block:: Python

    discipline = create_discipline("AnalyticDiscipline", expressions={"y": "x**2"})








.. GENERATED FROM PYTHON SOURCE LINES 63-64

This function is defined over the interval :math:`[-1,1]`:

.. GENERATED FROM PYTHON SOURCE LINES 64-67

.. code-block:: Python

    design_space = create_design_space()
    design_space.add_variable("x", lower_bound=-1, upper_bound=1)








.. GENERATED FROM PYTHON SOURCE LINES 68-71

We want to sample this discipline over this design space.
For that,
we express the sampling problem as a :class:`.DOEScenario`:

.. GENERATED FROM PYTHON SOURCE LINES 71-79

.. code-block:: Python

    scenario = create_scenario(
        [discipline],
        "y",
        design_space,
        scenario_type="DOE",
        formulation_name="DisciplinaryOpt",
    )








.. GENERATED FROM PYTHON SOURCE LINES 80-81

and solve it:

.. GENERATED FROM PYTHON SOURCE LINES 81-84

.. code-block:: Python

    scenario.execute(algo_name="OT_OPT_LHS", n_samples=2)
    scenario.formulation.optimization_problem.database.get_last_n_x_vect(2)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

        INFO - 16:25:33: *** Start DOEScenario execution ***
        INFO - 16:25:33: DOEScenario
        INFO - 16:25:33:    Disciplines: AnalyticDiscipline
        INFO - 16:25:33:    MDO formulation: DisciplinaryOpt
        INFO - 16:25:33: Optimization problem:
        INFO - 16:25:33:    minimize y(x)
        INFO - 16:25:33:    with respect to x
        INFO - 16:25:33:    over the design space:
        INFO - 16:25:33:       +------+-------------+-------+-------------+-------+
        INFO - 16:25:33:       | Name | Lower bound | Value | Upper bound | Type  |
        INFO - 16:25:33:       +------+-------------+-------+-------------+-------+
        INFO - 16:25:33:       | x    |      -1     |  None |      1      | float |
        INFO - 16:25:33:       +------+-------------+-------+-------------+-------+
        INFO - 16:25:33: Solving optimization problem with algorithm OT_OPT_LHS:
        INFO - 16:25:33:     50%|█████     | 1/2 [00:00<00:00, 602.11 it/sec, feas=True, obj=0.802]
        INFO - 16:25:33:    100%|██████████| 2/2 [00:00<00:00, 975.42 it/sec, feas=True, obj=0.54]
        INFO - 16:25:33: Optimization result:
        INFO - 16:25:33:    Optimizer info:
        INFO - 16:25:33:       Status: None
        INFO - 16:25:33:       Message: None
        INFO - 16:25:33:    Solution:
        INFO - 16:25:33:       Objective: 0.5398664901859535
        INFO - 16:25:33:       Design space:
        INFO - 16:25:33:          +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:33:          | Name | Lower bound |       Value        | Upper bound | Type  |
        INFO - 16:25:33:          +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:33:          | x    |      -1     | 0.7347560752970699 |      1      | float |
        INFO - 16:25:33:          +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:33: *** End DOEScenario execution (time: 0:00:00.005463) ***

    [array([-0.89534931]), array([0.73475608])]



.. GENERATED FROM PYTHON SOURCE LINES 85-87

When using the same DOE algorithm,
solving again this problem with the same scenario leads to a new result:

.. GENERATED FROM PYTHON SOURCE LINES 87-90

.. code-block:: Python

    scenario.execute(algo_name="OT_OPT_LHS", n_samples=2)
    scenario.formulation.optimization_problem.database.get_last_n_x_vect(2)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

        INFO - 16:25:33: *** Start DOEScenario execution ***
        INFO - 16:25:33: DOEScenario
        INFO - 16:25:33:    Disciplines: AnalyticDiscipline
        INFO - 16:25:33:    MDO formulation: DisciplinaryOpt
        INFO - 16:25:33: Optimization problem:
        INFO - 16:25:33:    minimize y(x)
        INFO - 16:25:33:    with respect to x
        INFO - 16:25:33:    over the design space:
        INFO - 16:25:33:       +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:33:       | Name | Lower bound |       Value        | Upper bound | Type  |
        INFO - 16:25:33:       +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:33:       | x    |      -1     | 0.7347560752970699 |      1      | float |
        INFO - 16:25:33:       +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:33: Solving optimization problem with algorithm OT_OPT_LHS:
        INFO - 16:25:33:     50%|█████     | 1/2 [00:00<00:00, 4634.59 it/sec, feas=True, obj=0.00248]
        INFO - 16:25:33:    100%|██████████| 2/2 [00:00<00:00, 3715.06 it/sec, feas=True, obj=0.237]
        INFO - 16:25:33: Optimization result:
        INFO - 16:25:33:    Optimizer info:
        INFO - 16:25:33:       Status: None
        INFO - 16:25:33:       Message: None
        INFO - 16:25:33:    Solution:
        INFO - 16:25:33:       Objective: 0.0024824697156153775
        INFO - 16:25:33:       Design space:
        INFO - 16:25:33:          +------+-------------+---------------------+-------------+-------+
        INFO - 16:25:33:          | Name | Lower bound |        Value        | Upper bound | Type  |
        INFO - 16:25:33:          +------+-------------+---------------------+-------------+-------+
        INFO - 16:25:33:          | x    |      -1     | 0.04982438876308848 |      1      | float |
        INFO - 16:25:33:          +------+-------------+---------------------+-------------+-------+
        INFO - 16:25:33: *** End DOEScenario execution (time: 0:00:00.003054) ***

    [array([0.04982439]), array([-0.48653892])]



.. GENERATED FROM PYTHON SOURCE LINES 91-94

The value of the default seed was incremented
from 0 (at first execution) to 1 (at last execution),
as we can check it by setting the custom ``"seed"`` to 1:

.. GENERATED FROM PYTHON SOURCE LINES 94-97

.. code-block:: Python

    scenario.execute(algo_name="OT_OPT_LHS", n_samples=2, seed=1)
    scenario.formulation.optimization_problem.database.get_last_n_x_vect(2)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

        INFO - 16:25:33: *** Start DOEScenario execution ***
        INFO - 16:25:33: DOEScenario
        INFO - 16:25:33:    Disciplines: AnalyticDiscipline
        INFO - 16:25:33:    MDO formulation: DisciplinaryOpt
        INFO - 16:25:33: Optimization problem:
        INFO - 16:25:33:    minimize y(x)
        INFO - 16:25:33:    with respect to x
        INFO - 16:25:33:    over the design space:
        INFO - 16:25:33:       +------+-------------+---------------------+-------------+-------+
        INFO - 16:25:33:       | Name | Lower bound |        Value        | Upper bound | Type  |
        INFO - 16:25:33:       +------+-------------+---------------------+-------------+-------+
        INFO - 16:25:33:       | x    |      -1     | 0.04982438876308848 |      1      | float |
        INFO - 16:25:33:       +------+-------------+---------------------+-------------+-------+
        INFO - 16:25:33: Solving optimization problem with algorithm OT_OPT_LHS:
        INFO - 16:25:34: Optimization result:
        INFO - 16:25:34:    Optimizer info:
        INFO - 16:25:34:       Status: None
        INFO - 16:25:34:       Message: None
        INFO - 16:25:34:    Solution:
        INFO - 16:25:34:       Objective: 0.0024824697156153775
        INFO - 16:25:34:       Design space:
        INFO - 16:25:34:          +------+-------------+---------------------+-------------+-------+
        INFO - 16:25:34:          | Name | Lower bound |        Value        | Upper bound | Type  |
        INFO - 16:25:34:          +------+-------------+---------------------+-------------+-------+
        INFO - 16:25:34:          | x    |      -1     | 0.04982438876308848 |      1      | float |
        INFO - 16:25:34:          +------+-------------+---------------------+-------------+-------+
        INFO - 16:25:34: *** End DOEScenario execution (time: 0:00:00.002448) ***

    [array([0.04982439]), array([-0.48653892])]



.. GENERATED FROM PYTHON SOURCE LINES 98-99

The default seed is incremented at each execution, whatever the custom one.

.. GENERATED FROM PYTHON SOURCE LINES 101-106

At the problem level
--------------------
Basic
~~~~~
Let us consider an :class:`.MDOFunction` representing the function :math:`y=x^2`:

.. GENERATED FROM PYTHON SOURCE LINES 106-108

.. code-block:: Python

    function = MDOFunction(lambda x: x**2, "f", input_names=["x"], output_names=["y"])








.. GENERATED FROM PYTHON SOURCE LINES 109-110

and defined over the unit interval :math:`x\in[0,1]`:

.. GENERATED FROM PYTHON SOURCE LINES 110-113

.. code-block:: Python

    design_space = create_design_space()
    design_space.add_variable("x", lower_bound=-1, upper_bound=1)








.. GENERATED FROM PYTHON SOURCE LINES 114-117

We want to sample this function over this design space.
For that,
we express the sampling problem as an :class:`.OptimizationProblem`:

.. GENERATED FROM PYTHON SOURCE LINES 117-120

.. code-block:: Python

    problem = OptimizationProblem(design_space)
    problem.objective = function








.. GENERATED FROM PYTHON SOURCE LINES 121-122

and solve it:

.. GENERATED FROM PYTHON SOURCE LINES 122-125

.. code-block:: Python

    execute_algo(problem, algo_name="OT_OPT_LHS", algo_type="doe", n_samples=2)
    problem.database.get_last_n_x_vect(2)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

        INFO - 16:25:34: Optimization problem:
        INFO - 16:25:34:    minimize f(x)
        INFO - 16:25:34:    with respect to x
        INFO - 16:25:34:    over the design space:
        INFO - 16:25:34:       +------+-------------+-------+-------------+-------+
        INFO - 16:25:34:       | Name | Lower bound | Value | Upper bound | Type  |
        INFO - 16:25:34:       +------+-------------+-------+-------------+-------+
        INFO - 16:25:34:       | x    |      -1     |  None |      1      | float |
        INFO - 16:25:34:       +------+-------------+-------+-------------+-------+
        INFO - 16:25:34: Solving optimization problem with algorithm OT_OPT_LHS:
        INFO - 16:25:34:     50%|█████     | 1/2 [00:00<00:00, 10618.49 it/sec, feas=True, obj=0.802]
        INFO - 16:25:34:    100%|██████████| 2/2 [00:00<00:00, 6232.25 it/sec, feas=True, obj=0.54]
        INFO - 16:25:34: Optimization result:
        INFO - 16:25:34:    Optimizer info:
        INFO - 16:25:34:       Status: None
        INFO - 16:25:34:       Message: None
        INFO - 16:25:34:    Solution:
        INFO - 16:25:34:       Objective: 0.5398664901859535
        INFO - 16:25:34:       Design space:
        INFO - 16:25:34:          +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34:          | Name | Lower bound |       Value        | Upper bound | Type  |
        INFO - 16:25:34:          +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34:          | x    |      -1     | 0.7347560752970699 |      1      | float |
        INFO - 16:25:34:          +------+-------------+--------------------+-------------+-------+

    [array([-0.89534931]), array([0.73475608])]



.. GENERATED FROM PYTHON SOURCE LINES 126-132

Note:
    We use the function :func:`.execute_algo`
    as the :class:`.OptimizationProblem` does not have a method :func:`execute`
    unlike the :class:`.Scenario`.

Solving again this problem with the same configuration leads to the same result:

.. GENERATED FROM PYTHON SOURCE LINES 132-135

.. code-block:: Python

    execute_algo(problem, algo_name="OT_OPT_LHS", algo_type="doe", n_samples=2)
    problem.database.get_last_n_x_vect(2)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

        INFO - 16:25:34: Optimization problem:
        INFO - 16:25:34:    minimize f(x)
        INFO - 16:25:34:    with respect to x
        INFO - 16:25:34:    over the design space:
        INFO - 16:25:34:       +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34:       | Name | Lower bound |       Value        | Upper bound | Type  |
        INFO - 16:25:34:       +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34:       | x    |      -1     | 0.7347560752970699 |      1      | float |
        INFO - 16:25:34:       +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34: Solving optimization problem with algorithm OT_OPT_LHS:
        INFO - 16:25:34: Optimization result:
        INFO - 16:25:34:    Optimizer info:
        INFO - 16:25:34:       Status: None
        INFO - 16:25:34:       Message: None
        INFO - 16:25:34:    Solution:
        INFO - 16:25:34:       Objective: 0.5398664901859535
        INFO - 16:25:34:       Design space:
        INFO - 16:25:34:          +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34:          | Name | Lower bound |       Value        | Upper bound | Type  |
        INFO - 16:25:34:          +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34:          | x    |      -1     | 0.7347560752970699 |      1      | float |
        INFO - 16:25:34:          +------+-------------+--------------------+-------------+-------+

    [array([-0.89534931]), array([0.73475608])]



.. GENERATED FROM PYTHON SOURCE LINES 136-138

and the result is still the same if we take 1 as random seed,
as 1 is the default value of this seed:

.. GENERATED FROM PYTHON SOURCE LINES 138-141

.. code-block:: Python

    execute_algo(problem, algo_name="OT_OPT_LHS", algo_type="doe", n_samples=2, seed=1)
    problem.database.get_last_n_x_vect(2)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

        INFO - 16:25:34: Optimization problem:
        INFO - 16:25:34:    minimize f(x)
        INFO - 16:25:34:    with respect to x
        INFO - 16:25:34:    over the design space:
        INFO - 16:25:34:       +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34:       | Name | Lower bound |       Value        | Upper bound | Type  |
        INFO - 16:25:34:       +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34:       | x    |      -1     | 0.7347560752970699 |      1      | float |
        INFO - 16:25:34:       +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34: Solving optimization problem with algorithm OT_OPT_LHS:
        INFO - 16:25:34: Optimization result:
        INFO - 16:25:34:    Optimizer info:
        INFO - 16:25:34:       Status: None
        INFO - 16:25:34:       Message: None
        INFO - 16:25:34:    Solution:
        INFO - 16:25:34:       Objective: 0.5398664901859535
        INFO - 16:25:34:       Design space:
        INFO - 16:25:34:          +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34:          | Name | Lower bound |       Value        | Upper bound | Type  |
        INFO - 16:25:34:          +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34:          | x    |      -1     | 0.7347560752970699 |      1      | float |
        INFO - 16:25:34:          +------+-------------+--------------------+-------------+-------+

    [array([-0.89534931]), array([0.73475608])]



.. GENERATED FROM PYTHON SOURCE LINES 142-144

If you want to use a different random seed,
you only have to change the value of ``seed``:

.. GENERATED FROM PYTHON SOURCE LINES 144-147

.. code-block:: Python

    execute_algo(problem, algo_name="OT_OPT_LHS", algo_type="doe", n_samples=2, seed=3)
    problem.database.get_last_n_x_vect(2)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

        INFO - 16:25:34: Optimization problem:
        INFO - 16:25:34:    minimize f(x)
        INFO - 16:25:34:    with respect to x
        INFO - 16:25:34:    over the design space:
        INFO - 16:25:34:       +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34:       | Name | Lower bound |       Value        | Upper bound | Type  |
        INFO - 16:25:34:       +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34:       | x    |      -1     | 0.7347560752970699 |      1      | float |
        INFO - 16:25:34:       +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34: Solving optimization problem with algorithm OT_OPT_LHS:
        INFO - 16:25:34:     50%|█████     | 1/2 [00:00<00:00, 11125.47 it/sec, feas=True, obj=0.867]
        INFO - 16:25:34:    100%|██████████| 2/2 [00:00<00:00, 6311.97 it/sec, feas=True, obj=0.663]
        INFO - 16:25:34: Optimization result:
        INFO - 16:25:34:    Optimizer info:
        INFO - 16:25:34:       Status: None
        INFO - 16:25:34:       Message: None
        INFO - 16:25:34:    Solution:
        INFO - 16:25:34:       Objective: 0.5398664901859535
        INFO - 16:25:34:       Design space:
        INFO - 16:25:34:          +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34:          | Name | Lower bound |       Value        | Upper bound | Type  |
        INFO - 16:25:34:          +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34:          | x    |      -1     | 0.7347560752970699 |      1      | float |
        INFO - 16:25:34:          +------+-------------+--------------------+-------------+-------+

    [array([0.93088508]), array([-0.81399054])]



.. GENERATED FROM PYTHON SOURCE LINES 148-154

Advanced
~~~~~~~~
You can also solve your problem with a lower level API
by directly instantiating the :class:`.BaseDOELibrary` of interest.
A :class:`.BaseDOELibrary` has a default seed generated by a :class:`.Seeder`
that is incremented at the beginning of each execution:

.. GENERATED FROM PYTHON SOURCE LINES 154-157

.. code-block:: Python

    algo = OpenTURNS("OT_OPT_LHS")
    algo.execute(problem, n_samples=2)
    problem.database.get_last_n_x_vect(2)




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

        INFO - 16:25:34: Optimization problem:
        INFO - 16:25:34:    minimize f(x)
        INFO - 16:25:34:    with respect to x
        INFO - 16:25:34:    over the design space:
        INFO - 16:25:34:       +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34:       | Name | Lower bound |       Value        | Upper bound | Type  |
        INFO - 16:25:34:       +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34:       | x    |      -1     | 0.7347560752970699 |      1      | float |
        INFO - 16:25:34:       +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34: Solving optimization problem with algorithm OT_OPT_LHS:
        INFO - 16:25:34: Optimization result:
        INFO - 16:25:34:    Optimizer info:
        INFO - 16:25:34:       Status: None
        INFO - 16:25:34:       Message: None
        INFO - 16:25:34:    Solution:
        INFO - 16:25:34:       Objective: 0.5398664901859535
        INFO - 16:25:34:       Design space:
        INFO - 16:25:34:          +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34:          | Name | Lower bound |       Value        | Upper bound | Type  |
        INFO - 16:25:34:          +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34:          | x    |      -1     | 0.7347560752970699 |      1      | float |
        INFO - 16:25:34:          +------+-------------+--------------------+-------------+-------+

    [array([0.93088508]), array([-0.81399054])]



.. GENERATED FROM PYTHON SOURCE LINES 158-159

Solving again the problem will give different samples:

.. GENERATED FROM PYTHON SOURCE LINES 159-161

.. code-block:: Python

    algo.execute(problem, n_samples=2)
    problem.database.get_last_n_x_vect(2)




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

        INFO - 16:25:34: Optimization problem:
        INFO - 16:25:34:    minimize f(x)
        INFO - 16:25:34:    with respect to x
        INFO - 16:25:34:    over the design space:
        INFO - 16:25:34:       +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34:       | Name | Lower bound |       Value        | Upper bound | Type  |
        INFO - 16:25:34:       +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34:       | x    |      -1     | 0.7347560752970699 |      1      | float |
        INFO - 16:25:34:       +------+-------------+--------------------+-------------+-------+
        INFO - 16:25:34: Solving optimization problem with algorithm OT_OPT_LHS:
        INFO - 16:25:34:     50%|█████     | 1/2 [00:00<00:00, 10459.61 it/sec, feas=True, obj=0.00248]
        INFO - 16:25:34:    100%|██████████| 2/2 [00:00<00:00, 5757.45 it/sec, feas=True, obj=0.237]
        INFO - 16:25:34: Optimization result:
        INFO - 16:25:34:    Optimizer info:
        INFO - 16:25:34:       Status: None
        INFO - 16:25:34:       Message: None
        INFO - 16:25:34:    Solution:
        INFO - 16:25:34:       Objective: 0.0024824697156153775
        INFO - 16:25:34:       Design space:
        INFO - 16:25:34:          +------+-------------+---------------------+-------------+-------+
        INFO - 16:25:34:          | Name | Lower bound |        Value        | Upper bound | Type  |
        INFO - 16:25:34:          +------+-------------+---------------------+-------------+-------+
        INFO - 16:25:34:          | x    |      -1     | 0.04982438876308848 |      1      | float |
        INFO - 16:25:34:          +------+-------------+---------------------+-------------+-------+

    [array([0.04982439]), array([-0.48653892])]



.. GENERATED FROM PYTHON SOURCE LINES 162-163

You can also use a specific seed instead of the default one:

.. GENERATED FROM PYTHON SOURCE LINES 163-165

.. code-block:: Python

    algo.execute(problem, n_samples=2, seed=123)
    problem.database.get_last_n_x_vect(2)




.. rst-class:: sphx-glr-script-out

 .. code-block:: none

        INFO - 16:25:34: Optimization problem:
        INFO - 16:25:34:    minimize f(x)
        INFO - 16:25:34:    with respect to x
        INFO - 16:25:34:    over the design space:
        INFO - 16:25:34:       +------+-------------+---------------------+-------------+-------+
        INFO - 16:25:34:       | Name | Lower bound |        Value        | Upper bound | Type  |
        INFO - 16:25:34:       +------+-------------+---------------------+-------------+-------+
        INFO - 16:25:34:       | x    |      -1     | 0.04982438876308848 |      1      | float |
        INFO - 16:25:34:       +------+-------------+---------------------+-------------+-------+
        INFO - 16:25:34: Solving optimization problem with algorithm OT_OPT_LHS:
        INFO - 16:25:34:     50%|█████     | 1/2 [00:00<00:00, 10459.61 it/sec, feas=True, obj=0.00904]
        INFO - 16:25:34:    100%|██████████| 2/2 [00:00<00:00, 5886.74 it/sec, feas=True, obj=0.968]
        INFO - 16:25:34: Optimization result:
        INFO - 16:25:34:    Optimizer info:
        INFO - 16:25:34:       Status: None
        INFO - 16:25:34:       Message: None
        INFO - 16:25:34:    Solution:
        INFO - 16:25:34:       Objective: 0.0024824697156153775
        INFO - 16:25:34:       Design space:
        INFO - 16:25:34:          +------+-------------+---------------------+-------------+-------+
        INFO - 16:25:34:          | Name | Lower bound |        Value        | Upper bound | Type  |
        INFO - 16:25:34:          +------+-------------+---------------------+-------------+-------+
        INFO - 16:25:34:          | x    |      -1     | 0.04982438876308848 |      1      | float |
        INFO - 16:25:34:          +------+-------------+---------------------+-------------+-------+

    [array([-0.09505332]), array([0.98400738])]




.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.039 seconds)


.. _sphx_glr_download_examples_doe_plot_seed.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_seed.ipynb <plot_seed.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_seed.py <plot_seed.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_seed.zip <plot_seed.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_