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Fix building of docs with the lastest numpy and sphinx #709

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merged 14 commits into from
Jul 24, 2023
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Fix building of docs with the lastest numpy and sphinx #709

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b52d5fd
Use float instead of np.float
keszybz Jul 17, 2023
b87ba7f
setup: fix git invocation for exlinks, add override
keszybz Jul 17, 2023
8320b2a
Fix CMA tanle miss align and conf modules
fmder Jul 23, 2023
9e0095c
Merge branch 'master' into keszybz-doc-build
fmder Jul 23, 2023
c3e13a9
Remove math warnings and align mathjax left
fmder Jul 24, 2023
7b337b7
Adjust doc to MetaEphemeral
fmder Jul 24, 2023
2e4b9ce
Fix title underscore too short warning
fmder Jul 24, 2023
0f05b73
Fix EDA example references
fmder Jul 24, 2023
9b7b3c8
Fix emphasis line
fmder Jul 24, 2023
9103dc2
Fix include path
fmder Jul 24, 2023
48f2214
Fix keyword
fmder Jul 24, 2023
2a5b6ec
Fixed reference to numpy tutorial
fmder Jul 24, 2023
649e5ee
Fixed reference to GA One Max
fmder Jul 24, 2023
3a764d7
Fix ref to fitness creation part
fmder Jul 24, 2023
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Fix EDA example references
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@fmder
fmder committed Jul 24, 2023
commit 0f05b73e693b22f71daaf01b0aea6a966ed09ce4
16 changes: 8 additions & 8 deletions doc/examples/eda.rst
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Expand Up @@ -15,13 +15,13 @@ The basic concept concept behind EDA is to sample :math:`\lambda` individuals
with a certain distribution and estimate the problem distribution from the
:math:`\mu` best individuals. This really simple concept adhere to the
generate-update logic. The strategy contains a random number generator which
is adapted from the population. The following :class:`EDA` class do just that.
is adapted from the population. The following :class:`EMNA` class do just that.

.. literalinclude:: /../examples/eda/fctmin.py
:pyobject: EDA
.. literalinclude:: /../examples/eda/emna.py
:pyobject: EMNA

A normal random number generator is initialized with a certain mean
(*centroid*) and standard deviation (*sigma*) for each
(*centroid*) and standard deviation (*sigma*) for each
dimension. The :meth:`generate` method uses numpy to generate *lambda_*
sequences in *dim* dimensions, then the sequences are used to initialize
individuals of class given in the *ind_init* argument. Finally, the
Expand All @@ -37,19 +37,19 @@ Two classes are needed, a minimization fitness and a individual that will
combine the fitness and the real values. Moreover, we will use
:class:`numpy.ndarray` as base class for our individuals.

.. literalinclude:: /../examples/eda/fctmin.py
.. literalinclude:: /../examples/eda/emna.py
:lines: 28-29

Operators
=========

The :func:`~deap.algorithms.eaGenerateUpdate` algorithm requires to set in a
toolbox an evaluation function, an generation method and an update method.
We will use the method of an initialized :class:`EDA`. For the generate
We will use the method of an initialized :class:`EMNA`. For the generate
method, we set the class that the individuals are transferred in to our
:class:`Individual` class containing a fitness.

.. literalinclude:: /../examples/eda/fctmin.py
.. literalinclude:: /../examples/eda/emna.py
:pyobject: main

The complete :example:`eda/fctmin`.
The complete :example:`eda/fctmin`.