..
   Copyright 2021 IRT Saint-Exupéry, https://www.irt-saintexupery.com

   This work is licensed under the Creative Commons Attribution-ShareAlike 4.0
   International License. To view a copy of this license, visit
   http://creativecommons.org/licenses/by-sa/4.0/ or send a letter to Creative
   Commons, PO Box 1866, Mountain View, CA 94042, USA.

.. _gen_clustering_algos:

Clustering algorithms
=====================

.. warning::
    Some algorithms may require the :ref:`installation <installation>` of |g| with :ref:`all its features <dependencies>`
    and some others may depend on :ref:`plugins <plugins>`.

.. note::
    All the features of the wrapped optimization libraries may not be exposed through |g|.

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      cursor: pointer;
    }

    th, td {
      text-align: center;
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    tr:hover {
        border: 1px solid black;
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    tr:nth-child(even) {
      background-color: #f2f2f2
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    <script>
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      dir = "asc";
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          y = rows[i + 1].getElementsByTagName("TD")[n];
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              shouldSwitch= true;
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.. _GaussianMixture_options:

GaussianMixture
---------------



Module: :class:`gemseo.mlearning.cluster.gaussian_mixture`





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    <dl class="field-list simple">
    <dt class="field-odd">Required parameters</dt>
    <dd class="field-odd"><ul class="simple">

    <li>

**data** : *Dataset*

The learning dataset.

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    </li>

    </ul>
    </dd>
    </dl>



    <dl class="field-list simple">
    <dt class="field-odd">Optional parameters</dt>
    <dd class="field-odd"><ul class="simple">


    <li>

**n_components** : *int, optional*

The number of components of the Gaussian mixture.


By default it is set to 5.


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    </li>

    <li>

**transformer** : *Mapping[str, TransformerType] | None, optional*

The strategies to transform the variables. The values are instances of :class:`.Transformer` while the keys are the names of either the variables or the groups of variables, e.g. "inputs" or "outputs" in the case of the regression algorithms. If a group is specified, the :class:`.Transformer` will be applied to all the variables of this group. If None, do not transform the variables.


By default it is set to None.


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    </li>

    <li>

**var_names** : *Iterable[str] | None, optional*

The names of the variables. If None, consider all variables mentioned in the learning dataset.


By default it is set to None.


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    </li>

    <li>

****parameters** : *int | float | str | bool | None*

The parameters of the machine learning algorithm.



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    </li>

    </ul>
    </dd>
    </dl>



.. _KMeans_options:

KMeans
------



Module: :class:`gemseo.mlearning.cluster.kmeans`





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    <dl class="field-list simple">
    <dt class="field-odd">Required parameters</dt>
    <dd class="field-odd"><ul class="simple">

    <li>

**data** : *Dataset*

The learning dataset.

.. raw:: html

    </li>

    </ul>
    </dd>
    </dl>



    <dl class="field-list simple">
    <dt class="field-odd">Optional parameters</dt>
    <dd class="field-odd"><ul class="simple">


    <li>

**n_clusters** : *int, optional*

The number of clusters of the K-means algorithm.


By default it is set to 5.


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    </li>

    <li>

**random_state** : *int | None, optional*

If None, use a random generation of the initial centroids. If not None, the integer is used to make the initialization deterministic.


By default it is set to 0.


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    </li>

    <li>

**transformer** : *Mapping[str, TransformerType] | None, optional*

The strategies to transform the variables. The values are instances of :class:`.Transformer` while the keys are the names of either the variables or the groups of variables, e.g. "inputs" or "outputs" in the case of the regression algorithms. If a group is specified, the :class:`.Transformer` will be applied to all the variables of this group. If None, do not transform the variables.


By default it is set to None.


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    </li>

    <li>

**var_names** : *Iterable[str] | None, optional*

The names of the variables. If None, consider all variables mentioned in the learning dataset.


By default it is set to None.


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    </li>

    <li>

****parameters** : *int | float | bool | str | None*

The parameters of the machine learning algorithm.



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    </li>

    </ul>
    </dd>
    </dl>



.. _MLPredictiveClusteringAlgo_options:

MLPredictiveClusteringAlgo
--------------------------



Module: :class:`gemseo.mlearning.cluster.cluster`





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    <dl class="field-list simple">
    <dt class="field-odd">Required parameters</dt>
    <dd class="field-odd"><ul class="simple">

    <li>

**data** : *Dataset*

The learning dataset.

.. raw:: html

    </li>

    </ul>
    </dd>
    </dl>



    <dl class="field-list simple">
    <dt class="field-odd">Optional parameters</dt>
    <dd class="field-odd"><ul class="simple">


    <li>

**transformer** : *Mapping[str, TransformerType] | None, optional*

The strategies to transform the variables. The values are instances of :class:`.Transformer` while the keys are the names of either the variables or the groups of variables, e.g. "inputs" or "outputs" in the case of the regression algorithms. If a group is specified, the :class:`.Transformer` will be applied to all the variables of this group. If None, do not transform the variables.


By default it is set to None.


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    </li>

    <li>

**var_names** : *Iterable[str] | None, optional*

The names of the variables. If None, consider all variables mentioned in the learning dataset.


By default it is set to None.


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    </li>

    <li>

****parameters** : *MLAlgoParameterType*

The parameters of the machine learning algorithm.



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    </li>

    </ul>
    </dd>
    </dl>