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<ul>
<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code>.fetch_olivetti_faces</a><ul>
<li><a class="reference internal" href="#sklearn.datasets.fetch_olivetti_faces"><code class="docutils literal notranslate"><span class="pre">fetch_olivetti_faces</span></code></a></li>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-fetch-olivetti-faces">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.fetch_olivetti_faces</span></code></a></li>
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<section id="sklearn-datasets-fetch-olivetti-faces">
<h1><a class="reference internal" href="../classes.html#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code></a>.fetch_olivetti_faces<a class="headerlink" href="#sklearn-datasets-fetch-olivetti-faces" title="Permalink to this heading">¶</a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="sklearn.datasets.fetch_olivetti_faces">
<span class="sig-prename descclassname"><span class="pre">sklearn.datasets.</span></span><span class="sig-name descname"><span class="pre">fetch_olivetti_faces</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_home</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">download_if_missing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_X_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/3f89022fa/sklearn/datasets/_olivetti_faces.py#L37"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#sklearn.datasets.fetch_olivetti_faces" title="Permalink to this definition">¶</a></dt>
<dd><p>Load the Olivetti faces data-set from AT&T (classification).</p>
<p>Download it if necessary.</p>
<table class="docutils align-default">
<tbody>
<tr class="row-odd"><td><p>Classes</p></td>
<td><p>40</p></td>
</tr>
<tr class="row-even"><td><p>Samples total</p></td>
<td><p>400</p></td>
</tr>
<tr class="row-odd"><td><p>Dimensionality</p></td>
<td><p>4096</p></td>
</tr>
<tr class="row-even"><td><p>Features</p></td>
<td><p>real, between 0 and 1</p></td>
</tr>
</tbody>
</table>
<p>Read more in the <a class="reference internal" href="../../datasets/real_world.html#olivetti-faces-dataset"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl>
<dt><strong>data_home</strong><span class="classifier">str or path-like, default=None</span></dt><dd><p>Specify another download and cache folder for the datasets. By default
all scikit-learn data is stored in ‘~/scikit_learn_data’ subfolders.</p>
</dd>
<dt><strong>shuffle</strong><span class="classifier">bool, default=False</span></dt><dd><p>If True the order of the dataset is shuffled to avoid having
images of the same person grouped.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=0</span></dt><dd><p>Determines random number generation for dataset shuffling. Pass an int
for reproducible output across multiple function calls.
See <a class="reference internal" href="../../glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
<dt><strong>download_if_missing</strong><span class="classifier">bool, default=True</span></dt><dd><p>If False, raise an OSError if the data is not locally available
instead of trying to download the data from the source site.</p>
</dd>
<dt><strong>return_X_y</strong><span class="classifier">bool, default=False</span></dt><dd><p>If True, returns <code class="docutils literal notranslate"><span class="pre">(data,</span> <span class="pre">target)</span></code> instead of a <code class="docutils literal notranslate"><span class="pre">Bunch</span></code> object. See
below for more information about the <code class="docutils literal notranslate"><span class="pre">data</span></code> and <code class="docutils literal notranslate"><span class="pre">target</span></code> object.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.22.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl>
<dt><strong>data</strong><span class="classifier"><a class="reference internal" href="sklearn.utils.Bunch.html#sklearn.utils.Bunch" title="sklearn.utils.Bunch"><code class="xref py py-class docutils literal notranslate"><span class="pre">Bunch</span></code></a></span></dt><dd><p>Dictionary-like object, with the following attributes.</p>
<dl class="simple">
<dt>data: ndarray, shape (400, 4096)</dt><dd><p>Each row corresponds to a ravelled
face image of original size 64 x 64 pixels.</p>
</dd>
<dt>images<span class="classifier">ndarray, shape (400, 64, 64)</span></dt><dd><p>Each row is a face image
corresponding to one of the 40 subjects of the dataset.</p>
</dd>
<dt>target<span class="classifier">ndarray, shape (400,)</span></dt><dd><p>Labels associated to each face image.
Those labels are ranging from 0-39 and correspond to the
Subject IDs.</p>
</dd>
<dt>DESCR<span class="classifier">str</span></dt><dd><p>Description of the modified Olivetti Faces Dataset.</p>
</dd>
</dl>
</dd>
<dt><strong>(data, target)</strong><span class="classifier">tuple if <code class="docutils literal notranslate"><span class="pre">return_X_y=True</span></code></span></dt><dd><p>Tuple with the <code class="docutils literal notranslate"><span class="pre">data</span></code> and <code class="docutils literal notranslate"><span class="pre">target</span></code> objects described above.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.22.</span></p>
</div>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<section id="examples-using-sklearn-datasets-fetch-olivetti-faces">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.fetch_olivetti_faces</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-fetch-olivetti-faces" title="Permalink to this heading">¶</a></h2>
<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example uses a large dataset of faces to learn a set of 20 x 20 images patches that consti..."><img alt="" src="../../_images/sphx_glr_plot_dict_face_patches_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/cluster/plot_dict_face_patches.html#sphx-glr-auto-examples-cluster-plot-dict-face-patches-py"><span class="std std-ref">Online learning of a dictionary of parts of faces</span></a></p>
<div class="sphx-glr-thumbnail-title">Online learning of a dictionary of parts of faces</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example applies to olivetti_faces_dataset different unsupervised matrix decomposition (dim..."><img alt="" src="../../_images/sphx_glr_plot_faces_decomposition_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/decomposition/plot_faces_decomposition.html#sphx-glr-auto-examples-decomposition-plot-faces-decomposition-py"><span class="std std-ref">Faces dataset decompositions</span></a></p>
<div class="sphx-glr-thumbnail-title">Faces dataset decompositions</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows the use of a forest of trees to evaluate the impurity based importance of th..."><img alt="" src="../../_images/sphx_glr_plot_forest_importances_faces_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/ensemble/plot_forest_importances_faces.html#sphx-glr-auto-examples-ensemble-plot-forest-importances-faces-py"><span class="std std-ref">Pixel importances with a parallel forest of trees</span></a></p>
<div class="sphx-glr-thumbnail-title">Pixel importances with a parallel forest of trees</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows the use of multi-output estimator to complete images. The goal is to predict..."><img alt="" src="../../_images/sphx_glr_plot_multioutput_face_completion_thumb.png" />
<p><a class="reference internal" href="../../auto_examples/miscellaneous/plot_multioutput_face_completion.html#sphx-glr-auto-examples-miscellaneous-plot-multioutput-face-completion-py"><span class="std std-ref">Face completion with a multi-output estimators</span></a></p>
<div class="sphx-glr-thumbnail-title">Face completion with a multi-output estimators</div>
</div></div><div class="clearer"></div></section>
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