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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code>.MiniBatchKMeans</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-cluster-minibatchkmeans">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.MiniBatchKMeans</span></code></a></li>
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<div class="section" id="sklearn-cluster-minibatchkmeans">
<h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code></a>.MiniBatchKMeans<a class="headerlink" href="#sklearn-cluster-minibatchkmeans" title="Permalink to this headline">¶</a></h1>
<dl class="py class">
<dt id="sklearn.cluster.MiniBatchKMeans">
<em class="property">class </em><code class="sig-prename descclassname">sklearn.cluster.</code><code class="sig-name descname">MiniBatchKMeans</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">n_clusters</span><span class="o">=</span><span class="default_value">8</span></em>, <em class="sig-param"><span class="o">*</span></em>, <em class="sig-param"><span class="n">init</span><span class="o">=</span><span class="default_value">'k-means++'</span></em>, <em class="sig-param"><span class="n">max_iter</span><span class="o">=</span><span class="default_value">100</span></em>, <em class="sig-param"><span class="n">batch_size</span><span class="o">=</span><span class="default_value">100</span></em>, <em class="sig-param"><span class="n">verbose</span><span class="o">=</span><span class="default_value">0</span></em>, <em class="sig-param"><span class="n">compute_labels</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">random_state</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">tol</span><span class="o">=</span><span class="default_value">0.0</span></em>, <em class="sig-param"><span class="n">max_no_improvement</span><span class="o">=</span><span class="default_value">10</span></em>, <em class="sig-param"><span class="n">init_size</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">n_init</span><span class="o">=</span><span class="default_value">3</span></em>, <em class="sig-param"><span class="n">reassignment_ratio</span><span class="o">=</span><span class="default_value">0.01</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/cluster/_kmeans.py#L1407"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans" title="Permalink to this definition">¶</a></dt>
<dd><p>Mini-Batch K-Means clustering.</p>
<p>Read more in the <a class="reference internal" href="../clustering.html#mini-batch-kmeans"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>n_clusters</strong><span class="classifier">int, default=8</span></dt><dd><p>The number of clusters to form as well as the number of
centroids to generate.</p>
</dd>
<dt><strong>init</strong><span class="classifier">{‘k-means++’, ‘random’}, callable or array-like of shape (n_clusters, n_features), default=’k-means++’</span></dt><dd><p>Method for initialization:</p>
<p>‘k-means++’ : selects initial cluster centers for k-mean
clustering in a smart way to speed up convergence. See section
Notes in k_init for more details.</p>
<p>‘random’: choose <code class="docutils literal notranslate"><span class="pre">n_clusters</span></code> observations (rows) at random from data
for the initial centroids.</p>
<p>If an array is passed, it should be of shape (n_clusters, n_features)
and gives the initial centers.</p>
<p>If a callable is passed, it should take arguments X, n_clusters and a
random state and return an initialization.</p>
</dd>
<dt><strong>max_iter</strong><span class="classifier">int, default=100</span></dt><dd><p>Maximum number of iterations over the complete dataset before
stopping independently of any early stopping criterion heuristics.</p>
</dd>
<dt><strong>batch_size</strong><span class="classifier">int, default=100</span></dt><dd><p>Size of the mini batches.</p>
</dd>
<dt><strong>verbose</strong><span class="classifier">int, default=0</span></dt><dd><p>Verbosity mode.</p>
</dd>
<dt><strong>compute_labels</strong><span class="classifier">bool, default=True</span></dt><dd><p>Compute label assignment and inertia for the complete dataset
once the minibatch optimization has converged in fit.</p>
</dd>
<dt><strong>random_state</strong><span class="classifier">int, RandomState instance or None, default=None</span></dt><dd><p>Determines random number generation for centroid initialization and
random reassignment. Use an int to make the randomness deterministic.
See <a class="reference internal" href="../../glossary.html#term-random_state"><span class="xref std std-term">Glossary</span></a>.</p>
</dd>
<dt><strong>tol</strong><span class="classifier">float, default=0.0</span></dt><dd><p>Control early stopping based on the relative center changes as
measured by a smoothed, variance-normalized of the mean center
squared position changes. This early stopping heuristics is
closer to the one used for the batch variant of the algorithms
but induces a slight computational and memory overhead over the
inertia heuristic.</p>
<p>To disable convergence detection based on normalized center
change, set tol to 0.0 (default).</p>
</dd>
<dt><strong>max_no_improvement</strong><span class="classifier">int, default=10</span></dt><dd><p>Control early stopping based on the consecutive number of mini
batches that does not yield an improvement on the smoothed inertia.</p>
<p>To disable convergence detection based on inertia, set
max_no_improvement to None.</p>
</dd>
<dt><strong>init_size</strong><span class="classifier">int, default=None</span></dt><dd><p>Number of samples to randomly sample for speeding up the
initialization (sometimes at the expense of accuracy): the
only algorithm is initialized by running a batch KMeans on a
random subset of the data. This needs to be larger than n_clusters.</p>
<p>If <code class="docutils literal notranslate"><span class="pre">None</span></code>, <code class="docutils literal notranslate"><span class="pre">init_size=</span> <span class="pre">3</span> <span class="pre">*</span> <span class="pre">batch_size</span></code>.</p>
</dd>
<dt><strong>n_init</strong><span class="classifier">int, default=3</span></dt><dd><p>Number of random initializations that are tried.
In contrast to KMeans, the algorithm is only run once, using the
best of the <code class="docutils literal notranslate"><span class="pre">n_init</span></code> initializations as measured by inertia.</p>
</dd>
<dt><strong>reassignment_ratio</strong><span class="classifier">float, default=0.01</span></dt><dd><p>Control the fraction of the maximum number of counts for a
center to be reassigned. A higher value means that low count
centers are more easily reassigned, which means that the
model will take longer to converge, but should converge in a
better clustering.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Attributes</dt>
<dd class="field-even"><dl>
<dt><strong>cluster_centers_</strong><span class="classifier">ndarray of shape (n_clusters, n_features)</span></dt><dd><p>Coordinates of cluster centers.</p>
</dd>
<dt><strong>labels_</strong><span class="classifier">int</span></dt><dd><p>Labels of each point (if compute_labels is set to True).</p>
</dd>
<dt><strong>inertia_</strong><span class="classifier">float</span></dt><dd><p>The value of the inertia criterion associated with the chosen
partition (if compute_labels is set to True). The inertia is
defined as the sum of square distances of samples to their nearest
neighbor.</p>
</dd>
<dt><strong>n_iter_</strong><span class="classifier">int</span></dt><dd><p>Number of batches processed.</p>
</dd>
<dt><strong>counts_</strong><span class="classifier">ndarray of shape (n_clusters,)</span></dt><dd><p>Weigth sum of each cluster.</p>
<div class="deprecated">
<p><span class="versionmodified deprecated">Deprecated since version 0.24: </span>This attribute is deprecated in 0.24 and will be removed in
1.1 (renaming of 0.26).</p>
</div>
</dd>
<dt><strong>init_size_</strong><span class="classifier">int</span></dt><dd><p>The effective number of samples used for the initialization.</p>
<div class="deprecated">
<p><span class="versionmodified deprecated">Deprecated since version 0.24: </span>This attribute is deprecated in 0.24 and will be removed in
1.1 (renaming of 0.26).</p>
</div>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.cluster.KMeans.html#sklearn.cluster.KMeans" title="sklearn.cluster.KMeans"><code class="xref py py-obj docutils literal notranslate"><span class="pre">KMeans</span></code></a></dt><dd><p>The classic implementation of the clustering method based on the Lloyd’s algorithm. It consumes the whole set of input data at each iteration.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>See <a class="reference external" href="https://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf">https://www.eecs.tufts.edu/~dsculley/papers/fastkmeans.pdf</a></p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="kn">import</span> <span class="n">MiniBatchKMeans</span>
<span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="gp">>>> </span><span class="n">X</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="gp">... </span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span>
<span class="gp">... </span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
<span class="gp">... </span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="c1"># manually fit on batches</span>
<span class="gp">>>> </span><span class="n">kmeans</span> <span class="o">=</span> <span class="n">MiniBatchKMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">kmeans</span> <span class="o">=</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">6</span><span class="p">,:])</span>
<span class="gp">>>> </span><span class="n">kmeans</span> <span class="o">=</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">partial_fit</span><span class="p">(</span><span class="n">X</span><span class="p">[</span><span class="mi">6</span><span class="p">:</span><span class="mi">12</span><span class="p">,:])</span>
<span class="gp">>>> </span><span class="n">kmeans</span><span class="o">.</span><span class="n">cluster_centers_</span>
<span class="go">array([[2. , 1. ],</span>
<span class="go"> [3.5, 4.5]])</span>
<span class="gp">>>> </span><span class="n">kmeans</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
<span class="go">array([0, 1], dtype=int32)</span>
<span class="gp">>>> </span><span class="c1"># fit on the whole data</span>
<span class="gp">>>> </span><span class="n">kmeans</span> <span class="o">=</span> <span class="n">MiniBatchKMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span>
<span class="gp">... </span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">kmeans</span><span class="o">.</span><span class="n">cluster_centers_</span>
<span class="go">array([[3.95918367, 2.40816327],</span>
<span class="go"> [1.12195122, 1.3902439 ]])</span>
<span class="gp">>>> </span><span class="n">kmeans</span><span class="o">.</span><span class="n">predict</span><span class="p">([[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">]])</span>
<span class="go">array([1, 0], dtype=int32)</span>
</pre></div>
</div>
<p class="rubric">Methods</p>
<table class="longtable docutils align-default">
<colgroup>
<col style="width: 10%" />
<col style="width: 90%" />
</colgroup>
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans.fit" title="sklearn.cluster.MiniBatchKMeans.fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code></a>(X[, y, sample_weight])</p></td>
<td><p>Compute the centroids on X by chunking it into mini-batches.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans.fit_predict" title="sklearn.cluster.MiniBatchKMeans.fit_predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_predict</span></code></a>(X[, y, sample_weight])</p></td>
<td><p>Compute cluster centers and predict cluster index for each sample.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans.fit_transform" title="sklearn.cluster.MiniBatchKMeans.fit_transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code></a>(X[, y, sample_weight])</p></td>
<td><p>Compute clustering and transform X to cluster-distance space.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans.get_params" title="sklearn.cluster.MiniBatchKMeans.get_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code></a>([deep])</p></td>
<td><p>Get parameters for this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans.partial_fit" title="sklearn.cluster.MiniBatchKMeans.partial_fit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">partial_fit</span></code></a>(X[, y, sample_weight])</p></td>
<td><p>Update k means estimate on a single mini-batch X.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans.predict" title="sklearn.cluster.MiniBatchKMeans.predict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">predict</span></code></a>(X[, sample_weight])</p></td>
<td><p>Predict the closest cluster each sample in X belongs to.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans.score" title="sklearn.cluster.MiniBatchKMeans.score"><code class="xref py py-obj docutils literal notranslate"><span class="pre">score</span></code></a>(X[, y, sample_weight])</p></td>
<td><p>Opposite of the value of X on the K-means objective.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans.set_params" title="sklearn.cluster.MiniBatchKMeans.set_params"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code></a>(**params)</p></td>
<td><p>Set the parameters of this estimator.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="#sklearn.cluster.MiniBatchKMeans.transform" title="sklearn.cluster.MiniBatchKMeans.transform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code></a>(X)</p></td>
<td><p>Transform X to a cluster-distance space.</p></td>
</tr>
</tbody>
</table>
<dl class="py method">
<dt id="sklearn.cluster.MiniBatchKMeans.fit">
<code class="sig-name descname">fit</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sample_weight</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/cluster/_kmeans.py#L1638"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the centroids on X by chunking it into mini-batches.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>Training instances to cluster. It must be noted that the data
will be converted to C ordering, which will cause a memory copy
if the given data is not C-contiguous.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present here for API consistency by convention.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>The weights for each observation in X. If None, all observations
are assigned equal weight (default: None).</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.20.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>self</dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="sklearn.cluster.MiniBatchKMeans.fit_predict">
<code class="sig-name descname">fit_predict</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sample_weight</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/cluster/_kmeans.py#L1055"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.fit_predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute cluster centers and predict cluster index for each sample.</p>
<p>Convenience method; equivalent to calling fit(X) followed by
predict(X).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>New data to transform.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present here for API consistency by convention.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>The weights for each observation in X. If None, all observations
are assigned equal weight.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>labels</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Index of the cluster each sample belongs to.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="sklearn.cluster.MiniBatchKMeans.fit_transform">
<code class="sig-name descname">fit_transform</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sample_weight</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/cluster/_kmeans.py#L1080"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.fit_transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute clustering and transform X to cluster-distance space.</p>
<p>Equivalent to fit(X).transform(X), but more efficiently implemented.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>New data to transform.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present here for API consistency by convention.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>The weights for each observation in X. If None, all observations
are assigned equal weight.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_new</strong><span class="classifier">ndarray of shape (n_samples, n_clusters)</span></dt><dd><p>X transformed in the new space.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="sklearn.cluster.MiniBatchKMeans.get_params">
<code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">deep</span><span class="o">=</span><span class="default_value">True</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/base.py#L178"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for this estimator.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>deep</strong><span class="classifier">bool, default=True</span></dt><dd><p>If True, will return the parameters for this estimator and
contained subobjects that are estimators.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>params</strong><span class="classifier">dict</span></dt><dd><p>Parameter names mapped to their values.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="sklearn.cluster.MiniBatchKMeans.partial_fit">
<code class="sig-name descname">partial_fit</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sample_weight</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/cluster/_kmeans.py#L1816"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.partial_fit" title="Permalink to this definition">¶</a></dt>
<dd><p>Update k means estimate on a single mini-batch X.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">array-like of shape (n_samples, n_features)</span></dt><dd><p>Coordinates of the data points to cluster. It must be noted that
X will be copied if it is not C-contiguous.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present here for API consistency by convention.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>The weights for each observation in X. If None, all observations
are assigned equal weight (default: None).</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>self</dt><dd></dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="sklearn.cluster.MiniBatchKMeans.predict">
<code class="sig-name descname">predict</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">sample_weight</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/cluster/_kmeans.py#L1894"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.predict" title="Permalink to this definition">¶</a></dt>
<dd><p>Predict the closest cluster each sample in X belongs to.</p>
<p>In the vector quantization literature, <code class="docutils literal notranslate"><span class="pre">cluster_centers_</span></code> is called
the code book and each value returned by <code class="docutils literal notranslate"><span class="pre">predict</span></code> is the index of
the closest code in the code book.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>New data to predict.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>The weights for each observation in X. If None, all observations
are assigned equal weight (default: None).</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>labels</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>Index of the cluster each sample belongs to.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="sklearn.cluster.MiniBatchKMeans.score">
<code class="sig-name descname">score</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em>, <em class="sig-param"><span class="n">y</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">sample_weight</span><span class="o">=</span><span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/cluster/_kmeans.py#L1164"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.score" title="Permalink to this definition">¶</a></dt>
<dd><p>Opposite of the value of X on the K-means objective.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>New data.</p>
</dd>
<dt><strong>y</strong><span class="classifier">Ignored</span></dt><dd><p>Not used, present here for API consistency by convention.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array-like of shape (n_samples,), default=None</span></dt><dd><p>The weights for each observation in X. If None, all observations
are assigned equal weight.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>score</strong><span class="classifier">float</span></dt><dd><p>Opposite of the value of X on the K-means objective.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="sklearn.cluster.MiniBatchKMeans.set_params">
<code class="sig-name descname">set_params</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">params</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/base.py#L202"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.set_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Set the parameters of this estimator.</p>
<p>The method works on simple estimators as well as on nested objects
(such as <a class="reference internal" href="sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline" title="sklearn.pipeline.Pipeline"><code class="xref py py-class docutils literal notranslate"><span class="pre">Pipeline</span></code></a>). The latter have
parameters of the form <code class="docutils literal notranslate"><span class="pre"><component>__<parameter></span></code> so that it’s
possible to update each component of a nested object.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>**params</strong><span class="classifier">dict</span></dt><dd><p>Estimator parameters.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>self</strong><span class="classifier">estimator instance</span></dt><dd><p>Estimator instance.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py method">
<dt id="sklearn.cluster.MiniBatchKMeans.transform">
<code class="sig-name descname">transform</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">X</span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/23d876161/sklearn/cluster/_kmeans.py#L1108"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.MiniBatchKMeans.transform" title="Permalink to this definition">¶</a></dt>
<dd><p>Transform X to a cluster-distance space.</p>
<p>In the new space, each dimension is the distance to the cluster
centers. Note that even if X is sparse, the array returned by
<code class="docutils literal notranslate"><span class="pre">transform</span></code> will typically be dense.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>X</strong><span class="classifier">{array-like, sparse matrix} of shape (n_samples, n_features)</span></dt><dd><p>New data to transform.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X_new</strong><span class="classifier">ndarray of shape (n_samples, n_clusters)</span></dt><dd><p>X transformed in the new space.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
</dd></dl>
<div class="section" id="examples-using-sklearn-cluster-minibatchkmeans">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.cluster.MiniBatchKMeans</span></code><a class="headerlink" href="#examples-using-sklearn-cluster-minibatchkmeans" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates the Spectral Co-clustering algorithm on the twenty newsgroups dataset..."><div class="figure align-default" id="id1">
<img alt="Biclustering documents with the Spectral Co-clustering algorithm" src="../../_images/sphx_glr_plot_bicluster_newsgroups_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/bicluster/plot_bicluster_newsgroups.html#sphx-glr-auto-examples-bicluster-plot-bicluster-newsgroups-py"><span class="std std-ref">Biclustering documents with the Spectral Co-clustering algorithm</span></a></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
</div>
</div><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..."><div class="figure align-default" id="id2">
<img alt="Online learning of a dictionary of parts of faces" src="../../_images/sphx_glr_plot_dict_face_patches_thumb.png" />
<p class="caption"><span class="caption-text"><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></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example compares the timing of Birch (with and without the global clustering step) and Min..."><div class="figure align-default" id="id3">
<img alt="Compare BIRCH and MiniBatchKMeans" src="../../_images/sphx_glr_plot_birch_vs_minibatchkmeans_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_birch_vs_minibatchkmeans.html#sphx-glr-auto-examples-cluster-plot-birch-vs-minibatchkmeans-py"><span class="std std-ref">Compare BIRCH and MiniBatchKMeans</span></a></span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="Evaluate the ability of k-means initializations strategies to make the algorithm convergence ro..."><div class="figure align-default" id="id4">
<img alt="Empirical evaluation of the impact of k-means initialization" src="../../_images/sphx_glr_plot_kmeans_stability_low_dim_dense_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_kmeans_stability_low_dim_dense.html#sphx-glr-auto-examples-cluster-plot-kmeans-stability-low-dim-dense-py"><span class="std std-ref">Empirical evaluation of the impact of k-means initialization</span></a></span><a class="headerlink" href="#id4" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="We want to compare the performance of the MiniBatchKMeans and KMeans: the MiniBatchKMeans is fa..."><div class="figure align-default" id="id5">
<img alt="Comparison of the K-Means and MiniBatchKMeans clustering algorithms" src="../../_images/sphx_glr_plot_mini_batch_kmeans_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_mini_batch_kmeans.html#sphx-glr-auto-examples-cluster-plot-mini-batch-kmeans-py"><span class="std std-ref">Comparison of the K-Means and MiniBatchKMeans clustering algorithms</span></a></span><a class="headerlink" href="#id5" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different clustering algorithms on datasets that are "int..."><div class="figure align-default" id="id6">
<img alt="Comparing different clustering algorithms on toy datasets" src="../../_images/sphx_glr_plot_cluster_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py"><span class="std std-ref">Comparing different clustering algorithms on toy datasets</span></a></span><a class="headerlink" href="#id6" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example applies to olivetti_faces_dataset different unsupervised matrix decomposition (dim..."><div class="figure align-default" id="id7">
<img alt="Faces dataset decompositions" src="../../_images/sphx_glr_plot_faces_decomposition_thumb.png" />
<p class="caption"><span class="caption-text"><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></span><a class="headerlink" href="#id7" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This is an example showing how the scikit-learn can be used to cluster documents by topics usin..."><div class="figure align-default" id="id8">
<img alt="Clustering text documents using k-means" src="../../_images/sphx_glr_plot_document_clustering_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/text/plot_document_clustering.html#sphx-glr-auto-examples-text-plot-document-clustering-py"><span class="std std-ref">Clustering text documents using k-means</span></a></span><a class="headerlink" href="#id8" title="Permalink to this image">¶</a></p>
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