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<ul>
<li><a class="reference internal" href="#"><code class="docutils literal"><span class="pre">sklearn.datasets</span></code>.make_blobs</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-make-blobs">Examples using <code class="docutils literal"><span class="pre">sklearn.datasets.make_blobs</span></code></a></li>
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<div class="section" id="sklearn-datasets-make-blobs">
<h1><a class="reference internal" href="../classes.html#module-sklearn.datasets" title="sklearn.datasets"><code class="xref py py-mod docutils literal"><span class="pre">sklearn.datasets</span></code></a>.make_blobs<a class="headerlink" href="#sklearn-datasets-make-blobs" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.datasets.make_blobs">
<code class="descclassname">sklearn.datasets.</code><code class="descname">make_blobs</code><span class="sig-paren">(</span><em>n_samples=100</em>, <em>n_features=2</em>, <em>centers=3</em>, <em>cluster_std=1.0</em>, <em>center_box=(-10.0</em>, <em>10.0)</em>, <em>shuffle=True</em>, <em>random_state=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/51a765a/sklearn/datasets/samples_generator.py#L682"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.datasets.make_blobs" title="Permalink to this definition">¶</a></dt>
<dd><p>Generate isotropic Gaussian blobs for clustering.</p>
<p>Read more in the <a class="reference internal" href="../../datasets/index.html#sample-generators"><span>User Guide</span></a>.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><p class="first"><strong>n_samples</strong> : int, optional (default=100)</p>
<blockquote>
<div><p>The total number of points equally divided among clusters.</p>
</div></blockquote>
<p><strong>n_features</strong> : int, optional (default=2)</p>
<blockquote>
<div><p>The number of features for each sample.</p>
</div></blockquote>
<p><strong>centers</strong> : int or array of shape [n_centers, n_features], optional</p>
<blockquote>
<div><p>(default=3)
The number of centers to generate, or the fixed center locations.</p>
</div></blockquote>
<p><strong>cluster_std: float or sequence of floats, optional (default=1.0)</strong> :</p>
<blockquote>
<div><p>The standard deviation of the clusters.</p>
</div></blockquote>
<p><strong>center_box: pair of floats (min, max), optional (default=(-10.0, 10.0))</strong> :</p>
<blockquote>
<div><p>The bounding box for each cluster center when centers are
generated at random.</p>
</div></blockquote>
<p><strong>shuffle</strong> : boolean, optional (default=True)</p>
<blockquote>
<div><p>Shuffle the samples.</p>
</div></blockquote>
<p><strong>random_state</strong> : int, RandomState instance or None, optional (default=None)</p>
<blockquote>
<div><p>If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by <cite>np.random</cite>.</p>
</div></blockquote>
</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body"><p class="first"><strong>X</strong> : array of shape [n_samples, n_features]</p>
<blockquote>
<div><p>The generated samples.</p>
</div></blockquote>
<p><strong>y</strong> : array of shape [n_samples]</p>
<blockquote class="last">
<div><p>The integer labels for cluster membership of each sample.</p>
</div></blockquote>
</td>
</tr>
</tbody>
</table>
<div class="admonition seealso">
<p class="first admonition-title">See also</p>
<dl class="last docutils">
<dt><a class="reference internal" href="sklearn.datasets.make_classification.html#sklearn.datasets.make_classification" title="sklearn.datasets.make_classification"><code class="xref py py-obj docutils literal"><span class="pre">make_classification</span></code></a></dt>
<dd>a more intricate variant</dd>
</dl>
</div>
<p class="rubric">Examples</p>
<div class="highlight-python"><div class="highlight"><pre><span class="gp">>>> </span><span class="kn">from</span> <span class="nn">sklearn.datasets.samples_generator</span> <span class="kn">import</span> <span class="n">make_blobs</span>
<span class="gp">>>> </span><span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">n_features</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="k">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="go">(10, 2)</span>
<span class="gp">>>> </span><span class="n">y</span>
<span class="go">array([0, 0, 1, 0, 2, 2, 2, 1, 1, 0])</span>
</pre></div>
</div>
</dd></dl>
<div class="section" id="examples-using-sklearn-datasets-make-blobs">
<h2>Examples using <code class="docutils literal"><span class="pre">sklearn.datasets.make_blobs</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-make-blobs" title="Permalink to this headline">¶</a></h2>
<div class="thumbnailContainer" tooltip="When performing classification you often want to predict not only the class label, but also the..."><div class="figure" id="id1">
<a class="reference external image-reference" href="./../../auto_examples/calibration/plot_calibration.html"><img alt="../../_images/plot_calibration1.png" src="../../_images/plot_calibration1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/calibration/plot_calibration.html#example-calibration-plot-calibration-py"><span>Probability calibration of classifiers</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="This example illustrates how sigmoid calibration changes predicted probabilities for a 3-class ..."><div class="figure" id="id2">
<a class="reference external image-reference" href="./../../auto_examples/calibration/plot_calibration_multiclass.html"><img alt="../../_images/plot_calibration_multiclass1.png" src="../../_images/plot_calibration_multiclass1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/calibration/plot_calibration_multiclass.html#example-calibration-plot-calibration-multiclass-py"><span>Probability Calibration for 3-class classification</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="Shows how shrinkage improves classification. "><div class="figure" id="id3">
<a class="reference external image-reference" href="./../../auto_examples/classification/plot_lda.html"><img alt="../../_images/plot_lda1.png" src="../../_images/plot_lda1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/classification/plot_lda.html#example-classification-plot-lda-py"><span>Normal and Shrinkage Linear Discriminant Analysis for classification</span></a></span></p>
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</div><div class="thumbnailContainer" tooltip="Reference:"><div class="figure" id="id4">
<a class="reference external image-reference" href="./../../auto_examples/cluster/plot_mean_shift.html"><img alt="../../_images/plot_mean_shift1.png" src="../../_images/plot_mean_shift1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_mean_shift.html#example-cluster-plot-mean-shift-py"><span>A demo of the mean-shift clustering algorithm</span></a></span></p>
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</div><div class="thumbnailContainer" tooltip="Reference: Brendan J. Frey and Delbert Dueck, "Clustering by Passing Messages Between Data Poin..."><div class="figure" id="id5">
<a class="reference external image-reference" href="./../../auto_examples/cluster/plot_affinity_propagation.html"><img alt="../../_images/plot_affinity_propagation1.png" src="../../_images/plot_affinity_propagation1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_affinity_propagation.html#example-cluster-plot-affinity-propagation-py"><span>Demo of affinity propagation clustering algorithm</span></a></span></p>
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</div><div class="thumbnailContainer" tooltip="This example is meant to illustrate situations where k-means will produce unintuitive and possi..."><div class="figure" id="id6">
<a class="reference external image-reference" href="./../../auto_examples/cluster/plot_kmeans_assumptions.html"><img alt="../../_images/plot_kmeans_assumptions1.png" src="../../_images/plot_kmeans_assumptions1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_kmeans_assumptions.html#example-cluster-plot-kmeans-assumptions-py"><span>Demonstration of k-means assumptions</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="Finds core samples of high density and expands clusters from them."><div class="figure" id="id7">
<a class="reference external image-reference" href="./../../auto_examples/cluster/plot_dbscan.html"><img alt="../../_images/plot_dbscan1.png" src="../../_images/plot_dbscan1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_dbscan.html#example-cluster-plot-dbscan-py"><span>Demo of DBSCAN clustering algorithm</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="This example compares the timing of Birch (with and without the global clustering step) and Min..."><div class="figure" id="id8">
<a class="reference external image-reference" href="./../../auto_examples/cluster/plot_birch_vs_minibatchkmeans.html"><img alt="../../_images/plot_birch_vs_minibatchkmeans1.png" src="../../_images/plot_birch_vs_minibatchkmeans1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_birch_vs_minibatchkmeans.html#example-cluster-plot-birch-vs-minibatchkmeans-py"><span>Compare BIRCH and MiniBatchKMeans</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="This example aims at showing characteristics of different clustering algorithms on datasets tha..."><div class="figure" id="id9">
<a class="reference external image-reference" href="./../../auto_examples/cluster/plot_cluster_comparison.html"><img alt="../../_images/plot_cluster_comparison1.png" src="../../_images/plot_cluster_comparison1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_cluster_comparison.html#example-cluster-plot-cluster-comparison-py"><span>Comparing different clustering algorithms on toy datasets</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="We want to compare the performance of the MiniBatchKMeans and KMeans: the MiniBatchKMeans is fa..."><div class="figure" id="id10">
<a class="reference external image-reference" href="./../../auto_examples/cluster/plot_mini_batch_kmeans.html"><img alt="../../_images/plot_mini_batch_kmeans1.png" src="../../_images/plot_mini_batch_kmeans1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_mini_batch_kmeans.html#example-cluster-plot-mini-batch-kmeans-py"><span>Comparison of the K-Means and MiniBatchKMeans clustering algorithms</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="Silhouette analysis can be used to study the separation distance between the resulting clusters..."><div class="figure" id="id11">
<a class="reference external image-reference" href="./../../auto_examples/cluster/plot_kmeans_silhouette_analysis.html"><img alt="../../_images/plot_kmeans_silhouette_analysis1.png" src="../../_images/plot_kmeans_silhouette_analysis1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_kmeans_silhouette_analysis.html#example-cluster-plot-kmeans-silhouette-analysis-py"><span>Selecting the number of clusters with silhouette analysis on KMeans clustering</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="Plot several randomly generated 2D classification datasets. This example illustrates the :func:..."><div class="figure" id="id12">
<a class="reference external image-reference" href="./../../auto_examples/datasets/plot_random_dataset.html"><img alt="../../_images/plot_random_dataset1.png" src="../../_images/plot_random_dataset1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/datasets/plot_random_dataset.html#example-datasets-plot-random-dataset-py"><span>Plot randomly generated classification dataset</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="Plot the maximum margin separating hyperplane within a two-class separable dataset using a line..."><div class="figure" id="id13">
<a class="reference external image-reference" href="./../../auto_examples/linear_model/plot_sgd_separating_hyperplane.html"><img alt="../../_images/plot_sgd_separating_hyperplane1.png" src="../../_images/plot_sgd_separating_hyperplane1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/linear_model/plot_sgd_separating_hyperplane.html#example-linear-model-plot-sgd-separating-hyperplane-py"><span>SGD: Maximum margin separating hyperplane</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="This example demonstrates the behaviour of the accuracy of the nearest neighbor queries of Loca..."><div class="figure" id="id14">
<a class="reference external image-reference" href="./../../auto_examples/neighbors/plot_approximate_nearest_neighbors_hyperparameters.html"><img alt="../../_images/plot_approximate_nearest_neighbors_hyperparameters1.png" src="../../_images/plot_approximate_nearest_neighbors_hyperparameters1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neighbors/plot_approximate_nearest_neighbors_hyperparameters.html#example-neighbors-plot-approximate-nearest-neighbors-hyperparameters-py"><span>Hyper-parameters of Approximate Nearest Neighbors</span></a></span></p>
</div>
</div><div class="thumbnailContainer" tooltip="This example studies the scalability profile of approximate 10-neighbors queries using the LSHF..."><div class="figure" id="id15">
<a class="reference external image-reference" href="./../../auto_examples/neighbors/plot_approximate_nearest_neighbors_scalability.html"><img alt="../../_images/plot_approximate_nearest_neighbors_scalability1.png" src="../../_images/plot_approximate_nearest_neighbors_scalability1.png" /></a>
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neighbors/plot_approximate_nearest_neighbors_scalability.html#example-neighbors-plot-approximate-nearest-neighbors-scalability-py"><span>Scalability of Approximate Nearest Neighbors</span></a></span></p>
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