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<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.datasets</span></code>.make_moons</a><ul>
<li><a class="reference internal" href="#examples-using-sklearn-datasets-make-moons">Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.make_moons</span></code></a></li>
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<div class="section" id="sklearn-datasets-make-moons">
<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>.make_moons<a class="headerlink" href="#sklearn-datasets-make-moons" title="Permalink to this headline">¶</a></h1>
<dl class="py function">
<dt id="sklearn.datasets.make_moons">
<code class="sig-prename descclassname">sklearn.datasets.</code><code class="sig-name descname">make_moons</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">n_samples</span><span class="o">=</span><span class="default_value">100</span></em>, <em class="sig-param"><span class="o">*</span></em>, <em class="sig-param"><span class="n">shuffle</span><span class="o">=</span><span class="default_value">True</span></em>, <em class="sig-param"><span class="n">noise</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">random_state</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/datasets/_samples_generator.py#L683"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.datasets.make_moons" title="Permalink to this definition">¶</a></dt>
<dd><p>Make two interleaving half circles.</p>
<p>A simple toy dataset to visualize clustering and classification
algorithms. Read more in the <a class="reference internal" href="../../datasets/sample_generators.html#sample-generators"><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_samples</strong><span class="classifier">int or tuple of shape (2,), dtype=int, default=100</span></dt><dd><p>If int, the total number of points generated.
If two-element tuple, number of points in each of two moons.</p>
<div class="versionchanged">
<p><span class="versionmodified changed">Changed in version 0.23: </span>Added two-element tuple.</p>
</div>
</dd>
<dt><strong>shuffle</strong><span class="classifier">bool, default=True</span></dt><dd><p>Whether to shuffle the samples.</p>
</dd>
<dt><strong>noise</strong><span class="classifier">float, default=None</span></dt><dd><p>Standard deviation of Gaussian noise added to the data.</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 dataset shuffling and noise.
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>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>X</strong><span class="classifier">ndarray of shape (n_samples, 2)</span></dt><dd><p>The generated samples.</p>
</dd>
<dt><strong>y</strong><span class="classifier">ndarray of shape (n_samples,)</span></dt><dd><p>The integer labels (0 or 1) for class membership of each sample.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<div class="section" id="examples-using-sklearn-datasets-make-moons">
<h2>Examples using <code class="docutils literal notranslate"><span class="pre">sklearn.datasets.make_moons</span></code><a class="headerlink" href="#examples-using-sklearn-datasets-make-moons" title="Permalink to this headline">¶</a></h2>
<div class="sphx-glr-thumbcontainer" tooltip="A comparison of a several classifiers in scikit-learn on synthetic datasets. The point of this ..."><div class="figure align-default" id="id1">
<img alt="Classifier comparison" src="../../_images/sphx_glr_plot_classifier_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/classification/plot_classifier_comparison.html#sphx-glr-auto-examples-classification-plot-classifier-comparison-py"><span class="std std-ref">Classifier comparison</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 shows characteristics of different linkage methods for hierarchical clustering on ..."><div class="figure align-default" id="id2">
<img alt="Comparing different hierarchical linkage methods on toy datasets" src="../../_images/sphx_glr_plot_linkage_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/cluster/plot_linkage_comparison.html#sphx-glr-auto-examples-cluster-plot-linkage-comparison-py"><span class="std std-ref">Comparing different hierarchical linkage methods on toy datasets</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 shows characteristics of different clustering algorithms on datasets that are "int..."><div class="figure align-default" id="id3">
<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="#id3" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows characteristics of different anomaly detection algorithms on 2D datasets. Da..."><div class="figure align-default" id="id4">
<img alt="Comparing anomaly detection algorithms for outlier detection on toy datasets" src="../../_images/sphx_glr_plot_anomaly_comparison_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/miscellaneous/plot_anomaly_comparison.html#sphx-glr-auto-examples-miscellaneous-plot-anomaly-comparison-py"><span class="std std-ref">Comparing anomaly detection algorithms for outlier detection on toy datasets</span></a></span><a class="headerlink" href="#id4" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="This example illustrates how to statistically compare the performance of models trained and eva..."><div class="figure align-default" id="id5">
<img alt="Statistical comparison of models using grid search" src="../../_images/sphx_glr_plot_grid_search_stats_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/model_selection/plot_grid_search_stats.html#sphx-glr-auto-examples-model-selection-plot-grid-search-stats-py"><span class="std std-ref">Statistical comparison of models using grid search</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 visualizes some training loss curves for different stochastic learning strategies,..."><div class="figure align-default" id="id6">
<img alt="Compare Stochastic learning strategies for MLPClassifier" src="../../_images/sphx_glr_plot_mlp_training_curves_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neural_networks/plot_mlp_training_curves.html#sphx-glr-auto-examples-neural-networks-plot-mlp-training-curves-py"><span class="std std-ref">Compare Stochastic learning strategies for MLPClassifier</span></a></span><a class="headerlink" href="#id6" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A comparison of different values for regularization parameter 'alpha' on synthetic datasets. Th..."><div class="figure align-default" id="id7">
<img alt="Varying regularization in Multi-layer Perceptron" src="../../_images/sphx_glr_plot_mlp_alpha_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/neural_networks/plot_mlp_alpha.html#sphx-glr-auto-examples-neural-networks-plot-mlp-alpha-py"><span class="std std-ref">Varying regularization in Multi-layer Perceptron</span></a></span><a class="headerlink" href="#id7" title="Permalink to this image">¶</a></p>
</div>
</div><div class="sphx-glr-thumbcontainer" tooltip="A demonstration of feature discretization on synthetic classification datasets. Feature discret..."><div class="figure align-default" id="id8">
<img alt="Feature discretization" src="../../_images/sphx_glr_plot_discretization_classification_thumb.png" />
<p class="caption"><span class="caption-text"><a class="reference internal" href="../../auto_examples/preprocessing/plot_discretization_classification.html#sphx-glr-auto-examples-preprocessing-plot-discretization-classification-py"><span class="std std-ref">Feature discretization</span></a></span><a class="headerlink" href="#id8" title="Permalink to this image">¶</a></p>
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var top = target.getBoundingClientRect().top;
return (top < 2) && (top > -2);
};
// Hide navbar on load if hash target is on top
var navBar = document.getElementById("navbar");
var navBarToggler = document.getElementById("sk-navbar-toggler");
var navBarHeightHidden = "-" + navBar.getBoundingClientRect().height + "px";
var $window = $(window);
hideNavBar = function() {
navBar.style.top = navBarHeightHidden;
};
showNavBar = function() {
navBar.style.top = "0";
}
if (hashTargetOnTop()) {
hideNavBar()
}
var prevScrollpos = window.pageYOffset;
hideOnScroll = function(lastScrollTop) {
if (($window.width() < 768) && (navBarToggler.getAttribute("aria-expanded") === 'true')) {
return;
}
if (lastScrollTop > 2 && (prevScrollpos <= lastScrollTop) || hashTargetOnTop()){
hideNavBar()
} else {
showNavBar()
}
prevScrollpos = lastScrollTop;
};
/*** high performance scroll event listener***/
var raf = window.requestAnimationFrame ||
window.webkitRequestAnimationFrame ||
window.mozRequestAnimationFrame ||
window.msRequestAnimationFrame ||
window.oRequestAnimationFrame;
var lastScrollTop = $window.scrollTop();
if (raf) {
loop();
}
function loop() {
var scrollTop = $window.scrollTop();
if (lastScrollTop === scrollTop) {
raf(loop);
return;
} else {
lastScrollTop = scrollTop;
hideOnScroll(lastScrollTop);
raf(loop);
}
}
})();
});
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