.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_basic_clustering.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_basic_clustering.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_basic_clustering.py:


===================================================================
DenMune: Basic Clustering of Non-Convex Shapes
===================================================================

This example demonstrates the primary use case of the DenMune algorithm:
identifying clusters of arbitrary shapes.

We generate a 'moons' dataset, which is a classic benchmark for clustering
algorithms that struggle with non-convex data, such as K-Means.

The plot shows that DenMune, similar to DBSCAN, can successfully separate the
two moon-shaped clusters. It also identifies "strong" points (core samples)
which form the skeleton of the clusters, and "weak" points (boundary samples)
that are attached to them.

.. GENERATED FROM PYTHON SOURCE LINES 17-25

.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np
    from sklearn.datasets import make_moons
    from sklearn.preprocessing import StandardScaler

    from denmune_skl import DenMune








.. GENERATED FROM PYTHON SOURCE LINES 26-28

Generate and prepare the data
------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 28-31

.. code-block:: Python

    X, y = make_moons(n_samples=250, noise=0.07, random_state=42)
    X = StandardScaler().fit_transform(X)








.. GENERATED FROM PYTHON SOURCE LINES 32-36

Fit the DenMune model
---------------------
We choose a `k_nearest` value that is appropriate for the density of the dataset.
Since the data is already 2D, we set `reduce_dims=False`.

.. GENERATED FROM PYTHON SOURCE LINES 36-43

.. code-block:: Python

    model = DenMune(k_nearest=20, reduce_dims=False, random_state=42)
    model.fit(X)
    labels = model.labels_
    n_clusters = model.n_clusters_

    print(f"Estimated number of clusters: {n_clusters}")





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Estimated number of clusters: 2




.. GENERATED FROM PYTHON SOURCE LINES 44-48

Visualize the clustering results
---------------------------------
We create a visualization that distinguishes between core points (strong points)
and boundary points (weak points).

.. GENERATED FROM PYTHON SOURCE LINES 48-92

.. code-block:: Python

    core_samples_mask = np.zeros_like(labels, dtype=bool)
    core_samples_mask[model.core_sample_indices_] = True
    unique_labels = set(labels)

    fig, ax = plt.subplots(figsize=(8, 6))
    colors = [plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))]

    for k, col in zip(unique_labels, colors):
        if k == -1:
            # Black used for noise.
            col = [0, 0, 0, 1]

        class_member_mask = labels == k

        # Plot core samples with larger markers
        xy = X[class_member_mask & core_samples_mask]
        ax.plot(
            xy[:, 0],
            xy[:, 1],
            "o",
            markerfacecolor=tuple(col),
            markeredgecolor="k",
            markersize=12,
            label=f"Cluster {k}",
        )

        # Plot non-core samples with smaller markers
        xy = X[class_member_mask & ~core_samples_mask]
        ax.plot(
            xy[:, 0],
            xy[:, 1],
            "o",
            markerfacecolor=tuple(col),
            markeredgecolor="k",
            markersize=6,
        )

    ax.set_title(
        f"DenMune Clustering (k={model.k_nearest})\nEstimated clusters: {n_clusters}"
    )
    ax.set_xlabel("Feature 1")
    ax.set_ylabel("Feature 2")
    ax.legend()
    plt.show()



.. image-sg:: /auto_examples/images/sphx_glr_plot_basic_clustering_001.png
   :alt: DenMune Clustering (k=20) Estimated clusters: 2
   :srcset: /auto_examples/images/sphx_glr_plot_basic_clustering_001.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.193 seconds)


.. _sphx_glr_download_auto_examples_plot_basic_clustering.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_basic_clustering.ipynb <plot_basic_clustering.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_basic_clustering.py <plot_basic_clustering.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_basic_clustering.zip <plot_basic_clustering.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_