.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/mixture/plot_gmm_pdf.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_mixture_plot_gmm_pdf.py>`
        to download the full example code or to run this example in your browser via JupyterLite or Binder

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

.. _sphx_glr_auto_examples_mixture_plot_gmm_pdf.py:


=========================================
Density Estimation for a Gaussian mixture
=========================================

Plot the density estimation of a mixture of two Gaussians. Data is
generated from two Gaussians with different centers and covariance
matrices.

.. GENERATED FROM PYTHON SOURCE LINES 11-54



.. image-sg:: /auto_examples/mixture/images/sphx_glr_plot_gmm_pdf_001.png
   :alt: Negative log-likelihood predicted by a GMM
   :srcset: /auto_examples/mixture/images/sphx_glr_plot_gmm_pdf_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np
    from matplotlib.colors import LogNorm

    from sklearn import mixture

    n_samples = 300

    # generate random sample, two components
    np.random.seed(0)

    # generate spherical data centered on (20, 20)
    shifted_gaussian = np.random.randn(n_samples, 2) + np.array([20, 20])

    # generate zero centered stretched Gaussian data
    C = np.array([[0.0, -0.7], [3.5, 0.7]])
    stretched_gaussian = np.dot(np.random.randn(n_samples, 2), C)

    # concatenate the two datasets into the final training set
    X_train = np.vstack([shifted_gaussian, stretched_gaussian])

    # fit a Gaussian Mixture Model with two components
    clf = mixture.GaussianMixture(n_components=2, covariance_type="full")
    clf.fit(X_train)

    # display predicted scores by the model as a contour plot
    x = np.linspace(-20.0, 30.0)
    y = np.linspace(-20.0, 40.0)
    X, Y = np.meshgrid(x, y)
    XX = np.array([X.ravel(), Y.ravel()]).T
    Z = -clf.score_samples(XX)
    Z = Z.reshape(X.shape)

    CS = plt.contour(
        X, Y, Z, norm=LogNorm(vmin=1.0, vmax=1000.0), levels=np.logspace(0, 3, 10)
    )
    CB = plt.colorbar(CS, shrink=0.8, extend="both")
    plt.scatter(X_train[:, 0], X_train[:, 1], 0.8)

    plt.title("Negative log-likelihood predicted by a GMM")
    plt.axis("tight")
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.117 seconds)


.. _sphx_glr_download_auto_examples_mixture_plot_gmm_pdf.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.4.X?urlpath=lab/tree/notebooks/auto_examples/mixture/plot_gmm_pdf.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/?path=auto_examples/mixture/plot_gmm_pdf.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

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


.. include:: plot_gmm_pdf.recommendations


.. only:: html

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

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