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

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

.. _sphx_glr_auto_examples_mixture_plot_gmm_sin.py:


=================================
Gaussian Mixture Model Sine Curve
=================================

This example demonstrates the behavior of Gaussian mixture models fit on data
that was not sampled from a mixture of Gaussian random variables. The dataset
is formed by 100 points loosely spaced following a noisy sine curve. There is
therefore no ground truth value for the number of Gaussian components.

The first model is a classical Gaussian Mixture Model with 10 components fit
with the Expectation-Maximization algorithm.

The second model is a Bayesian Gaussian Mixture Model with a Dirichlet process
prior fit with variational inference. The low value of the concentration prior
makes the model favor a lower number of active components. This models
"decides" to focus its modeling power on the big picture of the structure of
the dataset: groups of points with alternating directions modeled by
non-diagonal covariance matrices. Those alternating directions roughly capture
the alternating nature of the original sine signal.

The third model is also a Bayesian Gaussian mixture model with a Dirichlet
process prior but this time the value of the concentration prior is higher
giving the model more liberty to model the fine-grained structure of the data.
The result is a mixture with a larger number of active components that is
similar to the first model where we arbitrarily decided to fix the number of
components to 10.

Which model is the best is a matter of subjective judgment: do we want to
favor models that only capture the big picture to summarize and explain most of
the structure of the data while ignoring the details or do we prefer models
that closely follow the high density regions of the signal?

The last two panels show how we can sample from the last two models. The
resulting samples distributions do not look exactly like the original data
distribution. The difference primarily stems from the approximation error we
made by using a model that assumes that the data was generated by a finite
number of Gaussian components instead of a continuous noisy sine curve.

.. GENERATED FROM PYTHON SOURCE LINES 41-188



.. image-sg:: /auto_examples/mixture/images/sphx_glr_plot_gmm_sin_001.png
   :alt: Expectation-maximization, Bayesian Gaussian mixture models with a Dirichlet process prior for $\gamma_0=0.01$., Gaussian mixture with a Dirichlet process prior for $\gamma_0=0.01$ sampled with $2000$ samples., Bayesian Gaussian mixture models with a Dirichlet process prior for $\gamma_0=100$, Gaussian mixture with a Dirichlet process prior for $\gamma_0=100$ sampled with $2000$ samples.
   :srcset: /auto_examples/mixture/images/sphx_glr_plot_gmm_sin_001.png
   :class: sphx-glr-single-img





.. code-block:: default


    import itertools

    import matplotlib as mpl
    import matplotlib.pyplot as plt
    import numpy as np
    from scipy import linalg

    from sklearn import mixture

    color_iter = itertools.cycle(["navy", "c", "cornflowerblue", "gold", "darkorange"])


    def plot_results(X, Y, means, covariances, index, title):
        splot = plt.subplot(5, 1, 1 + index)
        for i, (mean, covar, color) in enumerate(zip(means, covariances, color_iter)):
            v, w = linalg.eigh(covar)
            v = 2.0 * np.sqrt(2.0) * np.sqrt(v)
            u = w[0] / linalg.norm(w[0])
            # as the DP will not use every component it has access to
            # unless it needs it, we shouldn't plot the redundant
            # components.
            if not np.any(Y == i):
                continue
            plt.scatter(X[Y == i, 0], X[Y == i, 1], 0.8, color=color)

            # Plot an ellipse to show the Gaussian component
            angle = np.arctan(u[1] / u[0])
            angle = 180.0 * angle / np.pi  # convert to degrees
            ell = mpl.patches.Ellipse(mean, v[0], v[1], angle=180.0 + angle, color=color)
            ell.set_clip_box(splot.bbox)
            ell.set_alpha(0.5)
            splot.add_artist(ell)

        plt.xlim(-6.0, 4.0 * np.pi - 6.0)
        plt.ylim(-5.0, 5.0)
        plt.title(title)
        plt.xticks(())
        plt.yticks(())


    def plot_samples(X, Y, n_components, index, title):
        plt.subplot(5, 1, 4 + index)
        for i, color in zip(range(n_components), color_iter):
            # as the DP will not use every component it has access to
            # unless it needs it, we shouldn't plot the redundant
            # components.
            if not np.any(Y == i):
                continue
            plt.scatter(X[Y == i, 0], X[Y == i, 1], 0.8, color=color)

        plt.xlim(-6.0, 4.0 * np.pi - 6.0)
        plt.ylim(-5.0, 5.0)
        plt.title(title)
        plt.xticks(())
        plt.yticks(())


    # Parameters
    n_samples = 100

    # Generate random sample following a sine curve
    np.random.seed(0)
    X = np.zeros((n_samples, 2))
    step = 4.0 * np.pi / n_samples

    for i in range(X.shape[0]):
        x = i * step - 6.0
        X[i, 0] = x + np.random.normal(0, 0.1)
        X[i, 1] = 3.0 * (np.sin(x) + np.random.normal(0, 0.2))

    plt.figure(figsize=(10, 10))
    plt.subplots_adjust(
        bottom=0.04, top=0.95, hspace=0.2, wspace=0.05, left=0.03, right=0.97
    )

    # Fit a Gaussian mixture with EM using ten components
    gmm = mixture.GaussianMixture(
        n_components=10, covariance_type="full", max_iter=100
    ).fit(X)
    plot_results(
        X, gmm.predict(X), gmm.means_, gmm.covariances_, 0, "Expectation-maximization"
    )

    dpgmm = mixture.BayesianGaussianMixture(
        n_components=10,
        covariance_type="full",
        weight_concentration_prior=1e-2,
        weight_concentration_prior_type="dirichlet_process",
        mean_precision_prior=1e-2,
        covariance_prior=1e0 * np.eye(2),
        init_params="random",
        max_iter=100,
        random_state=2,
    ).fit(X)
    plot_results(
        X,
        dpgmm.predict(X),
        dpgmm.means_,
        dpgmm.covariances_,
        1,
        "Bayesian Gaussian mixture models with a Dirichlet process prior "
        r"for $\gamma_0=0.01$.",
    )

    X_s, y_s = dpgmm.sample(n_samples=2000)
    plot_samples(
        X_s,
        y_s,
        dpgmm.n_components,
        0,
        "Gaussian mixture with a Dirichlet process prior "
        r"for $\gamma_0=0.01$ sampled with $2000$ samples.",
    )

    dpgmm = mixture.BayesianGaussianMixture(
        n_components=10,
        covariance_type="full",
        weight_concentration_prior=1e2,
        weight_concentration_prior_type="dirichlet_process",
        mean_precision_prior=1e-2,
        covariance_prior=1e0 * np.eye(2),
        init_params="kmeans",
        max_iter=100,
        random_state=2,
    ).fit(X)
    plot_results(
        X,
        dpgmm.predict(X),
        dpgmm.means_,
        dpgmm.covariances_,
        2,
        "Bayesian Gaussian mixture models with a Dirichlet process prior "
        r"for $\gamma_0=100$",
    )

    X_s, y_s = dpgmm.sample(n_samples=2000)
    plot_samples(
        X_s,
        y_s,
        dpgmm.n_components,
        1,
        "Gaussian mixture with a Dirichlet process prior "
        r"for $\gamma_0=100$ sampled with $2000$ samples.",
    )

    plt.show()


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

   **Total running time of the script:** (0 minutes 0.306 seconds)


.. _sphx_glr_download_auto_examples_mixture_plot_gmm_sin.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/1.3.X?urlpath=lab/tree/notebooks/auto_examples/mixture/plot_gmm_sin.ipynb
        :alt: Launch binder
        :width: 150 px



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

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

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

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


.. only:: html

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

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