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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_plot_hmm_sampling.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_plot_hmm_sampling.py:


Sampling from HMM
-----------------

This script shows how to sample points from a Hidden Markov Model (HMM):
we use a 4-state model with specified mean and covariance.

The plot show the sequence of observations generated with the transitions
between them. We can see that, as specified by our transition matrix,
there are no transition between component 1 and 3.

.. GENERATED FROM PYTHON SOURCE LINES 12-19

.. code-block:: default

    print(__doc__)

    import numpy as np
    import matplotlib.pyplot as plt

    from hmmlearn import hmm








.. GENERATED FROM PYTHON SOURCE LINES 20-22

Prepare parameters for a 4-components HMM
Initial population probability

.. GENERATED FROM PYTHON SOURCE LINES 22-46

.. code-block:: default

    startprob = np.array([0.6, 0.3, 0.1, 0.0])
    # The transition matrix, note that there are no transitions possible
    # between component 1 and 3
    transmat = np.array([[0.7, 0.2, 0.0, 0.1],
                         [0.3, 0.5, 0.2, 0.0],
                         [0.0, 0.3, 0.5, 0.2],
                         [0.2, 0.0, 0.2, 0.6]])
    # The means of each component
    means = np.array([[0.0,  0.0],
                      [0.0, 11.0],
                      [9.0, 10.0],
                      [11.0, -1.0]])
    # The covariance of each component
    covars = .5 * np.tile(np.identity(2), (4, 1, 1))

    # Build an HMM instance and set parameters
    model = hmm.GaussianHMM(n_components=4, covariance_type="full")

    # Instead of fitting it from the data, we directly set the estimated
    # parameters, the means and covariance of the components
    model.startprob_ = startprob
    model.transmat_ = transmat
    model.means_ = means
    model.covars_ = covars







.. GENERATED FROM PYTHON SOURCE LINES 47-62

.. code-block:: default


    # Generate samples
    X, Z = model.sample(500)

    # Plot the sampled data
    plt.plot(X[:, 0], X[:, 1], ".-", label="observations", ms=6,
             mfc="orange", alpha=0.7)

    # Indicate the component numbers
    for i, m in enumerate(means):
        plt.text(m[0], m[1], 'Component %i' % (i + 1),
                 size=17, horizontalalignment='center',
                 bbox=dict(alpha=.7, facecolor='w'))
    plt.legend(loc='best')
    plt.show()



.. image:: /auto_examples/images/sphx_glr_plot_hmm_sampling_001.png
    :alt: plot hmm sampling
    :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 0 minutes  0.696 seconds)


.. _sphx_glr_download_auto_examples_plot_hmm_sampling.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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