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.. "auto_examples/models/mixed_spn.py"
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.. only:: html

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

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

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

.. _sphx_glr_auto_examples_models_mixed_spn.py:


=========
Mixed SPN
=========

This demonstrates learning a Mixed Sum-Product Network (MSPN) where the data is
composed of variables drawn from multiple types of distributions.

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.. code-block:: default


    import numpy as np

    np.random.seed(123)

    from spn.algorithms.LearningWrappers import learn_mspn
    from spn.structure.Base import Context
    from spn.structure.StatisticalTypes import MetaType


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We will compose a data set from four features:

- two Discrete
- two Real valued

A and B are discrete, C and D are real-valued.

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.. code-block:: default


    a = np.random.randint(2, size=1000).reshape(-1, 1)
    b = np.random.randint(3, size=1000).reshape(-1, 1)
    c = np.r_[np.random.normal(10, 5, (300, 1)), np.random.normal(20, 10, (700, 1))]
    d = 5 * a + 3 * b + c

    train_data = np.c_[a, b, c, d]


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The types of distributions are known ahead of time, so we can add these
as ``meta_types`` in the ``Context``:

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.. code-block:: default


    ds_context = Context(meta_types=[MetaType.DISCRETE, MetaType.DISCRETE, MetaType.REAL, MetaType.REAL])
    ds_context.add_domains(train_data)


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Finally, we learn the MSPN:

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.. code-block:: default


    mspn = learn_mspn(train_data, ds_context, min_instances_slice=20)


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

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_models_mixed_spn.py:


.. only :: html

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

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



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

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


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