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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_models_cnets.py:


=======================
Cutset Networks (CNets)
=======================

With SPFlow we can learn both the structure and the parameters of CNets, a
particular kind of SPNs with CLTs as leaf providing exact MPE inference.

.. GENERATED FROM PYTHON SOURCE LINES 9-55

.. code-block:: default


    import numpy as np

    np.random.seed(123)


    from spn.structure.leaves.cltree.CLTree import create_cltree_leaf
    from spn.structure.Base import Context
    from spn.structure.leaves.parametric.Parametric import Bernoulli
    from spn.algorithms.LearningWrappers import learn_parametric, learn_cnet
    from spn.algorithms.Inference import log_likelihood
    from spn.algorithms.MPE import mpe

    train_data = np.random.binomial(1, [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.1], size=(100, 10))

    ds_context = Context(
        parametric_types=[
            Bernoulli,
            Bernoulli,
            Bernoulli,
            Bernoulli,
            Bernoulli,
            Bernoulli,
            Bernoulli,
            Bernoulli,
            Bernoulli,
            Bernoulli,
        ]
    ).add_domains(train_data)

    # Learning a CNet with a naive mle conditioning
    cnet_naive_mle = learn_cnet(train_data, ds_context, cond="naive_mle", min_instances_slice=20, min_features_slice=1)

    # Learning a CNet with random conditioning
    cnet_random = learn_cnet(train_data, ds_context, cond="random", min_instances_slice=20, min_features_slice=1)

    ll = log_likelihood(cnet_naive_mle, train_data)
    print("Naive mle conditioning", np.mean(ll))

    ll = log_likelihood(cnet_random, train_data)
    print("Random conditioning", np.mean(ll))

    # computing exact MPE
    train_data_mpe = train_data.astype(float)
    train_data_mpe[:, 0] = np.nan
    print(mpe(cnet_random, train_data_mpe))


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

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


.. _sphx_glr_download_auto_examples_models_cnets.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: cnets.py <cnets.py>`



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

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


.. only:: html

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

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