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        Click :ref:`here <sphx_glr_download_auto_examples_models_parametric_spn.py>`
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.. _sphx_glr_auto_examples_models_parametric_spn.py:


==============
Parametric SPN
==============

The setup is similar to the Mixed SPN, but here we learn a parametric SPN.

.. GENERATED FROM PYTHON SOURCE LINES 8-27

.. code-block:: default


    import numpy as np

    np.random.seed(123)

    from spn.algorithms.LearningWrappers import learn_parametric
    from spn.structure.Base import Context
    from spn.structure.leaves.parametric.Parametric import Categorical, Gaussian

    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]

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

    spn = learn_parametric(train_data, ds_context, min_instances_slice=20)


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.. _sphx_glr_download_auto_examples_models_parametric_spn.py:


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