.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/montecarlo/b07estimation_specification.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_montecarlo_b07estimation_specification.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_montecarlo_b07estimation_specification.py:


Specification of the mixtures of logit
======================================

Creation of the Biogeme object for a mixtures of logit models where
the integral is approximated using MonteCarlo integration.

:author: Michel Bierlaire, EPFL
:date: Thu Apr 13 21:04:47 2023

.. GENERATED FROM PYTHON SOURCE LINES 12-32

.. code-block:: default


    import biogeme.biogeme as bio
    from biogeme import models
    from biogeme.expressions import Beta, MonteCarlo, log, bioDraws
    from biogeme.tools import TemporaryFile

    from swissmetro import (
        database,
        TRAIN_TT_SCALED,
        TRAIN_COST_SCALED,
        SM_TT_SCALED,
        SM_COST_SCALED,
        CAR_TT_SCALED,
        CAR_CO_SCALED,
        TRAIN_AV_SP,
        SM_AV,
        CAR_AV_SP,
        CHOICE,
    )


.. GENERATED FROM PYTHON SOURCE LINES 33-36

.. code-block:: default

    R = 2000



.. GENERATED FROM PYTHON SOURCE LINES 37-78

.. code-block:: default

    def get_biogeme(the_draws: bioDraws, number_of_draws: int) -> bio.BIOGEME:
        """Function returning the Biogeme object as a function of the selected draws

        :param the_draws: expression representing the draws.
        :param number_of_draws: number of draws to generate.
        :return: Biogeme object.
        """

        asc_car = Beta('asc_car', 0, None, None, 0)
        asc_train = Beta('asc_train', 0, None, None, 0)
        b_time = Beta('b_time', 0, None, None, 0)
        b_time_s = Beta('b_time_s', 1, None, None, 0)
        b_cost = Beta('b_cost', 0, None, None, 0)

        # Define a random parameter, normally distributed, designed to be used
        # for Monte-Carlo simulation
        b_time_rnd = b_time + b_time_s * the_draws

        # Definition of the utility functions
        v_1 = asc_train + b_time_rnd * TRAIN_TT_SCALED + b_cost * TRAIN_COST_SCALED
        v_2 = b_time_rnd * SM_TT_SCALED + b_cost * SM_COST_SCALED
        v_3 = asc_car + b_time_rnd * CAR_TT_SCALED + b_cost * CAR_CO_SCALED

        # Associate utility functions with the numbering of alternatives
        utilities = {1: v_1, 2: v_2, 3: v_3}

        # Associate the availability conditions with the alternatives
        av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP}

        # The choice model is a logit, with availability conditions
        prob = models.logit(utilities, av, CHOICE)
        logprob = log(MonteCarlo(prob))

        with TemporaryFile() as filename:
            with open(filename, 'w', encoding='utf-8') as f:
                print('[MonteCarlo]', file=f)
                print(f'number_of_draws = {number_of_draws}', file=f)

            the_biogeme = bio.BIOGEME(database, logprob, parameter_file=filename)

        return the_biogeme


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

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


.. _sphx_glr_download_auto_examples_montecarlo_b07estimation_specification.py:

.. only:: html

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




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

      :download:`Download Python source code: b07estimation_specification.py <b07estimation_specification.py>`

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

      :download:`Download Jupyter notebook: b07estimation_specification.ipynb <b07estimation_specification.ipynb>`


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

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