core.model package
==================

This package contains classes that pertain to the occupancy estimation models.
All user-defined models should be put here.

core.model.superclass module
----------------------------

.. py:class:: core.model.superclass.NormalModel(train, test, thread_num=4)
    :noindex:
.. py:class:: core.model.superclass.DomainAdaptiveModel(source, target_retrain, target_test, thread_num=4)
    :noindex:

    :class:`~core.model.superclass.NormalModel` and :class:`~core.model.superclass.DomainAdaptiveModel`
    are the superclasses for all occupancy estimation models. :class:`~core.model.superclass.NormalModel` is the
    superclass for models that are designed for estimating binary occupancy states. In
    contrast, :class:`~core.model.superclass.DomainAdaptiveModel` is the superclass for models that
    are designed for estimating occupancy counts. They both have four methods:

    .. py:method:: get_all_model()
        :noindex:
    .. py:method:: add_model(list_of_model)
        :noindex:
    .. py:method:: remove_model(list_of_model)
        :noindex:
    .. py:method:: run_all_model()
        :noindex:

        These methods can be used to return estimations and allow the user to modify settings for a
        specific model at any time.

        .. code-block:: python

            import core

            # Load a sample data set from the package
            dataset = core.data.load_sample("umons-all")
            # Create train / test sets
            train, test = dataset.split(0.8)

            # ===== Sample Start Here =====
            # Select models you want to use
            model_set = core.model.NormalModel(train, test, thread_num=1)
            model_set.add_model(["YourModelName"])

            # Edit the settings of your model
            your_model = model_set.models["YourModelName"]
            your_model.alpha = 0.5
            # Run model to predict occupancy level
            model_set_results = model_set.run_all_model()

            print(model_set_results)