:py:mod:`mlair.model_modules.linear_model`
==========================================

.. py:module:: mlair.model_modules.linear_model

.. autoapi-nested-parse::

   Calculate ordinary least squared model.



Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   mlair.model_modules.linear_model.OrdinaryLeastSquaredModel




Attributes
~~~~~~~~~~

.. autoapisummary::

   mlair.model_modules.linear_model.__author__
   mlair.model_modules.linear_model.__date__


.. py:data:: __author__
   :annotation: = Felix Kleinert, Lukas Leufen

   

.. py:data:: __date__
   :annotation: = 2019-12-11

   

.. py:class:: OrdinaryLeastSquaredModel(generator)

   Implementation of an ordinary least squared model (OLS).

   Inputs and outputs are retrieved from a generator. This generator needs to return in xarray format and has to be
   iterable. OLS is calculated on initialisation using statsmodels package. Train your personal OLS using:

   .. code-block:: python

       # next(train_data) should be return (x, y)
       my_ols_model = OrdinaryLeastSquaredModel(train_data)

   After calculation, use your OLS model with

   ..  code-block:: python

       # input_data needs to be structured like train data
       result_ols = my_ols_model.predict(input_data)

   :param generator: generator object returning a tuple containing inputs and outputs as xarrays

   .. py:method:: _train_ols_model_from_generator(self)


   .. py:method:: _set_x_y_from_generator(self)


   .. py:method:: _concatenate(self, new, old)


   .. py:method:: predict(self, data)

      Apply OLS model on data.


   .. py:method:: flatten(data)
      :staticmethod:


   .. py:method:: reshape_xarray_to_numpy(data)
      :staticmethod:

      Reshape xarray data to numpy data and flatten.


   .. py:method:: ordinary_least_squared_model(x, y)
      :staticmethod:

      Calculate ols model using statsmodels.