:mod:`multiview_generator.sub_problems`
=======================================

.. py:module:: multiview_generator.sub_problems


sub_problems
------------


.. py:class:: BaseSubProblem(n_classes=2, n_features=2, random_vertices=True, errors=np.array([0.5, 0.5]), random_state=np.random.RandomState(42), n_samples_per_class=np.array([100, 100]), **configuration)

   The base class for all the sub-problem generators.


   .. py:attribute:: n_classes
      :value: 2



   .. py:attribute:: random_vertices
      :value: True



   .. py:attribute:: errors


   .. py:attribute:: n_features
      :value: 2



   .. py:attribute:: rs


   .. py:attribute:: n_samples_per_class


   .. py:attribute:: bayes_error


   .. py:attribute:: descriptions


   .. py:attribute:: config


   .. py:attribute:: view_name
      :value: 'generated'



   .. py:method:: gen_report()

      General method the generate the report on the view.

      :return: A string containing the general report for the view



.. py:class:: StumpsGenerator(n_classes=2, n_features=2, random_vertices=True, errors=np.array([0.5, 0.5]), random_state=np.random.RandomState(42), n_samples_per_class=np.array([100, 100]), **configuration)



   The base class for all the sub-problem generators.


   .. py:method:: gen_data()

      Generates the samples according to gaussian distributions with scales
      computed with the given error and class separation. This sub-problem is
      easily understandable by a decision tree.

      The features are built as :
      relevant_features : the  math.ceil(math.log2(self.n_classes)) first ones,
      uniform noise features : all the remaining ones


      :return: data a np.ndarray of dimension n_classes, n_samples_per_class,         n_features containing the samples' descriptions, sorted by class



   .. py:method:: gen_report()

      Generates the specific report for StumpsGenerator.



   .. py:method:: get_bayes_classifier()


.. py:class:: TreesGenerator(n_classes=2, n_features=2, random_vertices=True, errors=np.array([0.5, 0.5]), random_state=np.random.RandomState(42), n_samples_per_class=np.array([100, 100]), **configuration)



   Work in progress : Similar generator as StumpsGenerator, but that
   generates several blobs per class


   .. py:method:: gen_data()

      WIP



   .. py:method:: gen_report()

      WIP



   .. py:method:: get_bayes_classifier()


.. py:class:: RingsGenerator(n_classes=2, n_features=2, random_vertices=True, errors=np.array([0.5, 0.5]), random_state=np.random.RandomState(42), n_samples_per_class=np.array([100, 100]), **configuration)



   The base class for all the sub-problem generators.


   .. py:method:: gen_data()

      Generates the samples according to gaussian distributions with scales
      computed with the given error and class separation. The generator first
      computes a radius according to the gaussian distribution, then
      generates n_features-1 random angles to build the polar coordinates of
      the samples. The dataset returned is the cartesian version of this
      "polar" dataset.

      :return: data a np.ndarray of dimension n_classes, n_samples_per_class, n_features containing the samples' descriptions, sorted by class



   .. py:method:: gen_report()

      Generates the specific report for StumpsGenerator.



   .. py:method:: get_bayes_classifier()


.. py:function:: to_cartesian(radius, angles)

   Transforms polar coordinates to cartesian coordinates.