:mod:`multiview_generator.gaussian_classes`
===========================================

.. py:module:: multiview_generator.gaussian_classes


gaussian_classes
----------------


.. py:class:: MultiViewGaussianSubProblemsGenerator(random_state=42, n_samples=100, n_classes=4, n_views=4, error_matrix=None, n_features=3, class_weights=1.0, redundancy=0.05, complementarity=0.05, complementarity_level=3, mutual_error=0.01, name='generated_dataset', config_file=None, sub_problem_type='base', sub_problem_configurations=None, sub_problem_generators='StumpsGenerator', random_vertices=False, min_rndm_val=-1, max_rndm_val=1, **kwargs)



   This engine generates one monoview sub-problem for each view with independant data.
   If then switch descriptions between the samples to create error and difficulty in the dataset

   :param random_state: The random state or seed.
   :param n_samples: The number of samples that the dataset will contain
   :param n_classes: The number of classes in which the samples will be labelled
   :param n_views: The number of views describing the samples
   :param error_matrix: The error matrix giving in row i column j the error of the Bayes classifier on Class i for View j
   :param n_features: The number of features describing the samples for each view (can specify an int or array-like of length ``n_views``)
   :param class_weights: The proportion of the dataset that will be labelled in each class. Must specify an array-like of size n_classes ([0.1,0.45,0.45] will output a dataset with with 10% of the samples in the first class and 45% in the two others.)
   :param redundancy: The proportion of the samples that will be well-decribed by all the views.
   # :param complementarity: The proportion of samples that will be well-decribed only by some views
   :param complementarity_level: The number of views that will have a bad description of the complementray samples
   :param mutual_error: The proportion of samples that will be mis-described by all the views
   :param name: The name of the dataset (will be used to name the file)
   :param config_file: The path to the yaml config file. If provided, the config fil entries will overwrite the one passed as arguments.

   :type random_state: int or np.random.RandomState
   :type n_samples: int
   :type n_classes: int
   :type n_views: int
   :type error_matrix: np.ndarray
   :type n_features: int or array-like
   :type class_weights: float or array-like
   :type redundancy: float
   :type complementarity: float
   :type complementarity_level: float
   :type mutual_error: float
   :type name: str
   :type config_file: str
   :type sub_problem_type: str or list
   :type sub_problem_configurations: None, dict or list


   .. py:attribute:: random_vertices


   .. py:attribute:: sub_problem_generators


   .. py:method:: generate_multi_view_dataset()

      This is the main method. It will generate a multiview dataset according to the configuration.
      To do so,

      * it generates the labels of the multiview dataset,
      * then it assigns all the subsets of samples (redundant, ...)
      * finally, for each view it generates a monoview dataset according to the configuration


      :return: view_data a list containing the views np.ndarrays and y, the label array.



   .. py:method:: assign_mutual_error()

      Method assigning the mis-describing views to the mutual error samples.



   .. py:method:: assign_complementarity()

      Method assigning mis-described and well-described views to build
      complementary samples



   .. py:method:: assign_redundancy()

      Method assigning the well-describing views to the redundant samples.



   .. py:method:: get_distance()

      Method that records the distance of each description to the ideal
      decision limit, will be used later to quantify more precisely the
      quality of a description.