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 latent_size_multiplicator: The mutlitplicator for the latent space generated for each view. It is useful mainly if the dataset is un-balanced.
: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 proposrtion 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 proportion of views that will have a good description of the complementray samples
:param mutual_error: The propotion 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 passes as arguments.
:param sub_problem_type: The type of monoview-generator used in each view (make_classification, make_gaussian_quantiles, ...). If only a string is provided, the same generator will be used for all the views.
:param sub_problem_configurations: The configuration for each of the generators. If only a dict is provided, the same configuration will be used for all the views.
: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 latent_size_multiplicator: float
: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