:mod:`summit.multiview_platform.monoview_classifiers.scm`
=========================================================

.. py:module:: summit.multiview_platform.monoview_classifiers.scm


scm
---


.. py:data:: classifier_class_name
   :annotation: = SCM

   

.. py:class:: SCM(random_state=None, model_type='conjunction', max_rules=10, p=0.1, **kwargs)



   SCM  Classifier
   :param random_state (default:
   :type random_state (default: None)
   :param model_type:
   :type model_type: string (default: "conjunction")
   :param max_rules:
   :type max_rules: int number maximum of rules (default : 10)
   :param p:
   :type p: float value(default : 0.1 )
   :param kwarg:
   :type kwarg: others arguments

   .. attribute:: param_names



   .. attribute:: distribs



   .. attribute:: classed_params



   .. attribute:: weird_strings



   .. py:method:: fit(self, X, y, tiebreaker=None, iteration_callback=None, **fit_params)

      Fit a SCM model.

      Parameters:
      -----------
      X: array-like, shape=[n_examples, n_features]
          The feature of the input examples.
      y : array-like, shape = [n_samples]
          The labels of the input examples.
      tiebreaker: function(model_type, feature_idx, thresholds, rule_type)
          A function that takes in the model type and information about the
          equivalent rules and outputs the index of the rule to use. The lists
          respectively contain the feature indices, thresholds and type
          corresponding of the equivalent rules. If None, the rule that most
          decreases the training error is selected. Note: the model type is
          provided because the rules that are added to disjunction models
          correspond to the inverse of the rules that are handled during
          training. Handle this case with care.
      iteration_callback: function(model)
          A function that is called each time a rule is added to the model.

      Returns:
      --------
      self: object
          Returns self.



   .. py:method:: get_interpretation(self, directory, base_file_name, y_test, feature_ids, multi_class=False)

      Base method that returns an empty string if there is not interpretation
      method in the classifier's module



.. py:function:: paramsToSet(nIter, random_state)


