Added some doc

multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py

@@ -20,6 +20,29 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
                  n_stumps_per_attribute=None, use_r=True,
                  plotted_metric=Metrics.zero_one_loss):
         super(ColumnGenerationClassifierQar, self).__init__()
+        r"""
+
+            Parameters
+            ----------
+            n_max_iterations : int
+                Maximum number of iterations for the boosting algorithm.
+            estimators_generator : object
+                Sk-learn classifier object used to generate the hypotheses with the data.
+            random_state : np.random.RandomState or int
+                The random state, used in order to be reproductible
+            self_complemented : bool
+                If True, in the hypotheses generation process, for each hypothesis, it's complement will be generated too.
+            twice_the_same : bool
+                If True, the algorithm will be allowed to select twice the same hypothesis in the boosting process.
+            c_bound_choice : bool
+                If True, the C-Bound will be used to select the hypotheses. If False, the margin will be the criterion.
+            n_stumps_per_attribute : int
+                The number of hypotheses generated by data attribute 
+            use_r : bool
+                If True, uses edge to compute the performance of a voter. If False, use the error instead.
+            plotted_metric : Metric module
+                The metric that will be plotted for each iteration of boosting. 
+            """
 
         if type(random_state) is int:
             self.random_state = np.random.RandomState(random_state)
@@ -45,6 +68,9 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
         self.twice_the_same = params["twice_the_same"]
         self.c_bound_choice = params["c_bound_choice"]
         self.random_start = params["random_start"]
+        self.n_max_iterations = params["n_max_iterations"]
+        self.n_stumps = params["n_stumps_per_attribute"]
+        self.use_r = params["use_r"]
 
     def fit(self, X, y):
         start = time.time()