Introducing alpha_cut on MVFTS variables

pyFTS/models/multivariate/common.py

@@ -2,9 +2,9 @@ import numpy as np
 import pandas as pd
 
 
-def fuzzyfy_instance(data_point, var, alpha_cut=0.0):
+def fuzzyfy_instance(data_point, var):
     mv = np.array([var.partitioner.sets[key].membership(data_point) for key in var.partitioner.ordered_sets])
-    ix = np.ravel(np.argwhere(mv > alpha_cut))
+    ix = np.ravel(np.argwhere(mv > var.alpha_cut))
     sets = [(var.name, var.partitioner.ordered_sets[i]) for i in ix]
     return sets
 

pyFTS/models/multivariate/mvfts.py

@@ -41,7 +41,7 @@ class MVFTS(fts.FTS):
         lags = {}
         for vc, var in enumerate(self.explanatory_variables):
             data_point = data[var.data_label]
-            lags[vc] = common.fuzzyfy_instance(data_point, var, self.alpha_cut)
+            lags[vc] = common.fuzzyfy_instance(data_point, var)
 
         root = tree.FLRGTreeNode(None)
 

pyFTS/models/multivariate/variable.py

@@ -24,6 +24,7 @@ class Variable:
         self.transformation = kwargs.get('transformation', None)
         self.transformation_params = kwargs.get('transformation_params', None)
         self.partitioner = None
+        self.alpha_cut = kwargs.get('alpha_cut', 0.0)
 
         if kwargs.get('data', None) is not None:
             self.build(**kwargs)