diff --git a/pyFTS/models/multivariate/common.py b/pyFTS/models/multivariate/common.py
index 15475c1..45ca204 100644
--- a/pyFTS/models/multivariate/common.py
+++ b/pyFTS/models/multivariate/common.py
@@ -43,7 +43,7 @@ class MultivariateFuzzySet(Composite.FuzzySet):
 
 
 def fuzzyfy_instance(data_point, var, tuples=True):
-    fsets = FuzzySet.fuzzyfy(data_point, var.partitioner, mode='sets', method='fuzzy', alpha_cut=var.alpha_cut)
+    fsets = var.partitioner(data_point, mode='sets', method='fuzzy', alpha_cut=var.alpha_cut)
     if tuples:
         return [(var.name, fs) for fs in fsets]
     else:
diff --git a/pyFTS/partitioners/partitioner.py b/pyFTS/partitioners/partitioner.py
index 9c70793..2c25a3c 100644
--- a/pyFTS/partitioners/partitioner.py
+++ b/pyFTS/partitioners/partitioner.py
@@ -142,6 +142,7 @@ class Partitioner(object):
             ret = []
             for inst in data:
                 mv = self.fuzzyfy(inst, **kwargs)
+                ret.append(mv)
             return ret
 
         alpha_cut = kwargs.get('alpha_cut', 0.)
@@ -157,6 +158,7 @@ class Partitioner(object):
             mv[ix] = tmp if tmp >= alpha_cut else 0.
 
         ix = np.ravel(np.argwhere(mv > 0.))
+
         if ix.size == 0:
             mv[self.check_bounds(data)] = 1.
 
@@ -170,6 +172,10 @@ class Partitioner(object):
             mx = max(mv)
             ix = np.ravel(np.argwhere(mv == mx))
             return self.ordered_sets[ix[0]]
+        elif mode == 'both':
+            ix = np.ravel(np.argwhere(mv > 0.))
+            sets = [(self.ordered_sets[i], mv[i]) for i in ix]
+            return sets
 
     def check_bounds(self, data):
         if data < self.min:
diff --git a/pyFTS/tests/general.py b/pyFTS/tests/general.py
index cc3eac3..2bb39c6 100644
--- a/pyFTS/tests/general.py
+++ b/pyFTS/tests/general.py
@@ -11,19 +11,20 @@ import pandas as pd
 from pyFTS.common import Util as cUtil, FuzzySet
 from pyFTS.partitioners import Grid, Entropy, Util as pUtil, Simple
 from pyFTS.benchmarks import benchmarks as bchmk, Measures
-from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts
+from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts, tsaur, song, sadaei
 from pyFTS.common import Transformations, Membership
 
 from pyFTS.data import TAIEX
 
-fs = Grid.GridPartitioner(data=TAIEX.get_data(), npart=23)
+data = TAIEX.get_data()
 
-print(fs.min, fs.max)
+fs = Grid.GridPartitioner(data=data, npart=23)
 
-tmp = fs.search(5500)
-print(tmp)
-
-tmp = fs.fuzzyfy(5500, method='fuzzy', alpha_cut=0.3)
-print(tmp)
+test = [2000, 5000, 5500, 12000]
 
+for method in [yu.WeightedFTS, tsaur.MarkovWeightedFTS, song.ConventionalFTS, sadaei.ExponentialyWeightedFTS, ismailefendi.ImprovedWeightedFTS,
+               chen.ConventionalFTS, cheng.TrendWeightedFTS, hofts.HighOrderFTS, pwfts.ProbabilisticWeightedFTS]:
+    model = method(partitioner=fs)
+    model.fit(data)
+    print(model.forecast(test))