Bugfix on pwfts

pyFTS/common/FuzzySet.py

@@ -141,7 +141,7 @@ def fuzzyfy(data, partitioner, **kwargs):
         if mode == 'vector':
             return fuzzyfy_instance(data, partitioner.sets, partitioner.ordered_sets)
         elif mode == 'both':
-            mv = fuzzyfy_instances(data, partitioner.sets, partitioner.ordered_sets)
+            mv = fuzzyfy_instance(data, partitioner.sets, partitioner.ordered_sets)
             fsets = [(partitioner.ordered_sets[ix], mv[ix])
                      for ix in np.arange(len(mv))
                      if mv[ix] >= alpha_cut]

pyFTS/models/multivariate/cmvfts.py

@@ -69,7 +69,7 @@ class ClusteredMVFTS(mvfts.MVFTS):
         if self.pre_fuzzyfy:
             ndata = self.fuzzyfy(ndata)
         else:
-            ndata = [self.format(k) for k in ndata.to_dict('records')]
+            ndata = [self.format_data(k) for k in ndata.to_dict('records')]
 
         return self.model.forecast(ndata, fuzzyfied=self.pre_fuzzyfy, **kwargs)
 

pyFTS/tests/general.py

@@ -25,7 +25,7 @@ p = Grid.GridPartitioner(data=dataset, npart=20)
 
 print(p)
 
-model = hofts.HighOrderFTS(partitioner=p, order=2)
+model = pwfts.ProbabilisticWeightedFTS(partitioner=p, order=2)
 
 model.fit(dataset) #[22, 22, 23, 23, 24])
 

pyFTS/tests/multivariate.py

@@ -18,6 +18,17 @@ from pyFTS.models.multivariate import common, variable, mvfts, cmvfts
 from pyFTS.models.seasonal import partitioner as seasonal
 from pyFTS.models.seasonal.common import DateTime
 
+dataset = pd.read_csv('https://query.data.world/s/2bgegjggydd3venttp3zlosh3wpjqj', sep=';')
+
+dataset['data'] = pd.to_datetime(dataset["data"], format='%Y-%m-%d %H:%M:%S')
+
+data = dataset['glo_avg'].values
+
+train_mv = dataset.iloc[:24505]
+test_mv = dataset.iloc[24505:]
+
+
+
 '''
 model = Util.load_obj('/home/petronio/Downloads/ClusteredMVFTS1solarorder2knn3')
 
@@ -40,7 +51,7 @@ for ix, row in df.iterrows():
 '''
 
 # Multivariate time series
-
+'''
 dataset = pd.read_csv('https://query.data.world/s/2bgegjggydd3venttp3zlosh3wpjqj', sep=';')
 
 dataset['data'] = pd.to_datetime(dataset["data"], format='%Y-%m-%d %H:%M:%S')
@@ -63,8 +74,17 @@ model.append_variable(vavg)
 model.target_variable = vavg
 model.fit(train_mv)
 
+Util.persist_obj(model, model.shortname)
+'''
+
+#model = Util.load_obj("ClusteredMVFTS")
+
+model = Util.load_obj("ClusteredMVFTS2loadorder2knn2")
+
 print(model)
 
+print(model.predict(test_mv))
+
 '''
 train_mv = {}
 test_mv = {}