Refactorings on IncrementalEnsemble

pyFTS/models/incremental/IncrementalEnsemble.py

@@ -65,20 +65,21 @@ class IncrementalEnsembleFTS(ensemble.EnsembleFTS):
 
         ret = []
 
-        for k in np.arange(self.max_lag, l):
+        for k in np.arange(0, l):
 
-            data_window.append(data[k - self.max_lag])
+            data_window.append(data[k])
 
             if k >= self.window_length:
                 data_window.pop(0)
 
-            if k % self.batch_size == 0 and k - self.max_lag >= self.window_length:
+            if k % self.batch_size == 0 and k >= self.window_length:
                 self.train(data_window, **kwargs)
 
-            sample = data[k - self.max_lag: k]
+            if len(self.models) > 0:
-            tmp = self.get_models_forecasts(sample)
+                sample = data[k: k + self.max_lag]
-            point = self.get_point(tmp)
+                tmp = self.get_models_forecasts(sample)
-            ret.append(point)
+                point = self.get_point(tmp)
+                ret.append(point)
 
         return ret
 

pyFTS/tests/ensemble.py

@@ -7,19 +7,30 @@ import numpy as np
 import pandas as pd
 from pyFTS.partitioners import Grid
 from pyFTS.common import Transformations
-from pyFTS.models import chen
+from pyFTS.models import chen, hofts
 from pyFTS.models.incremental import IncrementalEnsemble, TimeVariant
 
-from pyFTS.data import AirPassengers
+from pyFTS.data import AirPassengers, artificial
+
+mu_local = 5
+sigma_local = 0.25
+mu_drift = 10
+sigma_drift = 1.
+deflen = 100
+totlen = deflen * 10
+order = 10
 
 
-passengers = AirPassengers.get_data()
+signal = artificial.SignalEmulator()\
+.stationary_gaussian(mu_local,sigma_local,length=deflen//2,it=10)\
+.stationary_gaussian(mu_drift,sigma_drift,length=deflen//2,it=10, additive=False)\
+.run()
 
-model = IncrementalEnsemble.IncrementalEnsembleFTS(order=2, window_length=20, batch_size=5)
+model2 = IncrementalEnsemble.IncrementalEnsembleFTS(partitioner_method=Grid.GridPartitioner, partitioner_params={'npart': 15},
+                              fts_method=hofts.WeightedHighOrderFTS, fts_params={}, order=2 ,
+                              batch_size=20, window_length=100, num_models=5)
 
-model.fit(passengers[:40])
+forecasts = model2.predict(signal)
-
-forecasts = model.predict(passengers[40:])
 
 print(forecasts)