Bugfixes on nonstationary methods

pyFTS/benchmarks/benchmarks.py

@@ -873,6 +873,15 @@ def plot_interval(axis, intervals, order, label, color='red', typeonlegend=False
     return [mi, ma]
 
 
+def plot_point(axis, points, order, label, color='red', ls='-', linewidth=1):
+    mi = min(points) * 0.95
+    ma = max(points) * 1.05
+    for k in np.arange(0, order):
+        points.insert(0, None)
+    axis.plot(points, color=color, label=label, ls=ls,linewidth=linewidth)
+    return [mi, ma]
+
+
 
 def plot_compared_series(original, models, colors, typeonlegend=False, save=False, file=None, tam=[20, 5],
                          points=True, intervals=True, linewidth=1.5):

pyFTS/models/nonstationary/nsfts.py

@@ -111,25 +111,17 @@ class NonStationaryFTS(fts.FTS):
 
             tdisp = common.window_index(k + time_displacement, window_size)
 
-            if self.method == 'fuzzy':
+            affected_sets = [[self.sets[key], self.sets[key].membership(ndata[k], tdisp)]
-                affected_sets = [[set, set.membership(ndata[k], tdisp)]
+                             for key in self.partitioner.ordered_sets
-                                 for set in self.sets if set.membership(ndata[k], tdisp) > 0.0]
+                             if self.sets[key].membership(ndata[k], tdisp) > 0.0]
-            elif self.method == 'maximum':
-                mv = [set.membership(ndata[k], tdisp) for set in self.sets]
-                ix = np.ravel(np.argwhere(mv == max(mv)))
-                affected_sets = [self.sets[x] for x in ix]
 
             if len(affected_sets) == 0:
-                if self.method == 'fuzzy':
+                affected_sets.append([common.check_bounds(ndata[k], self.partitioner, tdisp), 1.0])
-                    affected_sets.append([common.check_bounds(ndata[k], self.sets, tdisp), 1.0])
-                else:
-                    affected_sets.append(common.check_bounds(ndata[k], self.sets, tdisp))
 
             upper = []
             lower = []
 
             if len(affected_sets) == 1:
-                #print(2)
                 aset = affected_sets[0][0]
                 if aset.name in self.flrgs:
                     lower.append(self.flrgs[aset.name].get_lower(tdisp))
@@ -139,7 +131,6 @@ class NonStationaryFTS(fts.FTS):
                     upper.append(aset.get_upper(tdisp))
             else:
                 for aset in affected_sets:
-                    #print(aset)
                     if aset[0].name in self.flrgs:
                         lower.append(self.flrgs[aset[0].name].get_lower(tdisp) * aset[1])
                         upper.append(self.flrgs[aset[0].name].get_upper(tdisp) * aset[1])

pyFTS/models/nonstationary/util.py

@@ -15,27 +15,27 @@ def plot_sets(partitioner, start=0, end=10, step=1, tam=[5, 5], colors=None,
         fig, axes = plt.subplots(nrows=1, ncols=1, figsize=tam)
 
     for ct, key in enumerate(partitioner.ordered_sets):
-        set = partitioner.sets[key]
+        fset = partitioner.sets[key]
         if not only_lines:
             for t in range:
                 tdisp = t - (t % window_size)
-                set.membership(0, tdisp)
+                fset.membership(0, tdisp)
-                param = set.perturbated_parameters[str(tdisp)]
+                param = fset.perturbated_parameters[str(tdisp)]
 
-                if set.mf == Membership.trimf:
+                if fset.mf == Membership.trimf:
                     if t == start:
-                        line = axes.plot([t, t+1, t], param, label=set.name)
+                        line = axes.plot([t, t+1, t], param, label=fset.name)
-                        set.metadata['color'] = line[0].get_color()
+                        fset.metadata['color'] = line[0].get_color()
                     else:
-                        axes.plot([t, t + 1, t], param,c=set.metadata['color'])
+                        axes.plot([t, t + 1, t], param,c=fset.metadata['color'])
 
                 ticks.extend(["t+"+str(t),""])
         else:
             tmp = []
             for t in range:
                 tdisp = t - (t % window_size)
-                set.membership(0, tdisp)
+                fset.membership(0, tdisp)
-                param = set.perturbated_parameters[str(tdisp)]
+                param = fset.perturbated_parameters[str(tdisp)]
                 tmp.append(np.polyval(param, tdisp))
             axes.plot(range, tmp, ls="--", c="blue")
 

pyFTS/tests/general.py

@@ -31,25 +31,30 @@ train_split = 2000
 test_length = 200
 
 from pyFTS.partitioners import Grid, Util as pUtil
-#partitioner = Grid.GridPartitioner(data=dataset[:train_split], npart=30)
+partitioner = Grid.GridPartitioner(data=dataset[:train_split], npart=30)
-partitioner = Grid.GridPartitioner(data=dataset[:train_split], npart=10, transformation=tdiff)
+#partitioner = Grid.GridPartitioner(data=dataset[:train_split], npart=10, transformation=tdiff)
 
 from pyFTS.common import fts,tree
 from pyFTS.models import hofts, pwfts
 
 pfts1_taiex = pwfts.ProbabilisticWeightedFTS("1", partitioner=partitioner)
-pfts1_taiex.append_transformation(tdiff)
+#pfts1_taiex.append_transformation(tdiff)
 pfts1_taiex.fit(dataset[:train_split], save_model=True, file_path='pwfts')
 pfts1_taiex.shortname = "1st Order"
 
 print(pfts1_taiex)
 
-tmp = pfts1_taiex.predict(dataset[train_split:train_split+20], type='point',
+tmp = pfts1_taiex.predict(dataset[train_split:train_split+200], type='point',
-                          method='heuristic', steps_ahead=10)
+                          method='heuristic')
 
 
 print(tmp)
 
+tmp = pfts1_taiex.predict(dataset[train_split:train_split+200], type='point',
+                          method='expected_value')
+
+
+print(tmp)
 
 
 '''

pyFTS/tests/nonstationary.py

@@ -29,4 +29,6 @@ print(fs1)
 
 print(nsfts1.predict(test1))
 
-print(nsfts1)
+print(nsfts1)
+
+util.plot_sets(fs1, tam=[10, 5], start=0, end=100, step=2, data=lmv1[:100], window_size=35)