Split ifts.IntervalFTS(HighOrderFTS) from WeightedIntervalFTS(WeightedHighOrderFTS)

pyFTS/models/ifts.py

@@ -13,7 +13,7 @@ from pyFTS.common import FuzzySet, FLR, fts, tree
 from pyFTS.models import hofts
 
 
-class IntervalFTS(hofts.WeightedHighOrderFTS):
+class IntervalFTS(hofts.HighOrderFTS):
     """
     High Order Interval Fuzzy Time Series
     """
@@ -87,3 +87,79 @@ class IntervalFTS(hofts.WeightedHighOrderFTS):
             ret.append([lo_, up_])
 
         return ret
+
+
+class WeightedIntervalFTS(hofts.WeightedHighOrderFTS):
+    """
+    Weighted High Order Interval Fuzzy Time Series
+    """
+    def __init__(self, **kwargs):
+        super(IntervalFTS, self).__init__(**kwargs)
+        self.shortname = "IFTS"
+        self.name = "Interval FTS"
+        self.detail = "Silva, P.; Guimarães, F.; Sadaei, H. (2016)"
+        self.flrgs = {}
+        self.has_point_forecasting = False
+        self.has_interval_forecasting = True
+        self.is_high_order = True
+        self.min_order = 1
+
+    def get_upper(self, flrg):
+        ret = np.nan
+        if len(flrg.LHS) > 0:
+            if flrg.get_key() in self.flrgs:
+                tmp = self.flrgs[flrg.get_key()]
+                ret = tmp.get_upper(self.sets)
+            else:
+                ret = self.sets[flrg.LHS[-1]].upper
+        return ret
+
+    def get_lower(self, flrg):
+        ret = np.nan
+        if len(flrg.LHS) > 0:
+            if flrg.get_key() in self.flrgs:
+                tmp = self.flrgs[flrg.get_key()]
+                ret = tmp.get_lower(self.partitioner.sets)
+            else:
+                ret = self.partitioner.sets[flrg.LHS[-1]].lower
+        return ret
+
+    def get_sequence_membership(self, data, fuzzySets):
+        mb = [fuzzySets[k].membership(data[k]) for k in np.arange(0, len(data))]
+        return mb
+
+
+    def forecast_interval(self, ndata, **kwargs):
+
+        ret = []
+
+        l = len(ndata)
+
+        if l <= self.order:
+            return ndata
+
+        for k in np.arange(self.max_lag, l+1):
+
+            sample = ndata[k - self.max_lag: k]
+
+            flrgs = self.generate_lhs_flrg(sample)
+
+            up = []
+            lo = []
+            affected_flrgs_memberships = []
+
+            for flrg in flrgs:
+                if len(flrg.LHS) > 0:
+
+                    mv = flrg.get_membership(sample, self.sets)
+                    up.append(mv * self.get_upper(flrg))
+                    lo.append(mv * self.get_lower(flrg))
+                    affected_flrgs_memberships.append(mv)
+
+            # gerar o intervalo
+            norm = sum(affected_flrgs_memberships)
+            lo_ = sum(lo) / norm
+            up_ = sum(up) / norm
+            ret.append([lo_, up_])
+
+        return ret