- Introducing new superclass FLRG and general refactoring

pyFTS/chen.py

@@ -6,17 +6,15 @@ S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets
 
 import numpy as np
 from pyFTS.common import FuzzySet, FLR
-from pyFTS import fts
+from pyFTS import fts,flrg
 
 
-class ConventionalFLRG(object):
+class ConventionalFLRG(flrg.FLRG):
     """First Order Conventional Fuzzy Logical Relationship Group"""
-    def __init__(self, LHS):
+    def __init__(self, LHS, **kwargs):
+        super(ConventionalFLRG, self).__init__(1, **kwargs)
         self.LHS = LHS
         self.RHS = set()
-        self.midpoint = None
-        self.lower = None
-        self.upper = None
 
     def append(self, c):
         self.RHS.add(c)
@@ -30,8 +28,7 @@ class ConventionalFLRG(object):
             tmp2 = tmp2 + c.name
         return tmp + tmp2
 
-    def __len__(self):
-        return len(self.RHS)
+
 
 
 class ConventionalFTS(fts.FTS):

pyFTS/cheng.py

@@ -15,7 +15,7 @@ class TrendWeightedFLRG(yu.WeightedFLRG):
     First Order Trend Weighted Fuzzy Logical Relationship Group
     """
     def __init__(self, LHS, **kwargs):
-        super(TrendWeightedFLRG, self).__init__(LHS)
+        super(TrendWeightedFLRG, self).__init__(LHS, **kwargs)
 
     def weights(self):
         count_nochange = 0.0

pyFTS/flrg.py

@@ -0,0 +1,17 @@
+
+
+class FLRG(object):
+
+    def __init__(self, order, **kwargs):
+        self.LHS = None
+        self.RHS = None
+        self.order = order
+        self.midpoint = None
+        self.lower = None
+        self.upper = None
+
+    def __len__(self):
+        return len(self.RHS)
+
+
+

pyFTS/hofts.py

@@ -7,15 +7,15 @@ Fuzzy Sets Syst., vol. 81, no. 3, pp. 311–319, 1996.
 
 import numpy as np
 from pyFTS.common import FuzzySet,FLR
-from pyFTS import fts
+from pyFTS import fts, flrg
 
 
-class HighOrderFLRG(object):
+class HighOrderFLRG(flrg.FLRG):
     """Conventional High Order Fuzzy Logical Relationship Group"""
-    def __init__(self, order):
+    def __init__(self, order, **kwargs):
+        super(HighOrderFLRG, self).__init__(order, **kwargs)
         self.LHS = []
         self.RHS = {}
-        self.order = order
         self.strlhs = ""
 
     def appendRHS(self, c):

pyFTS/ismailefendi.py

@@ -7,12 +7,13 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1,
 
 import numpy as np
 from pyFTS.common import FuzzySet,FLR
-from pyFTS import fts
+from pyFTS import fts, flrg
 
 
-class ImprovedWeightedFLRG(object):
+class ImprovedWeightedFLRG(flrg.FLRG):
     """First Order Improved Weighted Fuzzy Logical Relationship Group"""
-    def __init__(self, LHS):
+    def __init__(self, LHS, **kwargs):
+        super(ImprovedWeightedFLRG, self).__init__(1, **kwargs)
         self.LHS = LHS
         self.RHS = {}
         self.count = 0.0

pyFTS/nonstationary/flrg.py

@@ -0,0 +1,34 @@
+
+from pyFTS import flrg
+
+
+class NonStationaryFLRG(flrg.FLRG):
+
+    def __init__(self, LHS, **kwargs):
+        super(NonStationaryFLRG, self).__init__(1, **kwargs)
+        self.LHS = LHS
+        self.RHS = set()
+
+    def get_midpoint(self, t):
+        if self.midpoint is None:
+            tmp = []
+            for r in self.RHS:
+                tmp.append(r.get_midpoint(t))
+            self.midpoint = sum(tmp) / len(tmp)
+        return self.midpoint
+
+    def get_lower(self, t):
+        if self.lower is None:
+            tmp = []
+            for r in self.RHS:
+                tmp.append(r.get_midpoint(t))
+            self.lower = min(tmp)
+        return self.lower
+
+    def get_upper(self, t):
+        if self.upper is None:
+            tmp = []
+            for r in self.RHS:
+                tmp.append(r.get_midpoint(t))
+            self.upper = max(tmp)
+        return self.upper

pyFTS/nonstationary/honsfts.py

@@ -0,0 +1,138 @@
+import numpy as np
+from pyFTS.common import FuzzySet, FLR
+from pyFTS import fts, hofts
+from pyFTS.nonstationary import common
+
+
+class HighOrderNonStationaryFLRG(hofts.HighOrderFLRG):
+    """First Order NonStationary Fuzzy Logical Relationship Group"""
+    def __init__(self, order):
+        super(HighOrderNonStationaryFLRG, self).__init__(order)
+
+    def get_midpoint(self, t):
+        if self.midpoint is None:
+            tmp = []
+            for r in self.RHS:
+                tmp.append(r.get_midpoint(t))
+            self.midpoint = sum(tmp)/len(tmp)
+        return self.midpoint
+
+    def get_lower(self, t):
+        if self.lower is None:
+            tmp = []
+            for r in self.RHS:
+                tmp.append(r.get_midpoint(t))
+            self.lower = min(tmp)
+        return self.lower
+
+    def get_upper(self, t):
+        if self.upper is None:
+            tmp = []
+            for r in self.RHS:
+                tmp.append(r.get_midpoint(t))
+            self.upper = max(tmp)
+        return self.upper
+
+
+class NonStationaryFTS(fts.FTS):
+    """NonStationaryFTS Fuzzy Time Series"""
+    def __init__(self, name, **kwargs):
+        super(NonStationaryFTS, self).__init__(1, "NSFTS " + name, **kwargs)
+        self.name = "Non Stationary FTS"
+        self.detail = ""
+        self.flrgs = {}
+
+    def generateFLRG(self, flrs):
+        flrgs = {}
+        for flr in flrs:
+            if flr.LHS.name in flrgs:
+                flrgs[flr.LHS.name].append(flr.RHS)
+            else:
+                flrgs[flr.LHS.name] = NonStationaryFLRG(flr.LHS)
+                flrgs[flr.LHS.name].append(flr.RHS)
+        return (flrgs)
+
+    def train(self, data, sets=None,order=1,parameters=None):
+
+        if sets is not None:
+            self.sets = sets
+        else:
+            self.sets = self.partitioner.sets
+
+        ndata = self.doTransformations(data)
+        tmpdata = common.fuzzySeries(ndata, self.sets)
+        flrs = FLR.generateNonRecurrentFLRs(tmpdata)
+        self.flrgs = self.generateFLRG(flrs)
+
+    def forecast(self, data, **kwargs):
+
+        time_displacement = kwargs.get("time_displacement",0)
+
+        ndata = np.array(self.doTransformations(data))
+
+        l = len(ndata)
+
+        ret = []
+
+        for k in np.arange(0, l):
+
+            #print("input: " + str(ndata[k]))
+
+            tdisp = k + time_displacement
+
+            affected_sets = [ [set, set.membership(ndata[k], tdisp)]
+                              for set in self.sets if set.membership(ndata[k], tdisp) > 0.0]
+
+            if len(affected_sets) == 0:
+                if self.sets[0].get_lower(tdisp) > ndata[k]:
+                    affected_sets.append([self.sets[0], 1.0])
+                elif self.sets[-1].get_upper(tdisp) < ndata[k]:
+                    affected_sets.append([self.sets[-1], 1.0])
+
+            #print(affected_sets)
+
+            tmp = []
+            for aset in affected_sets:
+                if aset[0] in self.flrgs:
+                    tmp.append(self.flrgs[aset[0].name].get_midpoint(tdisp) * aset[1])
+                else:
+                    tmp.append(aset[0].get_midpoint(tdisp) * aset[1])
+
+            pto = sum(tmp)
+
+            #print(pto)
+
+            ret.append(pto)
+
+        ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
+
+        return ret
+
+    def forecastInterval(self, data, **kwargs):
+
+        time_displacement = kwargs.get("time_displacement",0)
+
+        ndata = np.array(self.doTransformations(data))
+
+        l = len(ndata)
+
+        ret = []
+
+        for k in np.arange(0, l):
+
+            tdisp = k + time_displacement
+
+            affected_sets = [ [set.name, set.membership(ndata[k], tdisp)]
+                              for set in self.sets if set.membership(ndata[k], tdisp) > 0.0]
+
+            upper = []
+            lower = []
+            for aset in affected_sets:
+                lower.append(self.flrgs[aset[0]].get_lower(tdisp) * aset[1])
+                upper.append(self.flrgs[aset[0]].get_upper(tdisp) * aset[1])
+
+            ret.append([sum(lower), sum(upper)])
+
+        ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
+
+        return ret

pyFTS/nonstationary/nsfts.py

@@ -1,37 +1,28 @@
 import numpy as np
 from pyFTS.common import FuzzySet, FLR
 from pyFTS import fts, chen
-from pyFTS.nonstationary import common
+from pyFTS.nonstationary import common, flrg
 
 
-class NonStationaryFLRG(chen.ConventionalFLRG):
+class ConventionalNonStationaryFLRG(flrg.NonStationaryFLRG):
     """First Order NonStationary Fuzzy Logical Relationship Group"""
-    def __init__(self, LHS):
-        super(NonStationaryFLRG, self).__init__(LHS)
 
-    def get_midpoint(self, t):
-        if self.midpoint is None:
-            tmp = []
-            for r in self.RHS:
-                tmp.append(r.get_midpoint(t))
-            self.midpoint = sum(tmp)/len(tmp)
-        return self.midpoint
+    def __init__(self, LHS, **kwargs):
+        super(ConventionalNonStationaryFLRG, self).__init__(1, **kwargs)
+        self.LHS = LHS
+        self.RHS = set()
 
-    def get_lower(self, t):
-        if self.lower is None:
-            tmp = []
-            for r in self.RHS:
-                tmp.append(r.get_midpoint(t))
-            self.lower = min(tmp)
-        return self.lower
+    def append(self, c):
+        self.RHS.add(c)
 
-    def get_upper(self, t):
-        if self.upper is None:
-            tmp = []
-            for r in self.RHS:
-                tmp.append(r.get_midpoint(t))
-            self.upper = max(tmp)
-        return self.upper
+    def __str__(self):
+        tmp = self.LHS.name + " -> "
+        tmp2 = ""
+        for c in sorted(self.RHS, key=lambda s: s.name):
+            if len(tmp2) > 0:
+                tmp2 = tmp2 + ","
+            tmp2 = tmp2 + c.name
+        return tmp + tmp2
 
 
 class NonStationaryFTS(fts.FTS):
@@ -48,7 +39,7 @@ class NonStationaryFTS(fts.FTS):
             if flr.LHS.name in flrgs:
                 flrgs[flr.LHS.name].append(flr.RHS)
             else:
-                flrgs[flr.LHS.name] = NonStationaryFLRG(flr.LHS)
+                flrgs[flr.LHS.name] = ConventionalNonStationaryFLRG(flr.LHS)
                 flrgs[flr.LHS.name].append(flr.RHS)
         return (flrgs)
 

pyFTS/sadaei.py

@@ -7,16 +7,17 @@ refined exponentially weighted fuzzy time series and an improved harmony search,
 
 import numpy as np
 from pyFTS.common import FuzzySet,FLR
-from pyFTS import fts
+from pyFTS import fts, flrg
 
 
-class ExponentialyWeightedFLRG(object):
+class ExponentialyWeightedFLRG(flrg.FLRG):
     """First Order Exponentialy Weighted Fuzzy Logical Relationship Group"""
-    def __init__(self, LHS, c):
+    def __init__(self, LHS, **kwargs):
+        super(ExponentialyWeightedFLRG, self).__init__(1, **kwargs)
         self.LHS = LHS
         self.RHS = []
         self.count = 0.0
-        self.c = c
+        self.c = kwargs.get("c",2.0)
 
     def append(self, c):
         self.RHS.append(c)

pyFTS/yu.py

@@ -7,12 +7,13 @@ Phys. A Stat. Mech. its Appl., vol. 349, no. 3, pp. 609–624, 2005.
 
 import numpy as np
 from pyFTS.common import FuzzySet,FLR
-from pyFTS import fts
+from pyFTS import fts, flrg, chen
 
 
-class WeightedFLRG(object):
+class WeightedFLRG(flrg.FLRG):
     """First Order Weighted Fuzzy Logical Relationship Group"""
     def __init__(self, LHS, **kwargs):
+        super(WeightedFLRG, self).__init__(1, **kwargs)
         self.LHS = LHS
         self.RHS = []
         self.count = 1.0