Refatoração de PIFTS para PFTS; acrescentando as packages data e models

pfts.py

@@ -30,11 +30,11 @@ class ProbabilisticFLRG(hofts.HighOrderFLRG):
         return self.strLHS() + " -> " + tmp2
 
 
-class ProbabilisticIntervalFTS(ifts.IntervalFTS):
+class ProbabilisticFTS(ifts.IntervalFTS):
     def __init__(self, name):
-        super(ProbabilisticIntervalFTS, self).__init__("PIFTS")
+        super(ProbabilisticFTS, self).__init__("PIFTS")
         self.shortname = "PIFTS " + name
-        self.name = "Probabilistic Interval FTS"
+        self.name = "Probabilistic FTS"
         self.detail = "Silva, P.; Guimarães, F.; Sadaei, H."
         self.flrgs = {}
         self.globalFrequency = 0
@@ -65,6 +65,14 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
         else:
             return 1.0 / self.globalFrequency
 
+    def getMidpoints(self, flrg):
+        if flrg.strLHS() in self.flrgs:
+            tmp = self.flrgs[flrg.strLHS()]
+            ret = sum(np.array([tmp.getProbability(s) * self.setsDict[s].midpoint for s in tmp.RHS]))
+        else:
+            ret = sum(np.array([0.33 * s.midpoint for s in flrg.LHS]))
+        return ret
+
     def getUpper(self, flrg):
         if flrg.strLHS() in self.flrgs:
             tmp = self.flrgs[flrg.strLHS()]
@@ -89,6 +97,106 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
 
         ret = []
 
+        for k in np.arange(self.order - 1, l):
+
+            # print(k)
+
+            affected_flrgs = []
+            affected_flrgs_memberships = []
+            norms = []
+
+            mp = []
+
+            # Find the sets which membership > 0 for each lag
+            count = 0
+            lags = {}
+            if self.order > 1:
+                subset = ndata[k - (self.order - 1): k + 1]
+
+                for instance in subset:
+                    mb = FuzzySet.fuzzyInstance(instance, self.sets)
+                    tmp = np.argwhere(mb)
+                    idx = np.ravel(tmp)  # flatten the array
+
+                    if idx.size == 0:  # the element is out of the bounds of the Universe of Discourse
+                        if math.ceil(instance) <= self.sets[0].lower:
+                            idx = [0]
+                        elif math.ceil(instance) >= self.sets[-1].upper:
+                            idx = [len(self.sets) - 1]
+                        else:
+                            raise Exception(instance)
+
+                    lags[count] = idx
+                    count = count + 1
+
+                # Build the tree with all possible paths
+
+                root = tree.FLRGTreeNode(None)
+
+                self.buildTree(root, lags, 0)
+
+                # Trace the possible paths and build the PFLRG's
+
+                for p in root.paths():
+                    path = list(reversed(list(filter(None.__ne__, p))))
+                    flrg = hofts.HighOrderFLRG(self.order)
+                    for kk in path: flrg.appendLHS(self.sets[kk])
+
+                    assert len(flrg.LHS) == subset.size, str(subset) + " -> " + str([s.name for s in flrg.LHS])
+
+                    ##
+                    affected_flrgs.append(flrg)
+
+                    # Find the general membership of FLRG
+                    affected_flrgs_memberships.append(min(self.getSequenceMembership(subset, flrg.LHS)))
+
+            else:
+
+                mv = FuzzySet.fuzzyInstance(ndata[k], self.sets)  # get all membership values
+                tmp = np.argwhere(mv)  # get the indices of values > 0
+                idx = np.ravel(tmp)  # flatten the array
+
+                if idx.size == 0:  # the element is out of the bounds of the Universe of Discourse
+                    if math.ceil(ndata[k]) <= self.sets[0].lower:
+                        idx = [0]
+                    elif math.ceil(ndata[k]) >= self.sets[-1].upper:
+                        idx = [len(self.sets) - 1]
+                    else:
+                        raise Exception(ndata[k])
+
+                for kk in idx:
+                    flrg = hofts.HighOrderFLRG(self.order)
+                    flrg.appendLHS(self.sets[kk])
+                    affected_flrgs.append(flrg)
+                    affected_flrgs_memberships.append(mv[kk])
+
+            count = 0
+            for flrg in affected_flrgs:
+                # achar o os bounds de cada FLRG, ponderados pela probabilidade e pertinência
+                norm = self.getProbability(flrg) * affected_flrgs_memberships[count]
+                if norm == 0:
+                    norm = self.getProbability(flrg)  # * 0.001
+                mp.append(norm * self.getMidpoints(flrg))
+                norms.append(norm)
+                count = count + 1
+
+            # gerar o intervalo
+            norm = sum(norms)
+            if norm == 0:
+                ret.append([0, 0])
+            else:
+                ret.append(sum(mp) / norm)
+
+        return ret
+
+    def forecastInterval(self, data):
+
+        ndata = np.array(data)
+
+        l = len(ndata)
+
+        ret = []
+
         for k in np.arange(self.order - 1, l):
 
             # print(k)
@@ -184,6 +292,20 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
         return ret
 
     def forecastAhead(self, data, steps):
+        ret = [data[k] for k in np.arange(len(data) - self.order, len(data))]
+
+        for k in np.arange(self.order - 1, steps):
+
+            if ret[-1] <= self.sets[0].lower or ret[-1] >= self.sets[-1].upper:
+                ret.append(ret[-1])
+            else:
+                mp = self.forecast([ret[x] for x in np.arange(k - self.order, k)])
+
+                ret.append(mp)
+
+        return ret
+
+    def forecastAheadInterval(self, data, steps):
         ret = [[data[k], data[k]] for k in np.arange(len(data) - self.order, len(data))]
 
         for k in np.arange(self.order - 1, steps):
@@ -191,8 +313,8 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
             if ret[-1][0] <= self.sets[0].lower and ret[-1][1] >= self.sets[-1].upper:
                 ret.append(ret[-1])
             else:
-                lower = self.forecast([ret[x][0] for x in np.arange(k - self.order, k)])
+                lower = self.forecastInterval([ret[x][0] for x in np.arange(k - self.order, k)])
-                upper = self.forecast([ret[x][1] for x in np.arange(k - self.order, k)])
+                upper = self.forecastInterval([ret[x][1] for x in np.arange(k - self.order, k)])
 
                 ret.append([np.min(lower), np.max(upper)])
 
@@ -258,11 +380,11 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
         df = pd.DataFrame(ret, columns=sorted(grid))
         return df
 
-    def forecastDistributionAhead(self, data, steps, resolution):
+    def forecastAheadDistribution(self, data, steps, resolution):
 
         ret = []
 
-        intervals = self.forecastAhead(data, steps)
+        intervals = self.forecastAheadInterval(data, steps)
 
         for k in np.arange(self.order, steps):
 
@@ -271,13 +393,15 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
 
             for qt in np.arange(1, 50, 2):
                 # print(qt)
-                qtle_lower = self.forecast([intervals[x][0] + qt * (intervals[x][1] - intervals[x][0]) / 100 for x in
+                qtle_lower = self.forecastInterval(
+                    [intervals[x][0] + qt * (intervals[x][1] - intervals[x][0]) / 100 for x in
                      np.arange(k - self.order, k)])
                 grid = self.gridCount(grid, resolution, np.ravel(qtle_lower))
-                qtle_upper = self.forecast([intervals[x][1] - qt * (intervals[x][1] - intervals[x][0]) / 100 for x in
+                qtle_upper = self.forecastInterval(
+                    [intervals[x][1] - qt * (intervals[x][1] - intervals[x][0]) / 100 for x in
                      np.arange(k - self.order, k)])
                 grid = self.gridCount(grid, resolution, np.ravel(qtle_upper))
-            qtle_mid = self.forecast(
+            qtle_mid = self.forecastInterval(
                 [intervals[x][0] + (intervals[x][1] - intervals[x][0]) / 2 for x in np.arange(k - self.order, k)])
             grid = self.gridCount(grid, resolution, np.ravel(qtle_mid))