Refatoração de PIFTS para PFTS; acrescentando as packages data e models
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Petrônio Cândido de Lima e Silva
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2022c2a032
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data/__init__.py
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data/__init__.py
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models/__init__.py
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models/__init__.py
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@ -1,7 +1,7 @@
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import numpy as np
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import pandas as pd
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import math
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from pyFTS.common import FuzzySet,FLR
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from pyFTS.common import FuzzySet, FLR
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import hofts, ifts, tree
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@ -30,11 +30,11 @@ class ProbabilisticFLRG(hofts.HighOrderFLRG):
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return self.strLHS() + " -> " + tmp2
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class ProbabilisticIntervalFTS(ifts.IntervalFTS):
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class ProbabilisticFTS(ifts.IntervalFTS):
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def __init__(self, name):
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super(ProbabilisticIntervalFTS, self).__init__("PIFTS")
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super(ProbabilisticFTS, self).__init__("PIFTS")
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self.shortname = "PIFTS " + name
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self.name = "Probabilistic Interval FTS"
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self.name = "Probabilistic FTS"
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self.detail = "Silva, P.; Guimarães, F.; Sadaei, H."
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self.flrgs = {}
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self.globalFrequency = 0
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@ -65,6 +65,14 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
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else:
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return 1.0 / self.globalFrequency
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def getMidpoints(self, flrg):
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if flrg.strLHS() in self.flrgs:
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tmp = self.flrgs[flrg.strLHS()]
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ret = sum(np.array([tmp.getProbability(s) * self.setsDict[s].midpoint for s in tmp.RHS]))
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else:
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ret = sum(np.array([0.33 * s.midpoint for s in flrg.LHS]))
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return ret
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def getUpper(self, flrg):
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if flrg.strLHS() in self.flrgs:
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tmp = self.flrgs[flrg.strLHS()]
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@ -89,6 +97,106 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
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ret = []
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for k in np.arange(self.order - 1, l):
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# print(k)
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affected_flrgs = []
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affected_flrgs_memberships = []
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norms = []
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mp = []
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# Find the sets which membership > 0 for each lag
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count = 0
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lags = {}
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if self.order > 1:
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subset = ndata[k - (self.order - 1): k + 1]
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for instance in subset:
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mb = FuzzySet.fuzzyInstance(instance, self.sets)
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tmp = np.argwhere(mb)
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idx = np.ravel(tmp) # flatten the array
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if idx.size == 0: # the element is out of the bounds of the Universe of Discourse
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if math.ceil(instance) <= self.sets[0].lower:
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idx = [0]
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elif math.ceil(instance) >= self.sets[-1].upper:
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idx = [len(self.sets) - 1]
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else:
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raise Exception(instance)
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lags[count] = idx
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count = count + 1
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# Build the tree with all possible paths
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root = tree.FLRGTreeNode(None)
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self.buildTree(root, lags, 0)
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# Trace the possible paths and build the PFLRG's
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for p in root.paths():
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path = list(reversed(list(filter(None.__ne__, p))))
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flrg = hofts.HighOrderFLRG(self.order)
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for kk in path: flrg.appendLHS(self.sets[kk])
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assert len(flrg.LHS) == subset.size, str(subset) + " -> " + str([s.name for s in flrg.LHS])
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##
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affected_flrgs.append(flrg)
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# Find the general membership of FLRG
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affected_flrgs_memberships.append(min(self.getSequenceMembership(subset, flrg.LHS)))
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else:
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mv = FuzzySet.fuzzyInstance(ndata[k], self.sets) # get all membership values
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tmp = np.argwhere(mv) # get the indices of values > 0
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idx = np.ravel(tmp) # flatten the array
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if idx.size == 0: # the element is out of the bounds of the Universe of Discourse
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if math.ceil(ndata[k]) <= self.sets[0].lower:
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idx = [0]
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elif math.ceil(ndata[k]) >= self.sets[-1].upper:
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idx = [len(self.sets) - 1]
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else:
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raise Exception(ndata[k])
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for kk in idx:
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flrg = hofts.HighOrderFLRG(self.order)
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flrg.appendLHS(self.sets[kk])
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affected_flrgs.append(flrg)
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affected_flrgs_memberships.append(mv[kk])
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count = 0
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for flrg in affected_flrgs:
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# achar o os bounds de cada FLRG, ponderados pela probabilidade e pertinência
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norm = self.getProbability(flrg) * affected_flrgs_memberships[count]
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if norm == 0:
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norm = self.getProbability(flrg) # * 0.001
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mp.append(norm * self.getMidpoints(flrg))
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norms.append(norm)
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count = count + 1
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# gerar o intervalo
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norm = sum(norms)
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if norm == 0:
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ret.append([0, 0])
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else:
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ret.append(sum(mp) / norm)
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return ret
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def forecastInterval(self, data):
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ndata = np.array(data)
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l = len(ndata)
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ret = []
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for k in np.arange(self.order - 1, l):
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# print(k)
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@ -184,6 +292,20 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
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return ret
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def forecastAhead(self, data, steps):
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ret = [data[k] for k in np.arange(len(data) - self.order, len(data))]
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for k in np.arange(self.order - 1, steps):
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if ret[-1] <= self.sets[0].lower or ret[-1] >= self.sets[-1].upper:
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ret.append(ret[-1])
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else:
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mp = self.forecast([ret[x] for x in np.arange(k - self.order, k)])
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ret.append(mp)
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return ret
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def forecastAheadInterval(self, data, steps):
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ret = [[data[k], data[k]] for k in np.arange(len(data) - self.order, len(data))]
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for k in np.arange(self.order - 1, steps):
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@ -191,8 +313,8 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
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if ret[-1][0] <= self.sets[0].lower and ret[-1][1] >= self.sets[-1].upper:
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ret.append(ret[-1])
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else:
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lower = self.forecast([ret[x][0] for x in np.arange(k - self.order, k)])
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upper = self.forecast([ret[x][1] for x in np.arange(k - self.order, k)])
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lower = self.forecastInterval([ret[x][0] for x in np.arange(k - self.order, k)])
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upper = self.forecastInterval([ret[x][1] for x in np.arange(k - self.order, k)])
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ret.append([np.min(lower), np.max(upper)])
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@ -258,11 +380,11 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
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df = pd.DataFrame(ret, columns=sorted(grid))
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return df
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def forecastDistributionAhead(self, data, steps, resolution):
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def forecastAheadDistribution(self, data, steps, resolution):
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ret = []
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intervals = self.forecastAhead(data, steps)
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intervals = self.forecastAheadInterval(data, steps)
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for k in np.arange(self.order, steps):
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@ -271,13 +393,15 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
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for qt in np.arange(1, 50, 2):
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# print(qt)
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qtle_lower = self.forecast([intervals[x][0] + qt * (intervals[x][1] - intervals[x][0]) / 100 for x in
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np.arange(k - self.order, k)])
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qtle_lower = self.forecastInterval(
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[intervals[x][0] + qt * (intervals[x][1] - intervals[x][0]) / 100 for x in
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np.arange(k - self.order, k)])
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grid = self.gridCount(grid, resolution, np.ravel(qtle_lower))
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qtle_upper = self.forecast([intervals[x][1] - qt * (intervals[x][1] - intervals[x][0]) / 100 for x in
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np.arange(k - self.order, k)])
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qtle_upper = self.forecastInterval(
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[intervals[x][1] - qt * (intervals[x][1] - intervals[x][0]) / 100 for x in
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np.arange(k - self.order, k)])
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grid = self.gridCount(grid, resolution, np.ravel(qtle_upper))
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qtle_mid = self.forecast(
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qtle_mid = self.forecastInterval(
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[intervals[x][0] + (intervals[x][1] - intervals[x][0]) / 2 for x in np.arange(k - self.order, k)])
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grid = self.gridCount(grid, resolution, np.ravel(qtle_mid))
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