298 lines
10 KiB
Python
298 lines
10 KiB
Python
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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import hofts, ifts, tree
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class ProbabilisticFLRG(hofts.HighOrderFLRG):
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def __init__(self, order):
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super(ProbabilisticFLRG, self).__init__(order)
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self.RHS = {}
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self.frequencyCount = 0
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def appendRHS(self, c):
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self.frequencyCount = self.frequencyCount + 1
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if c.name in self.RHS:
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self.RHS[c.name] = self.RHS[c.name] + 1
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else:
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self.RHS[c.name] = 1
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def getProbability(self, c):
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return self.RHS[c] / self.frequencyCount
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def __str__(self):
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tmp2 = ""
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for c in sorted(self.RHS):
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if len(tmp2) > 0:
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tmp2 = tmp2 + ", "
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tmp2 = tmp2 + c + "(" + str(round(self.RHS[c] / self.frequencyCount, 3)) + ")"
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return self.strLHS() + " -> " + tmp2
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class ProbabilisticIntervalFTS(ifts.IntervalFTS):
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def __init__(self, name):
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super(ProbabilisticIntervalFTS, 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.detail = "Silva, P.; Guimarães, F.; Sadaei, H."
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self.flrgs = {}
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self.globalFrequency = 0
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self.isInterval = True
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self.isDensity = True
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def generateFLRG(self, flrs):
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flrgs = {}
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l = len(flrs)
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for k in np.arange(self.order + 1, l):
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flrg = ProbabilisticFLRG(self.order)
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for kk in np.arange(k - self.order, k):
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flrg.appendLHS(flrs[kk].LHS)
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if flrg.strLHS() in flrgs:
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flrgs[flrg.strLHS()].appendRHS(flrs[k].RHS)
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else:
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flrgs[flrg.strLHS()] = flrg;
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flrgs[flrg.strLHS()].appendRHS(flrs[k].RHS)
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self.globalFrequency = self.globalFrequency + 1
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return (flrgs)
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def getProbability(self, flrg):
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if flrg.strLHS() in self.flrgs:
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return self.flrgs[flrg.strLHS()].frequencyCount / self.globalFrequency
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else:
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return 1.0 / self.globalFrequency
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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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ret = sum(np.array([tmp.getProbability(s) * self.setsDict[s].upper for s in tmp.RHS]))
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else:
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ret = sum(np.array([0.33 * s.upper for s in flrg.LHS]))
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return ret
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def getLower(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].lower for s in tmp.RHS]))
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else:
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ret = sum(np.array([0.33 * s.lower for s in flrg.LHS]))
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return ret
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def forecast(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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affected_flrgs = []
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affected_flrgs_memberships = []
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norms = []
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up = []
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lo = []
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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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up.append(norm * self.getUpper(flrg))
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lo.append(norm * self.getLower(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(lo) / norm, sum(up) / norm])
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return ret
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def forecastAhead(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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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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ret.append([np.min(lower), np.max(upper)])
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return ret
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def getGridClean(self, resolution):
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grid = {}
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for sbin in np.arange(self.sets[0].lower, self.sets[-1].upper, resolution):
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grid[sbin] = 0
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return grid
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def gridCount(self, grid, resolution, interval):
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for sbin in sorted(grid):
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if sbin >= interval[0] and (sbin + resolution) <= interval[1]:
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grid[sbin] = grid[sbin] + 1
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return grid
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def forecastDistributionAhead2(self, data, steps, resolution):
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ret = []
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intervals = self.forecastAhead(data, steps)
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for k in np.arange(self.order, steps):
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grid = self.getGridClean(resolution)
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grid = self.gridCount(grid, resolution, intervals[k])
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lags = {}
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cc = 0
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for x in np.arange(k - self.order, k):
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tmp = []
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for qt in np.arange(0, 100, 5):
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tmp.append(intervals[x][0] + qt * (intervals[x][1] - intervals[x][0]) / 100)
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tmp.append(intervals[x][1] - qt * (intervals[x][1] - intervals[x][0]) / 100)
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tmp.append(intervals[x][0] + (intervals[x][1] - intervals[x][0]) / 2)
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lags[cc] = tmp
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cc = cc + 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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subset = [kk for kk in path]
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qtle = self.forecast(subset)
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grid = self.gridCount(grid, resolution, np.ravel(qtle))
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tmp = np.array([grid[k] for k in sorted(grid)])
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ret.append(tmp / sum(tmp))
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grid = self.getGridClean(resolution)
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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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ret = []
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intervals = self.forecastAhead(data, steps)
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for k in np.arange(self.order, steps):
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grid = self.getGridClean(resolution)
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grid = self.gridCount(grid, resolution, intervals[k])
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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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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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grid = self.gridCount(grid, resolution, np.ravel(qtle_upper))
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qtle_mid = self.forecast(
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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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tmp = np.array([grid[k] for k in sorted(grid)])
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ret.append(tmp / sum(tmp))
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grid = self.getGridClean(resolution)
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df = pd.DataFrame(ret, columns=sorted(grid))
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return df
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def __str__(self):
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tmp = self.name + ":\n"
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for r in sorted(self.flrgs):
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p = round(self.flrgs[r].frequencyCount / self.globalFrequency, 3)
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tmp = tmp + "(" + str(p) + ") " + str(self.flrgs[r]) + "\n"
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return tmp
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