169 lines
4.7 KiB
Python
169 lines
4.7 KiB
Python
"""
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Simple High Order extension of Conventional FTS by Chen (1996)
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[1] S.-M. Chen, “Forecasting enrollments based on fuzzy time series,”
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Fuzzy Sets Syst., vol. 81, no. 3, pp. 311–319, 1996.
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"""
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import numpy as np
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from pyFTS.common import FuzzySet,FLR
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from pyFTS import fts, flrg, tree
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class HighOrderFLRG(flrg.FLRG):
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"""Conventional High Order Fuzzy Logical Relationship Group"""
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def __init__(self, order, **kwargs):
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super(HighOrderFLRG, self).__init__(order, **kwargs)
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self.LHS = []
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self.RHS = {}
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self.strlhs = ""
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def appendRHS(self, c):
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if c.name not in self.RHS:
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self.RHS[c.name] = c
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def strLHS(self):
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if len(self.strlhs) == 0:
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for c in self.LHS:
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if len(self.strlhs) > 0:
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self.strlhs += ", "
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self.strlhs = self.strlhs + c.name
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return self.strlhs
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def appendLHS(self, c):
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self.LHS.append(c)
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def __str__(self):
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tmp = ""
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for c in sorted(self.RHS):
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if len(tmp) > 0:
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tmp = tmp + ","
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tmp = tmp + c
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return self.strLHS() + " -> " + tmp
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def __len__(self):
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return len(self.RHS)
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class HighOrderFTS(fts.FTS):
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"""Conventional High Order Fuzzy Time Series"""
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def __init__(self, name, **kwargs):
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super(HighOrderFTS, self).__init__(1, name="HOFTS" + name, **kwargs)
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self.name = "High Order FTS"
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self.shortname = "HOFTS" + name
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self.detail = "Chen"
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self.order = 1
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self.setsDict = {}
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self.is_high_order = True
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def build_tree(self, node, lags, level):
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if level >= self.order:
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return
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for s in lags[level]:
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node.appendChild(tree.FLRGTreeNode(s))
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for child in node.getChildren():
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self.build_tree(child, lags, level + 1)
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def build_tree_without_order(self, node, lags, level):
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if level not in lags:
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return
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for s in lags[level]:
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node.appendChild(tree.FLRGTreeNode(s))
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for child in node.getChildren():
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self.build_tree_without_order(child, lags, level + 1)
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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 = HighOrderFLRG(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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return (flrgs)
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def generate_flrg(self, data):
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flrgs = {}
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l = len(data)
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for k in np.arange(self.order, l):
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if self.dump: print("FLR: " + str(k))
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sample = data[k - self.order: k]
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rhs = [set for set in self.sets if set.membership(data[k]) > 0.0]
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lags = {}
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for o in np.arange(0, self.order):
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lhs = [set for set in self.sets if set.membership(sample[o]) > 0.0]
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lags[o] = lhs
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root = tree.FLRGTreeNode(None)
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self.build_tree_without_order(root, lags, 0)
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# Trace the possible paths
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for p in root.paths():
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flrg = HighOrderFLRG(self.order)
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path = list(reversed(list(filter(None.__ne__, p))))
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for lhs in enumerate(path, start=0):
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flrg.appendLHS(lhs)
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if flrg.strLHS() not in flrgs:
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flrgs[flrg.strLHS()] = flrg;
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for st in rhs:
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flrgs[flrg.strLHS()].appendRHS(st)
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return flrgs
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def train(self, data, sets, order=1,parameters=None):
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data = self.doTransformations(data, updateUoD=True)
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self.order = order
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self.sets = sets
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for s in self.sets: self.setsDict[s.name] = s
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self.flrgs = self.generate_flrg(data)
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def forecast(self, data, **kwargs):
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ret = []
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l = len(data)
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if l <= self.order:
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return data
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ndata = self.doTransformations(data)
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for k in np.arange(self.order, l+1):
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tmpdata = FuzzySet.fuzzySeries(ndata[k - self.order: k], self.sets)
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tmpflrg = HighOrderFLRG(self.order)
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for s in tmpdata: tmpflrg.appendLHS(s)
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if tmpflrg.strLHS() not in self.flrgs:
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ret.append(tmpdata[-1].centroid)
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else:
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flrg = self.flrgs[tmpflrg.strLHS()]
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ret.append(flrg.get_midpoint())
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ret = self.doInverseTransformations(ret, params=[data[self.order-1:]])
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return ret
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