pyFTS/models/cmsfts.py
Petrônio Cândido de Lima e Silva 279679b3a4 Refactoring to help tasks automotion
2017-02-10 11:09:59 -02:00

98 lines
2.7 KiB
Python

import numpy as np
from pyFTS.common import FuzzySet,FLR
from pyFTS import fts, sfts, chen
class ContextualSeasonalFLRG(object):
def __init__(self, seasonality):
self.season = seasonality
self.flrgs = {}
def append(self, flr):
if flr.LHS.name in self.flrgs:
self.flrgs[flr.LHS.name].append(flr.RHS)
else:
self.flrgs[flr.LHS.name] = chen.ConventionalFLRG(flr.LHS)
self.flrgs[flr.LHS.name].append(flr.RHS)
def __str__(self):
tmp = str(self.season) + ": \n "
tmp2 = "\t"
for r in sorted(self.flrgs):
tmp2 += str(self.flrgs[r]) + "\n\t"
return tmp + tmp2 + "\n"
class ContextualMultiSeasonalFTS(sfts.SeasonalFTS):
def __init__(self, name, indexer):
super(ContextualMultiSeasonalFTS, self).__init__("CMSFTS")
self.name = "Contextual Multi Seasonal FTS"
self.shortname = "CMSFTS " + name
self.detail = ""
self.seasonality = 1
self.hasSeasonality = True
self.hasPointForecasting = True
self.isHighOrder = True
self.isMultivariate = True
self.indexer = indexer
self.flrgs = {}
def generateFLRG(self, flrs):
flrgs = {}
for flr in flrs:
if str(flr.index) not in flrgs:
flrgs[str(flr.index)] = ContextualSeasonalFLRG(flr.index)
flrgs[str(flr.index)].append(flr)
return (flrgs)
def train(self, data, sets, order=1, parameters=None):
self.sets = sets
self.seasonality = parameters
flrs = FLR.generateIndexedFLRs(self.sets, self.indexer, data)
self.flrgs = self.generateFLRG(flrs)
def getMidpoints(self, flrg, data):
if data.name in flrg.flrgs:
ret = np.array([s.centroid for s in flrg.flrgs[data.name].RHS])
return ret
else:
return np.array([data.centroid])
def forecast(self, data):
ret = []
index = self.indexer.get_season_of_data(data)
ndata = self.indexer.get_data(data)
for k in np.arange(1, len(data)):
flrg = self.flrgs[str(index[k])]
d = FuzzySet.getMaxMembershipFuzzySet(ndata[k], self.sets)
mp = self.getMidpoints(flrg, d)
ret.append(sum(mp) / len(mp))
ret = self.doInverseTransformations(ret, params=[ndata[self.order - 1:]])
return ret
def forecastAhead(self, data, steps):
ret = []
for i in steps:
flrg = self.flrgs[str(i)]
mp = self.getMidpoints(flrg)
ret.append(sum(mp) / len(mp))
ret = self.doInverseTransformations(ret, params=data)
return ret