89 lines
2.3 KiB
Python
89 lines
2.3 KiB
Python
import numpy as np
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from pyFTS import *
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class FTS(object):
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def __init__(self, order, name):
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self.sets = {}
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self.flrgs = {}
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self.order = order
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self.shortname = name
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self.name = name
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self.detail = name
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self.isHighOrder = False
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self.minOrder = 1
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self.hasSeasonality = False
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self.hasPointForecasting = True
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self.hasIntervalForecasting = False
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self.hasDistributionForecasting = False
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self.dump = False
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self.transformations = []
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self.transformations_param = []
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def fuzzy(self, data):
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best = {"fuzzyset": "", "membership": 0.0}
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for f in self.sets:
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fset = self.sets[f]
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if best["membership"] <= fset.membership(data):
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best["fuzzyset"] = fset.name
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best["membership"] = fset.membership(data)
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return best
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def forecast(self, data):
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pass
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def forecastInterval(self, data):
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pass
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def forecastDistribution(self, data):
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pass
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def forecastAhead(self, data, steps):
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pass
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def forecastAheadInterval(self, data, steps):
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pass
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def forecastAheadDistribution(self, data, steps):
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pass
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def train(self, data, sets,order=1, parameters=None):
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pass
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def getMidpoints(self, flrg):
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ret = np.array([s.centroid for s in flrg.RHS])
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return ret
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def appendTransformation(self, transformation):
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self.transformations.append(transformation)
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def doTransformations(self,data,params=None):
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ndata = data
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if len(self.transformations) > 0:
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if params is None:
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params = [ None for k in self.transformations]
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for c, t in enumerate(self.transformations, start=0):
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ndata = t.apply(ndata,params[c])
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return ndata
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def doInverseTransformations(self, data, params=None):
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ndata = data
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if len(self.transformations) > 0:
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if params is None:
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params = [None for k in self.transformations]
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for c, t in enumerate(reversed(self.transformations), start=0):
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ndata = t.inverse(ndata, params[c])
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return ndata
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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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tmp = tmp + str(self.flrgs[r]) + "\n"
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return tmp
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