182 lines
4.7 KiB
Python
182 lines
4.7 KiB
Python
import numpy as np
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import pandas as pd
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from pyFTS import tree
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from pyFTS.common import FuzzySet, SortedCollection
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from pyFTS.benchmarks import Measures
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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.isMultivariate = 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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self.original_max = 0
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self.original_min = 0
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self.partitioner = None
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self.auto_update = False
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self.benchmark_only = False
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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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if transformation is not None:
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self.transformations.append(transformation)
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def doTransformations(self,data,params=None,updateUoD=False):
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ndata = data
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if updateUoD:
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if min(data) < 0:
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self.original_min = min(data) * 1.1
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else:
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self.original_min = min(data) * 0.9
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if max(data) > 0:
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self.original_max = max(data) * 1.1
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else:
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self.original_max = max(data) * 0.9
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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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def __len__(self):
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return len(self.flrgs)
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def len_total(self):
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return sum([len(k) for k in self.flrgs])
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def buildTreeWithoutOrder(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.buildTreeWithoutOrder(child, lags, level + 1)
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def inputoutputmapping(self,bins=100):
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dim_uod = tuple([bins for k in range(0,self.order)])
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dim_fs = tuple([ len(self.sets) for k in range(0, self.order)])
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simulation_uod = np.zeros(shape=dim_uod, dtype=float)
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simulation_fs = np.zeros(shape=dim_fs, dtype=float)
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percentiles = np.linspace(self.sets[0].lower, self.sets[-1].upper, bins).tolist()
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pdf_uod = {}
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for k in percentiles:
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pdf_uod[k] = 0
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pdf_fs = {}
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for k in self.sets:
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pdf_fs[k.name] = 0
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lags = {}
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for o in np.arange(0, self.order):
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lags[o] = percentiles
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# Build the tree with all possible paths
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root = tree.FLRGTreeNode(None)
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self.buildTreeWithoutOrder(root, lags, 0)
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# Trace the possible paths
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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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index_uod = tuple([percentiles.index(k) for k in path])
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index_fs = tuple([ FuzzySet.getMaxMembershipFuzzySetIndex(k, self.sets) for k in path])
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forecast = self.forecast(path)[0]
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simulation_uod[index_uod] = forecast
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simulation_fs[index_fs] = forecast
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return [simulation_fs, simulation_uod ]
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