pyFTS/ismailefendi.py

import numpy as np
from pyFTS.common import FuzzySet,FLR
from pyFTS import fts


class ImprovedWeightedFLRG(object):
    """First Order Improved Weighted Fuzzy Logical Relationship Group"""
    def __init__(self, LHS):
        self.LHS = LHS
        self.RHS = {}
        self.count = 0.0

    def append(self, c):
        if c.name not in self.RHS:
            self.RHS[c.name] = 1.0
        else:
            self.RHS[c.name] = self.RHS[c.name] + 1.0
        self.count = self.count + 1.0

    def weights(self):
        return np.array([self.RHS[c] / self.count for c in self.RHS.keys()])

    def __str__(self):
        tmp = self.LHS.name + " -> "
        tmp2 = ""
        for c in sorted(self.RHS):
            if len(tmp2) > 0:
                tmp2 = tmp2 + ","
            tmp2 = tmp2 + c + "(" + str(round(self.RHS[c] / self.count, 3)) + ")"
        return tmp + tmp2

    def __len__(self):
        return len(self.RHS)


class ImprovedWeightedFTS(fts.FTS):
    """First Order Improved Weighted Fuzzy Time Series"""
    def __init__(self, name, **kwargs):
        super(ImprovedWeightedFTS, self).__init__(1, "IWFTS " + name)
        self.name = "Improved Weighted FTS"
        self.detail = "Ismail & Efendi"
        self.setsDict = {}

    def generateFLRG(self, flrs):
        flrgs = {}
        for flr in flrs:
            if flr.LHS.name in flrgs:
                flrgs[flr.LHS.name].append(flr.RHS)
            else:
                flrgs[flr.LHS.name] = ImprovedWeightedFLRG(flr.LHS);
                flrgs[flr.LHS.name].append(flr.RHS)
        return (flrgs)

    def train(self, data, sets, order=1, parameters=None):
        self.sets = sets

        for s in self.sets:    self.setsDict[s.name] = s

        ndata = self.doTransformations(data)

        tmpdata = FuzzySet.fuzzySeries(ndata, self.sets)
        flrs = FLR.generateRecurrentFLRs(tmpdata)
        self.flrgs = self.generateFLRG(flrs)

    def getMidpoints(self, flrg):
        ret = np.array([self.setsDict[s].centroid for s in flrg.RHS])
        return ret

    def forecast(self, data, **kwargs):
        l = 1

        data = np.array(data)
        ndata = self.doTransformations(data)

        l = len(ndata)

        ret = []

        for k in np.arange(0, l):

            mv = FuzzySet.fuzzyInstance(ndata[k], self.sets)

            actual = self.sets[np.argwhere(mv == max(mv))[0, 0]]

            if actual.name not in self.flrgs:
                ret.append(actual.centroid)
            else:
                flrg = self.flrgs[actual.name]
                mp = self.getMidpoints(flrg)

                ret.append(mp.dot(flrg.weights()))

        ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])

        return ret