pyFTS/song.py

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

class ConventionalFTS(fts.FTS):
    """Conventional Fuzzy Time Series"""
    def __init__(self, name, **kwargs):
        super(ConventionalFTS, self).__init__(1, "FTS " + name)
        self.name = "Traditional FTS"
        self.detail = "Song & Chissom"
        self.R = None

    def flr_membership_matrix(self, flr):
        lm = [flr.LHS.membership(k.centroid) for k in self.sets]
        rm = [flr.RHS.membership(k.centroid) for k in self.sets]

        r = np.zeros((len(self.sets), len(self.sets)))
        for k in range(0,len(self.sets)):
            for l in range(0, len(self.sets)):
                r[k][l] = min(lm[k],rm[l])

        return r

    def operation_matrix(self, flrs):
        r = np.zeros((len(self.sets),len(self.sets)))
        for k in flrs:
            mm = self.flr_membership_matrix(k)
            for k in range(0, len(self.sets)):
                for l in range(0, len(self.sets)):
                    r[k][l] = max(r[k][l], mm[k][l])

        return r

    def train(self, data, sets,order=1,parameters=None):
        self.sets = sets
        ndata = self.doTransformations(data)
        tmpdata = FuzzySet.fuzzySeries(ndata, sets)
        flrs = FLR.generateNonRecurrentFLRs(tmpdata)
        self.R = self.operation_matrix(flrs)

    def forecast(self, data, **kwargs):

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

        l = len(ndata)
        npart = len(self.sets)

        ret = []

        for k in np.arange(0, l):
            mv = FuzzySet.fuzzyInstance(ndata[k], self.sets)

            r = [max([ min(self.R[i][j], mv[j]) for j in np.arange(0,npart) ]) for i in np.arange(0,npart)]

            fs = np.ravel(np.argwhere(r == max(r)))

            if len(fs) == 1:
                ret.append(self.sets[fs[0]].centroid)
            else:
                mp = [self.sets[s].centroid for s in fs]

                ret.append( sum(mp)/len(mp))


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

        return ret