"""
First Order Traditional Fuzzy Time Series method by Song & Chissom (1993)

Q. Song and B. S. Chissom, “Fuzzy time series and its models,” Fuzzy Sets Syst., vol. 54, no. 3, pp. 269–277, 1993.
"""

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


class ConventionalFTS(fts.FTS):
    """Traditional 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