# Source code for pyFTS.models.ismailefendi

    """
    First Order Improved Weighted Fuzzy Time Series by Efendi, Ismail and Deris (2013)

    R. Efendi, Z. Ismail, and M. M. Deris, “Improved weight Fuzzy Time Series as used in the exchange rates forecasting of
    US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1, p. 1350005, 2013.
    """

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

    class ImprovedWeightedFLRG(flrg.FLRG):
        """First Order Improved Weighted Fuzzy Logical Relationship Group"""
        def __init__(self, LHS, **kwargs):
            super(ImprovedWeightedFLRG, self).__init__(1, **kwargs)
            self.LHS = LHS
            self.RHS = {}
            self.rhs_counts = {}
            self.count = 0.0
            self.w = None

        def append_rhs(self, c, **kwargs):
            count = kwargs.get('count', 1.0)
            if c not in self.RHS:
                self.RHS[c] = c
                self.rhs_counts[c] = count
            else:
                self.rhs_counts[c] += count
            self.count += count

        def weights(self):
            if self.w is None:
                self.w = np.array([self.rhs_counts[c] / self.count for c in self.RHS.keys()])
            return self.w

        def __str__(self):
            tmp = self.LHS + " -> "
            tmp2 = ""
            for c in sorted(self.RHS.keys()):
                if len(tmp2) > 0:
                    tmp2 = tmp2 + ","
                tmp2 = tmp2 + c + "(" + str(round(self.rhs_counts[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, **kwargs):
            super(ImprovedWeightedFTS, self).__init__(order=1, name="IWFTS", **kwargs)
            self.name = "Improved Weighted FTS"
            self.detail = "Ismail & Efendi"

        def generate_flrg(self, flrs):
            for flr in flrs:
                if flr.LHS in self.flrgs:
                    self.flrgs[flr.LHS].append_rhs(flr.RHS)
                else:
                    self.flrgs[flr.LHS] = ImprovedWeightedFLRG(flr.LHS)
                    self.flrgs[flr.LHS].append_rhs(flr.RHS)

        def train(self, ndata, **kwargs):

            tmpdata = self.partitioner.fuzzyfy(ndata, method='maximum', mode='sets')
            flrs = FLR.generate_recurrent_flrs(tmpdata)
            self.generate_flrg(flrs)

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

            explain = kwargs.get('explain', False)

            if self.partitioner is not None:
                ordered_sets = self.partitioner.ordered_sets
            else:
                ordered_sets = FuzzySet.set_ordered(self.sets)

            ndata = np.array(ndata)

            l = len(ndata) if not explain else 1

            ret = []

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

                actual = FuzzySet.get_maximum_membership_fuzzyset(ndata[k], self.sets, ordered_sets)

                if explain:
                    print("Fuzzyfication:\n\n {} -> {} \n".format(ndata[k], actual.name))

                if actual.name not in self.flrgs:
                    ret.append(actual.centroid)

                    if explain:
                        print("Rules:\n\n {} -> {} (Naïve)\t Midpoint: {}  \n\n".format(actual.name, actual.name,actual.centroid))

                else:
                    flrg = self.flrgs[actual.name]
                    mp = flrg.get_midpoints(self.sets)

                    final = mp.dot(flrg.weights())

                    ret.append(final)

                    if explain:
                        print("Rules:\n\n {} \n\n ".format(str(flrg)))
                        print("Midpoints: \n\n {}\n\n".format(mp))

                        print("Deffuzyfied value: {} \n".format(final))

            return ret