# pyFTS.models.seasonal.cmsfts — pyFTS 1.6 documentation

# Source code for pyFTS.models.seasonal.cmsfts

    import numpy as np
    from pyFTS.common import FuzzySet, FLR
    from pyFTS.models.seasonal import sfts
    from pyFTS.models import chen

    class ContextualSeasonalFLRG(sfts.SeasonalFLRG):
        """
        Contextual Seasonal Fuzzy Logical Relationship Group
        """
        def __init__(self, seasonality):
            super(ContextualSeasonalFLRG, self).__init__(seasonality)
            self.RHS = {}

        def append_rhs(self, flr, **kwargs):
            if flr.LHS in self.RHS:
                self.RHS[flr.LHS].append_rhs(flr.RHS)
            else:
                self.RHS[flr.LHS] = chen.ConventionalFLRG(flr.LHS)
                self.RHS[flr.LHS].append_rhs(flr.RHS)

        def __str__(self):
            tmp = str(self.LHS) + ": \n "
            tmp2 = "\t"
            for r in sorted(self.RHS):
                tmp2 += str(self.RHS[r]) + "\n\t"
            return tmp + tmp2 + "\n"

    class ContextualMultiSeasonalFTS(sfts.SeasonalFTS):
        """
        Contextual Multi-Seasonal Fuzzy Time Series
        """
        def __init__(self, **kwargs):
            super(ContextualMultiSeasonalFTS, self).__init__(**kwargs)
            self.name = "Contextual Multi Seasonal FTS"
            self.shortname = "CMSFTS "
            self.detail = ""
            self.seasonality = 1
            self.has_seasonality = True
            self.has_point_forecasting = True
            self.is_high_order = True
            self.is_multivariate = True
            self.order = 1
            self.flrgs = {}

        def generate_flrg(self, flrs):
            for flr in flrs:

                if str(flr.index) not in self.flrgs:
                    self.flrgs[str(flr.index)] = ContextualSeasonalFLRG(flr.index)

                self.flrgs[str(flr.index)].append_rhs(flr)

        def train(self, data,  **kwargs):
            if kwargs.get('sets', None) is not None:
                self.sets = kwargs.get('sets', None)
            if kwargs.get('parameters', None) is not None:
                self.seasonality = kwargs.get('parameters', None)
            flrs = FLR.generate_indexed_flrs(self.sets, self.indexer, data,
                                             transformation=self.partitioner.transformation,
                                             alpha_cut=self.alpha_cut)
            self.generate_flrg(flrs)

        def get_midpoints(self, flrg, data):
            ret = []
            for d in data:
                if d in flrg.RHS:
                    ret.extend([self.sets[s].centroid for s in flrg.RHS[d].RHS])
                else:
                    ret.extend([self.sets[d].centroid])

            return np.array(ret)

        def forecast(self, data, **kwargs):
            ordered_sets = FuzzySet.set_ordered(self.sets)

            ret = []

            index = self.indexer.get_season_of_data(data)
            ndata = self.indexer.get_data(data)

            for k in np.arange(0, len(data)):

                if str(index[k]) in self.flrgs:

                    flrg = self.flrgs[str(index[k])]

                    d = FuzzySet.get_fuzzysets(ndata[k], self.sets, ordered_sets, alpha_cut=self.alpha_cut)

                    mp = self.get_midpoints(flrg, d)

                    ret.append(sum(mp) / len(mp))
                else:
                    ret.append(np.nan)

            return ret

        def forecast_ahead(self, data, steps, **kwargs):
            ret = []
            for i in steps:
                flrg = self.flrgs[str(i)]

                mp = self.get_midpoints(flrg)

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

            return ret