# pyFTS.models.nonstationary.honsfts — pyFTS 1.4 documentation

# Source code for pyFTS.models.nonstationary.honsfts

    import numpy as np
    from pyFTS.common import FuzzySet, FLR, fts, tree
    from pyFTS.models import hofts
    from pyFTS.models.nonstationary import common, flrg

    class HighOrderNonStationaryFLRG(flrg.NonStationaryFLRG):
        """First Order NonStationary Fuzzy Logical Relationship Group"""
        def __init__(self, order, **kwargs):
            super(HighOrderNonStationaryFLRG, self).__init__(order, **kwargs)

            self.LHS = []
            self.RHS = {}

        def append_rhs(self, c, **kwargs):
            if c.name not in self.RHS:
                self.RHS[c.name] = c

        def append_lhs(self, c):
            self.LHS.append(c)

        def __str__(self):
            tmp = ""
            for c in sorted(self.RHS):
                if len(tmp) > 0:
                    tmp = tmp + ","
                tmp = tmp + c
            return self.get_key() + " -> " + tmp

    class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
        """NonStationaryFTS Fuzzy Time Series"""
        def __init__(self, name, **kwargs):
            super(HighOrderNonStationaryFTS, self).__init__("HONSFTS " + name, **kwargs)
            self.name = "High Order Non Stationary FTS"
            self.detail = ""
            self.flrgs = {}

        def generate_flrg(self, data, **kwargs):
            l = len(data)
            window_size = kwargs.get("window_size", 1)
            for k in np.arange(self.order, l):
                if self.dump: print("FLR: " + str(k))

                sample = data[k - self.order: k]

                disp = common.window_index(k, window_size)

                rhs = [self.sets[key] for key in self.partitioner.ordered_sets
                       if self.sets[key].membership(data[k], disp) > 0.0]

                if len(rhs) == 0:
                    rhs = [common.check_bounds(data[k], self.partitioner, disp)]

                lags = {}

                for o in np.arange(0, self.order):
                    tdisp = common.window_index(k - (self.order - o), window_size)
                    lhs = [self.sets[key] for key in self.partitioner.ordered_sets
                       if self.sets[key].membership(sample[o], tdisp) > 0.0]

                    if len(lhs) == 0:
                        lhs = [common.check_bounds(sample[o], self.partitioner, tdisp)]

                    lags[o] = lhs

                root = tree.FLRGTreeNode(None)

                tree.build_tree_without_order(root, lags, 0)

                # Trace the possible paths
                for p in root.paths():
                    flrg = HighOrderNonStationaryFLRG(self.order)
                    path = list(reversed(list(filter(None.__ne__, p))))

                    for c, e in enumerate(path, start=0):
                        flrg.append_lhs(e)

                    if flrg.get_key() not in self.flrgs:
                        self.flrgs[flrg.get_key()] = flrg;

                    for st in rhs:
                        self.flrgs[flrg.get_key()].append_rhs(st)

            # flrgs = sorted(flrgs, key=lambda flrg: flrg.get_midpoint(0, window_size=1))

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

            if kwargs.get('order', None) is not None:
                self.order = kwargs.get('order', 1)

            if kwargs.get('sets', None) is not None:
                self.sets = kwargs.get('sets', None)

            window_size = kwargs.get('parameters', 1)
            self.generate_flrg(data, window_size=window_size)

        def _affected_flrgs(self, sample, k, time_displacement, window_size):
            # print("input: " + str(ndata[k]))

            affected_flrgs = []
            affected_flrgs_memberships = []

            lags = {}

            for ct, dat in enumerate(sample):
                tdisp = common.window_index((k + time_displacement) - (self.order - ct), window_size)

                sel = [ct for ct, key in enumerate(self.partitioner.ordered_sets)
                       if self.sets[key].membership(dat, tdisp) > 0.0]

                if len(sel) == 0:
                    sel.append(common.check_bounds_index(dat, self.partitioner, tdisp))

                lags[ct] = sel

            # Build the tree with all possible paths

            root = tree.FLRGTreeNode(None)

            tree.build_tree_without_order(root, lags, 0)

            # Trace the possible paths and build the PFLRG's

            for p in root.paths():
                path = list(reversed(list(filter(None.__ne__, p))))
                flrg = HighOrderNonStationaryFLRG(self.order)

                for kk in path:
                    flrg.append_lhs(self.sets[self.partitioner.ordered_sets[kk]])

                affected_flrgs.append(flrg)
                # affected_flrgs_memberships.append_rhs(flrg.get_membership(sample, disp))

                #                print(flrg.get_key())

                # the FLRG is here because of the bounds verification
                mv = []
                for ct, dat in enumerate(sample):
                    td = common.window_index((k + time_displacement) - (self.order - ct), window_size)
                    tmp = flrg.LHS[ct].membership(dat, td)

                    mv.append(tmp)
                # print(mv)

                affected_flrgs_memberships.append(np.prod(mv))

            return [affected_flrgs, affected_flrgs_memberships]

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

            time_displacement = kwargs.get("time_displacement",0)

            window_size = kwargs.get("window_size", 1)

            l = len(ndata)

            ret = []

            for k in np.arange(self.order, l+1):

                sample = ndata[k - self.order: k]

                affected_flrgs, affected_flrgs_memberships = self._affected_flrgs(sample, k,
                                                                                  time_displacement, window_size)

                #print([str(k) for k in affected_flrgs])
                #print(affected_flrgs_memberships)

                tmp = []
                tdisp = common.window_index(k + time_displacement, window_size)
                if len(affected_flrgs) == 0:
                    tmp.append(common.check_bounds(sample[-1], self.sets, tdisp))
                elif len(affected_flrgs) == 1:
                    flrg = affected_flrgs[0]
                    if flrg.get_key() in self.flrgs:
                        tmp.append(self.flrgs[flrg.get_key()].get_midpoint(tdisp))
                    else:
                        tmp.append(flrg.LHS[-1].get_midpoint(tdisp))
                else:
                    for ct, aset in enumerate(affected_flrgs):
                        if aset.get_key() in self.flrgs:
                            tmp.append(self.flrgs[aset.get_key()].get_midpoint(tdisp) *
                                       affected_flrgs_memberships[ct])
                        else:
                            tmp.append(aset.LHS[-1].get_midpoint(tdisp)*
                                       affected_flrgs_memberships[ct])
                pto = sum(tmp)

                #print(pto)

                ret.append(pto)

            return ret

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

            time_displacement = kwargs.get("time_displacement", 0)

            window_size = kwargs.get("window_size", 1)

            l = len(ndata)

            ret = []

            for k in np.arange(self.order, l + 1):

                sample = ndata[k - self.order: k]

                affected_flrgs, affected_flrgs_memberships = self._affected_flrgs(sample, k,
                                                                                  time_displacement, window_size)

                # print([str(k) for k in affected_flrgs])
                # print(affected_flrgs_memberships)

                upper = []
                lower = []

                tdisp = common.window_index(k + time_displacement, window_size)
                if len(affected_flrgs) == 0:
                    aset = common.check_bounds(sample[-1], self.sets, tdisp)
                    lower.append(aset.get_lower(tdisp))
                    upper.append(aset.get_upper(tdisp))
                elif len(affected_flrgs) == 1:
                    _flrg = affected_flrgs[0]
                    if _flrg.get_key() in self.flrgs:
                        lower.append(self.flrgs[_flrg.get_key()].get_lower(tdisp))
                        upper.append(self.flrgs[_flrg.get_key()].get_upper(tdisp))
                    else:
                        lower.append(_flrg.LHS[-1].get_lower(tdisp))
                        upper.append(_flrg.LHS[-1].get_upper(tdisp))
                else:
                    for ct, aset in enumerate(affected_flrgs):
                        if aset.get_key() in self.flrgs:
                            lower.append(self.flrgs[aset.get_key()].get_lower(tdisp) *
                                         affected_flrgs_memberships[ct])
                            upper.append(self.flrgs[aset.get_key()].get_upper(tdisp) *
                                         affected_flrgs_memberships[ct])
                        else:
                            lower.append(aset.LHS[-1].get_lower(tdisp) *
                                       affected_flrgs_memberships[ct])
                            upper.append(aset.LHS[-1].get_upper(tdisp) *
                                       affected_flrgs_memberships[ct])

                ret.append([sum(lower), sum(upper)])

            return ret