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

# Source code for pyFTS.models.nonstationary.nsfts

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

    class ConventionalNonStationaryFLRG(flrg.NonStationaryFLRG):
        """First Order NonStationary Fuzzy Logical Relationship Group"""

        def __init__(self, LHS, **kwargs):
            super(ConventionalNonStationaryFLRG, self).__init__(1, **kwargs)
            self.LHS = LHS
            self.RHS = set()

        def get_key(self):
            return self.LHS

        def append_rhs(self, c, **kwargs):
            self.RHS.add(c)

        def __str__(self):
            tmp = self.LHS + " -> "
            tmp2 = ""
            for c in sorted(self.RHS):
                if len(tmp2) > 0:
                    tmp2 = tmp2 + ","
                tmp2 = tmp2 + c
            return tmp + tmp2

    class NonStationaryFTS(fts.FTS):
        """NonStationaryFTS Fuzzy Time Series"""
        def __init__(self, **kwargs):
            super(NonStationaryFTS, self).__init__(**kwargs)
            self.name = "Non Stationary FTS"
            self.shortname = "NSFTS"
            self.detail = ""
            self.flrgs = {}
            self.method = kwargs.get('method','conditional')
            self.is_high_order = False
            if self.partitioner is not None:
                self.append_transformation(self.partitioner.transformation)

            if self.method == 'conditional':
                self.min_stack = [0, 0, 0]
                self.max_stack = [0, 0, 0]
                self.uod_clip = False
                self.order = 1
                self.min_order = 1
                self.max_lag = 1
                self.inputs = []
                self.forecasts = []
                self.residuals = []
                self.variance_residual = 0.
                self.mean_residual = 0.
                self.memory_window = kwargs.get("memory_window", 5)

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

        def _smooth(self, a):
            return .1 * a[0] + .3 * a[1] + .6 * a[2]

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

            if self.method == 'unconditional':
                window_size = kwargs.get('parameters', 1)
                tmpdata = common.fuzzySeries(data, self.sets,
                                             self.partitioner.ordered_sets,
                                             window_size, method='fuzzy')
            else:
                tmpdata = common.fuzzySeries(data, self.sets,
                                     self.partitioner.ordered_sets,
                                     method='fuzzy', const_t=0)

            flrs = FLR.generate_non_recurrent_flrs(tmpdata)
            self.generate_flrg(flrs)

            if self.method == 'conditional':
                self.forecasts = self.forecast(data, no_update=True)
                self.residuals = np.array(data[1:]) - np.array(self.forecasts[:-1])

                self.variance_residual = np.var(self.residuals)  # np.max(self.residuals
                self.mean_residual = np.mean(self.residuals)

                self.residuals = self.residuals[-self.memory_window:].tolist()
                self.forecasts = self.forecasts[-self.memory_window:]
                self.inputs = np.array(data[-self.memory_window:]).tolist()

        def conditional_perturbation_factors(self, data, **kwargs):
            npart = len(self.partitioner.sets)
            _max = 0
            _min = 0
            if data < self.original_min:
                _min = data - self.original_min if data < 0 else self.original_min - data
            elif data > self.original_max:
                _max = data - self.original_max if data > 0 else self.original_max - data
            self.min_stack.pop(2)
            self.min_stack.insert(0, _min)
            _min = min(self.min_stack)
            self.max_stack.pop(2)
            self.max_stack.insert(0, _max)
            _max = max(self.max_stack)

            _range = (_max - _min)/2

            translate = np.linspace(_min, _max, npart)

            var = np.std(self.residuals)

            var = 0 if var < 1 else var

            loc = (self.mean_residual + np.mean(self.residuals))

            location = [_range + w + loc + k for k in np.linspace(-var,var, npart) for w in translate]

            scale = [abs(location[0] - location[2])]
            scale.extend([abs(location[k - 1] - location[k + 1]) for k in np.arange(1, npart)])
            scale.append(abs(location[-1] - location[-3]))

            perturb = [[location[k], scale[k]] for k in np.arange(npart)]

            return perturb

        def _fsset_key(self, ix):
            return self.partitioner.ordered_sets[ix]

        def _affected_sets(self, sample, perturb):

            if self.method == 'conditional':

                affected_sets = [[ct, self.sets[self._fsset_key(ct)].membership(sample, perturb[ct])]
                                 for ct in np.arange(len(self.partitioner.sets))
                                 if self.sets[self._fsset_key(ct)].membership(sample, perturb[ct]) > 0.0]
                if len(affected_sets) == 0:

                    if sample < self.partitioner.lower_set().get_lower(perturb[0]):
                        affected_sets.append([0, 1])
                    elif sample > self.partitioner.upper_set().get_upper(perturb[-1]):
                        affected_sets.append([len(self.sets) - 1, 1])

            else:
                affected_sets = [[ct, self.sets[self._fsset_key(ct)].membership(sample, perturb)]
                                 for ct in np.arange(len(self.partitioner.sets))
                                 if self.sets[self._fsset_key(ct)].membership(sample, perturb) > 0.0]

                if len(affected_sets) == 0:

                    if sample < self.partitioner.lower_set().get_lower(perturb):
                        affected_sets.append([0, 1])
                    elif sample > self.partitioner.upper_set().get_upper(perturb):
                        affected_sets.append([len(self.sets) - 1, 1])

            return affected_sets

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

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

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

            no_update = kwargs.get("no_update", False)

            l = len(ndata)

            ret = []

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

                sample = ndata[k]

                if self.method == 'unconditional':
                    perturb = common.window_index(k + time_displacement, window_size)
                elif self.method == 'conditional':
                    if not no_update:
                        perturb = self.conditional_perturbation_factors(sample)
                    else:
                        perturb = [[0, 1] for k in np.arange(len(self.partitioner.sets))]

                affected_sets = self._affected_sets(sample, perturb)

                numerator = []
                denominator = []

                if len(affected_sets) == 1:
                    ix = affected_sets[0][0]
                    aset = self.partitioner.ordered_sets[ix]
                    if aset in self.flrgs:
                        numerator.append(self.flrgs[aset].get_midpoint(self.sets, perturb[ix]))
                    else:
                        fuzzy_set = self.sets[aset]
                        numerator.append(fuzzy_set.get_midpoint(perturb[ix]))
                    denominator.append(1)
                else:
                    for aset in affected_sets:
                        ix = aset[0]
                        fs = self.partitioner.ordered_sets[ix]
                        tdisp = perturb[ix]
                        if fs in self.flrgs:
                            numerator.append(self.flrgs[fs].get_midpoint(self.sets, tdisp) * aset[1])
                        else:
                            fuzzy_set = self.sets[fs]
                            numerator.append(fuzzy_set.get_midpoint(tdisp) * aset[1])
                        denominator.append(aset[1])

                if sum(denominator) > 0:
                    pto = sum(numerator) / sum(denominator)
                else:
                    pto = sum(numerator)

                ret.append(pto)

                if self.method == 'conditional' and not no_update:
                    self.forecasts.append(pto)
                    self.residuals.append(self.inputs[-1] - self.forecasts[-1])
                    self.inputs.append(sample)

                    self.inputs.pop(0)
                    self.forecasts.pop(0)
                    self.residuals.pop(0)

            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(0, l):

                # print("input: " + str(ndata[k]))

                tdisp = common.window_index(k + time_displacement, window_size)

                affected_sets = [[self.sets[key], self.sets[key].membership(ndata[k], tdisp)]
                                 for key in self.partitioner.ordered_sets
                                 if self.sets[key].membership(ndata[k], tdisp) > 0.0]

                if len(affected_sets) == 0:
                    affected_sets.append([common.check_bounds(ndata[k], self.partitioner, tdisp), 1.0])

                upper = []
                lower = []

                if len(affected_sets) == 1:
                    aset = affected_sets[0][0]
                    if aset.name in self.flrgs:
                        lower.append(self.flrgs[aset.name].get_lower(tdisp))
                        upper.append(self.flrgs[aset.name].get_upper(tdisp))
                    else:
                        lower.append(aset.get_lower(tdisp))
                        upper.append(aset.get_upper(tdisp))
                else:
                    for aset in affected_sets:
                        if aset[0].name in self.flrgs:
                            lower.append(self.flrgs[aset[0].name].get_lower(tdisp) * aset[1])
                            upper.append(self.flrgs[aset[0].name].get_upper(tdisp) * aset[1])
                        else:
                            lower.append(aset[0].get_lower(tdisp) * aset[1])
                            upper.append(aset[0].get_upper(tdisp) * aset[1])

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

            return ret