From 50c2b501b1efa2d102b122a67e97a2a035a78b08 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Petr=C3=B4nio=20C=C3=A2ndido?= Date: Wed, 11 Apr 2018 01:12:55 -0300 Subject: [PATCH] Refactoring to centralize the apply_transformations and apply_transformations_inverse inside fts.predict method --- pyFTS/common/fts.py | 38 +++++++++++++++++++-------- pyFTS/models/chen.py | 11 +++----- pyFTS/models/hofts.py | 12 +++------ pyFTS/models/hwang.py | 6 +---- pyFTS/models/ifts.py | 10 +++---- pyFTS/models/ismailefendi.py | 12 +++------ pyFTS/models/nonstationary/cvfts.py | 16 +++-------- pyFTS/models/nonstationary/honsfts.py | 19 +++----------- pyFTS/models/nonstationary/nsfts.py | 21 +++------------ pyFTS/models/pwfts.py | 29 +++++--------------- pyFTS/models/sadaei.py | 9 ++----- pyFTS/models/seasonal/cmsfts.py | 4 --- pyFTS/models/seasonal/msfts.py | 4 --- pyFTS/models/seasonal/sfts.py | 9 ++----- pyFTS/models/song.py | 10 +++---- pyFTS/models/yu.py | 14 +++------- 16 files changed, 67 insertions(+), 157 deletions(-) diff --git a/pyFTS/common/fts.py b/pyFTS/common/fts.py index acf404a..43f607c 100644 --- a/pyFTS/common/fts.py +++ b/pyFTS/common/fts.py @@ -70,6 +70,11 @@ class FTS(object): :return: a numpy array with the forecasted data """ + if self.is_multivariate: + ndata = data + else: + ndata = self.apply_transformations(data) + if 'distributed' in kwargs: distributed = kwargs.pop('distributed') else: @@ -85,17 +90,17 @@ class FTS(object): steps_ahead = kwargs.get("steps_ahead", None) if type == 'point' and steps_ahead == None: - return self.forecast(data, **kwargs) + ret = self.forecast(ndata, **kwargs) elif type == 'point' and steps_ahead != None: - return self.forecast_ahead(data, steps_ahead, **kwargs) + ret = self.forecast_ahead(ndata, steps_ahead, **kwargs) elif type == 'interval' and steps_ahead == None: - return self.forecast_interval(data, **kwargs) + ret = self.forecast_interval(ndata, **kwargs) elif type == 'interval' and steps_ahead != None: - return self.forecast_ahead_interval(data, steps_ahead, **kwargs) + ret = self.forecast_ahead_interval(ndata, steps_ahead, **kwargs) elif type == 'distribution' and steps_ahead == None: - return self.forecast_distribution(data, **kwargs) + ret = self.forecast_distribution(ndata, **kwargs) elif type == 'distribution' and steps_ahead != None: - return self.forecast_ahead_distribution(data, steps_ahead, **kwargs) + ret = self.forecast_ahead_distribution(ndata, steps_ahead, **kwargs) else: raise ValueError('The argument \'type\' has an unknown value.') @@ -104,7 +109,13 @@ class FTS(object): nodes = kwargs.get("nodes", ['127.0.0.1']) num_batches = kwargs.get('num_batches', 10) - return Util.distributed_predict(self, kwargs, nodes, data, num_batches) + ret = Util.distributed_predict(self, kwargs, nodes, ndata, num_batches) + + if type != 'distribution' and not self.is_multivariate: + interval = True if type == 'interval' else False + ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]], interval=interval) + + return ret def forecast(self, data, **kwargs): @@ -185,7 +196,7 @@ class FTS(object): """ pass - def fit(self, data, **kwargs): + def fit(self, ndata, **kwargs): """ :param data: the training time series @@ -204,6 +215,11 @@ class FTS(object): import datetime + if self.is_multivariate: + data = ndata + else: + data = self.apply_transformations(ndata) + dump = kwargs.get('dump', None) num_batches = kwargs.get('num_batches', None) @@ -245,11 +261,11 @@ class FTS(object): if dump == 'time': print("[{0: %H:%M:%S}] Starting batch ".format(datetime.datetime.now()) + str(bcount)) if self.is_multivariate: - ndata = data.iloc[ct - self.order:ct + batch_size] + mdata = data.iloc[ct - self.order:ct + batch_size] else: - ndata = data[ct - self.order : ct + batch_size] + mdata = data[ct - self.order : ct + batch_size] - self.train(ndata, **kwargs) + self.train(mdata, **kwargs) if batch_save: Util.persist_obj(self,file_path) diff --git a/pyFTS/models/chen.py b/pyFTS/models/chen.py index d85f624..207a15e 100644 --- a/pyFTS/models/chen.py +++ b/pyFTS/models/chen.py @@ -15,7 +15,7 @@ class ConventionalFLRG(flrg.FLRG): self.LHS = LHS self.RHS = set() - def get_key(self): + def get_key(self, sets): return sets[self.LHS].name def append_rhs(self, c, **kwargs): @@ -50,14 +50,11 @@ class ConventionalFTS(fts.FTS): def train(self, data, **kwargs): if kwargs.get('sets', None) is not None: self.sets = kwargs.get('sets', None) - ndata = self.apply_transformations(data) - tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets) + tmpdata = FuzzySet.fuzzyfy_series_old(data, self.sets) flrs = FLR.generate_non_recurrent_flrs(tmpdata) self.generate_flrg(flrs) - def forecast(self, data, **kwargs): - - ndata = np.array(self.apply_transformations(data)) + def forecast(self, ndata, **kwargs): l = len(ndata) @@ -76,6 +73,4 @@ class ConventionalFTS(fts.FTS): ret.append(_flrg.get_midpoint(self.sets)) - ret = self.apply_inverse_transformations(ret, params=[data]) - return ret diff --git a/pyFTS/models/hofts.py b/pyFTS/models/hofts.py index ac6a792..9e1e5ff 100644 --- a/pyFTS/models/hofts.py +++ b/pyFTS/models/hofts.py @@ -93,8 +93,6 @@ class HighOrderFTS(fts.FTS): def train(self, data, **kwargs): - data = self.apply_transformations(data, updateUoD=True) - self.order = kwargs.get('order',2) if kwargs.get('sets', None) is not None: @@ -102,16 +100,14 @@ class HighOrderFTS(fts.FTS): self.generate_flrg(data) - def forecast(self, data, **kwargs): + def forecast(self, ndata, **kwargs): ret = [] - l = len(data) + l = len(ndata) if l <= self.order: - return data - - ndata = self.apply_transformations(data) + return ndata for k in np.arange(self.order, l+1): flrgs = self.generate_lhs_flrg(ndata[k - self.order: k]) @@ -126,6 +122,4 @@ class HighOrderFTS(fts.FTS): ret.append(np.nanmean(tmp)) - ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]]) - return ret diff --git a/pyFTS/models/hwang.py b/pyFTS/models/hwang.py index 4cf2258..a382598 100644 --- a/pyFTS/models/hwang.py +++ b/pyFTS/models/hwang.py @@ -18,12 +18,10 @@ class HighOrderFTS(fts.FTS): self.shortname = "Hwang" + name self.detail = "Hwang" - def forecast(self, data, **kwargs): + def forecast(self, ndata, **kwargs): ordered_sets = FuzzySet.set_ordered(self.sets) - ndata = self.apply_transformations(data) - l = len(self.sets) cn = np.array([0.0 for k in range(l)]) @@ -52,8 +50,6 @@ class HighOrderFTS(fts.FTS): count += 1.0 ret.append(out / count) - ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]]) - return ret def train(self, data, **kwargs): diff --git a/pyFTS/models/ifts.py b/pyFTS/models/ifts.py index 37695fc..d4ff0bb 100644 --- a/pyFTS/models/ifts.py +++ b/pyFTS/models/ifts.py @@ -44,16 +44,14 @@ class IntervalFTS(hofts.HighOrderFTS): return mb - def forecast_interval(self, data, **kwargs): + def forecast_interval(self, ndata, **kwargs): ret = [] - l = len(data) + l = len(ndata) if l <= self.order: - return data - - ndata = self.apply_transformations(data) + return ndata for k in np.arange(self.order, l+1): @@ -78,6 +76,4 @@ class IntervalFTS(hofts.HighOrderFTS): up_ = sum(up) / norm ret.append([lo_, up_]) - ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]], interval=True) - return ret diff --git a/pyFTS/models/ismailefendi.py b/pyFTS/models/ismailefendi.py index c1352ef..ecd5122 100644 --- a/pyFTS/models/ismailefendi.py +++ b/pyFTS/models/ismailefendi.py @@ -60,24 +60,20 @@ class ImprovedWeightedFTS(fts.FTS): self.flrgs[flr.LHS] = ImprovedWeightedFLRG(flr.LHS); self.flrgs[flr.LHS].append_rhs(flr.RHS) - def train(self, data, **kwargs): + def train(self, ndata, **kwargs): if kwargs.get('sets', None) is not None: self.sets = kwargs.get('sets', None) - ndata = self.apply_transformations(data) - tmpdata = FuzzySet.fuzzyfy_series(ndata, self.sets, method="maximum") flrs = FLR.generate_recurrent_flrs(tmpdata) self.generate_flrg(flrs) - def forecast(self, data, **kwargs): + def forecast(self, ndata, **kwargs): l = 1 ordered_sets = FuzzySet.set_ordered(self.sets) - data = np.array(data) - ndata = self.apply_transformations(data) - + ndata = np.array(ndata) l = len(ndata) ret = [] @@ -94,6 +90,4 @@ class ImprovedWeightedFTS(fts.FTS): ret.append(mp.dot(flrg.weights())) - ret = self.apply_inverse_transformations(ret, params=[data]) - return ret diff --git a/pyFTS/models/nonstationary/cvfts.py b/pyFTS/models/nonstationary/cvfts.py index bec51cf..307185b 100644 --- a/pyFTS/models/nonstationary/cvfts.py +++ b/pyFTS/models/nonstationary/cvfts.py @@ -22,12 +22,10 @@ class ConditionalVarianceFTS(chen.ConventionalFTS): self.min_stack = [0,0,0] self.max_stack = [0,0,0] - def train(self, data, **kwargs): + def train(self, ndata, **kwargs): if kwargs.get('sets', None) is not None: self.sets = kwargs.get('sets', None) - ndata = self.apply_transformations(data) - self.min_tx = min(ndata) self.max_tx = max(ndata) @@ -84,9 +82,7 @@ class ConditionalVarianceFTS(chen.ConventionalFTS): return affected_sets - def forecast(self, data, **kwargs): - ndata = np.array(self.apply_transformations(data)) - + def forecast(self, ndata, **kwargs): l = len(ndata) ret = [] @@ -123,14 +119,10 @@ class ConditionalVarianceFTS(chen.ConventionalFTS): ret.append(pto) - ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]]) - return ret - def forecast_interval(self, data, **kwargs): - ndata = np.array(self.apply_transformations(data)) - + def forecast_interval(self, ndata, **kwargs): l = len(ndata) ret = [] @@ -171,6 +163,4 @@ class ConditionalVarianceFTS(chen.ConventionalFTS): ret.append(itvl) - ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]]) - return ret diff --git a/pyFTS/models/nonstationary/honsfts.py b/pyFTS/models/nonstationary/honsfts.py index 3a0f615..b252572 100644 --- a/pyFTS/models/nonstationary/honsfts.py +++ b/pyFTS/models/nonstationary/honsfts.py @@ -1,7 +1,7 @@ import numpy as np from pyFTS.common import FuzzySet, FLR, fts, tree from pyFTS.models import hofts -from pyFTS.nonstationary import common, flrg +from pyFTS.models.nonstationary import common, flrg class HighOrderNonStationaryFLRG(flrg.NonStationaryFLRG): @@ -90,11 +90,8 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS): if kwargs.get('sets', None) is not None: self.sets = kwargs.get('sets', None) - ndata = self.apply_transformations(data) - #tmpdata = common.fuzzyfy_series_old(ndata, self.sets) - #flrs = FLR.generate_recurrent_flrs(ndata) window_size = kwargs.get('parameters', 1) - self.generate_flrg(ndata, window_size=window_size) + self.generate_flrg(data, window_size=window_size) def _affected_flrgs(self, sample, k, time_displacement, window_size): # print("input: " + str(ndata[k])) @@ -155,14 +152,12 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS): return [affected_flrgs, affected_flrgs_memberships] - def forecast(self, data, **kwargs): + def forecast(self, ndata, **kwargs): time_displacement = kwargs.get("time_displacement",0) window_size = kwargs.get("window_size", 1) - ndata = np.array(self.apply_transformations(data)) - l = len(ndata) ret = [] @@ -201,18 +196,14 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS): ret.append(pto) - ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]]) - return ret - def forecast_interval(self, data, **kwargs): + def forecast_interval(self, ndata, **kwargs): time_displacement = kwargs.get("time_displacement", 0) window_size = kwargs.get("window_size", 1) - ndata = np.array(self.apply_transformations(data)) - l = len(ndata) ret = [] @@ -259,6 +250,4 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS): ret.append([sum(lower), sum(upper)]) - ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]]) - return ret diff --git a/pyFTS/models/nonstationary/nsfts.py b/pyFTS/models/nonstationary/nsfts.py index db1c19f..5920727 100644 --- a/pyFTS/models/nonstationary/nsfts.py +++ b/pyFTS/models/nonstationary/nsfts.py @@ -49,30 +49,23 @@ class NonStationaryFTS(fts.FTS): if kwargs.get('sets', None) is not None: self.sets = kwargs.get('sets', None) - ndata = self.apply_transformations(data) window_size = kwargs.get('parameters', 1) - tmpdata = common.fuzzySeries(ndata, self.sets, window_size, method=self.method) - #print([k[0].name for k in tmpdata]) + tmpdata = common.fuzzySeries(data, self.sets, window_size, method=self.method) flrs = FLR.generate_recurrent_flrs(tmpdata) - #print([str(k) for k in flrs]) self.generate_flrg(flrs) - def forecast(self, data, **kwargs): + def forecast(self, ndata, **kwargs): time_displacement = kwargs.get("time_displacement",0) window_size = kwargs.get("window_size", 1) - ndata = np.array(self.apply_transformations(data)) - 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) if self.method == 'fuzzy': @@ -89,8 +82,6 @@ class NonStationaryFTS(fts.FTS): else: affected_sets.append(common.check_bounds(ndata[k], self.sets, tdisp)) - #print(affected_sets) - tmp = [] if len(affected_sets) == 1 and self.method == 'fuzzy': @@ -118,18 +109,14 @@ class NonStationaryFTS(fts.FTS): ret.append(pto) - ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]]) - return ret - def forecast_interval(self, data, **kwargs): + def forecast_interval(self, ndata, **kwargs): time_displacement = kwargs.get("time_displacement", 0) window_size = kwargs.get("window_size", 1) - ndata = np.array(self.apply_transformations(data)) - l = len(ndata) ret = [] @@ -179,6 +166,4 @@ class NonStationaryFTS(fts.FTS): ret.append([sum(lower), sum(upper)]) - ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]]) - return ret \ No newline at end of file diff --git a/pyFTS/models/pwfts.py b/pyFTS/models/pwfts.py index 9b4266a..efbb4ac 100644 --- a/pyFTS/models/pwfts.py +++ b/pyFTS/models/pwfts.py @@ -266,9 +266,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): else: raise Exception("Unknown point forecasting method!") - def point_heuristic(self, data, **kwargs): - - ndata = np.array(self.apply_transformations(data)) + def point_heuristic(self, ndata, **kwargs): l = len(ndata) @@ -298,8 +296,6 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): if self.auto_update and k > self.order+1: self.update_model(ndata[k - self.order - 1 : k]) - ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]]) - return ret def point_expected_value(self, data, **kwargs): @@ -314,11 +310,9 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): ret.append(tmp) - ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]]) - return ret - def forecast_interval(self, data, **kwargs): + def forecast_interval(self, ndata, **kwargs): if 'method' in kwargs: self.interval_method = kwargs.get('method','heuristic') @@ -326,8 +320,6 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): if 'alpha' in kwargs: self.alpha = kwargs.get('alpha', 0.05) - ndata = np.array(self.apply_transformations(data)) - l = len(ndata) ret = [] @@ -339,15 +331,12 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): else: self.interval_quantile(k, ndata, ret) - ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]], interval=True) - return ret def interval_quantile(self, k, ndata, ret): dist = self.forecast_distribution(ndata) - lo_qt = dist[0].quantile(self.alpha) - up_qt = dist[0].quantile(1.0 - self.alpha) - ret.append([lo_qt, up_qt]) + itvl = dist[0].quantile([self.alpha, 1.0 - self.alpha]) + ret.append(itvl) def interval_heuristic(self, k, ndata, ret): @@ -375,14 +364,10 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): up_ = sum(up) / norm ret.append([lo_, up_]) - def forecast_distribution(self, data, **kwargs): - - if not isinstance(data, (list, set, np.ndarray)): - data = [data] + def forecast_distribution(self, ndata, **kwargs): smooth = kwargs.get("smooth", "none") - ndata = np.array(self.apply_transformations(data)) l = len(ndata) uod = self.get_UoD() @@ -457,14 +442,12 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS): return ret - def forecast_ahead_distribution(self, data, steps, **kwargs): + def forecast_ahead_distribution(self, ndata, steps, **kwargs): ret = [] smooth = kwargs.get("smooth", "none") - ndata = np.array(self.apply_transformations(data)) - uod = self.get_UoD() if 'bins' in kwargs: diff --git a/pyFTS/models/sadaei.py b/pyFTS/models/sadaei.py index 9309bb4..8803e41 100644 --- a/pyFTS/models/sadaei.py +++ b/pyFTS/models/sadaei.py @@ -69,20 +69,17 @@ class ExponentialyWeightedFTS(fts.FTS): self.c = kwargs.get('parameters', default_c) if kwargs.get('sets', None) is not None: self.sets = kwargs.get('sets', None) - ndata = self.apply_transformations(data) - tmpdata = FuzzySet.fuzzyfy_series(ndata, self.sets, method='maximum') + tmpdata = FuzzySet.fuzzyfy_series(data, self.sets, method='maximum') flrs = FLR.generate_recurrent_flrs(tmpdata) self.generate_flrg(flrs, self.c) - def forecast(self, data, **kwargs): + def forecast(self, ndata, **kwargs): l = 1 ordered_sets = FuzzySet.set_ordered(self.sets) data = np.array(data) - ndata = self.apply_transformations(data) - l = len(ndata) ret = [] @@ -99,6 +96,4 @@ class ExponentialyWeightedFTS(fts.FTS): ret.append(mp.dot(flrg.weights())) - ret = self.apply_inverse_transformations(ret, params=[data]) - return ret diff --git a/pyFTS/models/seasonal/cmsfts.py b/pyFTS/models/seasonal/cmsfts.py index e834f1b..b0722d9 100644 --- a/pyFTS/models/seasonal/cmsfts.py +++ b/pyFTS/models/seasonal/cmsfts.py @@ -85,8 +85,6 @@ class ContextualMultiSeasonalFTS(sfts.SeasonalFTS): ret.append(sum(mp) / len(mp)) - ret = self.doInverseTransformations(ret, params=[ndata]) - return ret def forecast_ahead(self, data, steps, **kwargs): @@ -98,6 +96,4 @@ class ContextualMultiSeasonalFTS(sfts.SeasonalFTS): ret.append(sum(mp) / len(mp)) - ret = self.doInverseTransformations(ret, params=data) - return ret diff --git a/pyFTS/models/seasonal/msfts.py b/pyFTS/models/seasonal/msfts.py index 43569e6..1863aba 100644 --- a/pyFTS/models/seasonal/msfts.py +++ b/pyFTS/models/seasonal/msfts.py @@ -52,8 +52,6 @@ class MultiSeasonalFTS(sfts.SeasonalFTS): ret.append(sum(mp) / len(mp)) - ret = self.apply_inverse_transformations(ret, params=[ndata]) - return ret def forecast_ahead(self, data, steps, **kwargs): @@ -65,6 +63,4 @@ class MultiSeasonalFTS(sfts.SeasonalFTS): ret.append(sum(mp) / len(mp)) - ret = self.apply_inverse_transformations(ret, params=data) - return ret diff --git a/pyFTS/models/seasonal/sfts.py b/pyFTS/models/seasonal/sfts.py index 64e1a68..77fe694 100644 --- a/pyFTS/models/seasonal/sfts.py +++ b/pyFTS/models/seasonal/sfts.py @@ -65,16 +65,13 @@ class SeasonalFTS(fts.FTS): def train(self, data, **kwargs): if kwargs.get('sets', None) is not None: self.sets = kwargs.get('sets', None) - ndata = self.apply_transformations(data) - tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets) + tmpdata = FuzzySet.fuzzyfy_series_old(data, self.sets) flrs = FLR.generate_recurrent_flrs(tmpdata) self.generate_flrg(flrs) def forecast(self, data, **kwargs): - ndata = np.array(self.apply_transformations(data)) - - l = len(ndata) + l = len(data) ret = [] @@ -88,6 +85,4 @@ class SeasonalFTS(fts.FTS): ret.append(np.percentile(mp, 50)) - ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]]) - return ret diff --git a/pyFTS/models/song.py b/pyFTS/models/song.py index e7a3be0..136934b 100644 --- a/pyFTS/models/song.py +++ b/pyFTS/models/song.py @@ -51,17 +51,15 @@ class ConventionalFTS(fts.FTS): def train(self, data, **kwargs): if kwargs.get('sets', None) is not None: self.sets = kwargs.get('sets', None) - ndata = self.apply_transformations(data) - tmpdata = FuzzySet.fuzzyfy_series(ndata, self.sets, method='maximum') + + tmpdata = FuzzySet.fuzzyfy_series(data, self.sets, method='maximum') flrs = FLR.generate_non_recurrent_flrs(tmpdata) self.operation_matrix(flrs) - def forecast(self, data, **kwargs): + def forecast(self, ndata, **kwargs): ordered_set = FuzzySet.set_ordered(self.sets) - ndata = np.array(self.apply_transformations(data)) - l = len(ndata) npart = len(self.sets) @@ -81,8 +79,6 @@ class ConventionalFTS(fts.FTS): ret.append( sum(mp)/len(mp)) - ret = self.apply_inverse_transformations(ret, params=[data]) - return ret def __str__(self): diff --git a/pyFTS/models/yu.py b/pyFTS/models/yu.py index 4013159..349a571 100644 --- a/pyFTS/models/yu.py +++ b/pyFTS/models/yu.py @@ -57,23 +57,19 @@ class WeightedFTS(fts.FTS): self.flrgs[flr.LHS] = WeightedFLRG(flr.LHS); self.flrgs[flr.LHS].append_rhs(flr.RHS) - def train(self, data, **kwargs): + def train(self, ndata, **kwargs): if kwargs.get('sets', None) is not None: self.sets = kwargs.get('sets', None) - ndata = self.apply_transformations(data) + tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets) flrs = FLR.generate_recurrent_flrs(tmpdata) self.generate_FLRG(flrs) - def forecast(self, data, **kwargs): + def forecast(self, ndata, **kwargs): ordered_sets = FuzzySet.set_ordered(self.sets) - l = 1 - - data = np.array(data) - - ndata = self.apply_transformations(data) + ndata = np.array(ndata) l = len(ndata) @@ -91,6 +87,4 @@ class WeightedFTS(fts.FTS): ret.append(mp.dot(flrg.weights(self.sets))) - ret = self.apply_inverse_transformations(ret, params=[data]) - return ret