- Optimizations and bugfixes on Multi Seasonal Ensemble
- Bugfixes on ProbabilityDistribution - Indexers on Partitioners
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@ -59,7 +59,10 @@ class EnsembleFTS(fts.FTS):
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forecast = int(forecast[-1])
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elif isinstance(forecast, (list,np.ndarray)) and len(forecast) == 0:
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forecast = np.nan
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tmp.append(forecast)
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if isinstance(forecast, list):
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tmp.extend(forecast)
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else:
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tmp.append(forecast)
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return tmp
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def get_point(self,forecasts, **kwargs):
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@ -44,9 +44,13 @@ class SeasonalEnsembleFTS(ensemble.EnsembleFTS):
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self.has_seasonality = True
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self.has_probability_forecasting = True
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def update_uod(self, data):
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self.original_max = max(self.indexer.get_data(data))
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self.original_min = min(self.indexer.get_data(data))
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def train(self, data, sets, order=1, parameters=None):
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self.original_max = max(data)
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self.original_min = min(data)
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self.original_max = max(self.indexer.get_data(data))
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self.original_min = min(self.indexer.get_data(data))
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num_cores = multiprocessing.cpu_count()
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@ -76,7 +80,9 @@ class SeasonalEnsembleFTS(ensemble.EnsembleFTS):
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tmp = self.get_models_forecasts(data.ix[k])
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dist = ProbabilityDistribution.ProbabilityDistribution("KDE",h=h,uod=[self.original_min, self.original_max])
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tmp = np.ravel(tmp).tolist()
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dist = ProbabilityDistribution.ProbabilityDistribution("KDE",h=h,uod=[self.original_min, self.original_max], data=tmp)
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ret.append(dist)
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@ -78,8 +78,8 @@ def c_means(k, dados, tam):
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class CMeansPartitioner(partitioner.Partitioner):
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def __init__(self, data, npart, func = Membership.trimf, transformation=None):
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super(CMeansPartitioner, self).__init__("CMeans", data, npart, func=func, transformation=transformation)
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def __init__(self, data, npart, func = Membership.trimf, transformation=None, indexer=None):
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super(CMeansPartitioner, self).__init__("CMeans", data, npart, func=func, transformation=transformation, indexer=indexer)
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def build(self, data):
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sets = []
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@ -79,8 +79,8 @@ def bestSplit(data, npart):
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class EntropyPartitioner(partitioner.Partitioner):
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"""Huarng Entropy Partitioner"""
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def __init__(self, data, npart, func = Membership.trimf, transformation=None):
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super(EntropyPartitioner, self).__init__("Entropy", data, npart, func=func, transformation=transformation)
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def __init__(self, data, npart, func = Membership.trimf, transformation=None, indexer=None):
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super(EntropyPartitioner, self).__init__("Entropy", data, npart, func=func, transformation=transformation, indexer=indexer)
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def build(self, data):
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sets = []
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@ -104,8 +104,8 @@ class FCMPartitioner(partitioner.Partitioner):
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"""
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"""
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def __init__(self, data,npart,func = Membership.trimf, transformation=None):
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super(FCMPartitioner, self).__init__("FCM", data, npart, func=func, transformation=transformation)
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def __init__(self, data,npart,func = Membership.trimf, transformation=None, indexer=None):
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super(FCMPartitioner, self).__init__("FCM", data, npart, func=func, transformation=transformation, indexer=indexer)
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def build(self,data):
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sets = []
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@ -8,8 +8,8 @@ from pyFTS.partitioners import partitioner
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class GridPartitioner(partitioner.Partitioner):
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"""Even Length Grid Partitioner"""
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def __init__(self, data, npart, func = Membership.trimf, transformation=None):
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super(GridPartitioner, self).__init__("Grid", data, npart, func=func, transformation=transformation)
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def __init__(self, data, npart, func = Membership.trimf, transformation=None, indexer=None):
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super(GridPartitioner, self).__init__("Grid", data, npart, func=func, transformation=transformation, indexer=indexer)
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def build(self, data):
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sets = []
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@ -12,8 +12,8 @@ from pyFTS.partitioners import partitioner
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class HuarngPartitioner(partitioner.Partitioner):
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"""Huarng Empirical Partitioner"""
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def __init__(self, data,npart,func = Membership.trimf, transformation=None):
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super(HuarngPartitioner, self).__init__("Huarng", data, npart, func=func, transformation=transformation)
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def __init__(self, data,npart,func = Membership.trimf, transformation=None, indexer=None):
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super(HuarngPartitioner, self).__init__("Huarng", data, npart, func=func, transformation=transformation, indexer=indexer)
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def build(self, data):
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diff = Transformations.Differential(1)
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@ -11,25 +11,20 @@ class ProbabilityDistribution(object):
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If type is histogram, the PDF is discrete
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If type is KDE the PDF is continuous
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"""
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def __init__(self,type, **kwargs):
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def __init__(self,type = "KDE", **kwargs):
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self.uod = kwargs.get("uod", None)
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if type is None:
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self.type = "KDE"
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self.type = type
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if self.type == "KDE":
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self.kde = kde.KernelSmoothing(kwargs.get("h", 10), kwargs.get("method", "epanechnikov"))
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else:
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self.type = type
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self.description = kwargs.get("description", None)
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self.nbins = kwargs.get("num_bins", 100)
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if self.type == "histogram":
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self.bins = kwargs.get("bins", None)
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self.labels = kwargs.get("bins_labels", None)
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self.bins = kwargs.get("bins", None)
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self.labels = kwargs.get("bins_labels", None)
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if self.bins is None:
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self.bins = np.linspace(self.uod[0], self.uod[1], self.nbins).tolist()
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self.bins = np.linspace(int(self.uod[0]), int(self.uod[1]), int(self.nbins)).tolist()
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self.labels = [str(k) for k in self.bins]
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self.index = SortedCollection.SortedCollection(iterable=sorted(self.bins))
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@ -37,7 +32,14 @@ class ProbabilityDistribution(object):
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self.count = 0
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for k in self.bins: self.distribution[k] = 0
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self.data = kwargs.get("data",None)
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self.data = []
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data = kwargs.get("data",None)
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if data is not None:
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self.append(data)
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self.name = kwargs.get("name", "")
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def append(self, values):
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if self.type == "histogram":
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@ -50,7 +52,7 @@ class ProbabilityDistribution(object):
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self.distribution = {}
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dens = self.density(self.bins)
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for v,d in enumerate(dens):
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self.distribution[v] = d
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self.distribution[self.bins[v]] = d
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def density(self, values):
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ret = []
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@ -20,7 +20,7 @@ from pyFTS.models.seasonal import SeasonalIndexer
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os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
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#diff = Transformations.Differential(1)
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diff = Transformations.Differential(1)
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#ix = SeasonalIndexer.LinearSeasonalIndexer([12, 24], [720, 1],[False, False])
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"""
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@ -72,22 +72,27 @@ sonda = sonda[:][527041:]
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sonda.index = np.arange(0,len(sonda.index))
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sonda_treino = sonda[:1051200]
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sonda_teste = sonda[1051201:]
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sonda_teste = sonda[1051901:1051910]
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ix_m15 = SeasonalIndexer.DateTimeSeasonalIndexer('data',[SeasonalIndexer.DateTime.minute],[15],'glo_avg', name='m15')
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fs1 = Grid.GridPartitioner(sonda_treino,50,transformation=diff, indexer=ix_m15)
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'''
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from pyFTS.models.seasonal import SeasonalIndexer
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indexers = []
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for i in ["models/sonda_ix_m15.pkl", "models/sonda_ix_Mh.pkl", "models/sonda_ix_Mhm15.pkl"]:
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for i in ["models/sonda_ix_Mhm15.pkl"]: #, "models/sonda_ix_m15.pkl", "models/sonda_ix_Mh.pkl", ]:
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obj = cUtil.load_obj(i)
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indexers.append( obj )
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print(obj)
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partitioners = []
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transformations = ["", "_diff"]
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for max_part in [10, 20, 30, 40, 50, 60]:
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transformations = [""] #, "_diff"]
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for max_part in [30, 40, 50, 60, 70, 80, 90]:
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for t in transformations:
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obj = cUtil.load_obj("models/sonda_fs_grid_" + str(max_part) + t + ".pkl")
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partitioners.append( obj )
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@ -96,17 +101,33 @@ for max_part in [10, 20, 30, 40, 50, 60]:
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from pyFTS.ensemble import ensemble, multiseasonal
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fts = multiseasonal.SeasonalEnsembleFTS("")
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fts = multiseasonal.SeasonalEnsembleFTS("sonda_msfts_Mhm15")
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fts.indexers = indexers
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fts.partitioners = partitioners
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fts.indexer = indexers[0]
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fts.train(sonda_treino, sets=None)
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'''
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ftse = cUtil.load_obj("models/sonda_msfts_ensemble.pkl")
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#'''
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tmp = ftse.forecastDistribution(sonda_teste)
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#ix = cUtil.load_obj("models/sonda_ix_m15.pkl")
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#ftse = cUtil.load_obj("models/msfts_Grid40_diff_Mhm15.pkl")
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#ftse.indexer = ix
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#ftse.update_uod(sonda_treino)
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#tmp = ftse.forecastDistribution(sonda_teste,h=1)
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#tmp = ftse.forecast(sonda_teste,h=1)
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#tmp[5].plot()
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#'''
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'''
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from pyFTS.benchmarks import benchmarks as bchmk
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#from pyFTS.benchmarks import distributed_benchmarks as bchmk
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#from pyFTS.benchmarks import parallel_benchmarks as bchmk
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@ -299,7 +320,7 @@ diff = Transformations.Differential(1)
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fs = Grid.GridPartitioner(sonda[:9000], 10, transformation=diff)
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'''
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tmp = sfts.SeasonalFTS("")
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tmp.indexer = ix
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tmp.appendTransformation(diff)
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