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) self.generate_flrg(flrs)
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) #ndata = self.indexer.set_data(data,self.doTransformations(self.indexer.get_data(data))) flrs = FLR.generate_indexed_flrs(self.sets, self.indexer, data) self.generate_flrg(flrs)
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)