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)