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Bugfixes on hofts and pwfts

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This commit is contained in:
Petrônio Cândido 2018-12-11 13:15:44 -02:00
parent fc2c779266
commit 8d9d0e09c1
3 changed files with 15 additions and 13 deletions
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3
pyFTS/common/fts.py
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@ -458,7 +458,8 @@ class FTS(object):
return data return data
def get_UoD(self): def get_UoD(self):
return [self.original_min, self.original_max] #return [self.original_min, self.original_max]
return [self.partitioner.min, self.partitioner.max]
def __str__(self): def __str__(self):
"""String representation of the model""" """String representation of the model"""

4
pyFTS/models/hofts.py
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@ -88,13 +88,13 @@ class HighOrderFTS(fts.FTS):
self.detail = "Severiano, Silva, Sadaei and Guimarães" self.detail = "Severiano, Silva, Sadaei and Guimarães"
self.is_high_order = True self.is_high_order = True
self.min_order = 1 self.min_order = 1
self.order= kwargs.get("order", 2) self.order= kwargs.get("order", self.min_order)
self.lags = kwargs.get("lags", None) self.lags = kwargs.get("lags", None)
self.configure_lags(**kwargs) self.configure_lags(**kwargs)
def configure_lags(self, **kwargs): def configure_lags(self, **kwargs):
if "order" in kwargs: if "order" in kwargs:
self.order = kwargs.get("order", 2) self.order = kwargs.get("order", self.min_order)
if "lags" in kwargs: if "lags" in kwargs:
self.lags = kwargs.get("lags", None) self.lags = kwargs.get("lags", None)

21
pyFTS/tests/general.py
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@ -17,21 +17,22 @@ from pyFTS.common import Transformations
tdiff = Transformations.Differential(1) tdiff = Transformations.Differential(1)
from pyFTS.data import TAIEX, SP500, NASDAQ, Malaysia from pyFTS.data import TAIEX, SP500, NASDAQ, Malaysia, Enrollments
dataset = Malaysia.get_data('temperature')[:1000] train_split = 2000
test_length = 200
p = Grid.GridPartitioner(data=dataset, npart=20) dataset = TAIEX.get_data()
print(p) partitioner = Grid.GridPartitioner(data=dataset[:train_split], npart=35)
partitioner_diff = Grid.GridPartitioner(data=dataset[:train_split], npart=5, transformation=tdiff)
model = pwfts.ProbabilisticWeightedFTS(partitioner=p, order=2) pfts1_taiex = pwfts.ProbabilisticWeightedFTS(partitioner=partitioner)
pfts1_taiex.fit(dataset[:train_split], save_model=True, file_path='pwfts', order=1)
pfts1_taiex.shortname = "1st Order"
#print(pfts1_taiex)
model.fit(dataset) #[22, 22, 23, 23, 24]) tmp = pfts1_taiex.predict(dataset[train_split:train_split+200], type='distribution')
print(model)
Measures.get_point_statistics(dataset, model)
''' '''
#dataset = SP500.get_data()[11500:16000] #dataset = SP500.get_data()[11500:16000]