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Bugfixes due to circular imports

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This commit is contained in:
Petrônio Cândido 2021-01-13 15:31:59 -03:00
parent 8bf3f7b1e9
commit 5b2b983619
2 changed files with 4 additions and 5 deletions
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3
pyFTS/common/transformations/som.py
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@ -2,9 +2,8 @@
Kohonen Self Organizing Maps for Fuzzy Time Series Kohonen Self Organizing Maps for Fuzzy Time Series
""" """
import pandas as pd import pandas as pd
from pyFTS.models.multivariate import wmvfts #from pyFTS.models.multivariate import wmvfts
from typing import Tuple from typing import Tuple
from pyFTS.common.Transformations import Transformation
from typing import List from typing import List
from pyFTS.common.transformations.transformation import Transformation from pyFTS.common.transformations.transformation import Transformation

6
pyFTS/tests/general.py
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@ -12,7 +12,7 @@ import pandas as pd
from pyFTS.partitioners import Grid #, Entropy, Util as pUtil, Simple from pyFTS.partitioners import Grid #, Entropy, Util as pUtil, Simple
#from pyFTS.benchmarks import benchmarks as bchmk, Measures #from pyFTS.benchmarks import benchmarks as bchmk, Measures
#from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts, tsaur, song, sadaei, ifts #from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts, tsaur, song, sadaei, ifts
from pyFTS.models import pwfts, hofts, chen from pyFTS.models import pwfts, hofts
#from pyFTS.models.ensemble import ensemble #from pyFTS.models.ensemble import ensemble
from pyFTS.common import Transformations, Membership, Util from pyFTS.common import Transformations, Membership, Util
#from pyFTS.benchmarks import arima, quantreg #BSTS, gaussianproc, knn #from pyFTS.benchmarks import arima, quantreg #BSTS, gaussianproc, knn
@ -38,8 +38,8 @@ l = len(dados)
particionador = Grid.GridPartitioner(data = dados, npart = 10, func = Membership.trimf) particionador = Grid.GridPartitioner(data = dados, npart = 10, func = Membership.trimf)
#modelo = pwfts.ProbabilisticWeightedFTS(partitioner = particionador, order = 1) modelo = pwfts.ProbabilisticWeightedFTS(partitioner = particionador, order = 1)
modelo = hofts.WeightedHighOrderFTS(partitioner = particionador, order = 1, standard_horizon=1, lags=[2]) #modelo = hofts.WeightedHighOrderFTS(partitioner = particionador, order = 1, standard_horizon=1, lags=[2])
#modelo = chen.ConventionalFTS(partitioner = particionador, standard_horizon=3) #modelo = chen.ConventionalFTS(partitioner = particionador, standard_horizon=3)
modelo.fit(dados) modelo.fit(dados)