From 5b2b983619cdcc4dbbfe231504d1ebe39ddbd6ee Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Petr=C3=B4nio=20C=C3=A2ndido?= Date: Wed, 13 Jan 2021 15:31:59 -0300 Subject: [PATCH] Bugfixes due to circular imports --- pyFTS/common/transformations/som.py | 3 +-- pyFTS/tests/general.py | 6 +++--- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/pyFTS/common/transformations/som.py b/pyFTS/common/transformations/som.py index e98b48b..064fcaf 100644 --- a/pyFTS/common/transformations/som.py +++ b/pyFTS/common/transformations/som.py @@ -2,9 +2,8 @@ Kohonen Self Organizing Maps for Fuzzy Time Series """ import pandas as pd -from pyFTS.models.multivariate import wmvfts +#from pyFTS.models.multivariate import wmvfts from typing import Tuple -from pyFTS.common.Transformations import Transformation from typing import List from pyFTS.common.transformations.transformation import Transformation diff --git a/pyFTS/tests/general.py b/pyFTS/tests/general.py index 6a0a2a8..aea02a3 100644 --- a/pyFTS/tests/general.py +++ b/pyFTS/tests/general.py @@ -12,7 +12,7 @@ import pandas as pd from pyFTS.partitioners import Grid #, Entropy, Util as pUtil, Simple #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 pwfts, hofts, chen +from pyFTS.models import pwfts, hofts #from pyFTS.models.ensemble import ensemble from pyFTS.common import Transformations, Membership, Util #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) -#modelo = pwfts.ProbabilisticWeightedFTS(partitioner = particionador, order = 1) -modelo = hofts.WeightedHighOrderFTS(partitioner = particionador, order = 1, standard_horizon=1, lags=[2]) +modelo = pwfts.ProbabilisticWeightedFTS(partitioner = particionador, order = 1) +#modelo = hofts.WeightedHighOrderFTS(partitioner = particionador, order = 1, standard_horizon=1, lags=[2]) #modelo = chen.ConventionalFTS(partitioner = particionador, standard_horizon=3) modelo.fit(dados)