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step_to parameter in predict function

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Petrônio Cândido 2020-11-23 16:22:41 -03:00
parent 4a05587485
commit 2d4785d053
2 changed files with 26 additions and 19 deletions
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5
pyFTS/models/pwfts.py
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@ -517,7 +517,10 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
start = kwargs.get('start_at', 0)
ret = data[start: start+self.max_lag].tolist()
if isinstance(data, np.ndarray):
data = data.tolist()
ret = data[start: start+self.max_lag]
for k in np.arange(self.max_lag, steps+self.max_lag):

40
pyFTS/tests/general.py
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@ -8,40 +8,44 @@ import matplotlib.pylab as plt
import pandas as pd
from pyFTS.common import Util as cUtil, FuzzySet
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.ensemble import ensemble
#from pyFTS.common import Util as cUtil, FuzzySet
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
#from pyFTS.models.ensemble import ensemble
from pyFTS.common import Transformations, Membership, Util
from pyFTS.benchmarks import arima, quantreg #BSTS, gaussianproc, knn
from pyFTS.fcm import fts, common, GA
from pyFTS.common import Transformations
#from pyFTS.benchmarks import arima, quantreg #BSTS, gaussianproc, knn
#from pyFTS.fcm import fts, common, GA
#from pyFTS.common import Transformations
from pyFTS.data import Enrollments
tdiff = Transformations.Differential(1)
#tdiff = Transformations.Differential(1)
boxcox = Transformations.BoxCox(0)
#boxcox = Transformations.BoxCox(0)
df = pd.read_csv('https://query.data.world/s/z2xo3t32pkl4mdzp63x6lyne53obmi')
#df = pd.read_csv('https://query.data.world/s/z2xo3t32pkl4mdzp63x6lyne53obmi')
#dados = df.iloc[2710:2960 , 0:1].values # somente a 1 coluna sera usada
dados = df['temperature'].values
#dados = df['temperature'].values
#dados = dados.flatten().tolist()
dados = Enrollments.get_data()
l = len(dados)
dados_treino = dados[:int(l*.7)]
dados_teste = dados[int(l*.7):]
#dados_treino = dados[:int(l*.7)]
#dados_teste = dados[int(l*.7):]
particionador = Grid.GridPartitioner(data = dados_treino, npart = 15, func = Membership.trimf)
particionador = Grid.GridPartitioner(data = dados, npart = 5, func = Membership.trimf)
modelo = pwfts.ProbabilisticWeightedFTS(partitioner = particionador, order = 2)
modelo = pwfts.ProbabilisticWeightedFTS(partitioner = particionador, order = 1)
modelo.fit(dados_treino)
modelo.fit(dados)
# print(modelo)
# Todo o procedimento de inferência é feito pelo método predict
predicoes = modelo.predict(dados_teste, step_to=30)
predicoes = modelo.predict(dados)
print(predicoes)