55 lines
1.5 KiB
Python
55 lines
1.5 KiB
Python
#!/usr/bin/python
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# -*- coding: utf8 -*-
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import os
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import numpy as np
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import pandas as pd
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import matplotlib as plt
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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import pandas as pd
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from pyFTS.partitioners import Grid
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from pyFTS.common import FLR,FuzzySet,Membership,Transformations
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from pyFTS import fts,sfts
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from pyFTS.models import msfts
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from pyFTS.benchmarks import benchmarks as bchmk
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from pyFTS.benchmarks import Measures
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os.chdir("/home/petronio/dados/Dropbox/Doutorado/Disciplinas/AdvancedFuzzyTimeSeriesModels/")
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sonda = pd.read_csv("DataSets/SONDA_BSB_CLEAN.csv", sep=";")
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sonda = sonda[:][527041:]
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sonda.index = np.arange(0,len(sonda.index))
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sonda_treino = sonda[:1051200]
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sonda_teste = sonda[1051201:]
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#res = bchmk.simpleSearch_RMSE(sonda_treino, sonda_teste,
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# sfts.SeasonalFTS,np.arange(3,30),[1],parameters=1440,
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# tam=[15,8], plotforecasts=False,elev=45, azim=40,
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# save=False,file="pictures/sonda_sfts_error_surface", intervals=False)
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from pyFTS.models.seasonal import SeasonalIndexer
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from pyFTS.models import msfts
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from pyFTS.common import FLR
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ix = SeasonalIndexer.DataFrameSeasonalIndexer(['day','min'],[30, 60],'glo_avg')
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fs = Grid.GridPartitionerTrimf(ix.get_data(sonda_treino),20)
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#mfts = msfts.MultiSeasonalFTS("",ix)
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#mfts.train(sonda_teste,fs)
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#print(str(mfts))
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#[10, 508]
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flrs = FLR.generateIndexedFLRs(fs, ix, sonda_treino[110000:111450])
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for i in flrs: #ix.get_data(sonda_treino[111430:111450]):
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print(i) |