88 lines
3.1 KiB
Python
88 lines
3.1 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, Entropy, FCM, Huarng
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from pyFTS.common import FLR,FuzzySet,Membership,Transformations
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from pyFTS import fts,hofts,ifts,pwfts,tree, chen
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from pyFTS.benchmarks import benchmarks as bchmk
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from pyFTS.benchmarks import naive, arima
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from pyFTS.benchmarks import Measures
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from numpy import random
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#print(FCM.FCMPartitionerTrimf.__module__)
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#gauss = random.normal(0,1.0,5000)
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#gauss_teste = random.normal(0,1.0,400)
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os.chdir("/home/petronio/dados/Dropbox/Doutorado/Disciplinas/AdvancedFuzzyTimeSeriesModels/")
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#taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
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#taiex = np.array(taiexpd["avg"][:5000])
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nasdaqpd = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",")
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nasdaq = np.array(nasdaqpd["avg"][:5000])
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#taiex = pd.read_csv("DataSets/TAIEX.csv", sep=",")
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#taiex_treino = np.array(taiex["avg"][2500:3900])
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#taiex_teste = np.array(taiex["avg"][3901:4500])
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#print(len(taiex))
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#from pyFTS.common import Util
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#, ,
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diff = Transformations.Differential(1)
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#bchmk.external_point_sliding_window([naive.Naive, arima.ARIMA, arima.ARIMA, arima.ARIMA, arima.ARIMA, arima.ARIMA, arima.ARIMA],
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# [None, (1,0,0),(1,1,0),(2,0,0), (2,1,0), (1,1,1), (1,0,1)],
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# gauss,2000,train=0.8, dump=True, save=True, file="experiments/arima_gauss.csv")
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bchmk.interval_sliding_window(nasdaq,2000,train=0.8, #transformation=diff, #models=[pwfts.ProbabilisticWeightedFTS], # #
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partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
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partitions= np.arange(10,200,step=5), #
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dump=True, save=True, file="experiments/nasdaq_interval.csv")
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#3bchmk.ahead_sliding_window(taiex,2000,train=0.8, steps=20, resolution=250, #transformation=diff, #models=[pwfts.ProbabilisticWeightedFTS], # #
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# partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
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# partitions= np.arange(10,200,step=10), #
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# dump=True, save=True, file="experiments/taiex_ahead.csv")
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#bchmk.allPointForecasters(taiex_treino, taiex_treino, 95, #transformation=diff,
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# models=[ naive.Naive, pfts.ProbabilisticFTS, pwfts.ProbabilisticWeightedFTS],
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# statistics=True, residuals=False, series=False)
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#data_train_fs = Grid.GridPartitioner(nasdaq[:1600], 95).sets
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#fts1 = pwfts.ProbabilisticWeightedFTS("")
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#fts1.appendTransformation(diff)
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#fts1.train(nasdaq[:1600], data_train_fs, order=1)
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#_crps1, _crps2, _t1, _t2 = bchmk.get_distribution_statistics(nasdaq[1600:2000], fts1, steps=20, resolution=200)
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#print(_crps1, _crps2, _t1, _t2)
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#print(fts1.forecast([5000, 5000]))
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#fts2 = pwfts.ProbabilisticWeightedFTS("")
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#fts2.appendTransformation(diff)
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#fts2.train(taiex_treino, data_train_fs, order=1)
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#print(fts2.forecast([5000, 5000]))
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#tmp = Grid.GridPartitioner(taiex_treino,7,transformation=diff)
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#for s in tmp.sets: print(s) |