126 lines
4.5 KiB
Python
126 lines
4.5 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, 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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os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
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#enrollments = pd.read_csv("DataSets/Enrollments.csv", sep=";")
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#enrollments = np.array(enrollments["Enrollments"])
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"""
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DATASETS
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"""
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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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#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"][0:5000])
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#sp500pd = pd.read_csv("DataSets/S&P500.csv", sep=",")
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#sp500 = np.array(sp500pd["Avg"][11000:])
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#del(sp500pd)
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#sondapd = pd.read_csv("DataSets/SONDA_BSB_HOURLY_AVG.csv", sep=";")
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#sondapd = sondapd.dropna(axis=0, how='any')
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#sonda = np.array(sondapd["ws_10m"])
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#del(sondapd)
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#bestpd = pd.read_csv("DataSets/BEST_TAVG.csv", sep=";")
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#best = np.array(bestpd["Anomaly"])
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#print(lag)
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#print(a)
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#from pyFTS.benchmarks import benchmarks as bchmk
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from pyFTS.benchmarks import distributed_benchmarks as bchmk
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#from pyFTS.benchmarks import parallel_benchmarks as bchmk
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from pyFTS.benchmarks import Util
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from pyFTS.benchmarks import arima, quantreg
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#Util.cast_dataframe_to_synthetic_point("experiments/taiex_point_analitic.csv","experiments/taiex_point_sintetic.csv",11)
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#Util.plot_dataframe_point("experiments/taiex_point_sintetic.csv","experiments/taiex_point_analitic.csv",11)
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#tmp = arima.ARIMA("")
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#tmp.train(taiex[:1600], None, order=(2,0,2))
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#teste = tmp.forecastInterval(taiex[1600:1605])
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#tmp = quan#treg.QuantileRegression("")
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#tmp.train(taiex[:1600], None, order=2)
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#teste = tmp.forecast(taiex[1600:1605])
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#print(taiex[1600:1605])
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#print(teste)
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#bchmk.teste(taiex,['192.168.0.109', '192.168.0.101'])
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diff = Transformations.Differential(1)
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"""
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bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4,#models=[yu.WeightedFTS], # #
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partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
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partitions= np.arange(10,200,step=10), #transformation=diff,
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dump=True, save=True, file="experiments/sondaws_point_analytic.csv",
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nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
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bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4, #models=[yu.WeightedFTS], # #
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partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
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partitions= np.arange(3,20,step=2), #transformation=diff,
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dump=True, save=True, file="experiments/sondaws_point_analytic_diff.csv",
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nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
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#"""
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bchmk.interval_sliding_window(nasdaq, 2000, train=0.8, inc=0.1,#models=[yu.WeightedFTS], # #
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partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
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partitions= np.arange(10,200,step=10), #transformation=diff,
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dump=True, save=True, file="experiments/nasdaq_interval_analytic.csv",
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nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
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bchmk.interval_sliding_window(nasdaq, 2000, train=0.8, inc=0.1, #models=[yu.WeightedFTS], # #
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partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
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partitions= np.arange(3,20,step=2), #transformation=diff,
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dump=True, save=True, file="experiments/nasdaq_interval_analytic_diff.csv",
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nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
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"""
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from pyFTS.partitioners import Grid
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from pyFTS import pwfts
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diff = Transformations.Differential(1)
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fs = Grid.GridPartitioner(taiex[:2000], 10, transformation=diff)
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tmp = pwfts.ProbabilisticWeightedFTS("")
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tmp.appendTransformation(diff)
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tmp.train(taiex[:1600], fs.sets, order=1)
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x = tmp.forecastInterval(taiex[1600:1610])
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print(taiex[1600:1610])
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print(x)
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""" |