82 lines
2.2 KiB
Python
82 lines
2.2 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,hofts,ifts,pwfts,tree, chen
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from pyFTS.benchmarks import benchmarks as bchmk
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from pyFTS.benchmarks import Measures
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from numpy import random
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#gauss_treino = random.normal(0,1.0,1600)
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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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#enrollments = pd.read_csv("DataSets/Enrollments.csv", sep=";")
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#enrollments = np.array(enrollments["Enrollments"])
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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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#nasdaq = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",")
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#nasdaq_treino = np.array(nasdaq["avg"][0:1600])
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#nasdaq_teste = np.array(nasdaq["avg"][1601:2000])
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diff = Transformations.Differential(1)
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fs = Grid.GridPartitionerTrimf(taiex_treino,10)
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#tmp = chen.ConventionalFTS("")
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pfts1 = pwfts.ProbabilisticWeightedFTS("1")
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#pfts1.appendTransformation(diff)
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pfts1.train(taiex_treino,fs,1)
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from pyFTS.benchmarks import ProbabilityDistribution as dist
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forecasts = pfts1.forecast(taiex_treino)
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pmf1 = dist.ProbabilityDistribution("Original",100,[min(taiex_treino),max(taiex_treino)],data=taiex_treino)
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#print(pmf1.entropy())
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pmf2 = dist.ProbabilityDistribution("Original",100,[min(taiex_treino),max(taiex_treino)],data=forecasts)
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#print(pmf2.entropy())
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#print(pmf2.kullbackleiblerdivergence(pmf1))
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#print(pmf2.crossentropy(pmf1))
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print(pmf1.averageloglikelihood(taiex_treino))
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print(pmf2.averageloglikelihood(taiex_treino))
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#pfts2 = pfts.ProbabilisticWeightedFTS("n = 2")
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#pfts2.appendTransformation(diff)
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#pfts2.train(gauss_treino,fs,2)
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#pfts3 = pfts.ProbabilisticWeightedFTS("n = 3")
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#pfts3.appendTransformation(diff)
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#pfts3.train(gauss_treino,fs,3)
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#densities1 = pfts1.forecastAheadDistribution(gauss_teste[:50],2,1.50, parameters=2)
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#print(bchmk.getDistributionStatistics(gauss_teste[:50], [pfts1,pfts2,pfts3], 20, 1.50))
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