#!/usr/bin/python # -*- coding: utf8 -*- import os import numpy as np import pandas as pd import matplotlib as plt import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D import pandas as pd from pyFTS.partitioners import Grid, Entropy, FCM, Huarng from pyFTS.common import FLR,FuzzySet,Membership,Transformations from pyFTS import fts,hofts,ifts,pwfts,tree, chen from pyFTS.benchmarks import benchmarks as bchmk from pyFTS.benchmarks import naive, arima from pyFTS.benchmarks import Measures from numpy import random #print(FCM.FCMPartitionerTrimf.__module__) #gauss = random.normal(0,1.0,5000) #gauss_teste = random.normal(0,1.0,400) os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/") taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",") taiex = np.array(taiexpd["avg"][:5000]) #from statsmodels.tsa.arima_model import ARIMA as stats_arima from statsmodels.tsa.tsatools import lagmat tmp = np.arange(10) lag, a = lagmat(tmp, maxlag=2, trim="both", original='sep') print(lag) print(a) #from pyFTS.benchmarks import distributed_benchmarks as bchmk #from pyFTS.benchmarks import parallel_benchmarks as bchmk #from pyFTS.benchmarks import benchmarks as bchmk #from pyFTS.benchmarks import arima #tmp = arima.ARIMA("") #tmp.train(taiex[:1600],None,parameters=(2,0,1)) #teste = tmp.forecast(taiex[1598:1601]) #print(teste) #bchmk.teste(taiex,['192.168.0.109', '192.168.0.101']) #bchmk.point_sliding_window(taiex,2000,train=0.8, #models=[yu.WeightedFTS], # # # partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ], # partitions= np.arange(10,200,step=5), #transformation=diff, # dump=False, save=True, file="experiments/nasdaq_point_distributed.csv", # nodes=['192.168.0.109', '192.168.0.101']) #, depends=[hofts, ifts]) #bchmk.testa(taiex,[10,20],partitioners=[Grid.GridPartitioner], nodes=['192.168.0.109', '192.168.0.101']) #parallel_util.explore_partitioners(taiex,20) #nasdaqpd = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",") #nasdaq = np.array(nasdaqpd["avg"][:5000]) #taiex = pd.read_csv("DataSets/TAIEX.csv", sep=",") #taiex_treino = np.array(taiex["avg"][2500:3900]) #taiex_teste = np.array(taiex["avg"][3901:4500]) #print(len(taiex)) #from pyFTS.common import Util #, , #diff = Transformations.Differential(1) #bchmk.external_point_sliding_window([naive.Naive, arima.ARIMA, arima.ARIMA, arima.ARIMA, arima.ARIMA, arima.ARIMA, arima.ARIMA], # [None, (1,0,0),(1,1,0),(2,0,0), (2,1,0), (1,1,1), (1,0,1)], # gauss,2000,train=0.8, dump=True, save=True, file="experiments/arima_gauss.csv") #bchmk.interval_sliding_window(nasdaq,2000,train=0.8, #transformation=diff, #models=[pwfts.ProbabilisticWeightedFTS], # # # partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ], # partitions= np.arange(10,200,step=5), # # dump=True, save=True, file="experiments/nasdaq_interval.csv") #3bchmk.ahead_sliding_window(taiex,2000,train=0.8, steps=20, resolution=250, #transformation=diff, #models=[pwfts.ProbabilisticWeightedFTS], # # # partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ], # partitions= np.arange(10,200,step=10), # # dump=True, save=True, file="experiments/taiex_ahead.csv") #bchmk.allPointForecasters(taiex_treino, taiex_treino, 95, #transformation=diff, # models=[ naive.Naive, pfts.ProbabilisticFTS, pwfts.ProbabilisticWeightedFTS], # statistics=True, residuals=False, series=False) #data_train_fs = Grid.GridPartitioner(nasdaq[:1600], 95).sets #fts1 = pwfts.ProbabilisticWeightedFTS("") #fts1.appendTransformation(diff) #fts1.train(nasdaq[:1600], data_train_fs, order=1) #_crps1, _crps2, _t1, _t2 = bchmk.get_distribution_statistics(nasdaq[1600:2000], fts1, steps=20, resolution=200) #print(_crps1, _crps2, _t1, _t2) #print(fts1.forecast([5000, 5000])) #fts2 = pwfts.ProbabilisticWeightedFTS("") #fts2.appendTransformation(diff) #fts2.train(taiex_treino, data_train_fs, order=1) #print(fts2.forecast([5000, 5000])) #tmp = Grid.GridPartitioner(taiex_treino,7,transformation=diff) #for s in tmp.sets: print(s)