- Deep refactor on project folders
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6e4df0ce33
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@ -5,8 +5,7 @@ import numpy as np
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import pandas as pd
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from statsmodels.tsa.arima_model import ARIMA as stats_arima
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import scipy.stats as st
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from pyFTS import fts
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from pyFTS.common import SortedCollection
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from pyFTS.common import SortedCollection, fts
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from pyFTS.probabilistic import ProbabilityDistribution
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@ -13,14 +13,14 @@ import matplotlib.cm as cmx
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import matplotlib.colors as pltcolors
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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#from mpl_toolkits.mplot3d import Axes3D
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from pyFTS.probabilistic import ProbabilityDistribution
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from pyFTS import song, chen, yu, ismailefendi, sadaei, hofts, pwfts, ifts, cheng, ensemble, hwang
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from pyFTS.models import song, chen, yu, ismailefendi, sadaei, hofts, pwfts, ifts, cheng, hwang
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from pyFTS.models.ensemble import ensemble
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from pyFTS.benchmarks import Measures, naive, arima, ResidualAnalysis, quantreg
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from pyFTS.benchmarks import Util as bUtil
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from pyFTS.common import Transformations, Util
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from pyFTS.common import Util
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# from sklearn.cross_validation import KFold
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from pyFTS.partitioners import Grid
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from matplotlib import rc
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@ -817,7 +817,6 @@ def plot_compared_intervals_ahead(original, models, colors, distributions, time_
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def plot_density_rectange(ax, cmap, density, fig, resolution, time_from, time_to):
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from matplotlib.patches import Rectangle
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from matplotlib.collections import PatchCollection
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from matplotlib.colorbar import ColorbarPatch
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patches = []
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colors = []
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for x in density.index:
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@ -840,7 +839,6 @@ from pyFTS.common import Transformations
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def plot_probabilitydistribution_density(ax, cmap, probabilitydist, fig, time_from):
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from matplotlib.patches import Rectangle
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from matplotlib.collections import PatchCollection
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from matplotlib.colorbar import ColorbarPatch
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patches = []
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colors = []
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for ct, dt in enumerate(probabilitydist):
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@ -8,13 +8,12 @@ python3 /usr/local/bin/dispynode.py -i [local IP] -d
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import datetime
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import time
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from copy import deepcopy
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import dispy
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import dispy.httpd
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import numpy as np
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from pyFTS.benchmarks import benchmarks, Util as bUtil, naive, quantreg, arima
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from pyFTS.benchmarks import benchmarks, Util as bUtil, quantreg, arima
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from pyFTS.common import Util
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from pyFTS.partitioners import Grid
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@ -32,7 +31,8 @@ def run_point(mfts, partitioner, train_data, test_data, window_key=None, transfo
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:return: a dictionary with the benchmark results
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"""
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import time
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from pyFTS import yu,chen,hofts,ifts,pwfts,ismailefendi,sadaei, song, cheng, hwang
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from pyFTS import yu, hofts, pwfts,ismailefendi,sadaei, song, cheng, hwang
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from pyFTS.models import chen
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from pyFTS.partitioners import Grid, Entropy, FCM
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from pyFTS.benchmarks import Measures, naive, arima, quantreg
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from pyFTS.common import Transformations
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@ -424,9 +424,10 @@ def run_ahead(mfts, partitioner, train_data, test_data, steps, resolution, windo
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"""
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import time
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import numpy as np
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from pyFTS import hofts, ifts, pwfts, ensemble
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from pyFTS import hofts, ifts, pwfts
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from pyFTS.models import ensemble
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from pyFTS.partitioners import Grid, Entropy, FCM
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from pyFTS.benchmarks import Measures, arima, quantreg
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from pyFTS.benchmarks import Measures, arima
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from pyFTS.models.seasonal import SeasonalIndexer
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tmp = [hofts.HighOrderFTS, ifts.IntervalFTS, pwfts.ProbabilisticWeightedFTS, arima.ARIMA, ensemble.AllMethodEnsembleFTS]
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@ -1,7 +1,7 @@
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#!/usr/bin/python
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# -*- coding: utf8 -*-
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from pyFTS import fts
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from pyFTS.common import fts
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class Naive(fts.FTS):
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@ -5,8 +5,7 @@ import numpy as np
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import pandas as pd
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from statsmodels.regression.quantile_regression import QuantReg
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from statsmodels.tsa.tsatools import lagmat
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from pyFTS import fts
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from pyFTS.common import SortedCollection
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from pyFTS.common import SortedCollection, fts
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from pyFTS.probabilistic import ProbabilityDistribution
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class QuantileRegression(fts.FTS):
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@ -5,8 +5,7 @@ S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets
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"""
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import numpy as np
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from pyFTS.common import FuzzySet, FLR
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from pyFTS import fts,flrg
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from pyFTS.common import FuzzySet, FLR, fts, flrg
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class ConventionalFLRG(flrg.FLRG):
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@ -6,8 +6,8 @@ Expert Syst. Appl., vol. 36, no. 2, pp. 1826–1832, 2009.
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"""
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import numpy as np
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from pyFTS.common import FuzzySet,FLR
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from pyFTS import fts, yu
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from pyFTS.common import FuzzySet, FLR, fts
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from pyFTS.models import yu
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class TrendWeightedFLRG(yu.WeightedFLRG):
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@ -3,16 +3,10 @@
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import numpy as np
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import pandas as pd
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import math
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from operator import itemgetter
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from pyFTS.common import FLR, FuzzySet, SortedCollection
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from pyFTS import fts, chen, cheng, hofts, hwang, ismailefendi, sadaei, song, yu
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from pyFTS.benchmarks import arima, quantreg
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from pyFTS.common import Transformations
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from pyFTS.common import SortedCollection, fts, tree
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from pyFTS.models import chen, cheng, hofts, hwang, ismailefendi, sadaei, song, yu
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import scipy.stats as st
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from pyFTS import tree
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from pyFTS.seasonal import sfts, msfts
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from pyFTS.probabilistic import ProbabilityDistribution, kde
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def sampler(data, quantiles):
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ret = []
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@ -239,7 +233,7 @@ class AllMethodEnsembleFTS(EnsembleFTS):
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self.original_min = min(data)
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fo_methods = [song.ConventionalFTS, chen.ConventionalFTS, yu.WeightedFTS, cheng.TrendWeightedFTS,
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sadaei.ExponentialyWeightedFTS, ismailefendi.ImprovedWeightedFTS]
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sadaei.ExponentialyWeightedFTS, ismailefendi.ImprovedWeightedFTS]
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ho_methods = [hofts.HighOrderFTS, hwang.HighOrderFTS]
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@ -2,17 +2,10 @@
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# -*- coding: utf8 -*-
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import numpy as np
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import pandas as pd
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import math
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from operator import itemgetter
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from pyFTS.common import FLR, FuzzySet, SortedCollection
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from pyFTS import fts, chen, cheng, hofts, hwang, ismailefendi, sadaei, song, yu, sfts
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from pyFTS.benchmarks import arima, quantreg
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from pyFTS.common import Transformations, Util as cUtil
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import scipy.stats as st
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from pyFTS.ensemble import ensemble
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from pyFTS.models import msfts, cmsfts
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from pyFTS.probabilistic import ProbabilityDistribution, kde
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from pyFTS.common import Util as cUtil
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from pyFTS.models.ensemble import ensemble
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from pyFTS.models.seasonal import cmsfts
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from pyFTS.probabilistic import ProbabilityDistribution
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from copy import deepcopy
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from joblib import Parallel, delayed
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import multiprocessing
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@ -6,9 +6,7 @@ Fuzzy Sets Syst., vol. 81, no. 3, pp. 311–319, 1996.
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"""
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import numpy as np
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from pyFTS.common import FuzzySet,FLR
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from pyFTS import fts, flrg, tree
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from pyFTS.common import FuzzySet, FLR, fts, flrg, tree
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class HighOrderFLRG(flrg.FLRG):
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"""Conventional High Order Fuzzy Logical Relationship Group"""
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@ -6,8 +6,7 @@ Fuzzy Sets Syst., no. 100, pp. 217–228, 1998.
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"""
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import numpy as np
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from pyFTS.common import FuzzySet,FLR,Transformations
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from pyFTS import fts
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from pyFTS.common import FuzzySet, FLR, Transformations, fts
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class HighOrderFTS(fts.FTS):
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@ -2,8 +2,8 @@
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# -*- coding: utf8 -*-
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import numpy as np
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from pyFTS.common import FuzzySet,FLR
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from pyFTS import hofts, fts, tree
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from pyFTS.common import FuzzySet, FLR, fts, tree
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from pyFTS.models import hofts
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class IntervalFTS(hofts.HighOrderFTS):
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@ -6,8 +6,7 @@ US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1,
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"""
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import numpy as np
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from pyFTS.common import FuzzySet,FLR
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from pyFTS import fts, flrg
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from pyFTS.common import FuzzySet, FLR, fts, flrg
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class ImprovedWeightedFLRG(flrg.FLRG):
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@ -9,7 +9,7 @@ import numpy as np
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from pyFTS import *
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from pyFTS.common import FuzzySet as FS, Membership, FLR
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from pyFTS.partitioners import partitioner
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from pyFTS.nonstationary import perturbation
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from pyFTS.models.nonstationary import perturbation
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class FuzzySet(FS.FuzzySet):
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@ -1,7 +1,7 @@
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import numpy as np
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from pyFTS import fts, flrg, chen
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from pyFTS.nonstationary import common, perturbation, nsfts
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from pyFTS.common import Transformations, FuzzySet, FLR
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from pyFTS.models import chen
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from pyFTS.models.nonstationary import common,nsfts
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from pyFTS.common import FLR
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class ConditionalVarianceFTS(chen.ConventionalFTS):
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@ -1,6 +1,6 @@
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from pyFTS import flrg
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from pyFTS.nonstationary import common
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from pyFTS.common import flrg
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from pyFTS.models.nonstationary import common
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class NonStationaryFLRG(flrg.FLRG):
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@ -1,8 +1,7 @@
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import numpy as np
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from pyFTS.common import FuzzySet, FLR
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from pyFTS import fts, hofts
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from pyFTS.common import FuzzySet, FLR, fts, tree
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from pyFTS.models import hofts
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from pyFTS.nonstationary import common, flrg
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from pyFTS import tree
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class HighOrderNonStationaryFLRG(flrg.NonStationaryFLRG):
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@ -1,6 +1,5 @@
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import numpy as np
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from pyFTS.common import FuzzySet, FLR
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from pyFTS import fts, chen
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from pyFTS.common import FLR, fts
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from pyFTS.nonstationary import common, flrg
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@ -1,6 +1,6 @@
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import numpy as np
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from pyFTS.partitioners import partitioner
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from pyFTS.nonstationary import common, perturbation
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from pyFTS.models.nonstationary import common, perturbation
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class PolynomialNonStationaryPartitioner(partitioner.Partitioner):
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@ -5,8 +5,8 @@ import numpy as np
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import pandas as pd
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import math
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from operator import itemgetter
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from pyFTS.common import FLR, FuzzySet, SortedCollection
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from pyFTS import hofts, ifts, tree
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from pyFTS.common import FLR, FuzzySet, tree
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from pyFTS.models import hofts, ifts
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from pyFTS.probabilistic import ProbabilityDistribution
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@ -6,8 +6,7 @@ refined exponentially weighted fuzzy time series and an improved harmony search,
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"""
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import numpy as np
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from pyFTS.common import FuzzySet,FLR
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from pyFTS import fts, flrg
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from pyFTS.common import FuzzySet,FLR,fts, flrg
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class ExponentialyWeightedFLRG(flrg.FLRG):
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@ -1,7 +1,6 @@
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import numpy as np
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import pandas as pd
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from enum import Enum
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from pyFTS.seasonal import common
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from pyFTS.models.seasonal import common
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class SeasonalIndexer(object):
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@ -1,6 +1,7 @@
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import numpy as np
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from pyFTS.common import FuzzySet,FLR
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from pyFTS import fts, sfts, chen
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from pyFTS.common import FuzzySet, FLR
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from pyFTS.models.seasonal import sfts
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from pyFTS.models import chen
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class ContextualSeasonalFLRG(object):
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@ -1,6 +1,6 @@
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import numpy as np
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from pyFTS.common import FuzzySet,FLR
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from pyFTS.seasonal import sfts
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from pyFTS.common import FLR
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from pyFTS.models.seasonal import sfts
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class MultiSeasonalFTS(sfts.SeasonalFTS):
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@ -7,8 +7,7 @@ S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets
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"""
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import numpy as np
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from pyFTS.common import FuzzySet,FLR
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from pyFTS import fts
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from pyFTS.common import FuzzySet, FLR, fts
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class SeasonalFLRG(FLR.FLR):
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@ -5,8 +5,7 @@ Q. Song and B. S. Chissom, “Fuzzy time series and its models,” Fuzzy Sets Sy
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"""
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import numpy as np
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from pyFTS.common import FuzzySet, FLR
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from pyFTS import fts
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from pyFTS.common import FuzzySet, FLR, fts
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class ConventionalFTS(fts.FTS):
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@ -6,8 +6,8 @@ Phys. A Stat. Mech. its Appl., vol. 349, no. 3, pp. 609–624, 2005.
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"""
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import numpy as np
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from pyFTS.common import FuzzySet,FLR
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from pyFTS import fts, flrg, chen
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from pyFTS.common import FuzzySet, FLR, fts, flrg
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from pyFTS.models import chen
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class WeightedFLRG(flrg.FLRG):
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@ -12,8 +12,8 @@ import datetime
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import pandas as pd
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from pyFTS.partitioners import Grid, CMeans, FCM, Entropy
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from pyFTS.common import FLR,FuzzySet,Membership,Transformations,Util
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from pyFTS import fts,sfts
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from pyFTS.common import FLR, FuzzySet, Membership, Transformations, Util, fts
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from pyFTS import 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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@ -1,13 +1,14 @@
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from pyFTS.partitioners import Grid
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from pyFTS import fts, flrg, song, chen, yu, sadaei, ismailefendi, cheng, hofts
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from pyFTS.models import chen
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from pyFTS.benchmarks import Measures
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from pyFTS.common import Util as cUtil
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from pyFTS.common import Util as cUtil, fts
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import pandas as pd
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import numpy as np
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import os
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from pyFTS.common import Transformations
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from copy import deepcopy
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from pyFTS.nonstationary import common, flrg, util, perturbation, nsfts, honsfts, partitioners
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from pyFTS.nonstationary import flrg, util, honsfts, partitioners
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from pyFTS.models.nonstationary import nsfts
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bc = Transformations.BoxCox(0)
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@ -19,10 +20,8 @@ os.chdir("/home/petronio/Dropbox/Doutorado/Codigos/")
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def evaluate_individual_model(model, partitioner, train, test, window_size, time_displacement):
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import numpy as np
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from pyFTS.common import FLR, FuzzySet
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from pyFTS.partitioners import Grid
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from pyFTS.benchmarks import Measures
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from pyFTS.nonstationary import common, flrg, util, perturbation, nsfts, honsfts, partitioners
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try:
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model.train(train, sets=partitioner.sets, order=model.order, parameters=window_size)
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@ -3,20 +3,13 @@
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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, song, yu, cheng, ismailefendi, sadaei, hwang
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from pyFTS.benchmarks import naive, arima
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from numpy import random
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from pyFTS.benchmarks import benchmarks as bchmk
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from pyFTS.benchmarks import arima, quantreg, Measures
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from pyFTS.ensemble import ensemble
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from pyFTS.partitioners import Grid
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from pyFTS.common import Transformations
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from pyFTS import hofts, song, yu, cheng, ismailefendi, sadaei, hwang
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from pyFTS.models import chen
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from pyFTS.models.ensemble import ensemble
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os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
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@ -3,20 +3,13 @@
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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, Util as cUtil
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from pyFTS import fts,hofts,ifts,pwfts,tree, chen
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from pyFTS.common import Transformations
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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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from pyFTS.seasonal import SeasonalIndexer
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os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
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@ -1,7 +1,8 @@
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import os
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import numpy as np
|
||||
from pyFTS.common import Membership, Transformations
|
||||
from pyFTS.nonstationary import common,perturbation, partitioners, util,nsfts, honsfts, cvfts
|
||||
from pyFTS.nonstationary import common,perturbation, partitioners, util, honsfts, cvfts
|
||||
from pyFTS.models.nonstationary import nsfts
|
||||
from pyFTS.partitioners import Grid
|
||||
import matplotlib.pyplot as plt
|
||||
from pyFTS.common import Util as cUtil
|
||||
|
@ -9,11 +9,9 @@ import matplotlib.pyplot as plt
|
||||
from mpl_toolkits.mplot3d import Axes3D
|
||||
|
||||
import pandas as pd
|
||||
from pyFTS.partitioners import Grid
|
||||
from pyFTS.common import FLR,FuzzySet,Membership,Transformations
|
||||
from pyFTS import fts,hofts,ifts,pwfts,tree, chen
|
||||
from pyFTS.common import FLR, FuzzySet, Membership, Transformations, fts
|
||||
from pyFTS.models import chen
|
||||
from pyFTS.benchmarks import benchmarks as bchmk
|
||||
from pyFTS.benchmarks import Measures
|
||||
from numpy import random
|
||||
|
||||
#gauss_treino = random.normal(0,1.0,1600)
|
||||
|
@ -3,20 +3,10 @@
|
||||
|
||||
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 datetime
|
||||
|
||||
import pandas as pd
|
||||
from pyFTS.partitioners import Grid, CMeans, FCM, Entropy
|
||||
from pyFTS.common import FLR,FuzzySet,Membership,Transformations,Util
|
||||
from pyFTS import fts
|
||||
from pyFTS.seasonal import sfts, msfts
|
||||
from pyFTS.common import Util
|
||||
from pyFTS.benchmarks import benchmarks as bchmk
|
||||
from pyFTS.benchmarks import Measures
|
||||
|
||||
os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
|
||||
|
||||
@ -38,10 +28,6 @@ sonda_teste = sonda[1051201:]
|
||||
# tam=[15,8], plotforecasts=False,elev=45, azim=40,
|
||||
# save=False,file="pictures/sonda_sfts_error_surface", intervals=False)
|
||||
|
||||
from pyFTS.models.seasonal import SeasonalIndexer
|
||||
from pyFTS.models import msfts
|
||||
from pyFTS.common import FLR
|
||||
|
||||
partitions = ['grid','entropy']
|
||||
|
||||
indexers = ['m15','Mh','Mhm15']
|
||||
|
11
setup.py
11
setup.py
@ -2,11 +2,12 @@ from distutils.core import setup
|
||||
|
||||
setup(
|
||||
name='pyFTS',
|
||||
packages=['pyFTS', 'pyFTS.benchmarks', 'pyFTS.common', 'pyFTS.data', 'pyFTS.ensemble',
|
||||
'pyFTS.models', 'pyFTS.seasonal', 'pyFTS.partitioners', 'pyFTS.probabilistic',
|
||||
'pyFTS.tests', 'pyFTS.nonstationary'],
|
||||
package_data={'benchmarks': ['*'], 'common': ['*'], 'data': ['*'], 'ensemble': ['*'], 'models': ['*'],
|
||||
'seasonal': ['*'], 'partitioners': ['*'], 'probabilistic': ['*'], 'tests': ['*']},
|
||||
packages=['pyFTS', 'pyFTS.benchmarks', 'pyFTS.common', 'pyFTS.data', 'pyFTS.models.ensemble',
|
||||
'pyFTS.models', 'pyFTS.models.seasonal', 'pyFTS.partitioners', 'pyFTS.probabilistic',
|
||||
'pyFTS.tests', 'pyFTS.models.nonstationary'],
|
||||
package_data={'benchmarks': ['*'], 'common': ['*'], 'data': ['*'],
|
||||
'models': ['*'], 'seasonal': ['*'], 'ensemble': ['*'],
|
||||
'partitioners': ['*'], 'probabilistic': ['*'], 'tests': ['*']},
|
||||
version='1.1.1',
|
||||
description='Fuzzy Time Series for Python',
|
||||
author='Petronio Candido L. e Silva',
|
||||
|
Loading…
Reference in New Issue
Block a user