def build(self, data):
diff = Transformations.Differential(1)
data2 = diff.apply(data)
davg = np.abs(np.mean(data2) / 2)
if davg <= 1.0:
base = 0.1
elif 1 < davg <= 10:
base = 1.0
elif 10 < davg <= 100:
base = 10
else:
base = 100
sets = {}
kwargs = {'type': self.type, 'variable': self.variable}
dlen = self.max - self.min
npart = math.ceil(dlen / base)
partition = math.ceil(self.min)
for c in range(npart):
_name = self.get_name(c)
if self.membership_function == Membership.trimf:
sets[_name] = FuzzySet.FuzzySet(
_name, Membership.trimf,
[partition - base, partition, partition + base], partition,
**kwargs)
elif self.membership_function == Membership.gaussmf:
sets[_name] = FuzzySet.FuzzySet(_name, Membership.gaussmf,
[partition, base / 2],
partition)
elif self.membership_function == Membership.trapmf:
sets[_name] = FuzzySet.FuzzySet(_name, Membership.trapmf, [
partition - base, partition - (base / 2), partition +
(base / 2), partition + base
], partition, **kwargs)
partition += base
return sets
#!/usr/bin/python # -*- coding: utf8 -*- import os import numpy as np import matplotlib.pylab as plt #from mpl_toolkits.mplot3d import Axes3D import pandas as pd from pyFTS.common import Transformations tdiff = Transformations.Differential(1) from pyFTS.data import TAIEX, SP500, NASDAQ dataset = TAIEX.get_data() #dataset = SP500.get_data()[11500:16000] #dataset = NASDAQ.get_data() #print(len(dataset)) from pyFTS.partitioners import Grid, Util as pUtil partitioner = Grid.GridPartitioner(data=dataset[:800], npart=10, transformation=tdiff) from pyFTS.common import Util as cUtil from pyFTS.benchmarks import benchmarks as bchmk, Util as bUtil, Measures, knn, quantreg, arima, naive from pyFTS.models import pwfts, song, chen, ifts, hofts from pyFTS.models.ensemble import ensemble model = chen.ConventionalFTS(partitioner=partitioner)
train_split = 1000
test_length = 200
from pyFTS.common import Transformations
from pyFTS.benchmarks import naive
from pyFTS.models.incremental import TimeVariant, IncrementalEnsemble
from pyFTS.models.nonstationary import common, perturbation, partitioners as nspart
from pyFTS.models.nonstationary import nsfts, util as nsUtil
from pyFTS.partitioners import Grid
from pyFTS.models import hofts, pwfts
from pyFTS.benchmarks import Measures
from pyFTS.common import Transformations, Util as cUtil
diff = Transformations.Differential(lag=1)
train = dataset[:1000]
test = dataset[1000:]
#grid = Grid.GridPartitioner(data=train, transformation=diff)
#model = pwfts.ProbabilisticWeightedFTS(partitioner=grid)
#model.append_transformation(diff)
model = naive.Naive()
model.fit(train)
for ct, ttrain, ttest in cUtil.sliding_window(test, 1000, .95, inc=.5):
if model.shortname not in ('PWFTS', 'Naive'):
model.predict(ttrain)
