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Replacing references to FuzzySet.fuzzyfy to Partitioner.fuzzyfy

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This commit is contained in:
Petrônio Cândido 2019-04-09 16:06:04 -03:00
parent d3fa1ea534
commit 0e67b7bc61
3 changed files with 16 additions and 9 deletions
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2
pyFTS/models/multivariate/common.py
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@ -43,7 +43,7 @@ class MultivariateFuzzySet(Composite.FuzzySet):
def fuzzyfy_instance(data_point, var, tuples=True):
fsets = FuzzySet.fuzzyfy(data_point, var.partitioner, mode='sets', method='fuzzy', alpha_cut=var.alpha_cut)
fsets = var.partitioner(data_point, mode='sets', method='fuzzy', alpha_cut=var.alpha_cut)
if tuples:
return [(var.name, fs) for fs in fsets]
else:

6
pyFTS/partitioners/partitioner.py
View File

@ -142,6 +142,7 @@ class Partitioner(object):
ret = []
for inst in data:
mv = self.fuzzyfy(inst, **kwargs)
ret.append(mv)
return ret
alpha_cut = kwargs.get('alpha_cut', 0.)
@ -157,6 +158,7 @@ class Partitioner(object):
mv[ix] = tmp if tmp >= alpha_cut else 0.
ix = np.ravel(np.argwhere(mv > 0.))
if ix.size == 0:
mv[self.check_bounds(data)] = 1.
@ -170,6 +172,10 @@ class Partitioner(object):
mx = max(mv)
ix = np.ravel(np.argwhere(mv == mx))
return self.ordered_sets[ix[0]]
elif mode == 'both':
ix = np.ravel(np.argwhere(mv > 0.))
sets = [(self.ordered_sets[i], mv[i]) for i in ix]
return sets
def check_bounds(self, data):
if data < self.min:

17
pyFTS/tests/general.py
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@ -11,19 +11,20 @@ import pandas as pd
from pyFTS.common import Util as cUtil, FuzzySet
from pyFTS.partitioners import Grid, Entropy, Util as pUtil, Simple
from pyFTS.benchmarks import benchmarks as bchmk, Measures
from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts
from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts, tsaur, song, sadaei
from pyFTS.common import Transformations, Membership
from pyFTS.data import TAIEX
fs = Grid.GridPartitioner(data=TAIEX.get_data(), npart=23)
data = TAIEX.get_data()
print(fs.min, fs.max)
fs = Grid.GridPartitioner(data=data, npart=23)
tmp = fs.search(5500)
print(tmp)
tmp = fs.fuzzyfy(5500, method='fuzzy', alpha_cut=0.3)
print(tmp)
test = [2000, 5000, 5500, 12000]
for method in [yu.WeightedFTS, tsaur.MarkovWeightedFTS, song.ConventionalFTS, sadaei.ExponentialyWeightedFTS, ismailefendi.ImprovedWeightedFTS,
chen.ConventionalFTS, cheng.TrendWeightedFTS, hofts.HighOrderFTS, pwfts.ProbabilisticWeightedFTS]:
model = method(partitioner=fs)
model.fit(data)
print(model.forecast(test))