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Bugfixes

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This commit is contained in:
Petrônio Cândido 2018-10-30 19:00:51 -03:00
parent 1cc022c7d6
commit 3f435222ac
4 changed files with 29 additions and 4 deletions
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8
pyFTS/models/seasonal/sfts.py
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@ -91,3 +91,11 @@ class SeasonalFTS(fts.FTS):
ret.append(np.percentile(mp, 50)) ret.append(np.percentile(mp, 50))
return ret return ret
def __str__(self):
"""String representation of the model"""
tmp = self.name + ":\n"
for r in self.flrgs:
tmp = tmp + str(self.flrgs[r]) + "\n"
return tmp

2
pyFTS/partitioners/Entropy.py
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@ -42,7 +42,7 @@ def bestSplit(data, npart):
if len(data) < 2: if len(data) < 2:
return None return None
count = 1 count = 1
ndata = list(set(data)) ndata = list(set(np.array(data).flatten()))
ndata.sort() ndata.sort()
l = len(ndata) l = len(ndata)
threshold = 0 threshold = 0

2
pyFTS/partitioners/partitioner.py
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@ -54,7 +54,7 @@ class Partitioner(object):
self.min = float(_min * 1.1 if _min < 0 else _min * 0.9) self.min = float(_min * 1.1 if _min < 0 else _min * 0.9)
_max = max(ndata) _max = np.nanmax(ndata)
self.max = float(_max * 1.1 if _max > 0 else _max * 0.9) self.max = float(_max * 1.1 if _max > 0 else _max * 0.9)
self.sets = self.build(ndata) self.sets = self.build(ndata)

21
pyFTS/tests/general.py
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@ -9,13 +9,30 @@ import matplotlib.pylab as plt
import pandas as pd import pandas as pd
from pyFTS.common import Util as cUtil, FuzzySet from pyFTS.common import Util as cUtil, FuzzySet
from pyFTS.partitioners import Grid, Util as pUtil from pyFTS.partitioners import Grid, Entropy, Util as pUtil
from pyFTS.benchmarks import benchmarks as bchmk from pyFTS.benchmarks import benchmarks as bchmk
from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts
from pyFTS.common import Transformations from pyFTS.common import Transformations
tdiff = Transformations.Differential(1) tdiff = Transformations.Differential(1)
data = pd.read_csv('/home/petronio/Downloads/priceHong').values
split = 24 * 800
train = data[:split].flatten()
test = data[split:].flatten()
print(train)
fs_grid = Grid.GridPartitioner(data=train,npart=25)
#fs_entr.plot(ax[1])
for method in [hofts.HighOrderFTS, pwfts.ProbabilisticWeightedFTS]:
for order in [2,3]:
model = method(partitioner=fs_grid, order=order)
model.fit(train)
'''
from pyFTS.data import TAIEX, SP500, NASDAQ from pyFTS.data import TAIEX, SP500, NASDAQ
dataset = TAIEX.get_data() dataset = TAIEX.get_data()
@ -30,7 +47,7 @@ model.fit(dataset[:800])
print(model) print(model)
ret = model.predict([5000.00, 5200.00, 5400.00], explain=True) ret = model.predict([5000.00, 5200.00, 5400.00], explain=True)
'''
''' '''
#dataset = SP500.get_data()[11500:16000] #dataset = SP500.get_data()[11500:16000]
#dataset = NASDAQ.get_data() #dataset = NASDAQ.get_data()