Bugfixes
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@ -91,3 +91,11 @@ class SeasonalFTS(fts.FTS):
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ret.append(np.percentile(mp, 50))
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return ret
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def __str__(self):
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"""String representation of the model"""
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tmp = self.name + ":\n"
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for r in self.flrgs:
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tmp = tmp + str(self.flrgs[r]) + "\n"
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return tmp
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@ -42,7 +42,7 @@ def bestSplit(data, npart):
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if len(data) < 2:
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return None
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count = 1
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ndata = list(set(data))
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ndata = list(set(np.array(data).flatten()))
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ndata.sort()
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l = len(ndata)
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threshold = 0
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@ -54,7 +54,7 @@ class Partitioner(object):
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self.min = float(_min * 1.1 if _min < 0 else _min * 0.9)
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_max = max(ndata)
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_max = np.nanmax(ndata)
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self.max = float(_max * 1.1 if _max > 0 else _max * 0.9)
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self.sets = self.build(ndata)
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@ -9,13 +9,30 @@ import matplotlib.pylab as plt
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import pandas as pd
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from pyFTS.common import Util as cUtil, FuzzySet
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from pyFTS.partitioners import Grid, Util as pUtil
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from pyFTS.partitioners import Grid, Entropy, Util as pUtil
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from pyFTS.benchmarks import benchmarks as bchmk
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from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts
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from pyFTS.common import Transformations
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tdiff = Transformations.Differential(1)
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data = pd.read_csv('/home/petronio/Downloads/priceHong').values
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split = 24 * 800
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train = data[:split].flatten()
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test = data[split:].flatten()
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print(train)
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fs_grid = Grid.GridPartitioner(data=train,npart=25)
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#fs_entr.plot(ax[1])
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for method in [hofts.HighOrderFTS, pwfts.ProbabilisticWeightedFTS]:
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for order in [2,3]:
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model = method(partitioner=fs_grid, order=order)
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model.fit(train)
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'''
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from pyFTS.data import TAIEX, SP500, NASDAQ
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dataset = TAIEX.get_data()
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@ -30,7 +47,7 @@ model.fit(dataset[:800])
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print(model)
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ret = model.predict([5000.00, 5200.00, 5400.00], explain=True)
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'''
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'''
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#dataset = SP500.get_data()[11500:16000]
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#dataset = NASDAQ.get_data()
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