From 8d9d0e09c1062013536cf0017f380c2449f29e14 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Petr=C3=B4nio=20C=C3=A2ndido?= Date: Tue, 11 Dec 2018 13:15:44 -0200 Subject: [PATCH] Bugfixes on hofts and pwfts --- pyFTS/common/fts.py | 3 ++- pyFTS/models/hofts.py | 4 ++-- pyFTS/tests/general.py | 21 +++++++++++---------- 3 files changed, 15 insertions(+), 13 deletions(-) diff --git a/pyFTS/common/fts.py b/pyFTS/common/fts.py index 82b602b..53d3b5b 100644 --- a/pyFTS/common/fts.py +++ b/pyFTS/common/fts.py @@ -458,7 +458,8 @@ class FTS(object): return data def get_UoD(self): - return [self.original_min, self.original_max] + #return [self.original_min, self.original_max] + return [self.partitioner.min, self.partitioner.max] def __str__(self): """String representation of the model""" diff --git a/pyFTS/models/hofts.py b/pyFTS/models/hofts.py index 4b2e698..33a6223 100644 --- a/pyFTS/models/hofts.py +++ b/pyFTS/models/hofts.py @@ -88,13 +88,13 @@ class HighOrderFTS(fts.FTS): self.detail = "Severiano, Silva, Sadaei and Guimarães" self.is_high_order = True self.min_order = 1 - self.order= kwargs.get("order", 2) + self.order= kwargs.get("order", self.min_order) self.lags = kwargs.get("lags", None) self.configure_lags(**kwargs) def configure_lags(self, **kwargs): if "order" in kwargs: - self.order = kwargs.get("order", 2) + self.order = kwargs.get("order", self.min_order) if "lags" in kwargs: self.lags = kwargs.get("lags", None) diff --git a/pyFTS/tests/general.py b/pyFTS/tests/general.py index 8f55561..02ef077 100644 --- a/pyFTS/tests/general.py +++ b/pyFTS/tests/general.py @@ -17,21 +17,22 @@ from pyFTS.common import Transformations tdiff = Transformations.Differential(1) -from pyFTS.data import TAIEX, SP500, NASDAQ, Malaysia +from pyFTS.data import TAIEX, SP500, NASDAQ, Malaysia, Enrollments -dataset = Malaysia.get_data('temperature')[:1000] +train_split = 2000 +test_length = 200 -p = Grid.GridPartitioner(data=dataset, npart=20) +dataset = TAIEX.get_data() -print(p) +partitioner = Grid.GridPartitioner(data=dataset[:train_split], npart=35) +partitioner_diff = Grid.GridPartitioner(data=dataset[:train_split], npart=5, transformation=tdiff) -model = pwfts.ProbabilisticWeightedFTS(partitioner=p, order=2) +pfts1_taiex = pwfts.ProbabilisticWeightedFTS(partitioner=partitioner) +pfts1_taiex.fit(dataset[:train_split], save_model=True, file_path='pwfts', order=1) +pfts1_taiex.shortname = "1st Order" +#print(pfts1_taiex) -model.fit(dataset) #[22, 22, 23, 23, 24]) - -print(model) - -Measures.get_point_statistics(dataset, model) +tmp = pfts1_taiex.predict(dataset[train_split:train_split+200], type='distribution') ''' #dataset = SP500.get_data()[11500:16000]