Refactorings on IncrementalEnsemble
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@ -65,20 +65,21 @@ class IncrementalEnsembleFTS(ensemble.EnsembleFTS):
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ret = []
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for k in np.arange(self.max_lag, l):
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for k in np.arange(0, l):
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data_window.append(data[k - self.max_lag])
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data_window.append(data[k])
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if k >= self.window_length:
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data_window.pop(0)
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if k % self.batch_size == 0 and k - self.max_lag >= self.window_length:
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if k % self.batch_size == 0 and k >= self.window_length:
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self.train(data_window, **kwargs)
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sample = data[k - self.max_lag: k]
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tmp = self.get_models_forecasts(sample)
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point = self.get_point(tmp)
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ret.append(point)
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if len(self.models) > 0:
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sample = data[k: k + self.max_lag]
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tmp = self.get_models_forecasts(sample)
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point = self.get_point(tmp)
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ret.append(point)
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return ret
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@ -7,19 +7,30 @@ import numpy as np
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import pandas as pd
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from pyFTS.partitioners import Grid
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from pyFTS.common import Transformations
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from pyFTS.models import chen
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from pyFTS.models import chen, hofts
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from pyFTS.models.incremental import IncrementalEnsemble, TimeVariant
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from pyFTS.data import AirPassengers
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from pyFTS.data import AirPassengers, artificial
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mu_local = 5
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sigma_local = 0.25
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mu_drift = 10
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sigma_drift = 1.
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deflen = 100
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totlen = deflen * 10
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order = 10
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passengers = AirPassengers.get_data()
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signal = artificial.SignalEmulator()\
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.stationary_gaussian(mu_local,sigma_local,length=deflen//2,it=10)\
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.stationary_gaussian(mu_drift,sigma_drift,length=deflen//2,it=10, additive=False)\
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.run()
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model = IncrementalEnsemble.IncrementalEnsembleFTS(order=2, window_length=20, batch_size=5)
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model2 = IncrementalEnsemble.IncrementalEnsembleFTS(partitioner_method=Grid.GridPartitioner, partitioner_params={'npart': 15},
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fts_method=hofts.WeightedHighOrderFTS, fts_params={}, order=2 ,
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batch_size=20, window_length=100, num_models=5)
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model.fit(passengers[:40])
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forecasts = model.predict(passengers[40:])
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forecasts = model2.predict(signal)
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print(forecasts)
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