In [1]:
from splearn.datasets.base import load_data_sample
from splearn.tests.datasets.get_dataset_path import get_dataset_path
from splearn import Spectral
train_file = '3.pautomac_light.train'
data = load_data_sample(adr=get_dataset_path(train_file))
sp = Spectral()
sp.fit(X=data.data)
Out [1]:
Start Hankel matrix computation
End of Hankel matrix computation
Start Building Automaton from Hankel matrix
End of Automaton computation
Out [1]:
Spectral(lcolumns=7, lrows=7, mode_quiet=False, partial=True, rank=5,
smooth_method='none', sparse=True, version='classic')