Hello everybody,
I am trying to fit HMM model with two components
*GaussianHMM(n_components = 2)*
to one dimensional vector:
# Code:
from sklearn.hmm import GaussianHMM
import numpy as np
import matplotlib.pyplot as plt
model = GaussianHMM(n_components = 2)
s1 = np.random.randn(50)
s2 = np.random.randn(50)+5
signal = np.concatenate([s1, s2])
#plt.plot(signal)
#plt.show()
model.fit(signal)
And it gives an error:
..lib/python2.7/site-packages/sklearn/hmm.pyc in _init(self, obs, params)
754 self.n_features))
755
--> 756 self.n_features = obs[0].shape[1]
757
758 if 'm' in params:
IndexError: tuple index out of range
I looked on the examples:
http://scikit-learn.org/stable/auto_examples/applications/plot_hmm_stock_analysis.html#example-applications-plot-hmm-stock-analysis-py
As input is passed : X = np.column_stack([diff, volume]) there.
Is it possible to fit HMM for one-dimensional data?
--
Best regards,
Alexandr
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