Yeah, just reshape s1 and s2 to be (50,1).
> s1 = np.random.randn(50,1)
> s2 = np.random.randn(50,1)+5
-Robert
On Oct 18, 2013, at 3:57 PM, Alexandr M wrote:
> 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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