No, it works, thanks!
I used model.fit(signal) instead of model.fit( [signal] )
model = GaussianHMM(n_components = 2)
s1 = np.random.randn(50,1)
s2 = np.random.randn(50,1)+5
signal = np.concatenate([s1, s2])
model.fit([signal])
BR,
Alexandr
On 21 October 2013 01:42, Robert McGibbon wrote:
>
Hi Robert,
No, it doesn't work:
model = GaussianHMM(n_components = 2)
s1 = np.random.randn(50,1)
s2 = np.random.randn(50,1)+5
signal = np.concatenate([s1, s2])
model.fit(signal)
.../lib/python2.7/site-packages/sklearn/hmm.pyc in _init(self, obs,
params)754
self.n_features))755 --> 756
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:
>
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.ran
