Hi Maheshakya.
Sorry for the late reply.
I'm actually not so familiar with the HMM module, but Fred Mailhot and
Robert McGibbon might be able to help you ;)
Both should be possible but not entirely convenient with the current
API. You can fit the model to the data, and then "predict"
the hidden variables. Using the hidden variable of the last state, you
can use "transmat" to get the distribution of the next state
according to the model. Then, using the means (for continuous variables)
you can infer the observed state.
No guarantee of correctness, though ;)
Cheers,
Andy
On 09/27/2013 05:11 AM, Maheshakya Wijewardena wrote:
Suppose there is a sequence of observations. for an example take
[1,2,3,5,5,5,2,3,2,3, ..., 3, 4]. (Those can be even real numbers).
How do I use the current implementation of HMM in Scikit-learn to
predict the next value of this observation sequence. I have 2
questions regarding this.
1. Given a sequence of observations, predicting the next
observation(as mentioned above)
2. Given many sequences of n observations and n+1 observations of
those sequences, can HMM be used to predict the (n+1)th observation of
a new sequence of n observations? If so how?
How do I use the HMM in Scikit-learn for the above tasks? I couldn't
grasp much about this from the documentation.
Thank you.
Maheshakya
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