That would only work for the generation of samples, but wouldn't work when
the task is predicting the underlying states. Say I choose _ as a stand in
for nothing, and only the silent state emits this. If I have observations:
GGTTAAAA, the model will predict that the silent state couldn't have been
visited in the generation of these observations.
Anas
2014-05-08 8:57 GMT+02:00 Andy <t3k...@gmail.com>:
> Why don't you just add another output state that stands for "nothing"
>
>
> On 05/07/2014 11:52 PM, anas elghafari wrote:
>
> Thank you, Kyle, for your answer. I think there is some massaging of the
> numbers going on. For example, I tried to specify a silent state (a state
> where all emission probabilities are 0).The result was that all emissions
> at that state were of the first signal. I.e. the probability of the first
> emission was rounded up to 1 (code below).
>
> My question: is there a way to specify a silent state? There are some
> applications that require such a thing (e.g. DNA sequences where one of the
> things that could happen is the deletion of a symbol. The deletion state
> would show up in the sequence of states, but would
> emit no signal)
>
> --------------
> Code sample: trying to build an HMM with the second state (state 1) as a
> silent state:
>
> def testHMM():
> startprob = np.array([0.5, 0.5])
> transmat = np.array([[0.5, 0.5], [0.5, 0.5]])
> emissions = np.array([[0,0.8,0.2], [0,0,0]])
> model = hmm.MultinomialHMM(2, startprob, transmat)
> model.emissionprob_ = emissions
> return model
>
> >>> m = HMM_test.testHMM()
> >>> m.sample(20)
> (array([2, 1, 1, 1, 2, 0, 1, 0, 1, 1, 0, 1, 0, 2, 0, 0, 1, 1, 1, 1],
> dtype=int64),
> array([0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0]))
>
> In this example, singal 0 should've never shown up (since its probability
> is 0 in both states). However, it is the only signal emitted by state 1.
>
> Anas
>
>
>
>
> 2014-05-07 21:34 GMT+02:00 Kyle Kastner <kastnerk...@gmail.com>:
>
>> If you are manually specifying the emission probabilities I don't think
>> there are any hooks/asserts to guarantee that variable is normalized I.E.
>> if you assign to the emissionprob_ instead of using the fit() function, I
>> think it is on you to make sure the emission probabilities you are
>> assigning *are* actually probabilities. From my perspective that is desired
>> behavior, but maybe someone with more experience on this HMM implementation
>> can comment.
>>
>> For future reference,
>> Hidden Markov Models were recently split into their own project: seen
>> here https://github.com/hmmlearn/hmmlearn
>>
>> Kyle
>>
>>
>> On Wed, May 7, 2014 at 10:41 AM, anas elghafari <
>> anas.elghaf...@gmail.com> wrote:
>>
>>> Hi scikit community,
>>>
>>> I am playing around with the Hidden Markov Mode module (multinomialHMM)
>>> and one thing I don't understand is why scikit accepts emission
>>> probabilities that add up to more or less than 1.
>>>
>>> Here is an example with two states and three emission signals:
>>>
>>> >>> import numpy
>>> >>> from sklearn import hmm
>>> >>> startprob = numpy.array([0.5, 0.5])
>>> >>> transition_matrix = numpy.array([[0.5, 0.5], [0.5, 0.5]])
>>> >>> model = hmm.MultinomialHMM(2, startprob, transition_matrix)
>>> >>> model.emissionprob_ = numpy.array([[0, 0, 0.2], [0.6, 0.35, 0.05]])
>>>
>>> As you can see, I am specifying the emission proabilities for state 0
>>> as [0, 0, 0.2]. Scikit accepts this and generates predications with no
>>> complaints. Is this desired behavior? Do these probabilities get normalized?
>>>
>>> Thanks,
>>>
>>> Anas
>>>
>>>
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