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
<mailto: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 <mailto: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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