Comments (3)
Hi,
Thanks for the suggestion :-)
It does not seems to make a difference at a first glance (see the screenshot) but you're right that there is some Any
types inside the function.
I'll look more into details.
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I don't know if we can do better in terms of time (2x17us ~= 35us), however we may be able to use slightly less memory (~40KiB instead of 79KiB):
@btime logpdf.(hmm.D[1], obs);
17.398 μs (5 allocations: 19.72 KiB)
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Thanks for your answer!
I used the log_likelihood function in another function and the compiler wasn't able to correctly guess the output - so I thought you might get some speed up when you are using your function in the MLE part of your module.
On a related note, I saw the graph where you guys benchmarked your package with hmmlearn - why this drastic time increase for the likelihood calculation? This should be already be a part in the forward pass.
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Related Issues (20)
- Error in mle.jl? HOT 1
- Benchmarks vs MS_HMMBase & question about messages_backwards_log
- 2 state and 3 observation HMM HOT 3
- Multiple sequence with different length HOT 17
- Stable documentation is not up-to-date HOT 1
- HMM with observations as probabilities HOT 13
- viterbi(hmm, y) got "ERROR: BoundsError: attempt to access T×5 Array{Int64,2} at index [T-1, 0]" HOT 2
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- Running into an argument error in Distributions when using fit_mle HOT 1
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