Comments (3)
Hi,
You're right. I've added a few benchmarks (in benchmark/
):
rand
log_likelihoods
messages_forwards
messages_backwards
viterbi
Note that the tables above were generated manually and I think I've mixed some time units. The code/notebooks in benchmark
should be more correct.
lls
is my shortcut for log-likelihoods. Since it is required for most of the algorithms, I separate the time it takes to compute the likelihood of each sample, vs. the time it takes to actually perform the core computations.
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Viterbi should be much faster in 0b993f3.
/ | hmmlearn | pyhsmm | HMMBase |
---|---|---|---|
Sample 10k | 981 | 189 | 1.818 |
lls | 1.11 | 3.562 | |
forward (w/o lls computation) | 5.08 | 1.510 | |
backward (w/o lls computation) | 5.02 | 1.402 | |
Viterbi (w/o lls computation) | 0.319 | 1.099 | |
Viterbi | 1.99 | 78.2 | 5.904 |
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Hello, the benchmark code in the repository only seems to show the forward and backward passes.
Would you be able to share or check-in the code you're using to generate the tables above?
Also, I'm wondering what lls
stands for...?
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Related Issues (20)
- Implement serialization/deserialization HOT 1
- Em Algorithm Speedup HOT 3
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- Compat for Distributions.jl v0.25? HOT 1
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