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Dockerfile
Dockerfile
 
 
Jamroot
Jamroot
 
 
LICENSE.md
LICENSE.md
 
 
README.md
README.md
 
 
algo.hpp
algo.hpp
 
 
algotest.cpp
algotest.cpp
 
 
align_viterbi.py
align_viterbi.py
 
 
asa121.cpp
asa121.cpp
 
 
asa121.hpp
asa121.hpp
 
 
backward.hpp
backward.hpp
 
 
baumwelch.hpp
baumwelch.hpp
 
 
build.sh
build.sh
 
 
common.cpp
common.cpp
 
 
common.hpp
common.hpp
 
 
computation.hpp
computation.hpp
 
 
dotio.py
dotio.py
 
 
emit.hpp
emit.hpp
 
 
emit_from_model.py
emit_from_model.py
 
 
env_debug.sh
env_debug.sh
 
 
env_release.sh
env_release.sh
 
 
forward.hpp
forward.hpp
 
 
gamma.hpp
gamma.hpp
 
 
logspace.hpp
logspace.hpp
 
 
make_hsmm_example.py
make_hsmm_example.py
 
 
memoized.hpp
memoized.hpp
 
 
model.cpp
model.cpp
 
 
model.hpp
model.hpp
 
 
ntuple.hpp
ntuple.hpp
 
 
perturb_model.hpp
perturb_model.hpp
 
 
prototest.cpp
prototest.cpp
 
 
python_extension.cpp
python_extension.cpp
 
 
python_helpers.hpp
python_helpers.hpp
 
 
random.hpp
random.hpp
 
 
read_fasta.cpp
read_fasta.cpp
 
 
read_fasta.hpp
read_fasta.hpp
 
 
read_matrix.cpp
read_matrix.cpp
 
 
read_matrix.hpp
read_matrix.hpp
 
 
score_sequences.py
score_sequences.py
 
 
sequential_processing.hpp
sequential_processing.hpp
 
 
templates.py
templates.py
 
 
test.cpp
test.cpp
 
 
test_asa121.cpp
test_asa121.cpp
 
 
test_fasta.cpp
test_fasta.cpp
 
 
test_fasta.hpp
test_fasta.hpp
 
 
test_hsmm.hs
test_hsmm.hs
 
 
test_logspace.cpp
test_logspace.cpp
 
 
test_v2.cpp
test_v2.cpp
 
 
testmodel.dat
testmodel.dat
 
 
tests.hpp
tests.hpp
 
 
train_single.py
train_single.py
 
 
training.py
training.py
 
 
v2.hpp
v2.hpp
 
 
viterbi.cpp
viterbi.cpp
 
 
viterbi.hpp
viterbi.hpp
 
 
white_box_testing.py
white_box_testing.py
 
 
xi.hpp
xi.hpp
 
 

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hmmdsl - Library for HMMs and HSMMs

This library implements Hidden Markov Models (HMMs) and Hidden semi-Markov Models (HSMMs). The models are implemented in C++, and are exposed as a python extension. Algorithms include Viterbi alignment and Baum-Welch learning. The C++ implementation uses the generic programming paradigm, and relies heavily on the Boost libraries.

Maturity: Basic functionality is working, but code is still being developed and tested, so don't use for any important purpose.

For contact information, go to: http://www.tau.ac.il/~mich1

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hmmdsl - Library for HMMs and HSMMs

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