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No. |
Time |
User |
SHA256 |
-r2 (hmm-lapack-0.3-r2) |
2019-01-05T19:33:44Z |
HenningThielemann |
f8656df12d55c26f2eca7ed2978f6e27c4a5c8ebec5ef0acae8fd715748377df
|
|
Changed homepage
from http://hub.darcs.net/thielema/hmm-hmatrix
to http://hub.darcs.net/thielema/hmm-lapack Changed source-repository
from source-repository this
type: darcs
location: http://hub.darcs.net/thielema/hmm-hmatrix
tag: 0.3
to source-repository this
type: darcs
location: http://hub.darcs.net/thielema/hmm-lapack
tag: 0.3
Changed source-repository
from source-repository head
type: darcs
location: http://hub.darcs.net/thielema/hmm-hmatrix
to source-repository head
type: darcs
location: http://hub.darcs.net/thielema/hmm-lapack
|
-r1 (hmm-lapack-0.3-r1) |
2019-01-05T19:29:19Z |
HenningThielemann |
1ac5c9b29c5764ca969ef58a853cad8dfa3ccbf8f56e9130c1dd6a8ea0279dd4
|
|
Changed synopsis
from Hidden Markov Models using HMatrix primitives
to Hidden Markov Models using LAPACK primitives Changed description
from Hidden Markov Models implemented using HMatrix data types and operations.
<http://en.wikipedia.org/wiki/Hidden_Markov_Model>
It implements:
* generation of samples of emission sequences,
* computation of the likelihood of an observed sequence of emissions,
* construction of most likely state sequence
that produces an observed sequence of emissions,
* supervised and unsupervised training of the model by Baum-Welch algorithm.
It supports any kind of emission distribution,
where discrete and multivariate Gaussian distributions
are implemented as examples.
For an introduction please refer to the examples:
* "Math.HiddenMarkovModel.Example.TrafficLight"
* "Math.HiddenMarkovModel.Example.SineWave"
* "Math.HiddenMarkovModel.Example.Circle"
An alternative package without foreign calls is @hmm@.
to Hidden Markov Models implemented using LAPACK data types and operations.
<http://en.wikipedia.org/wiki/Hidden_Markov_Model>
It implements:
* generation of samples of emission sequences,
* computation of the likelihood of an observed sequence of emissions,
* construction of most likely state sequence
that produces an observed sequence of emissions,
* supervised and unsupervised training of the model by Baum-Welch algorithm.
It supports any kind of emission distribution,
where discrete and multivariate Gaussian distributions
are implemented as examples.
For an introduction please refer to the examples:
* "Math.HiddenMarkovModel.Example.TrafficLight"
* "Math.HiddenMarkovModel.Example.SineWave"
* "Math.HiddenMarkovModel.Example.Circle"
An alternative package without foreign calls is @hmm@.
|
-r0 (hmm-lapack-0.3-r0) |
2019-01-05T19:20:07Z |
HenningThielemann |
fc3481ba66678ce59426e6638e7769b0f18776f7a02ae203fbbb4fbe6256d130
|
|
|