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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