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HMM initialization: don't use equal initial probabilities. #828

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Dec 22, 2016
Merged

HMM initialization: don't use equal initial probabilities. #828

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d000a5a
Don't use equal initial probabilities.
rcurtin Dec 12, 2016
c76f5ac
Remove random initialization since it is done by default now.
rcurtin Dec 22, 2016
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66 changes: 3 additions & 63 deletions src/mlpack/methods/hmm/hmm_train_main.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -29,9 +29,9 @@ PROGRAM_INFO("Hidden Markov Model (HMM) Training", "This program allows a "
"\n\n"
"The HMM is trained with the Baum-Welch algorithm if no labels are "
"provided. The tolerance of the Baum-Welch algorithm can be set with the "
"--tolerance option. In general it is a good idea to use random "
"initialization in this case, which can be specified with the "
"--random_initialization (-r) option."
"--tolerance option. By default, the transition matrix is randomly "
"initialized and the emission distributions are initialized to fit the "
"extent of the data."
"\n\n"
"Optionally, a pre-created HMM model can be used as a guess for the "
"transition matrix and emission probabilities; this is specifiable with "
Expand All @@ -54,8 +54,6 @@ PARAM_STRING_OUT("output_model_file", "File to save trained HMM to.", "M");
PARAM_INT_IN("seed", "Random seed. If 0, 'std::time(NULL)' is used.", "s", 0);
PARAM_DOUBLE_IN("tolerance", "Tolerance of the Baum-Welch algorithm.", "T",
1e-5);
PARAM_FLAG("random_initialization", "Initialize emissions and transition "
"matrices with a uniform random distribution.", "r");

using namespace mlpack;
using namespace mlpack::hmm;
Expand Down Expand Up @@ -305,21 +303,6 @@ int main(int argc, char** argv)
HMM<DiscreteDistribution> hmm(size_t(states),
DiscreteDistribution(maxEmission), tolerance);

// Initialize with random starting point.
if (CLI::HasParam("random_initialization"))
{
hmm.Transition().randu();
for (size_t c = 0; c < hmm.Transition().n_cols; ++c)
hmm.Transition().col(c) /= arma::accu(hmm.Transition().col(c));

for (size_t e = 0; e < hmm.Emission().size(); ++e)
{
hmm.Emission()[e].Probabilities().randu();
hmm.Emission()[e].Probabilities() /=
arma::accu(hmm.Emission()[e].Probabilities());
}
}

// Now train it. Pass the already-loaded training data.
Train::Apply(hmm, &trainSeq);
}
Expand All @@ -338,22 +321,6 @@ int main(int argc, char** argv)
HMM<GaussianDistribution> hmm(size_t(states),
GaussianDistribution(dimensionality), tolerance);

// Initialize with random starting point.
if (CLI::HasParam("random_initialization"))
{
hmm.Transition().randu();
for (size_t c = 0; c < hmm.Transition().n_cols; ++c)
hmm.Transition().col(c) /= arma::accu(hmm.Transition().col(c));

for (size_t e = 0; e < hmm.Emission().size(); ++e)
{
hmm.Emission()[e].Mean().randu();
// Generate random covariance.
arma::mat r = arma::randu<arma::mat>(dimensionality, dimensionality);
hmm.Emission()[e].Covariance(r * r.t());
}
}

// Now train it.
Train::Apply(hmm, &trainSeq);
}
Expand All @@ -376,33 +343,6 @@ int main(int argc, char** argv)
HMM<GMM> hmm(size_t(states), GMM(size_t(gaussians), dimensionality),
tolerance);

// Initialize with random starting point.
if (CLI::HasParam("random_initialization"))
{
hmm.Transition().randu();
for (size_t c = 0; c < hmm.Transition().n_cols; ++c)
hmm.Transition().col(c) /= arma::accu(hmm.Transition().col(c));

for (size_t e = 0; e < hmm.Emission().size(); ++e)
{
// Random weights.
hmm.Emission()[e].Weights().randu();
hmm.Emission()[e].Weights() /=
arma::accu(hmm.Emission()[e].Weights());

// Random means and covariances.
for (int g = 0; g < gaussians; ++g)
{
hmm.Emission()[e].Component(g).Mean().randu();

// Generate random covariance.
arma::mat r = arma::randu<arma::mat>(dimensionality,
dimensionality);
hmm.Emission()[e].Component(g).Covariance(r * r.t());
}
}
}

// Issue a warning if the user didn't give labels.
if (!CLI::HasParam("labels_file"))
Log::Warn << "Unlabeled training of GMM HMMs is almost certainly not "
Expand Down