[mlpack-git] master: Convert tabs to spaces. (1533288)
gitdub at big.cc.gt.atl.ga.us
gitdub at big.cc.gt.atl.ga.us
Thu Mar 5 21:59:14 EST 2015
Repository : https://github.com/mlpack/mlpack
On branch : master
Link : https://github.com/mlpack/mlpack/compare/904762495c039e345beba14c1142fd719b3bd50e...f94823c800ad6f7266995c700b1b630d5ffdcf40
>---------------------------------------------------------------
commit 15332886791b6b9e18009527741e0e7678eace22
Author: Ryan Curtin <ryan at ratml.org>
Date: Wed Aug 20 21:11:33 2014 +0000
Convert tabs to spaces.
>---------------------------------------------------------------
15332886791b6b9e18009527741e0e7678eace22
src/mlpack/tests/hmm_test.cpp | 245 +++++++++++++++++++++---------------------
1 file changed, 122 insertions(+), 123 deletions(-)
diff --git a/src/mlpack/tests/hmm_test.cpp b/src/mlpack/tests/hmm_test.cpp
index 2d5ec99..26d3b82 100644
--- a/src/mlpack/tests/hmm_test.cpp
+++ b/src/mlpack/tests/hmm_test.cpp
@@ -777,24 +777,24 @@ BOOST_AUTO_TEST_CASE(GMMHMMPredictTest)
gmms[0].Weights() = arma::vec("0.75 0.25");
// N([2.25 3.10], [1.00 0.20; 0.20 0.89])
- gmms[0].Component(0) = GaussianDistribution("4.25 3.10",
- "1.00 0.20; 0.20 0.89");
+ gmms[0].Component(0) = GaussianDistribution("4.25 3.10",
+ "1.00 0.20; 0.20 0.89");
// N([4.10 1.01], [1.00 0.00; 0.00 1.01])
- gmms[0].Component(1) = GaussianDistribution("7.10 5.01",
- "1.00 0.00; 0.00 1.01");
+ gmms[0].Component(1) = GaussianDistribution("7.10 5.01",
+ "1.00 0.00; 0.00 1.01");
gmms[1] = GMM<>(3, 2);
gmms[1].Weights() = arma::vec("0.4 0.2 0.4");
- gmms[1].Component(0) = GaussianDistribution("-3.00 -6.12",
- "1.00 0.00; 0.00 1.00");
+ gmms[1].Component(0) = GaussianDistribution("-3.00 -6.12",
+ "1.00 0.00; 0.00 1.00");
- gmms[1].Component(1) = GaussianDistribution("-4.25 -7.12",
- "1.50 0.60; 0.60 1.20");
+ gmms[1].Component(1) = GaussianDistribution("-4.25 -7.12",
+ "1.50 0.60; 0.60 1.20");
- gmms[1].Component(2) = GaussianDistribution("-6.15 -2.00",
- "1.00 0.80; 0.80 1.00");
+ gmms[1].Component(2) = GaussianDistribution("-6.15 -2.00",
+ "1.00 0.80; 0.80 1.00");
// Default MATLAB initial probabilities.
arma::vec initial("1 0");
@@ -846,20 +846,20 @@ BOOST_AUTO_TEST_CASE(GMMHMMLabeledTrainingTest)
gmms[0].Weights() = arma::vec("0.3 0.7");
// N([2.25 3.10], [1.00 0.20; 0.20 0.89])
- gmms[0].Component(0) = GaussianDistribution("4.25 3.10",
- "1.00 0.20; 0.20 0.89");
+ gmms[0].Component(0) = GaussianDistribution("4.25 3.10",
+ "1.00 0.20; 0.20 0.89");
// N([4.10 1.01], [1.00 0.00; 0.00 1.01])
- gmms[0].Component(1) = GaussianDistribution("7.10 5.01",
- "1.00 0.00; 0.00 1.01");
+ gmms[0].Component(1) = GaussianDistribution("7.10 5.01",
+ "1.00 0.00; 0.00 1.01");
gmms[1].Weights() = arma::vec("0.20 0.80");
- gmms[1].Component(0) = GaussianDistribution("-3.00 -6.12",
- "1.00 0.00; 0.00 1.00");
+ gmms[1].Component(0) = GaussianDistribution("-3.00 -6.12",
+ "1.00 0.00; 0.00 1.00");
- gmms[1].Component(1) = GaussianDistribution("-4.25 -2.12",
- "1.50 0.60; 0.60 1.20");
+ gmms[1].Component(1) = GaussianDistribution("-4.25 -2.12",
+ "1.50 0.60; 0.60 1.20");
// Transition matrix.
arma::mat transMat("0.40 0.60;"
@@ -923,22 +923,22 @@ BOOST_AUTO_TEST_CASE(GMMHMMLabeledTrainingTest)
gmms[0].Component(1).Mean()[1], 0.15);
BOOST_REQUIRE_SMALL(hmm.Emission()[0].Component(sortedIndices[0]).
- Covariance()(0, 0) - gmms[0].Component(0).Covariance()(0, 0), 0.3);
+ Covariance()(0, 0) - gmms[0].Component(0).Covariance()(0, 0), 0.3);
BOOST_REQUIRE_SMALL(hmm.Emission()[0].Component(sortedIndices[0]).
- Covariance()(0, 1) - gmms[0].Component(0).Covariance()(0, 1), 0.3);
- BOOST_REQUIRE_SMALL(hmm.Emission()[0].Component(sortedIndices[0]).
- Covariance()(1, 0) - gmms[0].Component(0).Covariance()(1, 0), 0.3);
- BOOST_REQUIRE_SMALL(hmm.Emission()[0].Component(sortedIndices[0]).
- Covariance()(1, 1) - gmms[0].Component(0).Covariance()(1, 1), 0.3);
+ Covariance()(0, 1) - gmms[0].Component(0).Covariance()(0, 1), 0.3);
+ BOOST_REQUIRE_SMALL(hmm.Emission()[0].Component(sortedIndices[0]).
+ Covariance()(1, 0) - gmms[0].Component(0).Covariance()(1, 0), 0.3);
+ BOOST_REQUIRE_SMALL(hmm.Emission()[0].Component(sortedIndices[0]).
+ Covariance()(1, 1) - gmms[0].Component(0).Covariance()(1, 1), 0.3);
BOOST_REQUIRE_SMALL(hmm.Emission()[0].Component(sortedIndices[1]).
- Covariance()(0, 0) - gmms[0].Component(1).Covariance()(0, 0), 0.3);
+ Covariance()(0, 0) - gmms[0].Component(1).Covariance()(0, 0), 0.3);
+ BOOST_REQUIRE_SMALL(hmm.Emission()[0].Component(sortedIndices[1]).
+ Covariance()(0, 1) - gmms[0].Component(1).Covariance()(0, 1), 0.3);
BOOST_REQUIRE_SMALL(hmm.Emission()[0].Component(sortedIndices[1]).
- Covariance()(0, 1) - gmms[0].Component(1).Covariance()(0, 1), 0.3);
- BOOST_REQUIRE_SMALL(hmm.Emission()[0].Component(sortedIndices[1]).
- Covariance()(1, 0) - gmms[0].Component(1).Covariance()(1, 0), 0.3);
- BOOST_REQUIRE_SMALL(hmm.Emission()[0].Component(sortedIndices[1]).
- Covariance()(1, 1) - gmms[0].Component(1).Covariance()(1, 1), 0.3);
+ Covariance()(1, 0) - gmms[0].Component(1).Covariance()(1, 0), 0.3);
+ BOOST_REQUIRE_SMALL(hmm.Emission()[0].Component(sortedIndices[1]).
+ Covariance()(1, 1) - gmms[0].Component(1).Covariance()(1, 1), 0.3);
// Sort the GMM.
@@ -960,22 +960,22 @@ BOOST_AUTO_TEST_CASE(GMMHMMLabeledTrainingTest)
gmms[1].Component(1).Mean()[1], 0.15);
BOOST_REQUIRE_SMALL(hmm.Emission()[1].Component(sortedIndices[0]).
- Covariance()(0, 0) - gmms[1].Component(0).Covariance()(0, 0), 0.3);
+ Covariance()(0, 0) - gmms[1].Component(0).Covariance()(0, 0), 0.3);
+ BOOST_REQUIRE_SMALL(hmm.Emission()[1].Component(sortedIndices[0]).
+ Covariance()(0, 1) - gmms[1].Component(0).Covariance()(0, 1), 0.3);
BOOST_REQUIRE_SMALL(hmm.Emission()[1].Component(sortedIndices[0]).
- Covariance()(0, 1) - gmms[1].Component(0).Covariance()(0, 1), 0.3);
- BOOST_REQUIRE_SMALL(hmm.Emission()[1].Component(sortedIndices[0]).
- Covariance()(1, 0) - gmms[1].Component(0).Covariance()(1, 0), 0.3);
- BOOST_REQUIRE_SMALL(hmm.Emission()[1].Component(sortedIndices[0]).
- Covariance()(1, 1) - gmms[1].Component(0).Covariance()(1, 1), 0.3);
+ Covariance()(1, 0) - gmms[1].Component(0).Covariance()(1, 0), 0.3);
+ BOOST_REQUIRE_SMALL(hmm.Emission()[1].Component(sortedIndices[0]).
+ Covariance()(1, 1) - gmms[1].Component(0).Covariance()(1, 1), 0.3);
BOOST_REQUIRE_SMALL(hmm.Emission()[1].Component(sortedIndices[1]).
- Covariance()(0, 0) - gmms[1].Component(1).Covariance()(0, 0), 0.3);
+ Covariance()(0, 0) - gmms[1].Component(1).Covariance()(0, 0), 0.3);
+ BOOST_REQUIRE_SMALL(hmm.Emission()[1].Component(sortedIndices[1]).
+ Covariance()(0, 1) - gmms[1].Component(1).Covariance()(0, 1), 0.3);
+ BOOST_REQUIRE_SMALL(hmm.Emission()[1].Component(sortedIndices[1]).
+ Covariance()(1, 0) - gmms[1].Component(1).Covariance()(1, 0), 0.3);
BOOST_REQUIRE_SMALL(hmm.Emission()[1].Component(sortedIndices[1]).
- Covariance()(0, 1) - gmms[1].Component(1).Covariance()(0, 1), 0.3);
- BOOST_REQUIRE_SMALL(hmm.Emission()[1].Component(sortedIndices[1]).
- Covariance()(1, 0) - gmms[1].Component(1).Covariance()(1, 0), 0.3);
- BOOST_REQUIRE_SMALL(hmm.Emission()[1].Component(sortedIndices[1]).
- Covariance()(1, 1) - gmms[1].Component(1).Covariance()(1, 1), 0.3);
+ Covariance()(1, 1) - gmms[1].Component(1).Covariance()(1, 1), 0.3);
}
/**
@@ -984,57 +984,56 @@ BOOST_AUTO_TEST_CASE(GMMHMMLabeledTrainingTest)
BOOST_AUTO_TEST_CASE(GMMHMMLoadSaveTest)
{
// Create a GMM HMM, save it, and load it.
- HMM<GMM<> > hmm(3, GMM<>(4, 3));
+ HMM<GMM<> > hmm(3, GMM<>(4, 3));
- for(size_t j = 0; j < hmm.Emission().size(); ++j)
+ for(size_t j = 0; j < hmm.Emission().size(); ++j)
{
- hmm.Emission()[j].Weights().randu();
- for (size_t i = 0; i < hmm.Emission()[j].Gaussians(); ++i)
- {
- hmm.Emission()[j].Component(i).Mean().randu();
- hmm.Emission()[j].Component(i).Covariance().randu();
- }
- }
+ hmm.Emission()[j].Weights().randu();
+ for (size_t i = 0; i < hmm.Emission()[j].Gaussians(); ++i)
+ {
+ hmm.Emission()[j].Component(i).Mean().randu();
+ hmm.Emission()[j].Component(i).Covariance().randu();
+ }
+ }
util::SaveRestoreUtility sr;
- hmm.Save(sr);
+ hmm.Save(sr);
sr.WriteFile("test-hmm-save.xml");
- util::SaveRestoreUtility sr2;
- sr2.ReadFile("test-hmm-save.xml");
+ util::SaveRestoreUtility sr2;
+ sr2.ReadFile("test-hmm-save.xml");
HMM<GMM<> > hmm2(3, GMM<>(4, 3));
hmm2.Load(sr2);
// Remove clutter.
remove("test-hmm-save.xml");
- for(size_t j = 0; j < hmm.Emission().size(); ++j)
- {
- BOOST_REQUIRE_EQUAL(hmm.Emission()[j].Gaussians(),
- hmm2.Emission()[j].Gaussians());
- BOOST_REQUIRE_EQUAL(hmm.Emission()[j].Dimensionality(),
- hmm2.Emission()[j].Dimensionality());
-
- for (size_t i = 0; i < hmm.Emission()[j].Dimensionality(); ++i)
- BOOST_REQUIRE_CLOSE(hmm.Emission()[j].Weights()[i],
- hmm2.Emission()[j].Weights()[i], 1e-3);
-
- for (size_t i = 0; i < hmm.Emission()[j].Gaussians(); ++i)
- {
- for (size_t l = 0; l < hmm.Emission()[j].Dimensionality(); ++l)
- {
- BOOST_REQUIRE_CLOSE(hmm.Emission()[j].Component(i).Mean()[l],
- hmm2.Emission()[j].Component(i).Mean()[l], 1e-3);
-
- for (size_t k = 0; k < hmm.Emission()[j].Dimensionality(); ++k)
- {
- BOOST_REQUIRE_CLOSE(hmm.Emission()[j].Component(i).Covariance()(l,k),
- hmm2.Emission()[j].Component(i).Covariance()(l, k), 1e-3);
- }
- }
- }
-
- }
+ for(size_t j = 0; j < hmm.Emission().size(); ++j)
+ {
+ BOOST_REQUIRE_EQUAL(hmm.Emission()[j].Gaussians(),
+ hmm2.Emission()[j].Gaussians());
+ BOOST_REQUIRE_EQUAL(hmm.Emission()[j].Dimensionality(),
+ hmm2.Emission()[j].Dimensionality());
+
+ for (size_t i = 0; i < hmm.Emission()[j].Dimensionality(); ++i)
+ BOOST_REQUIRE_CLOSE(hmm.Emission()[j].Weights()[i],
+ hmm2.Emission()[j].Weights()[i], 1e-3);
+
+ for (size_t i = 0; i < hmm.Emission()[j].Gaussians(); ++i)
+ {
+ for (size_t l = 0; l < hmm.Emission()[j].Dimensionality(); ++l)
+ {
+ BOOST_REQUIRE_CLOSE(hmm.Emission()[j].Component(i).Mean()[l],
+ hmm2.Emission()[j].Component(i).Mean()[l], 1e-3);
+
+ for (size_t k = 0; k < hmm.Emission()[j].Dimensionality(); ++k)
+ {
+ BOOST_REQUIRE_CLOSE(hmm.Emission()[j].Component(i).Covariance()(l,k),
+ hmm2.Emission()[j].Component(i).Covariance()(l, k), 1e-3);
+ }
+ }
+ }
+ }
}
/**
@@ -1043,44 +1042,44 @@ BOOST_AUTO_TEST_CASE(GMMHMMLoadSaveTest)
BOOST_AUTO_TEST_CASE(GaussianHMMLoadSaveTest)
{
// Create a Gaussian HMM, save it, and load it.
- HMM<GaussianDistribution> hmm(3, GaussianDistribution(2));
+ HMM<GaussianDistribution> hmm(3, GaussianDistribution(2));
- for(size_t j = 0; j < hmm.Emission().size(); ++j)
+ for(size_t j = 0; j < hmm.Emission().size(); ++j)
{
- hmm.Emission()[j].Mean().randu();
- hmm.Emission()[j].Covariance().randu();
- }
+ hmm.Emission()[j].Mean().randu();
+ hmm.Emission()[j].Covariance().randu();
+ }
util::SaveRestoreUtility sr;
- hmm.Save(sr);
+ hmm.Save(sr);
sr.WriteFile("test-hmm-save.xml");
- util::SaveRestoreUtility sr2;
- sr2.ReadFile("test-hmm-save.xml");
+ util::SaveRestoreUtility sr2;
+ sr2.ReadFile("test-hmm-save.xml");
HMM<GaussianDistribution> hmm2(3, GaussianDistribution(2));
hmm2.Load(sr2);
// Remove clutter.
remove("test-hmm-save.xml");
- for(size_t j = 0; j < hmm.Emission().size(); ++j)
- {
- BOOST_REQUIRE_EQUAL(hmm.Emission()[j].Dimensionality(),
- hmm2.Emission()[j].Dimensionality());
-
- for (size_t i = 0; i < hmm.Emission()[j].Dimensionality(); ++i)
- {
- BOOST_REQUIRE_CLOSE(hmm.Emission()[j].Mean()[i],
- hmm2.Emission()[j].Mean()[i], 1e-3);
- for (size_t k = 0; k < hmm.Emission()[j].Dimensionality(); ++k)
- {
- BOOST_REQUIRE_CLOSE(hmm.Emission()[j].Covariance()(i,k),
- hmm2.Emission()[j].Covariance()(i, k), 1e-3);
- }
- }
-
- }
+ for(size_t j = 0; j < hmm.Emission().size(); ++j)
+ {
+ BOOST_REQUIRE_EQUAL(hmm.Emission()[j].Dimensionality(),
+ hmm2.Emission()[j].Dimensionality());
+
+ for (size_t i = 0; i < hmm.Emission()[j].Dimensionality(); ++i)
+ {
+ BOOST_REQUIRE_CLOSE(hmm.Emission()[j].Mean()[i],
+ hmm2.Emission()[j].Mean()[i], 1e-3);
+ for (size_t k = 0; k < hmm.Emission()[j].Dimensionality(); ++k)
+ {
+ BOOST_REQUIRE_CLOSE(hmm.Emission()[j].Covariance()(i,k),
+ hmm2.Emission()[j].Covariance()(i, k), 1e-3);
+ }
+ }
+
+ }
}
/**
@@ -1090,7 +1089,7 @@ BOOST_AUTO_TEST_CASE(DiscreteHMMLoadSaveTest)
{
// Create a Discrete HMM, save it, and load it.
- std::vector<DiscreteDistribution> emission(4);
+ std::vector<DiscreteDistribution> emission(4);
emission[0].Probabilities() = arma::randu<arma::vec>(6);
emission[0].Probabilities() /= accu(emission[0].Probabilities());
emission[1].Probabilities() = arma::randu<arma::vec>(6);
@@ -1102,35 +1101,35 @@ BOOST_AUTO_TEST_CASE(DiscreteHMMLoadSaveTest)
// Create HMM object.
- HMM<DiscreteDistribution> hmm(3, DiscreteDistribution(3));
+ HMM<DiscreteDistribution> hmm(3, DiscreteDistribution(3));
- for(size_t j = 0; j < hmm.Emission().size(); ++j)
+ for(size_t j = 0; j < hmm.Emission().size(); ++j)
{
- hmm.Emission()[j].Probabilities() = arma::randu<arma::vec>(3);
- hmm.Emission()[j].Probabilities() /= accu(emission[j].Probabilities());
- }
+ hmm.Emission()[j].Probabilities() = arma::randu<arma::vec>(3);
+ hmm.Emission()[j].Probabilities() /= accu(emission[j].Probabilities());
+ }
util::SaveRestoreUtility sr;
- hmm.Save(sr);
+ hmm.Save(sr);
sr.WriteFile("test-hmm-save.xml");
- util::SaveRestoreUtility sr2;
- sr2.ReadFile("test-hmm-save.xml");
+ util::SaveRestoreUtility sr2;
+ sr2.ReadFile("test-hmm-save.xml");
HMM<DiscreteDistribution> hmm2(3, DiscreteDistribution(3));
hmm2.Load(sr2);
// Remove clutter.
remove("test-hmm-save.xml");
- for(size_t j = 0; j < hmm.Emission().size(); ++j)
- {
- for (size_t i = 0; i < hmm.Emission()[j].Probabilities().n_elem; ++i)
- {
- BOOST_REQUIRE_CLOSE(hmm.Emission()[j].Probabilities()[i],
- hmm2.Emission()[j].Probabilities()[i], 1e-3);
- }
- }
+ for(size_t j = 0; j < hmm.Emission().size(); ++j)
+ {
+ for (size_t i = 0; i < hmm.Emission()[j].Probabilities().n_elem; ++i)
+ {
+ BOOST_REQUIRE_CLOSE(hmm.Emission()[j].Probabilities()[i],
+ hmm2.Emission()[j].Probabilities()[i], 1e-3);
+ }
+ }
}
BOOST_AUTO_TEST_SUITE_END();
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