diff --git a/src/mlpack/tests/main_tests/hmm_generate_test.cpp b/src/mlpack/tests/main_tests/hmm_generate_test.cpp
index 73a7e6cddd..eddaaf5d59 100644
--- a/src/mlpack/tests/main_tests/hmm_generate_test.cpp
+++ b/src/mlpack/tests/main_tests/hmm_generate_test.cpp
@@ -120,55 +120,25 @@ BOOST_AUTO_TEST_CASE(HMMGenerateGaussianHMMCheckDimensionsTest)
 
 BOOST_AUTO_TEST_CASE(HMMGenerateGMMHMMCheckDimensionsTest)
 {
-  // Load data to train a Gaussian Mixture Model HMM model with.
-  std::vector<GMM> gmms(2, GMM(2, 2));
-  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");
-
-  // 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[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(1) = GaussianDistribution("-4.25 -2.12",
-                                              "1.50 0.60; 0.60 1.20");
-
-  // Transition matrix.
-  arma::mat transMat("0.40 0.60;"
-                     "0.60 0.40");
-
-  // Make a sequence of observations.
-  std::vector<arma::mat> observations(5, arma::mat(2, 50));
-  std::vector<arma::Row<size_t> > states(5, arma::Row<size_t>(50));
-  for (size_t obs = 0; obs < 5; obs++)
-  {
-    states[obs][0] = 0;
-    observations[obs].col(0) = gmms[0].Random();
-
-    for (size_t i = 1; i < 50; i++)
-    {
-      double randValue = (double) rand() / (double) RAND_MAX;
-
-      if (randValue <= transMat(0, states[obs][i - 1]))
-        states[obs][i] = 0;
-      else
-        states[obs][i] = 1;
-
-      observations[obs].col(i) = gmms[states[obs][i]].Random();
-    }
-  }
-
   // Initialize and train a GMM HMM model.
   HMMModel* h = new HMMModel(GaussianMixtureModelHMM);
-  h->PerformAction<InitHMMModel, std::vector<arma::mat>>(&observations);
-  h->PerformAction<TrainHMMModel, std::vector<arma::mat>>(&observations);
+  *(h->GMMHMM()) = HMM<GMM>(2, GMM(2, 2));
+
+  // Manually set the components.
+  h->GMMHMM()->Transition() = arma::mat("0.40 0.60; 0.60 0.40");
+  h->GMMHMM()->Emission().resize(2);
+  h->GMMHMM()->Emission()[0] = GMM(2, 2);
+  h->GMMHMM()->Emission()[0].Weights() = arma::vec("0.3 0.7");
+  h->GMMHMM()->Emission()[0].Component(0) = GaussianDistribution("4.25 3.10",
+      "1.00 0.20; 0.20 0.89");
+  h->GMMHMM()->Emission()[0].Component(1) = GaussianDistribution("7.10 5.01",
+      "1.00 0.00; 0.00 1.01");
+  h->GMMHMM()->Emission()[1] = GMM(2, 2);
+  h->GMMHMM()->Emission()[1].Weights() = arma::vec("0.20 0.80");
+  h->GMMHMM()->Emission()[1].Component(0) = GaussianDistribution("-3.00 -6.12",
+      "1.00 0.00; 0.00 1.00");
+  h->GMMHMM()->Emission()[1].Component(1) = GaussianDistribution("-4.25 -2.12",
+      "1.50 0.60; 0.60 1.20");
 
   // Now that we have a trained HMM model, we can use it to generate a sequence
   // of states and observations - using the hmm_generate utility.
@@ -184,16 +154,16 @@ BOOST_AUTO_TEST_CASE(HMMGenerateGMMHMMCheckDimensionsTest)
   // Get the generated observation sequence. Ensure that the generated sequence
   // has the correct length (as provided in the input).
   arma::mat obsSeq = CLI::GetParam<arma::mat>("output");
-  BOOST_REQUIRE_EQUAL(obsSeq.n_cols, (size_t)length);
-  BOOST_REQUIRE_EQUAL(obsSeq.n_rows, (size_t)2);
-  BOOST_REQUIRE_EQUAL(obsSeq.n_elem, (size_t)(length*2));
+  BOOST_REQUIRE_EQUAL(obsSeq.n_cols, (size_t) length);
+  BOOST_REQUIRE_EQUAL(obsSeq.n_rows, (size_t) 2);
+  BOOST_REQUIRE_EQUAL(obsSeq.n_elem, (size_t) (length*2));
 
   // Get the generated state sequence. Ensure that the generated sequence
   // has the correct length (as provided in the input).
   arma::Mat<size_t> stateSeq = CLI::GetParam<arma::Mat<size_t>>("state");
-  BOOST_REQUIRE_EQUAL(stateSeq.n_cols, (size_t)length);
-  BOOST_REQUIRE_EQUAL(stateSeq.n_rows, (size_t)1);
-  BOOST_REQUIRE_EQUAL(stateSeq.n_elem, (size_t)length);
+  BOOST_REQUIRE_EQUAL(stateSeq.n_cols, (size_t) length);
+  BOOST_REQUIRE_EQUAL(stateSeq.n_rows, (size_t) 1);
+  BOOST_REQUIRE_EQUAL(stateSeq.n_elem, (size_t) length);
 }
 
 BOOST_AUTO_TEST_CASE(HMMGenerateLengthPositiveTest)
diff --git a/src/mlpack/tests/main_tests/hmm_viterbi_test.cpp b/src/mlpack/tests/main_tests/hmm_viterbi_test.cpp
index 7b8dec4905..b66fed5fcf 100644
--- a/src/mlpack/tests/main_tests/hmm_viterbi_test.cpp
+++ b/src/mlpack/tests/main_tests/hmm_viterbi_test.cpp
@@ -106,62 +106,56 @@ BOOST_AUTO_TEST_CASE(HMMViterbiGaussianHMMCheckDimensionsTest)
 
 BOOST_AUTO_TEST_CASE(HMMViterbiGMMHMMCheckDimensionsTest)
 {
-  // Load data to train a Gaussian Mixture Model HMM model with.
   std::vector<GMM> gmms(2, GMM(2, 2));
   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");
-
-  // N([4.10 1.01], [1.00 0.00; 0.00 1.01])
+      "1.00 0.20; 0.20 0.89");
   gmms[0].Component(1) = GaussianDistribution("7.10 5.01",
-                                              "1.00 0.00; 0.00 1.01");
-
+      "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");
-
+      "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");
+      "1.50 0.60; 0.60 1.20");
 
   // Transition matrix.
-  arma::mat transMat("0.40 0.60;"
-                     "0.60 0.40");
+  arma::mat transMat("0.40 0.60; 0.60 0.40");
 
-  // Make a sequence of observations.
-  std::vector<arma::mat> observations(5, arma::mat(2, 50));
-  std::vector<arma::Row<size_t> > states(5, arma::Row<size_t>(50));
-  for (size_t obs = 0; obs < 5; obs++)
+  // Make some observations.
+  arma::mat observations(2, 50);
+  arma::Row<size_t> states(50);
+
+  states[0] = 0;
+  observations.col(0) = gmms[0].Random();
+
+  for (size_t i = 1; i < 50; ++i)
   {
-    states[obs][0] = 0;
-    observations[obs].col(0) = gmms[0].Random();
+    double randValue = (double) rand() / (double) RAND_MAX;
 
-    for (size_t i = 1; i < 50; i++)
-    {
-      double randValue = (double) rand() / (double) RAND_MAX;
+    if (randValue <= transMat(0, states[i - 1]))
+      states[i] = 0;
+    else
+      states[i] = 1;
 
-      if (randValue <= transMat(0, states[obs][i - 1]))
-        states[obs][i] = 0;
-      else
-        states[obs][i] = 1;
-
-      observations[obs].col(i) = gmms[states[obs][i]].Random();
-    }
+    observations.col(i) = gmms[states[i]].Random();
   }
 
   // Initialize and train a GMM HMM model.
   HMMModel* h = new HMMModel(GaussianMixtureModelHMM);
-  h->PerformAction<InitHMMModel, std::vector<arma::mat>>(&observations);
-  h->PerformAction<TrainHMMModel, std::vector<arma::mat>>(&observations);
+  *(h->GMMHMM()) = HMM<GMM>(2, GMM(2, 2));
+
+  // Manually set the components.
+  h->GMMHMM()->Transition() = transMat;
+  h->GMMHMM()->Emission() = gmms;
 
   // Now that we have a trained HMM model, we can use it to predict the state
   // sequence for a given observation sequence - using the Viterbi algorithm.
   // Load the input model to be used for inference and the sequence over which
   // inference is to be performed.
   SetInputParam("input_model", h);
-  SetInputParam("input", observations[0]);
+  SetInputParam("input", observations);
 
   // Call to hmm_viterbi_main.
   mlpackMain();
@@ -172,7 +166,7 @@ BOOST_AUTO_TEST_CASE(HMMViterbiGMMHMMCheckDimensionsTest)
   // Output sequence length must be the same as input sequence length and
   // there should only be one row (since states are single dimensional values).
   BOOST_REQUIRE_EQUAL(out.n_rows, 1);
-  BOOST_REQUIRE_EQUAL(out.n_cols, observations[0].n_cols);
+  BOOST_REQUIRE_EQUAL(out.n_cols, observations.n_cols);
 }
 
 BOOST_AUTO_TEST_SUITE_END();