diff --git a/fastlib/u/tqlong/hmm/#mixtureDST.cc# b/fastlib/u/tqlong/hmm/#mixtureDST.cc#
deleted file mode 100644
index 1d0f9a29aa..0000000000
--- a/fastlib/u/tqlong/hmm/#mixtureDST.cc#
+++ /dev/null
@@ -1,214 +0,0 @@
-
-#include "fastlib/fastlib.h"
-#include "support.h"
-#include "mixtureDST.h"
-
-void MixtureGauss::Init(int K, int N) {
-  means.Init();
-  for (int i = 0; i < K; i++) {
-    Vector v;
-    RAND_NORMAL_01_INIT(N, &v);
-    means.AddBackItem(v);
-  }
-  
-  covs.Init();
-  for (int i = 0; i < means.size(); i++) {
-    Matrix m;
-    m.Init(N, N); m.SetZero();
-    for (int j = 0; j < N; j++) m.ref(j, j) = 1.0;
-    covs.AddBackItem(m);
-  }
-
-  prior.Init(means.size());
-  for (int i = 0; i < prior.length(); i++) prior[i] = 1.0/K;
-
-  ACC_means.Copy(means);
-  ACC_covs.Copy(covs);
-  ACC_prior.Init(K);
-  inv_covs.Copy(covs);
-  det_covs.Init(covs.size());
-  for (int i = 0; i < K; i++) {
-    double det = la::Determinant(covs[i]);
-    la::InverseOverwrite(covs[i], &inv_covs[i]);
-    det_covs[i] = pow(2.0*math::PI, -N/2.0) * pow(det, -0.5);
-  }
-}
-
-void MixtureGauss::Init(int K, const Matrix& data, const ArrayList<int>& labels) {
-  means.Init();
-  int N = data.n_rows();
-  for (int i = 0; i < K; i++) {
-    Vector v;
-    v.Init(N);
-    means.AddBackItem(v);
-  }
-  
-  covs.Init();
-  for (int i = 0; i < means.size(); i++) {
-    Matrix m;
-    m.Init(N, N);
-    covs.AddBackItem(m);
-  }
-
-  prior.Init(means.size());
-
-  ACC_means.Copy(means);
-  ACC_covs.Copy(covs);
-  ACC_prior.Init(K);
-  inv_covs.Copy(covs);
-  det_covs.Init(covs.size());
-  start_accumulate();
-  //printf("cols = %d rows = %d\n", data.n_cols(), data.n_rows()); 
-  for (int i = 0; i < data.n_cols(); i++) {
-    Vector v;
-    data.MakeColumnVector(i, &v);
-    //printf("%d\n", i);
-    accumulate_cluster(labels[i], v);
-  }
-  end_accumulate_cluster();
-}
-
-void MixtureGauss::InitFromFile(const char* mean_fn, const char* covs_fn, const char* prior_fn) {
-  Matrix meansmat;
-  data::Load(mean_fn, &meansmat);
-  mat2arrlst(meansmat, &means);
-  int N = means[0].length();
-  int K = means.size();
-  if (covs_fn != NULL) {
-    Matrix covsmat;
-    data::Load(covs_fn, &covsmat);
-    mat2arrlstmat(N, covsmat, &covs);
-    DEBUG_ASSERT_MSG(K==covs.size(), "InitFromFile: sizes do not match !");
-  }
-  else {
-    covs.Init();
-    for (int i = 0; i < means.size(); i++) {
-      Matrix m;
-      m.Init(N, N); m.SetZero();
-      for (int j = 0; j < N; j++) m.ref(j, j) = 1.0;
-      covs.AddBackItem(m);
-    }
-  }
-  if (prior_fn != NULL) {
-    Matrix priormat;
-    data::Load(prior_fn, &priormat);
-    DEBUG_ASSERT_MSG(K==priormat.n_cols(), "InitFromFile: sizes do not match !!");
-    prior.Init(K);
-    for (int i = 0; i < K; i++) prior[i] = priormat.get(0, i);
-  }
-  else {
-    prior.Init(means.size());
-    for (int i = 0; i < prior.length(); i++) prior[i] = 1.0/K;
-  }
-  
-  ACC_means.Copy(means);
-  ACC_covs.Copy(covs);
-  ACC_prior.Init(K);
-  inv_covs.Copy(covs);
-  det_covs.Init(covs.size());
-  for (int i = 0; i < K; i++) {
-    double det = la::Determinant(covs[i]);
-    la::InverseOverwrite(covs[i], &inv_covs[i]);
-    det_covs[i] = pow(2.0*math::PI, -N/2.0) * pow(det, -0.5);
-  }
-}
-
-void Mixture::InitFromProfile(const ArrayList<Matrix>& matlst, int start, int N) {
-  DEBUG_ASSERT(matlst[start].n_cols() == 1);
-  Vector tmp;
-  matlst[start].MakeColumnVector(0, &tmp);
-  prior.Copy(tmp);
-
-  int K = prior.length();
-
-  for (int i = start+1; i < start+2*K+1; i+=2) {
-    DEBUG_ASSERT(matlst[i].n_rows()==N && matlst[starti].n_cols()==1);
-    DEBUG_ASSERT(matlst[i+1].n_rows()==N && matlst[i+1].n_cols()==N);
-    Vector m;
-    matlst[i].MakeColumnVector(0, &m);
-    means->AddBackItem(m);
-    covs->AddBackItem(matlst[i+1]);
-
-  }
-
-  Matrix meansmat;
-  data::Load(mean_fn, &meansmat);
-  mat2arrlst(meansmat, &means);
-  int N = means[0].length();
-  int K = means.size();
-  if (covs_fn != NULL) {
-    Matrix covsmat;
-    data::Load(covs_fn, &covsmat);
-    mat2arrlstmat(N, covsmat, &covs);
-    DEBUG_ASSERT_MSG(K==covs.size(), "InitFromFile: sizes do not match !");
-  }
-  else {
-    covs.Init();
-    for (int i = 0; i < means.size(); i++) {
-      Matrix m;
-      m.Init(N, N); m.SetZero();
-      for (int j = 0; j < N; j++) m.ref(j, j) = 1.0;
-      covs.AddBackItem(m);
-    }
-  }
-  if (prior_fn != NULL) {
-    Matrix priormat;
-    data::Load(prior_fn, &priormat);
-    DEBUG_ASSERT_MSG(K==priormat.n_cols(), "InitFromFile: sizes do not match !!");
-    prior.Init(K);
-    for (int i = 0; i < K; i++) prior[i] = priormat.get(0, i);
-  }
-  else {
-    prior.Init(means.size());
-    for (int i = 0; i < prior.length(); i++) prior[i] = 1.0/K;
-  }
-  
-  ACC_means.Copy(means);
-  ACC_covs.Copy(covs);
-  ACC_prior.Init(K);
-  inv_covs.Copy(covs);
-  det_covs.Init(covs.size());
-  for (int i = 0; i < K; i++) {
-    double det = la::Determinant(covs[i]);
-    la::InverseOverwrite(covs[i], &inv_covs[i]);
-    det_covs[i] = pow(2.0*math::PI, -N/2.0) * pow(det, -0.5);
-  }
-}
-
-void MixtureGauss::print_mixture(const char* s) const {
-  int K = means.size();
-  printf("%s - Mixture (%d)\n", s, K);
-  print_vector(prior, "  PRIOR");
-  for (int i = 0; i < K; i++) {
-    printf("  CLUSTER %d:\n", i);
-    print_vector(means[i], "  MEANS");
-    print_matrix(covs[i], "  COVS");
-  }
-}
-
-void MixtureGauss::generate(Vector* v) const {
-  int K = means.size();
-  double r = RAND_UNIFORM_01;
-  int cluster = K-1;
-  double s = 0;
-  for (int i = 0; i < K; i++) {
-    s += prior[i];
-    if (s >= r) {
-      cluster = i;
-      break;
-    }
-  }
-  RAND_NORMAL_INIT(means[cluster], covs[cluster], v);
-}
-
-double MixtureGauss::getPDF(const Vector& v) const {
-  int K = means.size();
-  double s = 0;
-  for (int i = 0; i < K; i++)
-    s += getPDF(i, v);
-  return s;
-}
-
-double MixtureGauss::getPDF(int cluster, const Vector& v) const {
-  return prior[cluster]*NORMAL_DENSITY(v, means[cluster], inv_covs[cluster], det_covs[cluster]);
-}
diff --git a/fastlib/u/tqlong/hmm/build.py b/fastlib/u/tqlong/hmm/build.py
index 81846bd40e..7b3bcbeab6 100644
--- a/fastlib/u/tqlong/hmm/build.py
+++ b/fastlib/u/tqlong/hmm/build.py
@@ -1,10 +1,33 @@
-binrule(
+librule(
     name = "hmm",                 # the executable name
-    sources = ["hmm.cc","support.cc","discreteHMM.cc","gaussianHMM.cc","mixgaussHMM.cc","mixtureDST.cc"],
+    sources = ["support.cc","discreteHMM.cc","gaussianHMM.cc","mixgaussHMM.cc","mixtureDST.cc"],
     headers = ["support.h", "discreteHMM.h","gaussianHMM.h","mixgaussHMM.h","mixtureDST.h"],
-    deplibs = ["fastlib:fastlib_int"]       # depends on example in this folder
+    deplibs = ["fastlib:fastlib_int"]       # depends on faslib core library
     )
 
+binrule(
+    name = "generate",                 # the executable name
+    sources = ["generate.cc"],
+    deplibs = [":hmm"]       # depends on hmm library in this folder
+    )
+
+binrule(
+    name = "loglik",                 # the executable name
+    sources = ["loglik.cc"],
+    deplibs = [":hmm"]       # depends on hmm library in this folder
+    )
+
+binrule(
+    name = "viterbi",                 # the executable name
+    sources = ["viterbi.cc"],
+    deplibs = [":hmm"]       # depends on hmm library in this folder
+    )
+
+binrule(
+    name = "train",                 # the executable name
+    sources = ["train.cc"],
+    deplibs = [":hmm"]       # depends on hmm library in this folder
+    )
 
 # to build:
 # 1. make sure have environment variables set up:
diff --git a/fastlib/u/tqlong/hmm/discreteHMM.cc b/fastlib/u/tqlong/hmm/discreteHMM.cc
index c57e20df26..a6dbcd6825 100644
--- a/fastlib/u/tqlong/hmm/discreteHMM.cc
+++ b/fastlib/u/tqlong/hmm/discreteHMM.cc
@@ -2,6 +2,143 @@
 #include "support.h"
 #include "discreteHMM.h"
 
+void DiscreteHMM::setModel(const Matrix& transmission, const Matrix& emission) {
+  DEBUG_ASSERT(transmission.n_rows() == transmission.n_cols());
+  DEBUG_ASSERT(transmission.n_rows() == emission.n_rows());
+  transmission_.Destruct();
+  emission_.Destruct();
+  transmission_.Copy(transmission);
+  emission_.Copy(emission);
+}
+
+void DiscreteHMM::Init(const Matrix& transmission, const Matrix& emission) {
+  transmission_.Copy(transmission);
+  emission_.Copy(emission);
+  DEBUG_ASSERT(transmission.n_rows() == transmission.n_cols());
+  DEBUG_ASSERT(transmission.n_rows() == emission.n_rows());
+}
+
+void DiscreteHMM::InitFromFile(const char* profile) {
+  ArrayList<Matrix> list_mat;
+  load_matrix_list(profile, &list_mat);
+  if (list_mat.size() < 2)
+    FATAL("Number of matrices in the file should be at least 2.");
+  else if (list_mat.size() > 2)
+    NONFATAL("Number of matrices in the file should be 2.");
+  transmission_.Own(&list_mat[0]);
+  emission_.Own(&list_mat[1]);
+  DEBUG_ASSERT(transmission_.n_rows() == transmission_.n_cols());
+  DEBUG_ASSERT(transmission_.n_rows() == emission_.n_rows());
+}
+
+void DiscreteHMM::InitFromData(const ArrayList<Vector>& list_data_seq, int numstate) {
+  int numsymbol = 0;
+  int maxseq = 0;
+  for (int i = 0; i < list_data_seq.size(); i++) 
+    if (list_data_seq[i].length() > list_data_seq[maxseq].length()) maxseq = i;
+  for (int i = 0; i < list_data_seq[maxseq].length(); i++)
+    if (list_data_seq[maxseq][i] > numsymbol) numsymbol = (int) list_data_seq[maxseq][i];
+  numsymbol++;
+  Vector states;
+  int L = list_data_seq[maxseq].length();
+  states.Init(L);
+  for (int i = 0; i < L; i++) states[i] = rand() % numstate;
+  hmm_estimateD_init(numsymbol, numstate, list_data_seq[maxseq], states, &transmission_, &emission_);  
+}
+
+void DiscreteHMM::LoadProfile(const char* profile) {
+  transmission_.Destruct();
+  emission_.Destruct();
+  InitFromFile(profile);
+}
+
+void DiscreteHMM::SaveProfile(const char* profile) const {
+  TextWriter w_pro;
+  if (!PASSED(w_pro.Open(profile))) {
+    NONFATAL("Couldn't open '%s' for writing.", profile);
+    return;
+  }
+
+  print_matrix(w_pro, transmission_, "%% transmision", "%f,");
+  print_matrix(w_pro, emission_, "%% emission", "%f,");
+}
+
+void DiscreteHMM::GenerateSequence(int length, Vector* data_seq, Vector* state_seq) const {
+  hmm_generateD_init(length, transmission_, emission_, data_seq, state_seq);
+}
+
+void DiscreteHMM::EstimateModel(const Vector& data_seq, const Vector& state_seq) {
+  transmission_.Destruct();
+  emission_.Destruct();
+  hmm_estimateD_init(data_seq, state_seq, &transmission_, &emission_);
+}
+
+void DiscreteHMM::EstimateModel(int numstate, int numsymbol, const Vector& data_seq, const Vector& state_seq) {
+  transmission_.Destruct();
+  emission_.Destruct();
+  hmm_estimateD_init(numsymbol, numstate, data_seq, state_seq, &transmission_, &emission_);
+}
+
+void DiscreteHMM::DecodeOverwrite(const Vector& data_seq, Matrix* state_prob_mat, Matrix* forward_prob_mat, Matrix* backward_prob_mat, Vector* scale_vec) const {
+  hmm_decodeD(data_seq, transmission_, emission_, state_prob_mat, forward_prob_mat, backward_prob_mat, scale_vec);
+}
+
+void DiscreteHMM::DecodeInit(const Vector& data_seq, Matrix* state_prob_mat, Matrix* forward_prob_mat, Matrix* backward_prob_mat, Vector* scale_vec) const {
+  int M = transmission_.n_rows();
+  int L = data_seq.length();
+  state_prob_mat->Init(M, L);
+  forward_prob_mat->Init(M, L);
+  backward_prob_mat->Init(M, L);
+  scale_vec->Init(L);
+  hmm_decodeD(data_seq, transmission_, emission_, state_prob_mat, forward_prob_mat, backward_prob_mat, scale_vec);
+}
+
+void forward_procedure(const Vector& seq, const Matrix& trans, const Matrix& emis, Vector *scales, Matrix* fs);
+
+double DiscreteHMM::ComputeLogLikelihood(const Vector& data_seq) const {
+  int L = data_seq.length();
+  int M = transmission_.n_rows();
+  Matrix fs(M, L);
+  Vector sc;
+  sc.Init(L);
+  forward_procedure(data_seq, transmission_, emission_, &sc, &fs);
+  double loglik = 0;
+  for (int t = 0; t < L; t++)
+    loglik += log(sc[t]);
+  return loglik;
+}
+
+void DiscreteHMM::ComputeLogLikelihood(const ArrayList<Vector>& list_data_seq, ArrayList<double>* list_likelihood) const {
+  int L = 0;
+  for (int i = 0; i < list_data_seq.size(); i++)
+    if (list_data_seq[i].length() > L) L = list_data_seq[i].length();
+  int M = transmission_.n_rows();
+  Matrix fs(M, L);
+  Vector sc;
+  sc.Init(L);
+  list_likelihood->Init();
+  for (int i = 0; i < list_data_seq.size(); i++) {
+    forward_procedure(list_data_seq[i], transmission_, emission_, &sc, &fs);
+    int L = list_data_seq[i].length();
+    double loglik = 0;
+    for (int t = 0; t < L; t++)
+      loglik += log(sc[t]);
+    list_likelihood->AddBackItem(loglik);
+  }
+}
+
+void DiscreteHMM::ComputeViterbiStateSequence(const Vector& data_seq, Vector* state_seq) const {
+  hmm_viterbiD_init(data_seq, transmission_, emission_, state_seq);
+}
+
+void DiscreteHMM::TrainBaumWelch(const ArrayList<Vector>& list_data_seq, int max_iteration, double tolerance) {
+  hmm_trainD(list_data_seq, &transmission_, &emission_, max_iteration, tolerance);
+}
+
+void DiscreteHMM::TrainViterbi(const ArrayList<Vector>& list_data_seq, int max_iteration, double tolerance) {
+  hmm_train_viterbiD(list_data_seq, &transmission_, &emission_, max_iteration, tolerance);
+}
+
 void hmm_generateD_init(int L, const Matrix& trans, const Matrix& emis, Vector* seq, Vector* states) {
   DEBUG_ASSERT_MSG((trans.n_rows()==trans.n_cols() && trans.n_rows()==emis.n_rows()), "hmm_generateD_init: matrices sizes do not match");
   Matrix trsum, esum;
@@ -110,6 +247,7 @@ void forward_procedure(const Vector& seq, const Matrix& trans, const Matrix& emi
   Vector& s_ = *scales;
 
   fs_.SetZero();
+  s_.SetZero();
   // NOTE: start state is 0
   // time t = 0
   int e = (int) seq[0];
@@ -161,9 +299,7 @@ double hmm_decodeD(const Vector& seq, const Matrix& trans, const Matrix& emis, M
 		    M==trans.n_cols() && M==emis.n_rows()),"hmm_decodeD: sizes do not match");
   
   Matrix& ps_ = *pstates;
-
   Vector& s_ = *scales;
-  s_.SetZero();
 
   forward_procedure(seq, trans, emis, &s_, fs);
   backward_procedure(seq, trans, emis, s_, bs);
diff --git a/fastlib/u/tqlong/hmm/discreteHMM.h b/fastlib/u/tqlong/hmm/discreteHMM.h
index 2034b63dfa..595c1837a6 100644
--- a/fastlib/u/tqlong/hmm/discreteHMM.h
+++ b/fastlib/u/tqlong/hmm/discreteHMM.h
@@ -1,8 +1,100 @@
+/**
+ * @file discreteHMM.h
+ *
+ * This file contains functions of a discrete Hidden Markov Models (in C) and a
+ * wrapper class (in C++) for these functions. It implements log-likelihood
+ * computation, viterbi algorithm for the most probable sequence, Baum-Welch
+ * algorithm and Viterbi-like algorithm for parameter estimation. It can also
+ * generate sequences from a Hidden Markov  Model.
+ */
+
 #ifndef FASTLIB_DISCRETE_HMM_H
 #define FASTLIB_DISCRETE_HMM_H
 
 #include "fastlib/fastlib.h"
 
+/**
+ * A wrapper class for HMM functionals in discrete case
+ * 
+ * This class maintains transition probabilities and emission probabilities
+ * matrices and performs basic HMM functionals, more details below.
+ * 
+ */
+
+class DiscreteHMM {
+  /////////// Member variables /////////////////////////////////////
+ private:
+  /** Transmission probabilities matrix between states */
+  Matrix transmission_;
+
+  /** Emission probabilities in each state */
+  Matrix emission_;
+
+ public:
+  /** Basic getters */
+  const Matrix& getTransmission() const { return transmission_; }
+  const Matrix& getEmission() const { return emission_; }
+
+  /** Setters used when already initialized */
+  void setModel(const Matrix& transmission, const Matrix& emission);
+
+  /** Initializes from computed transmission and emission matrices */
+  void Init(const Matrix& transmission, const Matrix& emission);
+
+  /** Initializes by loading from a file */
+  void InitFromFile(const char* profile);
+
+  /** Initializes randomly using data as a guide */
+  void InitFromData(const ArrayList<Vector>& list_data_seq, int numstate);
+
+  /** Load from file, used when already initialized */
+  void LoadProfile(const char* profile);
+
+  /** Save matrices to file */
+  void SaveProfile(const char* profile) const;
+
+  /** Generate a random data sequence of a given length */
+  void GenerateSequence(int length, Vector* data_seq, Vector* state_seq) const;
+
+  /** 
+   * Estimate the matrices by a data sequence and a state sequence 
+   * Must be already initialized
+   */
+  void EstimateModel(const Vector& data_seq, const Vector& state_seq);
+  void EstimateModel(int numstate, int numsymbol, const Vector& data_seq, const Vector& state_seq);
+
+  /** 
+   * Decode a sequence into probabilities of each state at each time step
+   * using scaled forward-backward algorithm.
+   * Also return forward, backward probabilities and scale factors
+   */
+  void DecodeOverwrite(const Vector& data_seq, Matrix* state_prob_mat, Matrix* forward_prob_mat, Matrix* backward_prob_mat, Vector* scale_vec) const;
+
+  /** A decode version that initialized the out matrices */
+  void DecodeInit(const Vector& data_seq, Matrix* state_prob_mat, Matrix* forward_prob_mat, Matrix* backward_prob_mat, Vector* scale_vec) const;
+
+  /** Compute the log-likelihood of a sequence */
+  double ComputeLogLikelihood(const Vector& data_seq) const;
+
+  /** Compute the log-likelihood of a list of sequences */
+  void ComputeLogLikelihood(const ArrayList<Vector>& list_data_seq, ArrayList<double>* list_likelihood) const;
+
+  /** Compute the most probable sequence (Viterbi) */
+  void ComputeViterbiStateSequence(const Vector& data_seq, Vector* state_seq) const;
+
+  /** 
+   * Train the model with a list of sequences, must be already initialized 
+   * using Baum-Welch EM algorithm
+   */
+  void TrainBaumWelch(const ArrayList<Vector>& list_data_seq, int max_iteration = 500, double tolerance = 1e-3);
+
+  /** 
+   * Train the model with a list of sequences, must be already initialized 
+   * using Viterbi algorithm to determine the state sequence of each sequence
+   */
+  void TrainViterbi(const ArrayList<Vector>& list_data_seq, int max_iteration = 500, double tolerance = 1e-3);
+};
+
 /**
 Generating a sequence and states using transition and emission probabilities.
 L: sequence length
@@ -13,7 +105,6 @@ states: uninitialized vector, will have length L
 */
 void hmm_generateD_init(int L, const Matrix& trans, const Matrix& emis, Vector* seq, Vector * states);
 
-
 /** Estimate transition and emission probabilities from sequence and states
  */
 void hmm_estimateD_init(const Vector& seq, const Vector& states, Matrix* trans, Matrix* emis);
diff --git a/fastlib/u/tqlong/hmm/gaussianHMM.cc b/fastlib/u/tqlong/hmm/gaussianHMM.cc
index 63399d9eb6..1540713ca7 100644
--- a/fastlib/u/tqlong/hmm/gaussianHMM.cc
+++ b/fastlib/u/tqlong/hmm/gaussianHMM.cc
@@ -2,12 +2,104 @@
 #include "support.h"
 #include "gaussianHMM.h"
 
+void GaussianHMM::InitFromFile(const char* profile) {
+  if (!PASSED(load_profileG(profile, &transmission_, &list_mean_vec_, &list_covariance_mat_)))
+    FATAL("Couldn't open '%s' for reading.", profile);
+  list_inverse_cov_mat_.Copy(list_covariance_mat_);
+  gauss_const_vec_.Init(list_covariance_mat_.size());
+  CalculateInverse();
+}
+
+void GaussianHMM::InitFromData(const ArrayList<Matrix>& list_data_seq, int numstate) {
+  init_gauss_param(numstate, list_data_seq, &transmission_, &list_mean_vec_, &list_covariance_mat_);
+  list_inverse_cov_mat_.Copy(list_covariance_mat_);
+  gauss_const_vec_.Init(list_covariance_mat_.size());
+  CalculateInverse();
+}
+
+void GaussianHMM::LoadProfile(const char* profile) {
+  transmission_.Destruct();
+  list_mean_vec_.Destruct();
+  list_covariance_mat_.Destruct();
+  list_inverse_cov_mat_.Destruct();
+  gauss_const_vec_.Destruct();
+  InitFromFile(profile);
+}
+
+void GaussianHMM::SaveProfile(const char* profile) {
+  save_profileG(profile, transmission_, list_mean_vec_, list_covariance_mat_);
+}
+
+void GaussianHMM::CalculateInverse() {
+  int M = transmission_.n_rows();
+  int N = list_mean_vec_[0].length();
+  for (int i = 0; i < M; i++) {
+    la::InverseOverwrite(list_covariance_mat_[i], &list_inverse_cov_mat_[i]);
+    gauss_const_vec_[i] = pow(2.0*math::PI, -N/2.0) * pow(la::Determinant(list_covariance_mat_[i]), -0.5);
+  }
+}
+
+void GaussianHMM::GenerateSequence(int L, Matrix* data_seq, Vector* state_seq) {
+  hmm_generateG_init(L, transmission_, list_mean_vec_, list_covariance_mat_, data_seq, state_seq);
+}
+
+void forward_procedure(int L, const Matrix& trans, const Matrix& emis_prob, Vector *scales, Matrix* fs);
+
+double GaussianHMM::ComputeLogLikelihood(const Matrix& data_seq) {
+  int L = data_seq.n_cols();
+  int M = transmission_.n_rows();
+  Matrix fs(M, L), emis_prob(M, L);
+  Vector sc;
+  sc.Init(L);
+  hmm_cal_emis_prob(data_seq, list_mean_vec_, list_inverse_cov_mat_, gauss_const_vec_, &emis_prob);
+  forward_procedure(L, transmission_, emis_prob, &sc, &fs);
+  double loglik = 0;
+  for (int t = 0; t < L; t++)
+    loglik += log(sc[t]);
+  return loglik;
+}
+
+void GaussianHMM::ComputeLogLikelihood(const ArrayList<Matrix>& list_data_seq, ArrayList<double>* list_likelihood) {
+  int L = 0;
+  for (int i = 0; i < list_data_seq.size(); i++)
+    if (list_data_seq[i].n_cols() > L) L = list_data_seq[i].n_cols();
+  int M = transmission_.n_rows();
+  Matrix fs(M, L), emis_prob(M, L);
+  Vector sc;
+  sc.Init(L);
+  list_likelihood->Init();
+  for (int i = 0; i < list_data_seq.size(); i++) {
+    int L = list_data_seq[i].n_cols();
+    hmm_cal_emis_prob(list_data_seq[i], list_mean_vec_, list_inverse_cov_mat_, gauss_const_vec_, &emis_prob);
+    forward_procedure(L, transmission_, emis_prob, &sc, &fs);
+    double loglik = 0;
+    for (int t = 0; t < L; t++)
+      loglik += log(sc[t]);
+    list_likelihood->AddBackItem(loglik);
+  }
+}
+
+void GaussianHMM::ComputeViterbiStateSequence(const Matrix& data_seq, Vector* state_seq) {
+  int M = transmission_.n_rows();
+  int L = data_seq.n_cols();
+  Matrix emis_prob(M, L);
+  hmm_cal_emis_prob(data_seq, list_mean_vec_, list_inverse_cov_mat_, gauss_const_vec_, &emis_prob);
+  hmm_viterbiG_init(transmission_, emis_prob, state_seq);
+}
+
+void GaussianHMM::TrainBaumWelch(const ArrayList<Matrix>& list_data_seq, int max_iteration, double tolerance) {
+  hmm_trainG(list_data_seq, &transmission_, &list_mean_vec_, &list_covariance_mat_, max_iteration, tolerance);
+}
+
+void GaussianHMM::TrainViterbi(const ArrayList<Matrix>& list_data_seq, int max_iteration, double tolerance) {
+  hmm_train_viterbiG(list_data_seq, &transmission_, &list_mean_vec_, &list_covariance_mat_, max_iteration, tolerance);
+}
+
 success_t load_profileG(const char* profile, Matrix* trans, ArrayList<Vector>* means, ArrayList<Matrix>* covs) {
   ArrayList<Matrix> matlst;
-  if (!PASSED(load_matrix_list(profile, &matlst))) {
-    NONFATAL("Couldn't open '%s' for reading.", profile);
+  if (!PASSED(load_matrix_list(profile, &matlst)))
     return SUCCESS_FAIL;
-  }
+
   DEBUG_ASSERT(matlst.size() > 0);
   trans->Copy(matlst[0]);
   means->Init();
@@ -35,15 +127,15 @@ success_t save_profileG(const char* profile, const Matrix& trans, const ArrayLis
   int M = trans.n_rows(); // num of states
   DEBUG_ASSERT(means.size() == M && covs.size() == M);
   int N = means[0].length(); // dimension
-  print_matrix(w_pro, trans, "% transmission", "%E,");
+  print_matrix(w_pro, trans, "% transmission", "%f,");
   for (int i = 0; i < M; i++) {
     DEBUG_ASSERT(means[i].length() == N);
     DEBUG_ASSERT(covs[i].n_rows()==N && covs[i].n_cols()==N);
     char s[100];
     sprintf(s, "%% mean - state %d", i);
-    print_vector(w_pro, means[i], s, "%E,");    
+    print_vector(w_pro, means[i], s, "%f,");    
     sprintf(s, "%% covariance - state%d", i);
-    print_matrix(w_pro, covs[i], s, "%E,");    
+    print_matrix(w_pro, covs[i], s, "%f,");    
   }
   return SUCCESS_PASS;
 }
@@ -175,6 +267,7 @@ void forward_procedure(int L, const Matrix& trans, const Matrix& emis_prob, Vect
   Vector& s_ = *scales;
 
   fs_.SetZero();
+  s_.SetZero();
   // NOTE: start state is 0
   // time t = 0
   for (int i = 0; i < M; i++) {
@@ -221,9 +314,7 @@ double hmm_decodeG(int L, const Matrix& trans, const Matrix& emis_prob, Matrix*
 		    M==trans.n_cols() && M==emis_prob.n_rows()),"hmm_decodeG: sizes do not match");
   
   Matrix& ps_ = *pstates;
-
   Vector& s_ = *scales;
-  s_.SetZero();
 
   forward_procedure(L, trans, emis_prob, &s_, fs);
   backward_procedure(L, trans, emis_prob, s_, bs);
@@ -359,7 +450,8 @@ void init_gauss_param(int M, const ArrayList<Matrix>& seqs, Matrix* guessTR, Arr
       la::MulExpert(1, false, tmp_cov, true, tmp_cov, 1, &gCO[i]);
     }
   }
-  
+  //printf("---3---\n");
+
   for (int i = 0; i < M; i++) 
     if (sumState[i] == 0) {
       for (int j = 0; j < M; j++) gTR.ref(i, j) = 0;
@@ -373,6 +465,7 @@ void init_gauss_param(int M, const ArrayList<Matrix>& seqs, Matrix* guessTR, Arr
       la::Scale(1.0/sumState[i], &gCO[i]);
       for (int j = 0; j < N; j++) gCO[i].ref(j, j) += 1e-3; // make sure the diagonal elements are not too small
     }
+  //printf("---4---\n");
 }
 
 void hmm_train_viterbiG(const ArrayList<Matrix>& seqs, Matrix* guessTR, ArrayList<Vector>* guessME, ArrayList<Matrix>* guessCO, int max_iter, double tol) {
@@ -554,10 +647,8 @@ void hmm_trainG(const ArrayList<Matrix>& seqs, Matrix* guessTR, ArrayList<Vector
 	  sumState[i] += ps.get(i, t);
 	  la::AddExpert(ps.get(i, t), e, &ME[i]);
 
-	  Vector d;
-	  la::SubInit(e, gME[i], &d);
 	  Matrix D;
-	  D.AliasColVector(d);
+	  D.AliasColVector(e);
 	  la::MulExpert(ps.get(i, t), false, D, true, D, 1.0, &CO[i]);
 	}
       }
@@ -578,7 +669,10 @@ void hmm_trainG(const ArrayList<Matrix>& seqs, Matrix* guessTR, ArrayList<Vector
       
       if (sumState[i] != 0) {
 	la::ScaleOverwrite(1.0/sumState[i], ME[i], &gME[i]);
-	la::ScaleOverwrite(1.0/sumState[i], CO[i], &gCO[i]);
+	Matrix D;
+	D.AliasColVector(gME[i]);
+	la::MulExpert(-1.0, false, D, true, D, 1.0/sumState[i], &CO[i]);
+	gCO[i].CopyValues(CO[i]);
       }
     }
     // end re-estimate
diff --git a/fastlib/u/tqlong/hmm/gaussianHMM.h b/fastlib/u/tqlong/hmm/gaussianHMM.h
index 06ce5a7588..947214b5eb 100644
--- a/fastlib/u/tqlong/hmm/gaussianHMM.h
+++ b/fastlib/u/tqlong/hmm/gaussianHMM.h
@@ -3,6 +3,37 @@
 
 #include "fastlib/fastlib.h"
 
+/**
+ * A wrapper class for HMM functionals in single Gaussian case
+ */
+
+class GaussianHMM {
+ private:
+  Matrix transmission_;
+  ArrayList<Vector> list_mean_vec_;
+  ArrayList<Matrix> list_covariance_mat_;
+  
+  ArrayList<Matrix> list_inverse_cov_mat_;
+  Vector gauss_const_vec_;
+  void CalculateInverse();
+ public:
+  void InitFromFile(const char* profile);
+  void InitFromData(const ArrayList<Matrix>& list_data_seq, int numstate);
+
+  void LoadProfile(const char* profile);
+  void SaveProfile(const char* profile);
+
+  void GenerateSequence(int L, Matrix* data_seq, Vector* state_seq);
+
+  double ComputeLogLikelihood(const Matrix& data_seq);
+  void ComputeLogLikelihood(const ArrayList<Matrix>& list_data_seq, ArrayList<double>* list_likelihood);
+
+  void ComputeViterbiStateSequence(const Matrix& data_seq, Vector* state_seq);
+
+  void TrainBaumWelch(const ArrayList<Matrix>& list_data_seq, int max_iteration = 500, double tolerance = 1e-3);
+  void TrainViterbi(const ArrayList<Matrix>& list_data_seq, int max_iteration = 500, double tolerance = 1e-3);
+};
+
 success_t load_profileG(const char* profile, Matrix* trans, ArrayList<Vector>* means, ArrayList<Matrix>* covs);
 success_t save_profileG(const char* profile, const Matrix& trans, const ArrayList<Vector>& means, const ArrayList<Matrix>& covs);
 /**
diff --git a/fastlib/u/tqlong/hmm/generate.cc b/fastlib/u/tqlong/hmm/generate.cc
new file mode 100644
index 0000000000..24272425f7
--- /dev/null
+++ b/fastlib/u/tqlong/hmm/generate.cc
@@ -0,0 +1,188 @@
+#include "fastlib/fastlib.h"
+#include "support.h"
+#include "discreteHMM.h"
+#include "gaussianHMM.h"
+#include "mixgaussHMM.h"
+#include "mixtureDST.h"
+
+success_t generate_discrete();
+success_t generate_gaussian();
+success_t generate_mixture();
+void usage();
+
+int main(int argc, char* argv[]) {
+  fx_init(argc, argv);
+  success_t s = SUCCESS_PASS;
+  if (fx_param_exists(NULL,"type")) {
+    const char* type = fx_param_str_req(NULL, "type");
+    if (strcmp(type, "discrete")==0)
+      s = generate_discrete();
+    else if (strcmp(type, "gaussian")==0) 
+      s = generate_gaussian();
+    else if (strcmp(type, "mixture")==0) 
+      s = generate_mixture();
+    else {
+      printf("Unrecognized type: must be: discrete | gaussian | mixture !!!\n");
+      return SUCCESS_PASS;
+    }
+  }
+  else {
+    printf("Unrecognized type: must be: discrete | gaussian | mixture  !!!\n");
+    s = SUCCESS_FAIL;
+  }
+  if (!PASSED(s)) usage();
+  fx_done();
+}
+
+void usage() {
+  printf("\nUsage:\n");
+  printf("  generate --type=={discrete|gaussian|mixture} OPTIONS\n");
+  printf("[OPTIONS]\n");
+  printf("  --profile=file   : file contains HMM profile\n");
+  printf("  --length=NUM     : sequence length\n");
+  printf("  --lenmax=NUM     : maximum sequence length, default = length\n");
+  printf("  --numseq=NUM     : number of sequence\n");
+  printf("  --seqfile=file   : output file for generated sequences\n");
+  printf("  --statefile=file : output file for generated state sequences\n");
+}
+
+success_t generate_mixture() {
+  if (!fx_param_exists(NULL, "profile")) {
+    printf("--profile must be defined.\n");
+    return SUCCESS_FAIL;
+  }
+  const char* profile = fx_param_str_req(NULL, "profile");
+  const int seqlen = fx_param_int(NULL, "length", 10);
+  const int seqlmax = fx_param_int(NULL, "lenmax", seqlen);
+  const int numseq = fx_param_int(NULL, "numseq", 10);
+  const char* seqout = fx_param_str(NULL, "seqfile", "seq.mix.out");
+  const char* stateout = fx_param_str(NULL, "statefile", "state.mix.out");
+
+  DEBUG_ASSERT_MSG(seqlen <= seqlmax, "LENMAX must bigger than LENGTH");
+  DEBUG_ASSERT_MSG(numseq > 0, "NUMSEQ must be positive");
+
+  double step = (double) (seqlmax-seqlen) / numseq;
+
+  MixtureofGaussianHMM hmm;
+  hmm.InitFromFile(profile);
+  
+  TextWriter w_seq, w_state;
+  if (!PASSED(w_seq.Open(seqout))) {
+    NONFATAL("Couldn't open '%s' for writing.", seqout);
+    return SUCCESS_FAIL;
+  }
+
+  if (!PASSED(w_state.Open(stateout))) {
+    NONFATAL("Couldn't open '%s' for writing.", stateout);
+    return SUCCESS_FAIL;
+  }
+
+  double L = seqlen;
+  for (int i = 0; i < numseq; i++, L+=step) {
+    Matrix seq;
+    Vector states;
+    char s[100];
+
+    hmm.GenerateSequence((int)L, &seq, &states);
+    
+    sprintf(s, "%% sequence %d", i);
+    print_matrix(w_seq, seq, s, "%E,");    
+    sprintf(s, "%% state sequence %d", i);
+    print_vector(w_state, states, s, "%.0f,");    
+  }
+  //printf("---END---");
+  return SUCCESS_PASS;
+}
+
+success_t generate_gaussian() {
+  if (!fx_param_exists(NULL, "profile")) {
+    printf("--profile must be defined.\n");
+    return SUCCESS_FAIL;
+  }
+  const char* profile = fx_param_str_req(NULL, "profile");
+  const int seqlen = fx_param_int(NULL, "length", 10);
+  const int seqlmax = fx_param_int(NULL, "lenmax", seqlen);
+  const int numseq = fx_param_int(NULL, "numseq", 10);
+  const char* seqout = fx_param_str(NULL, "seqfile", "seq.gauss.out");
+  const char* stateout = fx_param_str(NULL, "statefile", "state.gauss.out");
+
+  DEBUG_ASSERT_MSG(seqlen <= seqlmax, "LENMAX must bigger than LENGTH");
+  DEBUG_ASSERT_MSG(numseq > 0, "NUMSEQ must be positive");
+
+  double step = (double) (seqlmax-seqlen) / numseq;
+
+  GaussianHMM hmm;
+  hmm.InitFromFile(profile);
+  
+  TextWriter w_seq, w_state;
+  if (!PASSED(w_seq.Open(seqout))) {
+    NONFATAL("Couldn't open '%s' for writing.", seqout);
+    return SUCCESS_FAIL;
+  }
+
+  if (!PASSED(w_state.Open(stateout))) {
+    NONFATAL("Couldn't open '%s' for writing.", stateout);
+    return SUCCESS_FAIL;
+  }
+
+  double L = seqlen;
+  for (int i = 0; i < numseq; i++, L+=step) {
+    Matrix seq;
+    Vector states;
+    char s[100];
+
+    hmm.GenerateSequence((int)L, &seq, &states);
+    
+    sprintf(s, "%% sequence %d", i);
+    print_matrix(w_seq, seq, s, "%E,");    
+    sprintf(s, "%% state sequence %d", i);
+    print_vector(w_state, states, s, "%.0f,");    
+  }
+  return SUCCESS_PASS;
+}
+
+success_t generate_discrete() {
+  if (!fx_param_exists(NULL, "profile")) {
+    printf("--profile must be defined.\n");
+    return SUCCESS_FAIL;
+  }
+  const char* profile = fx_param_str_req(NULL, "profile");
+  const int seqlen = fx_param_int(NULL, "length", 10);
+  const int seqlmax = fx_param_int(NULL, "lenmax", seqlen);
+  const int numseq = fx_param_int(NULL, "numseq", 10);
+  const char* seqout = fx_param_str(NULL, "seqfile", "seq.out");
+  const char* stateout = fx_param_str(NULL, "statefile", "state.out");
+
+  DEBUG_ASSERT_MSG(seqlen <= seqlmax, "LENMAX must bigger than LENGTH");
+  DEBUG_ASSERT_MSG(numseq > 0, "NUMSEQ must be positive");
+
+  double step = (double) (seqlmax-seqlen) / numseq;
+
+  DiscreteHMM hmm;
+  hmm.InitFromFile(profile);
+
+  TextWriter w_seq, w_state;
+  if (!PASSED(w_seq.Open(seqout))) {
+    NONFATAL("Couldn't open '%s' for writing.", seqout);
+    return SUCCESS_FAIL;
+  }
+
+  if (!PASSED(w_state.Open(stateout))) {
+    NONFATAL("Couldn't open '%s' for writing.", stateout);
+    return SUCCESS_FAIL;
+  }
+
+  double L = seqlen;
+  for (int i = 0; i < numseq; i++, L+=step) {
+    Vector seq, states;
+    char s[100];
+
+    hmm.GenerateSequence((int)L, &seq, &states);
+    
+    sprintf(s, "%% sequence %d", i);
+    print_vector(w_seq, seq, s, "%.0f,");    
+    sprintf(s, "%% state sequence %d", i);
+    print_vector(w_state, states, s, "%.0f,");    
+  }
+  return SUCCESS_PASS;
+}
diff --git a/fastlib/u/tqlong/hmm/log.out b/fastlib/u/tqlong/hmm/log.out
index a65c6739da..7603de7d36 100644
--- a/fastlib/u/tqlong/hmm/log.out
+++ b/fastlib/u/tqlong/hmm/log.out
@@ -1,4 +1,10 @@
-% Loglik sequence 0
- -8188.898628
-% Loglik sequence 1
- -8206.808088
+-1.209996E+01
+-1.570194E+01
+-1.793114E+01
+-1.236031E+01
+-1.290610E+01
+-1.787109E+01
+-1.623056E+01
+-1.853745E+01
+-1.786704E+01
+-1.615386E+01
diff --git a/fastlib/u/tqlong/hmm/loglik.cc b/fastlib/u/tqlong/hmm/loglik.cc
new file mode 100644
index 0000000000..e5a51ace86
--- /dev/null
+++ b/fastlib/u/tqlong/hmm/loglik.cc
@@ -0,0 +1,136 @@
+#include "fastlib/fastlib.h"
+#include "support.h"
+#include "discreteHMM.h"
+#include "gaussianHMM.h"
+#include "mixgaussHMM.h"
+#include "mixtureDST.h"
+
+success_t loglik_discrete();
+success_t loglik_gaussian();
+success_t loglik_mixture();
+void usage();
+
+int main(int argc, char* argv[]) {
+  fx_init(argc, argv);
+  success_t s = SUCCESS_PASS;
+  if (fx_param_exists(NULL,"type")) {
+    const char* type = fx_param_str_req(NULL, "type");
+    if (strcmp(type, "discrete")==0)
+      s = loglik_discrete();
+    else if (strcmp(type, "gaussian")==0) 
+      s = loglik_gaussian();
+    else if (strcmp(type, "mixture")==0) 
+      s = loglik_mixture();
+    else {
+      printf("Unrecognized type: must be: discrete | gaussian | mixture !!!\n");
+      s = SUCCESS_FAIL;
+    }
+  }
+  else {
+    printf("Unrecognized type: must be: discrete | gaussian | mixture  !!!\n");
+    s = SUCCESS_FAIL;
+  }
+  if (!PASSED(s)) usage();
+  fx_done();
+}
+
+void usage() {
+  printf("\n"
+	 "Usage:\n"
+	 "  loglik --type=={discrete|gaussian|mixture} OPTIONS\n"
+	 "[OPTIONS]\n"
+	 "  --profile==file   : file contains HMM profile\n"
+	 "  --seqfile==file   : file contains input sequences\n"
+	 "  --logfile==file   : output file for log-likelihood of the sequences\n"
+	 );
+}
+
+success_t loglik_mixture() {
+  if (!fx_param_exists(NULL, "profile")) {
+    printf("--profile must be defined.\n");
+    return SUCCESS_FAIL;
+  }
+  const char* profile = fx_param_str_req(NULL, "profile");
+  const char* seqin = fx_param_str(NULL, "seqfile", "seq.mix.out");
+  const char* logout = fx_param_str(NULL, "logfile", "log.mix.out");
+
+  MixtureofGaussianHMM hmm;
+  hmm.InitFromFile(profile);
+
+  ArrayList<Matrix> seqs;
+  load_matrix_list(seqin, &seqs);
+
+  TextWriter w_log;
+  if (!PASSED(w_log.Open(logout))) {
+    NONFATAL("Couldn't open '%s' for writing.", logout);
+    return SUCCESS_FAIL;
+  }
+
+  ArrayList<double> list_loglik;
+  hmm.ComputeLogLikelihood(seqs, &list_loglik);
+
+  for (int i = 0; i < seqs.size(); i++)
+    w_log.Printf("%f\n", list_loglik[i]);
+  
+  return SUCCESS_PASS;
+}
+
+success_t loglik_gaussian() {
+  if (!fx_param_exists(NULL, "profile")) {
+    printf("--profile must be defined.\n");
+    return SUCCESS_FAIL;
+  }
+  const char* profile = fx_param_str_req(NULL, "profile");
+  const char* seqin = fx_param_str(NULL, "seqfile", "seq.gauss.out");
+  const char* logout = fx_param_str(NULL, "logfile", "log.gauss.out");
+
+  GaussianHMM hmm;
+  hmm.InitFromFile(profile);
+
+  ArrayList<Matrix> seqs;
+  load_matrix_list(seqin, &seqs);
+
+  TextWriter w_log;
+  if (!PASSED(w_log.Open(logout))) {
+    NONFATAL("Couldn't open '%s' for writing.", logout);
+    return SUCCESS_FAIL;
+  }
+
+  ArrayList<double> list_loglik;
+  hmm.ComputeLogLikelihood(seqs, &list_loglik);
+
+  for (int i = 0; i < seqs.size(); i++)
+    w_log.Printf("%f\n", list_loglik[i]);
+  
+  return SUCCESS_PASS;
+}
+
+success_t loglik_discrete() {
+  if (!fx_param_exists(NULL, "profile")) {
+    printf("--profile must be defined.\n");
+    return SUCCESS_FAIL;
+  }
+  const char* profile = fx_param_str_req(NULL, "profile");
+  const char* seqin = fx_param_str(NULL, "seqfile", "seq.out");
+  const char* logout = fx_param_str(NULL, "logfile", "log.out");
+
+  DiscreteHMM hmm;
+  hmm.InitFromFile(profile);
+
+  ArrayList<Vector> seqs;
+  load_vector_list(seqin, &seqs);
+
+  TextWriter w_log;
+  if (!PASSED(w_log.Open(logout))) {
+    NONFATAL("Couldn't open '%s' for writing.", logout);
+    return SUCCESS_FAIL;
+  }
+
+  ArrayList<double> list_loglik;
+  hmm.ComputeLogLikelihood(seqs, &list_loglik);
+
+  for (int i = 0; i < seqs.size(); i++)
+    w_log.Printf("%f\n", list_loglik[i]);
+  return SUCCESS_PASS;
+}
+
diff --git a/fastlib/u/tqlong/hmm/mixgaussHMM.cc b/fastlib/u/tqlong/hmm/mixgaussHMM.cc
index 13b5842a5e..914fedf67c 100644
--- a/fastlib/u/tqlong/hmm/mixgaussHMM.cc
+++ b/fastlib/u/tqlong/hmm/mixgaussHMM.cc
@@ -3,6 +3,77 @@
 #include "mixgaussHMM.h"
 #include "gaussianHMM.h"
 
+void MixtureofGaussianHMM::InitFromFile(const char* profile) {
+  if (!PASSED(load_profileM(profile, &transmission_, &list_mixture_gauss_)))
+    FATAL("Couldn't open '%s' for reading.", profile);
+}
+
+void MixtureofGaussianHMM::LoadProfile(const char* profile) {
+  transmission_.Destruct();
+  list_mixture_gauss_.Destruct();
+  InitFromFile(profile);
+}
+
+void MixtureofGaussianHMM::SaveProfile(const char* profile) {
+  save_profileM(profile, transmission_, list_mixture_gauss_);
+}
+
+void MixtureofGaussianHMM::GenerateSequence(int L, Matrix* data_seq, Vector* state_seq) {
+  hmm_generateM_init(L, transmission_, list_mixture_gauss_, data_seq, state_seq);
+}
+
+void forward_procedure(int L, const Matrix& trans, const Matrix& emis_prob, Vector *scales, Matrix* fs);
+
+double MixtureofGaussianHMM::ComputeLogLikelihood(const Matrix& data_seq) {
+  int L = data_seq.n_cols();
+  int M = transmission_.n_rows();
+  Matrix fs(M, L), emis_prob(M, L);
+  Vector sc;
+  sc.Init(L);
+  hmm_cal_emis_probM(data_seq, list_mixture_gauss_, &emis_prob);
+  forward_procedure(L, transmission_, emis_prob, &sc, &fs);
+  double loglik = 0;
+  for (int t = 0; t < L; t++)
+    loglik += log(sc[t]);
+  return loglik;
+}
+
+void MixtureofGaussianHMM::ComputeLogLikelihood(const ArrayList<Matrix>& list_data_seq, ArrayList<double>* list_likelihood) {
+  int L = 0;
+  for (int i = 0; i < list_data_seq.size(); i++)
+    if (list_data_seq[i].n_cols() > L) L = list_data_seq[i].n_cols();
+  int M = transmission_.n_rows();
+  Matrix fs(M, L), emis_prob(M, L);
+  Vector sc;
+  sc.Init(L);
+  list_likelihood->Init();
+  for (int i = 0; i < list_data_seq.size(); i++) {
+    int L = list_data_seq[i].n_cols();
+    hmm_cal_emis_probM(list_data_seq[i], list_mixture_gauss_, &emis_prob);
+    forward_procedure(L, transmission_, emis_prob, &sc, &fs);
+    double loglik = 0;
+    for (int t = 0; t < L; t++)
+      loglik += log(sc[t]);
+    list_likelihood->AddBackItem(loglik);
+  }
+}
+
+void MixtureofGaussianHMM::ComputeViterbiStateSequence(const Matrix& data_seq, Vector* state_seq) {
+  int M = transmission_.n_rows();
+  int L = data_seq.n_cols();
+  Matrix emis_prob(M, L);
+  hmm_cal_emis_probM(data_seq, list_mixture_gauss_, &emis_prob);
+  hmm_viterbiG_init(transmission_, emis_prob, state_seq);
+}
+
+void MixtureofGaussianHMM::TrainBaumWelch(const ArrayList<Matrix>& list_data_seq, int max_iteration, double tolerance) {
+  hmm_trainM(list_data_seq, &transmission_, &list_mixture_gauss_, max_iteration, tolerance);
+}
+
+void MixtureofGaussianHMM::TrainViterbi(const ArrayList<Matrix>& list_data_seq, int max_iteration, double tolerance) {
+  hmm_train_viterbiM(list_data_seq, &transmission_, &list_mixture_gauss_, max_iteration, tolerance);
+}
+
 success_t load_profileM(const char* profile, Matrix* trans, ArrayList<MixtureGauss>* mixs) {
   ArrayList<Matrix> matlst;
   if (!PASSED(load_matrix_list(profile, &matlst))) {
diff --git a/fastlib/u/tqlong/hmm/mixgaussHMM.h b/fastlib/u/tqlong/hmm/mixgaussHMM.h
index 54ff1a03b0..4efc60038b 100644
--- a/fastlib/u/tqlong/hmm/mixgaussHMM.h
+++ b/fastlib/u/tqlong/hmm/mixgaussHMM.h
@@ -4,6 +4,31 @@
 #include "fastlib/fastlib.h"
 #include "mixtureDST.h"
 
+class MixtureofGaussianHMM {
+ private:
+  Matrix transmission_;
+  ArrayList<MixtureGauss> list_mixture_gauss_;
+ public:
+  void InitFromFile(const char* profile);
+  void Init() {
+    transmission_.Init(0, 0);
+    list_mixture_gauss_.Init();
+  }
+
+  void LoadProfile(const char* profile);
+  void SaveProfile(const char* profile);
+
+  void GenerateSequence(int L, Matrix* data_seq, Vector* state_seq);
+
+  double ComputeLogLikelihood(const Matrix& data_seq);
+  void ComputeLogLikelihood(const ArrayList<Matrix>& list_data_seq, ArrayList<double>* list_likelihood);
+
+  void ComputeViterbiStateSequence(const Matrix& data_seq, Vector* state_seq);
+
+  void TrainBaumWelch(const ArrayList<Matrix>& list_data_seq, int max_iteration = 500, double tolerance = 1e-3);
+  void TrainViterbi(const ArrayList<Matrix>& list_data_seq, int max_iteration = 500, double tolerance = 1e-3);
+};
+
 success_t load_profileM(const char* profile, Matrix* trans, ArrayList<MixtureGauss>* mixs);
 success_t save_profileM(const char* profile, const Matrix& trans, const ArrayList<MixtureGauss>& mixs);
 
diff --git a/fastlib/u/tqlong/hmm/mixtureDST.h b/fastlib/u/tqlong/hmm/mixtureDST.h
index 0a27246360..708d2f3b93 100644
--- a/fastlib/u/tqlong/hmm/mixtureDST.h
+++ b/fastlib/u/tqlong/hmm/mixtureDST.h
@@ -60,13 +60,11 @@ class MixtureGauss {
     total++;
   }
   void accumulate(double p, int i, const Vector& v) {
-    ACC_prior[i] += p;
     la::AddExpert(p, v, &ACC_means[i]);
-    Vector d;
-    la::SubInit(v, means[i], &d);
-    Matrix D;
-    D.AliasColVector(d);
-    la::MulExpert(p, false, D, true, D, 1.0, &ACC_covs[i]);
+    Matrix V;
+    V.AliasColVector(v);
+    la::MulExpert(p, false, V, true, V, 1.0, &ACC_covs[i]);
+    ACC_prior[i] += p;
     total += p;
   }
   void end_accumulate_cluster() {
@@ -88,7 +86,10 @@ class MixtureGauss {
     for (int i = 0; i < means.size(); i++) {
       if (ACC_prior[i] != 0) {
 	la::ScaleOverwrite(1.0/ACC_prior[i], ACC_means[i], &means[i]);
-	la::ScaleOverwrite(1.0/ACC_prior[i], ACC_covs[i], &covs[i]);
+	Matrix M;
+	M.AliasColVector(means[i]);
+	la::MulExpert(-1.0, false, M, true, M, 1.0/ACC_prior[i], &ACC_covs[i]);
+	covs[i].CopyValues(ACC_covs[i]);
 	prior[i] = ACC_prior[i]/total;
 
 	double det = la::Determinant(covs[i]);
diff --git a/fastlib/u/tqlong/hmm/pro.dis.out b/fastlib/u/tqlong/hmm/pro.dis.out
index d7100b576c..d0ebccc87e 100644
--- a/fastlib/u/tqlong/hmm/pro.dis.out
+++ b/fastlib/u/tqlong/hmm/pro.dis.out
@@ -1,10 +1,10 @@
 %% transmision - Matrix (2 x 2) = 
-0.951217,0.000000,
-0.048783,1.000000,
+0.913559,0.000041,
+0.086441,0.999959,
 %% emission - Matrix (2 x 6) = 
-0.097562,0.124008,
-0.146342,0.122000,
-0.195122,0.122201,
-0.219511,0.121398,
-0.146342,0.117984,
-0.195122,0.392409,
+0.176788,0.032479,
+0.117033,0.002811,
+0.207122,0.000096,
+0.093345,0.052184,
+0.169223,0.048246,
+0.236488,0.864183,
diff --git a/fastlib/u/tqlong/hmm/pro.gauss.out b/fastlib/u/tqlong/hmm/pro.gauss.out
index 555d115c2d..6978b51471 100644
--- a/fastlib/u/tqlong/hmm/pro.gauss.out
+++ b/fastlib/u/tqlong/hmm/pro.gauss.out
@@ -1,13 +1,13 @@
 % transmission - Matrix (2 x 2) = 
-9.008915E-01,5.015528E-02,
-9.910846E-02,9.498447E-01,
+0.900891,0.050155,
+0.099109,0.949845,
 % mean - state 0 - Vector (2) = 
--1.558390E-02,-5.167563E-03,
+-0.015584,-0.005167,
 % covariance - state0 - Matrix (2 x 2) = 
-1.030753E+00,8.553333E-02,
-8.553333E-02,9.869338E-01,
+1.030753,0.085533,
+0.085533,0.986934,
 % mean - state 1 - Vector (2) = 
-1.971526E+00,1.991312E+00,
+1.971527,1.991312,
 % covariance - state1 - Matrix (2 x 2) = 
-1.027874E+00,1.014292E-01,
-1.014292E-01,1.015718E+00,
+1.027874,0.101429,
+0.101429,1.015717,
diff --git a/fastlib/u/tqlong/hmm/pro.mix.out b/fastlib/u/tqlong/hmm/pro.mix.out
index 6c65a4e91d..3cb9fc5f62 100644
--- a/fastlib/u/tqlong/hmm/pro.mix.out
+++ b/fastlib/u/tqlong/hmm/pro.mix.out
@@ -1,27 +1,27 @@
 % transmission - Matrix (2 x 2) = 
-9.013158E-01,4.244604E-02,
-9.868421E-02,9.575540E-01,
+9.011601E-01,4.249988E-02,
+9.883990E-02,9.575001E-01,
 % prior - state 0 - Vector (2) = 
-5.186639E-01,4.813361E-01,
+5.192414E-01,4.807586E-01,
 % mean 0 - state 0 - Vector (2) = 
--1.188965E-02,-3.753936E-03,
+-8.598510E-03,-2.119347E-03,
 % covariance 0 - state 0 - Matrix (2 x 2) = 
-9.505489E-01,5.312182E-02,
-5.312182E-02,1.016244E+00,
+9.602378E-01,5.660176E-02,
+5.660176E-02,1.017587E+00,
 % mean 1 - state 0 - Vector (2) = 
-2.486977E-03,4.987819E+00,
+-3.370962E-04,4.986145E+00,
 % covariance 1 - state 0 - Matrix (2 x 2) = 
-9.356824E-01,-2.942855E-02,
--2.942855E-02,1.143288E+00,
+9.294454E-01,-3.376060E-02,
+-3.376060E-02,1.142541E+00,
 % prior - state 1 - Vector (2) = 
-5.009455E-01,4.990545E-01,
+5.007006E-01,4.992994E-01,
 % mean 0 - state 1 - Vector (2) = 
-5.005855E+00,-5.875820E-03,
+5.006180E+00,-6.596133E-03,
 % covariance 0 - state 1 - Matrix (2 x 2) = 
-9.461827E-01,4.419038E-02,
-4.419038E-02,1.027436E+00,
+9.477496E-01,4.588493E-02,
+4.588493E-02,1.026848E+00,
 % mean 1 - state 1 - Vector (2) = 
-4.978663E+00,5.000389E+00,
+4.977038E+00,5.001125E+00,
 % covariance 1 - state 1 - Matrix (2 x 2) = 
-9.377226E-01,-5.077517E-02,
--5.077517E-02,9.670612E-01,
+9.423989E-01,-5.292953E-02,
+-5.292953E-02,9.673613E-01,
diff --git a/fastlib/u/tqlong/hmm/seq.out b/fastlib/u/tqlong/hmm/seq.out
index 01fb51bba0..732c63cd2a 100644
--- a/fastlib/u/tqlong/hmm/seq.out
+++ b/fastlib/u/tqlong/hmm/seq.out
@@ -1,4 +1,20 @@
-% sequence 0 - Vector (5000) = 
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,0,2,0,5,3,4,0,3,5,5,4,5,5,2,5,4,5,1,5,4,0,5,5,3,5,0,5,1,1,5,5,2,1,4,5,5,5,5,5,5,2,5,4,5,4,2,5,0,5,5,3,0,1,1,5,3,5,5,4,5,5,2,4,5,5,5,3,5,2,3,5,5,3,5,5,5,5,3,1,5,2,5,3,5,5,5,1,4,0,1,0,5,5,2,4,5,4,5,1,5,3,0,5,5,4,5,5,3,5,2,5,5,1,4,3,2,5,4,5,5,3,5,2,5,5,1,4,2,3,2,3,2,4,1,0,5,5,5,5,5,4,1,0,2,5,5,5,5,5,2,0,1,5,3,3,5,0,5,3,5,5,5,5,5,5,4,3,5,5,2,5,4,3,5,5,2,3,4,5,5,1,5,5,5,2,5,0,0,1,4,5,0,4,0,3,5,1,3,5,5,3,1,5,5,1,2,1,5,5,5,5,2,5,2,5,5,5,5,0,5,5,5,1,0,4,3,0,4,3,5,1,2,0,3,2,1,5,1,0,0,1,3,2,1,2,4,5,5,5,1,5,1,5,5,5,4,4,2,4,4,2,5,3,3,1,1,1,0,5,5,5,5,5,5,0,5,0,5,2,5,1,5,5,2,5,3,5,4,5,2,5,2,0,4,5,5,4,1,5,5,4,1,2,5,5,5,0,2,1,3,5,5,0,5,0,1,2,5,5,4,1,1,0,1,0,0,5,5,3,5,3,5,0,0,5,5,5,0,2,5,5,2,4,5,0,5,1,5,1,1,3,5,5,5,2,2,5,5,5,4,5,0,3,2,5,5,2,1,5,5,4,0,5,3,4,1,5,3,1,0,5,1,2,0,2,5,1,4,4,3,5,2,5,0,2,0,5,3,4,1,4,4,0,5,5,5,1,5,0,0,4,4,2,5,0,2,1,4,4,0,1,2,2,4,3,5,5,5,5,4,5,5,5,5,5,2,4,3,2,0,3,5,4,3,3,5,5,5,2,3,5,5,0,5,5,5,5,3,2,5,4,5,5,5,5,5,5,3,2,0,0,2,5,5,2,5,5,5,1,1,5,5,5,3,3,2,4,1,0,0,3,5,2,3,1,0,2,5,2,4,4,5,1,0,0,2,5,5,0,4,5,2,4,3,0,5,5,4,3,1,4,1,0,4,2,3,1,4,0,0,3,4,0,2,4,0,2,0,1,5,4,1,2,5,2,5,3,0,5,5,0,5,2,5,0,3,5,1,3,3,5,3,1,2,5,2,1,4,4,0,5,5,5,4,0,5,5,5,5,5,3,4,4,0,1,3,4,5,4,3,4,1,2,2,1,3,1,3,5,5,2,0,1,3,5,1,1,5,5,5,2,5,2,2,2,1,5,5,5,5,5,5,0,4,3,3,4,2,0,1,3,0,3,1,4,3,1,3,3,0,4,0,4,3,0,0,1,5,5,1,4,2,0,5,2,5,0,5,2,5,0,5,5,1,0,5,0,5,0,5,0,4,5,5,1,0,2,3,0,2,5,5,1,2,4,3,4,5,5,0,2,5,2,5,5,4,3,5,3,5,1,2,2,0,5,4,2,2,0,5,4,5,5,3,3,4,2,3,5,5,4,1,2,5,5,0,5,5,0,4,3,2,2,3,4,5,4,1,2,5,1,0,5,2,1,2,1,4,3,5,0,5,0,2,5,2,0,5,2,2,2,2,1,5,3,2,5,1,3,2,4,2,0,3,4,0,5,5,1,5,3,5,5,2,3,5,2,5,4,5,5,4,0,1,5,0,5,4,4,5,2,5,3,5,3,1,0,1,5,3,0,5,5,3,5,1,1,4,5,1,0,1,0,1,3,2,5,3,3,1,5,5,2,3,1,3,1,2,1,5,1,3,4,0,0,4,5,5,1,2,2,5,5,5,5,5,1,4,1,0,5,1,4,2,5,1,2,3,0,3,5,1,3,5,0,5,5,0,5,5,5,3,5,5,5,5,5,2,3,5,5,1,1,1,5,5,5,4,5,1,5,5,5,5,5,2,5,3,5,5,3,5,5,5,4,5,3,1,2,5,3,3,3,5,1,2,5,5,5,2,5,5,1,1,5,4,5,4,5,2,3,3,4,5,3,5,5,4,5,4,3,5,5,4,5,5,1,5,2,0,5,5,5,5,5,5,4,3,1,3,4,5,5,3,2,4,2,1,5,2,5,5,5,1,5,5,5,0,2,3,5,1,4,1,3,1,3,1,0,3,5,2,1,2,0,5,1,2,1,5,1,5,5,3,5,0,4,5,0,3,5,2,3,5,0,3,2,0,4,1,2,0,5,2,5,1,5,4,5,1,0,2,1,5,5,3,4,5,1,1,2,1,5,3,0,4,1,5,0,2,4,5,1,0,2,0,5,5,5,5,2,5,5,5,5,5,5,1,3,0,4,1,2,0,2,0,3,1,3,5,5,5,5,3,1,5,4,5,5,5,2,4,0,2,5,5,5,3,5,5,0,2,4,3,5,5,3,5,5,5,3,4,5,3,3,4,2,5,5,5,5,5,5,2,5,5,1,5,1,5,2,5,5,5,1,2,4,5,2,2,0,0,5,0,0,3,4,0,2,1,1,1,1,4,4,4,3,1,3,2,1,0,5,4,5,3,1,5,5,1,5,3,5,5,5,5,2,0,1,5,1,2,2,0,5,4,0,0,4,5,1,5,5,2,3,1,5,1,0,1,1,4,5,5,5,1,3,3,2,0,4,2,5,1,3,5,3,2,5,0,5,3,5,1,2,1,3,2,0,0,5,5,5,5,0,2,5,0,3,2,3,5,0,5,2,5,5,5,4,3,1,5,2,5,4,2,3,5,5,5,3,1,0,5,3,1,5,5,2,5,2,0,4,4,4,2,5,2,5,1,5,3,5,5,1,2,1,5,1,4,0,5,5,3,4,5,4,0,0,4,3,5,2,0,2,1,5,2,5,3,0,2,4,3,2,5,2,4,0,1,4,1,2,5,0,5,2,5,2,1,2,3,4,1,5,3,5,2,5,5,5,0,5,5,1,5,1,1,5,5,5,0,5,0,1,4,5,0,4,4,5,1,5,5,5,5,5,4,5,5,5,5,5,4,5,5,3,5,4,5,4,3,5,5,3,5,5,5,4,5,3,5,5,5,5,3,0,3,4,5,0,5,5,0,0,3,5,1,1,5,5,5,5,5,5,5,3,5,0,1,3,3,5,3,5,5,3,4,5,0,3,4,2,5,5,4,4,5,5,3,5,5,4,5,4,4,5,5,0,3,2,3,5,0,2,3,4,5,5,5,5,0,1,2,5,0,2,0,4,1,5,5,0,5,2,5,5,1,5,2,5,5,5,1,5,5,5,5,1,5,0,2,4,0,4,5,4,0,3,3,3,0,0,3,0,3,1,0,3,3,5,5,5,5,1,5,0,1,3,2,1,5,5,3,1,5,5,1,4,2,3,1,2,4,1,4,0,4,3,2,5,2,2,5,3,3,4,4,1,5,1,5,0,1,5,5,1,5,5,1,3,5,1,5,5,5,0,5,2,5,5,5,5,1,5,3,5,5,5,1,5,0,3,1,1,5,4,3,5,5,5,3,4,5,4,4,5,3,4,4,3,2,5,1,0,5,0,4,3,5,5,1,5,5,0,5,5,2,5,5,2,3,5,5,5,5,5,4,5,4,5,5,1,5,5,2,5,1,3,5,5,4,5,5,4,5,3,5,0,1,5,5,5,5,5,4,0,5,5,0,4,5,5,2,5,5,5,5,5,0,5,0,3,4,0,3,5,2,2,4,5,5,1,2,5,3,5,1,2,2,1,5,4,2,5,0,5,2,5,0,2,0,3,5,1,0,5,2,0,2,2,2,2,1,2,3,5,4,5,5,2,3,0,5,5,0,5,2,0,1,5,5,2,5,1,3,5,5,5,1,5,5,5,5,3,5,2,3,3,5,1,1,0,5,3,3,1,5,5,1,1,1,0,2,3,2,1,3,4,2,3,5,1,5,3,3,0,1,5,5,2,3,3,0,4,3,3,0,1,4,3,0,4,4,4,1,2,5,1,3,4,2,4,5,0,2,3,2,5,3,3,4,5,5,0,5,5,4,2,0,1,0,3,4,1,3,4,1,1,0,5,4,3,1,1,2,5,0,5,5,5,2,1,5,3,5,5,0,4,4,5,5,5,1,4,1,5,1,5,3,1,2,3,4,5,5,5,3,5,2,5,5,5,5,2,1,4,5,1,5,5,1,5,2,1,5,5,2,5,4,1,3,2,3,4,2,5,4,0,1,3,0,3,1,2,3,5,2,3,5,5,4,0,3,3,4,5,3,5,5,5,5,0,4,5,2,0,5,3,2,0,5,1,1,4,0,5,4,4,2,5,1,5,2,4,5,0,2,3,0,2,5,5,2,5,2,5,5,3,5,2,2,0,5,5,1,5,2,2,0,2,4,5,3,5,5,5,5,4,0,5,4,2,5,3,3,1,3,0,4,5,1,3,3,0,2,4,1,5,5,3,5,1,4,5,3,5,5,5,3,2,4,5,5,0,0,5,5,5,5,5,5,1,5,1,4,0,3,5,5,5,0,4,0,2,0,2,4,5,1,0,3,3,4,0,5,1,1,0,5,1,5,5,1,2,5,0,5,1,5,5,1,5,5,3,3,5,1,4,1,5,1,1,2,0,4,2,1,2,3,5,5,0,4,2,5,4,5,4,2,4,5,1,2,0,1,3,5,4,2,0,0,5,4,1,2,2,1,1,0,2,3,3,5,5,5,5,4,5,2,4,1,0,5,0,5,3,2,5,5,0,5,5,5,2,5,2,2,4,5,1,5,0,5,2,5,1,4,5,5,5,5,5,2,0,5,4,5,1,5,5,2,5,2,1,3,5,5,2,0,3,5,5,1,1,4,5,1,4,1,3,4,4,1,4,2,0,2,4,4,5,0,5,2,5,1,1,0,4,5,4,3,3,3,1,0,5,1,3,5,5,5,5,1,0,2,4,0,1,5,5,5,0,4,4,5,5,5,2,4,5,5,2,0,2,5,5,5,5,3,4,5,4,5,0,5,5,0,5,5,0,5,0,5,5,0,1,5,1,2,4,0,5,4,2,4,4,2,5,1,1,4,0,5,3,2,2,2,4,0,5,2,5,5,5,5,4,1,5,4,1,1,5,5,2,5,5,5,3,1,4,5,0,4,5,5,3,3,3,0,3,1,2,1,1,4,0,3,2,4,2,1,5,3,4,2,5,1,4,3,5,5,5,5,2,1,1,5,2,0,3,5,5,5,2,0,1,5,5,5,1,5,3,0,2,2,3,5,5,5,5,1,5,5,4,4,3,5,5,1,5,4,3,5,5,4,0,3,4,5,2,4,5,1,5,1,5,0,5,4,5,4,5,3,3,5,3,2,3,3,5,1,2,4,5,0,0,2,1,3,0,4,5,5,4,5,1,1,2,5,1,5,3,5,0,5,0,5,5,3,2,5,2,2,1,5,2,5,0,5,0,1,0,1,5,0,5,5,0,5,5,5,3,5,5,5,2,0,5,5,5,0,5,0,2,1,2,2,5,0,0,3,2,3,4,1,2,3,0,2,2,5,2,3,3,0,2,5,1,5,
-% sequence 1 - Vector (5000) = 
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,2,4,1,5,3,2,4,3,3,2,0,5,4,0,3,2,1,2,3,4,4,3,5,5,1,2,0,4,1,5,2,5,5,0,4,3,0,5,2,2,5,5,0,4,3,0,0,1,4,5,1,2,1,0,5,5,0,5,3,2,2,4,0,0,2,5,1,4,3,0,1,2,5,2,5,4,3,5,3,3,5,4,5,5,0,5,5,5,3,5,5,3,4,4,0,5,4,1,5,3,4,5,2,5,4,5,2,5,5,5,5,1,1,1,5,1,5,4,2,2,1,1,0,2,2,0,0,0,2,4,4,5,0,1,0,2,1,4,2,1,1,4,3,3,0,2,3,1,5,0,3,1,0,5,5,1,2,3,5,0,4,0,4,5,4,3,1,3,3,4,2,4,0,5,3,2,4,5,5,5,4,4,3,0,3,3,5,5,3,0,4,3,3,3,5,2,0,0,0,5,5,5,3,5,3,0,2,1,0,0,2,0,3,0,1,1,4,5,4,5,5,5,5,2,5,5,0,5,5,3,5,3,5,2,1,5,1,1,4,5,5,0,4,5,5,5,5,5,2,1,0,4,4,5,2,5,1,0,5,5,5,5,5,3,0,3,3,5,5,5,5,5,3,2,3,2,5,2,2,1,0,3,2,0,1,5,5,5,3,5,0,0,5,3,0,3,1,3,2,3,4,3,3,0,5,5,4,5,4,0,5,5,5,5,0,5,1,5,3,4,1,2,3,4,2,4,4,5,4,3,4,1,5,3,1,5,5,0,2,1,5,4,5,3,5,5,5,5,5,5,1,2,4,0,1,3,5,0,2,5,0,0,5,5,5,5,5,5,5,3,1,5,5,3,3,1,2,4,0,0,0,0,1,5,5,5,1,5,4,1,3,3,5,5,1,5,5,5,5,5,5,5,4,3,1,1,1,1,3,4,5,5,5,5,0,2,0,1,5,3,1,4,1,0,4,0,3,0,1,5,0,1,1,3,0,1,5,3,5,5,0,1,5,5,5,1,5,0,0,1,5,2,3,0,2,0,5,0,2,4,5,0,4,5,4,5,4,2,0,5,1,0,0,5,1,5,3,4,5,0,4,1,1,5,5,5,5,4,5,0,5,1,4,5,4,5,5,4,5,1,3,5,4,4,2,4,3,3,4,1,0,2,1,4,4,1,5,2,0,1,5,5,5,4,5,5,5,5,3,5,1,3,5,5,5,4,5,5,3,1,0,5,5,5,3,4,5,5,3,5,5,3,0,2,4,2,1,5,1,5,4,0,4,5,4,1,3,2,0,0,1,3,2,4,0,0,1,4,4,1,0,5,1,0,0,0,3,4,1,4,5,5,5,1,5,5,5,5,5,4,5,1,4,3,3,5,2,1,5,1,5,5,2,5,5,1,0,0,2,0,1,5,1,2,0,5,1,5,5,5,5,0,0,2,2,5,1,5,1,2,3,1,2,0,4,3,5,4,4,1,4,2,3,1,3,0,4,0,1,1,5,0,5,1,2,1,2,3,1,5,4,1,1,4,3,5,2,4,2,3,2,5,5,5,1,5,1,5,5,5,5,5,5,2,3,1,5,4,1,4,4,5,5,0,0,3,4,0,2,4,2,3,3,5,5,5,1,1,5,1,5,5,3,5,2,2,4,5,3,4,2,5,2,5,0,5,2,3,5,2,5,4,0,3,1,5,0,5,3,4,1,3,0,5,5,5,5,5,5,0,0,3,1,2,4,5,2,0,5,5,0,5,3,2,2,5,0,3,5,5,5,5,5,1,0,0,4,5,0,5,3,3,4,5,5,3,2,4,3,5,1,5,1,2,0,3,4,0,2,5,5,0,0,1,5,2,0,0,0,3,4,3,3,0,3,0,2,0,5,0,1,5,4,3,2,4,2,3,0,5,5,4,1,4,0,5,1,4,5,3,3,3,5,5,5,3,4,4,1,1,2,5,0,3,5,2,3,4,5,1,1,4,5,5,5,4,2,4,4,1,5,5,2,3,4,0,1,5,5,1,5,4,5,2,5,4,5,3,2,2,1,3,5,3,5,5,1,1,2,0,5,1,5,5,2,4,1,4,5,3,5,5,5,5,3,5,3,5,1,0,4,5,5,5,5,2,5,3,5,0,5,4,4,2,0,2,5,5,5,3,5,0,5,5,4,5,5,5,3,4,0,2,2,5,5,4,3,2,1,4,5,0,3,2,3,4,1,2,2,3,0,0,1,5,3,2,0,4,1,5,2,1,5,0,4,3,0,3,1,5,1,0,5,2,1,5,5,0,4,1,5,2,3,2,4,3,4,2,3,3,5,1,3,3,3,5,4,3,2,2,3,1,5,5,3,5,5,0,5,5,5,5,5,5,0,3,5,5,1,4,5,5,5,5,5,3,5,5,0,0,2,5,5,1,4,4,1,5,5,5,5,5,3,5,3,5,0,5,3,5,5,5,4,5,5,5,5,5,3,5,5,3,4,5,5,4,0,4,5,4,3,0,4,5,5,5,5,4,1,1,2,3,0,5,0,0,0,2,5,1,5,3,3,3,5,5,3,5,3,0,1,1,1,2,5,5,4,5,0,3,5,0,3,2,2,0,3,5,5,4,3,2,5,5,5,5,0,2,5,0,5,4,3,5,0,5,0,1,5,5,5,5,5,3,5,3,4,5,5,0,4,2,5,5,2,5,2,1,5,3,5,0,5,1,1,3,5,1,0,0,2,3,5,2,0,2,5,2,1,2,3,0,2,1,5,5,1,3,0,5,5,0,4,2,3,2,3,4,5,4,1,5,5,4,5,5,5,5,0,5,0,5,4,5,5,5,4,1,5,5,5,5,4,0,5,1,5,5,5,2,4,5,4,5,5,5,3,1,2,2,5,0,3,5,5,0,5,2,5,4,5,5,5,0,1,2,3,2,0,5,4,4,5,4,5,1,1,2,1,2,5,4,0,0,5,1,5,5,2,5,2,0,5,4,5,2,3,2,5,5,3,1,4,5,5,5,1,2,2,5,4,5,4,2,3,5,5,0,1,5,3,4,5,5,5,5,4,0,1,5,3,5,4,4,5,4,2,0,0,2,1,5,1,5,2,5,4,0,0,4,4,1,2,0,5,1,3,4,1,5,0,3,1,5,0,1,0,5,4,5,3,5,2,5,4,2,5,2,3,3,5,1,0,4,5,5,3,3,1,1,5,0,0,3,4,5,4,4,5,3,1,0,4,1,4,2,5,5,5,1,4,3,3,5,5,1,0,5,4,0,1,0,5,5,4,0,3,3,3,4,1,3,5,4,0,2,0,5,0,3,1,1,1,0,0,1,3,5,1,5,2,3,5,5,5,5,1,5,5,2,5,1,4,0,1,5,3,5,3,0,5,2,4,2,2,1,1,4,0,1,4,4,0,0,5,5,5,5,1,5,5,5,5,5,0,1,1,5,2,5,5,5,1,2,5,5,4,5,5,4,5,5,4,1,0,0,1,5,5,5,0,3,5,5,1,5,2,1,5,0,2,5,0,5,2,0,5,5,5,5,5,0,5,5,5,1,0,1,2,4,3,5,1,3,4,0,4,1,1,1,1,3,2,5,0,4,1,1,5,5,5,0,5,4,5,5,0,3,3,4,3,0,1,0,5,5,2,5,0,0,3,4,3,1,0,3,5,5,5,5,5,3,0,5,5,5,0,1,3,2,1,0,0,5,5,0,5,2,3,1,1,5,5,5,5,5,5,5,5,3,4,1,0,3,3,4,4,5,2,2,5,5,1,5,4,5,1,1,3,4,3,5,5,4,5,3,1,2,5,2,2,5,1,5,0,0,1,5,5,5,1,0,2,5,2,2,0,3,4,1,1,2,1,5,2,3,3,4,5,5,2,5,1,3,3,2,5,0,5,0,3,5,0,5,5,0,5,2,0,5,5,3,5,5,3,3,0,5,1,1,4,5,0,1,5,5,5,0,5,5,1,5,1,3,1,0,5,5,5,5,5,0,5,5,5,2,5,5,4,1,5,2,0,2,5,2,0,4,3,1,4,0,2,2,3,2,5,0,5,5,5,4,5,5,4,5,1,5,5,3,5,5,3,5,2,3,5,4,1,5,0,4,3,4,4,0,4,4,5,1,5,3,5,3,4,5,0,5,2,0,4,5,0,2,1,1,2,5,4,2,3,4,5,1,4,0,2,2,3,3,1,0,2,5,2,2,2,4,3,2,1,2,0,1,2,2,4,2,3,2,4,5,0,1,5,5,0,4,0,5,4,2,5,3,5,2,5,1,5,3,5,0,5,0,1,1,5,5,0,5,5,1,4,1,5,5,5,5,0,5,3,5,5,5,5,4,1,5,5,5,5,1,5,5,3,2,5,0,5,4,5,0,5,5,1,3,5,0,2,0,3,3,3,3,3,5,3,4,5,5,5,3,5,5,2,5,0,5,5,3,1,2,5,5,5,5,5,4,5,0,5,5,0,3,4,4,5,0,5,5,1,2,0,0,5,5,5,5,0,2,5,1,5,3,5,5,5,0,5,5,1,3,3,0,1,5,3,4,0,3,2,4,1,1,5,5,5,2,0,5,5,2,4,5,2,4,5,5,0,5,1,1,2,0,5,5,5,0,5,5,0,5,5,4,5,5,5,5,0,1,3,2,4,5,0,4,0,5,5,1,4,1,2,5,5,0,4,2,4,2,5,5,1,5,5,2,4,0,3,2,5,1,5,5,5,1,2,5,3,2,5,5,5,1,5,4,5,2,1,5,1,2,5,5,0,1,3,2,0,5,5,2,5,1,4,5,1,3,2,2,3,4,4,3,3,2,4,3,5,5,5,5,1,2,5,2,2,3,5,5,4,0,0,5,5,3,3,5,1,0,4,2,3,5,5,5,5,2,2,4,5,0,5,5,5,1,2,5,2,5,5,5,5,0,4,3,2,3,1,0,5,4,4,4,5,5,5,0,0,3,5,5,0,5,1,2,1,1,1,5,3,2,3,2,3,0,3,0,4,4,0,5,0,4,5,2,2,5,1,4,5,5,2,0,5,0,5,3,0,3,2,4,2,2,1,0,1,5,5,5,5,5,5,5,5,3,3,3,1,1,2,3,1,0,5,5,2,0,4,4,5,2,0,2,5,5,5,5,3,5,5,5,5,5,4,5,5,3,5,2,2,4,1,0,2,4,5,5,0,5,1,5,5,1,1,2,5,5,5,2,5,1,2,0,1,5,5,4,5,0,0,3,3,2,5,1,4,0,0,5,2,4,1,2,4,5,5,1,3,5,0,3,1,0,4,4,3,5,5,5,4,5,0,5,3,4,5,0,5,5,1,3,5,3,3,0,1,1,0,3,0,1,1,1,4,4,5,4,5,4,2,1,4,5,4,2,3,5,5,4,5,5,1,3,2,5,0,2,5,5,5,2,5,2,2,1,0,5,4,0,5,5,2,1,3,4,2,5,5,5,5,1,1,2,5,3,2,5,0,3,0,0,4,3,5,5,3,5,5,5,5,5,2,3,5,1,5,5,1,1,5,0,4,2,1,2,5,4,4,2,5,5,5,1,0,5,5,5,5,5,0,5,5,4,0,4,1,2,5,5,5,5,3,0,0,4,5,5,5,2,5,3,5,4,5,5,5,2,5,4,1,1,1,0,5,3,5,5,4,5,
+% sequence 0 - Vector (10) = 
+2,4,1,5,5,5,5,5,5,5,
+% sequence 1 - Vector (10) = 
+1,4,2,0,2,5,2,5,5,5,
+% sequence 2 - Vector (10) = 
+4,5,2,5,5,0,3,2,0,0,
+% sequence 3 - Vector (10) = 
+5,5,5,5,5,0,5,5,5,3,
+% sequence 4 - Vector (10) = 
+0,5,1,5,4,5,5,5,5,5,
+% sequence 5 - Vector (10) = 
+1,5,5,4,2,3,2,2,4,5,
+% sequence 6 - Vector (10) = 
+0,4,5,3,3,2,2,5,5,5,
+% sequence 7 - Vector (10) = 
+5,3,1,4,5,4,0,0,1,4,
+% sequence 8 - Vector (10) = 
+4,0,2,1,0,0,5,1,2,5,
+% sequence 9 - Vector (10) = 
+3,2,0,4,5,5,3,4,5,5,
diff --git a/fastlib/u/tqlong/hmm/state.out b/fastlib/u/tqlong/hmm/state.out
index a7f76c4448..a9ed056c7b 100644
--- a/fastlib/u/tqlong/hmm/state.out
+++ b/fastlib/u/tqlong/hmm/state.out
@@ -1,4 +1,20 @@
-% state sequence 0 - Vector (5000) = 
-0,0,1,1,1,1,1,1,1,1,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0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,
-% state sequence 1 - Vector (5000) = 
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,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,1,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,1,1,1,0,0,0,0,0,0,0,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,1,0,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,0,1,1,1,1,1,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,
+% state sequence 0 - Vector (10) = 
+0,0,1,1,1,1,1,1,1,1,
+% state sequence 1 - Vector (10) = 
+0,0,0,0,1,1,1,1,1,1,
+% state sequence 2 - Vector (10) = 
+0,0,0,0,0,0,0,0,0,0,
+% state sequence 3 - Vector (10) = 
+0,1,1,1,1,1,1,1,1,1,
+% state sequence 4 - Vector (10) = 
+0,0,0,0,0,1,1,1,1,1,
+% state sequence 5 - Vector (10) = 
+0,1,1,1,1,1,1,1,1,1,
+% state sequence 6 - Vector (10) = 
+0,0,1,1,1,1,1,1,1,1,
+% state sequence 7 - Vector (10) = 
+0,0,0,0,0,0,0,0,0,0,
+% state sequence 8 - Vector (10) = 
+0,0,1,1,1,1,0,0,0,1,
+% state sequence 9 - Vector (10) = 
+0,0,0,0,0,0,0,0,0,0,
diff --git a/fastlib/u/tqlong/hmm/state_viterbi.out b/fastlib/u/tqlong/hmm/state_viterbi.out
index 4db216396a..5860b30aaa 100644
--- a/fastlib/u/tqlong/hmm/state_viterbi.out
+++ b/fastlib/u/tqlong/hmm/state_viterbi.out
@@ -1,4 +1,20 @@
-% viterbi state sequence 0 - Vector (5000) = 
-0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0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,
-% viterbi state sequence 1 - Vector (5000) = 
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,
+% viterbi state sequence 0 - Vector (10) = 
+0,0,0,1,1,1,1,1,1,1,
+% viterbi state sequence 1 - Vector (10) = 
+0,0,0,0,0,1,1,1,1,1,
+% viterbi state sequence 2 - Vector (10) = 
+0,0,0,0,0,0,0,0,0,0,
+% viterbi state sequence 3 - Vector (10) = 
+1,1,1,1,1,1,1,1,1,1,
+% viterbi state sequence 4 - Vector (10) = 
+0,1,1,1,1,1,1,1,1,1,
+% viterbi state sequence 5 - Vector (10) = 
+0,0,0,0,0,0,0,0,0,0,
+% viterbi state sequence 6 - Vector (10) = 
+0,0,0,0,0,0,0,1,1,1,
+% viterbi state sequence 7 - Vector (10) = 
+0,0,0,0,0,0,0,0,0,0,
+% viterbi state sequence 8 - Vector (10) = 
+0,0,0,0,0,0,0,0,0,0,
+% viterbi state sequence 9 - Vector (10) = 
+0,0,0,0,1,1,1,1,1,1,
diff --git a/fastlib/u/tqlong/hmm/train.cc b/fastlib/u/tqlong/hmm/train.cc
new file mode 100644
index 0000000000..6c80862c66
--- /dev/null
+++ b/fastlib/u/tqlong/hmm/train.cc
@@ -0,0 +1,295 @@
+#include "fastlib/fastlib.h"
+#include "support.h"
+#include "discreteHMM.h"
+#include "gaussianHMM.h"
+#include "mixgaussHMM.h"
+#include "mixtureDST.h"
+
+success_t train_baumwelch();
+success_t train_viterbi();
+void usage();
+
+int main(int argc, char* argv[]) {
+  fx_init(argc, argv);
+  success_t s = SUCCESS_PASS;
+  if (fx_param_exists(NULL,"type")) {
+    const char* algorithm = fx_param_str(NULL, "algorithm", "baumwelch");
+    if (strcmp(algorithm,"baumwelch")==0)
+      s = train_baumwelch();
+    else if (strcmp(algorithm,"viterbi")==0)
+      s = train_viterbi();
+    else {
+      printf("Unrecognized algorithm: must be baumwelch or viterbi !!!\n");
+      s = SUCCESS_FAIL;
+    }
+  }
+  else {
+    printf("Unrecognized type: must be: discrete | gaussian | mixture  !!!\n");
+    s = SUCCESS_FAIL;
+  }
+  if (!PASSED(s)) usage();
+  fx_done();
+}
+
+success_t train_baumwelch_discrete();
+success_t train_baumwelch_gaussian();
+success_t train_baumwelch_mixture();
+
+success_t train_baumwelch() {
+  const char* type = fx_param_str_req(NULL, "type");
+  if (strcmp(type, "discrete")==0)
+    return train_baumwelch_discrete();
+  else if (strcmp(type, "gaussian")==0)
+    return train_baumwelch_gaussian();
+  else if (strcmp(type, "mixture")==0)
+    return train_baumwelch_mixture();
+  else {
+    printf("Unrecognized type: must be: discrete | gaussian | mixture !!!\n");
+    return SUCCESS_FAIL;
+  }
+}
+
+success_t train_viterbi_discrete();
+success_t train_viterbi_gaussian();
+success_t train_viterbi_mixture();
+
+success_t train_viterbi() {
+  const char* type = fx_param_str_req(NULL, "type");
+  if (strcmp(type, "discrete")==0)
+    return train_viterbi_discrete();
+  else if (strcmp(type, "gaussian")==0)
+    return train_viterbi_gaussian();
+  else if (strcmp(type, "mixture")==0)
+    return train_viterbi_mixture();
+  else {
+    printf("Unrecognized type: must be: discrete | gaussian | mixture !!!\n");
+    return SUCCESS_FAIL;
+  }
+}
+
+void usage() {
+  printf("\nUsage:\n"
+	 "  train --type=={discrete|gaussian|mixture} OPTION\n"
+	 "[OPTIONS]\n"
+	 "  --algorithm={baumwelch|viterbi} : algorithm used for training, default Baum-Welch\n"
+	 "  --seqfile=file   : file contains input sequences\n"
+	 "  --guess=file     : file contains guess HMM profile\n"
+	 "  --numstate=NUM   : if no guess profile is specified, at least specify the number of state\n"
+	 "  --profile=file   : output file for estimated HMM profile\n"
+	 "  --maxiter=NUM    : maximum number of iteration, default=500\n"
+	 "  --tolerance=NUM  : error tolerance on log-likelihood, default=1e-3\n"
+	 );
+}
+
+success_t train_baumwelch_mixture() {
+  if (!fx_param_exists(NULL, "seqfile")) {
+    printf("--seqfile must be defined.\n");
+    return SUCCESS_FAIL;
+  }
+
+  MixtureofGaussianHMM hmm;
+  ArrayList<Matrix> seqs;
+
+  const char* seqin = fx_param_str_req(NULL, "seqfile");
+  const char* proout = fx_param_str(NULL, "profile", "pro.mix.out");
+
+  load_matrix_list(seqin, &seqs);
+
+  if (fx_param_exists(NULL, "guess")) { // guessed parameters in a file
+    const char* guess = fx_param_str_req(NULL, "guess");
+    printf("Load parameters from file %s\n", guess);
+    hmm.InitFromFile(guess);
+  }
+  else {
+    hmm.Init();
+    printf("Automatic initialization not supported !!!");
+    return SUCCESS_FAIL;
+  }
+
+  int maxiter = fx_param_int(NULL, "maxiter", 500);
+  double tol = fx_param_double(NULL, "tolerance", 1e-3);
+
+  hmm.TrainBaumWelch(seqs, maxiter, tol);
+
+  hmm.SaveProfile(proout);
+
+  return SUCCESS_PASS;
+}
+
+success_t train_baumwelch_gaussian() {
+  if (!fx_param_exists(NULL, "seqfile")) {
+    printf("--seqfile must be defined.\n");
+    return SUCCESS_FAIL;
+  }
+  GaussianHMM hmm;
+  ArrayList<Matrix> seqs;
+
+  const char* seqin = fx_param_str_req(NULL, "seqfile");
+  const char* proout = fx_param_str(NULL, "profile", "pro.gauss.out");
+
+  load_matrix_list(seqin, &seqs);
+
+  if (fx_param_exists(NULL, "guess")) { // guessed parameters in a file
+    const char* guess = fx_param_str_req(NULL, "guess");
+    printf("Load parameters from file %s\n", guess);
+    hmm.InitFromFile(guess);
+  }
+  else { // otherwise initialized using information from the data
+    int numstate = fx_param_int_req(NULL, "numstate");
+    printf("Generate HMM parameters: NUMSTATE = %d\n", numstate);
+    hmm.InitFromData(seqs, numstate);
+    printf("Done.\n");
+  }
+
+  int maxiter = fx_param_int(NULL, "maxiter", 500);
+  double tol = fx_param_double(NULL, "tolerance", 1e-3);
+
+  printf("Training ...\n");
+  hmm.TrainBaumWelch(seqs, maxiter, tol);
+  printf("Done.\n");
+
+  hmm.SaveProfile(proout);
+
+  return SUCCESS_PASS;
+}
+
+success_t train_baumwelch_discrete() {
+  if (!fx_param_exists(NULL, "seqfile")) {
+    printf("--seqfile must be defined.\n");
+    return SUCCESS_FAIL;
+  }
+
+  const char* seqin = fx_param_str_req(NULL, "seqfile");
+  const char* proout = fx_param_str(NULL, "profile", "pro.dis.out");
+
+  ArrayList<Vector> seqs;
+  load_vector_list(seqin, &seqs);
+
+  DiscreteHMM hmm;
+
+  if (fx_param_exists(NULL, "guess")) { // guessed parameters in a file
+    const char* guess = fx_param_str_req(NULL, "guess");
+    printf("Load HMM parameters from file %s\n", guess);
+    hmm.InitFromFile(guess);
+  }
+  else { // otherwise randomly initialized using information from the data
+    int numstate = fx_param_int_req(NULL, "numstate");
+    printf("Randomly generate parameters: NUMSTATE = %d\n", numstate);
+    hmm.InitFromData(seqs, numstate);
+  }
+
+  int maxiter = fx_param_int(NULL, "maxiter", 500);
+  double tol = fx_param_double(NULL, "tolerance", 1e-3);
+
+  hmm.TrainBaumWelch(seqs, maxiter, tol);
+
+  hmm.SaveProfile(proout);
+
+  return SUCCESS_PASS;
+}
+
+success_t train_viterbi_mixture() {
+  if (!fx_param_exists(NULL, "seqfile")) {
+    printf("--seqfile must be defined.\n");
+    return SUCCESS_FAIL;
+  }
+  
+  MixtureofGaussianHMM hmm;
+  ArrayList<Matrix> seqs;
+
+  const char* seqin = fx_param_str_req(NULL, "seqfile");
+  const char* proout = fx_param_str(NULL, "profile", "pro.mix.out");
+
+  load_matrix_list(seqin, &seqs);
+
+  if (fx_param_exists(NULL, "guess")) { // guessed parameters in a file
+    const char* guess = fx_param_str_req(NULL, "guess");
+    printf("Load parameters from file %s\n", guess);
+    hmm.InitFromFile(guess);
+  }
+  else {
+    hmm.Init();
+    printf("Automatic initialization not supported !!!");
+    return SUCCESS_FAIL;
+  }
+
+  int maxiter = fx_param_int(NULL, "maxiter", 500);
+  double tol = fx_param_double(NULL, "tolerance", 1e-3);
+
+  hmm.TrainViterbi(seqs, maxiter, tol);
+
+  hmm.SaveProfile(proout);
+
+  return SUCCESS_PASS;
+}
+
+success_t train_viterbi_gaussian() {
+  if (!fx_param_exists(NULL, "seqfile")) {
+    printf("--seqfile must be defined.\n");
+    return SUCCESS_FAIL;
+  }
+  
+  GaussianHMM hmm;
+  ArrayList<Matrix> seqs;
+
+  const char* seqin = fx_param_str_req(NULL, "seqfile");
+  const char* proout = fx_param_str(NULL, "profile", "pro.gauss.viterbi.out");
+
+  load_matrix_list(seqin, &seqs);
+
+  if (fx_param_exists(NULL, "guess")) { // guessed parameters in a file
+    const char* guess = fx_param_str_req(NULL, "guess");
+    printf("Load parameters from file %s\n", guess);
+    hmm.InitFromFile(guess);
+  }
+  else { // otherwise initialized using information from the data
+    int numstate = fx_param_int_req(NULL, "numstate");
+    printf("Generate parameters: NUMSTATE = %d\n", numstate);
+    hmm.InitFromData(seqs, numstate);
+  }
+
+  int maxiter = fx_param_int(NULL, "maxiter", 500);
+  double tol = fx_param_double(NULL, "tolerance", 1e-3);
+
+  hmm.TrainViterbi(seqs, maxiter, tol);
+
+  hmm.SaveProfile(proout);
+
+  return SUCCESS_PASS;
+}
+
+success_t train_viterbi_discrete() {
+  if (!fx_param_exists(NULL, "seqfile")) {
+    printf("--seqfile must be defined.\n");
+    return SUCCESS_FAIL;
+  }
+
+  DiscreteHMM hmm;
+  ArrayList<Vector> seqs;
+
+  const char* seqin = fx_param_str_req(NULL, "seqfile");
+  const char* proout = fx_param_str(NULL, "profile", "pro.dis.viterbi.out");
+
+  load_vector_list(seqin, &seqs);
+
+  if (fx_param_exists(NULL, "guess")) { // guessed parameters in a file
+    ArrayList<Matrix> matlst;
+    const char* guess = fx_param_str_req(NULL, "guess");
+    printf("Load parameters from file %s\n", guess);
+    hmm.InitFromFile(guess);
+  }
+  else { // otherwise randomly initialized using information from the data
+    int numstate = fx_param_int_req(NULL, "numstate");
+    printf("Generate parameters with NUMSTATE = %d\n", numstate);
+    hmm.InitFromData(seqs, numstate);
+  }
+
+  int maxiter = fx_param_int(NULL, "maxiter", 500);
+  double tol = fx_param_double(NULL, "tolerance", 1e-3);
+
+  hmm.TrainViterbi(seqs, maxiter, tol);
+
+  hmm.SaveProfile(proout);
+
+  return SUCCESS_PASS;
+}
diff --git a/fastlib/u/tqlong/hmm/viterbi.cc b/fastlib/u/tqlong/hmm/viterbi.cc
new file mode 100644
index 0000000000..5a00afdbec
--- /dev/null
+++ b/fastlib/u/tqlong/hmm/viterbi.cc
@@ -0,0 +1,144 @@
+#include "fastlib/fastlib.h"
+#include "support.h"
+#include "discreteHMM.h"
+#include "gaussianHMM.h"
+#include "mixgaussHMM.h"
+#include "mixtureDST.h"
+
+success_t viterbi_discrete();
+success_t viterbi_gaussian();
+success_t viterbi_mixture();
+void usage();
+
+int main(int argc, char* argv[]) {
+  fx_init(argc, argv);
+  success_t s = SUCCESS_PASS;
+  if (fx_param_exists(NULL,"type")) {
+    const char* type = fx_param_str_req(NULL, "type");
+    if (strcmp(type, "discrete")==0)
+      s = viterbi_discrete();
+    else if (strcmp(type, "gaussian")==0)
+      s = viterbi_gaussian();
+    else if (strcmp(type, "mixture")==0)
+      s = viterbi_mixture();
+    else {
+      printf("Unrecognized type: must be: discrete | gaussian | mixture !!!\n");
+      s = SUCCESS_FAIL;
+    }
+  }
+  else {
+    printf("Unrecognized type: must be: discrete | gaussian | mixture  !!!\n");
+    s = SUCCESS_FAIL;
+  }
+  if (!PASSED(s)) usage();
+  fx_done();
+}
+
+void usage() {
+  printf("\n"
+	 "Usage:\n"
+	 "  viterbi --type=={discrete|gaussian|mixture} OPTIONS\n"
+	 "[OPTIONS]\n"
+	 "  --profile=file   : file contains HMM profile\n"
+	 "  --seqfile=file   : file contains input sequences\n"
+	 "  --statefile=file : output file for state sequences\n"
+	 );
+}
+
+success_t viterbi_mixture() {
+  if (!fx_param_exists(NULL, "profile")) {
+    printf("--profile must be defined.\n");
+    return SUCCESS_FAIL;
+  }
+  const char* profile = fx_param_str_req(NULL, "profile");
+  const char* seqin = fx_param_str(NULL, "seqfile", "seq.mix.out");
+  const char* stateout = fx_param_str(NULL, "statefile", "state.viterbi.mix.out");
+
+  MixtureofGaussianHMM hmm;
+  hmm.InitFromFile(profile);
+
+  ArrayList<Matrix> seqs;
+  load_matrix_list(seqin, &seqs);
+
+  TextWriter w_state;
+  if (!PASSED(w_state.Open(stateout))) {
+    NONFATAL("Couldn't open '%s' for writing.", stateout);
+    return SUCCESS_FAIL;
+  }
+
+  for (int i = 0; i < seqs.size(); i++) {
+    Vector states;
+    char s[100];
+
+    hmm.ComputeViterbiStateSequence(seqs[i], &states);
+    
+    sprintf(s, "%% viterbi state sequence %d", i);
+    print_vector(w_state, states, s, "%.0f,");
+  }
+  return SUCCESS_PASS;
+}
+
+success_t viterbi_gaussian() {
+  if (!fx_param_exists(NULL, "profile")) {
+    printf("--profile must be defined.\n");
+    return SUCCESS_FAIL;
+  }
+  const char* profile = fx_param_str_req(NULL, "profile");
+  const char* seqin = fx_param_str(NULL, "seqfile", "seq.gauss.out");
+  const char* stateout = fx_param_str(NULL, "statefile", "state.viterbi.gauss.out");
+  GaussianHMM hmm;
+  hmm.InitFromFile(profile);
+
+  ArrayList<Matrix> seqs;
+  load_matrix_list(seqin, &seqs);
+
+  TextWriter w_state;
+  if (!PASSED(w_state.Open(stateout))) {
+    NONFATAL("Couldn't open '%s' for writing.", stateout);
+    return SUCCESS_FAIL;
+  }
+
+  for (int i = 0; i < seqs.size(); i++) {
+    Vector states;
+    char s[100];
+    hmm.ComputeViterbiStateSequence(seqs[i], &states);
+    
+    sprintf(s, "%% viterbi state sequence %d", i);
+    print_vector(w_state, states, s, "%.0f,");
+  }
+  return SUCCESS_PASS;
+}
+
+success_t viterbi_discrete() {
+  if (!fx_param_exists(NULL, "profile")) {
+    printf("--profile must be defined.\n");
+    return SUCCESS_FAIL;
+  }
+  const char* profile = fx_param_str_req(NULL, "profile");
+  const char* seqin = fx_param_str(NULL, "seqfile", "seq.out");
+  const char* stateout = fx_param_str(NULL, "statefile", "state.viterbi.out");
+
+  DiscreteHMM hmm;
+
+  hmm.InitFromFile(profile);
+
+  ArrayList<Vector> seqs;
+  load_vector_list(seqin, &seqs);
+
+  TextWriter w_state;
+  if (!PASSED(w_state.Open(stateout))) {
+    NONFATAL("Couldn't open '%s' for writing.", stateout);
+    return SUCCESS_FAIL;
+  }
+
+  for (int i = 0; i < seqs.size(); i++) {
+    Vector states;
+    char s[100];
+    
+    hmm.ComputeViterbiStateSequence(seqs[i], &states);
+    
+    sprintf(s, "%% viterbi state sequence %d", i);
+    print_vector(w_state, states, s, "%.0f,");
+  }
+  return SUCCESS_PASS;
+}