diff --git a/fastlib/u/pram/mog_em/faster/build.py b/fastlib/u/pram/mog_em/faster/build.py
new file mode 100644
index 0000000000..6ec98e8330
--- /dev/null
+++ b/fastlib/u/pram/mog_em/faster/build.py
@@ -0,0 +1,26 @@
+
+librule(
+    name = "mog_em",                    # this line can be safely omitted
+    sources = ["mog.cc"],            # files that must be compiled
+    headers = ["mog.h","phi.h","math_functions.h"],# include files part of the 'lib'
+    deplibs = ["fastlib:fastlib"], # depends on fastlib core
+    #tests = ["mog_em_tests.cc"]        # this file contains a main with test functions
+    )
+
+binrule(
+    name = "mog_em_main",                  # the executable name
+    sources = ["mog_em_main.cc"],          # compile main.cc
+    #headers = ["mog_em_main.h"],           # no extra headers
+    deplibs = [":mog_em", "fastlib:fastlib"]           #
+    )
+
+# to build:
+# 1. make sure have environment variables set up:
+#    $ source /full/path/to/fastlib/script/fl-env /full/path/to/fastlib
+#    (you might want to put this in bashrc)
+# 2. fl-build main
+#    - this automatically will assume --mode=check, the default
+#    - type fl-build --help for help
+# 3. ./main
+#    - to build same target again, type: make
+#    - to force recompilation, type: make clean
diff --git a/fastlib/u/pram/mog_em/faster/data.arff b/fastlib/u/pram/mog_em/faster/data.arff
new file mode 100644
index 0000000000..d95f367d0f
--- /dev/null
+++ b/fastlib/u/pram/mog_em/faster/data.arff
@@ -0,0 +1,1000 @@
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diff --git a/fastlib/u/pram/mog_em/faster/math_functions.h b/fastlib/u/pram/mog_em/faster/math_functions.h
new file mode 100644
index 0000000000..2349bc9d06
--- /dev/null
+++ b/fastlib/u/pram/mog_em/faster/math_functions.h
@@ -0,0 +1,138 @@
+/**
+ * @author Parikshit Ram (pram@cc.gatech.edu)
+ * @file math_functions.h
+ *
+ * This file has certain functions that find the 
+ * highest or lowest element in an array or 
+ * in a row of a matrix and returns them
+ *
+ */
+ 
+#include "fastlib/fastlib.h"
+#include "fastlib/fastlib_int.h"
+
+
+/**
+ * Finds the index of the minimum element
+ * in each row of a matrix
+ *
+ * Example use:
+ * @code
+ * Matrix& mat;
+ * index_t indices[mat.n_rows()];
+ *
+ * ...
+ * 
+ * min_element(mat, indices);
+ * @endcode
+ */
+
+void min_element(Matrix& element, index_t *indices) {
+	
+	index_t last = element.n_cols() - 1;
+	index_t first, lowest;
+	index_t i;
+	
+	for (i = 0; i < element.n_rows(); i++) {
+		
+		first = lowest = 0;
+		if (first == last) {
+			indices[i] = last;
+		}
+		while (++first <= last) {
+			if (element.get(i, first) < element.get(i, lowest)) {
+				lowest = first;
+			}
+		}
+		indices[i] = lowest;
+	}
+	return;
+}
+
+/**
+ * Returns the index of the maximum element
+ * in a float array 'array' of length 'length'.
+ *
+ * Example use:
+ * @code
+ * index_t length, index;
+ * float array[length];
+ * ...
+ * index = max_element_index(array, length);
+ * @endcode
+ */
+int max_element_index(float *array, int length) {
+  
+	int last = length - 1;
+	int first = 0;
+       	int highest = 0;
+	
+	if (first == last) {
+		return last;
+	}
+	while (++first <= last) {
+		if (array[first] > array[highest]) {
+			highest = first;
+		}
+	}
+	return highest;
+}
+
+/**
+ * Finds the index of the maximum element in an arraylist
+ * of floats
+ *
+ * Example use:
+ * @code
+ * index_t index;
+ * ArrayList<float> array;
+ * ...
+ * index = max_element_index(array);
+ * @endcode
+ */
+int max_element_index(ArrayList<float>& array){
+
+        int last = array.size() - 1;
+        int first = 0;
+       	int highest = 0;
+
+	if (first == last) {
+                return last;
+        }
+        while (++first <= last) {
+                if (array[first] > array[highest]) {
+                        highest = first;
+                }
+	}
+	return highest;
+}
+
+/**
+ * Returns the index of the maximum element in 
+ * an arraylist of doubles
+ *
+ * Example use:
+ * @code
+ * index_t index;
+ * ArrayList<double> array;
+ * ...
+ * index = max_element_index(array);
+ * @endcode
+ */
+int max_element_index(ArrayList<double>& array) {
+
+        int last = array.size() - 1;
+        int first = 0;
+       	int highest = 0;
+
+	if (first == last) {
+                return last;
+        }
+        while (++first <= last) {
+                if(array[first] > array[highest]) {
+                        highest = first;
+                }
+	}
+	return highest;
+}
+
diff --git a/fastlib/u/pram/mog_em/faster/mog.cc b/fastlib/u/pram/mog_em/faster/mog.cc
new file mode 100644
index 0000000000..85d377d2ce
--- /dev/null
+++ b/fastlib/u/pram/mog_em/faster/mog.cc
@@ -0,0 +1,301 @@
+/**
+ * @author Parikshit Ram (pram@cc.gatech.edu)
+ * @file mog.cc
+ *
+ * Implementation for the loglikelihood function, the EM algorithm
+ * and also computes the K-means for getting an initial point
+ *
+ */
+
+#include "mog.h"
+#include "phi.h"
+#include "math_functions.h"
+
+void MoGEM::ExpectationMaximization(Matrix& data_points) {
+
+  // Declaration of the variables */
+  index_t num_points;
+  index_t dim, num_gauss;
+  double sum, tmp; 	
+  ArrayList<Vector> mu_temp, mu;
+  ArrayList<Matrix> sigma_temp, sigma;
+  Vector omega_temp, omega, x;
+  Matrix cond_prob;
+  long double l, l_old, best_l, INFTY = 99999, TINY = 1.0e-10;
+
+  // Initializing values
+  dim = dimension();
+  num_gauss = number_of_gaussians();
+  num_points = data_points.n_cols();
+
+  // Initializing the number of the vectors and matrices 
+  // according to the parameters input
+  mu_temp.Init(num_gauss);
+  mu.Init(num_gauss);
+  sigma_temp.Init(num_gauss);
+  sigma.Init(num_gauss);
+  omega_temp.Init(num_gauss);
+  omega.Init(num_gauss);
+  
+  // Allocating size to the vectors and matrices 
+  // according to the dimensionality of the data
+  for(index_t i = 0; i < num_gauss; i++) {
+    mu_temp[i].Init(dim);
+    mu[i].Init(dim);
+    sigma_temp[i].Init(dim, dim);    
+    sigma[i].Init(dim, dim);
+  }
+  x.Init(dim);
+  cond_prob.Init(num_gauss, num_points);
+  
+  best_l = -INFTY;
+  index_t restarts = 0;
+  // performing 5 restarts and choosing the best from them
+  while (restarts < 1) { 
+
+    // assign initial values to 'mu', 'sig' and 'omega' using k-means
+    KMeans(data_points, &mu_temp, &sigma_temp, &omega_temp, num_gauss);
+    
+    l_old = -INFTY;
+
+    // calculates the loglikelihood value
+    l = Loglikelihood(data_points, mu_temp, sigma_temp, omega_temp);
+    
+    // added a check here to see if any 
+    // significant change is being made 
+    // at every iteration
+    while (l - l_old > TINY) {
+      // calculating the conditional probabilities 
+      // of choosing a particular gaussian given 
+      // the data and the present theta value   
+      /**********
+      for (index_t j = 0; j < num_points; j++) {
+	x.CopyValues(data_points.GetColumnPtr(j));
+	sum = 0;
+	for (index_t i = 0; i < num_gauss; i++) {
+	  tmp = phi(x, mu_temp[i], sigma_temp[i]) * omega_temp.get(i); // can be made faster by sending all the data at once instead of looping over
+	  cond_prob.set(i, j, tmp);
+	  sum += tmp;	  
+	}
+	for (index_t i = 0; i < num_gauss; i++) {
+	  tmp = cond_prob.get(i, j);
+	  cond_prob.set(i, j, tmp / sum); 
+	}
+      }
+      ******/
+
+      /*******FIX THIS SOON********/
+      Vector phi_val;
+      phi_val.Init(num_points);
+
+      for(index_t i = 0; i < num_gauss; i++) {
+	phi_val.CopyValues(phi(data_points, mu_temp[i], sigma_temp[i]));
+	la::Scale(omega_temp.get(i), &phi_val);
+      */
+			
+      // calculating the new value of the mu 
+      // using the updated conditional probabilities
+      for (index_t i = 0; i < num_gauss; i++) {
+	sum = 0;
+	mu_temp[i].SetZero();
+	for (index_t j = 0; j < num_points; j++) {
+	  x.CopyValues(data_points.GetColumnPtr(j));
+	  la::AddExpert(cond_prob.get(i, j), x, &mu_temp[i]);
+	  sum += cond_prob.get(i, j); 
+	}
+	la::Scale((1.0 / sum), &mu_temp[i]);
+      }
+					
+      // calculating the new value of the sig 
+      // using the updated conditional probabilities 
+      // and the updated mu
+      for (index_t i = 0; i < num_gauss; i++) {
+	sum = 0;
+	sigma_temp[i].SetZero();
+	for (index_t j = 0; j < num_points; j++) {
+	  Matrix co, ro, c;
+	  c.Init(dim, dim);
+	  x.CopyValues(data_points.GetColumnPtr(j));
+	  la::SubFrom(mu_temp[i] , &x);
+	  co.AliasColVector(x);
+	  ro.AliasRowVector(x);
+	  la::MulOverwrite(co, ro, &c);
+	  la::AddExpert(cond_prob.get(i, j), c, &sigma_temp[i]);
+	  sum += cond_prob.get(i, j);	  
+	}
+	la::Scale((1.0 / sum), &sigma_temp[i]);
+      }
+			
+      // calculating the new values for omega 
+      // using the updated conditional probabilities 
+      Vector identity_vector;
+      identity_vector.Init(num_points);
+      identity_vector.SetAll(1.0 / num_points);
+      la::MulOverwrite(cond_prob, identity_vector, &omega_temp);
+      
+      l_old = l;
+      l = Loglikelihood(data_points, mu_temp, sigma_temp, omega_temp);
+    }
+    
+    // putting a check to see if the best one is chosen
+    if(l > best_l){
+      best_l = l;
+      for (index_t i = 0; i < num_gauss; i++) {
+	mu[i].CopyValues(mu_temp[i]);
+	sigma[i].CopyValues(sigma_temp[i]);
+      }
+      omega.CopyValues(omega_temp);
+    }
+    restarts++;
+  }	
+
+  for (index_t i = 0; i < num_gauss; i++) {
+    set_mu(i, mu[i]);
+    set_sigma(i, sigma[i]);
+  }
+  set_omega(omega);
+  
+  NOTIFY("loglikelihood value of the estimated model: %Lf\n", best_l);
+  return;
+}
+
+long double MoGEM::Loglikelihood(Matrix& data_points, ArrayList<Vector>& means,
+			       ArrayList<Matrix>& covars, Vector& weights) {
+	
+  index_t i, j;
+  Vector x;
+  long double likelihood, loglikelihood = 0;
+	
+  x.Init(data_points.n_rows());
+	
+  for (j = 0; j < data_points.n_cols(); j++) {
+    x.CopyValues(data_points.GetColumnPtr(j));
+    likelihood = 0;
+    for(i = 0; i < number_of_gaussians() ; i++){
+      likelihood += weights.get(i) * phi(x, means[i], covars[i]);
+    }
+    loglikelihood += log(likelihood);
+  }
+  return loglikelihood;
+}
+
+void MoGEM::KMeans(Matrix& data, ArrayList<Vector> *means,
+		 ArrayList<Matrix> *covars, Vector *weights, index_t value_of_k){
+
+	
+  ArrayList<Vector> mu, mu_old;
+  double* tmpssq;
+  double* sig;
+  double* sig_best;
+  index_t *y;
+  Vector x, diff;
+  Matrix ssq;
+  index_t i, j, k, n, t, dim;
+  double score, score_old, sum;
+	
+  n = data.n_cols();
+  dim = data.n_rows();
+  mu.Init(value_of_k);
+  mu_old.Init(value_of_k);
+  tmpssq = (double*)malloc(value_of_k * sizeof( double ));
+  sig = (double*)malloc(value_of_k * sizeof( double ));
+  sig_best = (double*)malloc(value_of_k * sizeof( double ));
+  ssq.Init(n, value_of_k);
+	
+  for( i = 0; i < value_of_k; i++){
+    mu[i].Init(dim);
+    mu_old[i].Init(dim);
+  }
+  x.Init(dim);
+  y = (index_t*)malloc(n * sizeof(index_t));
+  diff.Init(dim);
+	
+  score_old = 999999;
+	
+  // putting 5 random restarts to obtain the k-means
+  for(i = 0; i < 5; i++){
+    t = -1;
+    for (k = 0; k < value_of_k; k++){
+      t = (t + 1 + (rand()%((n - 1 - (value_of_k - k)) - (t + 1))));
+      mu[k].CopyValues(data.GetColumnPtr(t));
+      for(j = 0; j < n; j++){
+	x.CopyValues( data.GetColumnPtr(j));
+	la::SubOverwrite(mu[k], x, &diff);
+	ssq.set( j, k, la::Dot(diff, diff));
+      }      	
+    }
+    min_element(ssq, y);
+    
+    do{
+      for(k = 0; k < value_of_k; k++){
+	mu_old[k].CopyValues(mu[k]);
+      }
+			
+      for(k = 0; k < value_of_k; k++){
+	index_t p = 0;
+	mu[k].SetZero();
+	for(j = 0; j < n; j++){
+	  x.CopyValues(data.GetColumnPtr(j));
+	  if(y[j] == k){
+	    la::AddTo(x, &mu[k]);
+	    p++;
+	  }
+	}
+	
+	if(p == 0){ 
+	}
+	else{
+	  double sc = 1 ;
+	  sc = sc / p;
+	  la::Scale(sc , &mu[k]);
+	}
+	for(j = 0; j < n; j++){
+	  x.CopyValues(data.GetColumnPtr(j));
+	  la::SubOverwrite(mu[k], x, &diff);
+	  ssq.set(j, k, la::Dot(diff, diff));
+	}
+      }      
+      min_element(ssq, y);
+      
+      sum = 0;
+      for(k = 0; k < value_of_k; k++) {
+	la::SubOverwrite(mu[k], mu_old[k], &diff);
+	sum += la::Dot(diff, diff);
+      }
+    }while(sum != 0);
+		
+    for(k = 0; k < value_of_k; k++){
+      index_t p = 0;
+      tmpssq[k] = 0;
+      for(j = 0; j < n; j++){
+	if(y[j] == k){
+	  tmpssq[k] += ssq.get(j, k);
+	  p++;
+	}
+      }
+      sig[k] = sqrt(tmpssq[k] / p);
+    }	
+		
+    score = 0;
+    for(k = 0; k < value_of_k; k++){
+      score += tmpssq[k];
+    }
+    score = score / n;
+    
+    if (score < score_old) {
+      score_old = score;
+      for(k = 0; k < value_of_k; k++){
+	(*means)[k].CopyValues(mu[k]);
+	sig_best[k] = sig[k];	
+      }
+    }
+  }
+	
+  for(k = 0; k < value_of_k; k++){
+    x.SetAll(sig_best[k]);
+    (*covars)[k].SetDiagonal(x);
+  }
+  double tmp = 1;
+  (*weights).SetAll(tmp / value_of_k);
+  return;
+}
diff --git a/fastlib/u/pram/mog_em/faster/mog.h b/fastlib/u/pram/mog_em/faster/mog.h
new file mode 100644
index 0000000000..3acefe9bca
--- /dev/null
+++ b/fastlib/u/pram/mog_em/faster/mog.h
@@ -0,0 +1,244 @@
+/**
+ * @author Parikshit Ram (pram@cc.gatech.edu)
+ * @file mog.h
+ *
+ * Defines a Gaussian Mixture model and
+ * estimates the parameters of the model
+ */
+
+#ifndef MOGEM_H
+#define MOGEM_H
+
+#include <fastlib/fastlib.h>
+
+/**
+ * A Gaussian mixture model class.
+ * 
+ * This class uses maximum likelihood loss functions to
+ * estimate the parameters of a gaussian mixture 
+ * model on a given data via the EM algorithm.
+ * 
+ *
+ * Example use:
+ *
+ * @code
+ * MoGEM mog;
+ * ArrayList<double> results;
+ *
+ * mog.Init(number_of_gaussians, dimension);
+ * mog.ExpectationMaximization(data, &results, optim_flag);
+ * @endcode
+ */
+class MoGEM {
+
+ private:
+
+  // The parameters of the mixture model
+  ArrayList<Vector> mu_;
+  ArrayList<Matrix> sigma_;
+  Vector omega_;
+  index_t number_of_gaussians_;
+  index_t dimension_;
+
+ public:
+
+  MoGEM() {
+    mu_.Init(0);
+    sigma_.Init(0);
+  }
+
+  ~MoGEM() {
+  }
+
+  void Init(index_t num_gauss, index_t dimension) {
+
+    // Initialize the private variables
+    number_of_gaussians_ = num_gauss;
+    dimension_ = dimension;
+
+    // Resize the ArrayList of Vectors and Matrices
+    mu_.Resize(number_of_gaussians_);
+    sigma_.Resize(number_of_gaussians_);
+  }
+
+  void Init(datanode *mog_em_module) {
+    
+    index_t num_gauss = fx_param_int_req(mog_em_module, "K");
+    index_t dim = fx_param_int_req(mog_em_module, "D");
+    Init(num_gauss, dim);
+  }
+  // The get functions
+
+  ArrayList<Vector>& mu() {
+    return mu_;
+  }				
+		
+  ArrayList<Matrix>& sigma() {
+    return sigma_;
+  }
+		
+  Vector& omega() {
+    return omega_;
+  }
+		
+  index_t number_of_gaussians() {
+    return number_of_gaussians_;
+  }			
+		
+  index_t dimension() {
+    return dimension_;
+  }
+		
+  Vector& mu(index_t i) {
+    return mu_[i] ;
+  }
+		
+  Matrix& sigma(index_t i) {
+    return sigma_[i];
+  }
+		
+  double omega(index_t i) {
+    return omega_.get(i);
+  }
+
+  // The set functions
+
+  void set_mu(index_t i, Vector& mu) {
+    DEBUG_ASSERT(i < number_of_gaussians());
+    DEBUG_ASSERT(mu.length() == dimension()); 
+    mu_[i].Copy(mu);
+    return;
+  }
+
+  void set_mu(index_t i, index_t length, const double *mu) {
+    DEBUG_ASSERT(i < number_of_gaussians());
+    DEBUG_ASSERT(length == dimension());
+    mu_[i].Copy(mu, length);
+    return;
+  }
+
+  void set_sigma(index_t i, Matrix& sigma) {
+    DEBUG_ASSERT(i < number_of_gaussians());
+    DEBUG_ASSERT(sigma.n_rows() == dimension());
+    DEBUG_ASSERT(sigma.n_cols() == dimension());
+    sigma_[i].Copy(sigma);
+    return;
+  }
+
+  void set_omega(Vector& omega) {
+    DEBUG_ASSERT(omega.length() == number_of_gaussians());
+    omega_.Copy(omega);
+    return;
+  }
+
+  void set_omega(index_t length, const double *omega) {
+    DEBUG_ASSERT(length == number_of_gaussians());
+    omega_.Copy(omega, length);
+    return;
+  }
+
+
+  /**
+   * This function outputs the parameters of the model
+   * to an arraylist of doubles 
+   *
+   * @code
+   * ArrayList<double> results;
+   * mog.OutputResults(&results);
+   * @endcode
+   */
+  void OutputResults(ArrayList<double> *results) {
+
+    // Initialize the size of the output array
+    (*results).Init(number_of_gaussians_ * (1 + dimension_*(1 + dimension_)));
+
+    // Copy values to the array from the private variables of the class
+    for (index_t i = 0; i < number_of_gaussians_; i++) {
+      (*results)[i] = omega(i);
+      for (index_t j = 0; j < dimension_; j++) {
+	(*results)[number_of_gaussians_ + i*dimension_ + j] = mu(i).get(j);
+	for (index_t k = 0; k < dimension_; k++) {
+	  (*results)[number_of_gaussians_*(1 + dimension_) 
+		   + i*dimension_*dimension_ + j*dimension_ 
+		   + k] = sigma(i).get(j, k);
+	}
+      }
+    }
+  }
+
+  /**
+   * This function prints the parameters of the model
+   *
+   * @code
+   * mog.Display();
+   * @endcode
+   */
+  void Display(){
+
+    // Output the model parameters as the omega, mu and sigma			
+    printf(" Omega : [ ");
+    for (index_t i = 0; i < number_of_gaussians_; i++) {
+      printf("%lf ", omega(i));
+    }
+    printf("]\n");
+    printf(" Mu : \n[");
+    for (index_t i = 0; i < number_of_gaussians_; i++) {
+      for (index_t j = 0; j < dimension_ ; j++) {
+	printf("%lf ", mu(i).get(j));
+      }
+      printf(";");
+      if (i == (number_of_gaussians_ - 1)) {
+	printf("\b]\n");
+      }
+    }
+    printf("Sigma : ");
+    for (index_t i = 0; i < number_of_gaussians_; i++) {
+      printf("\n[");
+      for (index_t j = 0; j < dimension_ ; j++) {
+	for(index_t k = 0; k < dimension_ ; k++) {
+	  printf("%lf ",sigma(i).get(j, k));
+	}
+	printf(";");
+      }
+      printf("\b]");
+    }
+    printf("\n");
+  }
+
+  /**
+   * This function calculates the parameters of the model
+   * using the Maximum Likelihood function via the 
+   * Expectation Maximization (EM) Algorithm.
+   *
+   * @code
+   * MoG mog;
+   * Matrix data = "the data on which you want to fit the model";
+   * ArrayList<double> results;
+   * mog.ExpectationMaximization(data, &results);
+   * @endcode
+   */
+  void ExpectationMaximization(Matrix& data_points);
+
+  /**
+   * This function computes the loglikelihood of model.
+   * This function is used by the 'ExpectationMaximization'
+   * function.
+   * 
+   */
+  long double Loglikelihood(Matrix& data_points, ArrayList<Vector>& means,
+			    ArrayList<Matrix>& covars, Vector& weights);
+
+  /**
+   * This function computes the k-means of the data and stores
+   * the calculated means and covariances in the ArrayList
+   * of Vectors and Matrices passed to it. It sets the weights 
+   * uniformly. 
+   * 
+   * This function is used to obtain a starting point for 
+   * the optimization
+   */
+  void KMeans(Matrix& data, ArrayList<Vector> *means,
+	      ArrayList<Matrix> *covars, Vector *weights, index_t value_of_k);
+};
+
+#endif
diff --git a/fastlib/u/pram/mog_em/faster/mog_em_main.cc b/fastlib/u/pram/mog_em/faster/mog_em_main.cc
new file mode 100644
index 0000000000..6e64a9bf87
--- /dev/null
+++ b/fastlib/u/pram/mog_em/faster/mog_em_main.cc
@@ -0,0 +1,72 @@
+/**
+ * @author Parikshit Ram (pram@cc.gatech.edu)
+ * @file mog_l2e_main.cc
+ * 
+ * This program test drives the L2 estimation
+ * of a Gaussian Mixture model.
+ * 
+ * PARAMETERS TO BE INPUT:
+ * 
+ * --data 
+ * This is the file that contains the data on which 
+ * the model is to be fit
+ *
+ * --mog_em/K
+ * This is the number of gaussians we want to fit
+ * on the data, defaults to '1'
+ *
+ * --output
+ * This file will contain the parameters estimated,
+ * defaults to 'ouotput.csv'
+ *
+ */
+
+#include "mog.h"
+
+
+int main(int argc, char* argv[]) {
+
+  fx_init(argc, argv);
+
+  ////// READING PARAMETERS AND LOADING DATA //////
+  
+  const char *data_filename = fx_param_str_req(NULL, "data");
+
+  Matrix data_points;
+  data::Load(data_filename, &data_points);
+
+  ////// MIXTURE OF GAUSSIANS USING EM //////
+
+  MoGEM mog;
+
+  struct datanode* mog_em_module = fx_submodule(NULL, "mog_em", "mog_em");
+  fx_param_int(mog_em_module, "K", 1);
+  fx_format_param(mog_em_module, "D", "%d", data_points.n_rows());
+
+  ////// Timing the initialization of the mixture model //////
+  fx_timer_start(mog_em_module, "model_init");
+  mog.Init(mog_em_module);
+  fx_timer_stop(mog_em_module, "model_init");
+
+  ////// Computing the parameters of the model using the EM algorithm //////
+  ArrayList<double> results;
+
+  fx_timer_start(mog_em_module, "EM");
+  mog.ExpectationMaximization(data_points);
+  fx_timer_stop(mog_em_module, "EM");
+  
+  mog.Display();
+  mog.OutputResults(&results);
+
+  ////// OUTPUT RESULTS //////
+
+  const char *output_filename = fx_param_str(NULL, "output", "output.csv");
+
+  FILE *output_file = fopen(output_filename, "w");
+
+  ot::Print(results, output_file);
+  fclose(output_file);
+  fx_done();
+
+  return 1;
+}
diff --git a/fastlib/u/pram/mog_em/faster/phi.h b/fastlib/u/pram/mog_em/faster/phi.h
new file mode 100644
index 0000000000..e7f9a7c2ae
--- /dev/null
+++ b/fastlib/u/pram/mog_em/faster/phi.h
@@ -0,0 +1,142 @@
+/**
+ * @author Parikshit Ram (pram@cc.gatech.edu)
+ * @file phi.h
+ *
+ * This file computes the Gaussian probability
+ * density function
+ */
+#include "fastlib/fastlib.h"
+#include "fastlib/fastlib_int.h"
+#include <cmath>
+
+/**
+ * Calculates the multivariate Gaussian probability density function
+ * 
+ * Example use:
+ * @code
+ * Vector x, mean;
+ * Matrix cov;
+ * ....
+ * long double f = phi(x, mean, cov);
+ * @endcode
+ */
+
+long double phi(Vector& x , Vector& mean , Matrix& cov) {
+	
+  long double det, f;
+  double exponent;
+  index_t dim;
+  Matrix inv;
+  Vector diff, tmp;
+	
+    dim = x.length();
+  la::InverseInit(cov, &inv);
+  det = la::Determinant(cov);
+  
+  if( det < 0){
+    det = -det;
+  }
+  la::SubInit(mean,x,&diff);
+  la::MulInit(inv, diff, &tmp);
+  exponent = la::Dot(diff, tmp);
+  long double tmp1, tmp2, tmp3;
+  tmp1 = 1;
+  tmp2 = dim;
+  tmp2 = tmp2/2;
+  tmp2 = pow((2*(math::PI)),tmp2);
+  tmp1 = tmp1/tmp2;
+  tmp3 = 1;
+  tmp2 = sqrt(det);
+  tmp3 = tmp3/tmp2;
+  tmp2 = -exponent;
+  tmp2 = tmp2 / 2;
+  f = (tmp1*tmp3*exp(tmp2));
+
+  return f;
+}
+
+/**
+ * Calculates the univariate Gaussian probability density function
+ * 
+ * Example use:
+ * @code
+ * double x, mean, var;
+ * ....
+ * long double f = phi(x, mean, var);
+ * @endcode
+ */
+
+long double phi(double x, double mean, double var) {
+
+  long double f;
+	
+  f = exp(-1.0*((x-mean)*(x-mean)/(2*var)))/sqrt(2*math::PI*var);
+  return f;
+}
+
+/**
+ * Calculates the multivariate Gaussian probability density function 
+ * and also the gradients with respect to the mean and the variance
+ *
+ * Example use:
+ * @code
+ * Vector x, mean, g_mean, g_cov;
+ * ArrayList<Matrix> d_cov; // the dSigma
+ * ....
+ * long double f = phi(x, mean, cov, d_cov, &g_mean, &g_cov);
+ * @endcode
+ */
+
+long double phi(Vector& x, Vector& mean, Matrix& cov, ArrayList<Matrix>& d_cov, Vector *g_mean, Vector *g_cov){
+	
+  long double det, f;
+  double exponent;
+  index_t dim;
+  Matrix inv;
+  Vector diff, tmp;
+	
+  dim = x.length();
+  la::InverseInit(cov, &inv);
+  det = la::Determinant(cov);
+
+  if( det < 0){
+    det = -det;
+  }
+  la::SubInit(mean,x,&diff);
+  la::MulInit(inv, diff, &tmp);
+  exponent = la::Dot(diff, tmp);
+  long double tmp1, tmp2, tmp3;
+  tmp1 = 1;
+  tmp2 = dim;
+  tmp2 = tmp2/2;
+  tmp2 = pow((2*(math::PI)),tmp2);
+  tmp1 = tmp1/tmp2;
+  tmp3 = 1;
+  tmp2 = sqrt(det);
+  tmp3 = tmp3/tmp2;
+  tmp2 = -exponent;
+  tmp2 = tmp2 / 2;
+  f = (tmp1*tmp3*exp(tmp2));
+
+  // Calculating the g_mean values  which would be a (1 X dim) vector
+  la::ScaleInit(f,tmp,g_mean);
+	
+  // Calculating the g_cov values which would be a (1 X (dim*(dim+1)/2)) vector
+  double *g_cov_tmp;
+  g_cov_tmp = (double*)malloc(d_cov.size()*sizeof(double));
+  for(index_t i = 0; i < d_cov.size(); i++){
+    Vector tmp_d;
+    Matrix inv_d;
+    long double tmp_d_cov_d_r;
+		
+    la::MulInit(d_cov[i],tmp,&tmp_d);
+    tmp_d_cov_d_r = la::Dot(tmp_d,tmp);
+    la::MulInit(inv,d_cov[i],&inv_d);
+    for(index_t j = 0; j < dim; j++)
+      tmp_d_cov_d_r += inv_d.get(j,j);
+    g_cov_tmp[i] = f*tmp_d_cov_d_r/2;
+  }
+  g_cov->Copy(g_cov_tmp,d_cov.size());
+	
+  return f;
+}
diff --git a/fastlib/u/pram/mog_em/mog.cc b/fastlib/u/pram/mog_em/mog.cc
index 86d33ac061..4aeaf2b520 100644
--- a/fastlib/u/pram/mog_em/mog.cc
+++ b/fastlib/u/pram/mog_em/mog.cc
@@ -51,7 +51,7 @@ void MoGEM::ExpectationMaximization(Matrix& data_points) {
   best_l = -INFTY;
   index_t restarts = 0;
   // performing 5 restarts and choosing the best from them
-  while (restarts < 5) { 
+  while (restarts < 1) { 
 
     // assign initial values to 'mu', 'sig' and 'omega' using k-means
     KMeans(data_points, &mu_temp, &sigma_temp, &omega_temp, num_gauss);