diff --git a/bob/learn/misc/cpp/GMMMachine.cpp b/bob/learn/misc/cpp/GMMMachine.cpp
index 2d81331187ff24b46792660534f9586631662278..a8636ce1f7dadc0b10659de8ed23d0a25c190a42 100644
--- a/bob/learn/misc/cpp/GMMMachine.cpp
+++ b/bob/learn/misc/cpp/GMMMachine.cpp
@@ -26,8 +26,7 @@ bob::learn::misc::GMMMachine::GMMMachine(bob::io::base::HDF5File& config):
   load(config);
 }
 
-bob::learn::misc::GMMMachine::GMMMachine(const GMMMachine& other):
-  Machine<blitz::Array<double,1>, double>(other), m_gaussians(0)
+bob::learn::misc::GMMMachine::GMMMachine(const GMMMachine& other)  
 {
   copy(other);
 }
@@ -94,9 +93,6 @@ void bob::learn::misc::GMMMachine::copy(const GMMMachine& other) {
 
 bob::learn::misc::GMMMachine::~GMMMachine() { }
 
-void bob::learn::misc::GMMMachine::setNInputs(const size_t n_inputs) {
-  resize(m_n_gaussians,n_inputs);
-}
 
 void bob::learn::misc::GMMMachine::resize(const size_t n_gaussians, const size_t n_inputs) {
   m_n_gaussians = n_gaussians;
@@ -256,6 +252,7 @@ double bob::learn::misc::GMMMachine::logLikelihood_(const blitz::Array<double, 1
   return logLikelihood_(x,m_cache_log_weighted_gaussian_likelihoods);
 }
 
+/*
 void bob::learn::misc::GMMMachine::forward(const blitz::Array<double,1>& input, double& output) const {
   if(static_cast<size_t>(input.extent(0)) != m_n_inputs) {
     boost::format m("expected input size (%u) does not match the size of input array (%d)");
@@ -270,6 +267,7 @@ void bob::learn::misc::GMMMachine::forward_(const blitz::Array<double,1>& input,
     double& output) const {
   output = logLikelihood(input);
 }
+*/
 
 void bob::learn::misc::GMMMachine::accStatistics(const blitz::Array<double,2>& input,
     bob::learn::misc::GMMStats& stats) const {
diff --git a/bob/learn/misc/include/bob.learn.misc/GMMMachine.h b/bob/learn/misc/include/bob.learn.misc/GMMMachine.h
index 93ce2352a09a602acdc4fd7acf51b25d205322d1..331c1a49738d520e22959860ddbffe2bbf6b3b27 100644
--- a/bob/learn/misc/include/bob.learn.misc/GMMMachine.h
+++ b/bob/learn/misc/include/bob.learn.misc/GMMMachine.h
@@ -12,7 +12,6 @@
 #ifndef BOB_LEARN_MISC_GMMMACHINE_H
 #define BOB_LEARN_MISC_GMMMACHINE_H
 
-#include <bob.learn.misc/Machine.h>
 #include <bob.learn.misc/Gaussian.h>
 #include <bob.learn.misc/GMMStats.h>
 #include <bob.io.base/HDF5File.h>
@@ -26,7 +25,7 @@ namespace bob { namespace learn { namespace misc {
  * @brief This class implements a multivariate diagonal Gaussian distribution.
  * @details See Section 2.3.9 of Bishop, "Pattern recognition and machine learning", 2006
  */
-class GMMMachine: public Machine<blitz::Array<double,1>, double>
+class GMMMachine
 {
   public:
     /**
@@ -78,11 +77,6 @@ class GMMMachine: public Machine<blitz::Array<double,1>, double>
      */
     virtual ~GMMMachine();
 
-    /**
-     * Set the feature dimensionality
-     */
-    void setNInputs(const size_t n_inputs);
-
     /**
      * Get number of inputs
      */
@@ -225,14 +219,14 @@ class GMMMachine: public Machine<blitz::Array<double,1>, double>
      * (overrides Machine::forward)
      * Dimension of the input is checked
      */
-    void forward(const blitz::Array<double,1>& input, double& output) const;
+    //void forward(const blitz::Array<double,1>& input, double& output) const;
 
     /**
      * Output the log likelihood of the sample, x
      * (overrides Machine::forward_)
      * @warning Dimension of the input is not checked
      */
-    void forward_(const blitz::Array<double,1>& input, double& output) const;
+    //void forward_(const blitz::Array<double,1>& input, double& output) const;
 
     /**
      * Accumulates the GMM statistics over a set of samples.