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diff --git a/kaldi_io/src/kaldi/tree/clusterable-classes.h b/kaldi_io/src/kaldi/tree/clusterable-classes.h
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-// tree/clusterable-classes.h
-
-// Copyright 2009-2011  Microsoft Corporation;  Saarland University
-//                2014  Daniel Povey
-
-// See ../../COPYING for clarification regarding multiple authors
-//
-// Licensed under the Apache License, Version 2.0 (the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-//  http://www.apache.org/licenses/LICENSE-2.0
-//
-// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
-// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
-// MERCHANTABLITY OR NON-INFRINGEMENT.
-// See the Apache 2 License for the specific language governing permissions and
-// limitations under the License.
-
-#ifndef KALDI_TREE_CLUSTERABLE_CLASSES_H_
-#define KALDI_TREE_CLUSTERABLE_CLASSES_H_ 1
-
-#include <string>
-#include "itf/clusterable-itf.h"
-#include "matrix/matrix-lib.h"
-
-namespace kaldi {
-
-// Note: see sgmm/sgmm-clusterable.h for an SGMM-based clusterable
-// class.  We didn't include it here, to avoid adding an extra
-// dependency to this directory.
-
-/// \addtogroup clustering_group
-/// @{
-
-/// ScalarClusterable clusters scalars with x^2 loss.
-class ScalarClusterable: public Clusterable {
- public:
-  ScalarClusterable(): x_(0), x2_(0), count_(0) {}
-  explicit ScalarClusterable(BaseFloat x): x_(x), x2_(x*x), count_(1) {}
-  virtual std::string Type() const { return "scalar"; }
-  virtual BaseFloat Objf() const;
-  virtual void SetZero() { count_ = x_ = x2_ = 0.0; }
-  virtual void Add(const Clusterable &other_in);
-  virtual void Sub(const Clusterable &other_in);
-  virtual Clusterable* Copy() const;
-  virtual BaseFloat Normalizer() const {
-    return static_cast<BaseFloat>(count_);
-  }
-
-  // Function to write data to stream. Will organize input later [more complex]
-  virtual void Write(std::ostream &os, bool binary) const;
-  virtual Clusterable* ReadNew(std::istream &is, bool binary) const;
-
-  std::string Info();  // For debugging.
-  BaseFloat Mean() { return (count_ != 0 ? x_/count_ : 0.0); }
-  private:
-  BaseFloat x_;
-  BaseFloat x2_;
-  BaseFloat count_;
-
-  void Read(std::istream &is, bool binary);
-};
-
-
-/// GaussClusterable wraps Gaussian statistics in a form accessible
-/// to generic clustering algorithms.
-class GaussClusterable: public Clusterable {
- public:
-  GaussClusterable(): count_(0.0), var_floor_(0.0) {}
-  GaussClusterable(int32 dim, BaseFloat var_floor):
-      count_(0.0), stats_(2, dim), var_floor_(var_floor) {}
-
-  GaussClusterable(const Vector<BaseFloat> &x_stats,
-                   const Vector<BaseFloat> &x2_stats,
-                   BaseFloat var_floor, BaseFloat count);
-
-  virtual std::string Type() const {  return "gauss"; }
-  void AddStats(const VectorBase<BaseFloat> &vec, BaseFloat weight = 1.0);
-  virtual BaseFloat Objf() const;
-  virtual void SetZero();
-  virtual void Add(const Clusterable &other_in);
-  virtual void Sub(const Clusterable &other_in);
-  virtual BaseFloat Normalizer() const { return count_; }
-  virtual Clusterable *Copy() const;
-  virtual void Scale(BaseFloat f);
-  virtual void Write(std::ostream &os, bool binary) const;
-  virtual Clusterable *ReadNew(std::istream &is, bool binary) const;
-  virtual ~GaussClusterable() {}
-
-  BaseFloat count() const { return count_; }
-  // The next two functions are not const-correct, because of SubVector.
-  SubVector<double> x_stats() const { return stats_.Row(0); }
-  SubVector<double> x2_stats() const { return stats_.Row(1); }
- private:
-  double count_;
-  Matrix<double> stats_; // two rows: sum, then sum-squared.
-  double var_floor_;  // should be common for all objects created.
-
-  void Read(std::istream &is, bool binary);
-};
-
-/// @} end of "addtogroup clustering_group"
-
-inline void GaussClusterable::SetZero() {
-  count_ = 0;
-  stats_.SetZero();
-}
-
-inline GaussClusterable::GaussClusterable(const Vector<BaseFloat> &x_stats,
-                                          const Vector<BaseFloat> &x2_stats,
-                                          BaseFloat var_floor, BaseFloat count):
-    count_(count), stats_(2, x_stats.Dim()), var_floor_(var_floor) {
-  stats_.Row(0).CopyFromVec(x_stats);
-  stats_.Row(1).CopyFromVec(x2_stats);
-}
-
-
-/// VectorClusterable wraps vectors in a form accessible to generic clustering
-/// algorithms.  Each vector is associated with a weight; these could be 1.0.
-/// The objective function (to be maximized) is the negated sum of squared
-/// distances from the cluster center to each vector, times that vector's
-/// weight.
-class VectorClusterable: public Clusterable {
- public:
-  VectorClusterable(): weight_(0.0), sumsq_(0.0) {}
-
-  VectorClusterable(const Vector<BaseFloat> &vector,
-                    BaseFloat weight);
-
-  virtual std::string Type() const {  return "vector"; }
-  // Objf is negated weighted sum of squared distances.
-  virtual BaseFloat Objf() const;
-  virtual void SetZero() { weight_ = 0.0; sumsq_ = 0.0; stats_.Set(0.0); }
-  virtual void Add(const Clusterable &other_in);
-  virtual void Sub(const Clusterable &other_in);
-  virtual BaseFloat Normalizer() const { return weight_; }
-  virtual Clusterable *Copy() const;
-  virtual void Scale(BaseFloat f);
-  virtual void Write(std::ostream &os, bool binary) const;
-  virtual Clusterable *ReadNew(std::istream &is, bool binary) const;
-  virtual ~VectorClusterable() {}
-
- private:
-  double weight_;  // sum of weights of the source vectors.  Never negative.
-  Vector<double> stats_; // Equals the weighted sum of the source vectors.
-  double sumsq_;  // Equals the sum over all sources, of weight_ * vec.vec,
-                  // where vec = stats_ / weight_.  Used in computing
-                  // the objective function.
-  void Read(std::istream &is, bool binary);
-};
-
-
-
-}  // end namespace kaldi.
-
-#endif  // KALDI_TREE_CLUSTERABLE_CLASSES_H_