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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_