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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_
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