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src/transform/regression-tree.h
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// transform/regression-tree.h
// Copyright 2009-2011 Saarland University
// Author: Arnab Ghoshal
// 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_TRANSFORM_REGRESSION_TREE_H_
#define KALDI_TRANSFORM_REGRESSION_TREE_H_
#include <utility>
#include <vector>
#include "base/kaldi-common.h"
#include "tree/cluster-utils.h"
#include "gmm/am-diag-gmm.h"
#include "transform/transform-common.h"
namespace kaldi {
/** \class RegressionTree
* A regression tree is a clustering of Gaussian densities in an acoustic
* model, such that the group of Gaussians at each node of the tree are
* transformed by the same transform. Each node is thus called a regression
* class.
*/
class RegressionTree {
public:
RegressionTree() {}
/// Top-down clustering of the Gaussians in a model based on their means.
/// If sil_indices is nonempty, will put silence in a special class
/// using a top-level split.
void BuildTree(const Vector<BaseFloat> &state_occs,
const std::vector<int32> &sil_indices,
const AmDiagGmm &am,
int32 max_clusters);
/// Parses the regression tree and finds the nodes whose occupancies (read
/// from stats_in) are greater than min_count. The regclass_out vector has
/// size equal to number of baseclasses, and contains the regression class
/// index for each baseclass. The stats_out vector has size equal to number
/// of regression classes. Return value is true if at least one regression
/// class passed the count cutoff, false otherwise.
bool GatherStats(const std::vector<AffineXformStats*> &stats_in,
double min_count,
std::vector<int32> *regclasses_out,
std::vector<AffineXformStats*> *stats_out) const;
void Write(std::ostream &out, bool binary) const;
void Read(std::istream &in, bool binary, const AmDiagGmm &am);
/// Accessors (const)
int32 NumBaseclasses() const { return num_baseclasses_; }
const std::vector< std::pair<int32, int32> >& GetBaseclass(int32 bclass)
const { return baseclasses_[bclass]; }
int32 Gauss2BaseclassId(size_t pdf_id, size_t gauss_id) const {
return gauss2bclass_[pdf_id][gauss_id];
}
private:
int32 num_nodes_; ///< Total (non-leaf+leaf) nodes
/// For each node, index of its parent: size = num_nodes_
/// If 0 <= i < num_baseclasses_, then i is a leaf of the tree (a base class);
/// else a non-leaf node. parents_[i] > i, except for the top node
/// (last-numbered one), for which parents_[i] == i.
std::vector<int32> parents_;
int32 num_baseclasses_; ///< Number of leaf nodes
/// Each baseclass (leaf of regression tree) is a vector of Gaussian indices.
/// Each Gaussian in the model is indexed by (pdf, gaussian) indices pair.
std::vector< std::vector< std::pair<int32, int32> > > baseclasses_;
/// Mapping from (pdf, gaussian) indices to baseclasses
std::vector< std::vector<int32> > gauss2bclass_;
void MakeGauss2Bclass(const AmDiagGmm &am);
// Cannot have copy constructor and assigment operator
KALDI_DISALLOW_COPY_AND_ASSIGN(RegressionTree);
};
} // namespace kaldi
#endif // KALDI_TRANSFORM_REGRESSION_TREE_H_
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