Blame view
src/transform/mllt.h
4.06 KB
8dcb6dfcb
first commit
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 |
// transform/mllt.h
// Copyright 2009-2011 Microsoft Corporation
// 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_MLLT_H_
#define KALDI_TRANSFORM_MLLT_H_
#include <vector>
#include "base/kaldi-common.h"
#include "gmm/am-diag-gmm.h"
#include "transform/transform-common.h"
#include "transform/regression-tree.h"
#include "util/kaldi-table.h"
#include "util/kaldi-holder.h"
namespace kaldi {
/** A class for estimating Maximum Likelihood Linear Transform, also known
as global Semi-tied Covariance (STC), for GMMs.
The resulting transform left-multiplies the feature vector.
*/
class MlltAccs {
public:
MlltAccs(): rand_prune_(0.0), beta_(0.0) { }
/// Need rand_prune >= 0.
/// The larger it is, the faster it will be. Zero is exact.
/// If a posterior p < rand_prune, will set p to
/// rand_prune with probability (p/rand_prune), otherwise zero.
/// E.g. 10 will give 10x speedup.
MlltAccs(int32 dim, BaseFloat rand_prune = 0.25) { Init(dim, rand_prune); }
/// initializes (destroys anything that was there before).
void Init(int32 dim, BaseFloat rand_prune = 0.25);
void Read(std::istream &is, bool binary, bool add = false);
void Write(std::ostream &os, bool binary) const;
int32 Dim() { return G_.size(); }; // returns model dimension.
/// The Update function does the ML update; it requires that M has the
/// right size.
/// @param [in, out] M The output transform, will be of dimension Dim() x Dim().
/// At input, should be the unit transform (the objective function
/// improvement is measured relative to this value).
/// @param [out] objf_impr_out The objective function improvement
/// @param [out] count_out The data-count
void Update(MatrixBase<BaseFloat> *M,
BaseFloat *objf_impr_out,
BaseFloat *count_out) const {
Update(beta_, G_, M, objf_impr_out, count_out);
}
// A static version of the Update function, so it can
// be called externally, given the right stats.
static void Update(double beta,
const std::vector<SpMatrix<double> > &G,
MatrixBase<BaseFloat> *M,
BaseFloat *objf_impr_out,
BaseFloat *count_out);
void AccumulateFromPosteriors(const DiagGmm &gmm,
const VectorBase<BaseFloat> &data,
const VectorBase<BaseFloat> &posteriors);
// Returns GMM likelihood.
BaseFloat AccumulateFromGmm(const DiagGmm &gmm,
const VectorBase<BaseFloat> &data,
BaseFloat weight); // e.g. weight = 1.0
BaseFloat AccumulateFromGmmPreselect(const DiagGmm &gmm,
const std::vector<int32> &gselect,
const VectorBase<BaseFloat> &data,
BaseFloat weight); // e.g. weight = 1.0
// premultiplies the means of the model by M. typically called
// after update.
// removed since we now do this using different code.
// static void MultiplyGmmMeans(const Matrix<BaseFloat> &M,
// DiagGmm *gmm);
/// rand_prune_ controls randomized pruning; the larger it is, the
/// more pruning we do. Typical value is 0.1.
BaseFloat rand_prune_;
double beta_; // count.
std::vector<SpMatrix<double> > G_; // the G matrices (d matrices of size d x d)
};
} // namespace kaldi
#endif // KALDI_TRANSFORM_MLLT_H_
|