get-feature-transform.cc
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// nnet2/get-feature-transform.cc
// Copyright 2009-2011 Jan Silovsky
// 2013 Johns Hopkins University (author: 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.
#include "nnet2/get-feature-transform.h"
namespace kaldi {
void FeatureTransformEstimate::Estimate(const FeatureTransformEstimateOptions &opts,
Matrix<BaseFloat> *M,
TpMatrix<BaseFloat> *C) const {
double count;
Vector<double> total_mean;
SpMatrix<double> total_covar, between_covar;
GetStats(&total_covar, &between_covar, &total_mean, &count);
KALDI_LOG << "Data count is " << count;
EstimateInternal(opts, total_covar, between_covar, total_mean, M, C);
}
// static
void FeatureTransformEstimate::EstimateInternal(
const FeatureTransformEstimateOptions &opts,
const SpMatrix<double> &total_covar,
const SpMatrix<double> &between_covar,
const Vector<double> &total_mean,
Matrix<BaseFloat> *M,
TpMatrix<BaseFloat> *C) {
int32 target_dim = opts.dim, dim = total_covar.NumRows();
// Interpret zero or negative target_dim as the full dim
if (target_dim <= 0)
target_dim = dim;
// between-class covar is of most rank C-1
KALDI_ASSERT(target_dim <= dim);
// within-class covariance
SpMatrix<double> wc_covar(total_covar);
wc_covar.AddSp(-1.0, between_covar);
TpMatrix<double> wc_covar_sqrt(dim);
try {
wc_covar_sqrt.Cholesky(wc_covar);
if (C != NULL) {
C->Resize(dim);
C->CopyFromTp(wc_covar_sqrt);
}
} catch (...) {
BaseFloat smooth = 1.0e-03 * wc_covar.Trace() / wc_covar.NumRows();
KALDI_LOG << "Cholesky failed (possibly not +ve definite), so adding " << smooth
<< " to diagonal and trying again.\n";
for (int32 i = 0; i < wc_covar.NumRows(); i++)
wc_covar(i, i) += smooth;
wc_covar_sqrt.Cholesky(wc_covar);
}
Matrix<double> wc_covar_sqrt_mat(wc_covar_sqrt);
wc_covar_sqrt_mat.Invert();
SpMatrix<double> tmp_sp(dim);
tmp_sp.AddMat2Sp(1.0, wc_covar_sqrt_mat, kNoTrans, between_covar, 0.0);
Matrix<double> tmp_mat(tmp_sp);
Matrix<double> svd_u(dim, dim), svd_vt(dim, dim);
Vector<double> svd_d(dim);
tmp_mat.Svd(&svd_d, &svd_u, &svd_vt);
SortSvd(&svd_d, &svd_u);
KALDI_LOG << "LDA singular values are " << svd_d;
KALDI_LOG << "Sum of all singular values is " << svd_d.Sum();
KALDI_LOG << "Sum of selected singular values is " <<
SubVector<double>(svd_d, 0, target_dim).Sum();
Matrix<double> lda_mat(dim, dim);
lda_mat.AddMatMat(1.0, svd_u, kTrans, wc_covar_sqrt_mat, kNoTrans, 0.0);
// finally, copy first target_dim rows to m
M->Resize(target_dim, dim);
M->CopyFromMat(lda_mat.Range(0, target_dim, 0, dim));
if (opts.within_class_factor != 1.0) {
for (int32 i = 0; i < svd_d.Dim(); i++) {
BaseFloat old_var = 1.0 + svd_d(i), // the total variance of that dim..
new_var = opts.within_class_factor + svd_d(i), // the variance we want..
scale = sqrt(new_var / old_var);
if (i < M->NumRows())
M->Row(i).Scale(scale);
}
}
if (opts.max_singular_value > 0.0) {
int32 rows = M->NumRows(), cols = M->NumCols(),
min_dim = std::min(rows, cols);
Matrix<BaseFloat> U(rows, min_dim), Vt(min_dim, cols);
Vector<BaseFloat> s(min_dim);
M->Svd(&s, &U, &Vt); // decompose m = U diag(s) Vt.
BaseFloat max_s = s.Max();
int32 n;
s.ApplyCeiling(opts.max_singular_value, &n);
if (n > 0) {
KALDI_LOG << "Applied ceiling to " << n << " out of " << s.Dim()
<< " singular values of transform using ceiling "
<< opts.max_singular_value << ", max is " << max_s;
Vt.MulRowsVec(s);
// reconstruct m with the modified singular values:
M->AddMatMat(1.0, U, kNoTrans, Vt, kNoTrans, 0.0);
}
}
if (opts.remove_offset)
AddMeanOffset(total_mean, M);
}
void FeatureTransformEstimateMulti::EstimateTransformPart(
const FeatureTransformEstimateOptions &opts,
const std::vector<int32> &indexes,
const SpMatrix<double> &total_covar,
const SpMatrix<double> &between_covar,
const Vector<double> &mean,
Matrix<BaseFloat> *M) const {
int32 full_dim = Dim(), proj_dim = indexes.size();
Matrix<double> transform(proj_dim, full_dim); // projects from full to projected dim.
for (int32 i = 0; i < proj_dim; i++)
transform(i, indexes[i]) = 1.0;
SpMatrix<double> total_covar_proj(proj_dim), between_covar_proj(proj_dim);
Vector<double> mean_proj(proj_dim);
total_covar_proj.AddMat2Sp(1.0, transform, kNoTrans, total_covar, 0.0);
between_covar_proj.AddMat2Sp(1.0, transform, kNoTrans, between_covar, 0.0);
mean_proj.AddMatVec(1.0, transform, kNoTrans, mean, 0.0);
Matrix<BaseFloat> M_proj;
FeatureTransformEstimateOptions opts_tmp(opts);
opts_tmp.dim = proj_dim;
EstimateInternal(opts_tmp, total_covar_proj, between_covar_proj, mean_proj,
&M_proj, NULL);
if (M_proj.NumCols() == proj_dim + 1) { // Extend transform to add the extra "1" that we
// use to handle mean shifts..
transform.Resize(proj_dim + 1, full_dim + 1, kCopyData);
transform(proj_dim, full_dim) = 1.0;
}
M->Resize(proj_dim, transform.NumCols());
// Produce output..
M->AddMatMat(1.0, M_proj, kNoTrans, Matrix<BaseFloat>(transform),
kNoTrans, 0.0);
}
void FeatureTransformEstimateMulti::Estimate(
const FeatureTransformEstimateOptions &opts,
const std::vector<std::vector<int32> > &indexes,
Matrix<BaseFloat> *M) const {
int32 input_dim = Dim(), output_dim = 0, num_transforms = indexes.size();
for (int32 i = 0; i < num_transforms; i++) { // some input-checking.
KALDI_ASSERT(indexes[i].size() > 0);
std::vector<int32> this_indexes(indexes[i]);
std::sort(this_indexes.begin(), this_indexes.end());
KALDI_ASSERT(IsSortedAndUniq(this_indexes)); // check for duplicates.
KALDI_ASSERT(this_indexes.front() >= 0);
KALDI_ASSERT(this_indexes.back() < input_dim);
output_dim += this_indexes.size();
}
int32 input_dim_ext = (opts.remove_offset ? input_dim + 1 : input_dim);
M->Resize(output_dim, input_dim_ext);
double count;
Vector<double> total_mean;
SpMatrix<double> total_covar, between_covar;
GetStats(&total_covar, &between_covar, &total_mean, &count);
int32 cur_output_index = 0;
for (int32 i = 0; i < num_transforms; i++) {
Matrix<BaseFloat> M_tmp;
EstimateTransformPart(opts, indexes[i], total_covar, between_covar,
total_mean, &M_tmp);
int32 this_output_dim = indexes[i].size();
M->Range(cur_output_index, this_output_dim, 0, M->NumCols()).
CopyFromMat(M_tmp);
cur_output_index += this_output_dim;
}
}
} // End of namespace kaldi