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src/gmm/model-test-common.cc
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// gmm/model-test-common.cc
// Copyright 2009-2011 Microsoft Corporation; Jan Silovsky;
// Saarland University
// 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 <algorithm>
#include <vector>
#include "matrix/matrix-lib.h"
#include "gmm/model-test-common.h"
namespace kaldi {
namespace unittest {
void RandPosdefSpMatrix(int32 dim, SpMatrix<BaseFloat> *matrix,
TpMatrix<BaseFloat> *matrix_sqrt, BaseFloat *logdet) {
// generate random (non-singular) matrix
Matrix<BaseFloat> tmp(dim, dim);
while (1) {
tmp.SetRandn();
if (tmp.Cond() < 100) break;
KALDI_LOG << "Condition number of random matrix large "
<< static_cast<float>(tmp.Cond())
<< ", trying again (this is normal)
";
}
// tmp * tmp^T will give positive definite matrix
matrix->AddMat2(1.0, tmp, kNoTrans, 0.0);
if (matrix_sqrt != NULL) matrix_sqrt->Cholesky(*matrix);
if (logdet != NULL) *logdet = matrix->LogPosDefDet();
if ((matrix_sqrt == NULL) && (logdet == NULL)) {
TpMatrix<BaseFloat> sqrt(dim);
sqrt.Cholesky(*matrix);
}
}
void RandDiagGaussFeatures(int32 num_samples,
const VectorBase<BaseFloat> &mean,
const VectorBase<BaseFloat> &sqrt_var,
MatrixBase<BaseFloat> *feats) {
int32 dim = mean.Dim();
KALDI_ASSERT(feats != NULL);
KALDI_ASSERT(feats->NumRows() == num_samples &&
feats->NumCols() == dim);
KALDI_ASSERT(sqrt_var.Dim() == dim);
Vector<BaseFloat> rnd_vec(dim);
for (int32 counter = 0; counter < num_samples; counter++) {
for (int32 d = 0; d < dim; d++) {
rnd_vec(d) = RandGauss();
}
feats->Row(counter).CopyFromVec(mean);
feats->Row(counter).AddVecVec(1.0, sqrt_var, rnd_vec, 1.0);
}
}
void RandFullGaussFeatures(int32 num_samples,
const VectorBase<BaseFloat> &mean,
const TpMatrix<BaseFloat> &sqrt_var,
MatrixBase<BaseFloat> *feats) {
int32 dim = mean.Dim();
KALDI_ASSERT(feats != NULL);
KALDI_ASSERT(feats->NumRows() == num_samples && feats->NumCols() == dim);
KALDI_ASSERT(sqrt_var.NumRows() == dim);
Vector<BaseFloat> rnd_vec(dim);
for (int32 counter = 0; counter < num_samples; counter++) {
for (int32 d = 0; d < dim; d++) {
rnd_vec(d) = RandGauss();
}
feats->Row(counter).CopyFromVec(mean);
feats->Row(counter).AddTpVec(1.0, sqrt_var, kNoTrans, rnd_vec, 1.0);
}
}
void InitRandDiagGmm(int32 dim, int32 num_comp, DiagGmm *gmm) {
Vector<BaseFloat> weights(num_comp);
Matrix<BaseFloat> means(num_comp, dim), inv_vars(num_comp, dim);
for (int32 m = 0; m < num_comp; m++) {
weights(m) = Exp(RandGauss());
for (int32 d= 0; d < dim; d++) {
means(m, d) = RandGauss() / (1 + d);
inv_vars(m, d) = Exp(RandGauss() / (1 + d)) + 1e-2;
}
}
weights.Scale(1.0 / weights.Sum());
gmm->Resize(num_comp, dim);
gmm->SetWeights(weights);
gmm->SetInvVarsAndMeans(inv_vars, means);
gmm->ComputeGconsts();
}
void InitRandFullGmm(int32 dim, int32 num_comp, FullGmm *gmm) {
Vector<BaseFloat> weights(num_comp);
Matrix<BaseFloat> means(num_comp, dim);
std::vector< SpMatrix<BaseFloat> > invcovars(num_comp);
for (int32 mix = 0; mix < num_comp; mix++) {
invcovars[mix].Resize(dim);
}
BaseFloat tot_weight = 0.0;
for (int32 m = 0; m < num_comp; m++) {
weights(m) = RandUniform() + 1e-2;
for (int32 d= 0; d < dim; d++) {
means(m, d) = RandGauss();
}
RandPosdefSpMatrix(dim, &invcovars[m], NULL, NULL);
invcovars[m].InvertDouble();
tot_weight += weights(m);
}
weights.Scale(1/tot_weight);
gmm->Resize(num_comp, dim);
gmm->SetWeights(weights);
gmm->SetInvCovarsAndMeans(invcovars, means);
gmm->ComputeGconsts();
}
} // End namespace unittests
} // End namespace kaldi
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