mle-am-diag-gmm-test.cc
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// gmm/mle-am-diag-gmm-test.cc
// Copyright 2009-2012 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.
#include "gmm/model-test-common.h"
#include "gmm/am-diag-gmm.h"
#include "gmm/mle-am-diag-gmm.h"
#include "util/kaldi-io.h"
using kaldi::AmDiagGmm;
using kaldi::AccumAmDiagGmm;
using kaldi::int32;
using kaldi::BaseFloat;
namespace ut = kaldi::unittest;
using namespace kaldi;
// Tests the Read() and Write() methods for the accumulators, in both binary
// and ASCII mode.
void TestAmDiagGmmAccsIO(const AmDiagGmm &am_gmm,
const Matrix<BaseFloat> &feats) {
kaldi::GmmFlagsType flags = kaldi::kGmmAll;
AccumAmDiagGmm accs;
accs.Init(am_gmm, flags);
BaseFloat loglike = 0.0;
for (int32 i = 0; i < feats.NumRows(); i++) {
int32 state = RandInt(0, am_gmm.NumPdfs()-1);
loglike += accs.AccumulateForGmm(am_gmm, feats.Row(i), state, 1.0);
}
KALDI_LOG << "Data log-likelihood = " << loglike << " over "
<< feats.NumRows() << " frames.";
KALDI_LOG << "Accumulated values: log-like = " << accs.TotLogLike()
<< ", # frames = " << accs.TotCount();
AssertEqual(accs.TotLogLike(), loglike, 1e-5);
AssertEqual(accs.TotCount(), static_cast<BaseFloat>(feats.NumRows()), 1e-5);
MleDiagGmmOptions config;
AmDiagGmm *am_gmm1 = new AmDiagGmm();
am_gmm1->CopyFromAmDiagGmm(am_gmm);
MleAmDiagGmmUpdate(config, accs, flags, am_gmm1, NULL, NULL);
int32 check_pdf = RandInt(0, am_gmm.NumPdfs()-1),
check_frame = RandInt(0, feats.NumRows()-1);
BaseFloat loglike1 = am_gmm1->LogLikelihood(check_pdf, feats.Row(check_frame));
delete am_gmm1;
// First, non-binary write
accs.Write(kaldi::Output("tmpf", false).Stream(), false);
bool binary_in;
AccumAmDiagGmm *accs1 = new AccumAmDiagGmm();
// Non-binary read
kaldi::Input ki1("tmpf", &binary_in);
accs1->Read(ki1.Stream(), binary_in, false);
AmDiagGmm *am_gmm2 = new AmDiagGmm();
am_gmm2->CopyFromAmDiagGmm(am_gmm);
MleAmDiagGmmUpdate(config, accs, flags, am_gmm2, NULL, NULL);
BaseFloat loglike2 = am_gmm2->LogLikelihood(check_pdf, feats.Row(check_frame));
kaldi::AssertEqual(loglike1, loglike2, 1e-6);
delete am_gmm2;
delete accs1;
// Next, binary write
accs.Write(kaldi::Output("tmpfb", true).Stream(), true);
AccumAmDiagGmm *accs2 = new AccumAmDiagGmm();
// Binary read
kaldi::Input ki2("tmpfb", &binary_in);
accs2->Read(ki2.Stream(), binary_in, false);
AmDiagGmm *am_gmm3 = new AmDiagGmm();
am_gmm3->CopyFromAmDiagGmm(am_gmm);
MleAmDiagGmmUpdate(config, accs, flags, am_gmm3, NULL, NULL);
BaseFloat loglike3 = am_gmm3->LogLikelihood(check_pdf, feats.Row(check_frame));
kaldi::AssertEqual(loglike1, loglike3, 1e-6);
delete am_gmm3;
delete accs2;
unlink("tmpf");
unlink("tmpfb");
}
void UnitTestMleAmDiagGmm() {
int32 dim = 1 + kaldi::RandInt(0, 9), // random dimension of the gmm
num_pdfs = 5 + kaldi::RandInt(0, 9); // random number of states
AmDiagGmm am_gmm;
int32 total_num_comp = 0;
for (int32 i = 0; i < num_pdfs; i++) {
int32 num_comp = 1 + kaldi::RandInt(0, 9); // random mixture size
kaldi::DiagGmm gmm;
ut::InitRandDiagGmm(dim, num_comp, &gmm);
am_gmm.AddPdf(gmm);
total_num_comp += num_comp;
}
kaldi::Matrix<BaseFloat> feats;
{ // First, generate random means and variances
int32 num_feat_comp = total_num_comp + kaldi::RandInt(-total_num_comp/2,
total_num_comp/2);
kaldi::Matrix<BaseFloat> means(num_feat_comp, dim),
vars(num_feat_comp, dim);
for (int32 m = 0; m < num_feat_comp; m++) {
for (int32 d= 0; d < dim; d++) {
means(m, d) = kaldi::RandGauss();
vars(m, d) = Exp(kaldi::RandGauss()) + 1e-2;
}
}
// Now generate random features with those means and variances.
feats.Resize(num_feat_comp * 200, dim);
for (int32 m = 0; m < num_feat_comp; m++) {
kaldi::SubMatrix<BaseFloat> tmp(feats, m*200, 200, 0, dim);
ut::RandDiagGaussFeatures(200, means.Row(m), vars.Row(m), &tmp);
}
}
TestAmDiagGmmAccsIO(am_gmm, feats);
}
int main() {
// std::srand(time(NULL));
for (int i = 0; i < 10; i++)
UnitTestMleAmDiagGmm();
std::cout << "Test OK.\n";
return 0;
}