Blame view
src/gmm/am-diag-gmm-test.cc
4.03 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 |
// gmm/am-diag-gmm-test.cc
// 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.
#include "gmm/model-test-common.h"
#include "gmm/am-diag-gmm.h"
#include "util/kaldi-io.h"
using kaldi::AmDiagGmm;
using kaldi::int32;
using kaldi::BaseFloat;
namespace ut = kaldi::unittest;
// Tests the Read() and Write() methods, in both binary and ASCII mode, as well
// as Check(), CopyFromSgmm(), and methods in likelihood computations.
void TestAmDiagGmmIO(const AmDiagGmm &am_gmm) {
int32 dim = am_gmm.Dim();
kaldi::Vector<BaseFloat> feat(dim);
for (int32 d = 0; d < dim; d++) {
feat(d) = kaldi::RandGauss();
}
BaseFloat loglike = 0.0;
for (int32 i = 0; i < am_gmm.NumPdfs(); i++)
loglike += am_gmm.LogLikelihood(i, feat);
// First, non-binary write
am_gmm.Write(kaldi::Output("tmpf", false).Stream(), false);
bool binary_in;
AmDiagGmm *am_gmm1 = new AmDiagGmm();
// Non-binary read
kaldi::Input ki1("tmpf", &binary_in);
am_gmm1->Read(ki1.Stream(), binary_in);
BaseFloat loglike1 = 0.0;
for (int32 i = 0; i < am_gmm1->NumPdfs(); i++)
loglike1 += am_gmm1->LogLikelihood(i, feat);
kaldi::AssertEqual(loglike, loglike1, 1e-4);
// Next, binary write
am_gmm1->Write(kaldi::Output("tmpfb", true).Stream(), true);
delete am_gmm1;
AmDiagGmm *am_gmm2 = new AmDiagGmm();
// Binary read
kaldi::Input ki2("tmpfb", &binary_in);
am_gmm2->Read(ki2.Stream(), binary_in);
BaseFloat loglike2 = 0.0;
for (int32 i = 0; i < am_gmm2->NumPdfs(); i++)
loglike2 += am_gmm2->LogLikelihood(i, feat);
kaldi::AssertEqual(loglike, loglike2, 1e-4);
delete am_gmm2;
unlink("tmpf");
unlink("tmpfb");
}
void TestSplitStates(const AmDiagGmm &am_gmm) {
int32 target_comp = 2 * am_gmm.NumGauss();
kaldi::Vector<BaseFloat> occs(am_gmm.NumPdfs());
for (int32 i = 0; i < occs.Dim(); i++)
occs(i) = std::fabs(kaldi::RandGauss()) * (kaldi::RandUniform()+1) * 4;
AmDiagGmm *am_gmm1 = new AmDiagGmm();
am_gmm1->CopyFromAmDiagGmm(am_gmm);
am_gmm1->SplitByCount(occs, target_comp, 0.01, 0.2, 0.0);
int32 dim = am_gmm.Dim();
kaldi::Vector<BaseFloat> feat(dim);
for (int32 d = 0; d < dim; d++) {
feat(d) = kaldi::RandGauss();
}
BaseFloat loglike = am_gmm.LogLikelihood(0, feat);
BaseFloat loglike1 = am_gmm1->LogLikelihood(0, feat);
kaldi::AssertEqual(loglike, loglike1, 1e-2);
delete am_gmm1;
}
void TestClustering(const AmDiagGmm &am_gmm) {
int32 target_comp = am_gmm.NumGauss() / 5,
interm_comp = am_gmm.NumGauss() / 2;
kaldi::Vector<BaseFloat> occs(am_gmm.NumPdfs());
for (int32 i = 0; i < occs.Dim(); i++)
occs(i) = std::fabs(kaldi::RandGauss()) * (kaldi::RandUniform()+1) * 4;
kaldi::UbmClusteringOptions ubm_opts(target_comp, 0.2, interm_comp, 0.01, 30);
kaldi::DiagGmm ubm;
ClusterGaussiansToUbm(am_gmm, occs, ubm_opts, &ubm);
}
void UnitTestAmDiagGmm() {
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;
for (int32 i = 0; i < num_pdfs; i++) {
int32 num_comp = 1 + kaldi::RandInt(0, 9); // random number of mixtures
kaldi::DiagGmm gmm;
ut::InitRandDiagGmm(dim, num_comp, &gmm);
am_gmm.AddPdf(gmm);
}
TestAmDiagGmmIO(am_gmm);
TestSplitStates(am_gmm);
TestClustering(am_gmm);
}
int main() {
for (int i = 0; i < 5; i++)
UnitTestAmDiagGmm();
std::cout << "Test OK.
";
return 0;
}
|