Repository navigation
Expand file tree
/
Copy pathregression-tree-test.cc
More file actions
152 lines (125 loc) · 4.06 KB
/
Copy pathregression-tree-test.cc
File metadata and controls
152 lines (125 loc) · 4.06 KB
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
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
// transform/regression-tree-test.cc
// Copyright 2009-2011 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 "transform/regression-tree.h"
#include "util/common-utils.h"
using namespace kaldi;
void
test_io(const RegressionTree ®tree,
const AmDiagGmm &acmodel,
bool binary) {
std::cout << "Testing I/O, binary = " << binary << '\n';
regtree.Write(Output("tmp_regtree", binary).Stream(),
binary);
bool binary_in;
RegressionTree regtree2;
Input ki("tmp_regtree", &binary_in);
regtree2.Read(ki.Stream(),
binary_in, acmodel);
std::ostringstream s1, s2;
regtree.Write(s1, false);
regtree2.Write(s2, false);
KALDI_ASSERT(s1.str() == s2.str());
unlink("tmp_regtree");
}
// void
// join_gmm(const DiagGmm &gmm1, const DiagGmm &gmm2, DiagGmm *gmm) {
// KALDI_ASSERT(gmm1.Dimension() == gmm2.Dimension());
// size_t num_comp = gmm1.NumGauss() + gmm2.NumGauss();
// size_t dim = gmm1.Dimension();
//
// Matrix<BaseFloat> means1(gmm1.NumGauss());
// size_t num_comp
// }
void
rand_diag_gmm(size_t num_comp, size_t dim, DiagGmm *gmm) {
Vector<BaseFloat> weights(num_comp);
Matrix<BaseFloat> means(num_comp, dim);
Matrix<BaseFloat> vars(num_comp, dim);
BaseFloat tot_weight = 0.0;
for (size_t m = 0; m < num_comp; m++) {
weights(m) = kaldi::RandUniform();
for (size_t d= 0; d < dim; d++) {
means(m, d) = kaldi::RandGauss();
vars(m, d) = Exp(kaldi::RandGauss()) + 1e-5;
}
tot_weight += weights(m);
}
weights.Scale(1.0/tot_weight);
vars.InvertElements();
gmm->SetWeights(weights);
gmm->SetInvVarsAndMeans(vars, means);
gmm->ComputeGconsts();
}
void
UnitTestRegressionTree() {
// using namespace kaldi;
// dimension of the gmm
// size_t dim = kaldi::RandInt(5, 20);
size_t dim = 2;
// number of mixtures in the data
size_t num_comp = kaldi::RandInt(2, 2);;
std::cout << "Running test with " << num_comp << " components and "
<< dim << " dimensional vectors" << '\n';
// generate random gmm
DiagGmm gmm1;
gmm1.Resize(num_comp, dim);
rand_diag_gmm(num_comp, dim, &gmm1);
// shift means for components
Matrix<BaseFloat> means2(num_comp, dim);
Vector<BaseFloat> tmp_vec(dim);
gmm1.GetMeans(&means2);
for (int32 c = 0; c < static_cast<int32>(num_comp); c++) {
// tmp_vec.SetRandn();
// tmp_vec.Scale(0.01);
tmp_vec.Set(0.001 * means2.Row(c).Max());
means2.Row(c).AddVec(1.0, tmp_vec);
}
// let's have another gmm with shifted means
DiagGmm gmm2;
gmm2.CopyFromDiagGmm(gmm1);
gmm2.SetMeans(means2);
AmDiagGmm acmodel;
acmodel.AddPdf(gmm1);
acmodel.AddPdf(gmm2);
// let's have uniform occupancies
size_t num_pdfs = 2;
Vector<BaseFloat> occs(num_pdfs);
for (int32 i = 0; i < static_cast<int32>(num_pdfs); i++) {
occs(i) = 1.0/static_cast<BaseFloat>(num_pdfs*num_comp);
}
for (int32 i = 0; i < gmm1.NumGauss(); i++) {
gmm1.GetComponentMean(i, &tmp_vec);
tmp_vec.Write(std::cout, false);
gmm2.GetComponentMean(i, &tmp_vec);
tmp_vec.Write(std::cout, false);
}
RegressionTree regtree;
std::vector<int32> sil_pdfs;
if (Rand() % 2 == 0)
sil_pdfs.push_back(Rand() % 2);
regtree.BuildTree(occs, sil_pdfs, acmodel, 2);
// test I/O
test_io(regtree, acmodel, false);
// test_io(regtree, acmodel, true);
}
int
main() {
// repeat the test X times
for (int i = 0; i < 4; i++)
UnitTestRegressionTree();
std::cout << "Test OK.\n";
}