-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmain.cpp
205 lines (141 loc) · 5.07 KB
/
main.cpp
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
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
// Copyright (C) 2019 Piotr (Peter) Beben <[email protected]>
// See LICENSE included.
#include "MatchingPursuit.h"
#include "OrthogonalPursuit.h"
#include "ksvd.h"
#include "ksvd_dct2D.h"
#include "cosine_transform.h"
#include <Eigen/Dense>
#include <QCoreApplication>
#include <iostream>
#include <ctime>
#include <omp.h>
using std::cout;
using std::endl;
using std::vector;
using Eigen::MatrixXf;
using Eigen::Matrix;
using Eigen::VectorXf;
using Eigen::Index;
using Eigen::Map;
using Eigen::Dynamic;
using vectorf = vector<float, Eigen::aligned_allocator<float>>;
void test_ksvd();
void test_ksvd_dct2D();
//-----------------------------------------------------------
int main(int argc, char *argv[])
{
QCoreApplication a(argc, argv);
Eigen::initParallel();
test_ksvd();
test_ksvd_dct2D();
return a.exec();
}
//-----------------------------------------------------------
void test_ksvd()
{
Index ndim = 25; // Dimension of atoms and signals.
Index nsig = 500; // No. of signal.
Index natm = 100; // No. of atoms.
Index latm = 8; // Sparsity constraint.
int maxiters = 8; // No. of K-SVD iterations.
bool useOpenMP = true;
MatchingPursuit mp;
OrthogonalPursuit op;
srand(2);
//std::srand(std::time(nullptr));
MatrixXf Y = MatrixXf::Random(ndim,nsig);
MatrixXf D = MatrixXf::Random(ndim,natm);
MatrixXf X = MatrixXf::Random(natm,nsig);
D.colwise().normalize();
MatrixXf Dorig = D;
MatrixXf Xorig = X;
clock_t c_start;
std::clock_t c_end;
double time_elapsed_ms;
cout << "\n\n** Testing ksvd **" ;
cout << "\n<Error measured as average coordinate difference>\n";
cout << "\n* Using matching pursuit *" ;
cout << "\nInitial error: " ;
cout << (Y-(D*X)).cwiseAbs().sum()/(ndim*nsig);
c_start = std::clock();
ksvd(useOpenMP, Y, latm, maxiters, 0.0, 1, mp, D, X);
c_end = std::clock();
cout << "\nError after " << maxiters <<" iterations: " ;
cout << (Y-(D*X)).cwiseAbs().sum()/(ndim*nsig);
time_elapsed_ms = 1000.0 * (c_end-c_start) / CLOCKS_PER_SEC;
cout << "\nCPU time used: " << time_elapsed_ms << " ms\n";
D = Dorig;
X = Xorig;
cout << "\n* Using orthogonal pursuit *" ;
cout << "\nInitial error: " ;
cout << (Y-(D*X)).cwiseAbs().sum()/(ndim*nsig);
c_start = std::clock();
ksvd(useOpenMP, Y, latm, maxiters, 0.0, 1, op, D, X);
c_end = std::clock();
cout << "\nError after " << maxiters <<" iterations: " ;
cout << (Y-(D*X)).cwiseAbs().sum()/(ndim*nsig);
time_elapsed_ms = 1000.0 * (c_end-c_start) / CLOCKS_PER_SEC;
cout << "\nCPU time used: " << time_elapsed_ms << " ms\n";
}
//-----------------------------------------------------------
void test_ksvd_dct2D()
{
Index nfreq = 5; // No. of frequencies in cosine transform
Index ndim = nfreq*nfreq; // Dimension of atoms.
Index nsig = 500; // No. of signal.
Index natm = 100; // No. of atoms.
Index latm = 6; // Sparsity constraint.
Index maxSamples = ndim; // Max. signal samples.
int maxiters = 18; // No. of K-SVD iterations.
bool useOpenMP = true;
MatchingPursuit mp;
OrthogonalPursuit op;
srand(2);
//std::srand(std::time(nullptr));
vector<Index> ndimrand(nsig);
for(Index isig=0; isig < nsig; ++isig){
ndimrand[isig] = 1 + std::rand()/((RAND_MAX + 1u)/maxSamples);
}
vector<VectorXf> Ya(nsig);
vector<VectorXf> Ua(nsig);
vector<VectorXf> Va(nsig);
MatrixXf D = MatrixXf::Random(ndim,natm);
MatrixXf X = MatrixXf::Random(natm,nsig);
D.colwise().normalize();
for(Index isig=0; isig < nsig; ++isig){
Index ndimsig = ndimrand[isig];
Ya[isig] = VectorXf::Random(ndimsig);
Ua[isig] = VectorXf::Random(ndimsig);
Va[isig] = VectorXf::Random(ndimsig);
}
MatrixXf Dorig = D;
MatrixXf Xorig = X;
clock_t c_start;
std::clock_t c_end;
double time_elapsed_ms;
cout << "\n\n** Testing ksvd_dct2D **" ;
cout << "\n<Error measured as average (coord. diff., cosine angle, length vect. diff.)>\n" ;
cout << "\n* Using matching pursuit *" ;
cout << "\nInitial error: " ;
print_error_dct2D(Ya, Ua, Va, D, X, nfreq);
c_start = std::clock();
ksvd_dct2D(useOpenMP, Ya, Ua, Va, nfreq, latm, maxiters, 0.0, mp, D, X);
c_end = std::clock();
cout << "\nError after " << maxiters <<" iterations: " ;
print_error_dct2D(Ya, Ua, Va, D, X, nfreq);
time_elapsed_ms = 1000.0 * (c_end-c_start) / CLOCKS_PER_SEC;
cout << "\nCPU time used: " << time_elapsed_ms << " ms\n";
D = Dorig;
X = Xorig;
cout << "\n* Using orthogonal pursuit *" ;
cout << "\nInitial error: " ;
print_error_dct2D(Ya, Ua, Va, D, X, nfreq);
c_start = std::clock();
ksvd_dct2D(useOpenMP, Ya, Ua, Va, nfreq, latm, maxiters, 0.0, op, D, X);
c_end = std::clock();
cout << "\nError after " << maxiters <<" iterations: " ;
print_error_dct2D(Ya, Ua, Va, D, X, nfreq);
time_elapsed_ms = 1000.0 * (c_end-c_start) / CLOCKS_PER_SEC;
cout << "\nCPU time used: " << time_elapsed_ms << " ms\n";
}