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
|
/*********************************************************************
MLDemos: A User-Friendly visualization toolkit for machine learning
Copyright (C) 2010 Basilio Noris
Contact: mldemos@b4silio.com
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Library General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free
Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*********************************************************************/
#include <public.h>
#include "regressorKRLS.h"
using namespace std;
const char *RegressorKRLS::GetInfoString()
{
char *text = new char[255];
sprintf(text, "Kernel Ridge Least Squares\n");
sprintf(text, "%sCapacity: %d", text, capacity);
sprintf(text, "%sKernel: ", text);
switch(kernelType)
{
case 0:
sprintf(text, "%s linear", text);
break;
case 1:
sprintf(text, "%s polynomial (deg: %d width: %f)", text, kernelDegree, kernelParam);
break;
case 2:
sprintf(text, "%s rbf (gamma: %f)", text, kernelParam);
break;
}
sprintf(text, "%seps: %f\n", text, epsilon);
sprintf(text, "%sBasis Functions: %lu\n", text, (unsigned long)GetSVs().size());
return text;
}
RegressorKRLS::~RegressorKRLS()
{
DEL(linTrainer);
DEL(polTrainer);
DEL(rbfTrainer);
}
void RegressorKRLS::Train(std::vector< fvec > _samples, ivec _labels)
{
if(capacity == 1) capacity = 2;
samples.clear();
labels.clear();
if(!_samples.size()) return;
if(_samples[0].size() > 2) return; // no multi-dim for now...
dim = _samples[0].size();
FOR(i, _samples.size())
{
reg_sample_type samp;
samp(0) = _samples[i][0];
samples.push_back(samp);
labels.push_back(_samples[i][1]);
}
randomize_samples(samples, labels);
DEL(linTrainer);
DEL(polTrainer);
DEL(rbfTrainer);
switch(kernelType)
{
case 0:
{
linTrainer = new dlib::krls<reg_lin_kernel>(reg_lin_kernel(),epsilon,capacity ? capacity : 1000000);
FOR(i, samples.size())
{
linTrainer->train(samples[i], labels[i]);
}
linFunc = linTrainer->get_decision_function();
}
break;
case 1:
{
polTrainer = new dlib::krls<reg_pol_kernel>(reg_pol_kernel(1./kernelParam,0,kernelDegree),epsilon,capacity ? capacity : 1000000);
FOR(i, samples.size())
{
polTrainer->train(samples[i], labels[i]);
}
polFunc = polTrainer->get_decision_function();
}
break;
case 2:
{
rbfTrainer = new dlib::krls<reg_rbf_kernel>(reg_rbf_kernel(1./kernelParam),epsilon,capacity ? capacity : 1000000);
FOR(i, samples.size())
{
rbfTrainer->train(samples[i], labels[i]);
}
rbfFunc = rbfTrainer->get_decision_function();
}
break;
}
}
fvec RegressorKRLS::Test( const fvec &_sample )
{
fvec res;
res.resize(2,0);
if(!linTrainer && !polTrainer && !rbfTrainer) return res;
reg_sample_type sample;
sample(0) = _sample[0];
switch(kernelType)
{
case 0:
res[0] = (*linTrainer)(sample);
break;
case 1:
res[0] = (*polTrainer)(sample);
break;
case 2:
res[0] = (*rbfTrainer)(sample);
break;
}
return res;
}
fVec RegressorKRLS::Test( const fVec &_sample )
{
fVec res;
reg_sample_type sample;
sample(0) = _sample._[0];
switch(kernelType)
{
case 0:
res[0] = (*linTrainer)(sample);
break;
case 1:
res[0] = (*polTrainer)(sample);
break;
case 2:
res[0] = (*rbfTrainer)(sample);
break;
}
return res;
}
std::vector<fvec> RegressorKRLS::GetSVs()
{
vector<fvec> SVs;
if(kernelType == 0)
{
FOR(i, linFunc.basis_vectors.nr())
{
fvec sv;
sv.resize(2,0);
sv[0] = linFunc.basis_vectors(i)(0);
SVs.push_back(sv);
}
}
else if(kernelType == 1)
{
FOR(i, polFunc.basis_vectors.nr())
{
fvec sv;
sv.resize(2,0);
sv[0] = polFunc.basis_vectors(i)(0);
SVs.push_back(sv);
}
}
else if(kernelType == 2)
{
FOR(i, rbfFunc.basis_vectors.nr())
{
fvec sv;
sv.resize(2,0);
sv[0] = rbfFunc.basis_vectors(i)(0);
SVs.push_back(sv);
}
}
FOR(i, SVs.size())
{
int closest = 0;
double dist = DBL_MAX;
FOR(j, samples.size())
{
double d = abs(samples[j](0)-SVs[i][0]);
if(d < dist)
{
dist = d;
closest = j;
}
}
SVs[i][1] = labels[closest];
}
return SVs;
}
|
