-
-
Notifications
You must be signed in to change notification settings - Fork 1k
/
MulticlassLibLinear.cpp
154 lines (123 loc) · 4.1 KB
/
MulticlassLibLinear.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
/*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2012 Sergey Lisitsyn
* Copyright (C) 2012 Sergey Lisitsyn
*/
#include <shogun/lib/config.h>
#include <shogun/multiclass/MulticlassLibLinear.h>
#include <shogun/multiclass/MulticlassOneVsRestStrategy.h>
#include <shogun/mathematics/Math.h>
#include <shogun/lib/v_array.h>
#include <shogun/lib/Signal.h>
#include <shogun/labels/MulticlassLabels.h>
using namespace shogun;
CMulticlassLibLinear::CMulticlassLibLinear() :
CLinearMulticlassMachine()
{
init_defaults();
}
CMulticlassLibLinear::CMulticlassLibLinear(float64_t C, CDotFeatures* features, CLabels* labs) :
CLinearMulticlassMachine(new CMulticlassOneVsRestStrategy(),features,NULL,labs)
{
init_defaults();
set_C(C);
}
void CMulticlassLibLinear::init_defaults()
{
set_C(1.0);
set_epsilon(1e-2);
set_max_iter(10000);
set_use_bias(false);
set_save_train_state(false);
m_train_state = NULL;
}
void CMulticlassLibLinear::register_parameters()
{
SG_ADD(&m_C, "m_C", "regularization constant",MS_AVAILABLE);
SG_ADD(&m_epsilon, "m_epsilon", "tolerance epsilon",MS_NOT_AVAILABLE);
SG_ADD(&m_max_iter, "m_max_iter", "max number of iterations",MS_NOT_AVAILABLE);
SG_ADD(&m_use_bias, "m_use_bias", "indicates whether bias should be used",MS_NOT_AVAILABLE);
SG_ADD(&m_save_train_state, "m_save_train_state", "indicates whether bias should be used",MS_NOT_AVAILABLE);
}
CMulticlassLibLinear::~CMulticlassLibLinear()
{
reset_train_state();
}
SGVector<int32_t> CMulticlassLibLinear::get_support_vectors() const
{
if (!m_train_state)
SG_ERROR("Please enable save_train_state option and train machine.\n")
ASSERT(m_labels && m_labels->get_label_type() == LT_MULTICLASS)
int32_t num_vectors = m_features->get_num_vectors();
int32_t num_classes = ((CMulticlassLabels*) m_labels)->get_num_classes();
v_array<int32_t> nz_idxs;
nz_idxs.reserve(num_vectors);
for (int32_t i=0; i<num_vectors; i++)
{
for (int32_t y=0; y<num_classes; y++)
{
if (CMath::abs(m_train_state->alpha[i*num_classes+y])>1e-6)
{
nz_idxs.push(i);
break;
}
}
}
int32_t num_nz = nz_idxs.index();
nz_idxs.reserve(num_nz);
return SGVector<int32_t>(nz_idxs.begin,num_nz);
}
SGMatrix<float64_t> CMulticlassLibLinear::obtain_regularizer_matrix() const
{
return SGMatrix<float64_t>();
}
bool CMulticlassLibLinear::train_machine(CFeatures* data)
{
if (data)
set_features((CDotFeatures*)data);
ASSERT(m_features)
ASSERT(m_labels && m_labels->get_label_type()==LT_MULTICLASS)
ASSERT(m_multiclass_strategy)
int32_t num_vectors = m_features->get_num_vectors();
int32_t num_classes = ((CMulticlassLabels*) m_labels)->get_num_classes();
int32_t bias_n = m_use_bias ? 1 : 0;
liblinear_problem mc_problem;
mc_problem.l = num_vectors;
mc_problem.n = m_features->get_dim_feature_space() + bias_n;
mc_problem.y = SG_MALLOC(float64_t, mc_problem.l);
for (int32_t i=0; i<num_vectors; i++)
mc_problem.y[i] = ((CMulticlassLabels*) m_labels)->get_int_label(i);
mc_problem.x = m_features;
mc_problem.use_bias = m_use_bias;
SGMatrix<float64_t> w0 = obtain_regularizer_matrix();
if (!m_train_state)
m_train_state = new mcsvm_state();
float64_t* C = SG_MALLOC(float64_t, num_vectors);
for (int32_t i=0; i<num_vectors; i++)
C[i] = m_C;
CSignal::clear_cancel();
Solver_MCSVM_CS solver(&mc_problem,num_classes,C,w0.matrix,m_epsilon,
m_max_iter,m_max_train_time,m_train_state);
solver.solve();
m_machines->reset_array();
for (int32_t i=0; i<num_classes; i++)
{
CLinearMachine* machine = new CLinearMachine();
SGVector<float64_t> cw(mc_problem.n-bias_n);
for (int32_t j=0; j<mc_problem.n-bias_n; j++)
cw[j] = m_train_state->w[j*num_classes+i];
machine->set_w(cw);
if (m_use_bias)
machine->set_bias(m_train_state->w[(mc_problem.n-bias_n)*num_classes+i]);
m_machines->push_back(machine);
}
if (!m_save_train_state)
reset_train_state();
SG_FREE(C);
SG_FREE(mc_problem.y);
return true;
}