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DomainAdaptationSVM.cpp
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DomainAdaptationSVM.cpp
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/*
* 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) 2007-2011 Christian Widmer
* Copyright (C) 2007-2011 Max-Planck-Society
*/
#include <shogun/lib/config.h>
#ifdef USE_SVMLIGHT
#include <shogun/transfer/domain_adaptation/DomainAdaptationSVM.h>
#include <shogun/io/SGIO.h>
#include <iostream>
#include <vector>
using namespace shogun;
CDomainAdaptationSVM::CDomainAdaptationSVM() : CSVMLight()
{
}
CDomainAdaptationSVM::CDomainAdaptationSVM(float64_t C, CKernel* k, CLabels* lab, CSVM* pre_svm, float64_t B_param) : CSVMLight(C, k, lab)
{
init();
init(pre_svm, B_param);
}
CDomainAdaptationSVM::~CDomainAdaptationSVM()
{
SG_UNREF(presvm);
SG_DEBUG("deleting DomainAdaptationSVM\n");
}
void CDomainAdaptationSVM::init(CSVM* pre_svm, float64_t B_param)
{
// increase reference counts
SG_REF(pre_svm);
this->presvm=pre_svm;
this->B=B_param;
this->train_factor=1.0;
// set bias of parent svm to zero
this->presvm->set_bias(0.0);
// invoke sanity check
is_presvm_sane();
}
bool CDomainAdaptationSVM::is_presvm_sane()
{
if (!presvm) {
SG_ERROR("presvm is null");
}
if (presvm->get_num_support_vectors() == 0) {
SG_ERROR("presvm has no support vectors, please train first");
}
if (presvm->get_bias() != 0) {
SG_ERROR("presvm bias not set to zero");
}
if (presvm->get_kernel()->get_kernel_type() != this->get_kernel()->get_kernel_type()) {
SG_ERROR("kernel types do not agree");
}
if (presvm->get_kernel()->get_feature_type() != this->get_kernel()->get_feature_type()) {
SG_ERROR("feature types do not agree");
}
return true;
}
bool CDomainAdaptationSVM::train_machine(CFeatures* data)
{
if (data)
{
if (m_labels->get_num_labels() != data->get_num_vectors())
SG_ERROR("Number of training vectors does not match number of labels\n");
kernel->init(data, data);
}
int32_t num_training_points = get_labels()->get_num_labels();
float64_t* lin_term = SG_MALLOC(float64_t, num_training_points);
// grab current training features
CFeatures* train_data = get_kernel()->get_lhs();
// bias of parent SVM was set to zero in constructor, already contains B
CLabels* parent_svm_out = presvm->apply(train_data);
// pre-compute linear term
for (int32_t i=0; i<num_training_points; i++)
{
lin_term[i] = train_factor * B * get_label(i) * parent_svm_out->get_label(i) - 1.0;
}
//set linear term for QP
this->set_linear_term(SGVector<float64_t>(lin_term, num_training_points));
SG_FREE(lin_term);
//train SVM
bool success = CSVMLight::train_machine();
ASSERT(presvm)
return success;
}
CSVM* CDomainAdaptationSVM::get_presvm()
{
SG_REF(presvm);
return presvm;
}
float64_t CDomainAdaptationSVM::get_B()
{
return B;
}
float64_t CDomainAdaptationSVM::get_train_factor()
{
return train_factor;
}
void CDomainAdaptationSVM::set_train_factor(float64_t factor)
{
train_factor = factor;
}
CLabels* CDomainAdaptationSVM::apply(CFeatures* data)
{
ASSERT(presvm->get_bias()==0.0);
int32_t num_examples = data->get_num_vectors();
CLabels* out_current = CSVMLight::apply(data);
// recursive call if used on DomainAdaptationSVM object
CLabels* out_presvm = presvm->apply(data);
// combine outputs
for (int32_t i=0; i!=num_examples; i++)
{
float64_t out_combined = out_current->get_label(i) + B*out_presvm->get_label(i);
out_current->set_label(i, out_combined);
}
return out_current;
}
void CDomainAdaptationSVM::init()
{
presvm = NULL;
B = 0;
train_factor = 1.0;
m_parameters->add((CSGObject**) &presvm, "presvm",
"SVM to regularize against.");
m_parameters->add(&B, "B", "regularization parameter B.");
m_parameters->add(&train_factor,
"train_factor", "flag to switch off regularization in training.");
}
#endif //USE_SVMLIGHT