C++ (Cpp) malsar_joint_feature_learningの例

コード例 #1

ファイル: MultitaskL12LogisticRegression.cpp プロジェクト: AjayRamanathan/shogun

bool CMultitaskL12LogisticRegression::train_locked_implementation(SGVector<index_t>* tasks)
{
	SGVector<float64_t> y(m_labels->get_num_labels());
	for (int32_t i=0; i<y.vlen; i++)
		y[i] = ((CBinaryLabels*)m_labels)->get_label(i);

	malsar_options options = malsar_options::default_options();
	options.termination = m_termination;
	options.tolerance = m_tolerance;
	options.max_iter = m_max_iter;
	options.n_tasks = ((CTaskGroup*)m_task_relation)->get_num_tasks();
	options.tasks_indices = tasks;
#ifdef HAVE_EIGEN3
	malsar_result_t model = malsar_joint_feature_learning(
		features, y.vector, m_rho1, m_rho2, options);

	m_tasks_w = model.w;
	m_tasks_c = model.c;
#else
	SG_WARNING("Please install Eigen3 to use MultitaskL12LogisticRegression\n")
	m_tasks_w = SGMatrix<float64_t>(((CDotFeatures*)features)->get_dim_feature_space(), options.n_tasks);
	m_tasks_c = SGVector<float64_t>(options.n_tasks);
#endif

	return true;
}

コード例 #2

ファイル: MultitaskL12LogisticRegression.cpp プロジェクト: shogun-toolbox/shogun-gpl

bool CMultitaskL12LogisticRegression::train_machine(CFeatures* data)
{
	if (data && (CDotFeatures*)data)
		set_features((CDotFeatures*)data);

	ASSERT(features)
	ASSERT(m_labels)
	ASSERT(m_task_relation)

	SGVector<float64_t> y(m_labels->get_num_labels());
	for (int32_t i=0; i<y.vlen; i++)
		y[i] = ((CBinaryLabels*)m_labels)->get_label(i);

	malsar_options options = malsar_options::default_options();
	options.termination = m_termination;
	options.tolerance = m_tolerance;
	options.max_iter = m_max_iter;
	options.n_tasks = ((CTaskGroup*)m_task_relation)->get_num_tasks();
	options.tasks_indices = ((CTaskGroup*)m_task_relation)->get_tasks_indices();

	malsar_result_t model = malsar_joint_feature_learning(
		features, y.vector, self->m_rho1, self->m_rho2, options);

	m_tasks_w = model.w;
	m_tasks_c = model.c;

	SG_FREE(options.tasks_indices);

	return true;
}

コード例 #3

bool CMultitaskL1L2LogisticRegression::train_machine(CFeatures* data)
{
	if (data && (CDotFeatures*)data)
		set_features((CDotFeatures*)data);

	ASSERT(features);
	ASSERT(m_labels);

	SGVector<float64_t> y(m_labels->get_num_labels());
	for (int32_t i=0; i<y.vlen; i++)
		y[i] = ((CBinaryLabels*)m_labels)->get_label(i);
	
	slep_options options = slep_options::default_options();
	options.termination = m_termination;
	options.tolerance = m_tolerance;
	options.max_iter = m_max_iter;
	SGVector<index_t> ind = ((CIndexBlockGroup*)m_task_relation)->get_SLEP_ind();
	options.ind = ind.vector;
	options.n_tasks = ind.vlen-1;

#ifdef HAVE_EIGEN3
	slep_result_t model = malsar_joint_feature_learning(
		features, y.vector, m_rho1, m_rho2, options);

	m_tasks_w = model.w;
	m_tasks_c = model.c;
#endif

	ASSERT(m_task_relation);

	return true;
}

コード例 #4

ファイル: MultitaskL12LogisticRegression.cpp プロジェクト: shogun-toolbox/shogun-gpl

bool CMultitaskL12LogisticRegression::train_locked_implementation(SGVector<index_t>* tasks)
{
	SGVector<float64_t> y(m_labels->get_num_labels());
	for (int32_t i=0; i<y.vlen; i++)
		y[i] = ((CBinaryLabels*)m_labels)->get_label(i);

	malsar_options options = malsar_options::default_options();
	options.termination = m_termination;
	options.tolerance = m_tolerance;
	options.max_iter = m_max_iter;
	options.n_tasks = ((CTaskGroup*)m_task_relation)->get_num_tasks();
	options.tasks_indices = tasks;
	malsar_result_t model = malsar_joint_feature_learning(
		features, y.vector, self->m_rho1, self->m_rho2, options);

	m_tasks_w = model.w;
	m_tasks_c = model.c;

	return true;
}