Ejemplo n.º 1
0
bool CPrimalMosekSOSVM::train_machine(CFeatures* data)
{
	SG_DEBUG("Entering CPrimalMosekSOSVM::train_machine.\n");
	if (data)
		set_features(data);

	CFeatures* model_features = get_features();
	// Initialize the model for training
	m_model->init_training();
	// Check that the scenary is correct to start with training
	m_model->check_training_setup();
	SG_DEBUG("The training setup is correct.\n");

	// Dimensionality of the joint feature space
	int32_t M = m_model->get_dim();
	// Number of auxiliary variables in the optimization vector
	int32_t num_aux = m_model->get_num_aux();
	// Number of auxiliary constraints
	int32_t num_aux_con = m_model->get_num_aux_con();
	// Number of training examples
	int32_t N = model_features->get_num_vectors();

	SG_DEBUG("M=%d, N =%d, num_aux=%d, num_aux_con=%d.\n", M, N, num_aux, num_aux_con);

	// Interface with MOSEK
	CMosek* mosek = new CMosek(0, M+num_aux+N);
	SG_REF(mosek);
	REQUIRE(mosek->get_rescode() == MSK_RES_OK, "Mosek object could not be properly created in PrimalMosekSOSVM training.\n");

	// Initialize the terms of the optimization problem
	SGMatrix< float64_t > A, B, C;
	SGVector< float64_t > a, b, lb, ub;
	m_model->init_primal_opt(m_regularization, A, a, B, b, lb, ub, C);

	SG_DEBUG("Regularization used in PrimalMosekSOSVM equal to %.2f.\n", m_regularization);

	// Input terms of the problem that do not change between iterations
	REQUIRE(mosek->init_sosvm(M, N, num_aux, num_aux_con, C, lb, ub, A, b) == MSK_RES_OK,
		"Mosek error in PrimalMosekSOSVM initializing SO-SVM.\n")

	// Initialize the weight vector
	m_w = SGVector< float64_t >(M);
	m_w.zero();

	m_slacks = SGVector< float64_t >(N);
	m_slacks.zero();

	// Initialize the list of constraints
	// Each element in results is a list of CResultSet with the constraints
	// associated to each training example
	CDynamicObjectArray* results = new CDynamicObjectArray(N);
	SG_REF(results);
	for ( int32_t i = 0 ; i < N ; ++i )
	{
		CList* list = new CList(true);
		results->push_back(list);
	}

	// Initialize variables used in the loop
	int32_t     num_con     = num_aux_con;	// number of constraints
	int32_t     old_num_con = num_con;
	bool        exception   = false;
	index_t     iteration   = 0;

	SGVector< float64_t > sol(M+num_aux+N);
	sol.zero();

	SGVector< float64_t > aux(num_aux);

	do
	{
		SG_DEBUG("Iteration #%d: Cutting plane training with num_con=%d and old_num_con=%d.\n",
				iteration, num_con, old_num_con);

		old_num_con = num_con;

		for ( int32_t i = 0 ; i < N ; ++i )
		{
			// Predict the result of the ith training example (loss-aug)
			CResultSet* result = m_model->argmax(m_w, i);

			// Compute the loss associated with the prediction (surrogate loss, max(0, \tilde{H}))
			float64_t slack = CHingeLoss().loss( compute_loss_arg(result) );
			CList* cur_list = (CList*) results->get_element(i);

			// Update the list of constraints
			if ( cur_list->get_num_elements() > 0 )
			{
				// Find the maximum loss within the elements of
				// the list of constraints
				CResultSet* cur_res = (CResultSet*) cur_list->get_first_element();
				float64_t max_slack = -CMath::INFTY;

				while ( cur_res != NULL )
				{
					max_slack = CMath::max(max_slack, CHingeLoss().loss( compute_loss_arg(cur_res) ));

					SG_UNREF(cur_res);
					cur_res = (CResultSet*) cur_list->get_next_element();
				}

				if ( slack > max_slack + m_epsilon )
				{
					// The current training example is a
					// violated constraint
					if ( ! insert_result(cur_list, result) )
					{
						exception = true;
						break;
					}

					add_constraint(mosek, result, num_con, i);
					++num_con;
				}
			}
			else
			{
				// First iteration of do ... while, add constraint
				if ( ! insert_result(cur_list, result) )
				{
					exception = true;
					break;
				}

				add_constraint(mosek, result, num_con, i);
				++num_con;
			}

			SG_UNREF(cur_list);
			SG_UNREF(result);
		}

		// Solve the QP
		SG_DEBUG("Entering Mosek QP solver.\n");

		mosek->optimize(sol);
		for ( int32_t i = 0 ; i < M+num_aux+N ; ++i )
		{
			if ( i < M )
				m_w[i] = sol[i];
			else if ( i < M+num_aux )
				aux[i-M] = sol[i];
			else
				m_slacks[i-M-num_aux] = sol[i];
		}

		SG_DEBUG("QP solved. The primal objective value is %.4f.\n", mosek->get_primal_objective_value());

		++iteration;

	} while ( old_num_con != num_con && ! exception );

	po_value = mosek->get_primal_objective_value();

	// Free resources
	SG_UNREF(results);
	SG_UNREF(mosek);
	SG_UNREF(model_features);
	return true;
}
Ejemplo n.º 2
0
bool CPrimalMosekSOSVM::train_machine(CFeatures* data)
{
	if (data)
		set_features(data);

	CFeatures* model_features = get_features();
	// Check that the scenary is correct to start with training
	m_model->check_training_setup();

	// Dimensionality of the joint feature space
	int32_t M = m_model->get_dim();
	// Number of auxiliary variables in the optimization vector
	int32_t num_aux = m_model->get_num_aux();
	// Number of auxiliary constraints
	int32_t num_aux_con = m_model->get_num_aux_con();
	// Number of training examples
	int32_t N = m_model->get_features()->get_num_vectors();

	// Interface with MOSEK
	CMosek* mosek = new CMosek(0, M+num_aux+N);
	SG_REF(mosek);
	if ( mosek->get_rescode() != MSK_RES_OK )
	{
		SG_PRINT("Mosek object could not be properly created..."
			 "aborting training of PrimalMosekSOSVM\n");

		return false;
	}

	// Initialize the terms of the optimization problem
	SGMatrix< float64_t > A, B, C;
	SGVector< float64_t > a, b, lb, ub;
	m_model->init_opt(A, a, B, b, lb, ub, C);

	// Input terms of the problem that do not change between iterations
	if ( mosek->init_sosvm(M, N, num_aux, num_aux_con, C, lb, ub, A, b) != MSK_RES_OK )
	{
		// MOSEK error took place
		return false;
	}

	// Initialize the weight vector
	m_w = SGVector< float64_t >(M);
	m_w.zero();

	m_slacks = SGVector< float64_t >(N);
	m_slacks.zero();

	// Initialize the list of constraints
	// Each element in results is a list of CResultSet with the constraints 
	// associated to each training example
	CDynamicObjectArray* results = new CDynamicObjectArray(N);
	SG_REF(results);
	for ( int32_t i = 0 ; i < N ; ++i )
	{
		CList* list = new CList(true);
		results->push_back(list);
	}

	// Initialize variables used in the loop
	int32_t     num_con     = num_aux_con;	// number of constraints
	int32_t     old_num_con = num_con;
	float64_t   slack       = 0.0;
	float64_t   max_slack   = 0.0;
	CResultSet* result      = NULL;
	CResultSet* cur_res     = NULL;
	CList*      cur_list    = NULL;
	bool        exception   = false;

	SGVector< float64_t > sol(M+num_aux+N);
	sol.zero();

	SGVector< float64_t > aux(num_aux);

	do 
	{
		old_num_con = num_con;

		for ( int32_t i = 0 ; i < N ; ++i )
		{
			// Predict the result of the ith training example
			result = m_model->argmax(m_w, i);

			//SG_PRINT("loss %f %f\n", compute_loss_arg(result),  m_loss->loss( compute_loss_arg(result)) );
			// Compute the loss associated with the prediction
			slack = m_loss->loss( compute_loss_arg(result) );
			cur_list = (CList*) results->get_element(i);

			// Update the list of constraints
			if ( cur_list->get_num_elements() > 0 )
			{
				// Find the maximum loss within the elements of
				// the list of constraints
				cur_res = (CResultSet*) cur_list->get_first_element();
				max_slack = -CMath::INFTY;

				while ( cur_res != NULL )
				{
					max_slack = CMath::max(max_slack,
							m_loss->loss( compute_loss_arg(cur_res) ));

					SG_UNREF(cur_res);
					cur_res = (CResultSet*) cur_list->get_next_element();
				}

				if ( slack > max_slack )
				{
					// The current training example is a
					// violated constraint
					if ( ! insert_result(cur_list, result) )
					{
						exception = true;
						break;
					}

					add_constraint(mosek, result, num_con, i);
					++num_con;
				}
			}
			else
			{
				// First iteration of do ... while, add constraint
				if ( ! insert_result(cur_list, result) )
				{
					exception = true;
					break;
				}

				add_constraint(mosek, result, num_con, i);
				++num_con;
			}

			SG_UNREF(cur_list);
			SG_UNREF(result);
		}

		// Solve the QP
		mosek->optimize(sol);
		for ( int32_t i = 0 ; i < M+num_aux+N ; ++i )
		{
			if ( i < M )
				m_w[i] = sol[i];
			else if ( i < M+num_aux )
				aux[i-M] = sol[i];
			else
				m_slacks[i-M-num_aux] = sol[i];
		}

	} while ( old_num_con != num_con && ! exception );

	po_value = mosek->get_primal_objective_value();

	// Free resources
	SG_UNREF(results);
	SG_UNREF(mosek);
	SG_UNREF(model_features);
	return true;
}