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;
}
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;
}