void AABoxContainTest::testExtendVolumePt()
{
// Test empty box and pt
gmtl::AABoxf empty, result;
gmtl::Point3f origin;
result = empty;
gmtl::extendVolume(result, origin);
CPPUNIT_ASSERT(! result.isEmpty());
CPPUNIT_ASSERT(result.getMin() == origin);
CPPUNIT_ASSERT(result.getMax() == origin);
// Valid box against pt inside
gmtl::AABoxf box2(gmtl::Point3f(-1,-1,-1), gmtl::Point3f(1,1,1));
result = box2;
gmtl::extendVolume(result, origin);
CPPUNIT_ASSERT(! result.isEmpty());
CPPUNIT_ASSERT(result.getMin() == box2.getMin());
CPPUNIT_ASSERT(result.getMax() == box2.getMax());
// Valid box with pt outside
gmtl::Point3f pt(30, 30, -30);
gmtl::Point3f expMin(-1,-1,-30);
gmtl::Point3f expMax(30,30,1);
result = box2;
gmtl::extendVolume(result, pt);
CPPUNIT_ASSERT(! result.isEmpty());
CPPUNIT_ASSERT(result.getMin() == expMin);
CPPUNIT_ASSERT(result.getMax() == expMax);
}
void AABoxContainTest::testExtendVolumeAABox()
{
gmtl::AABoxf empty, result;
gmtl::AABoxf box(gmtl::Point3f(-1,-1,-1), gmtl::Point3f(1,1,1));
// Both boxes empty
{
gmtl::extendVolume(result, empty);
CPPUNIT_ASSERT(result.isEmpty());
}
// Empty box with valid box
{
result = empty;
gmtl::extendVolume(result, box);
CPPUNIT_ASSERT(! result.isEmpty());
CPPUNIT_ASSERT(result.getMin() == box.getMin());
CPPUNIT_ASSERT(result.getMax() == box.getMax());
}
// Overlapping valid boxes
{
gmtl::AABoxf box2(gmtl::Point3f(0,0,0), gmtl::Point3f(2,2,2));
gmtl::Point3f expMin(-1,-1,-1);
gmtl::Point3f expMax(2,2,2);
result = box;
gmtl::extendVolume(result, box2);
CPPUNIT_ASSERT(! result.isEmpty());
CPPUNIT_ASSERT(result.getMin() == expMin);
CPPUNIT_ASSERT(result.getMax() == expMax);
}
// Non-overlapping valid boxes
{
gmtl::AABoxf box2(gmtl::Point3f(2,2,2), gmtl::Point3f(4,4,4));
gmtl::Point3f expMin(-1,-1,-1);
gmtl::Point3f expMax(4,4,4);
result = box;
gmtl::extendVolume(result, box2);
CPPUNIT_ASSERT(! result.isEmpty());
CPPUNIT_ASSERT(result.getMin() == expMin);
CPPUNIT_ASSERT(result.getMax() == expMax);
}
}
void em(dd::CmdParser & cmd_parser){
// number of NUMA nodes
int n_numa_node = numa_max_node() + 1;
// number of max threads per NUMA node
int n_thread_per_numa = (sysconf(_SC_NPROCESSORS_CONF))/(n_numa_node);
// get command line arguments
std::string fg_file = cmd_parser.fg_file->getValue();
std::string weight_file = cmd_parser.weight_file->getValue();
std::string variable_file = cmd_parser.variable_file->getValue();
std::string factor_file = cmd_parser.factor_file->getValue();
std::string edge_file = cmd_parser.edge_file->getValue();
std::string meta_file = cmd_parser.meta_file->getValue();
std::string output_folder = cmd_parser.output_folder->getValue();
int n_learning_epoch = cmd_parser.n_learning_epoch->getValue();
int n_samples_per_learning_epoch = cmd_parser.n_samples_per_learning_epoch->getValue();
int n_inference_epoch = cmd_parser.n_inference_epoch->getValue();
double stepsize = cmd_parser.stepsize->getValue();
double stepsize2 = cmd_parser.stepsize2->getValue();
// hack to support two parameters to specify step size
if (stepsize == 0.01) stepsize = stepsize2;
double decay = cmd_parser.decay->getValue();
int n_datacopy = cmd_parser.n_datacopy->getValue();
double reg_param = cmd_parser.reg_param->getValue();
double reg1_param = cmd_parser.reg1_param->getValue();
bool is_quiet = cmd_parser.quiet->getValue();
bool check_convergence = cmd_parser.check_convergence->getValue();
bool sample_evidence = cmd_parser.sample_evidence->getValue();
int burn_in = cmd_parser.burn_in->getValue();
int n_iter = cmd_parser.n_iter->getValue();
int wl_conv = cmd_parser.wl_conv->getValue();
int delta = cmd_parser.delta->getValue();
bool learn_non_evidence = cmd_parser.learn_non_evidence->getValue();
Meta meta = read_meta(fg_file);
if (is_quiet) {
std::cout << "Running in quiet mode..." << std::endl;
} else {
std::cout << std::endl;
std::cout << "#################MACHINE CONFIG#################" << std::endl;
std::cout << "# # NUMA Node : " << n_numa_node << std::endl;
std::cout << "# # Thread/NUMA Node : " << n_thread_per_numa << std::endl;
std::cout << "################################################" << std::endl;
std::cout << std::endl;
std::cout << "#################GIBBS SAMPLING#################" << std::endl;
std::cout << "# fg_file : " << fg_file << std::endl;
std::cout << "# edge_file : " << edge_file << std::endl;
std::cout << "# weight_file : " << weight_file << std::endl;
std::cout << "# variable_file : " << variable_file << std::endl;
std::cout << "# factor_file : " << factor_file << std::endl;
std::cout << "# meta_file : " << meta_file << std::endl;
std::cout << "# output_folder : " << output_folder << std::endl;
std::cout << "# n_learning_epoch : " << n_learning_epoch << std::endl;
std::cout << "# n_samples/l. epoch : " << n_samples_per_learning_epoch << std::endl;
std::cout << "# n_inference_epoch : " << n_inference_epoch << std::endl;
std::cout << "# stepsize : " << stepsize << std::endl;
std::cout << "# decay : " << decay << std::endl;
std::cout << "# regularization : " << reg_param << std::endl;
std::cout << "# l1 regularization : " << reg1_param << std::endl;
std::cout << "################################################" << std::endl;
std::cout << "# IGNORE -s (n_samples/l. epoch). ALWAYS -s 1. #" << std::endl;
std::cout << "# IGNORE -t (threads). ALWAYS USE ALL THREADS. #" << std::endl;
std::cout << "################################################" << std::endl;
std::cout << "# nvar : " << meta.num_variables << std::endl;
std::cout << "# nfac : " << meta.num_factors << std::endl;
std::cout << "# nweight : " << meta.num_weights << std::endl;
std::cout << "# nedge : " << meta.num_edges << std::endl;
std::cout << "################################################" << std::endl;
}
// run on NUMA node 0
numa_run_on_node(0);
numa_set_localalloc();
// load factor graph
dd::FactorGraph fg(meta.num_variables, meta.num_factors, meta.num_weights, meta.num_edges);
fg.load(cmd_parser, is_quiet);
dd::GibbsSampling gibbs(&fg, &cmd_parser, n_datacopy, sample_evidence, burn_in, learn_non_evidence);
// Initialize EM instance
dd::ExpMax expMax(&fg, &gibbs, wl_conv, delta, check_convergence);
// number of inference epochs
int numa_aware_n_epoch;
int numa_aware_n_learning_epoch;
// EM init -- run Maximzation (semi-supervised learning)
// Maximization step
numa_aware_n_learning_epoch = (int)(n_learning_epoch/n_numa_node) +
(n_learning_epoch%n_numa_node==0?0:1);
expMax.maximization(numa_aware_n_learning_epoch, n_samples_per_learning_epoch,
stepsize, decay, reg_param, reg1_param, is_quiet);
/*expMax.maximization(numa_aware_n_learning_epoch, n_samples_per_learning_epoch,
stepsize, decay, reg_param, reg1_param, meta_file, is_quiet);*/
while (!expMax.hasConverged && n_iter > 0) {
// Expectation step
numa_aware_n_epoch = (int)(n_inference_epoch/n_numa_node) +
(n_inference_epoch%n_numa_node==0?0:1);
expMax.expectation(numa_aware_n_epoch,is_quiet);
// Maximization step
numa_aware_n_learning_epoch = (int)(n_learning_epoch/n_numa_node) +
(n_learning_epoch%n_numa_node==0?0:1);
/*expMax.maximization(numa_aware_n_learning_epoch, n_samples_per_learning_epoch,
stepsize, decay, reg_param, reg1_param, meta_file, is_quiet);*/
expMax.maximization(numa_aware_n_learning_epoch, n_samples_per_learning_epoch,
stepsize, decay, reg_param, reg1_param, is_quiet);
//Decrement iteration counter
n_iter--;
}
expMax.dump_weights(is_quiet);
expMax.aggregate_results_and_dump(is_quiet);
}
