int main(int argc, char **argv, char **envp) {
int ** matriz_adj = NULL;
int N;
int i;
if (argc != 2) {
printUsage();
exit(1);
}
if (strlen(argv[1]) < 4 || strcmp(argv[1] + strlen(argv[1]) - 4, ".adj") != 0) {
printUsage();
exit(1);
}
if (readMatrixFromFile(argv[1], &matriz_adj, &N)) {
printf("Erro na leitura da matriz do ficheiro!!!\n");
exit(3);
}
printf("Densidade do grafo: %.4f\n", getGraphDensity(matriz_adj, N));
printf("Grau maximo do grafo: %d\n", getGraphHighRank(matriz_adj, N));
writeMatrixToFile(argv[1], matriz_adj, N);
for (i=0;i<N;i++)
free(matriz_adj[i]);
free(matriz_adj);
return 0;
}
int main(int argc, char** argv) {
Matrix *m1 = NULL, *m2 = NULL, *resultSequential = NULL, *resultParallel = NULL;
int numParallelTasks = 1;
struct timeval diff;
checkArgsSize(argc);
m1 = readMatrixFromFile(M1);
m2 = readMatrixFromFile(M2);
checkInputMatrices(m1, m2);
checkNumTasks(argv, &numParallelTasks, m1);
diff = measureBalanceWork(m1, m2, numParallelTasks, &resultParallel, balanceWork);
writeMatrixInFile(OUT, resultParallel);
printTime("Tempo levado para executar a multiplicação com %d threads: ", numParallelTasks, diff);
pthread_exit(NULL);
}
int readTwoOnDimMatrixFromAFile(char *fileName , oneDimMatrix * a , oneDimMatrix * b){
FILE *fp;
int numberOfLines = 0;
fp = openAFileForReading(fileName);
numberOfLines = readMatrixFromFile(fp, numberOfLines, DETECT_SIZE, a);
printf("%d %d %d \n" ,numberOfLines , a->nrows , a->ncols );
numberOfLines= readMatrixFromFile(fp, numberOfLines, DETECT_SIZE , b);
printf("%d %d %d \n" ,numberOfLines , b->nrows , b->ncols);
if(a->nrows != b->nrows)
inconsistentErrorReport("Different number of rows in each matrix.",
numberOfLines, a->nrows , a->ncols);
fclose(fp);
*a = createMatrix(a->nrows , a->ncols);
*b = createMatrix(b->nrows , b->ncols);
fp = openAFileForReading(fileName);
numberOfLines = readMatrixFromFile(fp, numberOfLines, READ_MATRIX, a);
numberOfLines= readMatrixFromFile(fp, numberOfLines, READ_MATRIX , b);
fclose(fp);
/* printOneDimMatrix(a); printf("\n"); printOneDimMatrix(b); */
return numberOfLines;
}
MainWindow::MainWindow(QWidget *parent) :
QMainWindow(parent)
{
thread = new QThread();
reader = new Reader();
reader->moveToThread(thread);
connect(reader, SIGNAL(sendPoint(int,int,double)),
this, SLOT(setPointProb(int,int,double)));
connect(reader, SIGNAL(workRequested()), thread, SLOT(start()));
connect(thread, SIGNAL(started()), reader, SLOT(doWork()));
connect(reader, SIGNAL(finished()),
thread, SLOT(quit()) , Qt::DirectConnection);
connect(reader, SIGNAL(finished()),
this, SLOT(fileFinished()));
connect(reader, SIGNAL(sendReset()), this, SLOT(resetPositions()));
setFixedSize(800,600);
p_scene = new QGraphicsScene(this);
p_view = new QGraphicsView(p_scene, this);
QPixmap background(QCoreApplication::applicationDirPath() +
"/img/sol.png");
if (background.isNull()) {
std::cout << "Ouverture de sol.png échouée, ouverture de sol.jpg" << std::endl;
background = QPixmap(QCoreApplication::applicationDirPath() +
"/img/sol.jpg");
}
p_scene->setBackgroundBrush(QBrush(background));
p_view->setGeometry(10,10,505,505);
p_addPosition = new QPushButton("Ajouter position", this);
p_addPosition->setGeometry(590,90, 120, 30);
p_deletePos = new QPushButton("Réinitialiser positions", this);
p_deletePos->setGeometry(590,130, 120, 30);
p_openFile = new QPushButton("Ouvrir fichier", this);
p_openFile->setGeometry(590, 170, 120, 30);
p_changeTableSize = new QPushButton("Changer taille table", this);
p_changeTableSize->setGeometry(50, 560, 120, 30);
pauseButton = new QPushButton("", this);
QIcon pauseIcon(QCoreApplication::applicationDirPath() + "/img/pause.png");
if (pauseIcon.isNull()) {
std::cout << "Ouverture de pause.png échouée, ouverture de pause.jpg" << std::endl;
pauseIcon = QIcon((QCoreApplication::applicationDirPath() + "/img/pause.jpg"));
}
pauseButton->setIcon(pauseIcon);
pauseButton->setGeometry(590, 250, 30, 30);
pauseButton->setDisabled(true);
stopButton = new QPushButton("", this);
QIcon stopIcon(QCoreApplication::applicationDirPath() + "/img/stop.png");
if (stopIcon.isNull()) {
std::cout << "Ouverture de stop.png échouée, ouverture de stop.jpg" << std::endl;
pauseIcon = QIcon((QCoreApplication::applicationDirPath() + "/img/stop.jpg"));
}
stopButton->setIcon(stopIcon);
stopButton->setGeometry(630, 250, 30, 30);
stopButton->setDisabled(true);
stepButton = new QPushButton("", this);
QIcon stepIcon(QCoreApplication::applicationDirPath() + "/img/step.png");
if (stepIcon.isNull()) {
std::cout << "Ouverture de step.png échouée, ouverture de step.jpg" << std::endl;
stepIcon = QIcon((QCoreApplication::applicationDirPath() + "/img/step.jpg"));
}
stepButton->setIcon(stepIcon);
stepButton->setGeometry(670, 250, 30, 30);
stepButton->setDisabled(true);
p_quit = new QPushButton("Quitter", this);
p_quit->setGeometry(600, 560, 100, 30);
p_addPosX = new QLineEdit(this);
p_addPosY = new QLineEdit(this);
p_addPosP = new QLineEdit(this);
p_addPosX->setGeometry(580, 50, 40, 30);
p_addPosY->setGeometry(630, 50, 40, 30);
p_addPosP->setGeometry(680, 50, 40, 30);
p_addPosX->setPlaceholderText("Abscisse");
p_addPosY->setPlaceholderText("Ordonnée");
p_addPosP->setPlaceholderText("Proba");
currentDate = new QLabel("Pas de fichier en cours", this);
currentDate->setGeometry(580, 300, 140, 30);
QObject::connect(p_addPosition, SIGNAL(clicked()),
this, SLOT(sendPointProbValues()));
QObject::connect(this, SIGNAL(pointProbValues(int,int,double)),
this, SLOT(setPointProb(int,int,double)));
QObject::connect(p_deletePos, SIGNAL(clicked()),
this, SLOT(resetPositions()));
QObject::connect(p_quit, SIGNAL(clicked()),
qApp, SLOT(quit()));
QObject::connect(p_openFile, SIGNAL(clicked()),
this, SLOT(readMatrixFromFile()));
QObject::connect(p_changeTableSize, SIGNAL(clicked()),
this, SLOT(changeTableSize()));
QObject::connect(pauseButton, SIGNAL(clicked()),
this, SLOT(pauseRead()));
QObject::connect(stopButton, SIGNAL(clicked()),
this, SLOT(stopRead()));
QObject::connect(reader, SIGNAL(sendDate(QString)),
this, SLOT(setDate(QString)));
QObject::connect(stepButton, SIGNAL(clicked()),
this, SLOT(clickNextDate()));
}
void pcl::face_detection::FaceDetectorDataProvider<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::initialize(std::string & data_dir)
{
std::string start;
std::string ext = std::string ("pcd");
bf::path dir = data_dir;
std::vector < std::string > files;
getFilesInDirectory (dir, start, files, ext);
//apart from loading the file names, we will do some bining regarding pitch and yaw
std::vector < std::vector<int> > yaw_pitch_bins;
std::vector < std::vector<std::vector<std::string> > > image_files_per_bin;
float res_yaw = 15.f;
float res_pitch = res_yaw;
int min_yaw = -75;
int min_pitch = -60;
int num_yaw = static_cast<int>((std::abs (min_yaw) * 2) / static_cast<int>(res_yaw + 1.f));
int num_pitch = static_cast<int>((std::abs (min_pitch) * 2) / static_cast<int>(res_pitch + 1.f));
yaw_pitch_bins.resize (num_yaw);
image_files_per_bin.resize (num_yaw);
for (int i = 0; i < num_yaw; i++)
{
yaw_pitch_bins[i].resize (num_pitch);
image_files_per_bin[i].resize (num_pitch);
for (int j = 0; j < num_pitch; j++)
{
yaw_pitch_bins[i][j] = 0;
}
}
for (size_t i = 0; i < files.size (); i++)
{
std::stringstream filestream;
filestream << data_dir << "/" << files[i];
std::string file = filestream.str ();
std::string pose_file (files[i]);
boost::replace_all (pose_file, ".pcd", "_pose.txt");
Eigen::Matrix4f pose_mat;
pose_mat.setIdentity (4, 4);
std::stringstream filestream_pose;
filestream_pose << data_dir << "/" << pose_file;
pose_file = filestream_pose.str ();
bool result = readMatrixFromFile (pose_file, pose_mat);
if (result)
{
Eigen::Vector3f ea = pose_mat.block<3, 3> (0, 0).eulerAngles (0, 1, 2);
ea *= 57.2957795f; //transform it to degrees to do the binning
int y = static_cast<int>(pcl_round ((ea[0] + static_cast<float>(std::abs (min_yaw))) / res_yaw));
int p = static_cast<int>(pcl_round ((ea[1] + static_cast<float>(std::abs (min_pitch))) / res_pitch));
if (y < 0)
y = 0;
if (p < 0)
p = 0;
if (p >= num_pitch)
p = num_pitch - 1;
if (y >= num_yaw)
y = num_yaw - 1;
assert (y >= 0 && y < num_yaw);
assert (p >= 0 && p < num_pitch);
yaw_pitch_bins[y][p]++;
image_files_per_bin[y][p].push_back (file);
}
}
pcl::face_detection::showBining (num_pitch, res_pitch, min_pitch, num_yaw, res_yaw, min_yaw, yaw_pitch_bins);
int max_elems = 0;
int total_elems = 0;
for (int i = 0; i < num_yaw; i++)
{
for (int j = 0; j < num_pitch; j++)
{
total_elems += yaw_pitch_bins[i][j];
if (yaw_pitch_bins[i][j] > max_elems)
max_elems = yaw_pitch_bins[i][j];
}
}
float average = static_cast<float> (total_elems) / (static_cast<float> (num_pitch + num_yaw));
std::cout << "The average number of image per bin is:" << average << std::endl;
std::cout << "Total number of images in the dataset:" << total_elems << std::endl;
//reduce unbalance from dataset by capping the number of images per bin, keeping at least a certain min
if (min_images_per_bin_ != -1)
{
std::cout << "Reducing unbalance of the dataset." << std::endl;
for (int i = 0; i < num_yaw; i++)
{
for (int j = 0; j < num_pitch; j++)
{
if (yaw_pitch_bins[i][j] >= min_images_per_bin_)
{
std::random_shuffle (image_files_per_bin[i][j].begin (), image_files_per_bin[i][j].end ());
image_files_per_bin[i][j].resize (min_images_per_bin_);
yaw_pitch_bins[i][j] = min_images_per_bin_;
}
for (size_t ii = 0; ii < image_files_per_bin[i][j].size (); ii++)
{
image_files_.push_back (image_files_per_bin[i][j][ii]);
}
}
}
}
pcl::face_detection::showBining (num_pitch, res_pitch, min_pitch, num_yaw, res_yaw, min_yaw, yaw_pitch_bins);
std::cout << "Total number of images in the dataset:" << image_files_.size () << std::endl;
}
void pcl::face_detection::FaceDetectorDataProvider<FeatureType, DataSet, LabelType, ExampleIndex, NodeType>::getDatasetAndLabels(DataSet & data_set,
std::vector<LabelType> & label_data, std::vector<ExampleIndex> & examples)
{
srand (static_cast<unsigned int>(time (NULL)));
std::random_shuffle (image_files_.begin (), image_files_.end ());
std::vector < std::string > files;
files = image_files_;
files.resize (std::min (num_images_, static_cast<int> (files.size ())));
std::vector < TrainingExample > training_examples;
std::vector<float> labels;
int total_neg, total_pos;
total_neg = total_pos = 0;
#if PCL_FACE_DETECTION_VIS_TRAINING_FDDP == 1
pcl::visualization::PCLVisualizer vis("training");
#endif
for (size_t j = 0; j < files.size (); j++)
{
#if PCL_FACE_DETECTION_VIS_TRAINING_FDDP == 1
vis.removeAllPointClouds();
vis.removeAllShapes();
#endif
if ((j % 50) == 0)
{
std::cout << "Loading image..." << j << std::endl;
}
//1. Load clouds with and without labels
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr loaded_cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::io::loadPCDFile (files[j], *loaded_cloud);
pcl::PointCloud<pcl::PointXYZL>::Ptr cloud_labels (new pcl::PointCloud<pcl::PointXYZL>);
pcl::PointCloud<pcl::PointXYZL>::Ptr loaded_cloud_labels (new pcl::PointCloud<pcl::PointXYZL>);
pcl::io::loadPCDFile (files[j], *loaded_cloud_labels);
//crop images to remove as many NaNs as possible and reduce the memory footprint
{
size_t min_col, min_row;
size_t max_col, max_row;
min_col = min_row = std::numeric_limits<size_t>::max ();
max_col = max_row = 0;
for (size_t col = 0; col < loaded_cloud->width; col++)
{
for (size_t row = 0; row < loaded_cloud->height; row++)
{
if (pcl::isFinite (loaded_cloud->at (col, row)))
{
if (row < min_row)
min_row = row;
if (row > max_row)
max_row = row;
if (col < min_col)
min_col = col;
if (col > max_col)
max_col = col;
}
}
}
//std::cout << min_col << " - " << max_col << std::endl;
//std::cout << min_row << " - " << max_row << std::endl;
cropCloud<pcl::PointXYZ> (min_col, max_col, min_row, max_row, *loaded_cloud, *cloud);
cropCloud<pcl::PointXYZL> (min_col, max_col, min_row, max_row, *loaded_cloud_labels, *cloud_labels);
/*pcl::visualization::PCLVisualizer vis ("training");
vis.addPointCloud(loaded_cloud);
vis.spin();*/
}
//Compute integral image over depth
boost::shared_ptr < pcl::IntegralImage2D<float, 1> > integral_image_depth;
integral_image_depth.reset (new pcl::IntegralImage2D<float, 1> (false));
int element_stride = sizeof(pcl::PointXYZ) / sizeof(float);
int row_stride = element_stride * cloud->width;
const float *data = reinterpret_cast<const float*> (&cloud->points[0]);
integral_image_depth->setInput (data + 2, cloud->width, cloud->height, element_stride, row_stride);
//Compute normals and normal integral images
pcl::PointCloud<pcl::Normal>::Ptr normals (new pcl::PointCloud<pcl::Normal>);
if (USE_NORMALS_)
{
typedef typename pcl::IntegralImageNormalEstimation<pcl::PointXYZ, pcl::Normal> NormalEstimator_;
NormalEstimator_ n3d;
n3d.setNormalEstimationMethod (n3d.COVARIANCE_MATRIX);
n3d.setInputCloud (cloud);
n3d.setRadiusSearch (0.02);
n3d.setKSearch (0);
{
pcl::ScopeTime t ("compute normals...");
n3d.compute (*normals);
}
}
int element_stride_normal = sizeof(pcl::Normal) / sizeof(float);
int row_stride_normal = element_stride_normal * normals->width;
boost::shared_ptr < pcl::IntegralImage2D<float, 1> > integral_image_normal_x;
boost::shared_ptr < pcl::IntegralImage2D<float, 1> > integral_image_normal_y;
boost::shared_ptr < pcl::IntegralImage2D<float, 1> > integral_image_normal_z;
if (USE_NORMALS_)
{
integral_image_normal_x.reset (new pcl::IntegralImage2D<float, 1> (false));
const float *data_nx = reinterpret_cast<const float*> (&normals->points[0]);
integral_image_normal_x->setInput (data_nx, normals->width, normals->height, element_stride_normal, row_stride_normal);
integral_image_normal_y.reset (new pcl::IntegralImage2D<float, 1> (false));
const float *data_ny = reinterpret_cast<const float*> (&normals->points[0]);
integral_image_normal_y->setInput (data_ny + 1, normals->width, normals->height, element_stride_normal, row_stride_normal);
integral_image_normal_z.reset (new pcl::IntegralImage2D<float, 1> (false));
const float *data_nz = reinterpret_cast<const float*> (&normals->points[0]);
integral_image_normal_z->setInput (data_nz + 2, normals->width, normals->height, element_stride_normal, row_stride_normal);
}
//Using cloud labels estimate a 2D window from where to extract positive samples
//Rest can be used to extract negative samples
size_t min_col, min_row;
size_t max_col, max_row;
min_col = min_row = std::numeric_limits<size_t>::max ();
max_col = max_row = 0;
//std::cout << cloud_labels->width << " " << cloud_labels->height << std::endl;
for (size_t col = 0; col < cloud_labels->width; col++)
{
for (size_t row = 0; row < cloud_labels->height; row++)
{
if (cloud_labels->at (col, row).label == 1)
{
if (row < min_row)
min_row = row;
if (row > max_row)
max_row = row;
if (col < min_col)
min_col = col;
if (col > max_col)
max_col = col;
}
}
}
#if PCL_FACE_DETECTION_VIS_TRAINING_FDDP == 1
pcl::PointCloud<pcl::PointXYZI>::Ptr cloud_intensity(new pcl::PointCloud<pcl::PointXYZI>);
cloud_intensity->width = cloud->width;
cloud_intensity->height = cloud->height;
cloud_intensity->points.resize(cloud->points.size());
cloud_intensity->is_dense = cloud->is_dense;
for (int jjj = 0; jjj < static_cast<int>(cloud->points.size()); jjj++)
{
cloud_intensity->points[jjj].getVector4fMap() = cloud->points[jjj].getVector4fMap();
cloud_intensity->points[jjj].intensity = 0.f;
int row, col;
col = jjj % cloud->width;
row = jjj / cloud->width;
//std::cout << row << " " << col << std::endl;
if (check_inside(col, row, min_col, max_col, min_row, max_row))
{
cloud_intensity->points[jjj].intensity = 1.f;
}
}
pcl::visualization::PointCloudColorHandlerGenericField < pcl::PointXYZI > handler(cloud_intensity, "intensity");
vis.addPointCloud(cloud_intensity, handler, "intensity_cloud");
#endif
std::string pose_file (files[j]);
boost::replace_all (pose_file, ".pcd", "_pose.txt");
Eigen::Matrix4f pose_mat;
pose_mat.setIdentity (4, 4);
readMatrixFromFile (pose_file, pose_mat);
Eigen::Vector3f ea = pose_mat.block<3, 3> (0, 0).eulerAngles (0, 1, 2);
Eigen::Vector3f trans_vector = Eigen::Vector3f (pose_mat (0, 3), pose_mat (1, 3), pose_mat (2, 3));
pcl::PointXYZ center_point;
center_point.x = trans_vector[0];
center_point.y = trans_vector[1];
center_point.z = trans_vector[2];
int N_patches = patches_per_image_;
int pos_extracted = 0;
int neg_extracted = 0;
int w_size_2 = static_cast<int> (w_size_ / 2);
//************************************************
//2nd training style, fanelli's journal description
//************************************************
{
typedef std::pair<int, int> pixelpair;
std::vector < pixelpair > negative_p, positive_p;
//get negative and positive indices to sample from
for (int col = 0; col < (static_cast<int> (cloud_labels->width) - w_size_); col++)
{
for (int row = 0; row < (static_cast<int> (cloud_labels->height) - w_size_); row++)
{
if (!pcl::isFinite (cloud->at (col + w_size_2, row + w_size_2)))
continue;
//reject patches with more than percent invalid values
float percent = 0.5f;
if (static_cast<float>(integral_image_depth->getFiniteElementsCount (col, row, w_size_, w_size_)) < (percent * static_cast<float>(w_size_ * w_size_)))
continue;
pixelpair pp = std::make_pair (col, row);
if (cloud_labels->at (col + w_size_2, row + w_size_2).label == 1)
positive_p.push_back (pp);
else
negative_p.push_back (pp);
}
}
//shuffle and resize
std::random_shuffle (positive_p.begin (), positive_p.end ());
std::random_shuffle (negative_p.begin (), negative_p.end ());
positive_p.resize (N_patches);
negative_p.resize (N_patches);
//extract positive patch
for (size_t p = 0; p < positive_p.size (); p++)
{
int col, row;
col = positive_p[p].first;
row = positive_p[p].second;
pcl::PointXYZ patch_center_point;
patch_center_point.x = cloud->at (col + w_size_2, row + w_size_2).x;
patch_center_point.y = cloud->at (col + w_size_2, row + w_size_2).y;
patch_center_point.z = cloud->at (col + w_size_2, row + w_size_2).z;
TrainingExample te;
te.iimages_.push_back (integral_image_depth);
if (USE_NORMALS_)
{
te.iimages_.push_back (integral_image_normal_x);
te.iimages_.push_back (integral_image_normal_y);
te.iimages_.push_back (integral_image_normal_z);
}
te.row_ = row;
te.col_ = col;
te.wsize_ = w_size_;
te.trans_ = center_point.getVector3fMap () - patch_center_point.getVector3fMap ();
te.trans_ *= 1000.f; //transform it to millimeters
te.rot_ = ea;
te.rot_ *= 57.2957795f; //transform it to degrees
labels.push_back (1);
pos_extracted++;
total_pos++;
training_examples.push_back (te);
#if PCL_FACE_DETECTION_VIS_TRAINING_FDDP == 1
pcl::PointCloud<pcl::PointXYZI>::Ptr cloud_intensity2(new pcl::PointCloud<pcl::PointXYZI>(*cloud_intensity));
for (int jjj = col; jjj < (col + w_size_); jjj++)
{
for (int iii = row; iii < (row + w_size_); iii++)
{
cloud_intensity2->at(jjj, iii).intensity = 2.f;
}
}
vis.removeAllPointClouds();
pcl::visualization::PointCloudColorHandlerGenericField < pcl::PointXYZI > handler(cloud_intensity2, "intensity");
vis.addPointCloud(cloud_intensity2, handler, "cloud");
vis.spinOnce();
vis.spin();
sleep(1);
#endif
}
#if PCL_FACE_DETECTION_VIS_TRAINING_FDDP == 1
std::cout << "Going to extract negative patches..." << std::endl;
sleep(2);
#endif
for (size_t p = 0; p < negative_p.size (); p++)
{
int col, row;
col = negative_p[p].first;
row = negative_p[p].second;
TrainingExample te;
te.iimages_.push_back (integral_image_depth);
if (USE_NORMALS_)
{
te.iimages_.push_back (integral_image_normal_x);
te.iimages_.push_back (integral_image_normal_y);
te.iimages_.push_back (integral_image_normal_z);
}
te.row_ = row;
te.col_ = col;
te.wsize_ = w_size_;
labels.push_back (0);
neg_extracted++;
total_neg++;
training_examples.push_back (te);
#if PCL_FACE_DETECTION_VIS_TRAINING_FDDP == 1
pcl::PointCloud<pcl::PointXYZI>::Ptr cloud_intensity2(new pcl::PointCloud<pcl::PointXYZI>(*cloud_intensity));
for (int jjj = col; jjj < (col + w_size_); jjj++)
{
for (int iii = row; iii < (row + w_size_); iii++)
{
cloud_intensity2->at(jjj, iii).intensity = 2.f;
}
}
vis.removeAllPointClouds();
pcl::visualization::PointCloudColorHandlerGenericField < pcl::PointXYZI > handler(cloud_intensity2, "intensity");
vis.addPointCloud(cloud_intensity2, handler, "cloud");
vis.spinOnce();
vis.spin();
sleep(1);
#endif
}
if (neg_extracted != N_patches)
{
std::cout << "Extracted " << neg_extracted << " negative patches" << std::endl;
std::cout << files[j] << std::endl;
}
if (pos_extracted != N_patches)
{
std::cout << "Extracted " << pos_extracted << " positive patches" << std::endl;
std::cout << files[j] << std::endl;
}
}
}
std::cout << training_examples.size () << " " << labels.size () << " " << total_neg << " " << total_pos << std::endl;
//train random forest and make persistent
std::vector<int> example_indices;
for (size_t i = 0; i < labels.size (); i++)
example_indices.push_back (static_cast<int> (i));
label_data = labels;
data_set = training_examples;
examples = example_indices;
}
int main(int argc, char **argv){
double *a;
double *b;
double temp;
int initID = 0;
int n=0;
//int res;
char *name;
clock_t t;
double time=0.;
FILE *input;
if (argc==2){
if ( !(n = atoi(argv[1]))){
printf("This is not a moon!\nI need numberz!\n");
return -4;
}
//n = atoi(argv[1]);
//m = atoi(argv[2]);
a = new double[n*n];
if (a==NULL){
printf("Failed to allocate memory for matrix A\n");
return -2;
}
defineMatrixWithFunction(a, n);
initID=1;
}
else if (argc==3){
if ( !(n = atoi(argv[2]))){
printf("This is not a moon!\n");
return -4;
}
name = argv[1];
//n = atoi(argv[2]);
//m = atoi(argv[3]);
int size = n*n;
a = new double[size];
if (a==NULL){
printf("Failed to allocate memory for matrix A\n");
return -2;
}
input = fopen(name, "r");
readMatrixFromFile(input, a, n);
fclose(input);
initID=2;
}
else{
printf("Usage:\n %s n\n %s file_name n\n", argv[0], argv[0]);
return -1;
}
if (initID==0){
printf("Failed to initialize matrix\n");
delete[] a;
return -1;
}
b = new double[n];
//printMatrix(a, n, n);
//temp = someInvariant_ii(a, n);
//printf("Sum before step 1:%.2lf\n", temp);
t = clock();
qrAlmostUpperTriangle(a, n);
//printMatrix(a, n, n);
temp = traceMatrix(a, n);
printf("Trace after step 1:%.2lf\n", temp);
//temp = someInvariant_ii(a, n);
//printf("Sum after step 1:%.2lf\n", temp);
searchEigenValues(a, b, n);
printMatrix(a, n, n);
temp = traceMatrix(a, n);
printf("Trace after step 2:%.2lf\n", temp);
time = (clock()-t)/(double)(CLOCKS_PER_SEC);
printf("Total time:\n%.2lf sec\n", time);
for (int i=0; i<n; i++){
printf("Value %d: %.4lf\n", i, b[i]);
}
delete[] a;
delete[] b;
return 0;
}
Matrix::Matrix(std::string name)
{
//readFromFile(name);
readMatrixFromFile(name);
}