本文整理汇总了C++中vcl_vector::back方法的典型用法代码示例。如果您正苦于以下问题:C++ vcl_vector::back方法的具体用法?C++ vcl_vector::back怎么用?C++ vcl_vector::back使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类vcl_vector
的用法示例。
在下文中一共展示了vcl_vector::back方法的4个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: local_dynamic_programming
bool rgrsn_ldp::local_dynamic_programming(const vnl_matrix<double> & probMap, int nNeighborBin,
vcl_vector<int> & optimalBins)
{
const int N = probMap.rows();
const int nBin = probMap.cols();
// dynamic programming
vnl_matrix<double> accumulatedProbMap = vnl_matrix<double>(N, nBin);
accumulatedProbMap.fill(0.0);
vnl_matrix<int> lookbackTable = vnl_matrix<int>(N, nBin);
lookbackTable.fill(0);
// copy first row
for (int c = 0; c<probMap.cols(); c++) {
accumulatedProbMap[0][c] = probMap[0][c];
lookbackTable[0][c] = c;
}
for (int r = 1; r <N; r++) {
for (int c = 0; c<probMap.cols(); c++) {
// lookup all possible place in the window
double max_val = -1;
int max_index = -1;
for (int w = -nNeighborBin; w <= nNeighborBin; w++) {
if (c + w <0 || c + w >= probMap.cols()) {
continue;
}
double val = probMap[r][c] + accumulatedProbMap[r-1][c+w];
if (val > max_val) {
max_val = val;
max_index = c + w; // most probable path from the [r-1] row, in column c + w
}
}
assert(max_index != -1);
accumulatedProbMap[r][c] = max_val;
lookbackTable[r][c] = max_index;
}
}
// lookback the table
double max_prob = -1.0;
int max_prob_index = -1;
for (int c = 0; c<accumulatedProbMap.cols(); c++) {
if (accumulatedProbMap[N-1][c] > max_prob) {
max_prob = accumulatedProbMap[N-1][c];
max_prob_index = c;
}
}
// back track
optimalBins.push_back(max_prob_index);
for (int r = N-1; r > 0; r--) {
int bin = lookbackTable[r][optimalBins.back()];
optimalBins.push_back(bin);
}
assert(optimalBins.size() == N);
// vcl_reverse(optimalBins.begin(), optimalBins.end());
return true;
}
示例2: lineEllipseIntersection
int VglPlus::lineEllipseIntersection(const vgl_line_2d<double> & line, const vgl_ellipse_2d<double> & ellipse,
vgl_point_2d<double> & pt1, vgl_point_2d<double> & pt2, bool isMajorAxis)
{
vgl_polygon<double> poly = ellipse.polygon();
assert(poly.num_sheets() == 1);
int num = 0;
// printf("sheet number is %u\n", poly.num_sheets());
const vcl_vector< vgl_point_2d< double > > sheet = poly[0];
assert( sheet.size() > 1 );
for ( unsigned int v = 0; v < sheet.size() - 1; v++ )
{
vgl_line_segment_2d< double > edge( sheet[v], sheet[v+1] );
vgl_point_2d<double> p;
bool isIntersection = vgl_intersection(line, edge, &p);
if (isIntersection) {
if (num == 0) {
pt1 = p;
num++;
}
else if(num == 1)
{
pt2 = p;
num++;
}
}
if (num == 2) {
break;
}
}
// last line segment
if(num != 2)
{
vgl_line_segment_2d< double > edge( sheet.back(), sheet.front());
vgl_point_2d<double> p;
bool isIntersection = vgl_intersection(line, edge, &p);
if (isIntersection) {
if (num == 0) {
pt1 = p;
num++;
}
else if(num == 1)
{
pt2 = p;
num++;
}
}
}
double disDif = 20;
if (num == 2 && isMajorAxis) {
double dis = vgl_distance(pt1, pt2);
// distance of two points should be as the similar length of minor or major axis
double dis1 = vgl_distance(ellipse.major_diameter().point1(), ellipse.major_diameter().point2());
double dis2 = vgl_distance(ellipse.minor_diameter().point1(), ellipse.minor_diameter().point2());
if (fabs(dis - dis1) >= disDif && fabs(dis - dis2) >= disDif) {
num = 0;
}
}
return num;
}
示例3: local_dynamic_programming_log
bool rgrsn_ldp::local_dynamic_programming_log(const vnl_matrix<double> & probMap, int nNeighborBin,
vcl_vector<int> & optimalBins)
{
// find minimum path
const int N = probMap.rows();
const int nBin = probMap.cols();
const double epsilon = 0.01;
vnl_matrix<double> negLogProbMap(N, nBin);
for (int r = 0; r<N; r++) {
for (int c = 0; c <nBin; c++) {
negLogProbMap(r, c) = -log(probMap(r, c) + epsilon);
}
}
// dynamic programming
vnl_matrix<double> accumulatedMap = vnl_matrix<double>(N, nBin);
accumulatedMap.fill(0.0);
vnl_matrix<int> lookbackTable = vnl_matrix<int>(N, nBin);
lookbackTable.fill(0);
// copy first row
for (int c = 0; c<negLogProbMap.cols(); c++) {
accumulatedMap[0][c] = negLogProbMap[0][c];
}
for (int r = 1; r <N; r++) {
for (int c = 0; c<negLogProbMap.cols(); c++) {
// lookup all possible place in the window
double min_val = INT_MAX;
int index = -1;
for (int w = -nNeighborBin; w <= nNeighborBin; w++) {
if (c + w <0 || c + w >= negLogProbMap.cols()) {
continue;
}
double val = negLogProbMap[r][c] + accumulatedMap[r-1][c+w];
if (val < min_val) {
min_val = val;
index = c + w;
}
}
assert(index != -1);
accumulatedMap[r][c] = min_val;
lookbackTable[r][c] = index;
}
}
// lookback the table
double min_val = INT_MAX;
int initIndex = -1;
for (int c = 0; c<accumulatedMap.cols(); c++) {
if (accumulatedMap[N-1][c] < min_val) {
min_val = accumulatedMap[N-1][c];
initIndex = c;
}
}
// back track
optimalBins.push_back(initIndex);
for (int r = N-1; r > 0; r--) {
int bin = lookbackTable[r][optimalBins.back()];
optimalBins.push_back(bin);
}
assert(optimalBins.size() == N);
vcl_reverse(optimalBins.begin(), optimalBins.end());
return true;
}
示例4: viterbi
bool rgrsn_ldp::viterbi(const vnl_matrix<double> & prob_map, const vnl_vector<double> & transition,
vcl_vector<int> & optimal_bins)
{
const int N = prob_map.rows();
const int nBin = prob_map.cols();
const int nNeighborBin = transition.size()/2;
const double epsilon = 0.01;
// dynamic programming
vnl_matrix<double> log_accumulatedProbMap = vnl_matrix<double>(N, nBin);
log_accumulatedProbMap.fill(0.0);
vnl_matrix<int> lookbackTable = vnl_matrix<int>(N, nBin);
lookbackTable.fill(0);
// copy first row
for (int c = 0; c<prob_map.cols(); c++) {
log_accumulatedProbMap[0][c] = log(prob_map[0][c] + epsilon);
lookbackTable[0][c] = c;
}
vnl_vector<double> log_transition = vnl_vector<double>(transition.size(), 0);
for (int i = 0; i<transition.size(); i++) {
log_transition[i] = log(transition[i] + epsilon);
}
for (int r = 1; r <N; r++) {
for (int c = 0; c<prob_map.cols(); c++) {
// lookup all possible place in the window
double max_val = vcl_numeric_limits<int>::min();
int max_index = -1;
for (int w = -nNeighborBin; w <= nNeighborBin; w++) {
if (c + w < 0 || c + w >= prob_map.cols()) {
continue;
}
assert(w + nNeighborBin >= 0 && w + nNeighborBin < transition.size());
double val = log_accumulatedProbMap[r-1][c+w] + log_transition[w + nNeighborBin];
if (val > max_val) {
max_val = val;
max_index = c + w; // most probable path from the [r-1] row, in column c + w
}
}
assert(max_index != -1);
log_accumulatedProbMap[r][c] = max_val + log(prob_map[r][c] + epsilon);
lookbackTable[r][c] = max_index;
}
}
// lookback the table
double max_prob = vcl_numeric_limits<int>::min();
int max_prob_index = -1;
for (int c = 0; c<log_accumulatedProbMap.cols(); c++) {
if (log_accumulatedProbMap[N-1][c] > max_prob) {
max_prob = log_accumulatedProbMap[N-1][c];
max_prob_index = c;
}
}
// back track
optimal_bins.push_back(max_prob_index);
for (int r = N-1; r > 0; r--) {
int bin = lookbackTable[r][optimal_bins.back()];
optimal_bins.push_back(bin);
}
assert(optimal_bins.size() == N);
vcl_reverse(optimal_bins.begin(), optimal_bins.end());
return true;
}