本文整理汇总了C++中Neighborhood::append方法的典型用法代码示例。如果您正苦于以下问题：C++ Neighborhood::append方法的具体用法？C++ Neighborhood::append怎么用？C++ Neighborhood::append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Neighborhood的用法示例。
在下文中一共展示了Neighborhood::append方法的2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: knnFromSimmat
Neighborhood br::knnFromSimmat(const QList<cv::Mat> &simmats, int k)
{
Neighborhood neighborhood;
float globalMax = -std::numeric_limits<float>::max();
float globalMin = std::numeric_limits<float>::max();
int numGalleries = (int)sqrt((float)simmats.size());
if (numGalleries*numGalleries != simmats.size())
qFatal("Incorrect number of similarity matrices.");
// Process each simmat
for (int i=0; i<numGalleries; i++) {
QVector<Neighbors> allNeighbors;
int currentRows = -1;
int columnOffset = 0;
for (int j=0; j<numGalleries; j++) {
cv::Mat m = simmats[i * numGalleries + j];
if (j==0) {
currentRows = m.rows;
allNeighbors.resize(currentRows);
}
if (currentRows != m.rows) qFatal("Row count mismatch.");
// Get data row by row
for (int k=0; k<m.rows; k++) {
Neighbors &neighbors = allNeighbors[k];
neighbors.reserve(neighbors.size() + m.cols);
for (int l=0; l<m.cols; l++) {
float val = m.at<float>(k,l);
if ((i==j) && (k==l)) continue; // Skips self-similarity scores
if (val != -std::numeric_limits<float>::max()
&& val != -std::numeric_limits<float>::infinity()
&& val != std::numeric_limits<float>::infinity()) {
globalMax = std::max(globalMax, val);
globalMin = std::min(globalMin, val);
}
neighbors.append(Neighbor(l+columnOffset, val));
}
}
columnOffset += m.cols;
}
// Keep the top matches
for (int j=0; j<allNeighbors.size(); j++) {
Neighbors &val = allNeighbors[j];
const int cutoff = k; // Number of neighbors to keep
int keep = std::min(cutoff, val.size());
std::partial_sort(val.begin(), val.begin()+keep, val.end(), compareNeighbors);
neighborhood.append((Neighbors)val.mid(0, keep));
}
}
return neighborhood;
}示例2: getNeighborhood
Neighborhood getNeighborhood(const QStringList &simmats)
{
Neighborhood neighborhood;
float globalMax = -std::numeric_limits<float>::max();
float globalMin = std::numeric_limits<float>::max();
int numGalleries = (int)sqrt((float)simmats.size());
if (numGalleries*numGalleries != simmats.size())
qFatal("Incorrect number of similarity matrices.");
// Process each simmat
for (int i=0; i<numGalleries; i++) {
QVector<Neighbors> allNeighbors;
int currentRows = -1;
int columnOffset = 0;
for (int j=0; j<numGalleries; j++) {
QScopedPointer<br::Format> format(br::Factory<br::Format>::make(simmats[i*numGalleries+j]));
br::Template t = format->read();
cv::Mat m = t.m();
if (j==0) {
currentRows = m.rows;
allNeighbors.resize(currentRows);
}
if (currentRows != m.rows) qFatal("Row count mismatch.");
// Get data row by row
for (int k=0; k<m.rows; k++) {
Neighbors &neighbors = allNeighbors[k];
neighbors.reserve(neighbors.size() + m.cols);
for (int l=0; l<m.cols; l++) {
float val = m.at<float>(k,l);
if ((i==j) && (k==l)) continue; // Skips self-similarity scores
if (val != -std::numeric_limits<float>::max()
&& val != -std::numeric_limits<float>::infinity()
&& val != std::numeric_limits<float>::infinity()) {
globalMax = std::max(globalMax, val);
globalMin = std::min(globalMin, val);
}
neighbors.append(Neighbor(l+columnOffset, val));
}
}
columnOffset += m.cols;
}
// Keep the top matches
for (int j=0; j<allNeighbors.size(); j++) {
Neighbors &val = allNeighbors[j];
const int cutoff = 20; // Somewhat arbitrary number of neighbors to keep
int keep = std::min(cutoff, val.size());
std::partial_sort(val.begin(), val.begin()+keep, val.end(), compareNeighbors);
neighborhood.append((Neighbors)val.mid(0, keep));
}
}
// Normalize scores
for (int i=0; i<neighborhood.size(); i++) {
Neighbors &neighbors = neighborhood[i];
for (int j=0; j<neighbors.size(); j++) {
Neighbor &neighbor = neighbors[j];
if (neighbor.second == -std::numeric_limits<float>::infinity())
neighbor.second = 0;
else if (neighbor.second == std::numeric_limits<float>::infinity())
neighbor.second = 1;
else
neighbor.second = (neighbor.second - globalMin) / (globalMax - globalMin);
}
}
return neighborhood;
}