本文整理汇总了C++中PCA::computeVar方法的典型用法代码示例。如果您正苦于以下问题：C++ PCA::computeVar方法的具体用法？C++ PCA::computeVar怎么用？C++ PCA::computeVar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PCA的用法示例。
在下文中一共展示了PCA::computeVar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: main
//.........这里部分代码省略.........
add(meanXs, instance, meanXs);
}
for (int i = 0; i < Xp.cols; i++) {
instance = Xp.col(i);
add(meanXp, instance, meanXp);
}
// calculate total mean
meanX.convertTo(meanX, CV_32F, 1.0/static_cast<double>(X.cols));
meanXs.convertTo(meanXs, CV_32F, 1.0/static_cast<double>(Xs.cols));
meanXp.convertTo(meanXp, CV_32F, 1.0/static_cast<double>(Xp.cols));
// subtract the mean of matrix
for(int i=0; i<X.cols; i++) {
Mat c_i = X.col(i);
subtract(c_i, meanX.reshape(1,dim), c_i);
}
for(int i=0; i<Xs.cols; i++) {
Mat c_i = Xs.col(i);
subtract(c_i, meanXs.reshape(1,dim), c_i);
}
for(int i=0; i<Xp.cols; i++) {
Mat c_i = Xp.col(i);
subtract(c_i, meanXp.reshape(1,dim), c_i);
}
if(meanX.total() >= nTraining)
pca(X, Mat(), CV_PCA_DATA_AS_COL, nTraining-1);
else
pca.computeVar(X, Mat(), CV_PCA_DATA_AS_COL, .99);
Mat W1 = pca.eigenvectors.t();
Mat ldaData = (W1.t()*X).t();
lda.compute(ldaData, labels);
Mat W2 = lda.eigenvectors();
W2.convertTo(W2, CV_32F);
Mat projectionMatrix = (W2.t()*W1.t()).t();
//testing
#pragma omp parallel for private(temp)
for(uint i=0; i<nTestingSketches; i++){
temp = *(testingSketchesDescriptors[i]);
temp = bag(temp, bag_indexes, 154);
temp = projectionMatrix.t()*(temp-meanX);
if(b==0){
testingSketchesDescriptorsBag[i] = new Mat();
*(testingSketchesDescriptorsBag[i]) = temp.clone();
}
else{
vconcat(*(testingSketchesDescriptorsBag[i]), temp, *(testingSketchesDescriptorsBag[i]));
}
}
#pragma omp parallel for private(temp)
for(uint i=0; i<nTestingPhotos; i++){
temp = *(testingPhotosDescriptors[i]);
temp = bag(temp, bag_indexes, 154);
temp = projectionMatrix.t()*(temp-meanX);
if(b==0){
testingPhotosDescriptorsBag[i] = new Mat();
*(testingPhotosDescriptorsBag[i]) = temp.clone();
}示例2: main
//.........这里部分代码省略.........
Kernel k(trainingPhotosDescriptors1Temp, trainingSketchesDescriptors1Temp);
k.compute();
uint dim = (k.projectGallery(bag(*(trainingPhotosDescriptors1[0]), bag_indexes, 154))).total();
Mat X(dim, 2*nTraining2, CV_32F);
#pragma omp parallel for private(temp)
for(uint i=0; i<nTraining2; i++){
temp = *(trainingSketchesDescriptors2[i]);
temp = bag(temp, bag_indexes, 154);
temp = k.projectProbe(temp);
temp.copyTo(X.col(i));
}
#pragma omp parallel for private(temp)
for(uint i=0; i<nTraining2; i++){
temp = *(trainingPhotosDescriptors2[i]);
temp = bag(temp, bag_indexes, 154);
temp = k.projectGallery(temp);
temp.copyTo(X.col(i+nTraining2));
}
Mat meanX = Mat::zeros(dim, 1, CV_32F), instance;
// calculate sums
for (int i = 0; i < X.cols; i++) {
instance = X.col(i);
add(meanX, instance, meanX);
}
// calculate total mean
meanX.convertTo(meanX, CV_32F, 1.0/static_cast<double>(X.cols));
// subtract the mean of matrix
for(int i=0; i<X.cols; i++) {
Mat c_i = X.col(i);
subtract(c_i, meanX.reshape(1,dim), c_i);
}
pca.computeVar(X, Mat(), CV_PCA_DATA_AS_COL, .99);
Mat W1 = pca.eigenvectors.t();
Mat ldaData = (W1.t()*X).t();
lda.compute(ldaData, labels);
Mat W2 = lda.eigenvectors();
W2.convertTo(W2, CV_32F);
Mat projectionMatrix = (W2.t()*W1.t()).t();
//testing
#pragma omp parallel for private(temp)
for(uint i=0; i<nTestingSketches; i++){
temp = *(testingSketchesDescriptors[i]);
temp = bag(temp, bag_indexes, 154);
temp = k.projectProbe(temp);
temp = projectionMatrix.t()*(temp-meanX);
if(b==0){
testingSketchesDescriptorsBag[i] = new Mat();
*(testingSketchesDescriptorsBag[i]) = temp.clone();
}
else{
vconcat(*(testingSketchesDescriptorsBag[i]), temp, *(testingSketchesDescriptorsBag[i]));
}
}
#pragma omp parallel for private(temp)
for(uint i=0; i<nTestingPhotos; i++){
temp = *(testingPhotosDescriptors[i]);
temp = bag(temp, bag_indexes, 154);
temp = k.projectGallery(temp);
temp = projectionMatrix.t()*(temp-meanX);
if(b==0){
testingPhotosDescriptorsBag[i] = new Mat();
*(testingPhotosDescriptorsBag[i]) = temp.clone();
}
else{
vconcat(*(testingPhotosDescriptorsBag[i]), temp, *(testingPhotosDescriptorsBag[i]));
}
}
}
Mat distancesCosine = Mat::zeros(nTestingSketches,nTestingPhotos,CV_64F);
#pragma omp parallel for
for(uint i=0; i<nTestingSketches; i++){
for(uint j=0; j<nTestingPhotos; j++){
distancesCosine.at<double>(i,j) = abs(1-cosineDistance(*(testingSketchesDescriptorsBag[i]),*(testingPhotosDescriptorsBag[j])));
}
}
string file1name = "kernel-prs-" + filter + descriptor + database + to_string(nTraining) + string("cosine") + to_string(count) + string(".xml");
FileStorage file1(file1name, FileStorage::WRITE);
file1 << "distanceMatrix" << distancesCosine;
file1.release();
return 0;
}