Reputation: 5254
I need to find a way of computing the orthonormal basis for range of a matrix. In matlab this function does it.
I need to do this in c/c++ and I am actually working with OpenCV
However, I haven't found anything that provides this capability in OpenCV.
I've tried working with cvSVD, but my results aren't correct.
Any clues?
Upvotes: 1
Views: 1875
Reputation: 393
This is in openCV, and it works with rectangular matrix as long as m>n, according to this paper
- (CvMat *) buildOrthonormal:(CvMat *) matrix {
NSInteger rows = matrix->rows;
NSInteger cols = matrix->cols;
CvMat *Q = cvCreateMat(rows, cols, kMatrixType);
CvMat *R = cvCreateMat(cols, cols, kMatrixType);
for (NSInteger k = 0; k < cols; k++) {
cvSetReal2D(R, k, k, 0.0f);
for (NSInteger i = 0; i < rows; i++) {
double value = cvGetReal2D(R, k, k) + cvGetReal2D(matrix, i, k) * cvGetReal2D(matrix, i, k);
cvSetReal2D(R, k, k, value);
}
cvSetReal2D(R, k, k, sqrt(cvGetReal2D(R, k, k)));
for (NSInteger i = 0; i < rows; i++) {
double value = cvGetReal2D(matrix, i, k) / cvGetReal2D(R, k, k);
cvSetReal2D(Q, i, k, value);
}
for (NSInteger j = k + 1; j < cols; j++) {
cvSetReal2D(R, k, j, 0.0f);
for (NSInteger i = 0; i < rows; i++) {
double value = cvGetReal2D(R, k, j) + cvGetReal2D(Q, i, k) * cvGetReal2D(matrix, i, j);
cvSetReal2D(R, k, j, value);
}
for (NSInteger i = 0; i < rows; i++) {
double value = cvGetReal2D(matrix, i, j) - cvGetReal2D(R, k, j) * cvGetReal2D(Q, i, k);
cvSetReal2D(matrix, i, j, value);
}
}
}
cvReleaseMat(&R);
return Q;
}
Upvotes: 2
Reputation: 77
Matlab can generate codes.Why don't you try it??? First generate then examine and finally use it,that is all
Upvotes: 0
Reputation: 19333
If you need an existing toolkit/library to handle this, @PureW above has provided a valid answer. If you need to implement this function yourself, you're looking for an implementation of the Gram-Schmidt algorithm.
Here is an example problem to help you verify your code:
http://www.mia.uni-saarland.de/Teaching/NAVC-SS11/sol_c8.pdf
And here is the code (please see references for full credits). PLEASE NOTE: This example assumes that you have a set of data that is scaled decently. If you have a poorly scaled matrix, you may need to consider LU-decomposition or an appropriate pivot strategy. There are useful links on this topic in the references as well.
#include <cstdlib>
#include <iostream>
#include <math.h>
using namespace std;
// example: http://www.mia.uni-saarland.de/Teaching/NAVC-SS11/sol_c8.pdf
// page 5
double a[3][3] = {
{1.0, 2.0, 1.0},
{0.0, 1.0, 2.0},
{1.0, 2.0, 0.0}
};
// any column of a is a vector
double r[3][3], q[3][3];
int main(int argc, char *argv[]) {
int k, i, j;
for (k=0; k<3; k++){
r[k][k]=0; // equivalent to sum = 0
for (i=0; i<3; i++)
r[k][k] = r[k][k] + a[i][k] * a[i][k]; // rkk = sqr(a0k) + sqr(a1k) + sqr(a2k)
r[k][k] = sqrt(r[k][k]); // ||a||
cout << endl << "R"<<k<<k<<": " << r[k][k];
for (i=0; i<3; i++) {
q[i][k] = a[i][k]/r[k][k];
cout << " q"<<i<<k<<": "<<q[i][k] << " ";
}
for(j=k+1; j<3; j++) {
r[k][j]=0;
for(i=0; i<3; i++) r[k][j] += q[i][k] * a[i][j];
cout << endl << "r"<<k<<j<<": " <<r[k][j] <<endl;
for (i=0; i<3; i++) a[i][j] = a[i][j] - r[k][j]*q[i][k];
for (i=0; i<3; i++) cout << "a"<<j<<": " << a[i][j]<< " ";
}
}
system("PAUSE");
return EXIT_SUCCESS;
}
References:
Upvotes: 4
Reputation: 5075
You want to look into the Gnu Scientific Library which is a nice and well-tested library building on top of the BLAS-libraries. It implements a lot of different matrix operations and is usually where I would start for linear algebra stuff. Maybe one of these would suit you?
Upvotes: 1