fix randMVNormal in ML ()

Fix the failed assertion by replacing the GEMM call.

Also random numbers are generated from normal distribution ~N(0,1),
instead of uniform distribution ~U(0,1).
This commit is contained in:
Amro 2015-10-11 01:54:11 +03:00
parent 0f1fdd885d
commit 13a0a37e63

View File

@ -151,21 +151,28 @@ static void Cholesky( const Mat& A, Mat& S )
average row vector, <cov> - symmetric covariation matrix */
void randMVNormal( InputArray _mean, InputArray _cov, int nsamples, OutputArray _samples )
{
// check mean vector and covariance matrix
Mat mean = _mean.getMat(), cov = _cov.getMat();
int dim = (int)mean.total();
int dim = (int)mean.total(); // dimensionality
CV_Assert(mean.rows == 1 || mean.cols == 1);
CV_Assert(cov.rows == dim && cov.cols == dim);
mean = mean.reshape(1,1); // ensure a row vector
// generate n-samples of the same dimension, from ~N(0,1)
_samples.create(nsamples, dim, CV_32F);
Mat samples = _samples.getMat();
randu(samples, 0., 1.);
randn(samples, Scalar::all(0), Scalar::all(1));
// decompose covariance using Cholesky: cov = U'*U
// (cov must be square, symmetric, and positive semi-definite matrix)
Mat utmat;
Cholesky(cov, utmat);
int flags = mean.cols == 1 ? 0 : GEMM_3_T;
// transform random numbers using specified mean and covariance
for( int i = 0; i < nsamples; i++ )
{
Mat sample = samples.row(i);
gemm(sample, utmat, 1, mean, 1, sample, flags);
sample = sample * utmat + mean;
}
}