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Merge pull request #5486 from amroamroamro:fix_ml_randMVNormal

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Alexander Alekhin 2015-10-12 17:44:02 +00:00
commit 0d791189ee
1 changed files with 11 additions and 4 deletions
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15
modules/ml/src/inner_functions.cpp
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@ -151,21 +151,28 @@ static void Cholesky( const Mat& A, Mat& S )
average row vector, <cov> - symmetric covariation matrix */ average row vector, <cov> - symmetric covariation matrix */
void randMVNormal( InputArray _mean, InputArray _cov, int nsamples, OutputArray _samples ) void randMVNormal( InputArray _mean, InputArray _cov, int nsamples, OutputArray _samples )
{ {
// check mean vector and covariance matrix
Mat mean = _mean.getMat(), cov = _cov.getMat(); 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); _samples.create(nsamples, dim, CV_32F);
Mat samples = _samples.getMat(); 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; Mat utmat;
Cholesky(cov, 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++ ) for( int i = 0; i < nsamples; i++ )
{ {
Mat sample = samples.row(i); Mat sample = samples.row(i);
gemm(sample, utmat, 1, mean, 1, sample, flags); sample = sample * utmat + mean;
} }
} }