fix randMVNormal in ML (#5469)

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).

modules/ml/src/inner_functions.cpp

@@ -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;
     }
 }