// CovarianceMatrix returns the covariance matrix of the distribution. Upon
// return, the value at element {i, j} of the covariance matrix is equal to
// the covariance of the i^th and j^th variables.
// covariance(i, j) = E[(x_i - E[x_i])(x_j - E[x_j])]
// If the input matrix is nil a new matrix is allocated, otherwise the result
// is stored in-place into the input.
func (n *Normal) CovarianceMatrix(s *mat64.SymDense) *mat64.SymDense {
if s == nil {
s = mat64.NewSymDense(n.Dim(), nil)
}
sn := s.Symmetric()
if sn != n.Dim() {
panic("normal: input matrix size mismatch")
}
n.setSigma()
s.CopySym(n.sigma)
return s
}