Function svd

src/lapack/mod.rs:71–89

Perform Singular Value Decomposition This function factorizes a matrix A into two unitary matrices U and Vt, and a diagonal matrix S such that A = U∗S∗Vt If A has M rows and N columns, U is of the size M x M , V is of size N x N, and S is of size M x N # Parameters - `in` is the input matrix # Return Values A triplet of Arrays. The first Array is the output array containing U The second A

(input: &Array<T>)

///
/// The third Array is the output array containing V ^ H
pub fn svd<T>(input: &Array<T>) -> (Array<T>, Array<T::BaseType>, Array<T>)
where
    T: HasAfEnum + FloatingPoint,
    T::BaseType: HasAfEnum,
{
    unsafe {
        let mut u: af_array = std::ptr::null_mut();
        let mut s: af_array = std::ptr::null_mut();
        let mut vt: af_array = std::ptr::null_mut();
        let err_val = af_svd(
            &mut u as *mut af_array,
            &mut s as *mut af_array,
            &mut vt as *mut af_array,
            input.get(),
        );
        HANDLE_ERROR(AfError::from(err_val));
        (u.into(), s.into(), vt.into())
    }
}

/// Perform Singular Value Decomposition inplace
///