fun <T : Any> svd(a: KtNDArray<T>, fullMatrices: Boolean = true, computeUV: Boolean = true, hermitian: Boolean = false): List<KtNDArray<Double>> (source)
Singular Value Decomposition.
a - a real array with a.ndim >= 2.
fullMatrices - if True (default), u and vh have the shapes (..., M, M) and (..., N, N), respectively.
Otherwise, the shapes are (..., M, K) and (..., K, N), respectively, where K = min(M, N).
computeUV - whether or not to compute u and vh in addition to s. True by default.
hermitian - if true, a is assumed to be Hermitian (symmetric if real-valued),
enabling a more efficient method for finding singular values. Defaults to false.
Return
svd.