svd¶

ivy.
svd
(x, f=None)[source]¶ Singular Value Decomposition. When x is a 2D array, it is factorized as u @ numpy.diag(s) @ vh = (u * s) @ vh, where u and vh are 2D unitary arrays and s is a 1D array of a’s singular values. When x is higherdimensional, SVD is applied in batched mode.
 Parameters
x (array) – Input array with number of dimensions >= 2.
f (ml_framework, optional) – Machine learning framework. Inferred from inputs if None.
 Returns
u > { (…, M, M), (…, M, K) } array
Unitary array(s). The first (number of dims  2) dimensions have the same size as those of the input a. The size of the last two dimensions depends on the value of full_matrices.
s > (…, K) array
Vector(s) with the singular values, within each vector sorted in descending ord. The first (number of dims  2) dimensions have the same size as those of the input a.
vh > { (…, N, N), (…, K, N) } array
Unitary array(s). The first (number of dims  2) dimensions have the same size as those of the input a. The size of the last two dimensions depends on the value of full_matrices.