make_svd_non_negative#
- ivy.make_svd_non_negative(x, U, S, V, /, *, nntype='nndsvd')[source]#
Use NNDSVD method to transform SVD results into a non-negative form. This method leads to more efficient solving with NNMF [1].
- Parameters:
x (
Union
[Array
,NativeArray
]) – tensor being decomposed.U (
Union
[Array
,NativeArray
]) – left singular matrix from SVD.S (
Union
[Array
,NativeArray
]) – diagonal matrix from SVD.V (
Union
[Array
,NativeArray
]) – right singular matrix from SVD.nntype (
Optional
[Literal
['nndsvd'
,'nndsvda'
]], default:'nndsvd'
) –- whether to fill small values with 0.0 (nndsvd),
or the tensor mean (nndsvda, default).
[1] (Boutsidis & Gallopoulos. Pattern Recognition, 41(4): 1350-1362, 2008.) –
- Return type:
- Array.make_svd_non_negative(self, U, S, V, /, *, nntype='nndsvd')[source]#
ivy.Array instance method variant of ivy.make_svd_non_negative. This method simply wraps the function, and so the docstring for ivy.make_svd_non_negative also applies to this method with minimal changes.
- Parameters:
self (
Union
[Array
,NativeArray
]) – tensor being decomposed.U (
Union
[Array
,NativeArray
]) – left singular matrix from SVD.S (
Union
[Array
,NativeArray
]) – diagonal matrix from SVD.V (
Union
[Array
,NativeArray
]) – right singular matrix from SVD.nntype (
Optional
[Literal
['nndsvd'
,'nndsvda'
]], default:'nndsvd'
) – whether to fill small values with 0.0 (nndsvd), or the tensor mean (nndsvda, default).[1] (Boutsidis & Gallopoulos. Pattern Recognition, 41(4): 1350-1362, 2008.) –
- Return type:
Tuple
[Array
,Array
]
- Container.make_svd_non_negative(self, U, S, V, /, *, nntype='nndsvd', key_chains=None, to_apply=True, prune_unapplied=False, map_sequences=False)[source]#
ivy.Container instance method variant of ivy.make_svd_non_negative. This method simply wraps the function, and so the docstring for ivy.make_svd_non_negative applies to this method with minimal changes.
- Parameters:
self (
Union
[Array
,NativeArray
,Container
]) – tensor being decomposed.U (
Union
[Array
,NativeArray
,Container
]) – left singular matrix from SVD.S (
Union
[Array
,NativeArray
,Container
]) – diagonal matrix from SVD.V (
Union
[Array
,NativeArray
,Container
]) – right singular matrix from SVD.nntype (
Optional
[Union
[Literal
['nndsvd'
,'nndsvda'
],Container
]], default:'nndsvd'
) – whether to fill small values with 0.0 (nndsvd), or the tensor mean (nndsvda, default).[1] (Boutsidis & Gallopoulos. Pattern Recognition, 41(4): 1350-1362, 2008.) –
- Return type:
Tuple
[Container
,Container
]