U ʹj* @sdZddgZddlmZmZddlZddlmZddlmZ dd l m Z m Z dee ee eeed d d Zdeee ee eeeeeefd ddZdeee ee eeeeeefd ddZdeee ee eeeefdddZdS)zCImplement various linear algebra algorithms for low rank matrices. svd_lowrank pca_lowrank)OptionalTupleN)Tensor) _linalg_utils)handle_torch_functionhas_torch_function)AqniterMreturnc Cs|dkr dn|}|jdd\}}t|}tj}tj||||jd}t|} |dkrtj |||j } t |D],} tj || | j } tj ||| j } qtnrt|} tj ||||||j } t |D]@} tj || | || | j } tj ||| ||| j } q| S)a8Return tensor :math:`Q` with :math:`q` orthonormal columns such that :math:`Q Q^H A` approximates :math:`A`. If :math:`M` is specified, then :math:`Q` is such that :math:`Q Q^H (A - M)` approximates :math:`A - M`. .. note:: The implementation is based on the Algorithm 4.4 from Halko et al, 2009. .. note:: For an adequate approximation of a k-rank matrix :math:`A`, where k is not known in advance but could be estimated, the number of :math:`Q` columns, q, can be choosen according to the following criteria: in general, :math:`k <= q <= min(2*k, m, n)`. For large low-rank matrices, take :math:`q = k + 5..10`. If k is relatively small compared to :math:`min(m, n)`, choosing :math:`q = k + 0..2` may be sufficient. .. note:: To obtain repeatable results, reset the seed for the pseudorandom number generator Args:: A (Tensor): the input tensor of size :math:`(*, m, n)` q (int): the dimension of subspace spanned by :math:`Q` columns. niter (int, optional): the number of subspace iterations to conduct; ``niter`` must be a nonnegative integer. In most cases, the default value 2 is more than enough. M (Tensor, optional): the input tensor's mean of size :math:`(*, 1, n)`. References:: - Nathan Halko, Per-Gunnar Martinsson, and Joel Tropp, Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions, arXiv:0909.4061 [math.NA; math.PR], 2009 (available at `arXiv `_). Nr dtypedevice) shape_utilsget_floating_dtypematmultorchZrandnrZ transjugatelinalgZqrQrange) r r rrmnrrRZA_HriZM_Hr!M/var/www/html/bwave/python/venv/lib/python3.8/site-packages/torch/_lowrank.pyget_approximate_basiss"-      r#cCs\tjsL||f}ttt|tjtdfsLt|rLt t |||||dSt ||||dS)a_Return the singular value decomposition ``(U, S, V)`` of a matrix, batches of matrices, or a sparse matrix :math:`A` such that :math:`A \approx U diag(S) V^T`. In case :math:`M` is given, then SVD is computed for the matrix :math:`A - M`. .. note:: The implementation is based on the Algorithm 5.1 from Halko et al, 2009. .. note:: To obtain repeatable results, reset the seed for the pseudorandom number generator .. note:: The input is assumed to be a low-rank matrix. .. note:: In general, use the full-rank SVD implementation :func:`torch.linalg.svd` for dense matrices due to its 10-fold higher performance characteristics. The low-rank SVD will be useful for huge sparse matrices that :func:`torch.linalg.svd` cannot handle. Args:: A (Tensor): the input tensor of size :math:`(*, m, n)` q (int, optional): a slightly overestimated rank of A. niter (int, optional): the number of subspace iterations to conduct; niter must be a nonnegative integer, and defaults to 2 M (Tensor, optional): the input tensor's mean of size :math:`(*, 1, n)`. References:: - Nathan Halko, Per-Gunnar Martinsson, and Joel Tropp, Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions, arXiv:0909.4061 [math.NA; math.PR], 2009 (available at `arXiv `_). N)r rr) rjit is_scriptingsetmaptypeissubsetrr r r _svd_lowrank)r r rrZ tensor_opsr!r!r"rTs -  cCs|dkr dn|}|jdd\}}tj}|dkr6d}n t|}t|}||ks\||krt||||d} t| } |dkr||| } n||| ||| } | jd|kst| j|f| jd|kst| j|f| jd| jdkst| jtjj | dd\} } }|j }| |}nt||||d} t| } |dkrL||| }n||| ||| }t|} | jd|kst| j|f| jd|kst| j|f| jd| jdkst| jtjj | dd\} } }|j }| | } | | |fS)Nr$rrrF)Z full_matrices) rrr transposer# conjugateAssertionErrorrrZsvdZmH)r r rrrrrZM_tZA_trZQ_cZB_tUSZVhVBr!r!r"r+s@           r+T)r r centerrrcCstjs8t|tjk r8t|fr8tt|f||||dS|jdd\}}|dkr`t d||}n0|dkrv|t ||kst d|dt |||dkst d|d t |}|st |||dd St |rt|jd krt d tjj|d d|}|d}tjd t||j|jd} || d<tj| ||df||jd} tj|jddd|f||jd} t tj| | } t |||| d S|jd dd} t || ||dd SdS)aPerforms linear Principal Component Analysis (PCA) on a low-rank matrix, batches of such matrices, or sparse matrix. This function returns a namedtuple ``(U, S, V)`` which is the nearly optimal approximation of a singular value decomposition of a centered matrix :math:`A` such that :math:`A = U diag(S) V^T`. .. note:: The relation of ``(U, S, V)`` to PCA is as follows: - :math:`A` is a data matrix with ``m`` samples and ``n`` features - the :math:`V` columns represent the principal directions - :math:`S ** 2 / (m - 1)` contains the eigenvalues of :math:`A^T A / (m - 1)` which is the covariance of ``A`` when ``center=True`` is provided. - ``matmul(A, V[:, :k])`` projects data to the first k principal components .. note:: Different from the standard SVD, the size of returned matrices depend on the specified rank and q values as follows: - :math:`U` is m x q matrix - :math:`S` is q-vector - :math:`V` is n x q matrix .. note:: To obtain repeatable results, reset the seed for the pseudorandom number generator Args: A (Tensor): the input tensor of size :math:`(*, m, n)` q (int, optional): a slightly overestimated rank of :math:`A`. By default, ``q = min(6, m, n)``. center (bool, optional): if True, center the input tensor, otherwise, assume that the input is centered. niter (int, optional): the number of subspace iterations to conduct; niter must be a nonnegative integer, and defaults to 2. References:: - Nathan Halko, Per-Gunnar Martinsson, and Joel Tropp, Finding structure with randomness: probabilistic algorithms for constructing approximate matrix decompositions, arXiv:0909.4061 [math.NA; math.PR], 2009 (available at `arXiv `_). )r r5rrNr$rzq(=z>) must be non-negative integer and not greater than min(m, n)=zniter(=z) must be non-negative integerr,r z8pca_lowrank input is expected to be 2-dimensional tensor)r)dimrrT)r6Zkeepdim)rr%r&r)rr r rrmin ValueErrorrrr+Z is_sparselensparsesumindicesZzerosrrZsparse_coo_tensorvaluesZonesr.mmZmean)r r r5rrrrcZcolumn_indicesr<ZC_tZ ones_m1_trCr!r!r"rs\?    $)r N)r$r N)r$r N)NTr )__doc____all__typingrrrrrrZ overridesr r intr#rr+boolrr!r!r!r"sZ   H : 4