o h:)@sdZddgZddlmZddlZddlmZmZddlm Z m Z   dd ed e d ee d eed ef ddZ   dd ed ee d ee d eed e eeeff ddZ   dd ed ee d ee d eed e eeeff ddZ   dd ed ee ded e d e eeeff ddZdS)zBImplement various linear algebra algorithms for low rank matrices. svd_lowrank pca_lowrank)OptionalN) _linalg_utilsTensor)handle_torch_functionhas_torch_functionAqniterMreturnc Cs|durdn|}|st|n|j}tj}tj|jd|||jd}|||}|dur4||||}tj |j }t |D]2} ||j |}|durS|||j |}tj |j }|||}|durj||||}tj |j }q?|S)aReturn 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`. without instantiating any tensors of the size of :math:`A` or :math:`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:`(*, m, 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) is_complex_utilsget_floating_dtypermatmultorchrandnshaperlinalgqrQrangemH) r r r r rrRXr_r"b/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/torch/_lowrank.pyget_approximate_basis s$1    r$cCs\tjs&||f}ttt|tjtdfs&t|r&t t |||||dSt ||||dS)aReturn 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 \operatorname{diag}(S) V^{\text{H}}`. 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:: 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:: This is a randomized method. To obtain repeatable results, set the seed for the pseudorandom number generator .. note:: In general, use the full-rank SVD implementation :func:`torch.linalg.svd` for dense matrices due to its 10x 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:`(*, m, n)`, which will be broadcasted to the size of A in this function. 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 r r ) rjit is_scriptingsetmaptypeissubsetrrrr _svd_lowrank)r r r r tensor_opsr"r"r#rVs 5  c Cs|durdn|}|jdd\}}tj}|dur||}||kr-|j}|dur-|j}t||||d}||j|}|durG|||j|}tjj |dd\} } } | j} || } ||krc| | } } | | | fS)Nr%r r F) full_matrices) rrr broadcast_tosizerr$rrsvd) r r r r mnrrBUSVhVr"r"r#r,s&    r,TcentercCstjst|tjurt|frtt|f||||dS|jdd\}}|dur0t d||}n|dkr;|t ||ksHt d|dt |||dksTt d|d t |}|sct |||dd St |rt|jd krst d tjj|d d|}|d}tjd t||j|jd} || d<tj| ||df||jd} tj|jddd|f||jd} tj| | j} t |||| d S|jd dd} t || ||dd S)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 \approx U \operatorname{diag}(S) V^{\text{H}}` .. 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 r;r r.Nr%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.)dimrT)r<keepdim)rr&r'r*rrrrrmin ValueErrorrrr, is_sparselensparsesumindiceszerosrrsparse_coo_tensorvaluesonesmmmTmean)r r r;r r4r5rccolumn_indicesrEC_t ones_m1_tr Cr"r"r#rsJ B   $)r N)r%r N)NTr )__doc____all__typingrrrrrtorch.overridesrrintr$tuplerr,boolrr"r"r"r#sz  L  B  $