קgA)zdZddgZddlmZmZddlZddlmZmZddl m Z m Z  dd ed e d ee d eed ef dZ dd ed ee d ee d eed eeeeff dZ dd ed ee d ee d eed eeeeff dZ dd ed ee ded e d eeeeff dZdS)zBImplement various linear algebra algorithms for low rank matrices. svd_lowrank pca_lowrank)OptionalTupleN) _linalg_utilsTensor)handle_torch_functionhas_torch_functionAqniterMreturnc|dn|}|stj|n|j}tj}t j|jd|||j}|||}|||||z }t j |j }t|D]} ||j |}||||j |z }t j |j }|||}|||||z }t j |j }|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 rrrrRXris J/var/www/html/ai-engine/env/lib/python3.11/site-packages/torch/_lowrank.pyget_approximate_basisr& s9bAAEE01  KF %a ( ( (AGE ]F AGBK%AAAA q! A} q!  A 5\\!! F14OO =FF14OO#A LOOA    F1aLL =FF1aLL A LOOA    HcZtjs{||f}tt t |tjt dfs)t|rtt|||||St||||S)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 `_). 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