'}h*2dZddgZddlmZmZddlZddlmZddlmZ dd l m Z m Z  dd ed e d ee d eedef dZ dd ed ee d ee d eedeeeeff dZ dd ed ee d ee d eedeeeeff dZ dd ed ee ded e deeeeff dZy)zCImplement various linear algebra algorithms for low rank matrices. svd_lowrank pca_lowrank)OptionalTupleN)Tensor) _linalg_utils)handle_torch_functionhas_torch_functionAqniterMreturnc|dn|}|jdd\}}tj|}tj}t j ||||j }tj|} |tjj|||j} t|D]b} tjj|| | j} tjj||| j} d| Stj|} tjj||||||z j} t|D]v} tjj|| | || | z j} tjj||| ||| z j} x| 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 `_). Ndtypedevice) shape_utilsget_floating_dtypematmultorchrandnr transjugatelinalgqrQrange) r r rrmnrrRA_Hr iM_Hs M/var/www/html/test/engine/venv/lib/python3.12/site-packages/torch/_lowrank.pyget_approximate_basisr)sZAEE 7723>A ? Hc&tjjse||f}tt t |j tjt dfs t|rtt|||||St||||S)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 rr tensor_opss r(rrTs}Z 99 ! ! #V 3tZ()22 \\4: &  ,(Zau  Qeq 11r*c|dn|}|jdd\}}tj}|d}ntj|}tj|}||ks||kDrt ||||} tj | } | ||| } n||| ||| z } | jd|k(sJ| j|f| jd|k(sJ| j|f| jd| jdksJ| jt jj| d\} } }|j}| j|}nt ||||} tj | } | ||| }n||| ||| z }tj|} | jd|k(sJ| j|f| jd|k(sJ| j|f| jd| jdksJ| jt jj| d\} } }|j}| j| } | | |fS)NrrrF) full_matrices) rrr transposer) conjugaterrsvdmH)r r rrr"r#rM_tA_tr Q_cB_tUSVhVBs r(r2r2sT YAA 77231Cyy}!1CIIq>1!yy}!1CIIq>1!yy} " -8syy8-<<##Cu#=1b EE HHQK !!Qeq 9q! 9sC AsC 6#s#33Aq!yy}!1CIIq>1!yy}!1CIIq>1!yy} " -8syy8-<<##Cu#=1b EE HHQK a7Nr*centerc tjjs=t|tjur"t |frt t|f||||S|jdd\}}|td||}n/|dk\r|t||kstd|dt|||dk\std|d tj|}|st|||d Stj|r?t|jd k7r td tj j#|d |z }|j%d}tj&d t||j(|j*} || d<tj,| |j/|df||j*} tj0|jddd|fz||j*} tj2tj j5| | } t|||| S|j7d d} t|| z ||d 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 = 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. 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