o ֭j+@sdZdgZddlZddlmZddlmZddlm Z ddlm Z dd lm Z dd lm Z e j e je je jd Ze je je je jd Zd d dZGdddZ    dddZdS)a Python wrapper for PROPACK -------------------------- PROPACK is a collection of Fortran routines for iterative computation of partial SVDs of large matrices or linear operators. Based on BSD licensed pypropack project: http://github.com/jakevdp/pypropack Author: Jake Vanderplas PROPACK source is BSD licensed, and available at http://soi.stanford.edu/~rmunk/PROPACK/ _svdpN)aslinearoperator) LinAlgError) _spropack) _dpropack) _cpropack) _zpropack)fdFDLS)LMSMc@s8eZdZdZddZddZeddZedd Zd S) _AProdz Wrapper class for linear operator The call signature of the __call__ method matches the callback of the PROPACK routines. cCs6zt||_WdStytt||_YdSwN)rA TypeErrornpasarray)selfrrV/var/www/html/CropPilot/venv/lib/python3.10/site-packages/scipy/sparse/linalg/_svdp.py__init__9s  z_AProd.__init__cCs8|dkr|j||dd<dS|j||dd<dS)Nn)rmatvecrmatvec)rtransamrxysparmiparmrrr__call__?sz_AProd.__call__cCs|jjSr)rshaperrrrr&Esz _AProd.shapec Cs:z|jjWSty|jt|jjdjYSw)Nr)rdtypeAttributeErrorrrzerosr&r'rrrr(Is    z _AProd.dtypeN) __name__ __module__ __qualname____doc__rr%propertyr&r(rrrrr2s rrTFMb`?c+ Cs|durtd|}|dvrtd|s|dkrtdt|}|jj}z t|}t|}Wn(tyXt tj d|drHtt j}ntt j}t|}t|}Ynw|j \}}|d ksi|t||krmtd |durud |}|dur{d }t|d |d |}||krtd |d|d|rdnd}|rdnd}tj||d fd|d}tj||fd|d}|dur|j|d|dddf<t tj d|dr|dddfd|j|d7<nz ||dddf<Wntytd|w| dur tt|j} | durt|jd} |rItj| | | |f|d}|dur0||}|t||||kr?td|dkrHtdn tj| | | f|d}tjtt| tt|fdd}d}|sn|r||d|d||dtd||d|d|t||} d |}!n!||d|d!||dt||d|d} d!|d }!tj | |d}"tj |!tjd}#tj d |d}$tj d tjd}%|rtj ||d"||d}&|"|&|#f}'n|"|#f}'|r|t|||||||||||| g |'|||$|%R\}}(})}}*n|||||||||| g |'|||$|%R\}}(})}}*|*dkrFtd#|*d$|*dkrVtd%|d&|d'|ddd|f|(|ddd|fj|)fS)(ax Compute the singular value decomposition of a linear operator using PROPACK Parameters ---------- A : array_like, sparse matrix, or LinearOperator Operator for which SVD will be computed. If `A` is a LinearOperator object, it must define both ``matvec`` and ``rmatvec`` methods. k : int Number of singular values/vectors to compute which : {"LM", "SM"} Which singular triplets to compute: - 'LM': compute triplets corresponding to the `k` largest singular values - 'SM': compute triplets corresponding to the `k` smallest singular values `which='SM'` requires `irl_mode=True`. Computes largest singular values by default. irl_mode : bool, optional If `True`, then compute SVD using IRL (implicitly restarted Lanczos) mode. Default is `True`. kmax : int, optional Maximal number of iterations / maximal dimension of the Krylov subspace. Default is ``10 * k``. compute_u : bool, optional If `True` (default) then compute left singular vectors, `u`. compute_v : bool, optional If `True` (default) then compute right singular vectors, `v`. tol : float, optional The desired relative accuracy for computed singular values. If not specified, it will be set based on machine precision. v0 : array_like, optional Starting vector for iterations: must be of length ``A.shape[0]``. If not specified, PROPACK will generate a starting vector. full_output : bool, optional If `True`, then return sigma_bound. Default is `False`. delta : float, optional Level of orthogonality to maintain between Lanczos vectors. Default is set based on machine precision. eta : float, optional Orthogonality cutoff. During reorthogonalization, vectors with component larger than `eta` along the Lanczos vector will be purged. Default is set based on machine precision. anorm : float, optional Estimate of ``||A||``. Default is ``0``. cgs : bool, optional If `True`, reorthogonalization is done using classical Gram-Schmidt. If `False` (default), it is done using modified Gram-Schmidt. elr : bool, optional If `True` (default), then extended local orthogonality is enforced when obtaining singular vectors. min_relgap : float, optional The smallest relative gap allowed between any shift in IRL mode. Default is ``0.001``. Accessed only if ``irl_mode=True``. shifts : int, optional Number of shifts per restart in IRL mode. Default is determined to satisfy ``k <= min(kmax-shifts, m, n)``. Must be >= 0, but choosing 0 might lead to performance degradation. Accessed only if ``irl_mode=True``. maxiter : int, optional Maximum number of restarts in IRL mode. Default is ``1000``. Accessed only if ``irl_mode=True``. rng : `numpy.random.Generator`, optional Pseudorandom number generator state. When `rng` is None, a new `numpy.random.Generator` is created using entropy from the operating system. Types other than `numpy.random.Generator` are passed to `numpy.random.default_rng` to instantiate a ``Generator``. Returns ------- u : ndarray The `k` largest (``which="LM"``) or smallest (``which="SM"``) left singular vectors, ``shape == (A.shape[0], 3)``, returned only if ``compute_u=True``. sigma : ndarray The top `k` singular values, ``shape == (k,)`` vt : ndarray The `k` largest (``which="LM"``) or smallest (``which="SM"``) right singular vectors, ``shape == (3, A.shape[1])``, returned only if ``compute_v=True``. sigma_bound : ndarray the error bounds on the singular values sigma, returned only if ``full_output=True``. Nz:`rng` must be a normalized numpy.random.Generator instance>rrz#`which` must be either 'LM' or 'SM'rz#`which`='SM' requires irl_mode=Truer)r(rz.k must be positive and not greater than m or n iz3kmax must be greater than or equal to k, but kmax (z) < k ()r"rr )orderr()sizey?zv0 must be of length g?z0shifts must satisfy k <= min(kmax-shifts, m, n)!zshifts must be >= 0!i  z#An invariant subspace of dimension z was found.zk=z0 singular triplets did not converge within kmax=z iterations) ValueErrorupperrr(char_lansvd_irl_dict _lansvd_dictKeyErrorr iscomplexobjemptycomplexfloatr&minr*uniformsqrtfinfoepsarraylowerintboolmaxint32isupper_which_converterrconjT)+rkwhichirl_modekmax compute_u compute_vv0 full_outputtoldeltaetaanormcgselr min_relgapshiftsmaxiterrngaprodtyp lansvd_irllansvdr rjobujobvuvdoptionioption blocksizelworkliworkworkiworkdparmr$zworkworkssigmabndinforrrrQsY        $     "    6        2)rTNTTNFrNNrFTr0NNN)r.__all__numpyrscipy.sparse.linalgr scipy.linalgr_propackrrrr slansvddlansvdclansvdzlansvdrB slansvd_irl dlansvd_irl clansvd_irl zlansvd_irlrArTrrrrrrs8