o ֭j2@sdZddlmZddlmZmZddlZddlmZddl m Z m Z ddl m Z dd lmZd d ejd DZgd ZdddZdddZ  dddZdS)zLU decomposition functions.)warn)asarrayasarray_chkfiniteN)product) _datacopied LinAlgWarning)get_lapack_funcs) lu_dispatchercs&i|]dfdddDqS)csg|] }t|r|qS)npcan_cast).0yxr T/var/www/html/CropPilot/venv/lib/python3.10/site-packages/scipy/linalg/_decomp_lu.py sz.fdFD)join)rr rr srAll)lulu_solve lu_factorFTcCs|rt|}nt|}|jdkr!t|}tjdtjd}||fS|p't||}td|f\}|||d\}}}|dkrCt d| |dkrPt d|t dd||fS) al Compute pivoted LU decomposition of a matrix. The decomposition is:: A = P L U where P is a permutation matrix, L lower triangular with unit diagonal elements, and U upper triangular. Parameters ---------- a : (M, N) array_like Matrix to decompose overwrite_a : bool, optional Whether to overwrite data in A (may increase performance) check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs. Returns ------- lu : (M, N) ndarray Matrix containing U in its upper triangle, and L in its lower triangle. The unit diagonal elements of L are not stored. piv : (K,) ndarray Pivot indices representing the permutation matrix P: row i of matrix was interchanged with row piv[i]. Of shape ``(K,)``, with ``K = min(M, N)``. See Also -------- lu : gives lu factorization in more user-friendly format lu_solve : solve an equation system using the LU factorization of a matrix Notes ----- This is a wrapper to the ``*GETRF`` routines from LAPACK. Unlike :func:`lu`, it outputs the L and U factors into a single array and returns pivot indices instead of a permutation matrix. While the underlying ``*GETRF`` routines return 1-based pivot indices, the ``piv`` array returned by ``lu_factor`` contains 0-based indices. Examples -------- >>> import numpy as np >>> from scipy.linalg import lu_factor >>> A = np.array([[2, 5, 8, 7], [5, 2, 2, 8], [7, 5, 6, 6], [5, 4, 4, 8]]) >>> lu, piv = lu_factor(A) >>> piv array([2, 2, 3, 3], dtype=int32) Convert LAPACK's ``piv`` array to NumPy index and test the permutation >>> def pivot_to_permutation(piv): ... perm = np.arange(len(piv)) ... for i in range(len(piv)): ... perm[i], perm[piv[i]] = perm[piv[i]], perm[i] ... return perm ... >>> p_inv = pivot_to_permutation(piv) >>> p_inv array([2, 0, 3, 1]) >>> L, U = np.tril(lu, k=-1) + np.eye(4), np.triu(lu) >>> np.allclose(A[p_inv] - L @ U, np.zeros((4, 4))) True The P matrix in P L U is defined by the inverse permutation and can be recovered using argsort: >>> p = np.argsort(p_inv) >>> p array([1, 3, 0, 2]) >>> np.allclose(A - L[p] @ U, np.zeros((4, 4))) True or alternatively: >>> P = np.eye(4)[p] >>> np.allclose(A - P @ L @ U, np.zeros((4, 4))) True rdtype)getrf) overwrite_az>> import numpy as np >>> from scipy.linalg import lu_factor, lu_solve >>> A = np.array([[2, 5, 8, 7], [5, 2, 2, 8], [7, 5, 6, 6], [5, 4, 4, 8]]) >>> b = np.array([1, 1, 1, 1]) >>> lu, piv = lu_factor(A) >>> x = lu_solve((lu, piv), b) >>> np.allclose(A @ x - b, np.zeros((4,))) True rz Shapes of lu z and b z are incompatibler rr)getrs)trans overwrite_bz4illegal value in %dth argument of internal gesv|posv) rrrshaper&r"rr eyeronesr#r ) lu_and_pivbr-r.r(rr*b1mr,rr+r r rrs"0  *rcCs|rt|nt|}|jdkrtd|jjdvr5t|jj}|s,td|jd| |d}d}|j ^}}} t || } |jjdvrId nd } t |j dkr|rttj g||| |jd } tj g|| | |jd } | | fS|rtj g|dtj d n tj g|dd| d }tj g||| |jd }tj g|| | |jd } ||| fS|j d ddkr|rt||r|fS|fS|rtjg||td nt|}|t||r|fS|fSt||s|s|jdd}|jdr|jds |jdd}|s8tj |tj d }tj| | g|jd }t|||||| kr/|||fn|||f\}}} nptj g||tj d }|| krytjg|| | |jd } tdd|j dd DD]}t||| ||||qe|}n/tjg|| | |jd }tdd|j dd DD]}t|||||||q|} |s|s|rtjg|||| d }tjdd|Dt|g}d|g||R<|}ntj||g| d }d|t||f<|}|r|| fS||| fS)ar Compute LU decomposition of a matrix with partial pivoting. The decomposition satisfies:: A = P @ L @ U where ``P`` is a permutation matrix, ``L`` lower triangular with unit diagonal elements, and ``U`` upper triangular. If `permute_l` is set to ``True`` then ``L`` is returned already permuted and hence satisfying ``A = L @ U``. Parameters ---------- a : (M, N) array_like Array to decompose permute_l : bool, optional Perform the multiplication P*L (Default: do not permute) overwrite_a : bool, optional Whether to overwrite data in a (may improve performance) check_finite : bool, optional Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs. p_indices : bool, optional If ``True`` the permutation information is returned as row indices. The default is ``False`` for backwards-compatibility reasons. Returns ------- **(If `permute_l` is ``False``)** p : (..., M, M) ndarray Permutation arrays or vectors depending on `p_indices` l : (..., M, K) ndarray Lower triangular or trapezoidal array with unit diagonal. ``K = min(M, N)`` u : (..., K, N) ndarray Upper triangular or trapezoidal array **(If `permute_l` is ``True``)** pl : (..., M, K) ndarray Permuted L matrix. ``K = min(M, N)`` u : (..., K, N) ndarray Upper triangular or trapezoidal array Notes ----- Permutation matrices are costly since they are nothing but row reorder of ``L`` and hence indices are strongly recommended to be used instead if the permutation is required. The relation in the 2D case then becomes simply ``A = L[P, :] @ U``. In higher dimensions, it is better to use `permute_l` to avoid complicated indexing tricks. In 2D case, if one has the indices however, for some reason, the permutation matrix is still needed then it can be constructed by ``np.eye(M)[P, :]``. Examples -------- >>> import numpy as np >>> from scipy.linalg import lu >>> A = np.array([[2, 5, 8, 7], [5, 2, 2, 8], [7, 5, 6, 6], [5, 4, 4, 8]]) >>> p, l, u = lu(A) >>> np.allclose(A, p @ l @ u) True >>> p # Permutation matrix array([[0., 1., 0., 0.], # Row index 1 [0., 0., 0., 1.], # Row index 3 [1., 0., 0., 0.], # Row index 0 [0., 0., 1., 0.]]) # Row index 2 >>> p, _, _ = lu(A, p_indices=True) >>> p array([1, 3, 0, 2], dtype=int32) # as given by row indices above >>> np.allclose(A, l[p, :] @ u) True We can also use nd-arrays, for example, a demonstration with 4D array: >>> rng = np.random.default_rng() >>> A = rng.uniform(low=-4, high=4, size=[3, 2, 4, 8]) >>> p, l, u = lu(A) >>> p.shape, l.shape, u.shape ((3, 2, 4, 4), (3, 2, 4, 4), (3, 2, 4, 8)) >>> np.allclose(A, p @ l @ u) True >>> PL, U = lu(A, permute_l=True) >>> np.allclose(A, PL @ U) True r z1The input array must be at least two-dimensional.rz The dtype z5 cannot be cast to float(32, 64) or complex(64, 128).rTfFfd)r/rrN)rrC)order C_CONTIGUOUS WRITEABLEcSg|]}t|qSr rangerrr r rrmzlu..cSr>r r?rAr r rrsrBcSsg|]}t|qSr )r r$rAr r rr}sr)r rrndimr&rcharlapack_cast_dict TypeErrorastyper/minemptyr% ones_likecopyzerosintrflagsr rix_r$)r' permute_lrr( p_indicesr) dtype_charndr5nk real_dcharPLUPLpuindPand_ixr r rrsx`        &     r)FT)rFT)FFTF)__doc__warningsrnumpyrrr itertoolsr_miscrrlapackr _decomp_lu_cythonr typecodesrE__all__rrrr r r rs"      mH