o ֭jx<@sdZddlZddlmZdgZd!ddZd d Zed d Zed dZ ddZ ddZ ddZ ddZ d"ddZddZddZddZdd ZdS)#zSparse block 1-norm estimator. N)aslinearoperator onenormestFcCst|}|jd|jdkrtd|jd}||krhtt|t|}|j||fkr9tddt|jt |j dd}|j|fkrQtddt|jt |}t ||} |dd|f} ||} n t ||j||\} } } } } |sy|r| f}|r|| f7}|r|| f7}|S| S)a Compute a lower bound of the 1-norm of a sparse array. Parameters ---------- A : ndarray or other linear operator A linear operator that can be transposed and that can produce matrix products. t : int, optional A positive parameter controlling the tradeoff between accuracy versus time and memory usage. Larger values take longer and use more memory but give more accurate output. itmax : int, optional Use at most this many iterations. compute_v : bool, optional Request a norm-maximizing linear operator input vector if True. compute_w : bool, optional Request a norm-maximizing linear operator output vector if True. Returns ------- est : float An underestimate of the 1-norm of the sparse array. v : ndarray, optional The vector such that ||Av||_1 == est*||v||_1. It can be thought of as an input to the linear operator that gives an output with particularly large norm. w : ndarray, optional The vector Av which has relatively large 1-norm. It can be thought of as an output of the linear operator that is relatively large in norm compared to the input. Notes ----- This is algorithm 2.4 of [1]. In [2] it is described as follows. "This algorithm typically requires the evaluation of about 4t matrix-vector products and almost invariably produces a norm estimate (which is, in fact, a lower bound on the norm) correct to within a factor 3." .. versionadded:: 0.13.0 References ---------- .. [1] Nicholas J. Higham and Francoise Tisseur (2000), "A Block Algorithm for Matrix 1-Norm Estimation, with an Application to 1-Norm Pseudospectra." SIAM J. Matrix Anal. Appl. Vol. 21, No. 4, pp. 1185-1201. .. [2] Awad H. Al-Mohy and Nicholas J. Higham (2009), "A new scaling and squaring algorithm for the matrix exponential." SIAM J. Matrix Anal. Appl. Vol. 31, No. 3, pp. 970-989. Examples -------- >>> import numpy as np >>> from scipy.sparse import csc_array >>> from scipy.sparse.linalg import onenormest >>> A = csc_array([[1., 0., 0.], [5., 8., 2.], [0., -1., 0.]], dtype=float) >>> A.toarray() array([[ 1., 0., 0.], [ 5., 8., 2.], [ 0., -1., 0.]]) >>> onenormest(A) 9.0 >>> np.linalg.norm(A.toarray(), ord=1) 9.0 rz1expected the operator to act like a square matrixzinternal error: zunexpected shape axisN)rshape ValueErrornpasarraymatmatidentity Exceptionstrabssumargmaxelementary_vector_onenormest_coreH)Atitmax compute_v compute_wn A_explicit col_abs_sumsargmax_jvwestnmults nresamplesresultr&\/var/www/html/CropPilot/venv/lib/python3.10/site-packages/scipy/sparse/linalg/_onenormest.pyr s8J         csdfdd}|S)z Decorator for an elementwise function, to apply it blockwise along first dimension, to avoid excessive memory usage in temporaries. cs|jdkr |S|d}tj|jdf|jdd|jd}||d<~t|jdD]}||||||<q6|S)Nrrdtype)r r zerosr*range)xy0yj block_sizefuncr&r'wrappers& "z%_blocked_elementwise..wrapperr&)r3r4r&r1r'_blocked_elementwiseys r5cCs&|}d||dk<|t|}|S)a9 This should do the right thing for both real and complex matrices. From Higham and Tisseur: "Everything in this section remains valid for complex matrices provided that sign(A) is redefined as the matrix (aij / |aij|) (and sign(0) = 1) transposes are replaced by conjugate transposes." rr)copyr rXYr&r&r' sign_round_ups r:cCstjt|ddS)Nrr)r maxr)r8r&r&r'_max_abs_axis1sr<cCsZd}d}td|jd|D]}tjt||||dd}|dur&|}q ||7}q |S)Nr(rr)r,r r rr)r8r2rr0r/r&r&r'_sum_abs_axis0s  r>cCstj|td}d||<|S)Nr)r)r r+float)rir r&r&r'rsrcCs8|jdks |j|jkrtd|jd}t|||kS)Nrz2expected conformant vectors with entries in {-1,1}r)ndimr r r dot)r r!rr&r&r'vectors_are_parallels rCcs.|jD]tfdd|jDsdSqdS)Nc3|]}t|VqdSNrC.0r!r r&r' z;every_col_of_X_is_parallel_to_a_col_of_Y..FT)Tanyr7r&rIr'(every_col_of_X_is_parallel_to_a_col_of_Ys rNcsbj\}}dd|ftfddt|DrdS|dur/tfdd|jDr/dSdS)Nc3s&|]}tdd|fVqdSrErF)rHr0r8r r&r'rJs$z*column_needs_resampling..Tc3rDrErFrGrIr&r'rJrKF)r rMr,rL)r@r8r9rrr&rOr'column_needs_resamplings rPcCs0tjjdd|jdddd|dd|f<dS)Nrrsizer)r randomrandintr )r@r8r&r&r'resample_columns0rUcCst||p ||kSrE)r allclose)abr&r&r'less_than_or_closesrYcCst|}t|}|jd}t||f}|dkr1tjjdd||dfddd|ddddf<|t|}d}d}d} t|} t| |} t | } t | } | | ddd} t | }t| |}t|}| dkrtt|t|dd| f|dd| fr | | fSt|dddd|} || }t|D]}t|| ||dd|f<q| dkrt|d|dstdt|d| dstd| d krt|D]}t| |||std q| }|}| d7} qB) a" This is Algorithm 2.2. Parameters ---------- A : ndarray or other linear operator A linear operator that can produce matrix products. AT : ndarray or other linear operator The transpose of A. t : int, optional A positive parameter controlling the tradeoff between accuracy versus time and memory usage. Returns ------- g : sequence A non-negative decreasing vector such that g[j] is a lower bound for the 1-norm of the column of A of jth largest 1-norm. The first entry of this vector is therefore a lower bound on the 1-norm of the linear operator A. This sequence has length t. ind : sequence The ith entry of ind is the index of the column A whose 1-norm is given by g[i]. This sequence of indices has length t, and its entries are chosen from range(n), possibly with repetition, where n is the order of the operator A. Notes ----- This algorithm is mainly for testing. It uses the 'ind' array in a way that is similar to its usage in algorithm 2.4. This algorithm 2.2 may be easier to test, so it gives a chance of uncovering bugs related to indexing which could have propagated less noticeably to algorithm 2.4. rrrrQNTzinvariant (2.2) is violatedzinvariant (2.3) is violated)rr r onesrSrTr?r,r r r>rsortr:r<rYr;rBargsortrr)rATrA_linear_operatorAT_linear_operatorrr8g_prevh_prevkindr9gbest_jSZhr0r&r&r'_algorithm_2_2sT' 2   .  rkcCs t|}t|}|dkrtd|dkrtd|jd}||kr%tdd}d}tj||ftd} |dkr]td|D]} t| | q;t|D]} t| | r\t| | |d7}t| | sNqG| t|} tj dtj d} d} tj ||ftd} d}d} t | | }|d7}t |}t|}t|}|| ks|dkr|dkr||}|dd|f}|dkr|| kr| }n|} | }||krnt|} ~t| |rn|dkrt|D]} t| | |rt| | |d7}t| | |sq~t | | }|d7}t|}~|dkr t|||kr nlt|ddd d|t| }~|dkrGt|d|| r5nDt|| }t||||f}t|D]}t|||| dd|f<qK|d|t|d|| }t| |f} |d7}q{t||}|||||fS) a Compute a lower bound of the 1-norm of a sparse array. Parameters ---------- A : ndarray or other linear operator A linear operator that can produce matrix products. AT : ndarray or other linear operator The transpose of A. t : int, optional A positive parameter controlling the tradeoff between accuracy versus time and memory usage. itmax : int, optional Use at most this many iterations. Returns ------- est : float An underestimate of the 1-norm of the sparse array. v : ndarray, optional The vector such that ||Av||_1 == est*||v||_1. It can be thought of as an input to the linear operator that gives an output with particularly large norm. w : ndarray, optional The vector Av which has relatively large 1-norm. It can be thought of as an output of the linear operator that is relatively large in norm compared to the input. nmults : int, optional The number of matrix products that were computed. nresamples : int, optional The number of times a parallel column was observed, necessitating a re-randomization of the column. Notes ----- This is algorithm 2.4. rz$at least two iterations are requiredrzat least one column is requiredrz't should be smaller than the order of Ar)NTrZ)rr r r r\r?r,rUrPr+intpr r r>r;rr:rNr<r^lenr6isinall concatenater)rr_rrr`rarr#r$r8r@ind_histest_oldrhrdrer9magsr"rgind_bestr!S_oldrirjseenr0new_indr r&r&r'rDs)              (   " >r)rrFFrE)__doc__numpyr scipy.sparse.linalgr__all__rr5r:r<r>rrCrNrPrUrYrkrr&r&r&r's&  n     g