i%^dZdZgdZddlZddlmZddlZddlm Z dd l m Z dd l m Z mZmZmZmZmZmZdd lmZmZmZmZdd lmZmZdd lmZmZmZm Z m!Z!m"Z"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(ddl)m*Z*m+Z+ddl,Z,GddeeZ-dZ.dZ/dZ0dZ1dZ2dZ3ddZ4dZ5Gdde-eZ6Gdde e-Z7y)z2 A sparse matrix in COOrdinate or 'triplet' formatzrestructuredtext en) coo_array coo_matrixisspmatrix_cooN)warn)copy_if_needed)spmatrix) coo_tocsr coo_todensecoo_todense_nd coo_matvec coo_matvec_ndcoo_matmat_densecoo_matmat_dense_nd)issparseSparseEfficiencyWarning_spbasesparray) _data_matrix _minmax_mixin) upcast_char to_nativeisshapegetdtypegetdatadowncast_intp_indexget_index_dtype check_shapecheck_reshape_kwargs isscalarlike isintlikeisdense)_validate_indices_broadcast_arraysceZdZdZeddZd3dddZedZejdZed Z e jd Z d Z e jje _ d4d Ze jje_ d4d Ze jje_ dZd5dZe j"je_ d6dZe j$je_ d7dZe j&je_ d8dZd8dZd8dZd8dZe j.je_ d8dZe j0je_ d8dZe j2je_ d9dZej4je_ dZd:dZdZdZdZ d6dZ!dZ"d Z#d!Z$d"Z%d#Z&d$Z'd%Z(d&Z)d'Z*d(Z+d)Z,d*Z-d;d+Z.d,Z/d-Z0d.Z1d8d/Z2d0Z3d1Z4d2Z5y)< _coo_basecoor ANmaxprintc tj|||stt|trst ||j rt||j |_|jt|j t|t}t fdtt|jD|_t!j"g||_d|_n |\}}|/t-d |Dr t+d t d |D}t||j |_|j|t|j.d  t  fd |D|_t1|||_d|_nht3|r&|j4|j4k(rrt d|jD|_|j$j7t|||_t|j.|j |_|j&|_n|j9} t | j|_| j$j7t|| d|_t| j.|j |_d|_n6t!j:|} t|t<s=t!j>| } | j@dk7rt)d| j@dt| j.|j |_|At||j |jk7rd|d|j} t+| |jt|j| jC}t fd|D|_t1| |||_d|_t|jdkDr!t d|jD|_|jEy#t(t*f$r} t)d| d} ~ wwxYw)Nr*allow_ndmaxval)defaultc3LK|]}tjgywdtypeNnparray).0_ idx_dtypes f/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/scipy/sparse/_coo.py z%_coo_base.__init__..*s($G.E%'HHRy$A$A.E!$r4Tzinvalid input formatc38K|]}t|dk(yw)rNlenr9idxs r<r=z%_coo_base.__init__..5s;FS3s8q=Fsz4cannot infer dimensions from zero sized index arraysc3rK|]/}tjtj|dz1ywr N)operatorindexr7maxrBs r<r=z%_coo_base.__init__..8s."5-3c#+.."="A-3s57)r0check_contentsc3NK|]}tj|yw)copyr5Nr6)r9rCrLr;s r<r=z%_coo_base.__init__..>s)$806%'HHSt9$M$M06s"%rKFc3<K|]}|jywNrLrBs r<r=z%_coo_base.__init__..Es'Jks krOrz!expected 2D array or matrix, not Dzinconsistent shapes: z != c3DK|]}|jdywFrON)astype)r9rC index_dtypes r<r=z%_coo_base.__init__.._s&$806%(JJ{J$G06s c3^K|]%}|jtjd'ywrS)rTr7int64rBs r<r=z%_coo_base.__init__..es"XKS 288% @Ks+-)#r__init__r isinstancetupler _allow_ndr_shape_get_index_dtyperHrfloatrangerAcoordsr7r8datahas_canonical_format TypeError ValueErroranyshaperrformatrTtocooasarrayr atleast_2dndimnonzero_check)selfarg1rfr5rLr+ data_dtypeobjr`er(Mmessager;rUs ` @@r<rXz_coo_base.__init__ sdD8<!D dE "tdnn5)$H  11T[[9I1J %eU; #$G.3C 4D.E$GG HHRz: ,0)C"&KC=;F;;(*>??!"5-3"55E)%$..I  11&9>"RDK040I0ID-**$*/C"' "3DK #0D5 QDI"-cii$.."QDK05D-JJt$!$0 a(Avv{'*KAFF8ST(UVV)!''DNNK $"54>>BdkkQ$9%T[[M"R(11"333t{{;K3L #$806$88 #AfIDF ,0) t{{ a XDKKXXDK m":.C#$:;BCs9QQ:) Q55Q:c|jdkDr|jdStj|j}|j d|S)Nr F)write)rkr`r7 zeros_likecolsetflags)rnresults r<rowz _coo_base.rowis@ 99q=;;r? "txx(e$ c|jdkr tdtj||jdj }|jdd|fz|jddz|_y)Nrz8cannot set row attribute of a 1-dimensional sparse arrayrvr4)rkrdr7rir`r5)rnnew_rows r<r|z _coo_base.rowrs` 99q=WX X**WDKKO,A,ABkk#2&'3dkk"#6FF r}c |jdSNrr`)rns r<ryz _coo_base.colys{{2r}ctj||jdj}|jdd|fz|_y)Nrr4)r7rir`r5)rnnew_cols r<ryz _coo_base.col}s9**WDKKO,A,ABkk#2&'3 r}c t||j|j}t|\}}||jk(r|r|j S|St |j |j|}t|dk(r+|dk(rt||d}n.t||dddd}ntj|||}|j|j t| t fd |D}|r|jj }n |j}|j||f|d S) Nr-orderrCr rrr/c3LK|]}tj|ywr3r7ri)r9cor;s r<r=z$_coo_base.reshape..s PZr2::b ::Zr>FrfrL)rrfr[r rL _ravel_coordsr`rAdivmodr7 unravel_indexr]rHrZra __class__) rnargskwargsrfrrL flat_coords new_coordsnew_datar;s @r<reshapez_coo_base.reshapesD$**t~~F*62 t DJJ yy{" $DKK5I u:?|#Kq: #Kq:4R4@ ))+uEJJ))$++c%j)I PZPP  yy~~'HyyH~~x4E~NNr}c||dk(r|jdk(rt|jtfd|jDr t d|jjdk7std|jDr t dt S|dkr||jz }||jk\r t dtjt|jd|z |jd|z S) Nrr c3:K|]}t|k7ywrNr@)r9rCnnzs r<r=z$_coo_base._getnnz..s:ks3s8s?ksz3all index and data arrays must have the same lengthc3:K|]}|jdk7ywrE)rkrBs r<r=z$_coo_base._getnnz..s)O;C#((a-;z'coordinates and data arrays must be 1-Daxis out of bounds minlength) rkrArarer`rdintr7bincountrrf)rnaxisrs @r<_getnnzz_coo_base._getnnzs .s'5(3JJs)<<(3r>rzaxis z index z exceeds matrix dimension znegative axis z index: N)rkrAr`rd enumerater5kindrnamer]rHrfrZrrarmin)rnrrCr;s @r<rmz_coo_base._checks 99DKK( ($$' $4#5[ MN N  ,FAsyy~~$|A3&>syy~~>NaP !#- ))$++c$**o)N 5(, 55 dii( 88a<#DKK03779 1 -$uQCwswwykB99=A&IJJ779q=$~aS %LMM 1 r}c|tjddd}n{ttr[t |drt |jk7r t dt t|jk7rt d|dk7r t dtfd|D}tfd|D}jj|f|| S) Nr__len__z"axes don't match matrix dimensionszrepeated axis in transpose)r rzoSparse matrices do not support an 'axes' parameter because swapping dimensions is the only logical permutation.c3<K|]}j|ywrN)r\r9rrns r<r=z&_coo_base.transpose..s.s=1 Arr) r_rkrYrhasattrrArdsetrZrra)rnaxesrLpermuted_shapepermuted_coordss` r< transposez_coo_base.transposes <#DbD)D g &4+s4yDII/E !EFF3t9~* !=>> V^9: :.sM6L(#sC#I6Lsc3(K|] }| ywrNrr9rCrs r<r=z#_coo_base.resize..s#E#CI)rr[rkrdrArr`rfmathprodr7rrar\rrezip logical_andreduceallrZ) rnrfrmax_size tmp_shapetmp is_truncatingrCsizers @r<resizez_coo_base.resizesEDNN; 99q==> > u:>@A A u: !' TZZ@Kyy'H**;y+A5IDK )8,DIDK  u: ! OSZ!^,$))c%j012  ,,y)C**[c%j1DK))KSZ0DKMc$**e6LMM >>((,/ U,C*,CysDd ,C*D88:##E#EE  IIdO  *s H0 c @|j||}t|jj}|s!|jjs t d|j dk(rattjdg|j|j |jd|j|jd|n@|j dk(rY|j\}}t|||j|j |j"|j|jd|n|r7tj$dtj&|jdd}nBtj$tj&|jddddddddd}tj(|j}t||j|j ||j|jd||j+|jS)Nz&Output array must be C or F contiguousr rArr)_process_toarray_argsrflags f_contiguous c_contiguousrdrkr r7r8rr`raravelrfr r|ryappendcumprod concatenater) rnroutBfortranrsNstridesr`s r<toarrayz_coo_base.toarray!s  & &uc 2agg**+qww33EF F 99> 288QC=$((DII;;q>499aggclG M YY!^::DAq 1dhh$((DII g /))Arzz$**Sb/'BC))BJJtzz!"~dd/C$DTrT$JAN^^DKK0F 7DHHdii!499aggclG Eyy$$r}cr|jdk7rtd|jd|jdk(r'|j|j|j Sddlm}|j|j\}}}}|j|||f|}|js|j|S) aQConvert this array/matrix to Compressed Sparse Column format Duplicate entries will be summed together. Examples -------- >>> from numpy import array >>> from scipy.sparse import coo_array >>> row = array([0, 0, 1, 3, 1, 0, 0]) >>> col = array([0, 2, 1, 3, 1, 0, 0]) >>> data = array([1, 1, 1, 1, 1, 1, 1]) >>> A = coo_array((data, (row, col)), shape=(4, 4)).tocsc() >>> A.toarray() array([[3, 0, 1, 0], [0, 2, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1]]) rz+Cannot convert. CSC format must be 2D. Got rQrr4r ) csc_arrayrf) rkrdr_csc_containerrfr5_cscr_coo_to_compressed_swaprbr)rnrLrindptrindicesrarfxs r<tocscz_coo_base.tocsc<s( 99>J499+UVWX X 88q=&&tzz&D D '+/+B+B9??+S (FGT5##T7F$;5#IA,,  "Hr}c|jdkDrtd|jd|jdk(r'|j|j|j Sddlm}|j|j|}|\}}}}|j|||f|j }|js|j|S) aNConvert this array/matrix to Compressed Sparse Row format Duplicate entries will be summed together. Examples -------- >>> from numpy import array >>> from scipy.sparse import coo_array >>> row = array([0, 0, 1, 3, 1, 0, 0]) >>> col = array([0, 2, 1, 3, 1, 0, 0]) >>> data = array([1, 1, 1, 1, 1, 1, 1]) >>> A = coo_array((data, (row, col)), shape=(4, 4)).tocsr() >>> A.toarray() array([[3, 0, 1, 0], [0, 2, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1]]) rz*Cannot convert. CSR must be 1D or 2D. Got rQrr4r ) csr_arrayrOr) rkrdr_csr_containerrfr5_csrrrrrbr) rnrLrarraysrrrarfrs r<tocsrz_coo_base.tocsr]s( 99q=I$))TUVW W 88q=&&tzz&D D ',,Y__4,HF+1 (FGT5##T7F$;4::#NA,,  "Hr}c ^||j\}}|j|jt|j|}|j dk(r|r|jdj n|jd}t|}tjd|g|}|r|jj n |j} ||| |jfS||j\} } t| }| j|d} | j|d} tj|dz|}tj| |}tj|j|j} t!|||| | |j||| ||| |jfS)z?convert (shape, coords, data) to (indptr, indices, data, shape)r/r rr4FrO) _shape_as_2dr]r`rHrrkrLrAr7r8rarfrTempty empty_liker5r ) rnswaprLrsrr;rrrramajorminors r<rz_coo_base._coo_to_compresseds]D%%&1))$++c$((A>N)O 99>/3dkk!n))+QGg,CXXq#hi8F'+499>>#D7D$**4 4DKK( u%j YU 3 YU 3!a%y1--Y7}}TYYdjj9!QUE499fgtLwdjj00r}c*|r|jS|SrNrO)rnrLs r<rhz_coo_base.tocoos 99; Kr}c|jdk7rtd|jd|j|j|jz }t j |d\}}t|dkDrtdt|dtd |jjd k(r"t jd |j }nbt jt||jjd zf|j }|j|||jf<|j||f|j S)Nrz+Cannot convert. DIA format must be 2D. Got rQT)return_inversedzConstructing a DIA matrix with z diagonals is inefficientrr)rrr4r r)rkrdrryr|r7uniquerArrrarzerosr5rH_dia_containerrf)rnrLksdiagsdiag_idxras r<todiaz_coo_base.todias 99>J499+UVWX X  XX ))Bt<x u:  23u:,?""(Q 8 99>>Q 88F$**5D88SZ)9:$**MD'+yyD488# $""D%= "CCr}cn|jdkDrtd|jd|j|j|j|j }|jdk(r|j d}nt|j }tt||j|_ |S)Nrz*Cannot convert. DOK must be 1D or 2D. Got rQr4r r) rkrdr_dok_containerrfr5r`rdictra_dict)rnrLdokr`s r<todokz_coo_base.todoks 99q=I$))TUVW W !!$**DJJ!? 99>[[^F$++&FVTYY/0  r}c  |jdk7r td|j\}}|| ks||k\r+tjd|j j Stjt|t|dz|t|dz |j }|j|z|jk( |jr|j }|j }nCt fd|jD}|j||j \\}}}|||t|dz<|S)Nrz diagonal requires two dimensionsrr4c3(K|] }| ywrNr)r9rC diag_masks r<r=z%_coo_base.diagonal..s?;CY;r)rkrdrfr7rrar5rrrHr|ryrbrZr`_sum_duplicates) rnkrowscolsdiagr|raindsr:rs @r<diagonalz_coo_base.diagonals 99>?@ @ZZ d :d88ATYY__5 5xxD3q!9,dSAY.>?"jj*XX\dhh.  $ $((9%C99Y'D?4;;??D!11$ )8LMNHS!d $S3q!9_ r}cx|jdk7r td|j\}}|jr t|sy|jj }|j |jz |k7}|dkrt||z|}|jrt|t|}tj||j |k\}tj| | |z|} tj||} nt|||z }|jrt|t|}tj||j|k\}tj||} tj|||z|} |jr|d|} n&tj||j } || ddtj|j|| ftj|j || ff|_ tj|j|| f|_d|_y)Nrz*setting a diagonal requires two dimensionsrr4F)rkrdrfrAr|r5ryrr7 logical_orarangerrr`rarb) rnvaluesrrsrr; full_keep max_indexkeeprrrs r<_setdiagz_coo_base._setdiags 99>IJ Jzz1 ;;s6{ HHNN HHtxx'1, q5AaC I{{ 3v;7 ==DHH ,ABDiiQBN)DGii ;GAqs I{{ 3v;7 ==DHH ,ABDii ;Gii1y= BG ;;jy)Hxx ? $)!r}c|rtd|jD}n |j}|j||f|j|jS)zReturns a matrix with the same sparsity structure as self, but with different data. By default the index arrays are copied. c3<K|]}|jywrNrOrBs r<r=z'_coo_base._with_data.. s=#388:rPrfr5)rZr`rrfr5)rnrarLr`s r< _with_dataz_coo_base._with_datasE ===F[[F~~tVnDJJdjj~QQr}cft||j|j\}}}}tjt |j tj}g}g}g} tt||jD]\} \} } t| tr || | k(z}#t| tr| tdk(r|j| S| j|j| \} }}|dk7rD|dkr | | k| |kDz}n | | k\| |kz}tj | | z |\}}||dk(|zz}n| | k\| |kz}| | z }||z}|j||j| | j|  |dk(r8|j |j#j%|j&dS|D cgc]} | | }} |j |}| rt)| } | dj}tj*| j-t | dd}tj*|D cgc]} | | c} dddddf}||k(j/d}|j1\}}||}|D cgc]} | | }} t3tj4|| }t ||d|dz dzk(r|d}|d||z||dz}n||z}|r|rtj6|d}n7tj8t ||jdj&}|j;|d||ddD]"} |j;| |j=$t?||f||j& Scc} wcc} wcc} w) Nr4r rrFrOrrrr") r$rfrgr7onesrArabool_rrr`rYrslicerrrsumrTr5r%r8rrrllistrrxrinsertrLr)rnkeyrG new_shape arr_int_posnone_pos index_mask slice_coords arr_coords arr_indicesrrCrstartstopstepin_rangenew_ixmrr arr_shapekeyarrfoundarr_coarr_ixnew_arr_coordspos coord_likes r< __getitem__z_coo_base.__getitem__s2C T[[3 /y+xWWS^288<    %c%&=>LAyR#s#rSy) C'%+% ''+(+ DJJqM(B%E4qy!8(*e T 'BH(*e T 'BH$&IIb5j$$? "qAv&99 $&%KBI#>!#e"h.  ''/!!"%""3'-?0 ?99Z(,,.55djju5M M/;<|bn| <99Z( +[9K#A,,IXXk*223{3CQKFJ"GJb2j>J"GHAtTJz)..A.6E"]]_NFF'H/9:z"V*zJ:!""2"26"KLN;;r?[^#Ca#GG!!n'->CDAQQ ,j8 ]]:a=9 XXc(m4;;q>;O;OP   hqk: 6ab\!!!Z__%67"(J/y SS]=,#H;s N$ N) N.c t||j|j\}}}}|r4t|}|dddD]}|j |t |}|r[t|}|Dcic]}t ||x} r|| } }t| j} t| | D] \}} | ||< t|r*d|jvryt|||j\} } n@tj||j}|jdk(ryt!||\} } |j#|\}}t%| dk(r!t%| ddk(r||c|_|_yd}d}|rp|D]k}t+|t,rt |r |j}t%||d|dz dzk(r|d}nd}| ||t%|z}t/||}nd}i}t1|D]3\}}||k(r|t%|z }t+|t,s*|||<|dz }5dg|j2z}t%| }t1|D]\}}t |rtj4||f||<,t+|t,r4|j7|j|\}}}|| |||zz||<p|j9||<| j;}tj<|}tj>|dd\}}tj@|||g|_t dt||dd|fD|_d|_!ycc}w) Nrrr4r T)r return_indexc3FK|]}tj|ywrN)r7hstack)r9cs r<r=z(_coo_base.__setitem__..sV2UQBIIaL2Us!F)"r$rfrgr*poprZr"r%rrr_get_sparse_data_and_coordsr5r7rir_get_dense_data_and_coords _zero_manyrArar`rYr(rrrk broadcast_torrrLr8rrFrb)rnr,rrGr-r.r/jrarrarr_posr3x_datax_coordsold_data old_coordsr:r@rC x_arr_coox_arr_coo_ravelx_axx_axesrnew_nnzr4r5r6rr:inds r< __setitem__z_coo_base.__setitem__esb2C T[[3 /y+x YIdd^ a $i(I KE'2U{!)5QR8OC:Tq#v{GU+W^^-=>Kg{33a4 A;AGG|:1iT FH 1DJJ/Avv{9!YG FH $u5* x=A #hqk"2a"7%-z "DIt{    !#u-in # I ;'KOk!n,Lq,PQ)!n!)S3y>-A BI&3Iy&IO#(&FAsCxI&#u% q   'Vdii' f+&FAs~!#wj!A 1 C'$'KK 1 $>!tT!&&)):   %c%&=>LAyR#s#rSy) C'C5;,>$'KK 1 $>!tT19ax$&%KBI#>$&%KBI#>rEz40A16X"55J "e T :H(*JC'C5;,>!!"%""3')?. XXk*223{3CQKFJ"GJb2j>J"GHAtTJz)..A.6EJGQ]]:6N?CN:--/27; < . (J48;;?;RZK; ?ii , M))#H@s  I" I c|jry|j|j|j}|\|_|_d|_y)zeEliminate duplicate entries by adding them together This is an *in place* operation NT)rbrr`ra)rnsummeds r<rz_coo_base.sum_duplicatess@  $ $ %%dkk499=!' TY$(!r}c t|dk(r||fStj|dddtfd|D}|}tjj |Dcgc] }|dd|ddk7c}tj dtfd|D}tj\}tjj|t||j}||fScc}w)Nrrc3(K|] }| ywrNr)r9rCrs r<r=z,_coo_base._sum_duplicates..s4Vcs5zVrr Tc3(K|] }| ywrNr)r9rC unique_masks r<r=z,_coo_base._sum_duplicates..s:6Cs;'6rr4) rAr7lexsortrZrrrrladdreduceatrr5)rnr`rarC unique_indsrrfs @@r<rz_coo_base._sum_duplicates s t9>4<  6$B$<(4V44E{mm**+1, +1CCGs3Bx 6,  iik2 :6::zz+. vvt%8%ETZZXt|, s&C9c|jdk7|j|_tfd|jD|_y)z[Remove zero entries from the array/matrix This is an *in place* operation rc3(K|] }| ywrNrrs r<r=z,_coo_base.eliminate_zeros..%s=#CIrN)rarZr`)rnrs @r<eliminate_zerosz_coo_base.eliminate_zeross7 yyA~IIdO === r}c |j|jk7r&td|jd|jdt|jj|jj}t j ||d}t|jj}|jdk(ratt j dg|j|j|jd|j|jd|n@|jd k(rY|j \}}t#|||j|j$|j&|j|jd|n|r7t j(dt j*|jdd }nBt j(t j*|jddddd ddd d}t j,|j}t||j|j||j|jd||j/|d S) NIncompatible shapes ( and rT)r5rLr rrrrFrO)rfrdrr5charr7r8rrrrkr rr`rarrr r|ryrrr _container) rnotherr5r{rrsrrr`s r< _add_densez_coo_base._add_dense+s ;;$** $4TZZLekk]RSTU UDJJOOU[[-=-=>%u48fll//0 99> 288QC=$((DII;;q>499fll36G R YY!^$$DAq 1dhh$((DII S)7 4))Arzz$**Sb/'BC))BJJtzz!"~dd/C$DTrT$JAN^^DKK0F 7DHHdii!499fll3.? JvE22r}c|jdkr|jj|S|j|jk7r&t d|jd|jd|j |}t j|j|jf}tt j|j|jfd}|j ||f|j}|SNrrorprr r%r) rkr _add_sparserfrdrr7rrarZr`rnrsrrrs r<rwz_coo_base._add_sparseCs 99q=::<++E2 2 ;;$** $4TZZLekk]RSTU Uu%>>499ejj"9:2>>4;; *EANO NNHj1N Dr}c|jdkr|jj|S|j|jk7r&t d|jd|jd|j |}t j|j|j f}tt j|j|jfd}t||f|j}|Srv) rkr _sub_sparserfrdrr7rrarZr`rrxs r<rzz_coo_base._sub_sparseOs 99q=::<++E2 2 ;;$** $4TZZLekk]RSTU Uu%>>499uzzk":;2>>4;; *EANO x,DJJ ?r}c |jdkDr:tjtj|j ddt |jj|jj}tj|j }tjtj|ddddddddddd}tj|j}t|jt!|j |||j"|||j%|j dd}|S|jdkDr|j dnd}tj|t |jj|jj}|jdk(r|j&}|j(}nL|jdk(r%|jd}tj*|}nt-d|jt/|j|||j"||t1|t2r |dk(r|dS|S)Nrrr4r rz$coo_matvec not implemented for ndim=)rkr7rrrrfrr5rqr8rrrr`rrrArarryr|rxNotImplementedErrorrrYr) rnrsr{rfrr` result_shaperyr|s r<_matmul_vectorz_coo_base._matmul_vector[s 99q=XXdii 3B8$/ AQAQ$RTFHHTZZ(Eii 5":dd+; ((C((C YY!^++a.C--$C%6tyykBD D 488S#tyy%@ dG $):!9  r}ct|r|j|S |j}t t|ddt t|dddddz}|j|}|j}t t|ddt t|dddddz}|j|j|}|turtS|dk(s|dk(rt|j}nIt t|jddt t|jdddddz}|j|S#t$r%t j |}|j}Y\wxYw)Nrvrr ) r! _mul_scalarrkAttributeErrorr7rirZr_r_matmul_dispatchNotImplemented)rnrso_ndimpermtrs_ndimrets r<_rmatmul_dispatchz_coo_base._rmatmul_dispatch|s^  ##E* * $vs+,uU6]235G"5M/NND&BYYFvs+,uU6]235G"5M/NND..&77;Cn$%%{fkSXXU388_Sb12U5?23;OPTRTPT;U5VV==& &!" $ 5) $s E*FFct|r|j|St|s_t|sTt j |}|j dk(r#|jtjk(rtS |j|j dkr%|j dkrtj||S|jd}d}|jtjur"|j|fk(r|j!|S|j|dfk(r@|j!|j#}|j$g|jdddS|j dk(r#|d|d|jdd}t'||jd |k(rK|jdd }|jdd }||k7r t j(|||j+|St'|d |d|jd d t|r|j-|St|r]|j dk(} |j dk(} | r|j%|j.}| r |j%|jddf}||jd k7r!t'|d |d|jd d |j d kDs|j d kDr:|jdd }|jdd }||k7r t j(|||j1|}| r@|j%t3|jdd t3|jddz}| r|j%|jdd}|Sy#t$r|}YwxYw#t&$r t'd wxYw#t&$r t'd wxYw)Nrrrz)matmul: dimension mismatch with signaturer z (n,k=z),(k=z,)->(n,)rvz&Batch dimensions are not broadcastablez (n,..,k=z,..,m)->(n,..,m)r)r!multiplyrr#r7 asanyarrayrkr5object_rrfrrrrndarrayr~rrrdbroadcast_shapes_matmul_multivectorrr_matmul_sparserZ) rnrsother_ar err_prefixr{msg batch_shape_A batch_shape_B self_is_1d other_is_1ds r<rz_coo_base._matmul_dispatchs  ==' '75>mmE*G||q W]]bjj%@&%   99q=UZZ!^++D%8 8 JJrN@ ??bjj ({{qd"**511{{q!f$,,U[[];%v~~:tzz#2:::zzQ# F1#U5;;q>2B(K o%{{2!# $ 3B % CR 0  M1S++M=I//66 !l)A3eEKKO3DDTU  ##E* * E?aJ**/K||D$5$56 u{{1~q&9:EKKO# !l)A3eEKKO3DDTU yy1} Q $ 3B % CR 0  M1S++M=I((/Ffll3B.?(@(-fll23.?(@)AB Sb(9:MM M"  2&S()QRRSN&S()QRRSs*; NN#N; N N #N8;Oc 8t|jj|jj}|jdk\s|jdk\r|jdk(rn|j d|j dj |}|j t|j ddt|j ddzStj|j dd|j dd}||j ddz}||j ddz}|j|}tj||}||j ddz|j ddz}tj||}t|jt|j |j dtj |tj |tj"|j$|j&|j)d| |S|jdk(r7|j d|j df}|j*}|j,} nC|jdk(r4|j df}|j$d}tj.|} tj|}t1|j|j d |j&|j)d||j3t5| S) Nrr rrvrr4rr)type)rr5rqrkrrfrrZr7r _broadcast_torLrrrrAr8rr`rarryr|rxrviewr) rnrs result_dtyper{broadcast_shape self_shape other_shaper}ryr|s r<rz_coo_base._matmul_multivectorsf"4::??EKK4D4DE 99>UZZ1_yyA~aA7KKER~~eEKK,<&=ekkRTRUFV@W&WXX 11$**Sb/5;;sPRCSTO(4::bc?:J)EKK,<>RSAQQLXXl,?F #djj/5;;r? " 5rxx 7M "t{{ ; $ 5;;s+;V EM 99> JJqM5;;q>:L((C((C YY!^!KKN,L++a.C--$C,l;5;;r?CEKK$4f >{{U {,,r}ct|s|t|sqt|sftj|}|j dk(r5|j tjk(rtdt|d |jt|r||zS|jd|jdddk7r&td|jd|jd |j d kr|j d kr||zSt|r|j|S|j|jS#t$r|}YwxYw) aReturn the dot product of two arrays. Strictly speaking a dot product involves two vectors. But in the sense that an array with ndim >= 1 is a collection of vectors, the function computes the collection of dot products between each vector in the first array with each vector in the second array. The axis upon which the sum of products is performed is the last axis of the first array and the second to last axis of the second array. If the second array is 1-D, the last axis is used. Thus, if both arrays are 1-D, the inner product is returned. If both are 2-D, we have matrix multiplication. If `other` is 1-D, the sum product is taken along the last axis of each array. If `other` is N-D for N>=2, the sum product is over the last axis of the first array and the second-to-last axis of the second array. Parameters ---------- other : array_like (dense or sparse) Second array Returns ------- output : array (sparse or dense) The dot product of this array with `other`. It will be dense/sparse if `other` is dense/sparse. Examples -------- >>> import numpy as np >>> from scipy.sparse import coo_array >>> A = coo_array([[1, 2, 0], [0, 0, 3], [4, 0, 5]]) >>> v = np.array([1, 0, -1]) >>> A.dot(v) array([ 1, -3, -1], dtype=int64) For 2-D arrays it is the matrix product: >>> A = coo_array([[1, 0], [0, 1]]) >>> B = coo_array([[4, 1], [2, 2]]) >>> A.dot(B).toarray() array([[4, 1], [2, 2]]) For 3-D arrays the shape extends unused axes by other unused axes. >>> A = coo_array(np.arange(3*4*5*6)).reshape((3,4,5,6)) >>> B = coo_array(np.arange(3*4*5*6)).reshape((5,4,6,3)) >>> A.dot(B).shape (3, 4, 5, 5, 4, 3) rz"dot argument not supported type: ''rrvNzshapes rpz are not aligned for n-D dotr)rr#r!r7rrkr5rrcrrfrrd _dense_dot _sparse_dotrh)rnrso_arrays r<dotz _coo_base.dots+l75>\%5HmmE*G||q W]]bjj%@"DT%[MQR STT   %<  ::b>U[[-a0 0wtzzl% };<= = 99q=UZZ!^%<  5>??5) ) ..!"  s< D>> E  E ct||jdz g\}}t|td|jdz g\}}||jz}|j }||z}g}|r||fn|f} t |j | D]*\} } |jtj| | ,t|j|f|S)Nr rrr) _convert_to_2drkrHTrhrr`extendr7rrra) rnrsself_2d s_new_shapeother_2d o_new_shapercombined_shaper` new_shapesrGss r<rz_coo_base._sparse_dotes .dTYY]OD .us1ejj1n7M6N O+#zz|%{23>k;/[N  Z0DAq MM"**1a0 11$))V,NCCr}c"|j}|dkr|dk(rdn|jdf}|}nt||jdz g\}}|j}|dkr|dk(rdn|jdf}|}n|jdd|jddz}|dz gt|dz |dz }t j ||} | j |jdtj|f}||z} ||z} | j | S)Nrr rrrrv) rkrfrr_r7rrrr) rnrsrrrrrr reorder_dimso_reorgrrs r<rz_coo_base._dense_dotzs Q; &! "$**Q-1AKG#1$Q#H G[ Q; &! "%++b/1CKH++cr*U[[-==K"QJGvz):GFQJGLll5,7G B;9O'PQH!%{2||N++r}ctsqtsftj}|jdk(r5|j tj k(rtdtd jtjj|\}}tfdt||Dr tdtrj||Sj!||S#t$r|YwxYw)a Return the tensordot product with another array along the given axes. The tensordot differs from dot and matmul in that any axis can be chosen for each of the first and second array and the sum of the products is computed just like for matrix multiplication, only not just for the rows of the first times the columns of the second. It takes the dot product of the collection of vectors along the specified axes. Here we can even take the sum of the products along two or even more axes if desired. So, tensordot is a dot product computation applied to arrays of any dimension >= 1. It is like matmul but over arbitrary axes for each matrix. Given two tensors, `a` and `b`, and the desired axes specified as a 2-tuple/list/array containing two sequences of axis numbers, ``(a_axes, b_axes)``, sum the products of `a`'s and `b`'s elements (components) over the axes specified by ``a_axes`` and ``b_axes``. The `axes` input can be a single non-negative integer, ``N``; if it is, then the last ``N`` dimensions of `a` and the first ``N`` dimensions of `b` are summed over. Parameters ---------- a, b : array_like Tensors to "dot". axes : int or (2,) array_like * integer_like If an int N, sum over the last N axes of `a` and the first N axes of `b` in order. The sizes of the corresponding axes must match. * (2,) array_like A 2-tuple of sequences of axes to be summed over, the first applying to `a`, the second to `b`. The sequences must be the same length. The shape of the corresponding axes must match between `a` and `b`. Returns ------- output : coo_array The tensor dot product of this array with `other`. It will be dense/sparse if `other` is dense/sparse. See Also -------- dot Examples -------- >>> import numpy as np >>> import scipy.sparse >>> A = scipy.sparse.coo_array([[[2, 3], [0, 0]], [[0, 1], [0, 5]]]) >>> A.shape (2, 2, 2) Integer axes N are shorthand for (range(-N, 0), range(0, N)): >>> A.tensordot(A, axes=1).toarray() array([[[[ 4, 9], [ 0, 15]], [[ 0, 0], [ 0, 0]]], [[[ 0, 1], [ 0, 5]], [[ 0, 5], [ 0, 25]]]]) >>> A.tensordot(A, axes=2).toarray() array([[ 4, 6], [ 0, 25]]) >>> A.tensordot(A, axes=3) array(39) Using tuple for axes: >>> a = scipy.sparse.coo_array(np.arange(60).reshape(3,4,5)) >>> b = np.arange(24).reshape(4,3,2) >>> c = a.tensordot(b, axes=([1,0],[0,1])) >>> c.shape (5, 2) >>> c array([[4400, 4730], [4532, 4874], [4664, 5018], [4796, 5162], [4928, 5306]]) rz#tensordot arg not supported type: 'rc3bK|]&\}}j|j|k7(ywrNr)r9axbxrsrns r<r=z&_coo_base.tensordot..s29762rzz"~R07s,/z*sizes of the corresponding axes must match)r#rr7rrkr5rrcrrfr _process_axesrerrd_dense_tensordot_sparse_tensordot)rnrsr other_array axes_self axes_others`` r< tensordotz_coo_base.tensordotsru~huo--.K1$):):bjj)H"Ed5k]RS TUU $ !.diiT J : 9 J79 9IJ J 5>(( :F F))%JG G" $# $s3 C>> D  D ctt||\}}t||\}}||jz}t|s|S|j}||z} g} |r||fn|f} t |j | D]-\} } | s | j tj| | /t|j| f| S)Nr) rrrrhrr`rr7rrra)rnrss_axeso_axesrrrrrrr`rrGrs r<rz_coo_base._sparse_tensordots .dF; .uf =+#~Kzz|%{23>k;/[N  Z0DAq b..q!451 $))V,NCCr}c(t|j}t|j}t|Dcgc] }||vs| }}|Dcgc]}|j|}}|Dcgc]}|j|} }t|Dcgc] }||vs| } }|Dcgc]}|j|} }| Dcgc]}|j|} }|j||z} t j|| dd|z| ddz}g| t j |}g| ddt j | | dd}| j|j|j|Scc}wcc}wcc}wcc}wcc}wcc}wr) rArfr_rr7rrrr)rnrsrrrrr s_non_axes s_axes_shapes_non_axes_shape o_non_axes o_axes_shapeo_non_axes_shapeleftright reshape_left reshape_rights r<rz_coo_base._dense_tensordotsTZZU[[!!&vBA!6/a B/56v! 1 v 63=>:aDJJqM:>!&vBA!6/a B0671 A 74>?JqEKKNJ?~~j612 UJsOf$B7?s/ E6E6 E;(F FF!F =Fc|jdkr%|jdkrtj||S|j}|j}t j |dd|dd}t ||ddz}t ||ddz}|j|}|j|}t|} t|} | | zj} t| g||jd|jdS)a Perform sparse-sparse matrix multiplication for two n-D COO arrays. The method converts input n-D arrays to 2-D block array format, uses csr_matmat to multiply them, and then converts the result back to n-D COO array. Parameters: self (COO): The first n-D sparse array in COO format. other (COO): The second n-D sparse array in COO format. Returns: prod (COO): The resulting n-D sparse array after multiplication. rNrvrr) rkrrrfr7rrZr _block_diagrh_extract_block_diag) rnrsrrrself_new_shapeother_new_shapeself_broadcastedother_broadcastedself_block_diagother_block_diagprod_block_diags r<rz_coo_base._matmul_sparse4s 99q=UZZ!^))$6 6ZZ kk --j"o{3B?OP/*RS/A0;rs3CC--n=!//@&&67&'89+-==DDF# EOETZZ^EU[[_E  r}cb|j|k(r|r|jS|S|j}t|t|kr tddt|t|z zt |z}t dt ||Drtd|d|j|}t|jt|}|j}|j}|dd}d} |d|dk7rY|d} | | z} tj|| }tjtjd | | |j } | f}t#d t|dz dD]} || || k7r|| } | | z} t|} tj|| }t tj|| dzd| }tjtjd | | | } | f|z}tj|| | } | f|z}t%||f|S) NzDNew shape must have at least as many dimensions as the current shaperc3:K|]\}}|dk7xr||k7ywrEr)r9ons r<r=z*_coo_base._broadcast_to..ms'E/Dtq!Q!16!/Drzcurrent shape z- cannot be broadcast to new shape {new_shape}r/rr rr4rv)rfrLrArdrZrerrrr`rHrar7tilerepeatrrr_r)rnr-rL old_shaperfr;r`rr cum_repeat repeat_countnew_dimrrs r<rz_coo_base._broadcast_to^s' :: ""&499; 0D 0JJ  y>C N *56 6IY785;KK Es5)/DE E~i[9BBC C||E"#DKKIG 99BC[  9 " %$R=L , &Jwwx6Hii !\ KTXXVG!JrSZ\?B/AQx9Q<'(| l* (m778\:"277:acd+;\#JK ))BIIaY$OQTU%Z*4 ''&)Z8%Z*4 !0$(J/;;r}ct||\}}tj|jddf|}||zj ||d<|S)Nr r4.)rr7r&rfr)rnr res_dtyperA2dr-r&s r<_sum_ndz_coo_base._sum_ndsL'd3Yww ! a( :$J'' 2C r}ct||\}}|jd||}tj|jd|}t |j |f|S)Nr r)r_min_or_max_axisr7rr`rra)rnr min_or_maxexplicitrr-resunraveled_coordss r<_min_or_max_axis_ndz_coo_base._min_or_max_axis_ndsX'd3Y""1j(;++CJJqM9E#(($45yAAr}cjt||\}}|jd|||}|j|S)Nr )r_argminmax_axisr)rnr argminmaxcomparerrr-res_flats r<_argminmax_axis_ndz_coo_base._argminmax_axis_nds9'd3Y&&q)WhG **r})NNFrN)NF)returnN)NN)F)r)T)r)6__name__ __module__ __qualname___formatr_r[rXpropertyr|setterryrr__doc__rrrmrrrrrrrhrr rrrr#rBrZrKrrrmrtrwrzr~rrrrrrrrrrrrrrrr}r<r'r's)Ga IGTGR ZZGG  ZZ44!OFoo--GO;(oo--GO W$1199MN2?$ ))11I$L^^++FN%2oo--GOB D16 MM))EMD,MM))EM MM))EM($,,44H$*NRRThn*`)*V )&>30  B'2[z!-FN/`D*,6nH`D4L*( T52r *Qx&- 1 '4/+ dE*-.e <9: 8 99;8 8 r}cht|trL|dks|tkDr tdt t |z }t t |}nt|t tzrst|dk7r td|\}}t|t|k7r tdtfd|Dstfd|Drtdtd |Dcgc]}|d kr|zn|}}|Dcgc]}|d kr|zn|}}||fScc}wcc}w) Nr z.axes integer is out of bounds for input arraysrz%axes must be a tuple/list of length 2z,axes lists/tuples must be of the same lengthc38K|]}|k\xs| kywrNr)r9rndim_as r<r=z _process_axes..&$=frV|+rVG|+fc38K|]}|k\xs| kywrNr)r9rndim_bs r<r=z _process_axes..'rrz/axes indices are out of bounds for input arraysz3axes must be an integer or a tuple/list of integersr) rYrrrdr*r_rZrArerc)rrraxes_aaxes_brs`` r<rrs($ !8tc&&11MN NeFTM623eDk" D%$, ' t9>DE E v;#f+ %KL L =f= = =f= =NO OMNN>D EfdtaxdVmT1fF E>D EfdtaxdVmT1fF E 6>F Es 8D*D/c tfdD}tfdD}t||}tj}tfdt |D}|rctfd|D}tfd|D}t||} | |f} t j |t j |f} n|f} t j |f} d}tj| f| } | |fS)Nc3<K|]}j|ywrNrr9rr(s r<r=z!_convert_to_2d..2s4t! 1 trc3<K|]}j|ywrNrrs r<r=z!_convert_to_2d..3s2Tsyy|Trc3,K|] }|vs| ywrNr)r9rrs r<r=z!_convert_to_2d..7s= 1q}Q s c3<K|]}j|ywrNrrs r<r=z!_convert_to_2d..9s@x! 1 xrc3<K|]}j|ywrNrrs r<r=z!_convert_to_2d..:s>Xsyy|Xrrr) rZrrAr`r_rrrra) r(r axis_coords axis_shape axis_ravelrknon_axisnon_axis_coordsnon_axis_shapenon_axis_ravel coords_2dshape_2dnew_coos `` r<rr1s4t44K2T22J{J7J szz?D=d ==H@x@@>X>>&G#Z0 IIn-tyy/DEM IIj)+9-X>G N ""r}ct|dk(r|dSt|dk(r|\}}|\}}|dk(rI|td|dz ztd|dz z}t|}tj||||zS|dk(rI|td|dz ztd|dz z}t|}tj||||zSt dtj ||| S) z;Like np.ravel_multi_index, but avoids some overflow issues.r rrrr/r4Fz'order' must be 'C' or 'F'r)rArHrr7rrdravel_multi_index) r`rfrnrowsncolsr|ryr0r;s r<rrGs 6{aay 6{a uS C<c!UQY//#a2CCF'v6I;;uc;cA A c\c!UQY//#a2CCF'v6I;;uc;cA A9: :  U ;;r}c"t|tS)aIs `x` of coo_matrix type? Parameters ---------- x object to check for being a coo matrix Returns ------- bool True if `x` is a coo matrix, False otherwise Examples -------- >>> from scipy.sparse import coo_array, coo_matrix, csr_matrix, isspmatrix_coo >>> isspmatrix_coo(coo_matrix([[5]])) True >>> isspmatrix_coo(coo_array([[5]])) False >>> isspmatrix_coo(csr_matrix([[5]])) False )rYr)rs r<rr\s. a $$r}ceZdZdZy)ra  A sparse array in COOrdinate format. Also known as the 'ijv' or 'triplet' format. This can be instantiated in several ways: coo_array(D) where D is an ndarray coo_array(S) with another sparse array or matrix S (equivalent to S.tocoo()) coo_array(shape, [dtype]) to construct an empty sparse array with shape `shape` dtype is optional, defaulting to dtype='d'. coo_array((data, coords), [shape]) to construct from existing data and index arrays: 1. data[:] the entries of the sparse array, in any order 2. coords[i][:] the axis-i coordinates of the data entries Where ``A[coords] = data``, and coords is a tuple of index arrays. When shape is not specified, it is inferred from the index arrays. Attributes ---------- dtype : dtype Data type of the sparse array shape : tuple of integers Shape of the sparse array ndim : int Number of dimensions of the sparse array nnz size data COO format data array of the sparse array coords COO format tuple of index arrays has_canonical_format : bool Whether the matrix has sorted coordinates and no duplicates format T Notes ----- Sparse arrays can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power. Advantages of the COO format - facilitates fast conversion among sparse formats - permits duplicate entries (see example) - very fast conversion to and from CSR/CSC formats Disadvantages of the COO format - does not directly support: + arithmetic operations + slicing Intended Usage - COO is a fast format for constructing sparse arrays - Once a COO array has been constructed, convert to CSR or CSC format for fast arithmetic and matrix vector operations - By default when converting to CSR or CSC format, duplicate (i,j) entries will be summed together. This facilitates efficient construction of finite element matrices and the like. (see example) Canonical format - Entries and coordinates sorted by row, then column. - There are no duplicate entries (i.e. duplicate (i,j) locations) - Data arrays MAY have explicit zeros. Examples -------- >>> # Constructing an empty sparse array >>> import numpy as np >>> from scipy.sparse import coo_array >>> coo_array((3, 4), dtype=np.int8).toarray() array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], dtype=int8) >>> # Constructing a sparse array using ijv format >>> row = np.array([0, 3, 1, 0]) >>> col = np.array([0, 3, 1, 2]) >>> data = np.array([4, 5, 7, 9]) >>> coo_array((data, (row, col)), shape=(4, 4)).toarray() array([[4, 0, 9, 0], [0, 7, 0, 0], [0, 0, 0, 0], [0, 0, 0, 5]]) >>> # Constructing a sparse array with duplicate coordinates >>> row = np.array([0, 0, 1, 3, 1, 0, 0]) >>> col = np.array([0, 2, 1, 3, 1, 0, 0]) >>> data = np.array([1, 1, 1, 1, 1, 1, 1]) >>> coo = coo_array((data, (row, col)), shape=(4, 4)) >>> # Duplicate coordinates are maintained until implicitly or explicitly summed >>> np.max(coo.data) 1 >>> coo.toarray() array([[3, 0, 1, 0], [0, 2, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1]]) N)rrrrrr}r<rrwskr}rc"eZdZdZdZdZdZy)ra. A sparse matrix in COOrdinate format. Also known as the 'ijv' or 'triplet' format. This can be instantiated in several ways: coo_matrix(D) where D is a 2-D ndarray coo_matrix(S) with another sparse array or matrix S (equivalent to S.tocoo()) coo_matrix((M, N), [dtype]) to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype='d'. coo_matrix((data, (i, j)), [shape=(M, N)]) to construct from three arrays: 1. data[:] the entries of the matrix, in any order 2. i[:] the row indices of the matrix entries 3. j[:] the column indices of the matrix entries Where ``A[i[k], j[k]] = data[k]``. When shape is not specified, it is inferred from the index arrays Attributes ---------- dtype : dtype Data type of the matrix shape : 2-tuple Shape of the matrix ndim : int Number of dimensions (this is always 2) nnz size data COO format data array of the matrix row COO format row index array of the matrix col COO format column index array of the matrix has_canonical_format : bool Whether the matrix has sorted indices and no duplicates format T Notes ----- Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power. Advantages of the COO format - facilitates fast conversion among sparse formats - permits duplicate entries (see example) - very fast conversion to and from CSR/CSC formats Disadvantages of the COO format - does not directly support: + arithmetic operations + slicing Intended Usage - COO is a fast format for constructing sparse matrices - Once a COO matrix has been constructed, convert to CSR or CSC format for fast arithmetic and matrix vector operations - By default when converting to CSR or CSC format, duplicate (i,j) entries will be summed together. This facilitates efficient construction of finite element matrices and the like. (see example) Canonical format - Entries and coordinates sorted by row, then column. - There are no duplicate entries (i.e. duplicate (i,j) locations) - Data arrays MAY have explicit zeros. Examples -------- >>> # Constructing an empty matrix >>> import numpy as np >>> from scipy.sparse import coo_matrix >>> coo_matrix((3, 4), dtype=np.int8).toarray() array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], dtype=int8) >>> # Constructing a matrix using ijv format >>> row = np.array([0, 3, 1, 0]) >>> col = np.array([0, 3, 1, 2]) >>> data = np.array([4, 5, 7, 9]) >>> coo_matrix((data, (row, col)), shape=(4, 4)).toarray() array([[4, 0, 9, 0], [0, 7, 0, 0], [0, 0, 0, 0], [0, 0, 0, 5]]) >>> # Constructing a matrix with duplicate coordinates >>> row = np.array([0, 0, 1, 3, 1, 0, 0]) >>> col = np.array([0, 2, 1, 3, 1, 0, 0]) >>> data = np.array([1, 1, 1, 1, 1, 1, 1]) >>> coo = coo_matrix((data, (row, col)), shape=(4, 4)) >>> # Duplicate coordinates are maintained until implicitly or explicitly summed >>> np.max(coo.data) 1 >>> coo.toarray() array([[3, 0, 1, 0], [0, 2, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1]]) cd|vr%|jd|jdf|d<|jj|y)Nr`r|ry)rH__dict__update)rnstates r< __setstate__zcoo_matrix.__setstate__Ws> 5  %yy/51ABE(O U#r}ctd)Nz('coo_matrix' object is not subscriptablerc)rnr,s r<rBzcoo_matrix.__getitem__^sBCCr}ctd)Nz4'coo_matrix' object does not support item assignmentr5)rnr,rs r<rZzcoo_matrix.__setitem__asNOOr}N)rrrrr3rBrZrr}r<rrsn`$DPr}r)r)8r __docformat____all__rwarningsrnumpyr7 _lib._utilr_matrixr _sparsetoolsr r r rrrr_baserrrr_datarr_sputilsrrrrrrrrr r!r"r#_indexr$r%rFr'rrrIrJrrrrrrrr}r<rBs8% 7 '000GF.++++9K+ mK+\,F2=2.b .#,<*%6l 7l^|P9|Pr}