ó ʽ÷Xc@`sÜdZddlmZmZmZdZddgZddlZddl m Z dd l m Z m Z dd lmZmZmZmZmZmZmZdd lmZde efd „ƒYZed „Zd„ZdS(s LInked List sparse matrix class i(tdivisiontprint_functiontabsolute_importsrestructuredtext ent lil_matrixtisspmatrix_lilN(txrangei(tspmatrixt isspmatrix(tgetdtypetisshapet isscalarliket IndexMixint upcast_scalartget_index_dtypet isintlike(t _csparsetoolscB`seZdZdZdded„Zd„Zejje_e dej deƒZ d„Z d„Z d„Zd „Zdd „Zd „Zejje_ejje_d „Zd „Zd„Zd„Zd„Zd„Zd„Zd„Zd„Zd„Zd„Zejje_dd„Zejje_ddd„Zded„Zejje_ed„Z ej je _ed„Z!ej!je!_RS(sRow-based linked list sparse matrix This is a structure for constructing sparse matrices incrementally. Note that inserting a single item can take linear time in the worst case; to construct a matrix efficiently, make sure the items are pre-sorted by index, per row. This can be instantiated in several ways: lil_matrix(D) with a dense matrix or rank-2 ndarray D lil_matrix(S) with another sparse matrix S (equivalent to S.tolil()) lil_matrix((M, N), [dtype]) to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype='d'. 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 Number of nonzero elements data LIL format data array of the matrix rows LIL format row index array of the matrix Notes ----- Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power. Advantages of the LIL format - supports flexible slicing - changes to the matrix sparsity structure are efficient Disadvantages of the LIL format - arithmetic operations LIL + LIL are slow (consider CSR or CSC) - slow column slicing (consider CSC) - slow matrix vector products (consider CSR or CSC) Intended Usage - LIL is a convenient format for constructing sparse matrices - once a matrix has been constructed, convert to CSR or CSC format for fast arithmetic and matrix vector operations - consider using the COO format when constructing large matrices Data Structure - An array (``self.rows``) of rows, each of which is a sorted list of column indices of non-zero elements. - The corresponding nonzero values are stored in similar fashion in ``self.data``. tlilc C`stj|ƒt||dtƒ|_t|ƒr¯t|ƒrR|rR|jƒ}n |jƒ}|dk r||j |ƒ}n|j |_ |j|_|j |_ |j |_ nUt|tƒryt|ƒrj|dk råtdƒ‚n|\}}||f|_ tj|fdtƒ|_ tj|fdtƒ|_ x=t|ƒD] }g|j |Ås(R=R(R'((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/lil.pyRFÄscC`s|d}xkt|jƒD]Z\}}xKt|ƒD]=\}}|dt||fƒt|j||ƒf7}q/WqW|d S(Nts %s %s iÿÿÿÿ(t enumerateRtstrR(R'tvalR,RDtpostj((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/lil.pyt__str__Ês 9cC`sNtd|jdfd|jƒ}|j||jd<|j||jd<|S(s;Returns a view of the 'i'th row (without copying). iRi(RRRRR(R'R,tnew((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/lil.pyt getrowviewÑs"cC`s_|j|ƒ}td|jdfd|jƒ}|j||jd<|j||jd<|S(s)Returns a copy of the 'i'th row. iRi(t_check_row_boundsRRRRR(R'R,RO((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/lil.pytgetrowÙs "cC`sR|dkr ||jd7}n|dks?||jdkrNtdƒ‚n|S(Nisrow index out of bounds(Rt IndexError(R'R,((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/lil.pyRQâs  cC`sR|dkr ||jd7}n|dks?||jdkrNtdƒ‚n|S(Niiscolumn index out of bounds(RRS(R'RM((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/lil.pyt_check_col_boundsés  c C`sÇt|tƒr´t|ƒdkr´|\}}t|tƒsNt|tjƒr´t|tƒsot|tjƒr´tj|jd|jd|j |j ||ƒ}|j j |ƒSn|j |ƒ\}}t|ƒ}t|ƒ}|r/|r/tj|jd|jd|j |j ||ƒ}|j j |ƒS|sDt|tƒr|ro|j|ƒ}t||dƒ}n|r |j|ƒ}t||dƒ}d}nLt|tƒrÔt|j|jdƒŒ}d}ntj|ƒ}|j}|dks t|ƒdkr|j||ƒSn|j||ƒ\}}|jdkrZt|jd|j ƒSt|jd|j ƒ}t||ƒ\}}tj|jd|jd|j |j |j |j ||ƒ|S(s®Return the element(s) index=(i, j), where j may be a slice. This always returns a copy for consistency, since slices into Python lists return copies. iiiRN(RRR-R.R tintegerRtlil_get1RRRRttypet _unpack_indexRtsliceRTRQRRtindicest atleast_1dt_get_row_rangest_index_to_arraystsizeRt_prepare_index_for_memoryviewt lil_fancy_get( R'tindexR,RMtvt i_intliket j_intliketi_shapeRO((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/lil.pyt __getitem__ðsR! !!             c C`s§|j|jdƒ\}}}t|||ƒ}t|ƒ}tt|ƒ|fd|jƒ}tj|jd|jd|j|j |j|j |||||ƒ |S(sµ Fast path for indexing in the case where column index is slice. This gains performance improvement over brute force by more efficient skipping of zeros, by accessing the elements column-wise in order. Parameters ---------- rows : sequence or xrange Rows indexed. If xrange, must be within valid bounds. col_slice : slice Columns indexed iRi( RZRRR-RRRtlil_get_row_rangesRR( R'Rt col_slicetj_starttj_stoptj_stridet col_rangetnjRO((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/lil.pyR\0s !  cC`sUt|tƒrÙt|ƒdkrÙ|\}}t|tƒsNt|tjƒrÙt|tƒsot|tjƒrÙ|jj|ƒ}|jdkrŸt dƒ‚nt j |j d|j d|j |j|||ƒdSn|j|ƒ\}}t|ƒrt|tƒr|tdƒkrt|tƒr|tdƒkr|j |j krt|d|jƒ}|j |_ |j|_dS|j||ƒ\}}t|ƒr²|jƒ}ntj|d|jƒ}tj||ƒ\}}|j |j krt dƒ‚nt|||ƒ\}}}t j|j d|j d|j |j|||ƒdS(Niis&Trying to assign a sequence to an itemiRsshape mismatch in assignment(RRR-R.R RURRWR^RRt lil_insertRRRRXRRYRRR]ttoarraytasarraytbroadcast_arraysR_t lil_fancy_set(R'RatxR,RMt_((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/lil.pyt __setitem__Ms:! !!-!    cC`sœ|dkr't|jd|jƒ}nqt|j|ƒ}|jƒ}|j|ƒ}xAt|jƒD]0\}}g|D]}||^qw|j|.           @  0     cC`s |j|jkr'|j|jƒ}n'|j|jkrN|j|jƒ}n|jj sv|jtjtjfkr‹|jtjƒ}n|jj s³|jtjtjfkrÈ|jtjƒ}n|dk rü|jjsï|j ƒ}n|||fS||fSdS(s Convert index and data arrays to form suitable for passing to the Cython fancy getset routines. The conversions are necessary since to (i) ensure the integer index arrays are in one of the accepted types, and (ii) to ensure the arrays are writable so that Cython memoryview support doesn't choke on them. Parameters ---------- i, j Index arrays x : optional Data arrays Returns ------- i, j, x Re-formatted arrays (x is omitted, if input was None) N( RRtflagst writeableR tint32tint64R?RR(R,RMRs((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/lil.pyR_æs((   cC`s t|tƒS(N(RR(Rs((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/lil.pyRs(Rt __future__RRRt __docformat__t__all__tnumpyR tscipy._lib.sixRtbaseRRtsputilsRR R R R R RRHRRRR_R(((s//tmp/pip-build-7oUkmx/scipy/scipy/sparse/lil.pyts  4ÿÔ )