CpjOSrSr/SQrSSKrSSKJr SSKJrJ r SSK J r J r J r JrJr SS KJr SS KJr "S S \5rS r"SS\\ 5r"SS\\5rg)z#Compressed Sparse Row matrix formatzrestructuredtext en) csr_array csr_matrixisspmatrix_csrN)spmatrix)_spbasesparray) csr_tocsc csr_tobsrcsr_count_blocksget_csr_submatrixcsr_sample_values)upcast) _cs_matrixc^\rSrSrSrSrSSjr\R R\lSSjr \RR\ lSSjr \RR\ lSU4Sjjr \RR\ lSSjr \RR\ lSS jr \RR\ l\S 5rS rS rS rSrSrSrSrSrSrSrSrSrSrSrSrU=r$) _csr_basecsr)rcUbUS:wa [S5eURS:XaU(aUR5$U$URup4UR UR UR UR4XC4US9$)N)rrzvSparse arrays/matrices do not support an 'axes' parameter because swapping dimensions is the only logical permutation.rshapecopy) ValueErrorndimrr_csc_containerdataindicesindptr)selfaxesrMNs M/var/www/html/pdf-tiff/venv/lib/python3.13/site-packages/scipy/sparse/_csr.py transpose_csr_base.transposes  LM M 99>"&499; 0D 0zz""DIIt||$(KK$19:T#K KcURS:wa [S5eURURURS9nUR 5 UR URURpTnURURpv[URS5H6nX8n X8S-n XIU R5Xh'XYU R5Xx'M8 U$)Nrz.Cannot convert a 1d sparse array to lil formatdtyperr) rr_lil_containerrr*sum_duplicatesrrrrowsrangetolist) r rlilptrinddatr-rnstartends r$tolil_csr_base.tolil$s 99>MN N!!$**DJJ!? kk$,,tyyXXsxxdtzz!}%AFEc(Cn++-DGn++-DG &  r'c4U(aUR5$U$Nr)r rs r$tocsr_csr_base.tocsr7s 99; Kr'cD>[TU]US9nURUlU$)Nr;)supertocoohas_canonical_format)r rA __class__s r$r@_csr_base.tocoo?s( GMtM $"&!:!:r'c zURS:wa [S5eURup#URURUR 4[ URU5S9n[R"US-US9n[R"URUS9n[R"UR[UR5S9n[X#URRUSS9UR RUSS9URUUU5 URXvU4URS9nS UlU$) Nrz.Cannot convert a 1d sparse array to csc formatmaxvalrr)Fr;rT)rrr_get_index_dtyperrmaxnnznpemptyrr*r astyperrhas_sorted_indices) r rr"r# idx_dtyperrrrBs r$tocsc_csr_base.tocscIs 99>MN Nzz))4;; *E+.txx+;*= !a%y1((48895xxtzz(:;!++$$YU$;,,%%ie%<))      7tzz  J#r'c URS:wa [S5eUcSSKJn UR U"U5S9$US:XaNUR R SSS5URUR4nURX@RUS9$UupVURupxUS:dUS:dXu-S :wdX-S :wa[S U35e[XxXVURUR5n URURUR4[X-U 5S 9n [R"Xu-S-U S 9n [R"XS 9n [R "XU4UR"S 9n [%XxXVURR'U S S9URR'U S S9UR XU R)55 URXU 4URS9$)Nrz.Cannot convert a 1d sparse array to bsr formatr)estimate_blocksize) blocksize)rrrrzinvalid blocksize rFr)Fr;rH)rr_spfuncsrTtobsrrreshaperr_bsr_containerrr rIrJrLrMzerosr*r rNravel)r rUrrTarg1RCr"r#blksrPrrrs r$rX_csr_base.tobsras 99>MN N   4::(:4(@:A A % II%%b1-dll4;;GD&&t::D&I ICA**CA1uA!quz #5i[!ABB#ADKK ED--t{{DLL.I/214.@IXXad1fI6Fhht5G88TAJdjj9D aAkk(((?ll)))%)@iitzz|  5 &&'tzz' r'cU$)zBswap the members of x if this is a column-oriented matrix xs r$_swap_csr_base._swaps r'c## URS:XaURRS5nSn[URUR 5H&up4[ X2- 5HnUv M Uv US-nM( [ URSU- 5HnUv M g[R"SURRS9n[U[5(aURSSOSURS4nSnURSSH=n X- US'URXn UR Xn URXU4USS9v U nM? g7f)Nrrrr)Tr)rr*typeziprrr.rrLr[r isinstancer rC) r zerouvd_rri0i1rrs r$__iter___csr_base.__iter__s/ 99>::??1%DADLL$))4quAJ&E 5 4::a=1,- . !4;;#4#45",T7";"; 12!TZZPQ]AS ++ab/BF1Ill2)G99R#D..$!8D.Q QB "sEEc URS:Xa4US;a[SUS35eURSURS4SS9$URup#[ U5nUS:aX- nUS:dX:a[SUS35e[ X#UR URURXS-SU5 upEnURXeU4SU4URSS 9$) zMReturns a copy of row i of the matrix, as a (1 x n) CSR matrix (row vector). r)rrVindex () out of rangerTr;Frr*r) r IndexErrorrYrintr rrrrCr*r ir"r#rrrs r$_getrow_csr_base._getrows 99> 71#^!<==<<DJJqM 2<> >zz F q5 FA q5AFwqc89 9 1 $++t||TYYq5!Q!H~~tf5aV$(JJU< ST Tzz F q5 FA q5AFwqc89 9 1 $++t||TYY1Q!H~~tf5aV$(JJU< >"DJJ>7 7$**Q-1mmC1jj.hhsxxtzz2! T\\499((Cc 3"%1!1CII !  ~~ckk)455r'cBURU5RU5$r:)r}_minor_index_fancyr rrs r$_get_intXarray_csr_base._get_intXarrays||C 33C88r'c URS;aURXSS9$URup4URU5upVnURXS-upURXn UR Xn US:a X:X:-n O X:*X:-n [ U5S:a XU- U-S:H-n XU- U-n Xn [R"S[U 5/5n US:aU SSS2n [ U SSS25n S[S[[R"[Xe- 5U- 5554nURXU 4UURSS 9$) NrTr;rrrrVFrx)rrrrrrabsrLarraylenrJrzceilfloatrCr*)r rrr"r#r5stopstrideiijj row_indicesrow_datar2 row_indptrrs r$_get_intXslice_csr_base._get_intXslicesg 88y &&sd&; ;zz!kk!nVSQ'll2) 99R# A:'K,>?C'K,>?C v;? %'61Q6 6C"'%/F: =XXq#k"234 A:"~Hk$B$/0KC3rwwuT\':V'CDEFG~~xjA$(JJU<  F "33r'rc"[U[5$)a0Is `x` of csr_matrix type? .. warning:: SciPy sparse is shifting from a sparse matrix interface to a sparse array interface. In the next few releases we expect to deprecate the sparse matrix interface. For documentation of the matrix interface, see the :ref:`spmatrix interface docs `. For guidance on converting existing code to sparse arrays, see :ref:`Migration from spmatrix to sparray `. Parameters ---------- x object to check for being a csr matrix Returns ------- bool True if `x` is a csr matrix, False otherwise Examples -------- >>> from scipy.sparse import csr_array, csr_matrix, coo_matrix, isspmatrix_csr >>> isspmatrix_csr(csr_matrix([[5]])) True >>> isspmatrix_csr(csr_array([[5]])) False >>> isspmatrix_csr(coo_matrix([[5]])) False )rkrrds r$rr)s@ a $$r'c\rSrSrSrSrg)riMa Compressed Sparse Row array. This can be instantiated in several ways: csr_array(D) where D is a 2-D ndarray csr_array(S) with another sparse array or matrix S (equivalent to S.tocsr()) csr_array((M, N), [dtype]) to construct an empty array with shape (M, N) dtype is optional, defaulting to dtype='d'. csr_array((data, (row_ind, col_ind)), [shape=(M, N)]) where ``data``, ``row_ind`` and ``col_ind`` satisfy the relationship ``a[row_ind[k], col_ind[k]] = data[k]``. csr_array((data, indices, indptr), [shape=(M, N)]) is the standard CSR representation where the column indices for row i are stored in ``indices[indptr[i]:indptr[i+1]]`` and their corresponding values are stored in ``data[indptr[i]:indptr[i+1]]``. If the shape parameter is not supplied, the array dimensions are inferred from the index arrays. Attributes ---------- data : ndarray CSR format data array of the array indices : ndarray CSR format index array of the array indptr : ndarray CSR format index pointer array of the array has_sorted_indices : bool Whether indices are sorted has_canonical_format : bool Whether indices are sorted and no duplicate entries exist dtype : dtype Data type of the array shape : 2-tuple Shape of the array ndim : int Number of dimensions (this is always 2) format : str Three letter code for the format of the array storage, e.g. 'csr' nnz : int Number of values stored in the array size : int Number of values stored in the array T : csr_array The transpose of the array mT : csr_array The matrix transpose of the array Notes ----- Sparse arrays can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power. Advantages of the CSR format - efficient arithmetic operations CSR + CSR, CSR * CSR, etc. - efficient row slicing - fast matrix vector products Disadvantages of the CSR format - slow column slicing operations (consider CSC) - changes to the sparsity structure are expensive (consider LIL or DOK) Canonical Format - Within each row, indices are sorted by column. - There are no duplicate entries. Examples -------- >>> import numpy as np >>> from scipy.sparse import csr_array >>> csr_array((3, 4), dtype=np.int8).toarray() array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], dtype=int8) >>> row = np.array([0, 0, 1, 2, 2, 2]) >>> col = np.array([0, 2, 2, 0, 1, 2]) >>> data = np.array([1, 2, 3, 4, 5, 6]) >>> csr_array((data, (row, col)), shape=(3, 3)).toarray() array([[1, 0, 2], [0, 0, 3], [4, 5, 6]]) >>> indptr = np.array([0, 2, 3, 6]) >>> indices = np.array([0, 2, 2, 0, 1, 2]) >>> data = np.array([1, 2, 3, 4, 5, 6]) >>> csr_array((data, indices, indptr), shape=(3, 3)).toarray() array([[1, 0, 2], [0, 0, 3], [4, 5, 6]]) Duplicate entries are summed together: >>> row = np.array([0, 1, 2, 0]) >>> col = np.array([0, 1, 1, 0]) >>> data = np.array([1, 2, 4, 8]) >>> csr_array((data, (row, col)), shape=(3, 3)).toarray() array([[9, 0, 0], [0, 2, 0], [0, 4, 0]]) As an example of how to construct a CSR array incrementally, the following snippet builds a term-document array from texts: >>> docs = [["hello", "world", "hello"], ["goodbye", "cruel", "world"]] >>> indptr = [0] >>> indices = [] >>> data = [] >>> vocabulary = {} >>> for d in docs: ... for term in d: ... index = vocabulary.setdefault(term, len(vocabulary)) ... indices.append(index) ... data.append(1) ... indptr.append(len(indices)) ... >>> csr_array((data, indices, indptr), dtype=int).toarray() array([[2, 1, 0, 0], [0, 1, 1, 1]]) rcNrrrrrrrcr'r$rrMs@r'rc\rSrSrSrSrg)riah Compressed Sparse Row matrix. .. warning:: SciPy sparse is shifting from a sparse matrix interface to a sparse array interface. In the next few releases we expect to deprecate the sparse matrix interface. For documentation of the matrix interface, see the :ref:`spmatrix interface docs `. For guidance on converting existing code to sparse arrays, see :ref:`Migration from spmatrix to sparray `. This can be instantiated in several ways: csr_matrix(D) where D is a 2-D ndarray csr_matrix(S) with another sparse array or matrix S (equivalent to S.tocsr()) csr_matrix((M, N), [dtype]) to construct an empty matrix with shape (M, N) dtype is optional, defaulting to dtype='d'. csr_matrix((data, (row_ind, col_ind)), [shape=(M, N)]) where ``data``, ``row_ind`` and ``col_ind`` satisfy the relationship ``a[row_ind[k], col_ind[k]] = data[k]``. csr_matrix((data, indices, indptr), [shape=(M, N)]) is the standard CSR representation where the column indices for row i are stored in ``indices[indptr[i]:indptr[i+1]]`` and their corresponding values are stored in ``data[indptr[i]:indptr[i+1]]``. If the shape parameter is not supplied, the matrix dimensions are inferred from the index arrays. Attributes ---------- data : ndarray CSR format data array of the matrix indices : ndarray CSR format index array of the matrix indptr : ndarray CSR format index pointer array of the matrix has_sorted_indices : bool Whether indices are sorted has_canonical_format : bool Whether indices are sorted and no duplicate entries exist dtype : dtype Data type of the matrix shape : 2-tuple Shape of the matrix ndim : int Number of dimensions (this is always 2) format : str Three letter code for the format of the matrix storage, e.g. 'csr' nnz : int Number of values stored in the matrix size : int Number of values stored in the matrix T : csr_matrix The transpose of the matrix mT : csr_matrix The matrix transpose Notes ----- Sparse matrices can be used in arithmetic operations: they support addition, subtraction, multiplication, division, and matrix power. Advantages of the CSR format - efficient arithmetic operations CSR + CSR, CSR * CSR, etc. - efficient row slicing - fast matrix vector products Disadvantages of the CSR format - slow column slicing operations (consider CSC) - changes to the sparsity structure are expensive (consider LIL or DOK) Canonical Format - Within each row, indices are sorted by column. - There are no duplicate entries. Examples -------- >>> import numpy as np >>> from scipy.sparse import csr_matrix >>> csr_matrix((3, 4), dtype=np.int8).toarray() array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]], dtype=int8) >>> row = np.array([0, 0, 1, 2, 2, 2]) >>> col = np.array([0, 2, 2, 0, 1, 2]) >>> data = np.array([1, 2, 3, 4, 5, 6]) >>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray() array([[1, 0, 2], [0, 0, 3], [4, 5, 6]]) >>> indptr = np.array([0, 2, 3, 6]) >>> indices = np.array([0, 2, 2, 0, 1, 2]) >>> data = np.array([1, 2, 3, 4, 5, 6]) >>> csr_matrix((data, indices, indptr), shape=(3, 3)).toarray() array([[1, 0, 2], [0, 0, 3], [4, 5, 6]]) Duplicate entries are summed together: >>> row = np.array([0, 1, 2, 0]) >>> col = np.array([0, 1, 1, 0]) >>> data = np.array([1, 2, 4, 8]) >>> csr_matrix((data, (row, col)), shape=(3, 3)).toarray() array([[9, 0, 0], [0, 2, 0], [0, 4, 0]]) As an example of how to construct a CSR matrix incrementally, the following snippet builds a term-document matrix from texts: >>> docs = [["hello", "world", "hello"], ["goodbye", "cruel", "world"]] >>> indptr = [0] >>> indices = [] >>> data = [] >>> vocabulary = {} >>> for d in docs: ... for term in d: ... index = vocabulary.setdefault(term, len(vocabulary)) ... indices.append(index) ... data.append(1) ... indptr.append(len(indices)) ... >>> csr_matrix((data, indices, indptr), dtype=int).toarray() array([[2, 1, 0, 0], [0, 1, 1, 1]]) rcNrrcr'r$rrsIr'r)r __docformat____all__numpyrL_matrixr_baserr _sparsetoolsr r r r r_sputilsr _compressedrrrrrrcr'r$rsc)% 7#AA#T3 T3n %HA 7AHJ9Jr'