ʽXc@`szdZddlmZmZmZddlZddlZddlm Z ddl m Z m Z m Z mZmZddd d d gZd efd YZd efdYZd efdYZiddgd6ddgd6ddgd6ddgd6ddgd6ddgd6d d!gd"6d#d$gd%6d&d'gd(6d)d*gd+6d,d-gd.6d/d0gd16d2d3gd46d5d6gd76d8d9gd:6d;d<gd=6d>d?gd@6dAdBgdC6dDdEgdF6dGdHgdI6Zeejejejejejejejejejej ej!ej"ej#ej$ej%ej&ej'ej(gZ)dJZ*de+fdKYZ,dLZ-e-Z.dS(MsBase class for sparse matricesi(tdivisiontprint_functiontabsolute_importN(txrangei(tisdenset isscalarliket isintliket get_sum_dtypet validateaxistspmatrixt isspmatrixtissparset SparseWarningtSparseEfficiencyWarningcB`seZRS((t__name__t __module__(((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyR stSparseFormatWarningcB`seZRS((RR(((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRscB`seZRS((RR(((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyR ssCompressed Sparse ColumntcscsCompressed Sparse RowtcsrisDictionary Of Keystdokis LInked ListtlilisDictionary of DictionariestdodisSymmetric Sparse Skylinetsssit COOrdinatetcooisLinpack BAndedtlbais#Ellpack-itpack Generalized Diagonaltegdi tDIAgonaltdiai sBlock Sparse Rowtbsri sModified compressed Sparse Rowtmsri sBlock Sparse Columntbsci s!Modified compressed Sparse ColumntmscisSymmetric SKylinetsskisNonsymmetric SKylinetnskisJAgged DiagonaltjadisUnsymmetric Sparse SkylinetussisVariable Block Rowtvbrit Undefinedtundi2cB`sheZdZdZdZedZdZdZe dedeZ dd Z d Z d Z d Zd ZdZdWdZe dZdZdZdZdZeZdZdZdZdZdZdZdWdZdZ dZ!dZ"dZ#d Z$d!Z%d"Z&d#Z'd$Z(d%Z)d&Z*d'Z+d(Z,d)Z-d*Z.d+Z/d,Z0d-Z1d.Z2e3e3d/Z4d0Z5d1Z6d2Z7d3Z8d4Z9d5Z:d6Z;d7Z<d8Z=d9Z>d:Z?d;Z@dWe3d<ZAd=ZBd>ZCeBjeC_d?ZDd@ZEdAZFdBZGdCZHdDZIdWdWdEZJdWdWdFZKe3dGZLe3dHZMe3dIZNe3dJZOe3dKZPdWe3dLZQe3dMZRdNZSdWdWdWdOZTdWdWdWdPZUdQZVdRdSZWdTZXdUZYdVZZRS(Xs This class provides a base class for all sparse matrices. It cannot be instantiated. Most of the work is provided by subclasses. g333333$@icC`s7d|_|jjdkr*tdn||_dS(NR s7This class is not intended to be instantiated directly.(tNonet_shapet __class__Rt ValueErrortmaxprint(tselfR,((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt__init__Fs cC`st|}t|dkr-tdny$t|dt|df}WntdnX|ddko|ddkstdn|j|kr|jdk ry|j|}Wqtk rtd|j j qXn||_dS(sSee `reshape`.is1Only two-dimensional sparse arrays are supported.iis invalid shapes!Reshaping not implemented for %s.N( ttupletlenR+tintt TypeErrorR)R(treshapetNotImplementedErrorR*R(R-tshape((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt set_shapeMs  $  cC`s|jS(sGet shape of a matrix.(R)(R-((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt get_shapedstfgettfsettCcC`std|jjdS(s Gives a new shape to a sparse matrix without changing its data. Parameters ---------- shape : length-2 tuple of ints The new shape should be compatible with the original shape. order : 'C', optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value, as this argument is not used. Returns ------- reshaped_matrix : `self` with the new dimensions of `shape` See Also -------- np.matrix.reshape : NumPy's implementation of 'reshape' for matrices s!Reshaping not implemented for %s.N(R4R*R(R-R5torder((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyR3jscC`s|jj|j|jS(sCast the matrix elements to a specified type. The data will be copied. Parameters ---------- t : string or numpy dtype Typecode or data-type to which to cast the data. (ttocsrtastypetasformattformat(R-tt((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyR=s cC`sxddddg}|jj|kr(|Sx3|D]+}|jtj|kr/|j|Sq/Wtd|jjdS(s7Upcast matrix to a floating point format (if necessary)tftdtFtDs-cannot upcast [%s] to a floating point formatN(tdtypetchartnpR=R2tname(R-tfp_typestfp_type((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pytasfptypes cc`s7x0t|jdD]}||ddfVqWdS(Ni(RR5(R-tr((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt__iter__scC`s|jS(s3Maximum number of elements to display when printed.(R,(R-((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt getmaxprintscC`std|jjdS(s(Number of non-zero entries, equivalent to np.count_nonzero(a.toarray()) Unlike getnnz() and the nnz property, which return the number of stored entries (the length of the data attribute), this method counts the actual number of non-zero entries in data. s%count_nonzero not implemented for %s.N(R4R*R(R-((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt count_nonzeros cC`std|jjdS(sJNumber of stored values, including explicit zeros. Parameters ---------- axis : None, 0, or 1 Select between the number of values across the whole matrix, in each column, or in each row. See also -------- count_nonzero : Number of non-zero entries sgetnnz not implemented for %s.N(R4R*R(R-taxis((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pytgetnnzs cC`s |jS(sNumber of stored values, including explicit zeros. See also -------- count_nonzero : Number of non-zero entries (RQ(R-((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pytnnzscC`st|ddS(s.Format of a matrix representation as a string.R?R'(tgetattr(R-((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt getformatscC`s7t|j\}}d|j|jj|j|fS(NsH<%dx%d sparse matrix of type '%s' with %d stored elements in %s format>(t_formatsRTR5REttypeRR(R-t_t format_name((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt__repr__scC`s|j}|j}d}|j|kr|d}||j| |j| |j| }|d7}||d}|||j| |j| |j| 7}n||j|j|j}|S(NcS`sBttt|||}djg|D]}d|^q+S(Ns s %s %s(tziptlisttjoin(trowtcoltdatattriplesR@((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyttostrsis : : (RNttocooRRR]R^R_(R-R,tARathalftout((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt__str__s    ' 1cC`s,|jdkr|jdkStddS(Niis\The truth value of an array with more than one element is ambiguous. Use a.any() or a.all().(ii(R5RRR+(R-((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt__bool__s cC`stddS(Ns;sparse matrix length is ambiguous; use getnnz() or shape[0](R2(R-((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt__len__scC`s7|dks||jkr|St|d|SdS(sReturn this matrix in a given sparse format Parameters ---------- format : {string, None} desired sparse matrix format - None for no format conversion - "csr" for csr_matrix format - "csc" for csc_matrix format - "lil" for lil_matrix format - "dok" for dok_matrix format and so on ttoN(R(R?RS(R-R?((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyR>scC`s|jj|S(s4Point-wise multiplication by another matrix (R<tmultiply(R-tother((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRjscC`s|jj|S(s5Element-wise maximum between this and another matrix.(R<tmaximum(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRlscC`s|jj|S(s5Element-wise minimum between this and another matrix.(R<tminimum(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRmscC`s||S(s1Ordinary dot product Examples -------- >>> import numpy as np >>> from scipy.sparse import csr_matrix >>> A = csr_matrix([[1, 2, 0], [0, 0, 3], [4, 0, 5]]) >>> v = np.array([1, 0, -1]) >>> A.dot(v) array([ 1, -3, -1], dtype=int64) ((R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pytdot!s cC`s|jj|d|S(sElement-wise power.RE(R<tpower(R-tnRE((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRo0scC`s|jj|S(N(R<t__eq__(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRq4scC`s|jj|S(N(R<t__ne__(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRr7scC`s|jj|S(N(R<t__lt__(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRs:scC`s|jj|S(N(R<t__gt__(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRt=scC`s|jj|S(N(R<t__le__(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRu@scC`s|jj|S(N(R<t__ge__(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRvCscC`st|jS(N(tabsR<(R-((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt__abs__FscC`s|jj|S(N(R<t__add__(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRyIscC`s|jj|S(N(R<t__radd__(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRzLscC`s|jj|S(N(R<t__sub__(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyR{OscC`s|jj|S(N(R<t__rsub__(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyR|Ssc C`s|j\}}|jtjkr|j|fkr@|j|S|j|dfkrt|j|jj|dS|jdkr|jd|kr|j|Snt |r|j |St |r|jd|jdkrt dn|j |Stj|}|jdkr5|jtjkr5tSy |jWntk rY|}nX|jdks|jdkr8|jddkr8|j|fkr|j|dfkrt dn|jtj|}t|tjrtj|}n|jdkr4|jddkr4|jdd}n|S|jdkr|jd|jdkrpt dn|jtj|}t|tjrtj|}n|St ddS(sinterpret other and call one of the following self._mul_scalar() self._mul_vector() self._mul_multivector() self._mul_sparse_matrix() iiisdimension mismatchiscould not interpret dimensionsN(R5R*RGtndarrayt _mul_vectortravelR3tndimt_mul_multivectorRt _mul_scalarR R+t_mul_sparse_matrixt asanyarrayREtobject_tNotImplementedtAttributeErrort isinstancetmatrixtasmatrixtasarray(R-RktMtNtother_atresult((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt__mul__VsL  "    !   1'"cC`s|jj|S(N(R<R(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRscC`s|jj|S(N(R<R~(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyR~scC`s|jj|S(N(R<R(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRscC`s|jj|S(N(R<R(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRscC`sjt|r|j|Sy|j}Wn&tk rQtj|j}nX|j|jSdS(N(RRt transposeRRGR(R-Rkttr((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt__rmul__s   cC`s(t|rtdn|j|S(Ns0Scalar operands are not allowed, use '*' instead(RR+R(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt __matmul__s cC`s(t|rtdn|j|S(Ns0Scalar operands are not allowed, use '*' instead(RR+R(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt __rmatmul__s cC`st|r|rG|r.tj||jStj||jSn|rtj|jtjr|jtjj d|S|j d|}tj |j}tj |jtj rtj |tj r|j|jS|Snt |rg|s2|rtj|j|Stj|j|Sq|rNtj||jStj||jSnt|r|r|j||dtS|j}|rtj|jtjr|jtjj|S|j|SntSdS(Ng?trdivide(RRGt true_dividettodensetdividetcan_castREtfloat_R=RRt issubdtypetintegerRR t_dividetFalseR<t_divide_sparseR(R-RkRRRLt scalar_dtypetself_csr((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRs:    cC`s|j|dtS(NR(RtTrue(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt __truediv__scC`s|j|dtS(NR(RR(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt__div__scC`stS(N(R(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt __rtruediv__scC`stS(N(R(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt__rdiv__scC`s |j S(N(R<(R-((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt__neg__scC`stS(N(R(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt__iadd__ scC`stS(N(R(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt__isub__ scC`stS(N(R(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt__imul__scC`s |j|S(N(t __itruediv__(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt__idiv__scC`stS(N(R(R-Rk((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRscC`s|jd|jdkr)tdnt|rt|}|dkr\tdn|dkrddlm}||jdd|jS|dkr|jS|j |d}|dr|||S||Snt |rtdnt SdS( Niismatrix is not squaresexponent must be >= 0(teyeREisexponent must be an integer( R5R2RR1R+t constructRREtcopyt__pow__RR(R-RkRttmp((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRs$          cC`s|dkr|jS|dkr,|jS|dkrB|jS|dkrX|jS|dkrn|jS|dkr|jSt|ddS(NRctTtHtrealtimagtsizes not found(ttoarrayRtgetHt_realt_imagRQR(R-tattr((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt __getattr__2s            cC`s|jjd|d|S(s Reverses the dimensions of the sparse matrix. Parameters ---------- axes : None, optional This argument is in the signature *solely* for NumPy compatibility reasons. Do not pass in anything except for the default value. copy : bool, optional Indicates whether or not attributes of `self` should be copied whenever possible. The degree to which attributes are copied varies depending on the type of sparse matrix being used. Returns ------- p : `self` with the dimensions reversed. See Also -------- np.matrix.transpose : NumPy's implementation of 'transpose' for matrices taxesR(R<R(R-RR((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRBscC`s|jjS(sElement-wise complex conjugation. If the matrix is of non-complex data type, then this method does nothing and the data is not copied. (R<tconj(R-((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyR]scC`s |jS(N(R(R-((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt conjugateescC`s|jjS(sReturn the Hermitian transpose of this matrix. See Also -------- np.matrix.getH : NumPy's implementation of `getH` for matrices (RR(R-((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRkscC`s|jjS(N(R<R(R-((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRtscC`s|jjS(N(R<R(R-((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRwscC`s3|j}|jdk}|j||j|fS(sgnonzero indices Returns a tuple of arrays (row,col) containing the indices of the non-zero elements of the matrix. Examples -------- >>> from scipy.sparse import csr_matrix >>> A = csr_matrix([[1,2,0],[0,0,3],[4,0,5]]) >>> A.nonzero() (array([0, 0, 1, 2, 2]), array([0, 1, 2, 0, 2])) i(RbR_R]R^(R-Rctnz_mask((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pytnonzerozs cC`sddlm}|jd}|dkr6||7}n|dksN||kr]tdn|dg|gdggfd|dfd|j}||S(sgReturns a copy of column j of the matrix, as an (m x 1) sparse matrix (column vector). i(t csc_matrixisindex out of boundsR5RE(RRR5t IndexErrorRE(R-tjRRpt col_selector((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pytgetcols   cC`sddlm}|jd}|dkr6||7}n|dksN||kr]tdn|dgdg|ggfdd|fd|j}||S(s`Returns a copy of row i of the matrix, as a (1 x n) sparse matrix (row vector). i(t csr_matrixisindex out of boundsR5RE(RRR5RRE(R-tiRtmt row_selector((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pytgetrows   cC`stj|jd|d|S(s Return a dense matrix representation of this matrix. Parameters ---------- order : {'C', 'F'}, optional Whether to store multi-dimensional data in C (row-major) or Fortran (column-major) order in memory. The default is 'None', indicating the NumPy default of C-ordered. Cannot be specified in conjunction with the `out` argument. out : ndarray, 2-dimensional, optional If specified, uses this array (or `numpy.matrix`) as the output buffer instead of allocating a new array to return. The provided array must have the same shape and dtype as the sparse matrix on which you are calling the method. Returns ------- arr : numpy.matrix, 2-dimensional A NumPy matrix object with the same shape and containing the same data represented by the sparse matrix, with the requested memory order. If `out` was passed and was an array (rather than a `numpy.matrix`), it will be filled with the appropriate values and returned wrapped in a `numpy.matrix` object that shares the same memory. R;Re(RGRR(R-R;Re((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRscC`s"|jdtjd|d|S(s Return a dense ndarray representation of this matrix. Parameters ---------- order : {'C', 'F'}, optional Whether to store multi-dimensional data in C (row-major) or Fortran (column-major) order in memory. The default is 'None', indicating the NumPy default of C-ordered. Cannot be specified in conjunction with the `out` argument. out : ndarray, 2-dimensional, optional If specified, uses this array as the output buffer instead of allocating a new array to return. The provided array must have the same shape and dtype as the sparse matrix on which you are calling the method. For most sparse types, `out` is required to be memory contiguous (either C or Fortran ordered). Returns ------- arr : ndarray, 2-dimensional An array with the same shape and containing the same data represented by the sparse matrix, with the requested memory order. If `out` was passed, the same object is returned after being modified in-place to contain the appropriate values. RR;Re(RbRR(R-R;Re((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRscC`s|jd|jdtS(sConvert this matrix to Compressed Sparse Row format. With copy=False, the data/indices may be shared between this matrix and the resultant csr_matrix. R(RbR<R(R-R((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyR<scC`s|jd|jdtS(sConvert this matrix to Dictionary Of Keys format. With copy=False, the data/indices may be shared between this matrix and the resultant dok_matrix. R(RbttodokR(R-R((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRscC`s|jdtjd|S(sConvert this matrix to COOrdinate format. With copy=False, the data/indices may be shared between this matrix and the resultant coo_matrix. R(R<RRb(R-R((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRbscC`s|jdtjd|S(sConvert this matrix to LInked List format. With copy=False, the data/indices may be shared between this matrix and the resultant lil_matrix. R(R<Rttolil(R-R((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRscC`s|jd|jdtS(sConvert this matrix to sparse DIAgonal format. With copy=False, the data/indices may be shared between this matrix and the resultant dia_matrix. R(RbttodiaR(R-R((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRscC`s"|jdtjd|d|S(sConvert this matrix to Block Sparse Row format. With copy=False, the data/indices may be shared between this matrix and the resultant bsr_matrix. When blocksize=(R, C) is provided, it will be used for construction of the bsr_matrix. Rt blocksize(R<Rttobsr(R-RR((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRs cC`s|jd|jdtS(sConvert this matrix to Compressed Sparse Column format. With copy=False, the data/indices may be shared between this matrix and the resultant csc_matrix. R(R<ttocscR(R-R((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyR)scC`s|j|dtS(sReturns a copy of this matrix. No data/indices will be shared between the returned value and current matrix. R(R*R(R-((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyR1scC`s-t||j\}}t|j}|dkrl|tjtj|dfd|jd|d|S|dkr|d7}n|dkrtjtjd|fd||}n(|tjtj|dfd|}|dk r|j|jkrt dn|jdd d|d|S( sq Sum the matrix elements over a given axis. Parameters ---------- axis : {-2, -1, 0, 1, None} optional Axis along which the sum is computed. The default is to compute the sum of all the matrix elements, returning a scalar (i.e. `axis` = `None`). dtype : dtype, optional The type of the returned matrix and of the accumulator in which the elements are summed. The dtype of `a` is used by default unless `a` has an integer dtype of less precision than the default platform integer. In that case, if `a` is signed then the platform integer is used while if `a` is unsigned then an unsigned integer of the same precision as the platform integer is used. .. versionadded: 0.18.0 out : np.matrix, optional Alternative output matrix in which to place the result. It must have the same shape as the expected output, but the type of the output values will be cast if necessary. .. versionadded: 0.18.0 Returns ------- sum_along_axis : np.matrix A matrix with the same shape as `self`, with the specified axis removed. See Also -------- np.matrix.sum : NumPy's implementation of 'sum' for matrices iREReiisdimensions do not matchRPN(( RR5RRER(RGRtonestsumR+(R-RPREReRRpt res_dtypetret((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyR9s"&        c C`sEd}t||jj}||j}|d krO|ratj}qantj|j}|rptjn|}|j|}|d kr|tj|jd|jdj d|d|S|dkr|d7}n|dkr|d|jdj ddd|d|S|d|jdj ddd|d|Sd S( s Compute the arithmetic mean along the specified axis. Returns the average of the matrix elements. The average is taken over all elements in the matrix by default, otherwise over the specified axis. `float64` intermediate and return values are used for integer inputs. Parameters ---------- axis : {-2, -1, 0, 1, None} optional Axis along which the mean is computed. The default is to compute the mean of all elements in the matrix (i.e. `axis` = `None`). dtype : data-type, optional Type to use in computing the mean. For integer inputs, the default is `float64`; for floating point inputs, it is the same as the input dtype. .. versionadded: 0.18.0 out : np.matrix, optional Alternative output matrix in which to place the result. It must have the same shape as the expected output, but the type of the output values will be cast if necessary. .. versionadded: 0.18.0 Returns ------- m : np.matrix See Also -------- np.matrix.mean : NumPy's implementation of 'mean' for matrices cS`s(tj|tjp'tj|tjS(N(RGRRtbool_(RE((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt _is_integralsiiREReig?RPN( RRERVR(RGtfloat64R=tarrayR5R( R-RPREReRRtintegralt inter_dtypet inter_self((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pytmeans*%          cC`s|jjS(s0Returns the main diagonal of the matrix (R<tdiagonal(R-((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyRsicC`sl|j\}}|dkr'||ks@|dkrO| |krOtdn|jtj||dS(s Set diagonal or off-diagonal elements of the array. Parameters ---------- values : array_like New values of the diagonal elements. Values may have any length. If the diagonal is longer than values, then the remaining diagonal entries will not be set. If values if longer than the diagonal, then the remaining values are ignored. If a scalar value is given, all of the diagonal is set to it. k : int, optional Which off-diagonal to set, corresponding to elements a[i,i+k]. Default: 0 (the main diagonal). isk exceeds matrix dimensionsN(R5R+t_setdiagRGR(R-tvaluestkRR((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pytsetdiags1cC`s|j\}}|dkr|jdkrkt|||}xt|D]}|||||fsRERet__call__iiiRR.N($tanyR[RGRRRStpopR(RR/RjtaddRyRnRRtsubtractR{R|Rtsyst version_infoRRRRlRmtabsoluteRwt _ufuncs_with_fixed_point_at_zeroRthasattrR<RR R( R-tfunctmethodtpostinputstkwargst with_selfRet without_selfRRRt func_name((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyt__numpy_ufunc__sh                N([RRt__doc__t__array_priority__RtMAXPRINTR.R6R7tpropertyR5R3R=RKRMRNROR(RQRRRTRYRfRgt __nonzero__RhR>RjRlRmRnRoRqRrRsRtRuRvRxRyRzR{R|RRR~RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR<RRbRRRRRRRRRRRR(((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyR >s                               L       +                     #       HI    cC`s t|tS(N(RR (R((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pyR Ws(/Rt __future__RRRRtnumpyRGtscipy._lib.sixRtsputilsRRRRRt__all__tWarningR RR RUt frozensettsinttantarcsintarctantsinhttanhtarcsinhtarctanhtrinttsigntexpm1tlog1ptdeg2radtrad2degtfloortceilttrunctsqrtRRRR R R (((s0/tmp/pip-build-7oUkmx/scipy/scipy/sparse/base.pytsT  (                    *$'