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lil.cpython-35.pyc
�B�W�D � @ s� d Z d d l m Z m Z m Z d Z d d g Z d d l Z d d l m Z d d l m Z m Z d d l m Z m Z m Z m Z m Z m Z m Z d d l m Z Gd d � d e e � Z d d d � Z d d � Z d S)z LInked List sparse matrix class � )�division�print_function�absolute_importzrestructuredtext en� lil_matrix�isspmatrix_lilN)�xrange� )�spmatrix� isspmatrix)�getdtype�isshape� isscalarlike� IndexMixin� upcast_scalar�get_index_dtype� isintlike)� _csparsetoolsc @ s� e Z d Z d Z d Z d d d d d � Z d d � Z e d e j d e � Z d d � Z d d � Z d d � Z d d � Z d d d � Z d d � Z e j j e _ e j j e _ d d � Z d d � Z d d � Z d d � Z d d � Z d! d" � Z d# d$ � Z d% d& � Z d' d( � Z d) d* � Z d+ d, � Z e j j e _ d- d. d/ � Z e j j e _ d d d0 d1 � Z d d d2 d3 � Z d d4 d5 � Z e j j e _ d d6 d7 � Z e j j e _ d S)8r a Row-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``. ZlilNFc C s t j | � t | | d t �| _ t | � r� t | � rR | rR | j � } n | j � } | d k ry | j | � } | j | _ | j | _ | j | _ | j | _ nSt | t � rst | � rd| d k r� t d � � | \ } } | | f | _ t j | f d t �| _ t j | f d t �| _ x= t | � D] } g | j | <g | j | <q=Wq�t d � � n� y t j | � } Wn t k r�t d � � YnY Xd d l m } | | d | �j � } | j | _ | j | _ | j | _ | j | _ d S)N�defaultz invalid use of shape parameter�dtypez)unrecognized lil_matrix constructor usagezunsupported matrix typer )� csr_matrix)r �__init__r �floatr r r �copy�tolil�astype�shape�rows�data� isinstance�tupler � ValueError�np�empty�object�range� TypeErrorZasmatrix�csrr ) �selfZarg1r r r �A�M�N�ir � r, �/lil.pyr T sD zlil_matrix.__init__c C s� t | � } t | � d k r* t d � � y$ t | d � t | d � f } Wn t d � � Yn X| d d k o� | d d k s� t d � � | j | k r� | j d k r� y | j | � } Wn( t k r� t d | j j � � Yn X| | _ d S)N� z1Only two-dimensional sparse arrays are supported.r r z invalid shapez!Reshaping not implemented for %s.) r �lenr �intr% Z_shape�reshape�NotImplementedError� __class__�__name__)r' r r, r, r- � set_shape� s $ zlil_matrix.set_shape�fget�fsetc C s$ | | | d d � d d � f <| S)Nr, )r' �otherr, r, r- �__iadd__� s zlil_matrix.__iadd__c C s$ | | | d d � d d � f <| S)Nr, )r' r8 r, r, r- �__isub__� s zlil_matrix.__isub__c C s8 t | � r0 | | | d d � d d � f <| St Sd S)N)r �NotImplemented)r' r8 r, r, r- �__imul__� s zlil_matrix.__imul__c C s8 t | � r0 | | | d d � d d � f <| St Sd S)N)r r; )r' r8 r, r, r- � __itruediv__� s zlil_matrix.__itruediv__c C s� | d k r& t d d � | j D� � S| d k r< | d 7} | d k r� t j | j d d t j �} x | j D] } | | d 7<qq W| S| d k r� t j d d � | j D� d t j �St d � � d S) Nc S s g | ] } t | � � q Sr, )r/ )�.0�rowvalsr, r, r- � <listcomp>� s z%lil_matrix.getnnz.<locals>.<listcomp>r r. r r c S s g | ] } t | � � q Sr, )r/ )r> r? r, r, r- r@ � s zaxis out of bounds) �sumr r! Zzerosr �intpr �arrayr )r' Zaxis�out�rowr, r, r- �getnnz� s &zlil_matrix.getnnzc C s t d d � | j D� � S)Nc s s | ] } t j | � Vq d S)N)r! � count_nonzero)r> r? r, r, r- � <genexpr>� s z+lil_matrix.count_nonzero.<locals>.<genexpr>)rA r )r' r, r, r- rG � s zlil_matrix.count_nonzeroc C s� d } xk t | j � D]Z \ } } xK t | � D]= \ } } | d t | | f � t | j | | � f 7} q/ Wq W| d d � S)N� z %s %s r ���)� enumerater �strr )r' �valr+ rE �pos�jr, r, r- �__str__� s 9zlil_matrix.__str__c C sN t d | j d f d | j �} | j | | j d <| j | | j d <| S)z;Returns a view of the 'i'th row (without copying). r r r )r r r r r )r' r+ �newr, r, r- � getrowview� s "zlil_matrix.getrowviewc C sq | j | � } t d | j d f d | j �} | j | d d � | j d <| j | d d � | j d <| S)z)Returns a copy of the 'i'th row. r r Nr )�_check_row_boundsr r r r r )r' r+ rQ r, r, r- �getrow� s " zlil_matrix.getrowc C sL | d k r | | j d 7} | d k s< | | j d k rH t d � � | S)Nr zrow index out of bounds)r � IndexError)r' r+ r, r, r- rS � s z lil_matrix._check_row_boundsc C sL | d k r | | j d 7} | d k s< | | j d k rH t d � � | S)Nr r zcolumn index out of bounds)r rU )r' rO r, r, r- �_check_col_bounds� s z lil_matrix._check_col_boundsc C s� t | t � r� t | � d k r� | \ } } t | t � sN t | t j � r� t | t � so t | t j � r� t j | j d | j d | j | j | | � } | j j | � S| j | � \ } } t | � } t | � } | r,| r,t j | j d | j d | j | j | | � } | j j | � S| sAt | t � r| ri| j | � } t | | d � } | r�| j | � } t | | d � } d } nL t | t � r�t | j | j d � � } d } n t j | � } | j } | d k st | � d k r| j | | � S| j | | � \ } } | j d k rQt | j d | j �St | j d | j �} t | | � \ } } t j | j d | j d | j | j | j | j | | � | S)z�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. r. r r Nr )r r r/ r0 r! �integerr Zlil_get1r r r r �type� _unpack_indexr �slicerV rS r �indicesZ atleast_1d�_get_row_ranges�_index_to_arrays�sizer � _prepare_index_for_memoryviewZ lil_fancy_get) r' �indexr+ rO �vZ i_intlikeZ j_intlikeZi_shaperQ r, r, r- � __getitem__� sR ! !! zlil_matrix.__getitem__c C s� | j | j d � \ } } } t | | | � } t | � } t t | � | f d | j �} t j | j d | j d | j | j | j | j | | | | | � | S)a� 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 r r r ) r[ r r r/ r r r Zlil_get_row_rangesr r ) r' r Z col_sliceZj_startZj_stopZj_strideZ col_rangeZnjrQ r, r, r- r\ . s ! zlil_matrix._get_row_rangesc C sI t | t � r� t | � d k r� | \ } } t | t � sN t | t j � r� t | t � so t | t j � r� | j j | � } | j d k r� t d � � t j | j d | j d | j | j | | | � d S| j | � \ } } t | � ryt | t � ry| t d � k ryt | t � ry| t d � k ry| j | j k ryt | d | j �} | j | _ | j | _ d S| j | | � \ } } t | � r�| j � } t j | d | j �} t j | | � \ } } | j | j k r�t d � � t | | | � \ } } } t j | j d | j d | j | j | | | � d S)Nr. r z&Trying to assign a sequence to an itemr r z shape mismatch in assignment)r r r/ r0 r! rW r rX r^ r r Z lil_insertr r r rY r rZ r r] �toarray�asarrayZbroadcast_arraysr_ Z lil_fancy_set)r' r` �xr+ rO �_r, r, r- � __setitem__K s: ! !! -! zlil_matrix.__setitem__c s� � d k r' t | j d | j �} nm t | j � � } | j � } | j | � } x= t | j � D], \ } } � f d d � | D� | j | <qd W| S)Nr r c s g | ] } | � � q Sr, r, )r> rM )r8 r, r- r@ � s z*lil_matrix._mul_scalar.<locals>.<listcomp>)r r r r r r rK r )r' r8 rQ Z res_dtyperO r? r, )r8 r- � _mul_scalar{ s $zlil_matrix._mul_scalarc sn t � � r\ | j � } x= t | j � D], \ } } � f d d � | D� | j | <q( W| S| j � � Sd S)Nc s g | ] } | � � q Sr, r, )r> rM )r8 r, r- r@ � s z*lil_matrix.__truediv__.<locals>.<listcomp>)r r rK r �tocsr)r' r8 rQ rO r? r, )r8 r- � __truediv__� s $zlil_matrix.__truediv__c C sP d d l m } t | j d | j �} | | j � | _ | | j � | _ | S)Nr )�deepcopyr )r rk r r r r r )r' rk rQ r, r, r- r � s zlil_matrix.copy�Cc C s? t | � t k s | d k r* t d � � t | � t k rH t d � � t | � d k rf t d � � t | d | j �} | j d } | j d | j d | j d | j d k r� t d � � xt t | j � D]c \ } } xT t | � D]F \ } } t j | | | | � \ } } | | | f | | | f <q� Wq� W| S) Nrl z4Sparse matrices do not support an 'order' parameter.z%a tuple must be passed in for 'shape'r. z.a length-2 tuple must be passed in for 'shape'r r r z^the product of the dimensions for the new sparse matrix must equal that of the original matrix) rX rL r r r% r/ r r r rK r r! Z unravel_index) r' r �orderrQ Zj_maxr+ rE �colrO Znew_rZnew_cr, r, r- r1 � s 0 "zlil_matrix.reshapec C sn | j | | � } xU t | j � D]D \ } } x5 t | � D]' \ } } | j | | | | | f <q; Wq" W| S)z)See the docstring for `spmatrix.toarray`.)Z_process_toarray_argsrK r r )r' rm rD �dr+ rE rN rO r, r, r- rc � s #zlil_matrix.toarrayc C s" | j � j d | d | � j � S)N�axesr )ri � transposer )r' rp r r, r, r- rq � s zlil_matrix.transposec C s | r | j � S| Sd S)N)r )r' r r, r, r- r � s zlil_matrix.tolilc C s- d d � | j D� } t d t | j d t | � � � } t j | d | �} t j t j d g d | �t j | d | �f � } g } x | j D] } | j | � q� Wt j | d | �} g } x | j D] } | j | � q� Wt j | d | j �} d d l m } | | | | f d | j �S) Nc S s g | ] } t | � � q Sr, )r/ )r> re r, r, r- r@ � s z$lil_matrix.tocsr.<locals>.<listcomp>Zmaxvalr r r )r r )r r �maxr rA r! rd Z concatenaterC Zcumsum�extendr r r& r ) r' r ZlstZ idx_dtypeZindptrr[ re r r r, r, r- ri � s %zlil_matrix.tocsr)!r4 � __module__� __qualname__�__doc__�formatr r5 �propertyr Z get_shaper r9 r: r< r= rF rG rP rR rT rS rV rb r\ rg rh rj r r1 rc rq r ri r, r, r, r- r sB >. @ 0 c C s� | j | j k r' | j | j � } n$ | j | j k rK | j | j � } | j j ss | j t j t j f k r� | j t j � } | j j s� | j t j t j f k r� | j t j � } | d k r� | j j s� | j � } | | | f S| | f Sd S)a 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) r r �flagsZ writeabler! Zint32Zint64rB r )r+ rO re r, r, r- r_ � s (( r_ c C s t | t � S)N)r r )re r, r, r- r s )rv Z __future__r r r Z __docformat__�__all__Znumpyr! Zscipy._lib.sixr �baser r Zsputilsr r r r r r r rI r r r_ r r, r, r, r- �<module> s 4� �)
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__init__.cpython-35.opt-1.pyc
6603 bytes
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__init__.cpython-35.pyc
6603 bytes
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base.cpython-35.opt-1.pyc
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base.cpython-35.pyc
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bsr.cpython-35.opt-1.pyc
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bsr.cpython-35.pyc
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compressed.cpython-35.opt-1.pyc
30815 bytes
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compressed.cpython-35.pyc
30815 bytes
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construct.cpython-35.opt-1.pyc
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construct.cpython-35.pyc
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coo.cpython-35.opt-1.pyc
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coo.cpython-35.pyc
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csc.cpython-35.opt-1.pyc
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csc.cpython-35.pyc
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csr.cpython-35.opt-1.pyc
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csr.cpython-35.pyc
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data.cpython-35.opt-1.pyc
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data.cpython-35.pyc
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dia.cpython-35.opt-1.pyc
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dia.cpython-35.pyc
10965 bytes
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dok.cpython-35.opt-1.pyc
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dok.cpython-35.pyc
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extract.cpython-35.opt-1.pyc
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extract.cpython-35.pyc
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generate_sparsetools.cpython-35.opt-1.pyc
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generate_sparsetools.cpython-35.pyc
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lil.cpython-35.opt-1.pyc
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lil.cpython-35.pyc
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setup.cpython-35.opt-1.pyc
1957 bytes
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setup.cpython-35.pyc
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sparsetools.cpython-35.opt-1.pyc
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sparsetools.cpython-35.pyc
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spfuncs.cpython-35.opt-1.pyc
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spfuncs.cpython-35.pyc
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sputils.cpython-35.opt-1.pyc
10713 bytes
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sputils.cpython-35.pyc
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N4ST4R_ID | Naxtarrr
