`i3ddlZddlZddlmZddlmZddlmZddlmZddlm Z gdZ Gdd ej Z d Z d ZGd d eZdZdZedS)N)internal)_util)_base)_coo)_sputils)arcsinarcsinharctanarctanhceildeg2radexpm1floorlog1prad2degrintsignsinsinhsqrttantanhtruncceZdZdZedZddZdZdZdZ ddZ e j j j e _ d Ze j jj e_ d Zdd Zdd Zd S) _data_matrixc||_dSN)data)selfrs l/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/cupyx/scipy/sparse/_data.py__init__z_data_matrix.__init__s  c|jjS)zData type of the matrix.)rdtypers r r$z_data_matrix.dtypesyr"Tctr)NotImplementedError)rrcopys r _with_dataz_data_matrix._with_datas!!r"cP|t|jS)zElementwise absolute.)r)absrr%s r __abs__z_data_matrix.__abs__ss49~~...r"c8||j S)zElementwise negative.)r)rr%s r __neg__z_data_matrix.__neg__"s z***r"c\||j|S)zCasts the array to given data type. Args: dtype: Type specifier. Returns: A copy of the array with a given type. )r)rastype)rts r r0z_data_matrix.astype&s&ty//22333r"ctj|jtjr.||j|S|r|S|S)Nr()cupy issubdtyper$complexfloatingr)rconjr()rr(s r r7z_data_matrix.conj2sX ?4:t'; < < ??49>>#3#3$??? ?  99;; Kr"c^||jdS)NTr3)r)rr(r%s r r(z_data_matrix.copy<s$ty~~//d;;;r"c4tj|jS)aWReturns number of non-zero entries. .. note:: This method counts the actual number of non-zero entories, which does not include explicit zero entries. Instead ``nnz`` returns the number of entries including explicit zeros. Returns: Number of non-zero entries. )r4 count_nonzerorr%s r r:z_data_matrix.count_nonzeroAs!$),,,r"Nctj||j\}}|j}|||z}n |dvr|}n|}|||z |||S)aCompute the arithmetic mean along the specified axis. Args: axis (int or ``None``): Axis along which the sum is computed. If it is ``None``, it computes the average of all the elements. Select from ``{None, 0, 1, -2, -1}``. Returns: cupy.ndarray: Summed array. .. seealso:: :meth:`scipy.sparse.spmatrix.mean` N)r)r validateaxisshaperr(r)sum)raxisr$outnRownColrns r meanz_data_matrix.meanPs~ d###Z dy~~ <t AA W__AAAtax((,,T5#>>>r"c||j}n|j|d}||z}||S)zpElementwise power function. Args: n: Exponent. dtype: Type specifier. NTr3)rr(r0r))rrDr$rs r powerz_data_matrix.powerlsQ =9>>##DD9##E#55D  t$$$r")T)NNNr)__name__ __module__ __qualname__r!propertyr$r)r,r.r0r7rspmatrix__doc__r(r:rErGr"r rrsX""""///+++ 4 4 4>&.DL<<<>&.DL - - -????8 % % % % % %r"rctj|j}||k}tj||tj|j|kr|jndS)N)r4arangesizewhereanyargmaxasarray)indrD positionsdiffs r _find_missing_indexrZ|sd CH%%I ) D :    ASXX266 8 88r"ctj|j}||jkr|S|j|jdz|jz}t ||}tj||ktj |||S)N) npprodr>nnzrowcolrZr4rSminimum)matamzeromrRrWzero_inds r _non_zero_cmprhsx 739  D sw g ! $sw.&sD11z I L2 & &  r"cVeZdZdZdZdZdZdZd ddd Zd ddd Z d d Z d d Z dS) _minmax_mixinzlMixin for min and max methods. These are not implemented for dia_matrix, hence the separate class. c |j|}|dkrtd|jd|z }|dkr|n|}|||||}t j|}|dk} t j| |}t j| |}|dkrAtj |t j t||ff|j d|fStj ||t j t|ff|j |dfS)Nr&zero-size array to reduction operationr\)r$r>)r> ValueErrortocsctocsrsum_duplicates _minor_reducer4rQcompressr coo_matrixzeroslenr$) rr@ min_or_maxexplicitNMrcvalue major_indexmasks r _min_or_max_axisz_minmax_mixin._min_or_max_axissG Jt  66EFF F Jq4x "aiidjjlllTZZ\\ !!*dH==k!nn zmD+66  dE** 199?CJJ//=>jA000 0?djU&<&<=>jA000 0r"c@|tdtj||d|jvrtdt jd|j}|jdkr|S|||j }|r|S|jtj |jkrJ|tj urt j ||}n&|tjurt j||}nJ|S|dkr|dz }||||S)N2Sparse matrices do not support an 'out' parameter.rrlrN)r$F)rmrr=r>r4rtr$r_rprrr^minrbmaxmaximumr})rr@rArvrwrerfs r _min_or_maxz_minmax_mixin._min_or_maxs3 ?466 6 d### <DJ !IJJJ:b 333Dx1}}    ! ! ! 49%%A x8=4444)) T1--AA48++ T1--AA 5H !88 AID$$T:x@@@r"c.|j|dkrtd|dkr|n|}||||}|dkr |dddfS|dddfS)Nrz7Can't apply the operation along a zero-sized dimension.)r>rmrnrorp_arg_minor_reduce)rr@oprcrzs r _arg_min_or_max_axisz"_minmax_mixin._arg_min_or_max_axiss :d q *++ +#aiidjjlllTZZ\\ %%b$// 199qqq> !D> !r"c l|tdtj||d|jvrtd|jdkrdSt j|jd}| }| ||j }|j |}t j ||||j ||jdz|j|zt||||S|dkr|dz }|||S)Nrrz-Can't apply the operation to an empty matrix.r\r)rmrr=r>r_r4rVr$typetocoorprrSr`rarhr) rr@rArcomparerercrdrfs r _arg_min_or_maxz_minmax_mixin._arg_min_or_maxs< ?344 4 d### <DJ "4555x1}}q|DJOOA$6$677jjll""$$$R\\HRLzGAt$$cgbkCIaL&@372;&N!#r433555 !88 AID((r222r"NF)rwc|r3d|jj}tj||||t j|S)aReturns the maximum of the matrix or maximum along an axis. Args: axis (int): {-2, -1, 0, 1, ``None``} (optional) Axis along which the sum is computed. The default is to compute the maximum over all the matrix elements, returning a scalar (i.e. ``axis`` = ``None``). out (None): (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. explicit (bool): Return the maximum value explicitly specified and ignore all implicit zero entries. If the dimension has no explicit values, a zero is then returned to indicate that it is the only implicit value. This parameter is experimental and may change in the future. Returns: (cupy.ndarray or float): Maximum of ``a``. If ``axis`` is ``None``, the result is a scalar value. If ``axis`` is given, the result is an array of dimension ``a.ndim - 1``. This differs from numpy for computational efficiency. .. seealso:: min : The minimum value of a sparse matrix along a given axis. .. seealso:: numpy.matrix.max : NumPy's implementation of ``max`` for matrices z%explicit of cupyx.scipy.sparse.{}.max)format __class__rHr experimentalrr4rrr@rArwapi_names r rz_minmax_mixin.maxV<  )>EE'))H  x ( ( (c48X>>>r"c|r3d|jj}tj||||t j|S)aReturns the minimum of the matrix or maximum along an axis. Args: axis (int): {-2, -1, 0, 1, ``None``} (optional) Axis along which the sum is computed. The default is to compute the minimum over all the matrix elements, returning a scalar (i.e. ``axis`` = ``None``). out (None): (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. explicit (bool): Return the minimum value explicitly specified and ignore all implicit zero entries. If the dimension has no explicit values, a zero is then returned to indicate that it is the only implicit value. This parameter is experimental and may change in the future. Returns: (cupy.ndarray or float): Minimum of ``a``. If ``axis`` is None, the result is a scalar value. If ``axis`` is given, the result is an array of dimension ``a.ndim - 1``. This differs from numpy for computational efficiency. .. seealso:: max : The maximum value of a sparse matrix along a given axis. .. seealso:: numpy.matrix.min : NumPy's implementation of 'min' for matrices z%explicit of cupyx.scipy.sparse.{}.min)rrrHrrrr4rrs r rz_minmax_mixin.min&rr"cZ|||tjtjS)aReturns indices of maximum elements along an axis. Implicit zero elements are taken into account. If there are several maximum values, the index of the first occurrence is returned. If ``NaN`` values occur in the matrix, the output defaults to a zero entry for the row/column in which the NaN occurs. Args: axis (int): {-2, -1, 0, 1, ``None``} (optional) Axis along which the argmax is computed. If ``None`` (default), index of the maximum element in the flatten data is returned. out (None): (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: (cupy.narray or int): Indices of maximum elements. If array, its size along ``axis`` is 1. )rr4rUgreaterrr@rAs r rUz_minmax_mixin.argmaxJs",##D#t{DLIIIr"cZ|||tjtjS)a Returns indices of minimum elements along an axis. Implicit zero elements are taken into account. If there are several minimum values, the index of the first occurrence is returned. If ``NaN`` values occur in the matrix, the output defaults to a zero entry for the row/column in which the NaN occurs. Args: axis (int): {-2, -1, 0, 1, ``None``} (optional) Axis along which the argmin is computed. If ``None`` (default), index of the minimum element in the flatten data is returned. out (None): (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: (cupy.narray or int): Indices of minimum elements. If matrix, its size along ``axis`` is 1. )rr4argminlessrs r rz_minmax_mixin.argminbs".##D#t{DIFFFr")NN) rHrIrJrMr}rrrrrrUrrNr"r rjrjs 0004AAA@""" 333@"?5"?"?"?"?"?H"?5"?"?"?"?"?HJJJJ0GGGGGGr"rjc`fd}dz|_|_tt|dS)Ncxtt}||j}||Sr)getattrr4rr))rufuncresult func_names r fz_install_ufunc..f~s5i((ty!!v&&&r"zElementwise %s.)rMrHsetattrr)rrs` r _install_ufuncr|sI''''' "I-AIAJ L)Q'''''r"c8tD]}t|dSr)_ufuncsr)rs r _install_ufuncsrs,"" y!!!!""r")r4numpyr] cupy._corerrcupyx.scipy.sparserrrrrLrrZrhobjectrjrrrNr"r rsG $$$$$$######''''''   h%h%h%h%h%5>h%h%h%V888   gGgGgGgGgGFgGgGgGT ( ( (""" r"