o װi3@sddlZddlZddlmZddlmZddlmZddlmZddlm Z gdZ Gdd d ej Z d d Z d d ZGdddeZddZddZedS)N)internal)_util)_base)_coo)_sputils)arcsinarcsinharctanarctanhceildeg2radexpm1floorlog1prad2degrintsignsinsinhsqrttantanhtruncc@seZdZddZeddZdddZdd Zd d Zd d Z dddZ e j j j e _ ddZe j jj e_ ddZdddZdddZdS) _data_matrixcCs ||_dSN)data)selfrrL/home/ubuntu/.local/lib/python3.10/site-packages/cupyx/scipy/sparse/_data.py__init__s z_data_matrix.__init__cCs|jjS)zData type of the matrix.)rdtyperrrrr sz_data_matrix.dtypeTcCstr)NotImplementedError)rrcopyrrr _with_datasz_data_matrix._with_datacCs|t|jS)zElementwise absolute.)r$absrr!rrr__abs__sz_data_matrix.__abs__cCs||j S)zElementwise negative.)r$rr!rrr__neg__"sz_data_matrix.__neg__cCs||j|S)zCasts the array to given data type. Args: dtype: Type specifier. Returns: A copy of the array with a given type. )r$rastype)rtrrrr(&s z_data_matrix.astypecCs4t|jtjr|j|j|dS|r|S|S)Nr#)cupy issubdtyper complexfloatingr$rconjr#)rr#rrrr.2s z_data_matrix.conjcCs|j|jddS)NTr*)r$rr#r!rrrr#<sz_data_matrix.copycCs t|jS)aWReturns number of non-zero entries. .. note:: This method counts the actual number of non-zero entories, which does not include explicit zero entries. Instead ``nnz`` returns the number of entries including explicit zeros. Returns: Number of non-zero entries. )r+ count_nonzerorr!rrrr/As z_data_matrix.count_nonzeroNcCsZt||j\}}|j}|dur||}n |dvr|}n|}||||||S)aCompute the arithmetic mean along the specified axis. Args: axis (int or ``None``): Axis along which the sum is computed. If it is ``None``, it computes the average of all the elements. Select from ``{None, 0, 1, -2, -1}``. Returns: cupy.ndarray: Summed array. .. seealso:: :meth:`scipy.sparse.spmatrix.mean` N)r)r validateaxisshaperr#r$sum)raxisr outnRownColrnrrrmeanPs    z_data_matrix.meancCs6|dur |j}n|jj|dd}||C}||S)zpElementwise power function. Args: n: Exponent. dtype: Type specifier. NTr*)rr#r(r$)rr8r rrrrpowerls   z_data_matrix.power)T)NNNr)__name__ __module__ __qualname__rpropertyr r$r&r'r(r.rspmatrix__doc__r#r/r9r:rrrrrs     rcCsDt|j}||k}t||t|j|kr|jSdS)N)r+arangesizewhereanyargmaxasarray)indr8 positionsdiffrrr_find_missing_index|s rKcCsTt|j}||jkr |S|j|jd|j}t||}t||kt |||S)N) npprodr2nnzrowcolrKr+rDminimum)matamzeromrCrHzero_indrrr _non_zero_cmps    rXc@sdeZdZdZddZddZddZdd Zdd d d dZdd d ddZ dddZ dddZ d S) _minmax_mixinzlMixin for min and max methods. These are not implemented for dia_matrix, hence the separate class. c Cs|j|}|dkr td|jd|}|dkr|n|}|||||}t|}|dk} t| |}t| |}|dkrXt j |t t ||ff|j d|fdSt j ||t t |ff|j |dfdS)Nr&zero-size array to reduction operationrL)r r2)r2 ValueErrortocsctocsrsum_duplicates _minor_reducer+rBcompressr coo_matrixzeroslenr ) rr4 min_or_maxexplicitNMrSvalue major_indexmaskrrr_min_or_max_axiss(      z_minmax_mixin._min_or_max_axiscCs|durtdt||dur]d|jvrtdtjd|jd}|jdkr)|S|||j }|r6|S|jt |jkr[|tj urLt ||}|S|tjurYt||}|SJ|S|dkre|d7}||||S)N2Sparse matrices do not support an 'out' parameter.rrZr)r F)r[rr1r2r+rbr rOr^rrrNminrRmaxmaximumrk)rr4r5rdrerUrVrrr _min_or_maxs2       z_minmax_mixin._min_or_maxcCsj|j|dkr td|dkr|n|}||||}|dkr-|dddfS|dddfS)Nrz7Can't apply the operation along a zero-sized dimension.)r2r[r\r]r^_arg_minor_reduce)rr4oprSrhrrr_arg_min_or_max_axiss z"_minmax_mixin._arg_min_or_max_axisc Cs|durtdt||durXd|jvrtd|jdkr!dSt|jd}| }| ||j }|j |}t ||||j ||jd|j|t||||S|dkr`|d7}|||S)Nrlrz-Can't apply the operation to an empty matrix.rLrm)r[rr1r2rOr+rGr typetocoor^rrDrPrQrXrt) rr4r5rscomparerUrSrTrVrrr_arg_min_or_maxs(     $  z_minmax_mixin._arg_min_or_maxNF)recC.|rd|jj}t||||tj|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__r;r experimentalrqr+rorr4r5reapi_namerrrro  z_minmax_mixin.maxcCry)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)rzr{r;rr|rqr+rnr}rrrrn&rz_minmax_mixin.mincC|||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. )rxr+rFgreaterrr4r5rrrrFJsz_minmax_mixin.argmaxcCr)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. )rxr+argminlessrrrrrbsz_minmax_mixin.argmin)NN) r;r<r=r@rkrqrtrxrornrFrrrrrrYs  $ $rYcs,fdd}d|_|_tt|dS)Ncstt}||j}||Sr)getattrr+rr$)rufuncresult func_namerrf~s   z_install_ufunc..fzElementwise %s.)r@r;setattrr)rrrrr_install_ufunc|s  rcCstD]}t|qdSr)_ufuncsrrrrr_install_ufuncss r)r+numpyrM cupy._corerrcupyx.scipy.sparserrrrr?rrKrXobjectrYrrrrrrs      k  k