\;jlddlZddlZddlmZmZddlmZmZddlm Z m Z m Z ddl m Z ddlmZgZe j"j$j&e j"j$j(e j"j$j*e j"j$j,e j"j$j.e j"j$j0gZe d dZe d dZe d d Ze d d Zd!d Ze d d Ze d d Z e d dZ!e d dZ"e d dZ#e d dZ$e d dZ%e d dZ&e d"dZ'e d dZ(e d dZ)e d dZ*e d dZ+e d dZ,e d dZ-e d dZ.d dZ/d dZ0d dZ1d#dZ2y)$N)_C_opsin_dynamic_mode) check_typecheck_variable_and_dtype)convert_np_dtype_to_dtype_core dygraph_only)Variable) LayerHelperc,tj|S)a Calculate elementwise sin of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = sin(x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.sin(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[-0.90929741, 0.84147102]) )r sparse_sinxnames \G:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\paddle/sparse/unary.pysinr$<   Q c,tj|S)a Calculate elementwise tan of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = tan(x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.tan(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[2.18503976, 1.55740774]) )r sparse_tanrs rtanrErrc,tj|S)a Calculate elementwise asin of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = asin(x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.asin(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[nan , 1.57079625]) )r sparse_asinrs rasinrf<   a  rc.tj||S)a% Changes the perm order of ``x`` without changing its data, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = transpose(x, perm) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. perm (list|tuple): Permute the input according to the data of perm. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A transposed Sparse Tensor with the same data type as ``x``. Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([[-2., 0.], [1., 2.]]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.transpose(sparse_x, [1, 0]) >>> out Tensor(shape=[2, 2], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 0]], values=[[-2., 0.], [ 1., 2.]]) )rsparse_transpose)rpermrs r transposers@  " "1d ++rc d}| d}t|}trtj||||S|g}n|g}|||d}|r|j |j |dt |dgddt|d ttttdtfdd}t|}|r|j| } n|j|j } |j|d|id | i| | S) a Computes the sum of sparse tensor elements over the given dimension, requiring x to be a SparseCooTensor or SparseCsrTensor. Args: x (Tensor): An N-D Tensor, the data type is bool, float16, float32, float64, int32 or int64. axis (int|list|tuple, optional): The dimensions along which the sum is performed. If :attr:`None`, sum all elements of :attr:`x` and return a Tensor with a single element, otherwise must be in the range :math:`[-rank(x), rank(x))`. If :math:`axis[i] < 0`, the dimension to reduce is :math:`rank + axis[i]`. dtype (str, optional): The dtype of output Tensor. The default value is None, the dtype of output is the same as input Tensor `x`. keepdim (bool, optional): Whether to reserve the reduced dimension in the output Tensor. The result Tensor will have one fewer dimension than the :attr:`x` unless :attr:`keepdim` is true, default value is False. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: Results of summation operation on the specified axis of input Tensor `x`. if `x.dtype='bool'` or `x.dtype='int32'`, it's data type is `'int64'`, otherwise it's data type is the same as `x`. Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([[-2., 0.], [1., 2.]]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out1 = paddle.sparse.sum(sparse_x) >>> out1 Tensor(shape=[1], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[0], values=1.) >>> out2 = paddle.sparse.sum(sparse_x, axis=0) >>> out2 Tensor(shape=[1, 2], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0]], values=[[-1., 2.]]) >>> out3 = paddle.sparse.sum(sparse_x, axis=-1) >>> out3 Tensor(shape=[2], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 1]], values=[-2., 3.]) >>> out4 = paddle.sparse.sum(sparse_x, axis=1, keepdim=True) >>> out4 Tensor(shape=[2, 1], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 1]], values=[[-2.], [ 3.]]) FNT)axisdtypekeepdim)in_dtype out_dtyperboolfloat32float64int16int32int64 sparse_sumr!r"outtypeinputsoutputsattrs)rrrr-updater"rrintlisttupler1r r )create_sparse_variable_for_type_inference append_op) rr!r"r#r dtype_flagr4op_typehelperr/s rsumr>sjJ  *51  D%99 <D6D'B  LLaggEB C      &3eT$ZBL W% BBBOCBBggCC #qE3>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.atan(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[-1.10714877, 0.78539819]) )r sparse_atanrs ratanrArrc,tj|S)a Calculate elementwise sinh of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = sinh(x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.sinh(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[-3.62686038, 1.17520118]) )r sparse_sinhrs rsinhrD1rrc,tj|S)a Calculate elementwise asinh of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = asinh(x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.asinh(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[-1.44363546, 0.88137358]) )r sparse_asinhrs rasinhrGR<   q !!rc,tj|S)a Calculate elementwise atanh of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = atanh(x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.atanh(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[nan , inf.]) )r sparse_atanhrs ratanhrKsrHrc,tj|S)a Calculate elementwise tanh of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = tanh(x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.tanh(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[-0.96402758, 0.76159418]) )r sparse_tanhrs rtanhrNrrc,tj|S)a Calculate elementwise square of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = square(x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.square(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[4., 1.]) )r sparse_squarers rsquarerQs<    ""rc,tj|S)a Calculate elementwise sqrt of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = sqrt(x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.sqrt(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[nan, 1. ]) )r sparse_sqrtrs rsqrtrTrrc,tj|S)a Calculate the natural log of (1+x), requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = ln(1+x) Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2, 0, 1], dtype='float32') >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.log1p(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[nan , 0.69314718]) )r sparse_log1prs rlog1prWrHrc|r/t|tjjs t |}|r/t|tjjs t |}t j |||S)a cast non-zero-index of SparseTensor to `index_dtype`, non-zero-element of SparseTensor to `value_dtype` , requiring x to be a SparseCooTensor or SparseCsrTensor. Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. index_dtype (np.dtype|str, optional): Data type of the index of SparseCooTensor, or crows/cols of SparseCsrTensor. Can be uint8, int8, int16, int32, int64. value_dtype (np.dtype|str, optional): Data type of the value of SparseCooTensor, SparseCsrTensor. Can be bool, float16, float32, float64, int8, int32, int64, uint8. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2, 0, 1]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.cast(sparse_x, 'int32', 'float64') >>> out Tensor(shape=[3], dtype=paddle.float64, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[-2., 1.]) ) isinstancerVarDescVarTyperr sparse_cast)r index_dtype value_dtypers rcastr_sY>:k4<<3G3GH0= :k4<<3G3GH0=   ak ::rc@tj|t|S)a Calculate elementwise pow of x, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = x^{factor} Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. factor (float|int): factor of pow. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2, 0, 3], dtype='float32') >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.pow(sparse_x, 2) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[4., 9.]) )r sparse_powfloat)rfactorrs rpowrd>s>   Qf ..rc2tj|dddS)a Calculate elementwise negative of x, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = -x Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2, 0, 3], dtype='float32') >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.neg(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[ 2., -3.]) gT)r sparse_scalers rnegrh`s<   q$T 22rc,tj|S)a Calculate elementwise absolute value of x, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = |x| Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2, 0, 3], dtype='float32') >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.abs(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[2., 3.]) )r sparse_absrs rabsrkrrc,tj|S)a the coalesced operator include sorted and merge, after coalesced, the indices of x is sorted and unique. Parameters: x (Tensor): the input SparseCooTensor. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: return the SparseCooTensor after coalesced. Examples: .. code-block:: python >>> import paddle >>> indices = [[0, 0, 1], [1, 1, 2]] >>> values = [1.0, 2.0, 3.0] >>> sp_x = paddle.sparse.sparse_coo_tensor(indices, values) >>> sp_x = paddle.sparse.coalesce(sp_x) >>> print(sp_x.indices()) Tensor(shape=[2, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [[0, 1], [1, 2]]) >>> print(sp_x.values()) Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [3., 3.]) )rsparse_coalescers rcoalescerns<  ! !! $$rc|jtvr9tj|dtj j j}tj|dtjz ddS)a Convert each of the elements of input x from radian to degree, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: rad2deg(x) = 180/ \pi * x Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64, int32, int64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([3.142, 0., -3.142]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.rad2deg(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[ 180.02334595, -180.02334595]) Nf@rfT r" _int_dtype_rr\rrZr[FP32rgnppirs rrad2degrvsS> ww+   q$ (<(<(A(A B   q%"%%-d ;;rc|jtvr9tj|dtj j j}tj|tjdz ddS)a Convert each of the elements of input x from degree to radian, requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: deg2rad(x) = \pi * x / 180 Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64, int32, int64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-180, 0, 180]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.deg2rad(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[-3.14159274, 3.14159274]) NrprfTrqrs rdeg2radrxsS> ww+   q$ (<(<(A(A B   q"%%%-d ;;rc,tj|S)a Calculate elementwise `exp(x)-1` , requiring x to be a SparseCooTensor or SparseCsrTensor. .. math:: out = exp(x) - 1 Parameters: x (Tensor): The input Sparse Tensor with data type float32, float64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same data type and shape as ``x`` . Examples: .. code-block:: python >>> import paddle >>> dense_x = paddle.to_tensor([-2., 0., 1.]) >>> sparse_x = dense_x.to_sparse_coo(1) >>> out = paddle.sparse.expm1(sparse_x) >>> out Tensor(shape=[3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 2]], values=[-0.86466473, 1.71828187]) )r sparse_expm1rs rexpm1r{ rHrc trtj||St|dgddt |dt t fdd|i}d|i}td}|j|j}|jd|d|i||S)a Changes the shape of ``x`` without changing its value, requiring x to be a SparseCooTensor or SparseCsrTensor. Currently this function can only reshape the sparse dims of ``x`` , but ``shape`` argument must be specified as the shape of the reshaped tensor. Note that if x is a SparseCsrTensor, then len(shape) must be 2 or 3. There are some tricks when specifying the target shape. - 1. -1 means the value of this dimension is inferred from the total element number of x and remaining dimensions. Thus one and only one dimension can be set -1. - 2. 0 means the actual dimension value is going to be copied from the corresponding dimension of x. The indices of 0 in the target shape can not exceed the rank of x. Here are some examples to explain it. - 1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape is [6, 8], the reshape operator will transform x into a 2-D tensor with shape [6, 8] and leaving x's data unchanged. - 2. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape is [2, 3, -1, 2], the reshape operator will transform x into a 4-D tensor with shape [2, 3, 4, 2] and leaving x's data unchanged. In this case, one dimension of the target shape is set to -1, the value of this dimension is inferred from the total element number of x and remaining dimensions. - 3. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape is [-1, 0, 3, 2], the reshape operator will transform x into a 4-D tensor with shape [2, 4, 3, 2] and leaving x's data unchanged. In this case, besides -1, 0 means the actual dimension value is going to be copied from the corresponding dimension of x. Args: x (Tensor): The input sparse tensor with data type ``float32``, ``float64``, ``int32``, ``int64`` or ``bool``. shape (list|tuple): Define the target shape. At most one dimension of the target shape can be -1. The data type is ``int32``. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A reshaped Tensor with the same data type as ``x``. Examples: .. code-block:: python >>> import paddle >>> x_shape = [6, 2, 3] >>> new_shape = [1, 0, 2, -1, 3] >>> format = "coo" >>> dense_x = paddle.randint(-100, 100, x_shape) * paddle.randint(0, 2, x_shape) >>> if format == "coo": ... sp_x = dense_x.to_sparse_coo(len(x_shape)) >>> else: ... sp_x = dense_x.to_sparse_csr() >>> sp_out = paddle.sparse.reshape(sp_x, new_shape) >>> print(sp_out.shape) [1, 2, 2, 3, 3] r)float16r(r)r*r+r,r'uint16reshapeshapesparse_reshaper/r0) rrrrrr7r8r r9r"r:)rrrr2r4r=r/s rrr,sj$$Q..     5'D%=)<q% -.>>qwwG!CL   rctrtj|Sd}t|}|j |j }|j |d|id|ii|S)a Return whether every element of input tensor is `NaN` or not, requiring x to be a SparseCooTensor or SparseCsrTensor. Args: x (Tensor): The input tensor (SparseCooTensor or SparseCsrTensor), it's data type should be float16, float32, float64, int32, int64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: A Sparse Tensor with the same shape as ``x``, the bool result which shows every element of `x` whether it is `NaN` or not. Examples: .. code-block:: python >>> import paddle >>> import numpy as np >>> format = "coo" >>> np_x = np.asarray([[[0., 0], [1., 2.]], [[0., 0], [3., float('nan')]]]) >>> dense_x = paddle.to_tensor(np_x) >>> if format == "coo": ... sparse_x = dense_x.to_sparse_coo(len(np_x.shape)) >>> else: ... sparse_x = dense_x.to_sparse_csr() ... >>> sparse_out = paddle.sparse.isnan(sparse_x) >>> print(sparse_out) Tensor(shape=[2, 2, 2], dtype=paddle.bool, place=Place(gpu:0), stop_gradient=True, indices=[[0, 0, 1, 1], [1, 1, 1, 1], [0, 1, 0, 1]], values=[False, False, False, True ]) sparse_isnanrr/r0)rrrr r9r"r:)rrr<r=r/s risnanrsjH""1%% W%>>qwwG#qE3>> import paddle >>> import numpy as np >>> format = 'coo' >>> np_x = np.asarray([[4, 0, 7, 0], [0, 0, 5, 0], [-4, 2, 0, 0]]) >>> dense_x = paddle.to_tensor(np_x) >>> if format == 'coo': ... sp_x = dense_x.to_sparse_coo(len(np_x.shape)) >>> else: ... sp_x = dense_x.to_sparse_csr() ... >>> axes = [0, 1] >>> starts = [1, 0] >>> ends = [3, -2] >>> sp_out = paddle.sparse.slice(sp_x, axes, starts, ends) >>> # sp_out is x[1:3, 0:-2] >>> print(sp_out) Tensor(shape=[2, 2], dtype=paddle.int64, place=Place(cpu), stop_gradient=True, indices=[[1, 1], [0, 1]], values=[-4, 2]) )axesstartsendsrr& sparse_slicerrrr.r/r0) rrrrrr7r8r r9r"r:) rrrrrr4r<r=r/s rslicersn""1dFD99>     4$?68dE]NC4$? W%>>QWW>M#qE3>> # doctest: +REQUIRES(env:GPU) >>> import paddle >>> paddle.device.set_device('gpu') >>> format = "coo" >>> paddle.seed(2023) >>> dense_x = paddle.randn((5, 5), dtype='float64') >>> if format == "coo": ... sparse_x = dense_x.to_sparse_coo(len(dense_x.shape)) >>> else: ... sparse_x = dense_x.to_sparse_csr() >>> print("sparse.pca_lowrank API only support CUDA 11.x") >>> # U, S, V = None, None, None >>> # use code blow when your device CUDA version >= 11.0 >>> U, S, V = paddle.sparse.pca_lowrank(sparse_x) >>> print(U) Tensor(shape=[5, 5], dtype=float64, place=Place(gpu:0), stop_gradient=True, [[-0.31412600, 0.44814876, 0.18390454, -0.19967630, -0.79170452], [-0.31412600, 0.44814876, 0.18390454, -0.58579808, 0.56877700], [-0.31412600, 0.44814876, 0.18390454, 0.78547437, 0.22292751], [-0.38082462, 0.10982129, -0.91810233, 0.00000000, 0.00000000], [ 0.74762770, 0.62082796, -0.23585052, 0.00000000, -0.00000000]]) >>> print(S) Tensor(shape=[5], dtype=float64, place=Place(gpu:0), stop_gradient=True, [1.56031096, 1.12956227, 0.27922715, 0.00000000, 0.00000000]) >>> print(V) Tensor(shape=[5, 5], dtype=float64, place=Place(gpu:0), stop_gradient=True, [[ 0.88568469, -0.29081908, 0.06163676, 0.19597228, -0.29796422], [-0.26169364, -0.27616183, 0.43148760, -0.42522796, -0.69874939], [ 0.28587685, 0.30695344, -0.47790836, -0.76982533, -0.05501437], [-0.23958121, -0.62770647, -0.71141770, 0.11463224, -0.17125926], [ 0.08918713, -0.59238761, 0.27478686, -0.41833534, 0.62498824]]) c|j}|tjtjtjfvr|StjSN)r"paddler}r(r))rr"s rget_floating_dtypez'pca_lowrank..get_floating_dtypeLs5 V^^V^^V^^D DL~~rcF|jr|jS|Sr) is_complexconj)rs r conjugatezpca_lowrank..conjugateRs <<>668Orc|j}ttdt|}|dd|dgz|dgz}|j r t j j||St j||S)Nr)rr7rangelen is_sparsersparser)rrrs rrzpca_lowrank..transposeWstE!SZ()CRyDH:%b 2 ;;===**1d3 34((rc |Sr)rrrs r transjugatez pca_lowrank..transjugate_s1&&rNc|dn|}|jdd\}}tjj}tj||f|j }|} |} ||tj j||d} t|D]T} |tj j| | d} |tj j|| d} V| S|} |tj j||tj||z d} t|D]} |tj j| | tj| | z d} |tj j|| tj|| z d} | S)Nrrr.r) rrlinalgqrrandnr"rmatmulr)rqniterMmnrRA_tA_HQiM_Hrrrs rget_approximate_basisz*pca_lowrank..get_approximate_basisbs]wwrs|1 ]]   LL!Qqww /ln 96==''1-.q1A5\v}}++C34Q7v}}++Aq1215" a.C6==''1- a0CCDQGA5\v}}++C3fmmC6KKLQOv}}++Aq1FMM!Q4GGHK"rc*|dn|}|jdd\}}|d}n|}|}||ks||kDr(||||}|} |!tjj|| } n7tjj|| tj|| z } | jd|k(sJ| j|f| jd|k(sJ| j|f| jd| jdksJ| jtjj | d\} } } | }|j|}n.||||}|} |!tjj|| }n7tjj|| tj|| z }|} | jd|k(sJ| j|f| jd|k(sJ| j|f| jd| jdksJ| jtjj | d\} } } | }|j| } | | |fS)NrrrrrF) full_matrices)rrrrrsvd)rrrrrrM_trrQ_cB_tUSVhVBrrrrs r svd_lowrankz pca_lowrank..svd_lowrankysVAwwrs|1 9CA,Cl q5AE%c1ESAAA,Cymm**1c2mm**1c2V]]1c5JJ99R=A% 5 1~ 5%99R=A% 5 1~ 5%99R=CIIbM1 <399 <1}}((E(BHAq"BA A%a%1=AA,CyMM((c2MM((c2V]]35LLA,C99R=A% 5 1~ 5%99R=A% 5 1~ 5%99R=CIIbM1 <399 <1}}((E(BHAq"BA A!QwrzInput must be tensor, but got z#Input must be sparse, but got denseFalse.r z-sparse.pca_lowrank API only support CUDA 11.xrzq(=z>) must be non-negative integer and not greater than min(m, n)=zniter(=z) must be non-negative integerrz,input is expected to be 2-dimensional tensor)r!)r"placer.)rN)rrN)r is_tensor ValueErrorr1rversioncudar6splitrminrrr>valuessparse_coo_tensorindicesr"rzerosonesrto_dense)rrcenterrrrr cuda_versionrrr"s_sums_valccolumn_indicesrC_t ones_m1_trrrrrs @@@@r pca_lowrankrs~J  )'.%%N   A 9$q'CDD ;;=>??>>&&(L 7 " |!!#&q) *R /HII WWRS\FQy 1aL1fc!Qi!//21ayk ;   QJ75')GHII q !E 1au55 177|qGHH MM  ab  )E LLNQ E '' ekk ( AYY[^NllAs>23>;O;OPGGAJ -- ) )aV5 * C AGGCRLAq61?I&--  :;A q!5A ..rr)NNFN)NNN)NTrN)3numpyrtrrrpaddle.base.data_feederrrpaddle.base.frameworkrrr paddle.common_ops_importr paddle.frameworkr __all__rZr[UINT8INT8INT16INT32INT64BOOLrrrrrrr>rArDrGrKrNrQrTrWr_rdrhrkrnrvrxr{rrrrrrrrsd *HPP-(  LLLLLLLLLLLL    @  @!!@,,DcL!!@!!@""@""@!!@##@!!@""@";";J//B33@  @%%@ <