Table Of Contents
Table Of Contents

row_sparse_array

mxnet.ndarray.sparse.row_sparse_array(arg1, shape=None, ctx=None, dtype=None)[source]

Creates a RowSparseNDArray, a multidimensional row sparse array with a set of tensor slices at given indices.

The RowSparseNDArray can be instantiated in several ways:

  • row_sparse_array(D):
    to construct a RowSparseNDArray with a dense ndarray D
    • D (array_like) - An object exposing the array interface, an object whose __array__ method returns an array, or any (nested) sequence.
    • ctx (Context, optional) - Device context (default is the current default context).
    • dtype (str or numpy.dtype, optional) - The data type of the output array. The default dtype is D.dtype if D is an NDArray or numpy.ndarray, float32 otherwise.
  • row_sparse_array(S)
    to construct a RowSparseNDArray with a sparse ndarray S
    • S (RowSparseNDArray) - A sparse ndarray.
    • ctx (Context, optional) - Device context (default is the current default context).
    • dtype (str or numpy.dtype, optional) - The data type of the output array. The default dtype is S.dtype.
  • row_sparse_array((D0, D1 .. Dn))
    to construct an empty RowSparseNDArray with shape (D0, D1, ... Dn)
    • D0, D1 .. Dn (int) - The shape of the ndarray
    • ctx (Context, optional) - Device context (default is the current default context).
    • dtype (str or numpy.dtype, optional) - The data type of the output array. The default dtype is float32.
  • row_sparse_array((data, indices))

    to construct a RowSparseNDArray based on the definition of row sparse format using two separate arrays, where the indices stores the indices of the row slices with non-zeros, while the values are stored in data. The corresponding NDArray dense represented by RowSparseNDArray rsp has dense[rsp.indices[i], :, :, :, ...] = rsp.data[i, :, :, :, ...] The row indices for are expected to be sorted in ascending order. - data (array_like) - An object exposing the array interface, which holds all the non-zero row slices of the array.

    • indices (array_like) - An object exposing the array interface, which stores the row index for each row slice with non-zero elements.
    • shape (tuple of int, optional) - The shape of the array. The default shape is inferred from the indices and indptr arrays.
    • ctx (Context, optional) - Device context (default is the current default context).
    • dtype (str or numpy.dtype, optional) - The data type of the output array. The default dtype is float32.
Parameters:
  • arg1 (NDArray, numpy.ndarray, RowSparseNDArray, tuple of int or tuple of array_like) – The argument to help instantiate the row sparse ndarray. See above for further details.
  • shape (tuple of int, optional) – The shape of the row sparse ndarray.
  • ctx (Context, optional) – Device context (default is the current default context).
  • dtype (str or numpy.dtype, optional) – The data type of the output array.
Returns:

An RowSparseNDArray with the row_sparse storage representation.

Return type:

RowSparseNDArray

Example

>>> a = mx.nd.sparse.row_sparse_array(([[1, 2], [3, 4]], [1, 4]), shape=(6, 2))
>>> a.asnumpy()
array([[ 0.,  0.],
       [ 1.,  2.],
       [ 0.,  0.],
       [ 0.,  0.],
       [ 3.,  4.],
       [ 0.,  0.]], dtype=float32)

See also

RowSparseNDArray()
MXNet NDArray in row sparse format.