Table Of Contents
Table Of Contents

csr_matrix

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

Creates a CSRNDArray, an 2D array with compressed sparse row (CSR) format.

The CSRNDArray can be instantiated in several ways:

  • csr_matrix(D):
    to construct a CSRNDArray with a dense 2D array 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.
  • csr_matrix(S)
    to construct a CSRNDArray with a sparse 2D array S
    • S (CSRNDArray or scipy.sparse.csr.csr_matrix) - A sparse matrix.
    • 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.
  • csr_matrix((M, N))
    to construct an empty CSRNDArray with shape (M, N)
    • M (int) - Number of rows in the matrix
    • N (int) - Number of columns in the matrix
    • 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.
  • csr_matrix((data, indices, indptr))
    to construct a CSRNDArray based on the definition of compressed sparse row format using three separate arrays, where the column indices for row i are stored in indices[indptr[i]:indptr[i+1]] and their corresponding values are stored in data[indptr[i]:indptr[i+1]]. The column indices for a given row are expected to be sorted in ascending order. Duplicate column entries for the same row are not allowed.
    • data (array_like) - An object exposing the array interface, which holds all the non-zero entries of the matrix in row-major order.
    • indices (array_like) - An object exposing the array interface, which stores the column index for each non-zero element in data.
    • indptr (array_like) - An object exposing the array interface, which stores the offset into data of the first non-zero element number of each row of the matrix.
    • 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 data.dtype if data is an NDArray or numpy.ndarray, float32 otherwise.
  • csr_matrix((data, (row, col)))
    to construct a CSRNDArray based on the COOrdinate format using three seperate arrays, where row[i] is the row index of the element, col[i] is the column index of the element and data[i] is the data corresponding to the element. All the missing elements in the input are taken to be zeroes.
    • data (array_like) - An object exposing the array interface, which holds all the non-zero entries of the matrix in COO format.
    • row (array_like) - An object exposing the array interface, which stores the row index for each non zero element in data.
    • col (array_like) - An object exposing the array interface, which stores the col index for each non zero element in data.
    • shape (tuple of int, optional) - The shape of the array. The default shape is inferred from the row and col 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 (tuple of int, tuple of array_like, array_like, CSRNDArray, scipy.sparse.csr_matrix, scipy.sparse.coo_matrix, tuple of int or tuple of array_like) – The argument to help instantiate the csr matrix. See above for further details.
  • shape (tuple of int, optional) – The shape of the csr matrix.
  • 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:

A CSRNDArray with the csr storage representation.

Return type:

CSRNDArray

Example

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

See also

CSRNDArray()
MXNet NDArray in compressed sparse row format.