Table Of Contents
Table Of Contents

cast_storage

mxnet.ndarray.sparse.cast_storage(data=None, stype=_Null, out=None, name=None, **kwargs)

Casts tensor storage type to the new type.

When an NDArray with default storage type is cast to csr or row_sparse storage, the result is compact, which means:

  • for csr, zero values will not be retained
  • for row_sparse, row slices of all zeros will not be retained

The storage type of cast_storage output depends on stype parameter:

  • cast_storage(csr, ‘default’) = default
  • cast_storage(row_sparse, ‘default’) = default
  • cast_storage(default, ‘csr’) = csr
  • cast_storage(default, ‘row_sparse’) = row_sparse
  • cast_storage(csr, ‘csr’) = csr
  • cast_storage(row_sparse, ‘row_sparse’) = row_sparse

Example:

dense = [[ 0.,  1.,  0.],
         [ 2.,  0.,  3.],
         [ 0.,  0.,  0.],
         [ 0.,  0.,  0.]]

# cast to row_sparse storage type
rsp = cast_storage(dense, 'row_sparse')
rsp.indices = [0, 1]
rsp.values = [[ 0.,  1.,  0.],
              [ 2.,  0.,  3.]]

# cast to csr storage type
csr = cast_storage(dense, 'csr')
csr.indices = [1, 0, 2]
csr.values = [ 1.,  2.,  3.]
csr.indptr = [0, 1, 3, 3, 3]

Defined in src/operator/tensor/cast_storage.cc:L71

Parameters:
  • data (NDArray) – The input.
  • stype ({'csr', 'default', 'row_sparse'}, required) – Output storage type.
  • out (NDArray, optional) – The output NDArray to hold the result.
Returns:

out – The output of this function.

Return type:

NDArray or list of NDArrays