Table Of Contents
Table Of Contents

norm

mxnet.ndarray.sparse.norm(data=None, ord=_Null, axis=_Null, keepdims=_Null, out=None, name=None, **kwargs)

Computes the norm on an NDArray.

This operator computes the norm on an NDArray with the specified axis, depending on the value of the ord parameter. By default, it computes the L2 norm on the entire array. Currently only ord=2 supports sparse ndarrays.

Examples:

x = [[[1, 2],
      [3, 4]],
     [[2, 2],
      [5, 6]]]

norm(x, ord=2, axis=1) = [[3.1622777 4.472136 ]
                          [5.3851647 6.3245554]]

norm(x, ord=1, axis=1) = [[4., 6.],
                          [7., 8.]]

rsp = x.cast_storage('row_sparse')

norm(rsp) = [5.47722578]

csr = x.cast_storage('csr')

norm(csr) = [5.47722578]

Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L345

Parameters:
  • data (NDArray) – The input
  • ord (int, optional, default='2') – Order of the norm. Currently ord=1 and ord=2 is supported.
  • axis (Shape or None, optional, default=None) –
    The axis or axes along which to perform the reduction.
    The default, axis=(), will compute over all elements into a scalar array with shape (1,). If axis is int, a reduction is performed on a particular axis. If axis is a 2-tuple, it specifies the axes that hold 2-D matrices, and the matrix norms of these matrices are computed.
  • keepdims (boolean, optional, default=0) – If this is set to True, the reduced axis is left in the result as dimension with size one.
  • out (NDArray, optional) – The output NDArray to hold the result.
Returns:

out – The output of this function.

Return type:

NDArray or list of NDArrays