mars.tensor.asarray#

mars.tensor.asarray(x, dtype=None, order=None, chunk_size=None)[source]#

Convert the input to an array.

Parameters

  • a (array_like) – Input data, in any form that can be converted to a tensor. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists and tensors.

  • dtype (data-type, optional) – By default, the data-type is inferred from the input data.

  • order ({'C', 'F'}, optional) – Whether to use row-major (C-style) or column-major (Fortran-style) memory representation.

  • chunk_size (int, tuple, optional) – Specifies chunk size for each dimension.

Returns

out – Tensor interpretation of a. No copy is performed if the input is already an ndarray with matching dtype and order. If a is a subclass of ndarray, a base class ndarray is returned.

Return type

Tensor

See also

ascontiguousarray

Convert input to a contiguous tensor.

asfortranarray

Convert input to a tensor with column-major memory order.

Examples

Convert a list into a tensor:

>>> import mars.tensor as mt

>>> a = [1, 2]
>>> mt.asarray(a).execute()
array([1, 2])

Existing arrays are not copied:

>>> a = mt.array([1, 2])
>>> mt.asarray(a) is a
True

If dtype is set, array is copied only if dtype does not match:

>>> a = mt.array([1, 2], dtype=mt.float32)
>>> mt.asarray(a, dtype=mt.float32) is a
True
>>> mt.asarray(a, dtype=mt.float64) is a
False