mars.tensor.array#

mars.tensor.array(x, dtype=None, copy=True, order='K', ndmin=None, chunk_size=None)[source]#

Create a tensor.

Parameters

  • object (array_like) – An array, any object exposing the array interface, an object whose __array__ method returns an array, or any (nested) sequence.

  • dtype (data-type, optional) – The desired data-type for the array. If not given, then the type will be determined as the minimum type required to hold the objects in the sequence. This argument can only be used to ‘upcast’ the array. For downcasting, use the .astype(t) method.

  • copy (bool, optional) – If true (default), then the object is copied. Otherwise, a copy will only be made if __array__ returns a copy, if obj is a nested sequence, or if a copy is needed to satisfy any of the other requirements (dtype, order, etc.).

  • order ({'K', 'A', 'C', 'F'}, optional) –

    Specify the memory layout of the array. If object is not an array, the newly created array will be in C order (row major) unless ‘F’ is specified, in which case it will be in Fortran order (column major). If object is an array the following holds.

    order

    no copy

    copy=True

    ’K’

    unchanged

    F & C order preserved, otherwise most similar order

    ’A’

    unchanged

    F order if input is F and not C, otherwise C order

    ’C’

    C order

    C order

    ’F’

    F order

    F order

    When copy=False and a copy is made for other reasons, the result is the same as if copy=True, with some exceptions for A, see the Notes section. The default order is ‘K’.

  • ndmin (int, optional) – Specifies the minimum number of dimensions that the resulting array should have. Ones will be prepended to the shape as needed to meet this requirement.

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

Returns

out – An tensor object satisfying the specified requirements.

Return type

Tensor

See also

empty, empty_like, zeros, zeros_like, ones, ones_like, full, full_like

Examples

>>> import mars.tensor as mt

>>> mt.array([1, 2, 3]).execute()
array([1, 2, 3])

Upcasting:

>>> mt.array([1, 2, 3.0]).execute()
array([ 1.,  2.,  3.])

More than one dimension:

>>> mt.array([[1, 2], [3, 4]]).execute()
array([[1, 2],
       [3, 4]])

Minimum dimensions 2:

>>> mt.array([1, 2, 3], ndmin=2).execute()
array([[1, 2, 3]])

Type provided:

>>> mt.array([1, 2, 3], dtype=complex).execute()
array([ 1.+0.j,  2.+0.j,  3.+0.j])