mars.tensor.ravel#

mars.tensor.ravel(a, order='C')[source]#

Return a contiguous flattened tensor.

A 1-D tensor, containing the elements of the input, is returned. A copy is made only if needed.

Parameters

a (array_like) – Input tensor. The elements in a are packed as a 1-D tensor.
order ({'C','F', 'A', 'K'}, optional) – The elements of a are read using this index order. ‘C’ means to index the elements in row-major, C-style order, with the last axis index changing fastest, back to the first axis index changing slowest. ‘F’ means to index the elements in column-major, Fortran-style order, with the first index changing fastest, and the last index changing slowest. Note that the ‘C’ and ‘F’ options take no account of the memory layout of the underlying array, and only refer to the order of axis indexing. ‘A’ means to read the elements in Fortran-like index order if a is Fortran contiguous in memory, C-like order otherwise. ‘K’ means to read the elements in the order they occur in memory, except for reversing the data when strides are negative. By default, ‘C’ index order is used.

Returns

y – If a is a matrix, y is a 1-D tensor, otherwise y is a tensor of the same subtype as a. The shape of the returned array is (a.size,). Matrices are special cased for backward compatibility.

Return type

array_like

See also

Tensor.flat: 1-D iterator over an array.
Tensor.flatten: 1-D array copy of the elements of an array in row-major order.
Tensor.reshape: Change the shape of an array without changing its data.

Examples

It is equivalent to reshape(-1).

>>> import mars.tensor as mt

>>> x = mt.array([[1, 2, 3], [4, 5, 6]])
>>> print(mt.ravel(x).execute())
[1 2 3 4 5 6]

>>> print(x.reshape(-1).execute())
[1 2 3 4 5 6]

>>> print(mt.ravel(x.T).execute())
[1 4 2 5 3 6]

>>> a = mt.arange(12).reshape(2,3,2).swapaxes(1,2); a.execute()
array([[[ 0,  2,  4],
        [ 1,  3,  5]],
       [[ 6,  8, 10],
        [ 7,  9, 11]]])
>>> a.ravel().execute()
array([ 0,  2,  4,  1,  3,  5,  6,  8, 10,  7,  9, 11])