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# derived from copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
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from ..core import Tensor
from .array import tensor as astensor
from .diag import diag
[docs]def diagflat(v, k=0, sparse=None, gpu=None, chunk_size=None):
"""
Create a two-dimensional tensor with the flattened input as a diagonal.
Parameters
----------
v : array_like
Input data, which is flattened and set as the `k`-th
diagonal of the output.
k : int, optional
Diagonal to set; 0, the default, corresponds to the "main" diagonal,
a positive (negative) `k` giving the number of the diagonal above
(below) the main.
sparse: bool, optional
Create sparse tensor if True, False as default
gpu : bool, optional
Allocate the tensor on GPU if True, False as default
chunk_size : int or tuple of int or tuple of ints, optional
Desired chunk size on each dimension
Returns
-------
out : Tensor
The 2-D output tensor.
See Also
--------
diag : MATLAB work-alike for 1-D and 2-D tensors.
diagonal : Return specified diagonals.
trace : Sum along diagonals.
Examples
--------
>>> import mars.tensor as mt
>>> mt.diagflat([[1,2], [3,4]]).execute()
array([[1, 0, 0, 0],
[0, 2, 0, 0],
[0, 0, 3, 0],
[0, 0, 0, 4]])
>>> mt.diagflat([1,2], 1).execute()
array([[0, 1, 0],
[0, 0, 2],
[0, 0, 0]])
"""
if not isinstance(v, Tensor):
v = astensor(v).op.data
return diag(v.flatten(), k=k, sparse=sparse, gpu=gpu, chunk_size=chunk_size)