Source code for mars.tensor.datasource.diagflat

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# 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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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) return diag(v.flatten(), k=k, sparse=sparse, gpu=gpu, chunk_size=chunk_size)