#
# * The source code in this file is based on the soure code of CuPy.
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# Copyright (c) 2015 Preferred Infrastructure, Inc.
# Copyright (c) 2015 Preferred Networks, Inc.
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import nlcpy
import numpy
# ----------------------------------------------------------------------------
# building matrices
# see: https://docs.scipy.org/doc/numpy/reference/routines.array-creation.html
# ----------------------------------------------------------------------------
[ドキュメント]def diag(v, k=0):
"""Extracts a diagonal or constructs a diagonal array.
Parameters
----------
v : array_like
If *v* is a 2-D array, return a copy of its *k-th* diagonal. If *v* is a 1-D
array, return a 2-D array with *v* on the k-th diagonal.
k : int, optional
Diagonal in question. The default is 0. Use *k>0* for diagonals above the main
diagonal, and *k<0* for diagonals below the main diagonal.
Returns
-------
out : ndarray
The extracted diagonal or constructed diagonal array.
Examples
--------
>>> import nlcpy as vp
>>> x = vp.arange(9).reshape((3,3))
>>> x
array([[0, 1, 2],
[3, 4, 5],
[6, 7, 8]])
>>> vp.diag(x)
array([0, 4, 8])
>>> vp.diag(x, k=1)
array([1, 5])
>>> vp.diag(x, k=-1)
array([3, 7])
>>> vp.diag(vp.diag(x))
array([[0, 0, 0],
[0, 4, 0],
[0, 0, 8]])
"""
if isinstance(v, nlcpy.ndarray):
ndim = v.ndim
else:
ndim = numpy.ndim(v)
if ndim == 1:
v = nlcpy.array(v)
if ndim == 2:
# to save bandwidth, don't copy non-diag elements to GPU
v = numpy.array(v)
if ndim == 1:
size = v.size + abs(k)
ret = nlcpy.zeros((size, size), dtype=v.dtype)
ret.diagonal(k)[:] = v
return ret
elif ndim == 2:
return v.diagonal(k).copy()
else:
raise ValueError('Input must be 1- or 2-d.')