randsignal

Generate \(f = A^k x_{\eta} / r^k\),
with A the adjacency matrix and
\(x_{\eta}\) realization of Bernoulli random variables of parameter \(\eta\) and \(r\) the largest eigenvalue (in magnitude). The generation is carried out in sparse matrices in order to scale up.

Provides the standard operations for signal processing on graphs:
graph Fourier transform, spectral graph wavelet transform,
visualization tools. It also implements a data driven method
for graph signal denoising/regression, for details see
De Loynes, Navarro, Olivier (2019) <arxiv:1906.01882>.
The package also provides an interface to the SuiteSparse Matrix Collection,
<https://sparse.tamu.edu/>, a large and widely used set of sparse matrix
benchmarks collected from a wide range of applications.

Fabien Navarro

gasper

Graph Signal Processing

Basile de Loynes

Baptiste Olivier

randsignal function

<dl><dt>eta</dt>
<dd>Smoothness parameter.</dd>
<dt>k</dt>
<dd>Smoothness parameter.</dd>
<dt>A</dt>
<dd>Adjacency matrix.</dd>
<dt>r</dt>
<dd>Optional argument corresponding to the largest eigenvalue of A (in magnitude).</dd></dl>

Arguments

Generate random signal with varying regularity. — randsignal

<dl>

<dt>eta</dt>
<dd>Smoothness parameter.</dd>


<dt>k</dt>
<dd>Smoothness parameter.</dd>


<dt>A</dt>
<dd>Adjacency matrix.</dd>


<dt>r</dt>
<dd>Optional argument corresponding to the largest eigenvalue of A (in magnitude).</dd>

</dl>

randsignal: Generate random signal with varying regularity.

Description

Usage

Value

Arguments