torch_matrix_rank

Provides functionality to define and train neural networks similar to
'PyTorch' by Paszke et al (2019) <doi:10.48550/arXiv.1912.01703> but written entirely in R
using the 'libtorch' library. Also supports low-level tensor operations and
'GPU' acceleration.

Daniel Falbel

torch

Tensors and Neural Networks with 'GPU' Acceleration

Javier Luraschi

Dmitriy Selivanov

Athos Damiani

Christophe Regouby

Krzysztof Joachimiak

Hamada S. Badr

Sebastian Fischer

Maximilian Pichler

 RStudio

torch_matrix_rank function

<dl><dt>self</dt>
<dd>(Tensor) the input 2-D tensor</dd>
<dt>tol</dt>
<dd>(float, optional) the tolerance value. Default: <code>NULL</code></dd>
<dt>symmetric</dt>
<dd>(bool, optional) indicates whether <code>input</code> is symmetric. Default: <code>FALSE</code></dd></dl>

Arguments

Returns the numerical rank of a 2-D tensor. The method to compute the
matrix rank is done using SVD by default. If <code>symmetric</code> is <code>TRUE</code>,
then <code>input</code> is assumed to be symmetric, and the computation of the
rank is done by obtaining the eigenvalues.
<code>tol</code> is the threshold below which the singular values (or the eigenvalues
when <code>symmetric</code> is <code>TRUE</code>) are considered to be 0. If <code>tol</code> is not
specified, <code>tol</code> is set to <code>S.max() * max(S.size()) * eps</code> where <code>S</code> is the
singular values (or the eigenvalues when <code>symmetric</code> is <code>TRUE</code>), and <code>eps</code>
is the epsilon value for the datatype of <code>input</code>.

matrix_rank(input, tol=NULL, symmetric=False) -&gt; Tensor 

Matrix_rank — torch_matrix_rank

<dl>

<dt>self</dt>
<dd>(Tensor) the input 2-D tensor</dd>


<dt>tol</dt>
<dd>(float, optional) the tolerance value. Default: <code>NULL</code></dd>


<dt>symmetric</dt>
<dd>(bool, optional) indicates whether <code>input</code> is symmetric. Default: <code>FALSE</code></dd>

</dl>

Returns the numerical rank of a 2-D tensor. The method to compute the
matrix rank is done using SVD by default. If <code>symmetric</code> is <code>TRUE</code>,
then <code>input</code> is assumed to be symmetric, and the computation of the
rank is done by obtaining the eigenvalues.
<code>tol</code> is the threshold below which the singular values (or the eigenvalues
when <code>symmetric</code> is <code>TRUE</code>) are considered to be 0. If <code>tol</code> is not
specified, <code>tol</code> is set to <code>S.max() * max(S.size()) * eps</code> where <code>S</code> is the
singular values (or the eigenvalues when <code>symmetric</code> is <code>TRUE</code>), and <code>eps</code>
is the epsilon value for the datatype of <code>input</code>.

torch_matrix_rank: Matrix_rank

Description

Arguments

matrix_rank(input, tol=NULL, symmetric=False) -> Tensor

Description

Arguments

matrix_rank(input, tol=NULL, symmetric=False) -&gt; Tensor

matrix_rank(input, tol=NULL, symmetric=False) -> Tensor