SnnsRObject$predictCurrPatSet

SnnsR__predictCurrPatSet

predictCurrPatSet,SnnsR-method

is passed to <a rd-options="" href="/link/SnnsRObject%24whereAreResults?package=RSNNS&version=0.4-14" data-mini-rdoc="RSNNS::SnnsRObject$whereAreResults">SnnsRObject$whereAreResults</a>

outputMethod

parameters passed to the networks update function

updateFuncParams

SnnsR low-level function to predict values with a trained net.

The Stuttgart Neural Network Simulator (SNNS) is a library
containing many standard implementations of neural networks. This
package wraps the SNNS functionality to make it available from
within R. Using the 'RSNNS' low-level interface, all of the
algorithmic functionality and flexibility of SNNS can be accessed.
Furthermore, the package contains a convenient high-level
interface, so that the most common neural network topologies and
learning algorithms integrate seamlessly into R.

Christoph Bergmeir

RSNNS

Neural Networks using the Stuttgart Neural Network Simulator
(SNNS)

José M. Benítez

Andreas Zell

Niels Mache

Günter Mamier

Michael Vogt

Sven Döring

Ralf Hübner

Kai-Uwe Herrmann

Tobias Soyez

Michael Schmalzl

Tilman Sommer

Artemis Hatzigeorgiou

Dietmar Posselt

Tobias Schreiner

Bernward Kett

Martin Reczko

Martin Riedmiller

Mark Seemann

Marcus Ritt

Jamie DeCoster

Jochen Biedermann

Joachim Danz

Christian Wehrfritz

Patrick Kursawe

Andre El-Ama

SnnsRObject$predictCurrPatSet function

<dl><dt>outputMethod</dt>
<dd>is passed to SnnsRObject$whereAreResults</dd>
<dt>updateFuncParams</dt>
<dd>parameters passed to the networks update function</dd></dl>

Arguments

Predict values with a trained net — SnnsRObject$predictCurrPatSet

<dl>

<dt>outputMethod</dt>
<dd>is passed to SnnsRObject$whereAreResults</dd>


<dt>updateFuncParams</dt>
<dd>parameters passed to the networks update function</dd>

</dl>

Predict values with a trained net

SnnsRObject$predictCurrPatSet: Predict values with a trained net

Description

Usage

Value

Arguments

Details