| Title: | General regression neural network |
| Description: | The program GRNN implements the algorithm proposed by Specht (1991). |
| URL: | http://flow.chasset.net/r-grnn/ |
| Version: | 0.1.0 |
| Author: | Pierre-Olivier Chasset |
| Maintainer: | Pierre-Olivier Chasset <pierre-olivier@chasset.net> |
| License: | AGPL |
| Collate: | 'create.R' 'grnn-package.r' 'guess.r' 'kernel.R' 'learn.R' 'smooth.R' |
| Packaged: | 2013-05-16 14:16:40 UTC; petrus |
| NeedsCompilation: | no |
| Repository: | CRAN |
| Date/Publication: | 2013-05-16 17:39:51 |
GRNN
Description
General regression neural network.
Details
The program GRNN implements the algorithm proposed by Specht (1991).
Author(s)
Pierre-Olivier Chasset
References
Specht D.F. (1991). A general regression neural network. IEEE Transactions on Neural Networks, 2(6):568-576.
Guess
Description
Infers the value of a new observation.
Usage
guess(nn, X)
Arguments
nn |
A trained and smoothed General regression neural network. |
X |
A vector describing a new observation. |
See Also
Examples
n <- 100
set.seed(1)
x <- runif(n, -2, 2)
y0 <- x^3
epsilon <- rnorm(n, 0, .1)
y <- y0 + epsilon
grnn <- learn(data.frame(y,x))
grnn <- smooth(grnn, sigma=0.1)
guess(grnn, -2)
guess(grnn, -1)
guess(grnn, -0.2)
guess(grnn, -0.1)
guess(grnn, 0)
guess(grnn, 0.1)
guess(grnn, 0.2)
guess(grnn, 1)
guess(grnn, 2)
Learn
Description
Create or update a General regression neural network.
Usage
learn(set, nn, variable.column = 1)
Arguments
set |
Data frame representing the training set. The
first column is used to define the category of each
observation (set |
nn |
A General regression neural network with or without training. |
variable.column |
The field number of the variable (1 by default). |
See Also
Smooth
Description
Smooth a General regression neural network.
Usage
smooth(nn, sigma)
Arguments
nn |
A trained General regression neural network. |
sigma |
A scalar. |