| Type: | Package |
| Title: | An Implementation of the Artificial Hydrocarbon Networks |
| Version: | 0.3.1 |
| Description: | Implementation of the Artificial Hydrocarbon Networks for data modeling. |
| Depends: | R (≥ 3.3.0) |
| License: | GPL-3 | file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| Suggests: | knitr, rmarkdown |
| URL: | https://github.com/jroberayalas/ahnr |
| BugReports: | https://github.com/jroberayalas/ahnr/issues |
| VignetteBuilder: | knitr |
| Imports: | matrixcalc, pracma, purrr, pdist, ggplot2, visNetwork, magrittr |
| RoxygenNote: | 6.0.1 |
| NeedsCompilation: | no |
| Packaged: | 2018-06-18 20:23:14 UTC; JRAS |
| Author: | Jose Roberto Ayala Solares [aut, cre] |
| Maintainer: | Jose Roberto Ayala Solares <ichbinjras@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2018-06-18 21:57:16 UTC |
fit
Description
Function to train an Artificial Hydrocarbon Network (AHN).
Usage
fit(Sigma, n, eta, maxIter = 2000)
Arguments
Sigma |
a list with two data frames. One for the inputs X, and one for the outputs Y. |
n |
number of particles to use. |
eta |
learning rate of the algorithm. Default is |
maxIter |
maximum number of iterations. |
Value
an object of class "ahn" with the following components:
network: structure of the AHN trained.
Yo: original output variable.
Ym: predicted output variable.
eta: learning rate.
minOverallError: minimum error achieved.
variableNames: names of the input variables.
Examples
# Create data
x <- 2 * runif(1000) - 1;
x <- sort(x)
y <- (x < 0.1) * (0.05 * runif(100) + atan(pi*x)) +
(x >= 0.1 & x < 0.6) * (0.05 * runif(1000) + sin(pi*x)) +
(x >= 0.6) * (0.05 * runif(1000) + cos(pi*x))
# Create Sigma list
Sigma <- list(X = data.frame(x = x), Y = data.frame(y = y))
# Train AHN
ahn <- fit(Sigma, 5, 0.01, 500)
Checks if argument is a ahn object
Description
Checks if argument is a ahn object
Usage
is.ahn(x)
Arguments
x |
An R object |
predict
Description
Function to simulate a trained Artificial Hydrocarbon Network.
Usage
## S3 method for class 'ahn'
predict(object, ...)
Arguments
object |
an object of class " |
... |
further arguments passed to or from other methods. |
Value
predicted output values for inputs in newdata.
Examples
## Not run:
# Create data
x <- 2 * runif(1000) - 1;
x <- sort(x)
y <- (x < 0.1) * (0.05 * runif(100) + atan(pi*x)) +
(x >= 0.1 & x < 0.6) * (0.05 * runif(1000) + sin(pi*x)) +
(x >= 0.6) * (0.05 * runif(1000) + cos(pi*x))
# Create Sigma list
Sigma <- list(X = data.frame(x = x), Y = data.frame(y = y))
# Train AHN
ahn <- fit(Sigma, 5, 0.01, 500)
# Test AHN
X <- data.frame(x = x)
ysim <- predict(ahn, X)
## End(Not run)
Summary Artificial Hydrocarbon Network
Description
Summary method for objects of class ahn.
Usage
## S3 method for class 'ahn'
summary(object, ...)
Arguments
object |
an object of class " |
... |
further arguments passed to or from other methods. |
Value
summary description of the AHN.
Examples
## Not run:
# Create data
x <- 2 * runif(1000) - 1;
x <- sort(x)
y <- (x < 0.1) * (0.05 * runif(100) + atan(pi*x)) +
(x >= 0.1 & x < 0.6) * (0.05 * runif(1000) + sin(pi*x)) +
(x >= 0.6) * (0.05 * runif(1000) + cos(pi*x))
# Create Sigma list
Sigma <- list(X = data.frame(x = x), Y = data.frame(y = y))
# Train AHN
ahn <- fit(Sigma, 5, 0.01, 500)
# Summary AHN
summary(ahn)
## End(Not run)
Visualize Artificial Hydrocarbon Network
Description
Visualize method for objects of class ahn.
Usage
visualize(x, ...)
Arguments
x |
an object of class " |
... |
further arguments passed to visNetwork functions. |
Value
dynamic visualization of the AHN.
Examples
## Not run:
# Create data
x <- 2 * runif(1000) - 1;
x <- sort(x)
y <- (x < 0.1) * (0.05 * runif(100) + atan(pi*x)) +
(x >= 0.1 & x < 0.6) * (0.05 * runif(1000) + sin(pi*x)) +
(x >= 0.6) * (0.05 * runif(1000) + cos(pi*x))
# Create Sigma list
Sigma <- list(X = data.frame(x = x), Y = data.frame(y = y))
# Train AHN
ahn <- fit(Sigma, 5, 0.01, 500)
# Visualize AHN
visualize(ahn)
## End(Not run)