Package 'ahnr'

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 0.01.
maxIter	maximum number of iterations.

Value

an object of class "ahn" with the following components:

• network: structure of the AHN trained. It contains the hydrogen coefficient H, position of the molecules Pi, data cluster ID per molecule moleculeNumber, number of molecules n, and carbon structure C

• 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)

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Checks if argument is a ahn object

Description

Checks if argument is a ahn object

Usage

is.ahn(x)

Arguments

x	An R object

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predict

Description

Function to simulate a trained Artificial Hydrocarbon Network.

Usage

## S3 method for class 'ahn'
predict(object, ...)

Arguments

object	an object of class "ahn" produced from the fit function.
...	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)

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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 "ahn" produced from the fit function.
...	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)

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Visualize Artificial Hydrocarbon Network

Description

Visualize method for objects of class ahn.

Usage

visualize(x, ...)

Arguments

x	an object of class "ahn" produced from the fit function.
...	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)

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