| grnn.predone {yager} | R Documentation |
Calculate a predicted value of GRNN
Description
The function grnn.predone calculates a predicted value of GRNN based on an input vector
The function grnn.predone calculates a predicted value of GRNN based on an input vector
Usage
grnn.predone(net, x, type = 1)
grnn.predone(net, x, type = 1)
Arguments
net |
The GRNN object generated by grnn.fit() |
x |
The vector of input predictors |
type |
A scalar, 1 for euclidean distance and 2 for manhattan distance |
Value
A scalar of the predicted value
A scalar of the predicted value
References
Donald Specht. (1991). A General Regression Neural Network.
Donald Specht. (1991). A General Regression Neural Network.
See Also
Examples
data(iris, package = "datasets")
Y <- ifelse(iris[, 5] == "setosa", 1, 0)
X <- scale(iris[, 1:4])
gnet <- grnn.fit(x = X, y = Y)
for (i in seq(5)) print(grnn.predone(gnet, X[i, ]))
data(iris, package = "datasets")
Y <- ifelse(iris[, 5] == "setosa", 1, 0)
X <- scale(iris[, 1:4])
gnet <- grnn.fit(x = X, y = Y)
for (i in seq(5)) print(grnn.predone(gnet, X[i, ]))
[Package yager version 0.1.1 Index]