Predict New Examples by a Trained Neural Net

Usage

predict.nnet(object, x, type=c("raw","class"))

Arguments

object an object of class nnet as returned by nnet.
x matrix or data frame of test examples. A vector is considered to be a row vector comprising a single case.

Description

Predict new examples by a trained neural net.

Details

This function is a method for the generic function predict() for class nnet. It can be invoked by calling predict(x) for an object x of the appropriate class, or directly by calling predict.nnet(x) regardless of the class of the object.

Value

If type="raw", the matrix of values returned by the trained network; if type="class", the corresponding class (which is probably only useful if the net was generated by nnet.formula).

See Also

nnet, which.is.max

Examples

data(iris3)
# use half the iris data
ir <- rbind(iris3[,,1],iris3[,,2],iris3[,,3])
targets <- class.ind( c(rep("s", 50), rep("c", 50), rep("v", 50)) )
samp <- c(sample(1:50,25), sample(51:100,25), sample(101:150,25))
ir1 <- nnet(ir[samp,], targets[samp,],size=2, rang=0.1,
            decay=5e-4, maxit=200)
test.cl <- function(true, pred){
        true <- max.col(true)
        cres <- max.col(pred)
        table(true, cres)
}
test.cl(targets[-samp,], predict(ir1, ir[-samp,]))

# or
ird <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]),
	species=c(rep("s",50), rep("c", 50), rep("v", 50)))
ir.nn2 <- nnet(species ~ ., data=ird, subset=samp, size=2, rang=0.1, 
            decay=5e-4, maxit=200)
table(ird$species[-samp], predict(ir.nn2, ird[-samp,], type="class"))

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