Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Library: nn
See also: rbftest rbftrain rbfsave rbfload rbfinfo rbftrain2

Quantlet: rbfpredict
Description: predicts the output of given RBF neural network

Reference(s):

Usage: netOut = rbfpredict(x,rbfnet,miny,maxy)
Input:
x (n x p) matrix, predictor variables
rbfnet composed object (list), RBF network as computed by rbftrain or rbftrain2
miny (q x 1) vector, minimum values for the output vector
maxy (q x 1) vector, maximum values for the output vector
Output:
netOut (n x q) matrix, output of the rbfnet

Example:
library("nn")		; load the library
; training set
randomize(2020)
n  = 10
xt = normal(n,2)+#(-1,-1)' | normal(n,2)+#(+1,+1)'
yt =(matrix(n)-1)|matrix(n)
; build the RBF network
clusters = 2
learn    = 0.1|0.2|0.1
epochs   = 5|5
mMSE     = 0.05
activ    = 0
rbfnet = rbftrain(xt,yt,clusters,learn,epochs,mMSE,activ)
; testing set
randomize(1140)
x = normal(n,2)+#(-1,-1)' | normal(n,2)+#(+1,+1)'
; predict the output
y = rbfpredict(x,rbfnet,0,1)
sum((y > 0.5) != yt)		; only one observation is misclassified

Result:
Contents of t
[1,] " An  2 - 2 - 1 RBF-network"
[2,] " training epochs: 5 - 5"
[3,] " cluster's learning rates: 0.2000 - 0.1000"
[4,] " output's learning rate: 0.1000"
[5,] " minimum mean squared error: 0.050"
[6,] " BINARY sigmoid activation function"
[7,] " minimum MSE reached: 0.190974 "

Contents of sum
[1,]        1



Author: K. Komorad, 20021212 license MD* Tech
(C) MD*TECH Method and Data Technologies, 05.02.2006