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

 Quantlet: rbftest Description: tests the given rbfnet network

Reference(s):
Komorad, K. (2002): On Credit Scoring Estimation, Humbold-University Berlin, unpublished.

 Usage: {netOut,AED,MSE} = rbftest(x,y,rbfnet) Input: x (n x p) matrix, predictor variables y (n x q) matrix, response variables rbfnet composed object (list), RBF network as computed by rbftrain or rbftrain2 Output: netOut (n x q) matrix, output of the rbfnet AED (n x q) matrix, absolute error difference, i.e. abs(netOut - y) MSE scalar, mean squared error

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)
; test the predicted values
test = rbftest(x,y,rbfnet)
test.MSE

```
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

Contents of MSE
[1,]  0.0030565
```