Library: | nn |
See also: | rbftest rbfpredict rbftrain rbfload rbfinfo rbftrain2 |
Quantlet: | rbfsave | |
Description: | saves given radial basis function network into the given file |
Usage: | rbfsave(rbfnet,name) | |
Input: | ||
rbfnet | composed object (list), RBF network as computed by rbftrain or rbftrain2 | |
name | string, name of the file which will hold the network |
All components of the RBF network are composed into a matrix, which is explained in the reference book.
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) rbfsave(rbfnet,"rbf_net")
Saves the RBF network into two files "rbf_net.rbf" and "rbf_net.err" 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 "