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* This program runs the aspect-angle dependent data from Gorman and 
* Sejnowski's article: "Analysis of Hidden Units in a Layered Network 
* Trained to Classify Sonar Targets", in Neural Networks, volume 1, 
* number 1, 1988.  The data has been modified so that class 0 is now 
* class 1 and what was class 1 is now class 2.  In addition, G&J gave 
* training data where all the class 1 patterns came first and all the 
* class 2 patterns came second.  With the online backprop method this 
* approach gives rather poor results unless the patterns are selected in 
* a random order (not implemented in this version) or you intermix 
* patterns from the two classes.  The file sonarmix.tra contains 
* patterns that were mixed and the file sonar.tra contains the original 
* unmixed patterns.  You might want to try it both ways. 
* 
* The periodic update setting given below is close to the one Gorman 
* and Sejnowski report using.  Note that when using the periodic update 
* settings the number of patterns you get correct will be much higher 
* than it really is because the tests are made as the program does each 
* pattern.  To get the real performance measure you must do an 
* independent test of the whole pattern set after each complete pass 
* thru the data.  To do this use: "f u+". 
* 
* The quickprop setting gives its best results by around 300 iterations 
* however the performance on the test set is worse than the periodic 
* update method.  Perhaps quickprop doesn't generalize as well as the 
* conventional algorithm? 
* 
m 60 6 2 
a dd uq 
qp e 0.01 
e 2 
a 0 
t 0.49 
f ir pc 
*rt sonar.tra 
rt sonarmix.tra 
tf sonar.tes 
s 0 
ci 0.3