* Found out what was wrong with the update functions.  The problem was that they were functions and so wouldn't update the weights outside of their own (Lexical?) environment.  I could change that without a problem by setting the increment fuction in work-horse instead of in the function, but that would mean that I'd need to put in an (incf) for every operation in addition to calling the update-increment code itself.  I'm lazy, and don't feel like doing that, so I decided to just change the functions into macros.  Now the only (incf) coded is part of the macro itself.  One incf to rule them all.  Lol.  I kill myself. * Yesterday, work-horse was finding 699 points to be the floor of aggregate error in the training-set itself.  Now it takes its time, but reaches just above 691 points.  I'm not going to see how far down it can go because the error in the validation set has bottomed out at just above 846 points when training-set error was 703.  At that point, the test-set error was ~696 points, which means that mean error was still just less than 44 pips in the test-set.  This is with 5 hidden nodes.  I think I'm tapped out, as far as hidden nodes go.  Perhaps its time to look into another input vector.
* Found out what was wrong with the update functions.  The problem was that they were functions and so wouldn't update the weights outside of their own (Lexical?) environment.  I could change that without a problem by setting the increment fuction in work-horse instead of in the function, but that would mean that I'd need to put in an (incf) for every operation in addition to calling the update-increment code itself.  I'm lazy, and don't feel like doing that, so I decided to just change the functions into macros.  Now the only (incf) coded is part of the macro itself.  One incf to rule them all.  Lol.  I kill myself.
* Yesterday, work-horse was finding 699 points to be the floor of aggregate error in the training-set itself.  Now it takes its time, but reaches just above 691 points.  I'm not going to see how far down it can go because the error in the validation set has bottomed out at just above 846 points when training-set error was 703.  At that point, the test-set error was ~696 points, which means that mean error was still just less than 44 pips in the test-set.  This is with 5 hidden nodes.  I think I'm tapped out, as far as hidden nodes go.  Perhaps its time to look into another input vector.

ravster/pleasance,ravster/pleasance,ravster/pleasance,ravster/pleasance,ravster/pleasance