Hey, I have a question for someone familiar with the fuzzy artmap algorithm implemented in python in the "brain" area.
It seems in the classification that they're using fuzzy "and" to calculate how close the input vectors are to the weight vectors that they build up in training. There are two places where this happens -- one where they're getting the "activation levels" and one where they are calculating the match value. The activation levels are only used to order the categories so that (presumably) the most likely categories get checked first. Then, the match is calculated to see if it exceeds the vigilance level to signal a match. The thing that's concerning is that fuzzy "and" isn't symmetric across these vectors. a ^ b != b ^ a. And, one of the calculations is input ^ weight and the other is weight ^ input. Does anyone know what value is derived by the different orientations of the equation? (I'm asking because of a low level optimization I could make if the comparisons were the same... I'm assuming they are not for some good reason, but I'm not intuiting what that reason might be...) Doug
_______________________________________________ Pyro-users mailing list [EMAIL PROTECTED] http://emergent.brynmawr.edu/mailman/listinfo/pyro-users
