anchor.diff:               :status "learning"
                :elapsed 0
                :iteration 0
  -             :mse 1.0))
  +             :mse (min-mse net)))
     (when randomize-weights
       (if (listp randomize-weights)
           (apply #'randomize-weights (cons net randomize-weights))
  
positive.code:                                    (string-downcase (id net))))
  
          (training-segments net) (make-training-segments tset max-segment-size)
          (max-mse net) -1000000.0
          (min-mse net) 1000000.0
          (last-anneal-iteration net) 0
          (anneal net) nil
          (randomize net) nil
  
negative.code:             (truncate (* (/ report-frequency 1000.0)
                           internal-time-units-per-second))
            for i from 1 to max-iterations
            for mse = (min-mse net) 
            then (present-vectors
                  net
                  outputs-first
                  output-labels
                  i
                  start-time
                  report-function
                  time-span-between-reports)
            while (and (> mse target-mse) (not (stop-training net)))
            when (or
                  (and (> i 1)
  