anchor.diff:            for i upfrom 0 do
             (log-msg "Starting to train level ~S RBM in DBN.~%" i)
             (train (make-sampler *training-stories*
  -                               :max-n (* (if (zerop i) 20 20)
  +                               :max-n (* (if (zerop i) 200 200)
                                            (length *training-stories*)))
                    (make-instance
                     'mr-rbm-trainer
  -                  :n-gibbs 1
  +                  :n-gibbs 5
  +                  :n-particles 100
  +                  :visible-sampling t
                     :segmenter
                     (lambda (cloud)
                       (multiple-value-bind (decay learning-rate)
  
positive.code: 
  (defmethod set-input (samples (bpn mr-bpn))
    (let* ((inputs (find-lump (chunk-lump-name 'inputs nil) bpn :errorp t))
           (label (find-lump (chunk-lump-name 'input-label nil) bpn :errorp nil))
           (expectations (find-lump 'expectations bpn :errorp t))
           (inputs* (storage (nodes inputs)))
           (expectations* (storage (nodes expectations))))
  
negative.code:                          ((-1) (setf (aref nodes (+ start 0)) (flt 1)))
                           ((1) (setf (aref nodes (+ start 1)) (flt 1))))))))))))
  
  (defclass mr-rbm-trainer (mr-base-trainer bm-pcd-trainer) ())
  
  #+nil
  (defmethod initialize-trainer ((trainer mr-rbm-trainer) rbm)
    (call-next-method)
    (when (typep trainer 'bm-pcd-trainer)
      (describe (persistent-chains trainer))
      (let ((inputs (find 'inputs (visible-chunks rbm) :key #'name)))
        (when inputs
  