Trading on the Edge/Trading On The Edge - CD-ROM Toolkit (Wayzata Technology)(2031)(1994).bin/pc/mac_file/vendor_d/neuralwa/nw2v50/fzartmap.nnc

Text File | 1993-08-23 | 3KB | 86 lines

csv3.8        !file format is Control Strategy Version 3.8
! Jun 1992  (fzartmap.nnc)  Fuzzy ARTMAP Network control strategy
!************************************************************************
!*                                    *
!*    Fuzzy ARTMAP Control Strategy                    *
!*                                    *
!************************************************************************
! Note:  This implementation has been written with performance
! rather than neuralogical plausability in mind.  The algorithmic
! form of Fuzzy ARTMAP has been mapped to the NeuralWorks model
! in as efficient a way as possible.  Therefore, there is no
! explicit representation of the two layer resonance, gain control
! sub-system and orienting sub-system which can be seen in the
! NeuralWorks implementation of ART1.
!
!MASK     label    op-code    operands    comment
L_saR_sa    trace    aux3        !  0 set trace option to aux3
L_saRi_a    cset    recall,0    !  1 recall count
!
! Get input (learn/test/recall) and desired output (learn/test)
!
L_saRi_a    lset    in        !  2 input layer
L___Ri_a    io    read        !  3 get input data (recall)
L_saR___    io    lrnin        !  4 get input data (learn)
L_saRi_a    lset    out        !  5 output layer
L_saR___    io    lrnout        !  6 get desired output (learn)
L___Ri_a    io    rcltst        !  7 get desired output (test)
L_saRi_a    math    ce=e|fire    !  8 Save desired output
!
! Process Input Layer
!
L_saRi_a    lset    in        !  9 input layer
L___Ri_a     math    sum|rnoise|tran|output      ! 10
L_saR___    math    sum|lnoise|tran|output|fire ! 11
!
! Complement Coding (Pre-processing)
!
L_saRi_a        lset    in,1            ! 12 Complement layer
L_saRi_a        math    sum|tran|output|fire  ! 13 Obtain complements
!
! Category Layer
!
L_saRisa        lset    out,-1          ! 14 Category layer
L_saRi_a        math    sum|tran|fire    ! 15 Initialize outputs
L_saR_sa @ctout math    o=0|output|fire    ! 16 Current output
!
! Output Layer
!
L_saR_sa    lset    out             ! 17 Output layer
L_saR_sa    math    sum|e=ce|tran|e-=w|fire ! 18 Error before output
L_saR_sa    math    output|fire    ! 19 Generate output
!
! Check if it was an uncommitted node that won or if there are no
! nodes left
!
L_saR___        ccmp    wrk2,0          ! 20 node already committed?
L_saR___        beq     @nolrn          ! 21 yes
L_saR___        ccmp    wrk2,2          ! 22 out of nodes?
L_saR___        beq     @doout          ! 23 yes
L_saR___        math    learn|fire      ! 24 Learn output map
L_saR___    math    output|fire    ! 25 Regenerate output value
L_saR___        br      @lncat        ! 26
!
! Test out results of match
L_saR___ @nolrn    ccmp    wrk1,0          ! 27 wrk1=1 if error no good
L_saR___        beq     @lncat        ! 28
!
! Go back to category layer and get another winner
!
L_saR___        cadd    recall,1        ! 29 increment recall counter
L_saR___    lset    out        ! 30 output layer
L_saR___        io      wrstep        ! 31 learn step
L_saR___     lset    out,-1          ! 32 category layer
L_saR___        brct    @ctout          ! 33 try again
!
! Update category weights
!
L_saR___ @lncat lset    out,-1          ! 34 category layer
L_saR___    math    learn|fire    ! 35
L_saR_sa @doout    lset    out        ! 36 output layer
L_saR_sa    math    ce=e|fire    ! 37 final error
L_saR___    io    lrnrslt        ! 38 learn result
L___R_sa    io    write        ! 39 recall result
!
LisaRisa    trace   0               ! 40  turn off any trace function