/* --- Copyright University of Sussex 1989. All Rights Reserved -------- > File: \$popneural/demos/verthorz.p > Purpose: learn grid layout > Author: David Young, 1989 > Documentation: > Related Files: */ uses complearn; define genlines(n) -> stim; lvars n stim; lvars x y i; array_of_double([1 12 1 6 1 ^n],0.0) -> stim; for i from 1 to n do if random(2) == 1 then random(6) -> x; for y from 1 to 6 do 1.0 -> stim(y,1,i); 1.0 -> stim(y+6,x,i) endfor else random(6) + 6 -> y; for x from 1 to 6 do 1.0 -> stim(1,x,i); 1.0 -> stim(y,x,i) endfor endif endfor; newanyarray([1 72 1 ^n],stim) -> stim enddefine; define printresvh(m); lvars m; lvars x y stim stim1 winners outvec; array_of_double([1 ^(m.clnoutunits)]) -> outvec; npr('Horizontal lines'); for x from 1 to 6 do pr(x); pr(': '); array_of_double([1 12 1 6],0.0) -> stim; newanyarray([1 72],stim) -> stim1; for y from 7 to 12 do 1.0 -> stim(y,x) endfor; cl_response(stim1, m, outvec); cl_activunits(m) -> winners; pr('layer 2 units: '), pr(winners(1)(1)), pr(' '), pr(winners(1)(2)); pr('; layer 3 unit: '),npr(winners(2)(1)); endfor; npr('Vertical lines'); for y from 7 to 12 do pr(y-6); pr(': '); array_of_double([1 12 1 6],0.0) -> stim; newanyarray([1 72],stim) -> stim1; for x from 1 to 6 do 1.0 -> stim(y,x) endfor; cl_response(stim1, m, outvec); cl_activunits(m) -> winners; pr('layer 2 units: '), pr(winners(1)(1)), pr(' '), pr(winners(1)(2)); pr('; layer 3 unit: '),npr(winners(2)(1)); endfor enddefine; vars h1v2=0, h2v1=0, nowinner=0; define checkresvh(m); lvars m; lvars x y stim stim1 h1 h2 v1 v2 outvec; array_of_double([1 ^(m.clnoutunits)]) -> outvec; array_of_double([1 12 1 6],0.0) -> stim; newanyarray([1 72],stim) -> stim1; for x from 1 to 6 do for y from 7 to 12 do 1.0 -> stim(y,x) endfor; cl_response(stim1, m, outvec); for y from 7 to 12 do 0.0 -> stim(y,x) endfor; if cl_activunits(m)(2)(1) == 1 then h1 + 1 -> h1 else h2 + 1 -> h2 endif endfor; for y from 7 to 12 do for x from 1 to 6 do 1.0 -> stim(y,x) endfor; cl_response(stim1, m, outvec); for x from 1 to 6 do 0.0 -> stim(y,x) endfor; if cl_activunits(m)(2)(1) == 1 then v1 + 1 -> v1 else v2 + 1 -> v2 endif endfor; if h2 == 0 and v1 == 0 then h1v2 + 1 -> h1v2 elseif h1 == 0 and v2 == 0 then h2v1 + 1 -> h2v1 else nowinner + 1 -> nowinner endif; enddefine; define verthorzdemo; dlocal pop_readline_prompt; lvars switchedscreen; dlocal popmemlim = max(popmemlim,200000); lvars machine stim; nl(1); ;;; this should put ved into the output file if (vedediting and (vedscreenlength /== vedwindowlength)) ->> switchedscreen then vedsetwindow(); endif; npr('Competitive learning of vertical and horizontal lines.'); npr('See Rumelhart & McClelland, p. 184 et seq.'); nl(1); npr('The results show which unit in each cluster wins on each of'); npr(' the 12 possible stimuli (without the training cue).'); npr(' See the diagram on p. 189 for the layout of units.'); nl(1); npr('The machine should end up with one of the layer 3 units'); npr(' responding only to horizontal lines and the other only to'); npr(' vertical lines. It is inevitable that not all trials will'); npr(' succeed as the process relies on the layer 2 clusters\''); npr(' responding differently to each other. Procedure -vhstats-'); npr(' is available to give statistics for success, but takes some'); npr(' time to run'); nl(1); make_clnet(72,{{4 4} {2}},0.02,false,0.02,0.001) -> machine; npr('Initial state of the machine:'); printresvh(machine); genlines(2000) -> stim; 'Type to start' -> pop_readline_prompt; readline().erase; npr('Now doing 500 learning presentations (one line per presentation)'); cl_learn_set(stim, false, 500, true, machine, array_of_double([%1, machine.clnoutunits%],0.0s0)); npr('Results after 500 presentations'); printresvh(machine); npr('Now doing another 1500 presentations'); cl_learn_set(stim, false, 1500, true, machine, array_of_double([%1, machine.clnoutunits%],0.0s0)); npr('Results after 1000 presentations'); printresvh(machine); 'Type to finish' -> pop_readline_prompt; readline().erase; if switchedscreen then vedsetwindow() endif; enddefine; define vhstats; lvars machine stim; vars h1v2=0, h2v1=0, nowinner=0; dlocal popmemlim = max(popmemlim,200000); genlines(2000) -> stim; repeat 100 times make_clnet(72,{{4 4} {2}},0.02,false,0.02,0.001) -> machine; cl_learn_set(stim, false, 2000, true, machine, array_of_double([%1, machine.clnoutunits%],0.0s0)); checkresvh(machine); endrepeat; pr('Out of 100 runs of 200 presentations, perfect discrimination'); pr (' was achieved in '); npr(100-nowinner); enddefine; /* --- Revision History --------------------------------------------------- Julian Clinton, Aug 5 1993 Changed array_of_real to array_of_double so example can be used with C version of complearn. */