Dec 072017
Programmer’s Journal Volume 7 Number 3 Part 1. | |||
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File Name | File Size | Zip Size | Zip Type |
BTRIEVE.C | 9895 | 3027 | deflated |
CUT.C | 7063 | 2498 | deflated |
CUT.EXE | 35000 | 10256 | deflated |
DELIM.C | 6413 | 2067 | deflated |
DELIM.EXE | 17774 | 9847 | deflated |
DISPLAY.CLS | 2942 | 899 | deflated |
DISPLAY.CPP | 4765 | 1660 | deflated |
DOTS.CPP | 1100 | 526 | deflated |
DOTS.EXE | 8558 | 4705 | deflated |
ESQL.C | 927 | 316 | deflated |
ESQL.COB | 3967 | 996 | deflated |
ESQL.PAS | 934 | 307 | deflated |
EVGALINE.ASM | 13388 | 3062 | deflated |
EVGALINE.C | 6662 | 1874 | deflated |
FASTDEMO.EXE | 11256 | 6040 | deflated |
GETOPT.C | 1479 | 668 | deflated |
GETOPT3.1 | 2904 | 1311 | deflated |
GETOPT3.C | 2542 | 1155 | deflated |
GETOPTD.OBJ | 1411 | 926 | deflated |
KEYBOARD.CLS | 1937 | 713 | deflated |
KEYBOARD.CPP | 505 | 314 | deflated |
LINEDEMO.C | 2590 | 791 | deflated |
MOUSE.CLS | 964 | 501 | deflated |
MOUSE.CPP | 2218 | 808 | deflated |
NET7 | 0 | 0 | stored |
FOO.BAS | 1474 | 654 | deflated |
FOO.TXT | 773 | 303 | deflated |
NET7.BAS | 7639 | 3502 | deflated |
NET7.TXT | 5227 | 1631 | deflated |
NETM.BAS | 8833 | 3823 | deflated |
NETM.TXT | 6340 | 1826 | deflated |
NETME.BAS | 8843 | 3842 | deflated |
NETME.TXT | 6376 | 1842 | deflated |
NETME1.BAS | 9067 | 3986 | deflated |
NETME1.TXT | 6616 | 1930 | deflated |
NETME2.BAS | 8912 | 3888 | deflated |
NETME2.TXT | 6414 | 1859 | deflated |
NEURAL.BAS | 2304 | 754 | deflated |
PJINDEX.66 | 8992 | 2748 | deflated |
PMDEV | 0 | 0 | stored |
PMAUX | 0 | 0 | stored |
PMAUX | 1054 | 517 | deflated |
PMAUX.C | 2226 | 1041 | deflated |
PMAUX.DEF | 360 | 255 | deflated |
PMAUX.EXE | 8544 | 4599 | deflated |
PMAUX.H | 1625 | 780 | deflated |
PMAUX.MAP | 11823 | 2044 | deflated |
PMAUX.OBJ | 1304 | 915 | deflated |
PMAUX.RC | 979 | 566 | deflated |
PMAUX.RES | 601 | 348 | deflated |
PMAUXFN.C | 3652 | 1696 | deflated |
PMAUXFN.OBJ | 1620 | 1152 | deflated |
PMAUXNT.C | 4557 | 1712 | deflated |
PMAUXNT.OBJ | 1753 | 1196 | deflated |
TTYCLS.OBJ | 2364 | 1583 | deflated |
POINT.CLS | 816 | 404 | deflated |
PWCOMMON | 0 | 0 | stored |
TTYCLS | 0 | 0 | stored |
ASCII.H | 731 | 248 | deflated |
TTYCLS.C | 9392 | 2877 | deflated |
TTYCLS.H | 921 | 435 | deflated |
README7.3 | 9359 | 2969 | deflated |
RECT.CLS | 909 | 430 | deflated |
SLOWDEMO.EXE | 12411 | 6532 | deflated |
WINDEV | 0 | 0 | stored |
WINAUX | 0 | 0 | stored |
TTYCLS.OBJ | 2218 | 1499 | deflated |
WINAUX | 1101 | 490 | deflated |
WINAUX.C | 2429 | 991 | deflated |
WINAUX.DEF | 360 | 249 | deflated |
WINAUX.EXE | 9552 | 5236 | deflated |
WINAUX.H | 1507 | 707 | deflated |
WINAUX.MAP | 13240 | 2292 | deflated |
WINAUX.OBJ | 1136 | 824 | deflated |
WINAUX.RC | 1301 | 626 | deflated |
WINAUX.RES | 527 | 358 | deflated |
WINAUX.SYM | 1668 | 1138 | deflated |
WINAUXFN.C | 3661 | 1556 | deflated |
WINAUXFN.OBJ | 1646 | 1175 | deflated |
WINAUXNT.C | 7501 | 2473 | deflated |
WINAUXNT.OBJ | 2821 | 1647 | deflated |
WINVUE | 0 | 0 | stored |
CSET.TXT | 544 | 355 | deflated |
LONGSTR.H | 75 | 47 | deflated |
TTYCLS.OBJ | 2218 | 1497 | deflated |
WINVUE | 1182 | 507 | deflated |
WINVUE.C | 3451 | 1328 | deflated |
WINVUE.DEF | 467 | 298 | deflated |
WINVUE.EXE | 10624 | 5820 | deflated |
WINVUE.H | 2681 | 1189 | deflated |
WINVUE.MAP | 15389 | 2555 | deflated |
WINVUE.OBJ | 1578 | 1070 | deflated |
WINVUE.RC | 2045 | 935 | deflated |
WINVUE.RES | 747 | 501 | deflated |
WINVUE.SYM | 1732 | 1161 | deflated |
WINVUEFN.C | 8087 | 2802 | deflated |
WINVUEFN.OBJ | 2879 | 1835 | deflated |
WINVUEMS.C | 9141 | 3090 | deflated |
WINVUEMS.OBJ | 2162 | 1423 | deflated |
WINVUENT.C | 6146 | 2286 | deflated |
WINVUENT.OBJ | 1931 | 1306 | deflated |
Download File PJ73.ZIP Here
Contents of the FOO.TXT file
100 REM prog name is net7
110 REM
120 inum% = 2: REM this is the number of inputs
130 jnum% = 12: REM this is the number of input layer neurons
140 knum% = 6: REM this is the number of center layer neurons
150 lnum% = 1: REM this is the number of output layer neurons
160 tsnum% = 121: REM this is the number of training sets
170 epsilon = 1: REM this is the convergence factor
180 alpha = .5: REM this is the momentum factor
190 REM
200 DIM SHARED I(tsnum%, inum%), T(tsnum%, lnum%), W1(jnum%, inum%)
210 DIM SHARED M1(jnum%, inum%), B1(jnum%), M4(jnum%), O1(jnum%)
220 DIM SHARED M2(knum%, jnum%), B2(knum%), M5(knum%), O2(knum%)
230 DIM SHARED M3(lnum%, knum%), B3(lnum%), M6(lnum%), O3(lnum%)
240 DIM SHARED W2(knum%, jnum%), W3(lnum%, knum%), N%(640)
110 REM
120 inum% = 2: REM this is the number of inputs
130 jnum% = 12: REM this is the number of input layer neurons
140 knum% = 6: REM this is the number of center layer neurons
150 lnum% = 1: REM this is the number of output layer neurons
160 tsnum% = 121: REM this is the number of training sets
170 epsilon = 1: REM this is the convergence factor
180 alpha = .5: REM this is the momentum factor
190 REM
200 DIM SHARED I(tsnum%, inum%), T(tsnum%, lnum%), W1(jnum%, inum%)
210 DIM SHARED M1(jnum%, inum%), B1(jnum%), M4(jnum%), O1(jnum%)
220 DIM SHARED M2(knum%, jnum%), B2(knum%), M5(knum%), O2(knum%)
230 DIM SHARED M3(lnum%, knum%), B3(lnum%), M6(lnum%), O3(lnum%)
240 DIM SHARED W2(knum%, jnum%), W3(lnum%, knum%), N%(640)
December 7, 2017
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