ProfitPress Mega CDROM2 Shareware Freeware (MSDOS)(1992)(Eng)

home *** CD-ROM | disk | FTP | other *** search

/ ProfitPress Mega CDROM2 …eeware (MSDOS)(1992)(Eng) / ProfitPress-MegaCDROM2.B6I / PROG / MISC / DATAREP.ZIP / MANUAL.LNI < prev next >

Wrap

Text File | 1990-02-22 | 47.7 KB | 1,062 lines

¢7 I ¢11h ¢?20 J ¢3300t ¢1;3300r ¢1;65536s ¢0d ¢12m How-To-Use-It-Manual Stimulus Generation and Association Programs Brown University Neural Modeling Group Release 2.0 BSB and ASSOCIATE March 29, 1989 James A. Anderson Department of Cognitive and Linguistic Sciences and Department of Psychology, Brown University Providence, RI 02912 (c) Copyright James A. Anderson, 1989 Page 2 Introduction. This manual will tell you how to use a set of programs developed at the Department of Cognitive and Linguistic Sciences, Brown University. The programs are research programs designed to generate, maintain, learn, and use sets of stimuli and matrices used in neural modelling research. The programs are written in VMS Pascal to run on a VAX. There are two programs of major interest. One is called ASSOCIATE and generates the matrices that associate pairs of vectors. (Sometimes a version of this program that involved a file transfer through an MS/DOS system will name it ASSOC. Same program.) Actually, the internal representation of a matrix is in the form of a FILE of a Pascal data type called Neuron, but acts mathematically like a matrix. The other is a program with several parts. It is called BSB. One part generates stimuli which are large state vectors. These state vectors represent strings of 25 characters in a 200 dimensional system. Once generated, the state vectors can be associated together using ASSOCIATE. Another part realizes the dynamics of the simple non-linear model called the `Brain-state-in-a-Box'. (See Anderson, Silverstein, Ritz and Jones, 1977, Anderson and Mozer, 1981; Anderson, 1986; and Anderson and Murphy, 1986) Files Several files must be ASSIGNed so the programs will know where to read and write the data. Below is a set of ASSIGN statements in the appropriate form. The example chosen is the file assignments from the `OHMS' demonstration, though the OHMS.COM file actually contains more material than this, since it also runs the demonstration. These VMS commands should be put in a command file (in this case, OHMS.COM) which makes the appropriate assignments for convenience. $assign fohms.stm ffile $assign gohms.stm gfile $assign tohms.stm tfile $assign nohms.neu nfile Then execute the command file by typing @OHMS to make the assignments. There are enough files to make life complicated unless some systematic way is found to label the files. By convention, the input and output vectors are given the extension `.STM' and the file containing the matrix are given the extension `.NEU'. As a general point, the files constructed with these programs can be quite large. In the 200 dimensional system the files associated with the matrix are 633 blocks long. Making the Stimuli using BSB. The first step in making stimuli is to RUN BSB. The program will ask you if you are using a VT100 or not. (I am assuming you are in this manual.) Then some initial information appears. The general pattern of displays in BSB is to have status information on the top 5 lines of the screen and the command prompts on the bottom line. Since the author of these programs was influenced by UCSD Pascal at an impressionable age, extensive use is made of self documenting prompts. For example, 'T)hreshold' means that if 'T' (or 't') is typed at the command prompt ('>') the threshold interpretation parameter can be changed from its default. In general, typing a single letter, followed by <Return> will cause an action. All commands can be upper or lower case and most are protected to some extent (not totally!) against Page 3 erroneous inputs. Not all commands appear on the prompt lines, for reasons of space, particularly for setting some of the less frequently used parameters in the simulation. At first, there are no files present for BSB to work with. Let us assume you want to create a series of stimuli for future use. The prompt line tells you how many stimuli are present in the F File, the G File, and the T File. When the program first appears, these values are all zero. F File and G File have the customary meanings they have in our neural modelling literature: i.e. the F File is the input set of state vectors and the G File is the output set of state vectors. The T file contains a set of 'Test' vectors, usually partial input stimuli, which are used to test the reconstructive and processing powers of the system. Although this simple form of our programs only performs autoassociation, more complex versions can have different input and output sets. Input vectors are constructed from strings of characters. We will assume the system is 200 dimensional. A character in the string is represented by its ASCII number, i.e. 'a' is 97, '5' is 53, '=' is 61, and so on. These numbers are converted into binary representation, so 'a' becomes '01100001'. Then the zeros are replaced with minus ones, so we can maintain rough equality of numbers of positive and negative elements which is convenient for the models, so the actual internal representation of 'a' ---> -1,1,1,-1,-1,-1,-1,1. Each character requires 8 vector elements, so a 200 dimensional system contains enough elements for 25 characters. The parity bit is computed but not used in the simulations at present. One reason for its presence, is that eight bits allow the construction of `orthogonal' character representations, which can sometimes be a convenience. If anyone wanted to actually use these programs for anything substatial it is strongly recommended that a less arbitrary way of coding letters be used. The state vectors contain single precision floating point numbers. Currently no more than 100 stimuli are allowed in any one file but this can be easily changed in the programs if necessary. The technically inclined might like to know that stimuli are a Pascal Record composed of two pieces: CONST Dimensionality = 200; TYPE Vector = ARRAY [1..Dimensionality] OF REAL; String = PACKED ARRAY [1..60] OF CHAR; Stimulus = RECORD Name: String; Val : Vector; END; Page 4 Figure 1. Initial appearance of the screen. --------------------- BSB Neural Net Programs. 3-89 B)SB. C)hange. E)xit. H)elp. L)ist. M)ode. N)eurons. R)ead. S)ave. T)hreshold. W)rite. Threshold: 5.0E-01 F File: 0 G File: 0 T File: 0 NO NEURON FILE. Display TF. Initializing Files. Reading neuron and stimulus files from disk. Reading F file. Reading G file. Reading T file. Reading neuron file (autoassociative). C> --------------------- The program will automatically look for the assigned stimulus and neuron files, and write its progress to the screen. If it doesn't find a file, it will write 'Stimulus file not found' to the command line (i.e. the 'C>' line). You may have to watch carefully to see this line appear, and then disappear when the next file is read. The information at the top of the screen will also inform you of progress. In the example, there are no stimuli in the F file, G file, and T file and this is noted in the third line on the screen. There is also a comment that there is 'NO NEURON FILE' on the fourth line. To contruct a stimulus, type 'C' from the first command level. This allows you to 'C)hange' the stimuli in the Files, which currently contain no stimulus vectors. There are a number of options in 'C)hange' to allow you to do various useful things to the stimuli. Suppose you want to make a new stimulus, say the first stimulus in the F File. Type 'R', for 'Replace.' The program will then prompt you as to which stimulus to replace. If you say 'F1', the F[1] position in the F File (which starts off as all blanks) will be replaced with what you type and the appropriate state vector will be constructed. Figure 2 shows the screen after the contents of several vectors (f1, f2, and f3) have been replaced with ASCII character strings and f4 is about to be replaced. The names of f1, f2 and f3 were listed and appear in the center of the screen. Page 5 Figure 2. Appearance after several f stimuli are made. -------------------- Template: 1234567890123456789012345 New : This will be f4. F[ 1]. This is stimulus f1.____ G[ 1]. F[ 2]. This is stimulus f2.____ G[ 2]. F[ 3]. And this is f3._________ G[ 3]. Replace which stimulus (set, number) : f4 -------------------- After you choose the stimulus to replace, 'Template:' will appear at the top of the screen. This is a set of 25 digits to serve as a reference marks for constructing your stimulus. Underneath it is 'New :' which is where the cursor is and where you can write the new string. Any ASCII character except '_'can be placed here and will be represented as its appropriate byte. The ASCII character '_' is represented in the vector as all zeros. If you hit return before the end of the string, the vector will be filled out with zeros, represented as underlines. If you type in more than 25 characters, the excess will be ignored. Remember to use <Delete> to remove characters from a line, as you always do on DEC systems. Using the <Backspace> key will insert non-printing control characters into the line, causing erratic behavior of the system that is difficult to detect. After you have typed in a few stimuli, you can see the current set by typing 'L' for 'L)ist'. This will list either the F file and the G File or the T file and the F file, depending on which M)ode you have chosen. If there are more stimuli than will fit on the screen you can move the file B)ackwards or F)orwards by typing 'b' or 'f'. Typing any other letter will return you to where you were. The listing will remain on the screen for reference until new information has to be written over it. Briefly the commands in C)hange are: A)dd. Form a state vector as the sum of other state vectors. The resulting vector values are divided by the number in the sum (i.e. the average of the vectors is taken). The '.Name' of this type of state vector will appear Page 6 in the L)isting as, for example, 'Sum f01 f02 f03' if it was the sum of stimulus vectors F[1], F[2], and F[3]. Up to 16 vectors can be summed. C)opy. Copy a state vector in one location to another location. The program will prompt you for the stimulus to be copied and where to put it. E)dit. This useful command will let you 'edit' an already existing stimulus. It will ask you for the stimulus number to edit. It will then give the old stimulus as the template and let you type in the new stimulus right below it. When you type <Return> the new stimulus vector will replace the old one. I)ndividual values. Will show the individual element values. The elements arranged on a byte per line with the number of the first and last elements on the line numbered on left and right sides of the screen, and the ASCII interpretation of the byte on the left. This command has its own command line. C)hoose chooses which vector to show. D)isplay displays the elements. M)ode (see below). Q)uit returns to C)hange. T)hreshold (see below). X)change allows you to change individual values. L)ist. Lists the state vectors in the files. If the display is in FG mode, the F File and the G File will be listed. If the display is in T mode, the T File and the F File will be listed. If more than 16 vectors are present, only sixteen at a time will be shown. The 16 stimuli displayed can be shifted forward or backward by typing 'F' or 'B'. L)ist can be left by typing any character other than 'F' or 'B'. M)ode. Change listing mode from displaying the F File and the G File to displaying the T File and the F File or vice versa. Q)uit. Return to wherever you entered C)hange from. C)hange can also be entered from BSB. R)eplace. Replace a member of one of the files with a new stimulus. S)ave. Save the file of stimuli to a disk file. The program will prompt you as to which file(s) you want to save. The saved file will be given the names you ASSIGNed to it earlier. If there is already a file of that name present, VMS will give the new file a later version number. For example, if F15.STM;2 is the ASSIGNed initial stimulus file, if you S)ave a later version of the file, it will get the name F15.STM;3, and so on. If you purge your directory, you will delete the earlier versions of the stimulus file. Don't forget to S)ave your work! >>>The program does NOT automatically save new stimuli.<<< Page 7 T)emplate. Sometimes it is convenient to use a template other than digits to make remembering what goes where easier. T)emplate allows you to make your own string of ASCII characters to serve as a template, or to use a member of one of the Stimulus files as a model for a template. Using ASSOCIATE to Make a New Learning Matrix. Suppose you have constructed a set of F and G stimuli. You have saved them to appropriate disk files, and now wish to form the association matrix between the input and output sets. This is done with the program named ASSOCIATE. ASSOCIATE can take sizeable amounts of computer time, so be careful! To learn a set of 12 200 dimensional vectors using the Widrow-Hoff error correction procedure for a total of 500 presentations can take 10 to 15 minutes of VAX CPU time, even if the system is partially connected. Also the data files ASSOCIATE automatically creates to store the results are very large (633 blocks in a 200 dimensional system) so be sure you have enough free space in your directory to write such a large file, otherwise the results of all the computing may be lost. If you RUN ASSOCIATE, first the F and G files will be listed so you can see if they are satisfactory. A listing of a session using ASSOCIATE to generate an Association matrix follows. The data set is a set of ambiguous descriptors used in the disambiguation demonstration. Page 8 ASSOCIATE program. March 19, 1989. The program is reading the FFILE. The dimensionality of the system is 200 The program is reading the GFILE. F and G stimuli used. F[ 1] : BaseballGameBat BallDiamd G[ 1] : BaseballGameBat BallDiamd F[ 2] : Vampire MythBat NiteDracu G[ 2] : Vampire MythBat NiteDracu F[ 3] : Animal LiveBat WingFlyng G[ 3] : Animal LiveBat WingFlyng F[ 4] : Poker GameBeerTablCards G[ 4] : Poker GameBeerTablCards F[ 5] : Tennis GameCortBallRackt G[ 5] : Tennis GameCortBallRackt F[ 6] : Dancing RichPrtyBallSocty G[ 6] : Dancing RichPrtyBallSocty F[ 7] : GeoShapeTwoDCrclSqreDiamd G[ 7] : GeoShapeTwoDCrclSqreDiamd F[ 8] : GeoModelTreDSphrBallTetra G[ 8] : GeoModelTreDSphrBallTetra F[ 9] : ExpJewelRichRubyOpalDiamd G[ 9] : ExpJewelRichRubyOpalDiamd Seed for RN generator : 123123 Number of associations to learn : 100 Use CORRECTION procedure? Y or N: y Use old Nfile as start? Y or N : n Number of synapses : 100 Setup completed. 10 Nr: 8 Cosine: 5.16E-01 20 Nr: 5 Cosine: 9.01E-01 30 Nr: 4 Cosine: 9.48E-01 40 Nr: 1 Cosine: 9.60E-01 50 Nr: 2 Cosine: 9.25E-01 60 Nr: 6 Cosine: 9.47E-01 70 Nr: 5 Cosine: 9.87E-01 80 Nr: 2 Cosine: 9.74E-01 90 Nr: 4 Cosine: 9.97E-01 100 Nr: 3 Cosine: 9.70E-01 Accuracy of recall of input set. 1 Name: BaseballGameBat BallDiamd Page 9 Cosine: 9.80E-01 Length: 1.30E+01 2 Name: Vampire MythBat NiteDracu Cosine: 9.93E-01 Length: 1.27E+01 3 Name: Animal LiveBat WingFlyng Cosine: 9.84E-01 Length: 1.28E+01 4 Name: Poker GameBeerTablCards Cosine: 9.97E-01 Length: 1.36E+01 5 Name: Tennis GameCortBallRackt Cosine: 9.78E-01 Length: 1.21E+01 6 Name: Dancing RichPrtyBallSocty Cosine: 9.94E-01 Length: 1.33E+01 7 Name: GeoShapeTwoDCrclSqreDiamd Cosine: 9.70E-01 Length: 1.16E+01 8 Name: GeoModelTreDSphrBallTetra Cosine: 9.97E-01 Length: 1.33E+01 9 Name: ExpJewelRichRubyOpalDiamd Cosine: 9.93E-01 Length: 1.33E+01 Writing to neuron output file. Page 10 The Prompts are mostly self explanatory. 1. Seed starts off the random number generator. Pairs of associations from the set of stimulus pairs are presented randomly. A particular sequence can be repeated if the same seed is used. 2. The number to be learned depends on the number of items in the stimulus sets. If the Widrow-Hoff error correction procedure is used, about 30 presentations per pair is a good place to start. If the linear associator is used, only one presentation of each pair is necessary. 3. Correction Procedure. If 'Y' or 'y', the Widrow-Hoff error correction procedure will be used, otherwise, the linear associator without error correction will be used. 4. Used Assigned Ninfile. If 'Yes', a previously constructed neuron file will be used as a starting point, otherwise all matrix elements start off at zero. You can teach an old matrix new tricks if you wish. If 'No', then a new matrix will be created. 5. Number of synapses is the number of non-zero synapses in the matrix. The rest of the synapses are set identically to zero. Execution speed is a linear function of number of synapses because of the way the programs are written and the files are constructed. This is because the matrices are not stored as matrices but as lists of non-zero synaptic connections. Fewer synapses means less computation time. Usually 50% connectivity is adequate for most systems we have investigated up to now and seems to make the systems work better than full connectivity, for reasons we now have some insight into. It takes a while to set up the matrix, so expect a wait. If you set a connectivity of over 75%, you will get a warning message, telling you to expect a VERY long wait. However, 100% connectivity is handled separately, and sets up very fast. After the matrix is set up and the system starts learning, the program will print out a progress report every 10 presentations. It will tell you the cumulative number of trials, the stimulus pair presented that trial, and the cosine between obtained and desired output vectors, so you can check on progress and make sure the stimuli are being presented correctly. After learning is over, the program will test how well the system learned. It will take each F in the F File in turn as input, compute the output, and compare that output with the correct association by computing the cosine between the actual and correct answers. A cosine of 1.0 is perfect. The length of the vectors is also computed. The new neuron file will then be written to whatever file is ASSIGNed to Noutfile. Remember, in a 200 dimensional system, these files are 633 blocks long so make sure you have enough free space in your account. Using the Matrix. To test the behavior of a previously constructed matrix, RUN BSB. You must read in the matrix and the stimulus sets you wish to test. This is done automatically in BSB. The program will inform you of its progress and whether or not it found the ASSIGNed files. Success or failure in retrieving files will appear on the status line (i.e. the bottom line). This line will appear and disappear so you have to watch carefully if you are interested. The status box (the top five lines) will tell you how many stimuli are in the stimulus files (0 if the files were not present) and whether neuron files are present. (See Figure 1.) Page 11 If the program cannot find the required files, it will tell you, but will not stop the program. Some other input errors may abnormally terminate the program and you will get a VMS error message. If you exit the program abnormally, by hitting <CTRL>C, or if you manage to do something fatal to the input, expect problems. Since your terminal has been reset to allow for the convenient display format, strange things may have happpened to your display, like having all input and output appear on one line. You can either write a small program to reset your terminal to normal or run BSB again and simply type 'e' to E)xit from the program normally which will reset your VT-100. A useful short program is called RESET which will reset your terminal after an abnormal exit from ITERATE: PROGRAM Reset (Input, Output); {Resets terminal.} BEGIN {Reset.} WRITE (CHR(27), '[1;24r'); {Restore_scrolling.} WRITE (CHR(27), '[f', CHR (27), '[2J'); {Clear_screen.} END. {Reset.} The Brain State in a Box Model. To use the Brain-State-in-a-Box (BSB) Model, type 'B', for B)SB. There now appear new prompt and command lines. There are a number of parameters of the simulation which can be changed if necessary, but the system comes with appropriate defaults for general use. Figure 3 shows the appearance of the initial BSB screen. Page 12 Figure 3. Initial appearance of BSB Screen -------------------- BSB>P)asses : 16 U)Limit: 1.3E+00 T)hreshold: 5.0E-01 Stim. #) : T 1 Mx: Synapses: 95 F)eedback: 7.0E-01 D)ecay: 6.5E-01 RESTART TF BSB Mx 1) 2) B)oth X)cute C)hange L)ist R)estart V)als Q)uit > -------------------- The variable x is the system state vector. The actual equation being used is x(t + 1) = Decay * x(t) + Feedback * A x(t) + {f(0)}. Decay is a decay parameter which measures the length of duration of the `short term memory' of the system. (If there was no feedback, activity would decay geometrically, since this fraction is usually less than one.) Feedback is a parameter multiplying the feedback through the matrix. If it is enabled, the term {f(0)} will add the initial state vector to the state vector at every step. It could correspond to a continous input from an earlier processing stage. This is related to what is called 'clamping' in the Boltzmann machine literature and effectively holds non-zero input vector elements constant so they cannot change in later iterations. This option is used in the DRUGS simulation. It is enabled if 'O' or 'o' is typed from the command line. The possible commands in the BSB system are: C)hange. Change the state vectors in the F File, G File or T File. (see earlier description of this part of the program). D)ecay allows change of the decay constant in the above equation. F)eedback allows change of the feedback constants in the above equation. L)ist. List the vectors in the files. If the display is in FG mode, the F File and the G File will be listed. If the display is in T mode, the T File and the F File will be listed. If more than 16 vectors are present, only sixteen at Page 13 a time will be shown. The part displayed can be moved forward or backward by typing 'F' or 'B'. L)ist can be left by typing any character other than 'F' or 'B'. M)ode. Changes the files L)isted from F and G to T and F or vice versa. O)riginal stimulus. This option adds the original stimulus vector to each iteration. It is closely related to what is called `clamping' in the Boltzmann machine literature since it effectively ensures that initial values never decay away but are always present at high amplitude. A comment will appear on the fifth line of the display if this option is in effect. If O) is typed again, the option is cancelled and the original stimulus will not be added. P)asses. The number of iterations to be performed before the command line returns and you can cease iterations or change parameters. The default value for P)asses is 16, the number of lines on the scrolling portion of the CRT screen. Q)uit. Return to the highest command level. R)estart the simulation with the initial stimulus. Otherwise typing X)cute will simply have the program continue what it was doing. The left part of the fourth status line will say RESTART is the system will start from zero iterations. Otherwise, the number of completed iterations will appear here. T)hreshold allows change of the interpretation threshold. When the output of the equation above is generated it is simply a 200 dimensional vector of floating point numbers. In order to see what it is actually doing, this vector is interpreted, i.e. turned back into an ASCII string. This is done by letting every value greater than +Threshold be give value +1 and every value less than -Threshold be given value -1. Values nearer to zero than plus or minus threshold are considered uninterpretable, and this particular byte is interpreted as the zero character, '_'. Non-printing characters are represented as '#'. This thresholding algorithm is only for the operator's convenience (i.e. it eliminates low amplitude ASCII garbage from the interpretations and makes them easier to read) and it has no effect on the values in the vector that are used for the next iteration. The parity bit is not used in the interpretation and it is not checked for correctness. U)limit. Allows change of the upper and lower limits in the BSB equation. Note upper and lower limit need not be the same. V)alues. Will display the values of the current state vector in terms of tenths of upper or lower limit. If the value is equal to the upper or lower limit, +L or -L will appear. A digit refers to tenths of the appropriate limit, i.e. +5 means that the value of that element is between 0.5 and 0.6 of the upper limit. X)cute. Start iterating the current state vector for P)asses iterations. Will continue from where it stopped if not R)eset. #) allows choice of the stimulus number to be used for staring the iterations if the system is R)eset. Page 14 Examples Several examples of iterations using the matrices generated by a disambiguation simulation are shown next. Figure 5 shows the pairs of associations. Figure 6 shows the results of the first 16 iterations and the next 16 iterations on 'bat ball'. Figure 7 shows the results of the first 16 and second 16 iterations on 'bat nite'. (The common word, 'bat', is ambiguous and the context is able to choose the correct meaning. More details are given in Anderson and Murphy, 1986.) 'Check' refers to the number of element values equal to the Upper or Lower limit and is a rough measure of the length of the vector and how rapidly the vector is changing. Positive feedback rapidly increases the number of saturated elements. Page 15 Figure 5. Stimulus Set for Disambiguation Example -------------------- BSB Neural Net Program. 3-89 B)SB. C)hange. E)xit. H)elp. L)ist. M)ode. N)eurons. R)ead. S)ave. T)hreshold. W)rite. Threshold: 5.0E-01 F File: 9 G File: 9 T File: 40 Synapses : 100 Display FG. F[ 1]. BaseballGameBat BallDiamd G[ 1]. BaseballGameBat BallDiamd F[ 2]. Vampire MythBat NiteDracu G[ 2]. Vampire MythBat NiteDracu F[ 3]. Animal LiveBat WingFlyng G[ 3]. Animal LiveBat WingFlyng F[ 4]. Poker GameBeerTablCards G[ 4]. Poker GameBeerTablCards F[ 5]. Tennis GameCortBallRackt G[ 5]. Tennis GameCortBallRackt F[ 6]. Dancing RichPrtyBallSocty G[ 6]. Dancing RichPrtyBallSocty F[ 7]. GeoShapeTwoDCrclSqreDiamd G[ 7]. GeoShapeTwoDCrclSqreDiamd F[ 8]. GeoModelTreDSphrBallTetra G[ 8]. GeoModelTreDSphrBallTetra F[ 9]. ExpJewelRichRubyOpalDiamd G[ 9]. ExpJewelRichRubyOpalDiamd C> -------------------- Page 16 Figure 6. Bat and Ball After 16 and 32 Iterations -------------------- BSB>P)asses : 16 U)Limit: 1.3E+00 T)hreshold: 5.0E-01 Stim. #) : t 5 Mx: Synapses: 100 F)eedback: 2.0E-01 D)ecay: 9.0E-01 RESTART 1. ____________Bat Ball_____ Check: 0 2. ____________Bat Ball_____ Check: 0 3. ____________Bat Ball_____ Check: 0 4. ____________Bat Ball_____ Check: 1 5. ____________Bat Ball_____ Check: 5 6. ____________Bat Ball_____ Check: 10 7. _a__________Bat Ball_____ Check: 14 8. _a__________Bat Ball_____ Check: 16 9. _a__________Bat Ball_____ Check: 21 10. _a__________Bat Ball_____ Check: 25 11. _a_e________Bat Ball__a__ Check: 45 12. _a_e________Bat Ball__a__ Check: 56 13. _a_e_______eBat Ball__a__ Check: 67 14. _a_e_______eBat Ball__a__ Check: 105 15. Ba_e____G__eBat Ball__a__ Check: 108 16. Ba_e____G__eBat Ball__a__ Check: 116 TF BSB X)ecute C)hange L)ist R)estart V)als Q)uit > -------------------- -------------------- BSB>P)asses : 16 U)Limit: 1.3E+00 T)hreshold: 5.0E-01 Stim. #) : t 5 Mx: Synapses: 100 F)eedback: 2.0E-01 D)ecay: 9.0E-01 It: 16 17. Ba_e____G__eBat Ball__a__ Check: 124 18. Ba_e____G__eBat Ball__a__ Check: 129 19. Ba_e____Ga_eBat Ball__a__ Check: 135 20. Ba_e____Ga_eBat Ball__a__ Check: 138 21. Ba_e____Ga_eBat BallD_a__ Check: 144 22. Ba_e__l_Ga_eBat BallDia__ Check: 149 23. Ba_e__l_Ga_eBat BallDia__ Check: 154 24. Ba_e__l_GameBat BallDia__ Check: 159 25. Base__llGameBat BallDiam_ Check: 161 26. Base__llGameBat BallDiam_ Check: 170 27. Baseb_llGameBat BallDiam_ Check: 174 28. BaseballGameBat BallDiamd Check: 179 29. BaseballGameBat BallDiamd Check: 181 30. BaseballGameBat BallDiamd Check: 187 31. BaseballGameBat BallDiamd Check: 192 32. BaseballGameBat BallDiamd Check: 197 TF BSB X)ecute C)hange L)ist R)estart V)als Q)uit > -------------------- Page 17 Figure 7 Bat and Nite after 16 and 32 Iterations -------------------- BSB>P)asses : 16 U)Limit: 1.3E+00 T)hreshold: 5.0E-01 Stim. #) : t16 Mx: Synapses: 100 F)eedback: 2.0E-01 D)ecay: 9.0E-01 RESTART 1. ____________Bat Nite_____ Check: 0 2. ____________Bat Nite_____ Check: 0 3. ____________Bat Nite_____ Check: 0 4. ____________Bat Nite_____ Check: 0 5. ____________Bat Nite_____ Check: 0 6. ____________Bat Nite_____ Check: 1 7. ____________Bat Nite_____ Check: 4 8. ____________Bat Nite_____ Check: 10 9. ________M___Bat Ni_eD____ Check: 21 10. _a______M___Bat Ni_eD____ Check: 24 11. _a______M_t_Bat Ni_eD_a__ Check: 31 12. _a_____ M_t_Bat Ni_eD_a__ Check: 51 13. _a_____ M_t_Bat Ni_eD_a__ Check: 71 14. _a_____ M_t_Bat Ni_eD_a__ Check: 100 15. _a_____ M_t_Bat Ni_eD_a__ Check: 119 16. _a_____ M_t_Bat Ni_eD_a__ Check: 123 TF BSB X)ecute C)hange L)ist R)estart V)als Q)uit > -------------------- -------------------- BSB>P)asses : 16 U)Limit: 1.3E+00 T)hreshold: 5.0E-01 Stim. #) : t16 Mx: Synapses: 100 F)eedback: 2.0E-01 D)ecay: 9.0E-01 It: 16 17. _a__i__ M_t_Bat NiteD_a__ Check: 127 18. _am_i_e Myt_Bat NiteD_a__ Check: 141 19. _am_i_e MythBat NiteD_ac_ Check: 147 20. _ampire MythBat NiteDracu Check: 158 21. Vampire MythBat NiteDracu Check: 166 22. Vampire MythBat NiteDracu Check: 169 23. Vampire MythBat NiteDracu Check: 171 24. Vampire MythBat NiteDracu Check: 173 25. Vampire MythBat NiteDracu Check: 180 26. Vampire MythBat NiteDracu Check: 193 27. Vampire MythBat NiteDracu Check: 199 28. Vampire MythBat NiteDracu Check: 200 >> Fully limited. Finished. TF BSB X)ecute C)hange L)ist R)estart V)als Q)uit > -------------------- Page 18 Programming the System. The system as described is primarily interactive. However, it is often convenient, when doing a systematic study of a system, to look at a great many test vectors. This release of the programs does not allow non-interactive programming. However, VMS command files will allow you to do this conveniently from outside the program. Command files are files of system and program commands that are automatically executed when prefaced with '@'. System commands are preceded with '$' and lines to serve as input to a program are simply typed. Suppose we had a set of say, five test stimuli, an autoassociative system, and we wished to iterate every test stimulus 50 times and save the output of the program for future study. Figure 8 shows a simple command file to accomplish this. This command file expects to find all the appropriate files in the directory drb2:[anderson.code]. The first lines make all the assignments, and since they are system commands they are preceded by a $. The line $assign drb2:[anderson.code]all159.out sys$output assigns the output from the program to a file all159.out which can be typed or studied with a text editor. Then BSB is run and the next lines form input to the program. We are not using a VT100, so the first line is n)o. Then the input files are l)isted. We want to use the b)sb model. In this case, we want each p)ass to consist of 50 iterations and the o)riginal input vector to be added after each iteration. Then test stimuli 's t1 through t5 are successively x)ecuted, the system being r)eset after each new test stimulus. Then b)sb is q)uit and the program e)xited. The output will be found in drb2:[anderson.code]all159.out. This convenient technique allows large studies and parameter variants to be run in batch mode as well by using the VMS SUBMIT command with the command file. Page 19 Figure 8 $set default drb2:[anderson.code] $assign f159.stm ffile $assign g159.stm gfile $assign ti159.stm tfile $assign n159.neu nfile $assign [anderson.test]all159.out sys$output $run bsb n l m l b p 50 o # t1 x r # t2 x r # t3 x r # t4 x r # t5 x r q e Demonstration Command Files Because it is sometimes convenient to be able to regenerate matrices or make stimulus files, we have provided command files for three demonstrations. We have already given examples of one demonstration, on lexical disambiguation. There are two other neural net demonstrations available. One is a simple drug data base, which contains information about drugs, diseases and microorganisms. There is also a demonstration of an associative system learning Ohm's Law, which can 'reason' about functional dependencies. Each of the demonstrations has three command files associated with it: Page 20 Demonstration Generates Generates Runs Matrix Stimuli Demo (.neu files) (.stm files) (Uses ASSOCIATE) (Uses BSB) (Uses BSB) Disambiguation: learndisambiguation.com makedisambiguation.com disambiguation.com Ohm's Law: learnohms.com makeohms.com ohms.com Drug data base: learndrugs.com makedrugs.com drugs.com (Sometimes these command files may have shorter file names, by an obvious transformation, such as, learndrugs.com = ldrugs.com. Some operating systems do not accept long file names.) It may take 10 or 20 minutes of MicroVAX CPU time to make the connection matrices, so beware! Making the stimulus files is quite rapid. The command files are basically strings of keystrokes, and it may be of some value to have complete working `scripts' for some common operations of BSB and ASSOCIATE Ohms and Drugs are described at length in a manuscript entitled `DEMOS.RNO' (the RUNOFF formatted source) and `DEMOS.MEM', the final version, which is complete with escape sequences for an LA-100 printer. It contains most of the material presented in the demonstrations. A compressed version of this manuscript appeared in (Anderson, 1986). Acknowledgement This research was primarily supported by National Science Foundation Grants BNS-82-14728 and BNS-85-18675 to James Anderson, Department of Cognitive and Linguistic Sciences, Brown University, Providence, RI, 02912. Please acknowledge this grant if you make use of these programs in published material. Page 21 References J.A. Anderson, Neural models for cognitive computation. IEEE Transactions: Systems, Man, and Cybernetics. 1983, SMC-13, 799-815. (Reprinted in V. Vemuri (Ed.), Artificial Neural Networks: Theoretical Concepts, Washington, DC: Computer Society Press of the IEEE, 1988.) J.A. Anderson, Cognitive Capabilities of a Parallel System. In E. Bienenstock, F. Foglemann, and G. Weisbuch. (Eds.) Disordered Systems and Biological Organization, Berlin: Springer, 1986. J.A. Anderson and M. Mozer, Categorization and selective neurons. In: G. Hinton and J. Anderson (Eds.), Parallel Models for Associative Memory. Hillsdale, New Jersey: Erlbaum Associates, 1981. Revised Edition, 1988. J.A. Anderson and G.L. Murphy, Psychological Concepts in a Parallel System. In J.D. Farmer, A. Lapedes, N. Packard, and B. Wendroff. (Eds.) Evolution, Games, and Learning.. New York: North Holland, 1986. (With Gregory L. Murphy). J.A. Anderson, J. Silverstein, S. Ritz, and R. Jones, Distinctive features, categorical perception and probability learning: some applications of a neural model. Psychological Review, 1978, 85, 597-603. (Reprinted in J.A. Anderson and E. Rosenfeld (Eds.), Neurocomputing, Cambridge, MA: MIT Press, 1988). Page 22 Appendix Naming Conventions: 1) Pascal source programs are given the extension .PAS (of course) and the executable files .EXE. 2) BSB.PAS uses a number of INCLUDEd files written in Pascal, which are given the extension .INC. 3) Files containing sets of stimulus vectors are given the extension .STM. They are Pascal records containing a 60 character header and a 200 dimensional floating point array. 4) Files containing matrices (actually files of a Pascal RECORD type Neuron), are given the extension .NEU. They are Pascal records containing 200 `Neurons' which contain some constants and an array of `Synapses'. 5) Files containing text are given the extension .RNO if the text is in RUNOFF format and .MEM if it has formatted. 6) Command files are given the extension .COM. 7) Program output to a terminal is given the extension .OUT.