文件列表:

artmap_init.m (7775, 2003-02-15)
artmap_shell.m (3962, 2003-02-15)
artmap_shell_vote.m (3044, 2003-05-10)
artmap_test_large.m (7405, 2003-02-15)
artmap_test_small.m (3803, 2003-02-15)
artmap_train_large.m (7578, 2003-02-15)
artmap_train_small.m (10110, 2003-02-15)
input.dat (34000, 2002-02-13)
Makefile (771, 2002-02-13)
output.dat (5000, 2002-02-13)
te_input.dat (340000, 2002-02-13)
te_output.dat (50000, 2002-02-13)

/******************************************************************************* * README v.2 (Feb. 9, 02) * * Description: * Readme for artmap_m.zip * See http://www.cns.bu.edu/~artmap for further details. * Authors: * Suhas Chelian, Norbert Kopco ******************************************************************************/ /******************************************************************************* * Contents ******************************************************************************/ The attached archive artmap_m.zip contains a sample implementation of the ARTMAP neural network, in many of its variations. The archive contains the following files: artmap_shell.m - a sample program of how to use a single ARTMAP network artmap_shell_vote.m - a sample program of how to use voting with ARTMAP networks artmap_init.m - initialization for a single ARTMAP network artmap_train_large.m - training an ARTMAP network on a set artmap_train_small.m - training an ARTMAP network for a single input artmap_test_large.m - testing an ARTMAP network on a set artmap_test_small.m - testing an ARTMAP network for a single input input.dat - sample training set containing 1000 training points for the Circle-in-the-Square problem (every row of this text file contains the input values for one training pattern) output.dat - the file containing expected output classes for the training set (every row of this text file contains the class label for the corresponding input in the file input.dat) te_input.dat - sample testing set containing 10000 testing points for the Circle-in-the-Square problem (format as in input.dat) te_output.dat - file containing expected output classes for the testing set (format as in output.dat) Makefile - useful for zipping this package README - this file The training and testing files (input.dat, output.dat, te_input.dat, te_output.dat) must be placed into the same directory as the code. /******************************************************************************* * Compiling and Running ******************************************************************************/ The code, written in Matlab, can be run on almost any platform with Matlab. To simulate a single ARTMAP network, run it under Matlab use following command: artmap_shell Toggle "traceInit" if you want to see how the network was initialized; toggle "traceTrain" if you want to see what weights the network developed after training. You can also simulate multiple ARTMAP networks that vote to make a decision by typing: artmap_shell_vote You can change the number of voters through the "numVoters" variable, and decide whether WTA compression is done for each network before voting or not through the "voteWTA" variable. /******************************************************************************* * Configuration ******************************************************************************/ To choose which ARTMAP system you would like, set MAPTYPEArg in artmap_init.m to 1 for Fuzzy ARTMAP, 2 for ART-EMAP, 3 for ARTMAP-IC, or 4 for dARTMAP; see artmap_shell.m for an example. From there, the system configuration is automatic. See http://www.cns.bu.edu/~artmap for the papers from which one can compare the different versions of ARTMAP. If you would like to do advanced configuration, set defaultParams to 0 in artmap_init.m, and pass in a cell array; see artmap_shell.m for an example. Please note that dARTMAP must use the Choice-by-difference signal rule, and the Increased CAM Gradient during training and testing. Other ARTMAP's can still use the Choice-by-difference signal rule, even though they were not orignally designed with it. Similarly, other ARTMAP's did not used Increased CAM Gradient during testing but they now can (do not use it during training). If you wish, you can change to using the Simple CAM Gradient by toggling the DO_TEST_SCG flag (through 'varargin'). Also, dARTMAP gets greatest code compression (fewer nodes) with DO_KAPPA_VEC set to 0. dARTMAP was not originally designed to handle ARTb output, which DO_KAPPA_VEC simulates. In addition, although MT- was not orignally applied to Fuzzy ARTMAP or ART-EMAP, it can still be used with them. If you wish, you can change to MT+ (epsilon > 0, again through 'varargin'), but you then lose the ability to encode inconsistent case. (Inconsistent cases are cases when the same input is mapped to different outputs.) In addition, note that each implementation differs slightly from the (Carpenter et al., 19***b) paper in that Theta is now defined to be Theta from the paper - M. This changes the Increased CAM Gradient set { j | (2-alpha)M-Tj > 0 } to { j | M-Tj > 0 } (see Step 2a in Training, and 2i in Testing); also Tu is now alpha*M, not M. These changes effect the dynamic range of the match function, T, but not the algorithms overall behavior. You can toggle back to the original paper's definition by setting DO_OLD_Tj (through 'varargin'). Before compilation of the code, the following parameters of the system need to be specified for each file: ******************************************************************************** artmap_init.m: MAPTYPE 1 - Fuzzy ARTMAP 2 - ART-EMAP 3 - ARTMAP_IC 4 - DIST_ARTMAP M The number of input dimensions, not including complement coding. L The number of output classes, labeled 1 to L. MAX_F2_SIZE The maximum number of F2 nodes. defaultParams Whether to use default parameters or not. Other parameters can be set by using 'varargin': alpha Parameter in the Choice-by-difference and Weber signal rule Loosely speaking, large values of alpha will not recruit uncommited nodes as quickly. This can also be achieved by making T_u small with the Choice-by-difference signal rule. p Power in the increased gradient or simple CAM rule p determines the degree contrast enchancement in F2 for testing. Large p are equivalent to Winner-take-all (choice behavior), and small p lead to a more distributed activity. Setting p < 1 is not recommended. beta Learning rate epsilon Match tracking parameter rho_a_bar ARTa baseline vigilance parameter rho_ab Mapfield vigilance T_u F0 -> F2 signal for uncommited nodes MAX_F2_SIZE This constant defines the upper bound on the number of coding units in the Layer F2 (F2 size is also equal to the number of units in F3 layer.) See the papers at http://www.cns.bu.edu/~artmap for further information on how the parameters effect performance. ******************************************************************************** artmap_train_large.m: trainArg The training points. The first 1:M columns are the inputs, and the M+1st column is the output. trainNArg The number of training points. forceInputHC Whether to force input hypercubing or not. forceOutputHC Whether to force proper indexing of output classes or not. verbose A value of 1 will show the beginning and ending of training; a value of 2 will show progress incrementally. defaultEpochs Whether to use default training regime or not. Other training regimes can be set by using 'varargin': EPOCHS Number of passes over set shuffle Whether to shuffle training points after each epoch or not shuffleSeed What to seed the shuffle order with Please note that input must be in the unit hypercube (all dimensions are placed in the [0,1] interval). The algorithm will not work properly if this is not so. Similarly, the output must be indexed from 1 to L. ******************************************************************************** artmap_test_large.m testArg The testing points. The first 1:M columns are the inputs, and the M+1st column is the output. testNArg The number of testing points. forceInputHC Whether to force input hypercubing or not. forceOutputHC Whether to force proper indexing of output classes or not. verbose A value of 1 will show the beginning and ending of training; a value of 2 will show progress incrementally; a value of 3 will show the index of each incorrectly predicted output. Please note that input must be in the unit hypercube (all dimensions are placed in the [0,1] interval). The algorithm will not work properly if this is not so. Similarly, the output must be indexed from 1 to L. Common abbreviations you may run into include: CBD Choice-by-difference WTA Winner-take-all (choice) ICG Increased CAM gradient IC Instance counting SCG Simple CAM gradient Lastly, the code has been broken into functions to allow greater readibility, but if you prefer monolothic code, see version 1.

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