Create and train an (auto-)associative memory
The autoassociative memory performs clustering by finding a prototype to the given input.
The implementation assumes two-dimensional input and output (cf. art1).
assoz(x, ...) ## Default S3 method: assoz(x, dimX, dimY, maxit = 100, initFunc = "RM_Random_Weights", initFuncParams = c(1, -1), learnFunc = "RM_delta", learnFuncParams = c(0.01, 100, 0, 0, 0), updateFunc = "Auto_Synchronous", updateFuncParams = c(50), shufflePatterns = TRUE, ...)
x |
a matrix with training inputs for the network |
... |
additional function parameters (currently not used) |
dimX |
x dimension of inputs and outputs |
dimY |
y dimension of inputs and outputs |
maxit |
maximum of iterations to learn |
initFunc |
the initialization function to use |
initFuncParams |
the parameters for the initialization function |
learnFunc |
the learning function to use |
learnFuncParams |
the parameters for the learning function |
updateFunc |
the update function to use |
updateFuncParams |
the parameters for the update function |
shufflePatterns |
should the patterns be shuffled? |
The default initialization and update functions are the only ones suitable for this kind of
network. The update function takes one parameter, which is the number of iterations that will
be performed. The default of 50 usually does not have to be modified. For learning, RM_delta
and Hebbian functions can be used, though the first one usually performs better.
A more detailed description of the theory and the parameters is available from the SNNS documentation and the other referenced literature.
an rsnns object. The fitted.values member contains the
activation patterns for all inputs.
Palm, G. (1980), 'On associative memory', Biological Cybernetics 36, 19-31.
Rojas, R. (1996), Neural networks :a systematic introduction, Springer-Verlag, Berlin.
Zell, A. et al. (1998), 'SNNS Stuttgart Neural Network Simulator User Manual, Version 4.2', IPVR, University of Stuttgart and WSI, University of Tübingen. http://www.ra.cs.uni-tuebingen.de/SNNS/welcome.html
## Not run: demo(assoz_letters) ## Not run: demo(assoz_lettersSnnsR) data(snnsData) patterns <- snnsData$art1_letters.pat model <- assoz(patterns, dimX=7, dimY=5) actMaps <- matrixToActMapList(model$fitted.values, nrow=7) par(mfrow=c(3,3)) for (i in 1:9) plotActMap(actMaps[[i]])
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