nnlib2Rcpp (version 0.2.9)

MAM-class: Class "MAM"

Description

A single Matrix Associative Memory (MAM) implemented as a (supervised) NN.

Extends

Class "RcppClass", directly.

All reference classes extend and inherit methods from "envRefClass".

Author

Vasilis N. Nikolaidis <[e-mail]>

Fields

.CppObject:

Object of class C++Object ~~

.CppClassDef:

Object of class activeBindingFunction ~~

.CppGenerator:

Object of class activeBindingFunction ~~

Methods

encode( data_in, data_out ):

Setup a new MAM NN and encode input-output data pairs. Parameters are:

• data_in: numeric matrix, input data to be encoded in MAM, a numeric matrix (2d, of n rows). Each row will be paired to the corresponding data_out row, forming an input-output vector pair.

• data_out: numeric matrix, output data to be encoded in MAM, a numeric matrix (2d, also of n rows). Each row will be paired to the corresponding data_in row, forming an input-output vector pair.

Note: to encode additional input-output vector pairs in an existing MAM, use train_single method (see below).

recall(data):

Get output for a dataset (numeric matrix data) from the (trained) MAM NN.

train_single (data_in, data_out):

Encode an input-output vector pair in the MAM NN. Vector sizes should be compatible to the current NN (as resulted from the encode method).

print():

print NN structure.

show():

print NN structure.

load(filename):

retrieve the NN from specified file.

save(filename):

save the NN to specified file.

The following methods are inherited (from the corresponding class): objectPointer ("RcppClass"), initialize ("RcppClass"), show ("RcppClass")

References

Pao Y (1989). Adaptive Pattern Recognition and Neural Networks. Reading, MA (US); Addison-Wesley Publishing Co., Inc.

See Also

BP,LVQs.

Examples

iris_s            <- as.matrix(scale(iris[1:4]))
class_ids         <- as.integer(iris$Species)
num_classes       <- max(class_ids)

# create output dataset to be used for training, Here we encode class as -1s and 1s
iris_data_out <- matrix( data = -1, nrow = nrow(iris_s), ncol = num_classes)

# now for each case, assign a 1 to the column corresponding to its class
for(r in 1:nrow(iris_data_out)) iris_data_out[r,class_ids[r]]=1

# Finally apply MAM:
# Encode train pairs in MAM and then get output dataset by recalling the test data.

mam <- new("MAM")

mam$encode(iris_s,iris_data_out)

# test the encoding by recalling the original input data...
mam_data_out <- mam$recall(iris_s)

# find which MAM output has the largest value and use this as the final cluster tag.
mam_recalled_cluster_ids = apply(mam_data_out,1,which.max)

plot(iris_s, pch=mam_recalled_cluster_ids, main="MAM recalled Iris data classes")

cat("MAM recalled these IDs:\n")
print(mam_recalled_cluster_ids)