Description Usage Arguments Value Examples
Creates a model for AMR-ELM.
1 2 | ## Default S3 method:
amrElmSSL(X, y, hidden_neurons, nl, affinity = "cosine")
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X |
training data, numerical with zero mean and unit variance and patterns in the lines, attributes in the columns - the unlabeled patterns must came after the labeled ones |
y |
training data labels (only two classes, with labels equals to -1 or +1, and 0 for the unlabeled patterns) |
hidden_neurons |
the number of hidden neurons |
nl |
the number of labeled patterns |
affinity |
- only cosine implemented |
The amrElm model for semissupervised problems - a list with: Z: hidden layer weights H: hidden layer output weights: output layer weights affinity: the affinity used to generate the model (e.g.: cosine affinity) X: training data for generating affinity matrix.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | ## Not run:
library(amrElm)
data(heart)
data <- heart$data
labels <- heart$labels
hidden_neurons <- 500
nl <- 50
N <- nrow(data)
randomPatterns <- seq(N)
data <- data[randomPatterns,]
labels <- labels[randomPatterns]
n <- floor(2*N/3)
nTest <- N - n
data <- data[randomPatterns,]
labels <- labels[randomPatterns]
X <- data[1:n,]
XTest <- data[(n+1):N,]
y <- labels[1:n]
y[(nl+1):n] <- 0
model <- amrElmSSLTrain(hidden_neurons,nl,X,y)
testOutput <- amrElmTest(XTest, model)
## End(Not run)
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