amrElmSSL <- function(x, ...) UseMethod("amrElmSSL")
#' Creates a model for AMR-ELM.
#'
#' @param hidden_neurons the number of hidden neurons
#' @param nl the number of labeled patterns
#' @param 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
#' @param y training data labels (only two classes, with labels equals to -1 or +1, and 0 for the unlabeled patterns)
#' @param affinity - only cosine implemented
#' @return 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.
#' @examples
#'
#' \dontrun{
#' 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)
#' }
#'
#' @export
amrElmSSL.default <- function(X, y, hidden_neurons, nl,
affinity="cosine") {
n <- nrow(X)
p <- ncol(X)
# Get random Z matrix (hidden layer weight matrix)
Z <- replicate(hidden_neurons, stats::runif(p + 1, -0.5, 0.5))
# Add bias to the X matrix
X_aug <- as.matrix(cbind(replicate(n, 1), X))
# Apply the sigmoid function to the hidden layer's output matrix (H)
H <- X_aug %*% Z
H <- e1071::sigmoid(H)
if (affinity == "cosine") {
affinity_matrix <- cosine(X)
} else {
stop("Wrong affinity option")
}
# Calculates the perturbated matrix (P %*% H) for AMRELM
HL <- affinity_matrix %*% H;
# Calculates the output layer weight matrix (weights) and
# the network output for the train data - AMELM
weights <- corpcor::pseudoinverse(HL[1:nl, ]) %*% y[1:nl];
return(structure(
list(weights = weights,
Z = Z,
H = H,
affinity = affinity,
X = X),
class = "amrElmSSL"
))
}
predict.amrElmSSL <- function(model, newdata) {
predict.amrElm(model, newdata)
}
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