#' @include Generics.R
#' Classifier class
#'
#' Top level classifier class
setClass("Classifier",
representation(name="character",
modelform="ANY",
classname="ANY",
classnames="ANY",
scaling="ANY"),
prototype(modelform=NULL, classname=NULL, classnames=NULL,scaling=NULL)
)
#' Show the contents of a classifier
#' @rdname rssl-formatting
#' @aliases show,Classifier-method
setMethod("show", signature(object="Classifier"), function(object) {
cat(object@name,"\n\n")
if (!is.null(object@modelform)) {
cat("Formula: ",Reduce(paste, deparse(object@modelform)),"\n")
}
cat("Classnames:\n",object@classnames,"\n")
if (.hasSlot(object,"theta")) {
cat("Classifier weights: ",object@theta,"\n")
}
if (.hasSlot(object,"w")) {
cat("Classifier weights: ",object@theta,"\n")
}
if (!is.null(object@scaling)) {
cat("Normalization applied:\n")
print(object@scaling)
}
})
setClass("LinearClassifier",
representation(w="ANY"),
prototype(w=NULL)
)
#' Classifier used for enabling shared documenting of parameters
#' @param X matrix; Design matrix for labeled data
#' @param y factor or integer vector; Label vector
#' @param X_u matrix; Design matrix for unlabeled data
#' @param verbose logical; Controls the verbosity of the output
#' @param scale logical; Should the features be normalized? (default: FALSE)
#' @param eps numeric; Stopping criterion for the maximinimization
#' @param x_center logical; Should the features be centered?
#' @param intercept logical; Whether an intercept should be included
#' @param lambda numeric; L2 regularization parameter
#' @param y_scale logical; whether the target vector should be centered
#' @param kernel kernlab::kernel to use
#' @param use_Xu_for_scaling logical; whether the unlabeled objects should be used to determine the mean and scaling for the normalization
#' @param ... Not used
#' @export
BaseClassifier <- function(X,y,X_u,verbose,scale,eps,x_center,intercept,lambda,y_scale,kernel,use_Xu_for_scaling,...) {}
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