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```
#' Implementing a Random Forest of SDTs.
#'
#' \code{SoftClassForest} creates categorical Random Forests of Soft Decision Trees while returning
#' the fitted classification given by the majority vote of individual SDTs.
#'
#' \code{SoftClassForest} individually fits a Random Forest for each possible classification response using \code{SoftForestPredFeeder} function
#' one classification at a time. The result from each one of these SDTs is a fitted probability of \code{0} or \code{1}.
#' Once all classifications have a fitted probability, the observation is classified as the maximum a posteriori probability.
#' Given a Random Forest of SDTs, the final Random Forest classification goes to the majority vote from the SDTs.
#'
#' @param trainresponses A matrix or data frame of responses \code{0} and \code{1} for the training set with length equal to the number of observations in the training set and width equal to the number of possible classifications.
#' @param train A matrix or data frame consisting of all possible variables to attempt for the training set.
#' @param test A matrix or data frame consisting of all possible variables to attempt for the test set.
#' @param ntry A numeric of the number of variables from the \code{num.features} to attempt to split. This is useful for building random forests. For a standard tree, choose \code{ntry = num.features}.
#' @param ntrees A numeric of the number of SDTs to build in the Random Forest.
#' @param depth A numeric of the number of the depth each SDT should be. Here this ends with \eqn{2^{depth - 1}} terminal nodes.
#' @param bag Logical if Random Forests should be built with bootstrap aggregating (TRUE) or raw data (FALSE).
#' @return A vector of the final classifications based on the Random Forest generated.
#'
#' @export
#'
#' @examples
#' Responses = SoftClassMatrix(as.vector(iris$Species))
#' SoftClassForest(trainresponses = Responses, train = iris[,1:4], test = iris[,1:4],
#' ntry = 2, ntrees = 15, depth = 2, bag = TRUE)
SoftClassForest = function(trainresponses, train, test, ntry, ntrees, depth, bag = TRUE)
{
if(sum(is.data.frame(trainresponses), is.matrix(trainresponses), is.vector(trainresponses)) != 1) stop("Responses must be matrix or data frame.")
if(sum(is.data.frame(train), is.matrix(train)) != 1) stop("Training data must be a matrix or data frame.")
if(sum(is.data.frame(test), is.matrix(test)) != 1) stop("Test data must be a matrix or data frame.")
stopifnot(is.numeric(ntry))
stopifnot(length(ntry) == 1)
stopifnot(is.numeric(ntrees))
stopifnot(length(ntrees) == 1)
stopifnot(is.numeric(depth))
stopifnot(length(depth) == 1)
if(sum(bag == TRUE, bag == FALSE) != 1) stop("Paramater bag must be either TRUE or FALSE")
if(ncol(train) != ncol(test)) stop("Must be equal number of columns in the training and test set.")
if(nrow(train) != nrow(trainresponses)) stop("Number of observations in Training set and Training responses must be equal.")
if(ntry > ncol(train)) stop("Cannot attempt to try more variables than exist.")
classes = ncol(trainresponses)
num.features = ncol(train)
softvotes = matrix(NA, nrow = nrow(test), ncol = ntrees)
for(i in 1:ntrees)
{
softpredmatrix = matrix(NA, nrow = nrow(test), ncol = classes)
if(bag == TRUE) index = sample(1:nrow(trainresponses), nrow(trainresponses), replace = TRUE)
if(bag == FALSE) index = 1:nrow(trainresponses)
for(j in 1:classes)
{
softpredmatrix[,j] = SoftForestPredFeeder(trainresponses[index,j], train[index,], test, num.features, ntry, depth)
}
softprediction = rep(NA, nrow(test))
for(j in 1:nrow(softpredmatrix))
{
softprediction[j] = which.max(softpredmatrix[j,])
}
softvotes[,i] = softprediction
}
finalprediction = rep(NA, nrow(test))
for(j in 1:nrow(softvotes))
{
finalprediction[j] = as.numeric(ClassMode(softvotes[j,]))
}
return(finalprediction)
}
```

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