Joel Therrien
fdc708dad5
Add support for making predictions without specifying training data Add support for adding trees to an existing forest Add support for toggling displayProgress Also reduced the size of the package by removing some unused dependency classes.
37 lines
No EOL
1.7 KiB
R
37 lines
No EOL
1.7 KiB
R
#' Connect To Data
|
|
#'
|
|
#' When a trained forest is saved, the training dataset is not saved alongside
|
|
#' it. When it's loaded back up, it can be more convenient (and in some cases
|
|
#' necessary) to import the training dataset back into the Java environment so
|
|
#' that it's readily accessible. There are only two functions that look for the
|
|
#' training dataset: \code{predict}, where you can easily just specify an
|
|
#' alternative dataset, or \code{\link{addTrees}}, which requires the training
|
|
#' dataset be connected.
|
|
#' @param forest The forest to connect data too
|
|
#' @param responses The responses in the data; aka the left hand side of the formula
|
|
#' @param covariateData A data.frame containing all of the covariates used in the training dataset
|
|
#' @return The same forest, but connected to the training data.
|
|
#' @export
|
|
#' @examples
|
|
#' data <- data.frame(x1=rnorm(1000), x2=rnorm(1000), y=rnorm(1000))
|
|
#' forest <- train(y~x1+x2, data, ntree=100, numberOfSplits=0, nodeSize=1, mtry=1)
|
|
#' forest$dataset <- NULL # what the forest looks like after being loaded
|
|
#'
|
|
#' forest <- connectToData(forest, data$y, data)
|
|
connectToData <- function(forest, responses, covariateData){
|
|
covariateList <- forest$covariateList
|
|
|
|
numCovariates <- .jcall(covariateList, "I", "size")
|
|
covariateNames <- character(numCovariates)
|
|
|
|
for(j in 1:numCovariates){
|
|
covariate <- .jcall(covariateList, makeResponse(.class_Object), "get", as.integer(j-1))
|
|
covariate <- .jcast(covariate, .class_Covariate)
|
|
covariateNames[j] <- .jcall(covariate, makeResponse(.class_String), "getName")
|
|
}
|
|
|
|
forest$dataset <- loadData(covariateData, covariateNames, responses, covariateList)$dataset
|
|
|
|
return(forest)
|
|
|
|
} |