37 lines
1.7 KiB
R
37 lines
1.7 KiB
R
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#' Connect To Data
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#'
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#' When a trained forest is saved, the training dataset is not saved alongside
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#' it. When it's loaded back up, it can be more convenient (and in some cases
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#' necessary) to import the training dataset back into the Java environment so
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#' that it's readily accessible. There are only two functions that look for the
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#' training dataset: \code{predict}, where you can easily just specify an
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#' alternative dataset, or \code{\link{addTrees}}, which requires the training
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#' dataset be connected.
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#' @param forest The forest to connect data too
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#' @param responses The responses in the data; aka the left hand side of the formula
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#' @param covariateData A data.frame containing all of the covariates used in the training dataset
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#' @return The same forest, but connected to the training data.
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#' @export
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#' @examples
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#' data <- data.frame(x1=rnorm(1000), x2=rnorm(1000), y=rnorm(1000))
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#' forest <- train(y~x1+x2, data, ntree=100, numberOfSplits=0, nodeSize=1, mtry=1)
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#' forest$dataset <- NULL # what the forest looks like after being loaded
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#'
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#' forest <- connectToData(forest, data$y, data)
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connectToData <- function(forest, responses, covariateData){
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covariateList <- forest$covariateList
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numCovariates <- .jcall(covariateList, "I", "size")
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covariateNames <- character(numCovariates)
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for(j in 1:numCovariates){
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covariate <- .jcall(covariateList, makeResponse(.class_Object), "get", as.integer(j-1))
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covariate <- .jcast(covariate, .class_Covariate)
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covariateNames[j] <- .jcall(covariate, makeResponse(.class_String), "getName")
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}
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forest$dataset <- loadData(covariateData, covariateNames, responses, covariateList)$dataset
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return(forest)
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}
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