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#
# Template for a data mining process (both with or w/o CV)
# (dataset: Sonar, method: Random Forest)
#
# Use "browser()" if you want to look at the variables inside
#
# Author: Wolfgang Konen, FHK, Oct'2009 - Apr'2014
#
# Example usage:
# result <- main_sonar(opts);
#
#readCmdSonar <- function(filename,opts) {
# read.csv2(file=paste(opts$path,opts$dir.data, filename, sep="/"), dec=".", sep=",", nrow=opts$READ.NROW,header=FALSE);
#}
readTrnSonar <- function(opts) {
read.csv2(file=paste(opts$path,opts$dir.data, opts$filename, sep="/"), dec=".", sep=",", nrow=opts$READ.NROW,header=FALSE);
}
main_sonar <- function(opts,dset=NULL,tset=NULL) {
opts <- tdmOptsDefaultsSet(opts); # fill in all opts params which are not yet set (see tdmOptsDefaults.r)
gdObj <- tdmGraAndLogInitialize(opts); # init graphics and log file
#===============================================
# PART 1: READ DATA
#===============================================
if (is.null(dset)) {
cat1(opts,opts$filename,": Read data ...\n")
dset <- tdmReadDataset(opts);
}
names(dset)[61] <- "Class"
if (!is.null(tset)) names(tset)[61] <- "Class"
# alternative way (but this requires mlbench):
#require(mlbench); data(Sonar); # 60 columns V1,...,V60 with input data,
#dset <- Sonar; # one response column "Class" with levels ["M" (metal) | "R" (rock)]
# which variable is response variable:
response.variable <- "Class"
ID.variable <- NULL
# which variables are input variables (in this case all others):
input.variables <- setdiff(names(dset), c(response.variable,ID.variable))
#===============================================
# PART 2 - 6
#===============================================
result <- tdmClassifyLoop(dset,response.variable,input.variables,opts,tset);
# print summary output and attach certain columns (here: y,sd.y,dset) to list result:
result <- tdmClassifySummary(result,opts,dset);
tdmGraAndLogFinalize(opts,gdObj); # close graphics and log file
result;
}
#result = main_sonar()
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