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#*# --------- demo/demo00sonar.r ---------
#*# This demo shows a simple data mining process (level 1 of TDMR) for the classification task
#*# SONAR (from UCI repository,
#*# http://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+%28Sonar,+Mines+vs.+Rocks%29).
#*# The data mining process is in main_sonar.r, which calls tdmClassifyLoop and tdmClassify.
#*# with Random Forest as the prediction model.
## path is the dir with data and main_*.r file:
path <- paste(find.package("TDMR"), "demo02sonar",sep="/");
#path <- paste("../inst", "demo02sonar",sep="/");
source(paste(path,"main_sonar.r",sep="/")); # needed to define readTrnSonar
controlDM <- function() {
#
# settings for the DM process (former sonar_00.apd file):
# (see ?tdmOptsDefaultsSet for a complete list of all default settings
# and many explanatory comments)
#
opts = list(path = path,
dir.data = "data", # relative to path
filename = "sonar.txt",
READ.TrnFn = readTrnSonar, # defined in main_sonar.r
data.title = "Sonar Data",
NRUN = 1, # how many runs with different train & test samples - or -
# how many CV-runs, if TST.kind="cv"
VERBOSE = 2
);
opts <- setParams(opts, defaultOpts(), keepNotMatching = TRUE);
# defaultOpts() fills in sensible defaults for all other controls
# See tdmOptsDefaults.r for the list of those elements and many
# explanatory comments.
# Keep all elements present in opts, but NULL in defaultOpts().
}
opts <- controlDM();
result <- main_sonar(opts);
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