$ make
gcc --std=c99 -D_GNU_SOURCE -Wall -Werror -g -c C/common.c -o C/common.o
gcc --std=c99 -D_GNU_SOURCE -Wall -Werror -g -c C/object.c -o C/object.o
gcc --std=c99 -D_GNU_SOURCE -Wall -Werror -g -c C/strfun.c -o C/strfun.o
gcc --std=c99 -D_GNU_SOURCE -Wall -Werror -g -c C/readwrite.c -o C/readwrite.o
gcc --std=c99 -D_GNU_SOURCE -Wall -Werror -g -c C/argparse.c -o C/argparse.o
gcc --std=c99 -D_GNU_SOURCE -Wall -Werror -g -c C/argparse_iforest.c -o C/argparse_iforest.o
gcc --std=c99 -D_GNU_SOURCE -Wall -Werror -g -c C/frames.c -o C/frames.o
ld -r C/common.o C/object.o C/strfun.o C/readwrite.o C/argparse.o C/argparse_iforest.o C/frames.o -o cincl.o
g++ --std=c++11 -Wall -Werror -g   -c Forest.cpp -o Forest.o
g++ --std=c++11 -Wall -Werror -g   -c IsolationForest.cpp -o IsolationForest.o
g++ --std=c++11 -Wall -Werror -g   -c Tree.cpp -o Tree.o
g++ --std=c++11 -Wall -Werror -g   -c utility.cpp -o utility.o
g++ --std=c++11 -Wall -Werror -g   -c OnlineIF.cpp -o OnlineIF.o
g++ --std=c++11 -Wall -Werror -g   -c main.cpp -o main.o
g++ --std=c++11 -Wall -Werror -g   -o iforest.exe cincl.o Forest.o IsolationForest.o Tree.o utility.o OnlineIF.o main.o

$ ../iforest.exe
Usage: D:\Codes\githubFeedbackIF\feedbackisolationforest\iforest.exe [options]
Options:
        -i FILE, --infile=FILE
                Specify path to input data file. (Required).
        -o FILE, --outfile=FILE
                Specify path to output results file. (Required).
        -m COLS, --metacol=COLS
                Specify columns to preserve as meta-data. (Separated by ',' Use '-' to specify ranges).
        -t N, --ntrees=N
                Specify number of trees to build.
                Default value is 100.
        -s S, --sampsize=S
                Specify subsampling rate for each tree. (Value of 0 indicates to use entire data set).
                Default value is 2048.
        -d MAX, --maxdepth=MAX
                Specify maximum depth of trees. (Value of 0 indicates no maximum).
                Default value is 0.
        -H, --header
                Toggle whether or not to expect a header input.
                Default value is true.
        -v, --verbose
                Toggle verbose ouput.
                Default value is false.
        -w W, --RegType=W
                Type of regularizer: 1 indicates l1 and 2 indicates l2
                Default value is 1.
        -c N, --columns=N
                specify number of columns to use.
                Default value is 0.
        -r R, --regularizer=R
                specify regularization constant.
                Default value is 0.
        -u U, --updatetype=U
                specify type of update to perform on weights.
                Default value is 0.
        -f F, --numfeedback=F
                specify number of feedback iteration to perform.
                Default value is 100.
        -x X, --numiter=X
                specify number of times experiments to rerun.
                Default value is 1.
        -l L, --losstype=L
                specify type of loss to use.
                Default value is 0.
        -g G, --numgradupd=G
                specify number of gradient update to run.
                Default value is 1.
        -a A, --learningrate=A
                specify learning rate for gradient update. Set 0 for variable learning rate 1/sq(t).
                Default value is 1.
        -p P, --posweight=P
                specify whether weights to be restricted to be positive only.
                Default value is 0.
        -z Z, --reInitWeights=Z
                specify whether weights reset to 1 after each feedback.(for stochastic and batch update)
                Default value is 0.
        -h, --help
                Print this help message and exit.

$ cd test
$ ../iforest.exe -i datasets/anomaly/ann_thyroid_1v3/fullsamples/ann_thyroid_1v3_1.csv -o outtest/ann_thyroid_1v3_1 -t 100 -s 256 -m 1 -x 5 -f 10 -w 2 -l 2 -a 1
# Trees          = 100
# Samples        = 256
MaxHeight        = 0
Orig Dimension   = 3251,21
# Iterations     = 5
# Feedbacks      = 10
Loss   type      = logistic
Update type      = online
Num Grad Upd     = 1
Reg. Constant    = 0
Learning Rate    = 1
Variable LRate   = 0
Positive W only  = 0
ReInitWgts       = 0
Regularizer type = L2
iter 0, # Anomaly: Baseline -> 2 Feedback -> 8
iter 1, # Anomaly: Baseline -> 1 Feedback -> 1
iter 2, # Anomaly: Baseline -> 2 Feedback -> 8
iter 3, # Anomaly: Baseline -> 2 Feedback -> 5
iter 4, # Anomaly: Baseline -> 2 Feedback -> 7
Avg: Baseline -> 1.8 Feedback -> 5.8
Time elapsed: 2 seconds