rename examples to code;

Signed-off-by: vsoch <vsoch@users.noreply.github.com>

association-analysis/hill-climb/README.md

@@ -12,7 +12,7 @@ $ source env/bin/activate
 $ pip install -r ../requirements.txt
 ```
 
-Make sure examples are cloned one directory up!
+Make sure examples (code) are cloned one directory up!
 
 ### Generate Flags
 
@@ -29,26 +29,26 @@ $ python hill-climb.py run ../../data/gpp_flags_filtered.json Prog.cpp
 for a specific example:
 
 ```bash
-$ python hill-climb.py run ../../data/gpp_flags_filtered.json ../examples/Aliases/Prog.cpp
+$ python hill-climb.py run ../../data/gpp_flags_filtered.json ../code/Aliases/Prog.cpp
 ```
 
 In practice, I found that using parallel made more sense (no workers in Python).
 Here is how to test a single script:
 
 ```bash
-$ python hill-climb.py run ../../data/gpp_flags.json "../examples/sizeof Operator/Prog.cpp" --outdir-num 1
+$ python hill-climb.py run ../../data/gpp_flags.json "../code/sizeof Operator/Prog.cpp" --outdir-num 1
 ```
 
 And then to run using parallel (`apt-get install -y parallel`)
 
 ```bash
-$ find ../examples -name "*Prog.cpp" | parallel -I% --max-args 1 python hill-climb.py run ../../data/gpp_flags.json "%" --outdir-num 1
+$ find ../code -name "*Prog.cpp" | parallel -I% --max-args 1 python hill-climb.py run ../../data/gpp_flags.json "%" --outdir-num 1
 ```
 
 There is a [run.sh](run.sh) script that I used, and ultimately ran between a range of 0 and 29 (to generate 30 runs of the same predictions for 100 iterations each). Finally, to run on a SLURM cluster:
 
 ```bash
-for iter in {11..30}; do
+for iter in {0..1}; do
    sbatch run_slurm.sh $iter
 done
 ```

association-analysis/hill-climb/run.sh

@@ -1,4 +1,4 @@
 #!/bin/bash
 for iter in {0..9}; do
-  find ../examples -name "*Prog.cpp" | parallel -I% --max-args 1 python hill-climb.py run ../../data/gpp_flags.json "%" --outdir-num $iter
+  find ../code -name "*Prog.cpp" | parallel -I% --max-args 1 python hill-climb.py run ../../data/gpp_flags.json "%" --outdir-num $iter
 done

association-analysis/hill-climb/run_slurm.sh

@@ -1,2 +1,2 @@
 #!/bin/bash
-find ../examples -name "*Prog.cpp" | parallel -I% --max-args 1 python hill-climb.py run ../../data/gpp_flags.json "%" --outdir-num $1
+find ../code -name "*Prog.cpp" | parallel -I% --max-args 1 python hill-climb.py run ../../data/gpp_flags.json "%" --outdir-num $1

association-analysis/montecarlo/README.md

@@ -21,7 +21,7 @@ $ source env/bin/activate
 $ pip install -r requirements.txt
 ```
 
-Make sure examples are cloned one directory up!
+Make sure examples (code) is cloned one directory up!
 
 ### Generate Flags
 
@@ -40,13 +40,13 @@ In practice, I found that using parallel made more sense (no workers in Python).
 Here is how to test a single script:
 
 ```bash
-$ python montecarlo-parallel.py run ../../data/gpp_flags.json "../examples/sizeof Operator/Prog.cpp" --outdir-num 1 --num-iter 2000
+$ python montecarlo-parallel.py run ../../data/gpp_flags.json "../code/sizeof Operator/Prog.cpp" --outdir-num 1 --num-iter 2000
 ```
 
 And then to run using parallel (`apt-get install -y parallel`)
 
 ```bash
-$ find ../examples -name "*Prog.cpp" | parallel -I% --max-args 1 python montecarlo-parallel.py run ../../data/gpp_flags.json "%" --outdir-num 1 --num-iter 2000
+$ find ../code -name "*Prog.cpp" | parallel -I% --max-args 1 python montecarlo-parallel.py run ../../data/gpp_flags.json "%" --outdir-num 1 --num-iter 2000
 ```
 
 There is a [run.sh](run.sh) script that I used, and ultimately ran between a range of 0 and 29 (to generate 30 runs of the same predictions for 100 iterations each). Finally, to run on a SLURM cluster:

association-analysis/montecarlo/run.sh

@@ -1,4 +1,4 @@
 #!/bin/bash
 for iter in {0..9}; do
-  find ../examples -name "*Prog.cpp" | parallel -I% --max-args 1 python montecarlo-parallel.py run ../../data/gpp_flags.json "%" --outdir-num $iter --num-iter 2000
+  find ../code -name "*Prog.cpp" | parallel -I% --max-args 1 python montecarlo-parallel.py run ../../data/gpp_flags.json "%" --outdir-num $iter --num-iter 2000
 done

association-analysis/montecarlo/run_slurm.sh

@@ -1,2 +1,2 @@
 #!/bin/bash
-find ../examples -name "*Prog.cpp" | parallel -I% --max-args 1 python montecarlo-parallel.py run ../../data/gpp_flags.json "%" --outdir-num $1 --num-iter 5000
+find ../code -name "*Prog.cpp" | parallel -I% --max-args 1 python montecarlo-parallel.py run ../../data/gpp_flags.json "%" --outdir-num $1 --num-iter 5000