rename directory

parameter_space_partitioning/README.md

@@ -23,8 +23,8 @@
 ## alarm-red-green-plots
 	- contains all data relevant to construct the 2D and 3D graphs for alarm
 		
-## generated_plots
-	- contains all the generated plots (also included in the paper)	
+## generated_files
+	- contains all the generated plots (also included in the paper) and the PLA table for win95pts
 	
 ## output
 	- contains the output data for the specific threshhold values (e.g. 0.3 for hailfinder)

parameter_space_partitioning/perform_parameter_space_partitioning.sh

@@ -5,7 +5,7 @@ flag=$1
 
 #create 2D plot
 python3 scripts/create_2D_graph.py $flag
-cd generated_plots
+cd generated_files
 pdflatex --enable-write18 --extra-mem-top=100000000 --synctex=1 ../alarm-red-green-plots/2D/alarm_pla_graph_2D.tex 
 rm alarm_pla_graph_2D.aux
 rm alarm_pla_graph_2D.log
@@ -14,7 +14,7 @@ cd ..
 
 #create 3D plot
 python3 scripts/create_3D_graph.py $flag
-cd generated_plots
+cd generated_files
 pdflatex --enable-write18 --extra-mem-top=100000000 --synctex=1 ../alarm-red-green-plots/3D/alarm_pla_graph_3D.tex 
 rm alarm_pla_graph_3D.aux
 rm alarm_pla_graph_3D.log
@@ -23,7 +23,7 @@ cd ..
 
 #run experiments for the networks 'win95pts', 'hailfinder', 'hepar2'
 python3 scripts/run_pla_experiments.py $flag
-cd generated_plots
+cd generated_files
 pdflatex ../scripts/pla_plot.tex 
 rm pla_plot.aux
 rm pla_plot.log

parameter_space_partitioning/scripts/make_pla_table.py

@@ -19,7 +19,7 @@ def make_pla_table():
         mem = mem.replace('\n', '')
         df = pd.DataFrame([[coverage, sat, unsat, unknown, regions, time, mem]],columns=['coverage','accepting(%)','rejecting(%)', 'unknown(%)', '#regions', 'time', 'mem'])            
         data = pd.concat([data,df],ignore_index=True)
-    data.to_csv('table.csv', index=False)
+    data.to_csv('generated_files/PLA_table.csv', index=False)
     print(data.tail(16))
 
 if __name__ == "__main__":