Clean repo and debug setup.sh

2 files changed, 9 insertions, 6 deletions

diff --git a/analysis/days.R b/analysis/in-situ.R
index 93d478e..2d57666 100644
--- a/analysis/days.R
+++ b/analysis/in-situ.R
@@ -27,6 +27,8 @@ library("latex2exp")
 ## energy         [0,+inf)
 ## nDataRcv       [0,+inf)
 
+## WARNING: Goto line 138 first and set the boolean to T (populate the R environment with accuracy results) afterwhich you can set it to FALSE (save time)
+
 nseed=200
 nwakeupfor=2
 nwireless=2
@@ -75,7 +77,7 @@ generate_accuracy_for=function(ignore_hint=FALSE,seed_max=200,attempts_max=2,wrl
             data_ml=data_ml%>%filter(simkey!="hint")
         }
         train_set=data_ml%>%filter(seed<=seed_max)%>%select(-seed)     # train data on seed_max*3 days
-        test_set=data_ml%>%select(-seed)                               # build test_sed
+        test_set=data_ml%>%select(-seed)                               # build test_set
         #print(paste0("Test set ",NROW(test_set)))
         #print(paste0("Train set ",NROW(train_set)))
         ## KNN training
@@ -86,7 +88,7 @@ generate_accuracy_for=function(ignore_hint=FALSE,seed_max=200,attempts_max=2,wrl
         #print(knn_accuracy)
         knn_prop_table=round(prop.table(knn_cont_table),digits=2)
         knn_f1_score=F1_Score2(test_set$simkey,knn_predictions)
-        
+
         ## Decision tree
         tree=rpart(
             simkey ~ wireless + wakeupfor + energy + coverage,
@@ -111,23 +113,24 @@ generate_accuracy_for=function(ignore_hint=FALSE,seed_max=200,attempts_max=2,wrl
 
 }
 
+########## Train models from seed 0 to seed 160 (this code assumes that each day, a given policy is used)
 generate_accuracy_energy = function(wireless,wakeupfor,steps=1, accuracy=10,ignore_hint=FALSE){
     ## Setup variables
     npolicies=4
     data_seed_for_energy=data_seed%>%ungroup()
     if(ignore_hint){npolicies=npolicies-1;data_seed_for_energy=data_seed%>%filter(simkey!="hint")}
-    data_seed_for_energy=data_seed_for_energy%>%filter(wireless==!!wireless,wakeupfor==!!wakeupfor)
+    data_seed_for_energy=data_seed_for_energy%>%filter(wireless==!!wireless,wakeupfor==!!wakeupfor) # Note !! do unquote, here we select the proper scenario
 
     ## Generate inputs
     result=tibble()
     result_energy=tibble()
-    for(i in seq(1,160,by=steps)){ # We stop at 80% of the data (this way test set is at least 20%)
+    for(i in seq(1,160,by=steps)){ # We stop at 80% of the data (this way test set is at least 20%) here 160 over 200 seeds
         print(paste("Step",i))
         acc=generate_accuracy_for(ignore_hint=ignore_hint,seed=i,attempts_max=accuracy,wrl=wireless,wuf=wakeupfor)
-        result_energy=rbind(result_energy,data_seed_for_energy%>%filter(seed<=i)%>%summarize(energy=sum(energy),seed_max=i,days=i*npolicies,setup=paste0(!!wireless," ",!!wakeupfor,"s"),wireless=!!wireless,wakeupfor=!!wakeupfor))
+        result_energy=rbind(result_energy,data_seed_for_energy%>%filter(seed<=i)%>%summarize(energy=sum(energy),seed_max=i,days=i*npolicies,setup=paste0(!!wireless," ",!!wakeupfor,"s"),wireless=!!wireless,wakeupfor=!!wakeupfor)) # days=i*npolicies since 1 policy only per day is used (see paper section IV.A)
         result=rbind(result,acc)
     }
-   list(accuracy=result%>%mutate(days=seed_max*npolicies), # Since 3 policies (since ignore_hint=TRUE)
+   list(accuracy=result%>%mutate(days=seed_max*npolicies), # Since 1 policy per days see L130
         energy=result_energy)
 }
 
diff --git a/analysis/learning.R b/analysis/offline.R
index da444d8..da444d8 100644
--- a/analysis/learning.R
+++ b/analysis/offline.R