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diff --git a/analysis/knn.R b/analysis/knn.R
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-library("tidyverse")
-library("class")
-library("rpart")
-library("rpart.plot")
-library("viridis")
-
-## Simulation Parameters:
-## simkey         {baseline,extended,hint,hintandextended}
-## wireless       {lora,nbiot}
-## wakeupfor      {60s,180s}
-## seed           [1,200]
-## node           on[0,12]
-## isSender       {0,1}
-## dataSize       {1MB}
-
-## Metrics:
-## energy         [0,+inf)
-## nDataRcv       [0,+inf)
-
-nseed=200
-nwakeupfor=2
-nwireless=2
-nsimkey=4
-nsimulations=nseed*nwakeupfor*nwireless*nsimkey # Must be 3200
-
-## Load data
-data=read_csv("../CCGRID2022.csv")%>%distinct() # Note that in the data experiment wireless=="lora",seed==1,wakeupfor==60,simkey=="baseline" is present 2 times in the CSV file
-tmp_data_coverage=data%>%group_by(simkey,wireless,wakeupfor,seed)%>%mutate(coverage=sum(nDataRcv))%>%ungroup()%>%filter(isSender==1)%>%select(simkey,wireless,wakeupfor,seed,coverage)
-data_seed_isSender=data%>%group_by(simkey,wireless,wakeupfor,seed,isSender)%>%summarize(energy_mean=mean(energy))%>%
-    left_join(tmp_data_coverage,by=c("simkey","wireless","wakeupfor","seed"))%>%
-    mutate(efficiency=energy_mean/coverage)%>%
-    ungroup()
-data_seed=data%>%group_by(simkey,wireless,wakeupfor,seed)%>%summarize(energy=sum(energy),coverage=sum(nDataRcv))%>%
-    mutate(efficiency=energy/coverage)%>%
-    ungroup()
-
-## Prepare data for traning
-set.seed(1) # Reproducibility
-wireless_map=c("lora"=1,"nbiot"=2)
-data_ml=data_seed%>%select(-efficiency,-seed)%>%mutate(wireless=wireless_map[data_seed$wireless])#%>%filter(simkey!="hint")
-train_set=data_ml%>%sample_frac(0.8)     # 80% of the data
-test_set=data_ml%>%anti_join(train_set)  # 20% of the data
-
-## KNN training
-knn_predictions=knn(train=train_set%>%select(-simkey),test=test_set%>%select(-simkey),cl=train_set$simkey,k=10)
-## KNN analysis
-knn_cont_table=table(knn_predictions,test_set$simkey)
-knn_accuracy=round((sum(diag(knn_cont_table)/sum(rowSums(knn_cont_table))))*100)
-knn_prop_table=round(prop.table(knn_cont_table),digits=2)
-
-## Decision tree
-tree=rpart(
-    simkey ~ wireless + wakeupfor + energy + coverage,
-    data=train_set,
-    method="class",
-    minsplit=60,
-    minbucket=1)
-tree_predictions=predict(tree,newdata=test_set%>%select(-simkey),type="class")
-tree_cont_table=table(tree_predictions,test_set$simkey)
-tree_accuracy=round((sum(diag(tree_cont_table)/sum(rowSums(tree_cont_table))))*100)
-tree_prop_table=round(prop.table(tree_cont_table),digits=2)
-
-## Prints
-print(paste0("Accuracy: KNN=",knn_accuracy,"% CART=",tree_accuracy,"%"))
-pdf("figures/tree.pdf")
-tree_plot=rpart.plot(tree,box.palette=as.list(viridis::viridis(4,begin=0.48)))
-silent_call=dev.off()
-## Notes: KNN accuracy jump to 76% and CART to 80% accuracy without the hint policy
-
-## Generate simulation inputs
-inputs=tibble(
-wakeupfor = c(60,180,60,180),
-wireless  = c("lora", "lora", "nbiot", "nbiot"))
-constraints=apply(inputs,1,function(row){
-    wi=row["wireless"]
-    wa=as.numeric(row["wakeupfor"])
-    ## First extract energy/coverage boundaries
-    min_energy=min((data_seed%>%filter(wireless==wi,wakeupfor==wa))$energy)
-    max_energy=max((data_seed%>%filter(wireless==wi,wakeupfor==wa))$energy)
-    min_coverage=min((data_seed%>%filter(wireless==wi,wakeupfor==wa))$coverage)
-    max_coverage=max((data_seed%>%filter(wireless==wi,wakeupfor==wa))$coverage)
-    ## Generate random points (10 per scenarios)
-    n=10
-    current_inputs=tibble(
-        wireless=rep(wi,n),
-        wakeupfor=rep(wa,n),
-        energy_constraint=runif(n,min_energy,max_energy),
-        coverage_constraint=round(runif(n,min_coverage,max_coverage)))
-    predictions_knn=knn(train=train_set%>%select(-simkey),test=current_inputs%>%
-                                              rename(energy=energy_constraint,coverage=coverage_constraint)%>%
-                                              mutate(wireless=wireless_map[wireless]),cl=train_set$simkey,k=10)
-    predictions_tree=predict(tree,newdata=current_inputs%>%
-                                              rename(energy=energy_constraint,coverage=coverage_constraint)%>%
-                                              mutate(wireless=wireless_map[wireless]),type="class")
-    knn_final=tibble(cbind(current_inputs,tibble(simkey=predictions_knn,model="knn")))
-    tree_final=tibble(cbind(current_inputs,tibble(simkey=predictions_tree,model="tree")))
-    rbind(knn_final,tree_final)
-})
-inputs=do.call("rbind",constraints)
-## Dimension Energy/Coverage
-ggplot(data_seed%>%mutate(wakeupfor=as.character(wakeupfor)),
-       aes(coverage,energy,color=simkey))+geom_point()+
-    geom_point(data=inputs%>%mutate(wakeupfor=as.character(wakeupfor)),aes(coverage_constraint,energy_constraint),size=3,pch=5)+
-    ggtitle("Dimension Energy/Coverage")+xlab("Coverage")+ylab("Sum of nodes energy consumption (J)")+
-    facet_wrap(~wakeupfor+wireless,scale="free")
-ggsave("figures/random_inputs.pdf")
-write.csv(inputs,"inputs.csv",row.names=FALSE)