diff options
Diffstat (limited to 'analysis/knn.R')
| -rw-r--r-- | analysis/knn.R | 47 |
1 files changed, 43 insertions, 4 deletions
diff --git a/analysis/knn.R b/analysis/knn.R index 729f332..d8a6ce1 100644 --- a/analysis/knn.R +++ b/analysis/knn.R @@ -34,12 +34,12 @@ data_seed=data%>%group_by(simkey,wireless,wakeupfor,seed)%>%summarize(energy=sum mutate(efficiency=energy/coverage)%>% ungroup() - ## Prepare data for traning set.seed(1) # Reproducibility -data_seed=data_seed%>%select(-efficiency,-seed)%>%mutate(wireless=as.numeric(as.factor(data_seed$wireless)))#%>%filter(simkey!="hint") -train_set=data_seed%>%sample_frac(0.8) # 80% of the data -test_set=data_seed%>%anti_join(train_set) # 20% of the data +wireless_map=c("lora"=1,"nbiot"=2) +data_ml=data_seed%>%select(-efficiency,-seed)%>%mutate(wireless=wireless_map[data_seed$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$simkey,k=10) @@ -66,3 +66,42 @@ pdf("figures/tree.pdf") tree_plot=rpart.plot(tree,box.palette=as.list(viridis::viridis(4,begin=0.48))) silent_call=dev.off() ## Notes: KNN accuracy jump to 76% and CART to 80% accuracy without the hint policy + +## Generate simulation inputs +inputs=tibble( +wakeupfor = c(60,180,60,180), +wireless = c("lora", "lora", "nbiot", "nbiot")) +constraints=apply(inputs,1,function(row){ + wi=row["wireless"] + wa=as.numeric(row["wakeupfor"]) + ## First extract energy/coverage boundaries + min_energy=min((data_seed%>%filter(wireless==wi,wakeupfor==wa))$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) |