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diff --git a/R Scripts/benchmarking.R b/R Scripts/benchmarking.R
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+library(igraph)
+setwd('~/Documents/Cascade Project')
+load('Raw Data/lcc.RData')
+load('Results/hyper-lcc.RData')
+load('Results/dag_dat_all.RData')
+source('Scripts/temporal.R')
+source('Scripts/structural.R')
+
+##### Initialize data
+formula = vic ~ sex + race + age + gang.member + gang.name
+lcc_verts$sex = as.factor(lcc_verts$sex)
+lcc_verts$race = as.factor(lcc_verts$race)
+lcc_verts$age = as.numeric(lcc_verts$age)
+lcc_verts$gang.name = as.factor(lcc_verts$gang.name)
+# sum(hyp_lcc_verts$vic)/length(days)
+
+##### Loop through days
+alpha = 1/100
+gamma = 0.18
+days = 70:max(hyp_lcc_verts$vic.day, na.rm=T)
+lambdas = 0#c(0, exp(seq(log(0.0000001), log(.0005), length.out=150)), 1)
+nvics = sum(hyp_lcc_verts$vic.day %in% days)
+correct_rank = matrix(nrow=nvics, ncol=length(lambdas))
+# correct_rank1 = correct_rank2 = correct_rank3 = c()
+edges_all = dag_dat_all[dag_dat_all$dist<2,]
+ptm = proc.time()
+for (day in days){
+  if (day %% 100 == 0) print(day)
+  
+  ##### Demographics model
+#   vics = match(unique(hyp_lcc_verts$ir_no[which(hyp_lcc_verts$vic.day<day)]),lcc_verts$name)
+#   victims = lcc_verts[,c('vic','sex','race','age','gang.member','gang.name')]
+#   victims$vic[vics] = TRUE
+#   victims$vic[-vics] = FALSE
+# #   glm.fit = glm(formula, data=victims, family=binomial)
+#   glm.fit = lm(formula, data=victims)
+#   glm.probs = predict(glm.fit, newdata=lcc_verts, type='response')
+  
+  ##### Cascade Model
+  edges = edges_all[which(edges_all$t1<day),]
+  f = temporal(edges$t1, day, alpha)
+  h = structural(gamma,edges$dist)
+  weights = f*h
+  ids = edges$to
+  irs = hyp_lcc_verts$ir_no[ids]
+  risk = data.frame(id=ids, ir=irs, weight=weights)
+  risk = risk[order(weights, decreasing=T),]
+  risk = risk[match(unique(risk$ir),risk$ir),]
+# maybe need to change this to reflect new algorithm that accounts for \tilde{p}
+
+  ##### Combined Model
+#   combined = data.frame(ir=attr(glm.probs,'name'), dem=as.numeric(glm.probs), cas=0, comb=0)
+  combined$cas[match(risk$ir, attr(glm.probs,'name'))] = risk$weight
+  
+  ##### Gather results
+  infected_irs = hyp_lcc_verts$ir_no[which(hyp_lcc_verts$vic.day==day)]
+  for (lambda in lambdas){
+    combined$comb = combined$cas#lambda*combined$dem + (1-lambda)*(1-combined$dem)*combined$cas
+    c_idx = which(lambdas==lambda)
+    r_idx = head(which(is.na(correct_rank[,c_idx])),length(infected_irs))
+    # !! order should be first: rank of (3,5,5,7) should be (1,2,2,4), may need to do n-rank
+    correct_rank[r_idx,c_idx] = match(infected_irs, combined$ir[order(combined$comb, decreasing=T)])
+    # maybe should also mark down vic/nonvic status of each?
+  }
+  
+}
+print(proc.time()-ptm)
+
+
+##### Plot results
+hist(correct_rank3,150,xlim=c(0,vcount(lcc)),col=rgb(0,0,1,1/8),
+     xlab='Risk Ranking of Victims',main='')
+hist(correct_rank1,150,xlim=c(0,vcount(lcc)),col=rgb(1,0,1,1/8),add=T)
+hist(correct_rank2,150,xlim=c(0,vcount(lcc)),col=rgb(1,0,1,1/8),add=T)
+legend("topright", c("Demographics Model", "Cascade Model"), 
+       fill=c(rgb(1,0,1,1/8), rgb(0,0,1,1/8)))
+
+counts = matrix(c(colSums(correct_rank<(vcount(lcc)/1000))*100/nvics,
+                  colSums(correct_rank<(vcount(lcc)/200))*100/nvics,
+                  colSums(correct_rank<(vcount(lcc)/100))*100/nvics),
+                nrow=3, byrow=T)
+plot(lambdas,counts[1,],log='x',type='l')
+
+correct_rank1 = correct_rank[,length(lambdas)]
+correct_rank2 = correct_rank[,1]
+correct_rank3 = correct_rank[,which.min(colMeans(correct_rank))]
+counts = matrix(c(sum(correct_rank1<(vcount(lcc)*0.001)),
+               sum(correct_rank1<(vcount(lcc)*0.005)),
+               sum(correct_rank1<(vcount(lcc)*0.01)),
+               sum(correct_rank2<(vcount(lcc)*0.001)),
+               sum(correct_rank2<(vcount(lcc)*0.005)),
+               sum(correct_rank2<(vcount(lcc)*0.01)),
+               sum(correct_rank3<(vcount(lcc)*0.001)),
+               sum(correct_rank3<(vcount(lcc)*0.005)),
+               sum(correct_rank3<(vcount(lcc)*0.01))),
+               nrow=3, byrow=T)
+counts = counts*100/nvics
+barplot(counts, 
+        xlab="Size of High-Risk Population",
+        ylab="Percent of Victims Predicted",
+        names.arg=c('0.1%','0.5%','1%'),ylim=c(0,max(counts)*1.1),
+        col=c(rgb(0,0,1,1/2),rgb(1,0,0,1/2),rgb(0,1,0,1/2)),
+        beside=TRUE)
+legend("topleft", inset=0.05, 
+       c("Demographics Model", "Cascade Model", "Combined Model"), 
+       fill=c(rgb(0,0,1,1/2),rgb(1,0,0,1/2),rgb(0,1,0,1/2)))
+box(which='plot')
+par(new=T)
+counts = counts/(100/nvics)
+barplot(counts, 
+        ylim=c(0,max(counts)*1.1),
+        col=c(rgb(0,0,1,0),rgb(1,0,0,0),rgb(0,1,0,0)),
+        beside=TRUE)
+axis(side = 4)
+mtext(side = 4, line = 3, "Number of Victims Predicted")
+
+popsizes = c(0.1, 0.5, 1)
+plot(popsizes,counts[1,],type='l',ylim=c(0,max(counts)))
+lines(popsizes,counts[2,])
+lines(popsizes,counts[3,])
+lines(c(0,1),c(0,1))
+
+#### Precision-Recall Curve
+plot(ecdf(correct_rank1),col='red',xlim=c(0,vcount(lcc)),lwd=2)
+plot(ecdf(correct_rank2),col='darkblue',lwd=2,add=T)
+plot(ecdf(correct_rank3),col='darkgreen',lwd=2,add=T)
+legend("bottomright", inset=0.05, 
+       c("Demographics Model", "Cascade Model", "Combined Model"), 
+       fill=c('red','darkblue','darkgreen'))
+lines(c(0,vcount(lcc)),c(0,1))