From 8e09ca6ca68c71bdab65525b529e2adfa281823c Mon Sep 17 00:00:00 2001
From: Ben Green <ben@SEASITs-MacBook-Pro.local>
Date: Wed, 1 Jul 2015 00:49:23 -0400
Subject: Got predict-victims running in parallel, drastically reducing the
 time for each test. Also changed how we get the rankings of infected
 individuals each day.

---
 R Scripts/find-parents.R          |  15 ++-
 R Scripts/generate-network.R      |  40 +++---
 R Scripts/predict-victims-plots.R |   9 +-
 R Scripts/predict-victims.R       |  39 +++---
 experiments/build_network.py      |   3 +-
 experiments/ml2.so                | Bin 123652 -> 123652 bytes
 experiments/out.log               | 255 +++++---------------------------------
 experiments/process.py            |  42 +++----
 8 files changed, 113 insertions(+), 290 deletions(-)

diff --git a/R Scripts/find-parents.R b/R Scripts/find-parents.R
index 3ec8809..023d7ba 100644
--- a/R Scripts/find-parents.R	
+++ b/R Scripts/find-parents.R	
@@ -6,8 +6,8 @@
 # source('criminal_cascades/R Scripts/structural.R')
 
 ##### Initialize parameters based on what ml2 found
-alpha = 0.061
-delta = 0.082
+alpha = 0.18
+delta = 0.09
 
 ##### Get weights
 edges = dag_dat_test[!is.na(dag_dat_test$t2),]
@@ -21,20 +21,27 @@ weights = p/p_tilde
 edges$weight = weights
 
 ##### Find most likely parents
-parents = data.frame(vic=0,Npars=0,par_rank=0)
+parents = data.frame(vic=0,Npars=0,par_rank=0,rand_rank=0)
 vics = setdiff(vic_ids,seeds)
+print(length(vics))
 for (u in vics){
+  if(which(vics==u) %% 500 == 0) print(which(vics==u))
   u_parents = edges[edges$to==u,]
   u_parents = u_parents[order(u_parents$weight,decreasing=T),]
   Nparents = dim(u_parents)[1]
   infector = V(g)$infector[u]
   infectorID = which(u_parents$from==infector)
-  parents[which(vics==u),] = c(u, Nparents, infectorID)
+  randID = sample(1:Nparents,1)
+  parents[which(vics==u),] = c(u, Nparents, infectorID, randID)
 }
 
 ##### Get some summary statistics on how well 
+mean(parents$par_rank==1)
 median(parents$par_rank[parents$Npars>9])
 median(parents$par_rank[parents$Npars>99])
 
+edges[edges$to==2847,]
 
+## baseline alg
+# for each vic, find potential parents, pick one at random
 
diff --git a/R Scripts/generate-network.R b/R Scripts/generate-network.R
index 3b40969..dab81a4 100644
--- a/R Scripts/generate-network.R	
+++ b/R Scripts/generate-network.R	
@@ -3,20 +3,20 @@ setwd("~/Documents/Cascade Project/")
 source('criminal_cascades/R Scripts/temporal.R')
 source('criminal_cascades/R Scripts/structural.R')
 
-alpha = 1/100
-beta = 0.02
-delta = 0.15
+alpha = 1/10
+beta = 0.01
+delta = 0.25
 # lmbda = 1/10
 t_max = 1000
 
 N = 5000
 g = forest.fire.game(nodes=N, fw.prob=0.3, ambs=1, directed=F)
-plot(g, vertex.size=5, vertex.label=NA)
+plot(g, vertex.size=3, vertex.label=NA)
 
 V(g)$seed = runif(vcount(g))<beta
 seeds = which(V(g)$seed)
 V(g)$vic = V(g)$seed
-V(g)$vic.day[V(g)$seed] = sample(1:t_max, sum(V(g)$seed))
+V(g)$vic.day[V(g)$seed] = 1#sample(1:t_max, sum(V(g)$seed))
 V(g)$spawn.date = 0
 V(g)$infector = NA
 
@@ -28,17 +28,19 @@ for (day in 1:t_max){
     dists = as.numeric(shortest.paths(g,vic,neighbors))
     infected = neighbors[which(runif(length(neighbors))<structural(delta, dists))]
     infected = setdiff(infected,seeds) # don't try to infect seeds
-    inf.days = day + ceiling(alpha*rexp(length(infected),alpha))
+    inf.days = day + ceiling(rexp(length(infected),alpha))
+    realized = ((inf.days <= V(g)$vic.day[infected]) %in% c(NA,T)) & (inf.days<=t_max)
+    infected = infected[realized]
     V(g)$vic[infected] = TRUE
-    infects = (inf.days <= V(g)$vic.day[infected]) %in% c(NA,T)
-    V(g)$vic.day[infected[infects]] = inf.days[infects]
-    V(g)$infector[infected[infects]] = vic
+    V(g)$vic.day[infected] = inf.days[realized]
+    V(g)$infector[infected] = vic
   }
 }
 
 vic_ids = which(V(g)$vic)
+print(length(vic_ids))
 cols = rep('lightblue',N); cols[V(g)$vic]='red'; cols[V(g)$seed]='darkred'
-plot(g, vertex.size=5, vertex.label=NA, vertex.color=cols)
+plot(g, vertex.size=3, vertex.label=NA, vertex.color=cols)
 
 ##### generate dag_dat
 dag_dat_test = data.frame(matrix(nrow=1,ncol=10))
@@ -68,12 +70,12 @@ rownames(dag_dat_test) = NULL
 
 write.csv(dag_dat_test, file='Results/dag_dat_test.csv')
 
-##### analyze performance of recovery algorithm
-recovered = read.csv('Results/infectors.csv',header=F,col.names=c('victim','infector'))
-recovered = recovered[order(recovered$victim),]
-infectors = cbind(setdiff(vic_ids,seeds),
-                  V(g)$infector[setdiff(vic_ids,seeds)],
-                  recovered$infector[recovered$victim %in% setdiff(vic_ids,seeds)])
-mean(infectors[,2]==infectors[,3])
-
-dag_dat_test[dag_dat_test$to==4984,]
\ No newline at end of file
+##### analyze performance of recovery algorithm ------
+# recovered = read.csv('Results/infectors.csv',header=F,col.names=c('victim','infector'))
+# recovered = recovered[order(recovered$victim),]
+# infectors = cbind(setdiff(vic_ids,seeds),
+#                   V(g)$infector[setdiff(vic_ids,seeds)],
+#                   recovered$infector[recovered$victim %in% setdiff(vic_ids,seeds)])
+# mean(infectors[,2]==infectors[,3])
+# 
+# dag_dat_test[dag_dat_test$to==4984,]
diff --git a/R Scripts/predict-victims-plots.R b/R Scripts/predict-victims-plots.R
index 8a93667..2ac62c8 100644
--- a/R Scripts/predict-victims-plots.R	
+++ b/R Scripts/predict-victims-plots.R	
@@ -2,7 +2,7 @@
 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)
+hist(correct_rank2,150,xlim=c(0,vcount(lcc)),col=rgb(0,0,1,1/8),add=T)
 legend("topright", c("Demographics Model", "Cascade Model"), 
        fill=c(rgb(1,0,1,1/8), rgb(0,0,1,1/8)))
 
@@ -12,9 +12,9 @@ counts = matrix(c(colSums(correct_rank<(vcount(lcc)/1000))*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))]
+correct_rank1 = correct_rank[,length(lambdas)] # demographics model
+correct_rank2 = correct_rank[,1] # cascade model
+correct_rank3 = correct_rank[,which.min(colMeans(correct_rank))] # best combined model
 counts = matrix(c(sum(correct_rank1<(vcount(lcc)*0.001)),
                   sum(correct_rank1<(vcount(lcc)*0.005)),
                   sum(correct_rank1<(vcount(lcc)*0.01)),
@@ -59,3 +59,4 @@ 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))
+
diff --git a/R Scripts/predict-victims.R b/R Scripts/predict-victims.R
index 470815d..2bda7e2 100644
--- a/R Scripts/predict-victims.R	
+++ b/R Scripts/predict-victims.R	
@@ -1,4 +1,7 @@
 library(igraph)
+library(foreach)
+library(doMC)
+registerDoMC(cores=4)
 setwd('~/Documents/Cascade Project')
 load('Raw Data/lcc.RData')
 load('Results/hyper-lcc.RData')
@@ -7,34 +10,36 @@ source('criminal_cascades/R Scripts/temporal.R')
 source('criminal_cascades/R Scripts/structural.R')
 
 ##### Initialize data
-formula = vic ~ sex + race + age + gang.member + gang.name
+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$vic)/length(days)
+df = data.frame(ir=lcc_verts$ir_no, dem=0, cas=0, comb=0)
 
 alpha = 0.0028
 delta = 0.06
 days = sort(unique(hyp_lcc_verts$vic.day)) # 70:max(hyp_lcc_verts$vic.day, na.rm=T)
 lambdas = c(0,1)#c(0, exp(seq(log(0.0000001), log(.0005), length.out=150)), 1)
-nvics = sum(lcc_verts$vic)#sum(hyp_lcc_verts$vic.day %in% days)
-correct_rank = matrix(nrow=nvics, ncol=length(lambdas))
+nvics = sum(hyp_lcc_verts$vic.day %in% days)
 edges_all = dag_dat_all
 
 ##### Loop through days
+writeLines(c(""), "Results/log.txt")
 ptm = proc.time()
-for (day in days){
-  if (which(day==days) %% 100 == 0) print(day)
-  
+correct_rank = foreach (day = days, .combine=rbind) %dopar% {
+  if (which(day==days) %% 100 == 0){sink("Results/log.txt", append=TRUE);cat(paste("day:",day,"\n"))}
+
   ##### 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')
+  fit = lm(formula, data=victims)
+#   fit = glm(formula, data=victims, family=binomial)
+#   fit = randomForest(formula, data=victims[,1:5], ntree=100)
+  probs = predict(fit, newdata=lcc_verts, type='response')
   
   ##### Cascade Model
   edges = edges_all[which(edges_all$t1<day),]
@@ -49,19 +54,21 @@ for (day in days){
 # 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$weight
+  combined = df#data.frame(ir=attr(probs,'name'), dem=as.numeric(probs), cas=0, comb=0)
+  combined$dem[match(attr(probs,'name'), df$ir)] = as.numeric(probs)
+  combined$cas[match(risk$ir, attr(probs,'name'))] = risk$weight
   
   ##### Gather results
   infected_irs = hyp_lcc_verts$ir_no[which(hyp_lcc_verts$vic.day==day)]
+  crday = matrix(nrow=length(infected_irs), ncol=length(lambdas))
   for (lambda in lambdas){
     combined$comb = lambda*combined$dem + (1-lambda)*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$comb, decreasing=T)])
-    # maybe should also mark down vic/nonvic status of each?
+    crday[,c_idx] = rank(-combined$comb,ties.method='average')[match(infected_irs,combined$ir)]
   }
-  
+
+  return(crday)
 }
 print(proc.time()-ptm)
+
+# save(correct_rank, file='Results/correct_rank_62815.RData') 
\ No newline at end of file
diff --git a/experiments/build_network.py b/experiments/build_network.py
index 0e912b0..2226070 100644
--- a/experiments/build_network.py
+++ b/experiments/build_network.py
@@ -8,6 +8,7 @@ def build_network(filename):
     victims = {}
     non_victims = {}
     age = 0
+    t_max = 1000
     with open(filename) as fh:
         reader = DictReader(fh)
         for row in reader:
@@ -29,7 +30,7 @@ def build_network(filename):
                     victims[from_] = []
             # 'to' is not a victim
             else:
-                dt = 1000 - int(row["t1"])
+                dt = t_max - int(row["t1"])
                 parent = (from_, dist, dt, w1, w2, w3)
                 if to not in non_victims:
                     # age += 3012 - int(row["spawn2"])
diff --git a/experiments/ml2.so b/experiments/ml2.so
index 515e455..37ff3ea 100755
Binary files a/experiments/ml2.so and b/experiments/ml2.so differ
diff --git a/experiments/out.log b/experiments/out.log
index 6ac961e..98ddbb1 100644
--- a/experiments/out.log
+++ b/experiments/out.log
@@ -1,225 +1,30 @@
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-0.00316227766017	0.0199526231497	1e-05	11270	-220033.988259
-0.00517947467923	0.0001	1e-05	11270	-220027.468461
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-0.00848342898244	0.0001	1e-05	11270	-220027.496279
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-0.00848342898244	0.00141253754462	1e-05	11270	-220028.473821
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-0.0227584592607	0.0001	1e-05	11270	-220027.604764
-0.0227584592607	0.000145977430289	1e-05	11270	-220027.688627
-0.0227584592607	0.000213094101537	1e-05	11270	-220027.811148
-0.0227584592607	0.00031106929352	1e-05	11270	-220027.990215
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-0.0227584592607	0.000967641053709	1e-05	11270	-220029.196809
-0.0227584592607	0.00141253754462	1e-05	11270	-220030.020972
-0.0227584592607	0.0020619860095	1e-05	11270	-220031.233691
-0.0227584592607	0.00301003418958	1e-05	11270	-220033.024747
-0.0227584592607	0.00439397056076	1e-05	11270	-220035.684297
-0.0227584592607	0.00641420531224	1e-05	11270	-220039.664958
-0.0227584592607	0.00936329208824	1e-05	11270	-220045.692799
-0.0227584592607	0.0136682931808	1e-05	11270	-220054.977676
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-0.0372759372031	0.0001	1e-05	11270	-220027.698422
-0.0372759372031	0.000145977430289	1e-05	11270	-220027.825392
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-0.0372759372031	0.000967641053709	1e-05	11270	-220030.108881
-0.0372759372031	0.00141253754462	1e-05	11270	-220031.35677
-0.0372759372031	0.0020619860095	1e-05	11270	-220033.193044
-0.0372759372031	0.00301003418958	1e-05	11270	-220035.905157
-0.0372759372031	0.00439397056076	1e-05	11270	-220039.932658
-0.0372759372031	0.00641420531224	1e-05	11270	-220045.961343
-0.0372759372031	0.00936329208824	1e-05	11270	-220055.091575
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-0.0610540229659	0.0001	1e-05	11270	-220027.823505
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-0.0610540229659	0.00141253754462	1e-05	11270	-220033.140891
-0.0610540229659	0.0020619860095	1e-05	11270	-220035.810071
-0.0610540229659	0.00301003418958	1e-05	11270	-220039.752546
-0.0610540229659	0.00439397056076	1e-05	11270	-220045.607535
-0.0610540229659	0.00641420531224	1e-05	11270	-220054.372543
-0.0610540229659	0.00936329208824	1e-05	11270	-220067.648403
-0.0610540229659	0.0136682931808	1e-05	11270	-220088.103672
-0.0610540229659	0.0199526231497	1e-05	11270	-220120.413364
-0.1	0.0001	1e-05	11270	-220027.97435
-0.1	0.000145977430289	1e-05	11270	-220028.228322
-0.1	0.000213094101537	1e-05	11270	-220028.599372
-0.1	0.00031106929352	1e-05	11270	-220029.141677
-0.1	0.000454090961097	1e-05	11270	-220029.934723
-0.1	0.000662870316183	1e-05	11270	-220031.095385
-0.1	0.000967641053709	1e-05	11270	-220032.796098
-0.1	0.00141253754462	1e-05	11270	-220035.292484
-0.1	0.0020619860095	1e-05	11270	-220038.966138
-0.1	0.00301003418958	1e-05	11270	-220044.392434
-0.1	0.00439397056076	1e-05	11270	-220052.451402
-0.1	0.00641420531224	1e-05	11270	-220064.51647
-0.1	0.00936329208824	1e-05	11270	-220082.791961
-0.1	0.0136682931808	1e-05	11270	-220110.952545
-0.1	0.0199526231497	1e-05	11270	-220155.435246
+0.15	0.08	0.1	47	-9267.41771294
+0.15	0.09	0.1	47	-9252.48080998
+0.15	0.1	0.1	47	-9284.46218119
+0.16	0.08	0.1	47	-9264.20398958
+0.16	0.09	0.1	47	-9248.68415885
+0.16	0.1	0.1	47	-9280.20703963
+0.17	0.08	0.1	47	-9262.86382359
+0.17	0.09	0.1	47	-9246.64573861
+0.17	0.1	0.1	47	-9278.035135
+0.18	0.08	0.1	47	-9263.17461439
+0.18	0.09	0.1	47	-9246.47738823
+0.18	0.1	0.1	47	-9276.64980284
+0.19	0.08	0.1	47	-9264.88048993
+0.19	0.09	0.1	47	-9246.9887665
+0.19	0.1	0.1	47	-9276.34422971
+0.2	0.08	0.1	47	-9267.64171914
+0.2	0.09	0.1	47	-9248.12259288
+0.2	0.1	0.1	47	-9276.81325712
+0.21	0.08	0.1	47	-9270.44267951
+0.21	0.09	0.1	47	-9249.95231714
+0.21	0.1	0.1	47	-9277.29709878
+0.22	0.08	0.1	47	-9273.93909307
+0.22	0.09	0.1	47	-9252.08035758
+0.22	0.1	0.1	47	-9279.14946242
+0.23	0.08	0.1	47	-9277.80574763
+0.23	0.09	0.1	47	-9255.27746396
+0.23	0.1	0.1	47	-9281.37029413
+0.24	0.08	0.1	47	-9282.57182493
+0.24	0.09	0.1	47	-9258.93035339
+0.24	0.1	0.1	47	-9284.2416322
diff --git a/experiments/process.py b/experiments/process.py
index f31e0d4..0115bd4 100644
--- a/experiments/process.py
+++ b/experiments/process.py
@@ -15,30 +15,30 @@ if __name__ == "__main__":
         sys.exit("usage: {0} <file>".format(sys.argv[0]))
     root_victims, victims, non_victims, age = load(open(sys.argv[1]))
     
-    # alphas = np.arange(1e-3, 1e-2, 8e-4)  # parameter of the time component
-    alphas = np.logspace(-4,-1,num=15)
-    # deltas = np.arange(0.001, 0.3, 0.008)  # parameter of the structural component
-    deltas = np.logspace(-4,-1.7,num=15)
-    with open("out.log", "w") as fh:
-        for alpha, delta in product(alphas, deltas):
-            beta, roots, ll = ml3(root_victims, victims, non_victims, age, alpha, delta)
-            print "\t".join(map(str, [alpha, delta, beta, roots, ll]))
-            fh.write("\t".join(map(str, [alpha, delta, beta, roots, ll])) + "\n")
-            fh.flush()
-
-    # alphas = np.arange(.04, .075, .003)  # parameter of the time component
-    # # alphas = np.logspace(-4,-1,num=20)
-    # deltas = np.arange(.07, .11, .003)  # parameter of the structural component
-    # # deltas = np.logspace(-2,-.5,num=20)
-    # lmbda = 0.10#np.logspace(-12,-3,num=10)
+    # # alphas = np.arange(1e-3, 1e-2, 8e-4)  # parameter of the time component
+    # alphas = np.logspace(-4,-1,num=15)
+    # # deltas = np.arange(0.001, 0.3, 0.008)  # parameter of the structural component
+    # deltas = np.logspace(-4,-1.7,num=15)
     # with open("out.log", "w") as fh:
     #     for alpha, delta in product(alphas, deltas):
-    #         lmbda, roots, ll = ml2(root_victims, victims, non_victims, alpha, delta, lmbda)
-    #         print "\t".join(map(str, [alpha, delta, lmbda, roots, ll]))
-    #         fh.write("\t".join(map(str, [alpha, delta, lmbda, roots, ll])) + "\n")
+    #         beta, roots, ll = ml3(root_victims, victims, non_victims, age, alpha, delta)
+    #         print "\t".join(map(str, [alpha, delta, beta, roots, ll]))
+    #         fh.write("\t".join(map(str, [alpha, delta, beta, roots, ll])) + "\n")
     #         fh.flush()
 
-    # alpha = 0.061
-    # delta = 0.082
+    alphas = np.arange(.15, .25, .01)  # parameter of the time component
+    # alphas = np.logspace(-4,-.5,num=15)
+    deltas = np.arange(.08, .1, .01)  # parameter of the structural component
+    # deltas = np.logspace(-2,-.1,num=15)
+    lmbda = 0.10#np.logspace(-12,-3,num=10)
+    with open("out.log", "w") as fh:
+        for alpha, delta in product(alphas, deltas):
+            lmbda, roots, ll = ml2(root_victims, victims, non_victims, alpha, delta, lmbda)
+            print "\t".join(map(str, [alpha, delta, lmbda, roots, ll]))
+            fh.write("\t".join(map(str, [alpha, delta, lmbda, roots, ll])) + "\n")
+            fh.flush()
+
+    # alpha = .016
+    # delta = 0.077
     # beta, roots, ll = ml2(root_victims, victims, non_victims, alpha, delta, 1.)
     # print "\t".join(map(str, [alpha, delta, beta, roots, ll]))
\ No newline at end of file
-- 
cgit v1.2.3-70-g09d2


From 375f29ab4306821c888fd9ef0637f9ab2879e375 Mon Sep 17 00:00:00 2001
From: Ben Green <ben@SEASITs-MacBook-Pro.local>
Date: Thu, 2 Jul 2015 00:41:49 -0400
Subject: in which the model works!!

---
 R Scripts/create-hyper-lcc.R                       |  126 -
 R Scripts/generate-hyper-lcc.R                     |  126 +
 R Scripts/predict-victims-plots.R                  |    3 +-
 experiments/build/temp.macosx-10.6-x86_64-2.7/ml.o |  Bin 299264 -> 306196 bytes
 .../build/temp.macosx-10.6-x86_64-2.7/ml3.o        |  Bin 285944 -> 275440 bytes
 experiments/build_network.py                       |    5 +-
 experiments/ml.c                                   | 3145 ++++++++------------
 experiments/ml.pyx                                 |   56 +-
 experiments/ml.so                                  |  Bin 108744 -> 119284 bytes
 experiments/ml3.c                                  |  966 +++---
 experiments/ml3.pyx                                |   27 +-
 experiments/ml3.so                                 |  Bin 110884 -> 106596 bytes
 experiments/out.log                                |  130 +-
 experiments/process.py                             |   38 +-
 14 files changed, 2047 insertions(+), 2575 deletions(-)
 delete mode 100644 R Scripts/create-hyper-lcc.R
 create mode 100644 R Scripts/generate-hyper-lcc.R

diff --git a/R Scripts/create-hyper-lcc.R b/R Scripts/create-hyper-lcc.R
deleted file mode 100644
index 786b694..0000000
--- a/R Scripts/create-hyper-lcc.R	
+++ /dev/null
@@ -1,126 +0,0 @@
-library(igraph)
-setwd("~/Documents/Cascade Project/")
-load('Raw Data/lcc.RData')
-
-lcc_verts = get.data.frame(lcc,'vertices')
-lcc_edges = get.data.frame(lcc,'edges')
-
-
-##### Create new vertices dataframe
-cols = c('id','ir_no','sex','race','dob','age.arrest','arrest.day',
-         'gang.member','gang.name','faction.name')
-hyp_lcc_verts = data.frame(matrix(ncol=length(cols)+1,nrow=0))
-colnames(hyp_lcc_verts) = c(cols,'spawn.date')
-  
-ptm = proc.time()
-ri = 1
-for (i in 1:dim(lcc_verts)[1]){
-  if (i%%10000==0) print(i)
-  if (lcc_verts$vic[i]){
-    if (lcc_verts$vic.nonfatal[i]>0){
-      # create nodes for each nonfatal shooting
-      for (nf in 1:lcc_verts$vic.nonfatal[i]){
-        hyp_lcc_verts[ri,cols] = lcc_verts[i,cols]
-        hyp_lcc_verts$id[ri] = ri
-        hyp_lcc_verts$vic[ri] = T
-        hyp_lcc_verts$vic.type[ri] = 'nonfatal'
-        if(nf==1){
-          hyp_lcc_verts$vic.day[ri] = lcc_verts$nonfatal_day_1[i]
-          hyp_lcc_verts$spawn.date[ri] = 0
-        } else if(nf==2){
-          hyp_lcc_verts$vic.day[ri] = lcc_verts$nonfatal_day_2[i]
-          hyp_lcc_verts$spawn.date[ri] = lcc_verts$nonfatal_day_1[i]
-        } else if(nf==3){
-          hyp_lcc_verts$vic.day[ri] = lcc_verts$nonfatal_day_3[i]
-          hyp_lcc_verts$spawn.date[ri] = lcc_verts$nonfatal_day_2[i]
-        } else if(nf==4){
-          hyp_lcc_verts$vic.day[ri] = lcc_verts$nonfatal_day_4[i]
-          hyp_lcc_verts$spawn.date[ri] = lcc_verts$nonfatal_day_3[i]
-        } else if(nf==5){
-          hyp_lcc_verts$vic.day[ri] = lcc_verts$nonfatal_day_5[i]
-          hyp_lcc_verts$spawn.date[ri] = lcc_verts$nonfatal_day_4[i]
-        }
-        ri = ri+1
-      }
-      # if no fatal infection, create uninfected duplicate
-      if (!lcc_verts$vic.fatal[i]){
-        hyp_lcc_verts[ri,cols] = lcc_verts[i,cols]
-        hyp_lcc_verts$id[ri] = ri
-        hyp_lcc_verts$vic[ri] = F
-        hyp_lcc_verts$vic.type[ri] = NA
-        hyp_lcc_verts$vic.day[ri] = NA
-        hyp_lcc_verts$spawn.date[ri] = max(lcc_verts$nonfatal_day_1[i],lcc_verts$nonfatal_day_2[i],
-                                           lcc_verts$nonfatal_day_3[i],lcc_verts$nonfatal_day_4[i],
-                                           lcc_verts$nonfatal_day_5[i],na.rm=T)
-        ri = ri+1
-      }
-    }
-    # create a node for each fatal shooting
-    # if also nonfatal shootings, spawn at last nonfatal shooting
-    if (lcc_verts$vic.fatal[i]){
-      hyp_lcc_verts[ri,cols] = lcc_verts[i,cols]
-      hyp_lcc_verts$id[ri] = ri
-      hyp_lcc_verts$vic[ri] = T
-      hyp_lcc_verts$vic.type[ri] = 'fatal'
-      hyp_lcc_verts$vic.day[ri] = lcc_verts$fatal_day[i] 
-      if (lcc_verts$vic.nonfatal[i]>0){
-        hyp_lcc_verts$spawn.date[ri] = max(lcc_verts$nonfatal_day_1[i],lcc_verts$nonfatal_day_2[i],
-                                           lcc_verts$nonfatal_day_3[i],lcc_verts$nonfatal_day_4[i],
-                                           lcc_verts$nonfatal_day_5[i],na.rm=T)
-      } else {
-        hyp_lcc_verts$spawn.date[ri] = 0
-      }
-      ri = ri+1
-    }
-  } 
-  # create an uninfected node for each uninfected person
-  else{
-    hyp_lcc_verts[ri,cols] = lcc_verts[i,cols]
-    hyp_lcc_verts$id[ri] = ri
-    hyp_lcc_verts$vic[ri] = F
-    hyp_lcc_verts$vic.type[ri] = NA
-    hyp_lcc_verts$vic.day[ri] = NA
-    hyp_lcc_verts$spawn.date[ri] = 0
-    ri = ri+1
-  }
-}
-print(proc.time()-ptm) # 1.5 hrs
-row.names(hyp_lcc_verts) = NULL
-n.nodes = sum(sum(V(lcc)$vic.fatal),sum(V(lcc)$vic.nonfatal),
-              sum(V(lcc)$vic.nonfatal>0 & !V(lcc)$vic.fatal),sum(V(lcc)$vic==FALSE))
-stopifnot(dim(hyp_lcc_verts)[1] == n.nodes)
-
-##### Create new edgelist
-print('Edges')
-hyp_lcc_edges = data.frame(from=0, to=0, weight=0)
-ei = 1
-ptm = proc.time()
-for(i in 1:dim(hyp_lcc_verts)[1]){
-  if (i%%10000==0) print(i)
-  ego_id = hyp_lcc_verts$id[i]
-  ego_ir = hyp_lcc_verts$ir_no[i]
-  alter_irs = union(lcc_edges$from[which(lcc_edges$to==ego_ir)],
-                    lcc_edges$to[which(lcc_edges$from==ego_ir)])
-#   alter_irs = union(ego_ir, alter_irs) # add edges to other infected selves
-  alter_ids = hyp_lcc_verts$id[which(hyp_lcc_verts$ir_no %in% alter_irs)]
-  alter_ids = alter_ids[ego_id<alter_ids] # avoid double-counting edges
-  for(alter_id in alter_ids){
-    weight=Inf
-    alter_ir = hyp_lcc_verts$ir_no[alter_id]
-    if(ego_ir!=alter_ir){
-      edge_id = which((lcc_edges$from %in% c(ego_ir,alter_ir) + lcc_edges$to %in% c(ego_ir,alter_ir))==2)
-      weight = lcc_edges$weight[edge_id]
-    }
-    hyp_lcc_edges[ei,] = c(ego_id, alter_id, weight)
-    ei = ei + 1
-  }
-}
-print(proc.time()-ptm) #10 hrs
-
-##### Create new graph
-hyp_lcc = graph.data.frame(hyp_lcc_edges, directed=FALSE, vertices=hyp_lcc_verts)
-
-save(hyp_lcc_edges, hyp_lcc_verts, hyp_lcc, file='Results/hyper-lcc.RData')
-write.csv(hyp_lcc_edges, file='Results/hyp_lcc_edges.csv')
-write.csv(hyp_lcc_verts[,c('id','spawn.date','vic','vic.type','vic.day')], file='Results/hyp_lcc_verts.csv')
-
diff --git a/R Scripts/generate-hyper-lcc.R b/R Scripts/generate-hyper-lcc.R
new file mode 100644
index 0000000..786b694
--- /dev/null
+++ b/R Scripts/generate-hyper-lcc.R	
@@ -0,0 +1,126 @@
+library(igraph)
+setwd("~/Documents/Cascade Project/")
+load('Raw Data/lcc.RData')
+
+lcc_verts = get.data.frame(lcc,'vertices')
+lcc_edges = get.data.frame(lcc,'edges')
+
+
+##### Create new vertices dataframe
+cols = c('id','ir_no','sex','race','dob','age.arrest','arrest.day',
+         'gang.member','gang.name','faction.name')
+hyp_lcc_verts = data.frame(matrix(ncol=length(cols)+1,nrow=0))
+colnames(hyp_lcc_verts) = c(cols,'spawn.date')
+  
+ptm = proc.time()
+ri = 1
+for (i in 1:dim(lcc_verts)[1]){
+  if (i%%10000==0) print(i)
+  if (lcc_verts$vic[i]){
+    if (lcc_verts$vic.nonfatal[i]>0){
+      # create nodes for each nonfatal shooting
+      for (nf in 1:lcc_verts$vic.nonfatal[i]){
+        hyp_lcc_verts[ri,cols] = lcc_verts[i,cols]
+        hyp_lcc_verts$id[ri] = ri
+        hyp_lcc_verts$vic[ri] = T
+        hyp_lcc_verts$vic.type[ri] = 'nonfatal'
+        if(nf==1){
+          hyp_lcc_verts$vic.day[ri] = lcc_verts$nonfatal_day_1[i]
+          hyp_lcc_verts$spawn.date[ri] = 0
+        } else if(nf==2){
+          hyp_lcc_verts$vic.day[ri] = lcc_verts$nonfatal_day_2[i]
+          hyp_lcc_verts$spawn.date[ri] = lcc_verts$nonfatal_day_1[i]
+        } else if(nf==3){
+          hyp_lcc_verts$vic.day[ri] = lcc_verts$nonfatal_day_3[i]
+          hyp_lcc_verts$spawn.date[ri] = lcc_verts$nonfatal_day_2[i]
+        } else if(nf==4){
+          hyp_lcc_verts$vic.day[ri] = lcc_verts$nonfatal_day_4[i]
+          hyp_lcc_verts$spawn.date[ri] = lcc_verts$nonfatal_day_3[i]
+        } else if(nf==5){
+          hyp_lcc_verts$vic.day[ri] = lcc_verts$nonfatal_day_5[i]
+          hyp_lcc_verts$spawn.date[ri] = lcc_verts$nonfatal_day_4[i]
+        }
+        ri = ri+1
+      }
+      # if no fatal infection, create uninfected duplicate
+      if (!lcc_verts$vic.fatal[i]){
+        hyp_lcc_verts[ri,cols] = lcc_verts[i,cols]
+        hyp_lcc_verts$id[ri] = ri
+        hyp_lcc_verts$vic[ri] = F
+        hyp_lcc_verts$vic.type[ri] = NA
+        hyp_lcc_verts$vic.day[ri] = NA
+        hyp_lcc_verts$spawn.date[ri] = max(lcc_verts$nonfatal_day_1[i],lcc_verts$nonfatal_day_2[i],
+                                           lcc_verts$nonfatal_day_3[i],lcc_verts$nonfatal_day_4[i],
+                                           lcc_verts$nonfatal_day_5[i],na.rm=T)
+        ri = ri+1
+      }
+    }
+    # create a node for each fatal shooting
+    # if also nonfatal shootings, spawn at last nonfatal shooting
+    if (lcc_verts$vic.fatal[i]){
+      hyp_lcc_verts[ri,cols] = lcc_verts[i,cols]
+      hyp_lcc_verts$id[ri] = ri
+      hyp_lcc_verts$vic[ri] = T
+      hyp_lcc_verts$vic.type[ri] = 'fatal'
+      hyp_lcc_verts$vic.day[ri] = lcc_verts$fatal_day[i] 
+      if (lcc_verts$vic.nonfatal[i]>0){
+        hyp_lcc_verts$spawn.date[ri] = max(lcc_verts$nonfatal_day_1[i],lcc_verts$nonfatal_day_2[i],
+                                           lcc_verts$nonfatal_day_3[i],lcc_verts$nonfatal_day_4[i],
+                                           lcc_verts$nonfatal_day_5[i],na.rm=T)
+      } else {
+        hyp_lcc_verts$spawn.date[ri] = 0
+      }
+      ri = ri+1
+    }
+  } 
+  # create an uninfected node for each uninfected person
+  else{
+    hyp_lcc_verts[ri,cols] = lcc_verts[i,cols]
+    hyp_lcc_verts$id[ri] = ri
+    hyp_lcc_verts$vic[ri] = F
+    hyp_lcc_verts$vic.type[ri] = NA
+    hyp_lcc_verts$vic.day[ri] = NA
+    hyp_lcc_verts$spawn.date[ri] = 0
+    ri = ri+1
+  }
+}
+print(proc.time()-ptm) # 1.5 hrs
+row.names(hyp_lcc_verts) = NULL
+n.nodes = sum(sum(V(lcc)$vic.fatal),sum(V(lcc)$vic.nonfatal),
+              sum(V(lcc)$vic.nonfatal>0 & !V(lcc)$vic.fatal),sum(V(lcc)$vic==FALSE))
+stopifnot(dim(hyp_lcc_verts)[1] == n.nodes)
+
+##### Create new edgelist
+print('Edges')
+hyp_lcc_edges = data.frame(from=0, to=0, weight=0)
+ei = 1
+ptm = proc.time()
+for(i in 1:dim(hyp_lcc_verts)[1]){
+  if (i%%10000==0) print(i)
+  ego_id = hyp_lcc_verts$id[i]
+  ego_ir = hyp_lcc_verts$ir_no[i]
+  alter_irs = union(lcc_edges$from[which(lcc_edges$to==ego_ir)],
+                    lcc_edges$to[which(lcc_edges$from==ego_ir)])
+#   alter_irs = union(ego_ir, alter_irs) # add edges to other infected selves
+  alter_ids = hyp_lcc_verts$id[which(hyp_lcc_verts$ir_no %in% alter_irs)]
+  alter_ids = alter_ids[ego_id<alter_ids] # avoid double-counting edges
+  for(alter_id in alter_ids){
+    weight=Inf
+    alter_ir = hyp_lcc_verts$ir_no[alter_id]
+    if(ego_ir!=alter_ir){
+      edge_id = which((lcc_edges$from %in% c(ego_ir,alter_ir) + lcc_edges$to %in% c(ego_ir,alter_ir))==2)
+      weight = lcc_edges$weight[edge_id]
+    }
+    hyp_lcc_edges[ei,] = c(ego_id, alter_id, weight)
+    ei = ei + 1
+  }
+}
+print(proc.time()-ptm) #10 hrs
+
+##### Create new graph
+hyp_lcc = graph.data.frame(hyp_lcc_edges, directed=FALSE, vertices=hyp_lcc_verts)
+
+save(hyp_lcc_edges, hyp_lcc_verts, hyp_lcc, file='Results/hyper-lcc.RData')
+write.csv(hyp_lcc_edges, file='Results/hyp_lcc_edges.csv')
+write.csv(hyp_lcc_verts[,c('id','spawn.date','vic','vic.type','vic.day')], file='Results/hyp_lcc_verts.csv')
+
diff --git a/R Scripts/predict-victims-plots.R b/R Scripts/predict-victims-plots.R
index 2ac62c8..553aa89 100644
--- a/R Scripts/predict-victims-plots.R	
+++ b/R Scripts/predict-victims-plots.R	
@@ -1,5 +1,5 @@
 ##### Plot results
-hist(correct_rank3,150,xlim=c(0,vcount(lcc)),col=rgb(0,0,1,1/8),
+hist(correct_rank1,150,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(0,0,1,1/8),add=T)
@@ -36,6 +36,7 @@ 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, 
diff --git a/experiments/build/temp.macosx-10.6-x86_64-2.7/ml.o b/experiments/build/temp.macosx-10.6-x86_64-2.7/ml.o
index 5861fce..8667998 100644
Binary files a/experiments/build/temp.macosx-10.6-x86_64-2.7/ml.o and b/experiments/build/temp.macosx-10.6-x86_64-2.7/ml.o differ
diff --git a/experiments/build/temp.macosx-10.6-x86_64-2.7/ml3.o b/experiments/build/temp.macosx-10.6-x86_64-2.7/ml3.o
index 8ac85ca..9a4f66b 100644
Binary files a/experiments/build/temp.macosx-10.6-x86_64-2.7/ml3.o and b/experiments/build/temp.macosx-10.6-x86_64-2.7/ml3.o differ
diff --git a/experiments/build_network.py b/experiments/build_network.py
index 2226070..a481a90 100644
--- a/experiments/build_network.py
+++ b/experiments/build_network.py
@@ -8,7 +8,7 @@ def build_network(filename):
     victims = {}
     non_victims = {}
     age = 0
-    t_max = 1000
+    t_max = 3012
     with open(filename) as fh:
         reader = DictReader(fh)
         for row in reader:
@@ -40,13 +40,10 @@ def build_network(filename):
                     age += int(row["t1"]) - int(row["spawn1"])
                     victims[from_] = []
     root_victims = {}
-    print sorted(victims.keys())
-    # print victims
     for victim in victims.keys():
         if not victims[victim]:
             del victims[victim]
             root_victims[victim] = []
-    print sorted(root_victims.keys())
     print len(root_victims), len(victims), len(non_victims)
     return root_victims, victims, non_victims, age
 
diff --git a/experiments/ml.c b/experiments/ml.c
index eb67db6..92e313f 100644
--- a/experiments/ml.c
+++ b/experiments/ml.c
@@ -720,8 +720,6 @@ typedef __pyx_t_5numpy_float_t __pyx_t_2ml_DTYPE_t;
 
 
 /*--- Type declarations ---*/
-struct __pyx_obj_2ml___pyx_scope_struct__ml;
-struct __pyx_obj_2ml___pyx_scope_struct_1_genexpr;
 
 /* "../../../../Library/Enthought/Canopy_64bit/User/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":765
  * ctypedef npy_longdouble longdouble_t
@@ -759,38 +757,6 @@ typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t;
  */
 typedef npy_cdouble __pyx_t_5numpy_complex_t;
 
-/* "ml.pyx":35
- *     return result
- * 
- * def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age,             # <<<<<<<<<<<<<<
- *        DTYPE_t alpha, DTYPE_t delta):
- *     cdef:
- */
-struct __pyx_obj_2ml___pyx_scope_struct__ml {
-  PyObject_HEAD
-  PyObject *__pyx_v_failures;
-  PyObject *__pyx_v_successes;
-};
-
-
-/* "ml.pyx":59
- *                      for (dist, dt, w1, w2, w3) in parents]
- *         # find parent that maximizes log(p) - log(\tilde{p})
- *         probs[i] = max(s - failures[l] for l, s in enumerate(successes))             # <<<<<<<<<<<<<<
- * 
- *     # loop through non-victims
- */
-struct __pyx_obj_2ml___pyx_scope_struct_1_genexpr {
-  PyObject_HEAD
-  struct __pyx_obj_2ml___pyx_scope_struct__ml *__pyx_outer_scope;
-  Py_ssize_t __pyx_v_l;
-  PyObject *__pyx_v_s;
-  Py_ssize_t __pyx_t_0;
-  PyObject *__pyx_t_1;
-  Py_ssize_t __pyx_t_2;
-};
-
-
 /* --- Runtime support code (head) --- */
 #ifndef CYTHON_REFNANNY
   #define CYTHON_REFNANNY 0
@@ -883,29 +849,6 @@ static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], \
 static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed,
     const char *name, int exact);
 
-static CYTHON_INLINE void __Pyx_RaiseClosureNameError(const char *varname);
-
-#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \
-    __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) : \
-    (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) : \
-               __Pyx_GetItemInt_Generic(o, to_py_func(i))))
-#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \
-    __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) : \
-    (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL))
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
-                                                              int wraparound, int boundscheck);
-#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \
-    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \
-    __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) : \
-    (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL))
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
-                                                              int wraparound, int boundscheck);
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
-                                                     int is_list, int wraparound, int boundscheck);
-
 static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name);
 
 #if CYTHON_COMPILING_IN_CPYTHON
@@ -983,6 +926,27 @@ static double __Pyx__PyObject_AsDouble(PyObject* obj);
  PyFloat_AS_DOUBLE(obj) : __Pyx__PyObject_AsDouble(obj))
 #endif
 
+#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \
+    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \
+    __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) : \
+    (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) : \
+               __Pyx_GetItemInt_Generic(o, to_py_func(i))))
+#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \
+    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \
+    __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) : \
+    (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL))
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
+                                                              int wraparound, int boundscheck);
+#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \
+    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \
+    __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) : \
+    (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL))
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
+                                                              int wraparound, int boundscheck);
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
+                                                     int is_list, int wraparound, int boundscheck);
+
 static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb);
 static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb);
 
@@ -1008,8 +972,6 @@ static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) {
     #define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key)
 #endif
 
-#include <string.h>
-
 typedef struct {
     int code_line;
     PyCodeObject* code_object;
@@ -1160,41 +1122,6 @@ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
 
 static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
 
-static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type);
-
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb);
-
-static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg);
-
-#define __Pyx_Generator_USED
-#include <structmember.h>
-#include <frameobject.h>
-typedef PyObject *(*__pyx_generator_body_t)(PyObject *, PyObject *);
-typedef struct {
-    PyObject_HEAD
-    __pyx_generator_body_t body;
-    PyObject *closure;
-    PyObject *exc_type;
-    PyObject *exc_value;
-    PyObject *exc_traceback;
-    PyObject *gi_weakreflist;
-    PyObject *classobj;
-    PyObject *yieldfrom;
-    PyObject *gi_name;
-    PyObject *gi_qualname;
-    int resume_label;
-    char is_running;
-} __pyx_GeneratorObject;
-static __pyx_GeneratorObject *__Pyx_Generator_New(__pyx_generator_body_t body,
-                                                  PyObject *closure, PyObject *name, PyObject *qualname);
-static int __pyx_Generator_init(void);
-static int __Pyx_Generator_clear(PyObject* self);
-#if 1 || PY_VERSION_HEX < 0x030300B0
-static int __Pyx_PyGen_FetchStopIterationValue(PyObject **pvalue);
-#else
-#define __Pyx_PyGen_FetchStopIterationValue(pvalue) PyGen_FetchStopIterationValue(pvalue)
-#endif
-
 static int __Pyx_check_binary_version(void);
 
 #if !defined(__Pyx_PyIdentifier_FromString)
@@ -1242,8 +1169,6 @@ static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, cha
 /* Module declarations from 'libc.math' */
 
 /* Module declarations from 'ml' */
-static PyTypeObject *__pyx_ptype_2ml___pyx_scope_struct__ml = 0;
-static PyTypeObject *__pyx_ptype_2ml___pyx_scope_struct_1_genexpr = 0;
 static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_success(int, int, __pyx_t_2ml_DTYPE_t, __pyx_t_2ml_DTYPE_t, __pyx_t_2ml_DTYPE_t, __pyx_t_2ml_DTYPE_t, __pyx_t_2ml_DTYPE_t); /*proto*/
 static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_failure(int, int, __pyx_t_2ml_DTYPE_t, __pyx_t_2ml_DTYPE_t, __pyx_t_2ml_DTYPE_t, __pyx_t_2ml_DTYPE_t, __pyx_t_2ml_DTYPE_t); /*proto*/
 static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_2ml_DTYPE_t = { "DTYPE_t", NULL, sizeof(__pyx_t_2ml_DTYPE_t), { 0 }, 0, 'R', 0, 0 };
@@ -1253,16 +1178,12 @@ int __pyx_module_is_main_ml = 0;
 /* Implementation of 'ml' */
 static PyObject *__pyx_builtin_enumerate;
 static PyObject *__pyx_builtin_sum;
-static PyObject *__pyx_builtin_max;
 static PyObject *__pyx_builtin_ValueError;
 static PyObject *__pyx_builtin_range;
 static PyObject *__pyx_builtin_RuntimeError;
-static PyObject *__pyx_pf_2ml_2ml_genexpr(PyObject *__pyx_self); /* proto */
 static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_root_victims, PyObject *__pyx_v_victims, PyObject *__pyx_v_non_victims, CYTHON_UNUSED __pyx_t_2ml_DTYPE_t __pyx_v_age, __pyx_t_2ml_DTYPE_t __pyx_v_alpha, __pyx_t_2ml_DTYPE_t __pyx_v_delta); /* proto */
 static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */
 static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info); /* proto */
-static PyObject *__pyx_tp_new_2ml___pyx_scope_struct__ml(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static PyObject *__pyx_tp_new_2ml___pyx_scope_struct_1_genexpr(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
 static char __pyx_k_B[] = "B";
 static char __pyx_k_H[] = "H";
 static char __pyx_k_I[] = "I";
@@ -1277,6 +1198,7 @@ static char __pyx_k_h[] = "h";
 static char __pyx_k_i[] = "i";
 static char __pyx_k_l[] = "l";
 static char __pyx_k_q[] = "q";
+static char __pyx_k_s[] = "s";
 static char __pyx_k_t[] = "t";
 static char __pyx_k_Zd[] = "Zd";
 static char __pyx_k_Zf[] = "Zf";
@@ -1289,31 +1211,30 @@ static char __pyx_k_w1[] = "w1";
 static char __pyx_k_w2[] = "w2";
 static char __pyx_k_w3[] = "w3";
 static char __pyx_k_age[] = "age";
+static char __pyx_k_dts[] = "dts";
 static char __pyx_k_inf[] = "-inf";
-static char __pyx_k_max[] = "max";
 static char __pyx_k_sum[] = "sum";
-static char __pyx_k_args[] = "args";
 static char __pyx_k_beta[] = "beta";
 static char __pyx_k_dist[] = "dist";
 static char __pyx_k_main[] = "__main__";
-static char __pyx_k_send[] = "send";
+static char __pyx_k_prnt[] = "prnt";
+static char __pyx_k_prob[] = "prob";
 static char __pyx_k_test[] = "__test__";
 static char __pyx_k_DTYPE[] = "DTYPE";
 static char __pyx_k_alpha[] = "alpha";
-static char __pyx_k_close[] = "close";
 static char __pyx_k_delta[] = "delta";
+static char __pyx_k_dists[] = "dists";
 static char __pyx_k_dtype[] = "dtype";
 static char __pyx_k_numpy[] = "numpy";
 static char __pyx_k_probs[] = "probs";
 static char __pyx_k_range[] = "range";
 static char __pyx_k_roots[] = "roots";
-static char __pyx_k_throw[] = "throw";
+static char __pyx_k_seeds[] = "seeds";
 static char __pyx_k_zeros[] = "zeros";
 static char __pyx_k_arange[] = "arange";
 static char __pyx_k_import[] = "__import__";
 static char __pyx_k_thresh[] = "thresh";
 static char __pyx_k_float64[] = "float64";
-static char __pyx_k_genexpr[] = "genexpr";
 static char __pyx_k_n_nodes[] = "n_nodes";
 static char __pyx_k_n_roots[] = "n_roots";
 static char __pyx_k_parents[] = "parents";
@@ -1325,15 +1246,17 @@ static char __pyx_k_probs_nv[] = "probs_nv";
 static char __pyx_k_enumerate[] = "enumerate";
 static char __pyx_k_max_roots[] = "max_roots";
 static char __pyx_k_n_victims[] = "n_victims";
+static char __pyx_k_non_seeds[] = "non_seeds";
 static char __pyx_k_successes[] = "successes";
 static char __pyx_k_ValueError[] = "ValueError";
 static char __pyx_k_itervalues[] = "itervalues";
+static char __pyx_k_parent_dts[] = "parent_dts";
 static char __pyx_k_probs_fail[] = "probs_fail";
 static char __pyx_k_non_victims[] = "non_victims";
 static char __pyx_k_RuntimeError[] = "RuntimeError";
 static char __pyx_k_max_beta_add[] = "max_beta_add";
+static char __pyx_k_parent_dists[] = "parent_dists";
 static char __pyx_k_root_victims[] = "root_victims";
-static char __pyx_k_ml_locals_genexpr[] = "ml.<locals>.genexpr";
 static char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous";
 static char __pyx_k_Users_ben_Documents_Cascade_Pro[] = "/Users/ben/Documents/Cascade Project/criminal_cascades/experiments/ml.pyx";
 static char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)";
@@ -1351,18 +1274,17 @@ static PyObject *__pyx_n_s_ValueError;
 static PyObject *__pyx_n_s_age;
 static PyObject *__pyx_n_s_alpha;
 static PyObject *__pyx_n_s_arange;
-static PyObject *__pyx_n_s_args;
 static PyObject *__pyx_n_s_beta;
 static PyObject *__pyx_n_s_beta_add;
-static PyObject *__pyx_n_s_close;
 static PyObject *__pyx_n_s_delta;
 static PyObject *__pyx_n_s_dist;
+static PyObject *__pyx_n_s_dists;
 static PyObject *__pyx_n_s_dt;
+static PyObject *__pyx_n_s_dts;
 static PyObject *__pyx_n_s_dtype;
 static PyObject *__pyx_n_s_enumerate;
 static PyObject *__pyx_n_s_failures;
 static PyObject *__pyx_n_s_float64;
-static PyObject *__pyx_n_s_genexpr;
 static PyObject *__pyx_n_s_i;
 static PyObject *__pyx_n_s_import;
 static PyObject *__pyx_kp_s_inf;
@@ -1370,43 +1292,47 @@ static PyObject *__pyx_n_s_itervalues;
 static PyObject *__pyx_n_s_l;
 static PyObject *__pyx_n_s_ll;
 static PyObject *__pyx_n_s_main;
-static PyObject *__pyx_n_s_max;
 static PyObject *__pyx_n_s_max_beta;
 static PyObject *__pyx_n_s_max_beta_add;
 static PyObject *__pyx_n_s_max_roots;
 static PyObject *__pyx_n_s_ml;
-static PyObject *__pyx_n_s_ml_locals_genexpr;
 static PyObject *__pyx_n_s_n_nodes;
 static PyObject *__pyx_n_s_n_roots;
 static PyObject *__pyx_n_s_n_victims;
 static PyObject *__pyx_kp_u_ndarray_is_not_C_contiguous;
 static PyObject *__pyx_kp_u_ndarray_is_not_Fortran_contiguou;
+static PyObject *__pyx_n_s_non_seeds;
 static PyObject *__pyx_n_s_non_victims;
 static PyObject *__pyx_n_s_np;
 static PyObject *__pyx_n_s_numpy;
+static PyObject *__pyx_n_s_parent_dists;
+static PyObject *__pyx_n_s_parent_dts;
 static PyObject *__pyx_n_s_parents;
+static PyObject *__pyx_n_s_prnt;
+static PyObject *__pyx_n_s_prob;
 static PyObject *__pyx_n_s_probs;
 static PyObject *__pyx_n_s_probs_fail;
 static PyObject *__pyx_n_s_probs_nv;
 static PyObject *__pyx_n_s_range;
 static PyObject *__pyx_n_s_root_victims;
 static PyObject *__pyx_n_s_roots;
-static PyObject *__pyx_n_s_send;
+static PyObject *__pyx_n_s_s;
+static PyObject *__pyx_n_s_seeds;
 static PyObject *__pyx_n_s_successes;
 static PyObject *__pyx_n_s_sum;
 static PyObject *__pyx_n_s_t;
 static PyObject *__pyx_n_s_test;
 static PyObject *__pyx_n_s_thresh;
-static PyObject *__pyx_n_s_throw;
 static PyObject *__pyx_kp_u_unknown_dtype_code_in_numpy_pxd;
 static PyObject *__pyx_n_s_victims;
 static PyObject *__pyx_n_s_w1;
 static PyObject *__pyx_n_s_w2;
 static PyObject *__pyx_n_s_w3;
 static PyObject *__pyx_n_s_zeros;
-static PyObject *__pyx_float__2;
-static PyObject *__pyx_float__002;
-static PyObject *__pyx_float_0_001;
+static PyObject *__pyx_float_1_;
+static PyObject *__pyx_float__01;
+static PyObject *__pyx_float_0_01;
+static PyObject *__pyx_int_148152;
 static PyObject *__pyx_tuple_;
 static PyObject *__pyx_tuple__2;
 static PyObject *__pyx_tuple__3;
@@ -1436,33 +1362,33 @@ static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_success(int __pyx_v_dist, int __py
   /* "ml.pyx":13
  *     """weight for successful infection, exponential time model"""
  *     cdef DTYPE_t structural, temporal, result
- *     structural = dist * log(delta)             # <<<<<<<<<<<<<<
- *     # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta)
- *     temporal = log(exp(alpha)-1.) - alpha*dt
+ *     structural = delta ** dist             # <<<<<<<<<<<<<<
+ *     temporal = log(exp(alpha)-1.) - alpha*dt/7.
+ *     result = log(structural) + temporal
+ */
+  __pyx_v_structural = pow(__pyx_v_delta, ((__pyx_t_2ml_DTYPE_t)__pyx_v_dist));
+
+  /* "ml.pyx":14
+ *     cdef DTYPE_t structural, temporal, result
+ *     structural = delta ** dist
+ *     temporal = log(exp(alpha)-1.) - alpha*dt/7.             # <<<<<<<<<<<<<<
+ *     result = log(structural) + temporal
+ *     return result
  */
-  __pyx_v_structural = (__pyx_v_dist * log(__pyx_v_delta));
+  __pyx_v_temporal = (log((exp(__pyx_v_alpha) - 1.)) - ((__pyx_v_alpha * __pyx_v_dt) / 7.));
 
   /* "ml.pyx":15
- *     structural = dist * log(delta)
- *     # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta)
- *     temporal = log(exp(alpha)-1.) - alpha*dt             # <<<<<<<<<<<<<<
- *     # temporal = 1 - exp(-alpha*dt)
- *     # if exp(-alpha*dt)==0.: print 'UNDERFLOW ERROR'
- */
-  __pyx_v_temporal = (log((exp(__pyx_v_alpha) - 1.)) - (__pyx_v_alpha * __pyx_v_dt));
-
-  /* "ml.pyx":19
- *     # if exp(-alpha*dt)==0.: print 'UNDERFLOW ERROR'
- *     # temporal = 1. / (1. + (dt - 1.)/alpha)**0.01 - 1. / (1. + dt/alpha)**0.01
- *     result = structural + temporal             # <<<<<<<<<<<<<<
- *     # print 'st', structural, temporal
+ *     structural = delta ** dist
+ *     temporal = log(exp(alpha)-1.) - alpha*dt/7.
+ *     result = log(structural) + temporal             # <<<<<<<<<<<<<<
  *     return result
+ * 
  */
-  __pyx_v_result = (__pyx_v_structural + __pyx_v_temporal);
+  __pyx_v_result = (log(__pyx_v_structural) + __pyx_v_temporal);
 
-  /* "ml.pyx":21
- *     result = structural + temporal
- *     # print 'st', structural, temporal
+  /* "ml.pyx":16
+ *     temporal = log(exp(alpha)-1.) - alpha*dt/7.
+ *     result = log(structural) + temporal
  *     return result             # <<<<<<<<<<<<<<
  * 
  * cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,
@@ -1484,7 +1410,7 @@ static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_success(int __pyx_v_dist, int __py
   return __pyx_r;
 }
 
-/* "ml.pyx":23
+/* "ml.pyx":18
  *     return result
  * 
  * cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,             # <<<<<<<<<<<<<<
@@ -1500,36 +1426,36 @@ static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_failure(int __pyx_v_dist, int __py
   __Pyx_RefNannyDeclarations
   __Pyx_RefNannySetupContext("weight_failure", 0);
 
-  /* "ml.pyx":27
+  /* "ml.pyx":22
  *     """weight for failed infection, exponential time model"""
  *     cdef DTYPE_t structural, temporal, result
  *     structural = delta ** dist             # <<<<<<<<<<<<<<
- *     # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta)
- *     temporal = exp(-alpha * dt)
+ *     temporal = exp(-alpha * dt/7.)
+ *     result = log(1. - structural + structural * temporal)
  */
   __pyx_v_structural = pow(__pyx_v_delta, ((__pyx_t_2ml_DTYPE_t)__pyx_v_dist));
 
-  /* "ml.pyx":29
+  /* "ml.pyx":23
+ *     cdef DTYPE_t structural, temporal, result
  *     structural = delta ** dist
- *     # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta)
- *     temporal = exp(-alpha * dt)             # <<<<<<<<<<<<<<
- *     # temporal = 1.  - 1. / (1. + dt/alpha)**0.01
+ *     temporal = exp(-alpha * dt/7.)             # <<<<<<<<<<<<<<
  *     result = log(1. - structural + structural * temporal)
+ *     return result
  */
-  __pyx_v_temporal = exp(((-__pyx_v_alpha) * __pyx_v_dt));
+  __pyx_v_temporal = exp((((-__pyx_v_alpha) * __pyx_v_dt) / 7.));
 
-  /* "ml.pyx":31
- *     temporal = exp(-alpha * dt)
- *     # temporal = 1.  - 1. / (1. + dt/alpha)**0.01
+  /* "ml.pyx":24
+ *     structural = delta ** dist
+ *     temporal = exp(-alpha * dt/7.)
  *     result = log(1. - structural + structural * temporal)             # <<<<<<<<<<<<<<
- *     # print 'stnv', structural, temporal
  *     return result
+ * 
  */
   __pyx_v_result = log(((1. - __pyx_v_structural) + (__pyx_v_structural * __pyx_v_temporal)));
 
-  /* "ml.pyx":33
+  /* "ml.pyx":25
+ *     temporal = exp(-alpha * dt/7.)
  *     result = log(1. - structural + structural * temporal)
- *     # print 'stnv', structural, temporal
  *     return result             # <<<<<<<<<<<<<<
  * 
  * def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
@@ -1537,7 +1463,7 @@ static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_failure(int __pyx_v_dist, int __py
   __pyx_r = __pyx_v_result;
   goto __pyx_L0;
 
-  /* "ml.pyx":23
+  /* "ml.pyx":18
  *     return result
  * 
  * cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,             # <<<<<<<<<<<<<<
@@ -1551,7 +1477,7 @@ static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_failure(int __pyx_v_dist, int __py
   return __pyx_r;
 }
 
-/* "ml.pyx":35
+/* "ml.pyx":27
  *     return result
  * 
  * def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age,             # <<<<<<<<<<<<<<
@@ -1599,31 +1525,31 @@ static PyObject *__pyx_pw_2ml_1ml(PyObject *__pyx_self, PyObject *__pyx_args, Py
         case  1:
         if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_victims)) != 0)) kw_args--;
         else {
-          __Pyx_RaiseArgtupleInvalid("ml", 1, 6, 6, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+          __Pyx_RaiseArgtupleInvalid("ml", 1, 6, 6, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 27; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
         }
         case  2:
         if (likely((values[2] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_non_victims)) != 0)) kw_args--;
         else {
-          __Pyx_RaiseArgtupleInvalid("ml", 1, 6, 6, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+          __Pyx_RaiseArgtupleInvalid("ml", 1, 6, 6, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 27; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
         }
         case  3:
         if (likely((values[3] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_age)) != 0)) kw_args--;
         else {
-          __Pyx_RaiseArgtupleInvalid("ml", 1, 6, 6, 3); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+          __Pyx_RaiseArgtupleInvalid("ml", 1, 6, 6, 3); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 27; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
         }
         case  4:
         if (likely((values[4] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_alpha)) != 0)) kw_args--;
         else {
-          __Pyx_RaiseArgtupleInvalid("ml", 1, 6, 6, 4); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+          __Pyx_RaiseArgtupleInvalid("ml", 1, 6, 6, 4); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 27; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
         }
         case  5:
         if (likely((values[5] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_delta)) != 0)) kw_args--;
         else {
-          __Pyx_RaiseArgtupleInvalid("ml", 1, 6, 6, 5); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+          __Pyx_RaiseArgtupleInvalid("ml", 1, 6, 6, 5); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 27; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
         }
       }
       if (unlikely(kw_args > 0)) {
-        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "ml") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "ml") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 27; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
       }
     } else if (PyTuple_GET_SIZE(__pyx_args) != 6) {
       goto __pyx_L5_argtuple_error;
@@ -1638,21 +1564,21 @@ static PyObject *__pyx_pw_2ml_1ml(PyObject *__pyx_self, PyObject *__pyx_args, Py
     __pyx_v_root_victims = ((PyObject*)values[0]);
     __pyx_v_victims = ((PyObject*)values[1]);
     __pyx_v_non_victims = ((PyObject*)values[2]);
-    __pyx_v_age = __pyx_PyFloat_AsDouble(values[3]); if (unlikely((__pyx_v_age == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-    __pyx_v_alpha = __pyx_PyFloat_AsDouble(values[4]); if (unlikely((__pyx_v_alpha == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 36; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
-    __pyx_v_delta = __pyx_PyFloat_AsDouble(values[5]); if (unlikely((__pyx_v_delta == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 36; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+    __pyx_v_age = __pyx_PyFloat_AsDouble(values[3]); if (unlikely((__pyx_v_age == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 27; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+    __pyx_v_alpha = __pyx_PyFloat_AsDouble(values[4]); if (unlikely((__pyx_v_alpha == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 28; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+    __pyx_v_delta = __pyx_PyFloat_AsDouble(values[5]); if (unlikely((__pyx_v_delta == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 28; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
   }
   goto __pyx_L4_argument_unpacking_done;
   __pyx_L5_argtuple_error:;
-  __Pyx_RaiseArgtupleInvalid("ml", 1, 6, 6, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  __Pyx_RaiseArgtupleInvalid("ml", 1, 6, 6, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 27; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
   __pyx_L3_error:;
   __Pyx_AddTraceback("ml.ml", __pyx_clineno, __pyx_lineno, __pyx_filename);
   __Pyx_RefNannyFinishContext();
   return NULL;
   __pyx_L4_argument_unpacking_done:;
-  if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_root_victims), (&PyDict_Type), 1, "root_victims", 1))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_victims), (&PyDict_Type), 1, "victims", 1))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_non_victims), (&PyDict_Type), 1, "non_victims", 1))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_root_victims), (&PyDict_Type), 1, "root_victims", 1))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 27; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_victims), (&PyDict_Type), 1, "victims", 1))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 27; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_non_victims), (&PyDict_Type), 1, "non_victims", 1))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 27; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-static PyObject *__pyx_tp_new_2ml___pyx_scope_struct__ml(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
-  PyObject *o;
-  if (CYTHON_COMPILING_IN_CPYTHON && likely((__pyx_freecount_2ml___pyx_scope_struct__ml > 0) & (t->tp_basicsize == sizeof(struct __pyx_obj_2ml___pyx_scope_struct__ml)))) {
-    o = (PyObject*)__pyx_freelist_2ml___pyx_scope_struct__ml[--__pyx_freecount_2ml___pyx_scope_struct__ml];
-    memset(o, 0, sizeof(struct __pyx_obj_2ml___pyx_scope_struct__ml));
-    (void) PyObject_INIT(o, t);
-    PyObject_GC_Track(o);
-  } else {
-    o = (*t->tp_alloc)(t, 0);
-    if (unlikely(!o)) return 0;
-  }
-  return o;
-}
-
-static void __pyx_tp_dealloc_2ml___pyx_scope_struct__ml(PyObject *o) {
-  struct __pyx_obj_2ml___pyx_scope_struct__ml *p = (struct __pyx_obj_2ml___pyx_scope_struct__ml *)o;
-  PyObject_GC_UnTrack(o);
-  Py_CLEAR(p->__pyx_v_failures);
-  Py_CLEAR(p->__pyx_v_successes);
-  if (CYTHON_COMPILING_IN_CPYTHON && ((__pyx_freecount_2ml___pyx_scope_struct__ml < 8) & (Py_TYPE(o)->tp_basicsize == sizeof(struct __pyx_obj_2ml___pyx_scope_struct__ml)))) {
-    __pyx_freelist_2ml___pyx_scope_struct__ml[__pyx_freecount_2ml___pyx_scope_struct__ml++] = ((struct __pyx_obj_2ml___pyx_scope_struct__ml *)o);
-  } else {
-    (*Py_TYPE(o)->tp_free)(o);
-  }
-}
-
-static int __pyx_tp_traverse_2ml___pyx_scope_struct__ml(PyObject *o, visitproc v, void *a) {
-  int e;
-  struct __pyx_obj_2ml___pyx_scope_struct__ml *p = (struct __pyx_obj_2ml___pyx_scope_struct__ml *)o;
-  if (p->__pyx_v_failures) {
-    e = (*v)(p->__pyx_v_failures, a); if (e) return e;
-  }
-  if (p->__pyx_v_successes) {
-    e = (*v)(p->__pyx_v_successes, a); if (e) return e;
-  }
-  return 0;
-}
-
-static int __pyx_tp_clear_2ml___pyx_scope_struct__ml(PyObject *o) {
-  PyObject* tmp;
-  struct __pyx_obj_2ml___pyx_scope_struct__ml *p = (struct __pyx_obj_2ml___pyx_scope_struct__ml *)o;
-  tmp = ((PyObject*)p->__pyx_v_failures);
-  p->__pyx_v_failures = ((PyObject*)Py_None); Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  tmp = ((PyObject*)p->__pyx_v_successes);
-  p->__pyx_v_successes = ((PyObject*)Py_None); Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  return 0;
-}
-
-static PyTypeObject __pyx_type_2ml___pyx_scope_struct__ml = {
-  PyVarObject_HEAD_INIT(0, 0)
-  "ml.__pyx_scope_struct__ml", /*tp_name*/
-  sizeof(struct __pyx_obj_2ml___pyx_scope_struct__ml), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc_2ml___pyx_scope_struct__ml, /*tp_dealloc*/
-  0, /*tp_print*/
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #else
-  0, /*reserved*/
-  #endif
-  0, /*tp_repr*/
-  0, /*tp_as_number*/
-  0, /*tp_as_sequence*/
-  0, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  0, /*tp_str*/
-  0, /*tp_getattro*/
-  0, /*tp_setattro*/
-  0, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
-  0, /*tp_doc*/
-  __pyx_tp_traverse_2ml___pyx_scope_struct__ml, /*tp_traverse*/
-  __pyx_tp_clear_2ml___pyx_scope_struct__ml, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  0, /*tp_methods*/
-  0, /*tp_members*/
-  0, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  0, /*tp_dictoffset*/
-  0, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new_2ml___pyx_scope_struct__ml, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  0, /*tp_version_tag*/
-  #if PY_VERSION_HEX >= 0x030400a1
-  0, /*tp_finalize*/
-  #endif
-};
-
-static struct __pyx_obj_2ml___pyx_scope_struct_1_genexpr *__pyx_freelist_2ml___pyx_scope_struct_1_genexpr[8];
-static int __pyx_freecount_2ml___pyx_scope_struct_1_genexpr = 0;
-
-static PyObject *__pyx_tp_new_2ml___pyx_scope_struct_1_genexpr(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
-  PyObject *o;
-  if (CYTHON_COMPILING_IN_CPYTHON && likely((__pyx_freecount_2ml___pyx_scope_struct_1_genexpr > 0) & (t->tp_basicsize == sizeof(struct __pyx_obj_2ml___pyx_scope_struct_1_genexpr)))) {
-    o = (PyObject*)__pyx_freelist_2ml___pyx_scope_struct_1_genexpr[--__pyx_freecount_2ml___pyx_scope_struct_1_genexpr];
-    memset(o, 0, sizeof(struct __pyx_obj_2ml___pyx_scope_struct_1_genexpr));
-    (void) PyObject_INIT(o, t);
-    PyObject_GC_Track(o);
-  } else {
-    o = (*t->tp_alloc)(t, 0);
-    if (unlikely(!o)) return 0;
-  }
-  return o;
-}
-
-static void __pyx_tp_dealloc_2ml___pyx_scope_struct_1_genexpr(PyObject *o) {
-  struct __pyx_obj_2ml___pyx_scope_struct_1_genexpr *p = (struct __pyx_obj_2ml___pyx_scope_struct_1_genexpr *)o;
-  PyObject_GC_UnTrack(o);
-  Py_CLEAR(p->__pyx_outer_scope);
-  Py_CLEAR(p->__pyx_v_s);
-  Py_CLEAR(p->__pyx_t_1);
-  if (CYTHON_COMPILING_IN_CPYTHON && ((__pyx_freecount_2ml___pyx_scope_struct_1_genexpr < 8) & (Py_TYPE(o)->tp_basicsize == sizeof(struct __pyx_obj_2ml___pyx_scope_struct_1_genexpr)))) {
-    __pyx_freelist_2ml___pyx_scope_struct_1_genexpr[__pyx_freecount_2ml___pyx_scope_struct_1_genexpr++] = ((struct __pyx_obj_2ml___pyx_scope_struct_1_genexpr *)o);
-  } else {
-    (*Py_TYPE(o)->tp_free)(o);
-  }
-}
-
-static int __pyx_tp_traverse_2ml___pyx_scope_struct_1_genexpr(PyObject *o, visitproc v, void *a) {
-  int e;
-  struct __pyx_obj_2ml___pyx_scope_struct_1_genexpr *p = (struct __pyx_obj_2ml___pyx_scope_struct_1_genexpr *)o;
-  if (p->__pyx_outer_scope) {
-    e = (*v)(((PyObject*)p->__pyx_outer_scope), a); if (e) return e;
-  }
-  if (p->__pyx_v_s) {
-    e = (*v)(p->__pyx_v_s, a); if (e) return e;
-  }
-  if (p->__pyx_t_1) {
-    e = (*v)(p->__pyx_t_1, a); if (e) return e;
-  }
-  return 0;
-}
-
-static int __pyx_tp_clear_2ml___pyx_scope_struct_1_genexpr(PyObject *o) {
-  PyObject* tmp;
-  struct __pyx_obj_2ml___pyx_scope_struct_1_genexpr *p = (struct __pyx_obj_2ml___pyx_scope_struct_1_genexpr *)o;
-  tmp = ((PyObject*)p->__pyx_outer_scope);
-  p->__pyx_outer_scope = ((struct __pyx_obj_2ml___pyx_scope_struct__ml *)Py_None); Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  tmp = ((PyObject*)p->__pyx_v_s);
-  p->__pyx_v_s = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  tmp = ((PyObject*)p->__pyx_t_1);
-  p->__pyx_t_1 = Py_None; Py_INCREF(Py_None);
-  Py_XDECREF(tmp);
-  return 0;
-}
-
-static PyTypeObject __pyx_type_2ml___pyx_scope_struct_1_genexpr = {
-  PyVarObject_HEAD_INIT(0, 0)
-  "ml.__pyx_scope_struct_1_genexpr", /*tp_name*/
-  sizeof(struct __pyx_obj_2ml___pyx_scope_struct_1_genexpr), /*tp_basicsize*/
-  0, /*tp_itemsize*/
-  __pyx_tp_dealloc_2ml___pyx_scope_struct_1_genexpr, /*tp_dealloc*/
-  0, /*tp_print*/
-  0, /*tp_getattr*/
-  0, /*tp_setattr*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*tp_compare*/
-  #else
-  0, /*reserved*/
-  #endif
-  0, /*tp_repr*/
-  0, /*tp_as_number*/
-  0, /*tp_as_sequence*/
-  0, /*tp_as_mapping*/
-  0, /*tp_hash*/
-  0, /*tp_call*/
-  0, /*tp_str*/
-  0, /*tp_getattro*/
-  0, /*tp_setattro*/
-  0, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
-  0, /*tp_doc*/
-  __pyx_tp_traverse_2ml___pyx_scope_struct_1_genexpr, /*tp_traverse*/
-  __pyx_tp_clear_2ml___pyx_scope_struct_1_genexpr, /*tp_clear*/
-  0, /*tp_richcompare*/
-  0, /*tp_weaklistoffset*/
-  0, /*tp_iter*/
-  0, /*tp_iternext*/
-  0, /*tp_methods*/
-  0, /*tp_members*/
-  0, /*tp_getset*/
-  0, /*tp_base*/
-  0, /*tp_dict*/
-  0, /*tp_descr_get*/
-  0, /*tp_descr_set*/
-  0, /*tp_dictoffset*/
-  0, /*tp_init*/
-  0, /*tp_alloc*/
-  __pyx_tp_new_2ml___pyx_scope_struct_1_genexpr, /*tp_new*/
-  0, /*tp_free*/
-  0, /*tp_is_gc*/
-  0, /*tp_bases*/
-  0, /*tp_mro*/
-  0, /*tp_cache*/
-  0, /*tp_subclasses*/
-  0, /*tp_weaklist*/
-  0, /*tp_del*/
-  0, /*tp_version_tag*/
-  #if PY_VERSION_HEX >= 0x030400a1
-  0, /*tp_finalize*/
-  #endif
-};
-
 static PyMethodDef __pyx_methods[] = {
   {0, 0, 0, 0}
 };
@@ -5226,18 +5304,17 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
   {&__pyx_n_s_age, __pyx_k_age, sizeof(__pyx_k_age), 0, 0, 1, 1},
   {&__pyx_n_s_alpha, __pyx_k_alpha, sizeof(__pyx_k_alpha), 0, 0, 1, 1},
   {&__pyx_n_s_arange, __pyx_k_arange, sizeof(__pyx_k_arange), 0, 0, 1, 1},
-  {&__pyx_n_s_args, __pyx_k_args, sizeof(__pyx_k_args), 0, 0, 1, 1},
   {&__pyx_n_s_beta, __pyx_k_beta, sizeof(__pyx_k_beta), 0, 0, 1, 1},
   {&__pyx_n_s_beta_add, __pyx_k_beta_add, sizeof(__pyx_k_beta_add), 0, 0, 1, 1},
-  {&__pyx_n_s_close, __pyx_k_close, sizeof(__pyx_k_close), 0, 0, 1, 1},
   {&__pyx_n_s_delta, __pyx_k_delta, sizeof(__pyx_k_delta), 0, 0, 1, 1},
   {&__pyx_n_s_dist, __pyx_k_dist, sizeof(__pyx_k_dist), 0, 0, 1, 1},
+  {&__pyx_n_s_dists, __pyx_k_dists, sizeof(__pyx_k_dists), 0, 0, 1, 1},
   {&__pyx_n_s_dt, __pyx_k_dt, sizeof(__pyx_k_dt), 0, 0, 1, 1},
+  {&__pyx_n_s_dts, __pyx_k_dts, sizeof(__pyx_k_dts), 0, 0, 1, 1},
   {&__pyx_n_s_dtype, __pyx_k_dtype, sizeof(__pyx_k_dtype), 0, 0, 1, 1},
   {&__pyx_n_s_enumerate, __pyx_k_enumerate, sizeof(__pyx_k_enumerate), 0, 0, 1, 1},
   {&__pyx_n_s_failures, __pyx_k_failures, sizeof(__pyx_k_failures), 0, 0, 1, 1},
   {&__pyx_n_s_float64, __pyx_k_float64, sizeof(__pyx_k_float64), 0, 0, 1, 1},
-  {&__pyx_n_s_genexpr, __pyx_k_genexpr, sizeof(__pyx_k_genexpr), 0, 0, 1, 1},
   {&__pyx_n_s_i, __pyx_k_i, sizeof(__pyx_k_i), 0, 0, 1, 1},
   {&__pyx_n_s_import, __pyx_k_import, sizeof(__pyx_k_import), 0, 0, 1, 1},
   {&__pyx_kp_s_inf, __pyx_k_inf, sizeof(__pyx_k_inf), 0, 0, 1, 0},
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   {&__pyx_n_s_main, __pyx_k_main, sizeof(__pyx_k_main), 0, 0, 1, 1},
-  {&__pyx_n_s_max, __pyx_k_max, sizeof(__pyx_k_max), 0, 0, 1, 1},
   {&__pyx_n_s_max_beta, __pyx_k_max_beta, sizeof(__pyx_k_max_beta), 0, 0, 1, 1},
   {&__pyx_n_s_max_beta_add, __pyx_k_max_beta_add, sizeof(__pyx_k_max_beta_add), 0, 0, 1, 1},
   {&__pyx_n_s_max_roots, __pyx_k_max_roots, sizeof(__pyx_k_max_roots), 0, 0, 1, 1},
   {&__pyx_n_s_ml, __pyx_k_ml, sizeof(__pyx_k_ml), 0, 0, 1, 1},
-  {&__pyx_n_s_ml_locals_genexpr, __pyx_k_ml_locals_genexpr, sizeof(__pyx_k_ml_locals_genexpr), 0, 0, 1, 1},
   {&__pyx_n_s_n_nodes, __pyx_k_n_nodes, sizeof(__pyx_k_n_nodes), 0, 0, 1, 1},
   {&__pyx_n_s_n_roots, __pyx_k_n_roots, sizeof(__pyx_k_n_roots), 0, 0, 1, 1},
   {&__pyx_n_s_n_victims, __pyx_k_n_victims, sizeof(__pyx_k_n_victims), 0, 0, 1, 1},
   {&__pyx_kp_u_ndarray_is_not_C_contiguous, __pyx_k_ndarray_is_not_C_contiguous, sizeof(__pyx_k_ndarray_is_not_C_contiguous), 0, 1, 0, 0},
   {&__pyx_kp_u_ndarray_is_not_Fortran_contiguou, __pyx_k_ndarray_is_not_Fortran_contiguou, sizeof(__pyx_k_ndarray_is_not_Fortran_contiguou), 0, 1, 0, 0},
+  {&__pyx_n_s_non_seeds, __pyx_k_non_seeds, sizeof(__pyx_k_non_seeds), 0, 0, 1, 1},
   {&__pyx_n_s_non_victims, __pyx_k_non_victims, sizeof(__pyx_k_non_victims), 0, 0, 1, 1},
   {&__pyx_n_s_np, __pyx_k_np, sizeof(__pyx_k_np), 0, 0, 1, 1},
   {&__pyx_n_s_numpy, __pyx_k_numpy, sizeof(__pyx_k_numpy), 0, 0, 1, 1},
+  {&__pyx_n_s_parent_dists, __pyx_k_parent_dists, sizeof(__pyx_k_parent_dists), 0, 0, 1, 1},
+  {&__pyx_n_s_parent_dts, __pyx_k_parent_dts, sizeof(__pyx_k_parent_dts), 0, 0, 1, 1},
   {&__pyx_n_s_parents, __pyx_k_parents, sizeof(__pyx_k_parents), 0, 0, 1, 1},
+  {&__pyx_n_s_prnt, __pyx_k_prnt, sizeof(__pyx_k_prnt), 0, 0, 1, 1},
+  {&__pyx_n_s_prob, __pyx_k_prob, sizeof(__pyx_k_prob), 0, 0, 1, 1},
   {&__pyx_n_s_probs, __pyx_k_probs, sizeof(__pyx_k_probs), 0, 0, 1, 1},
   {&__pyx_n_s_probs_fail, __pyx_k_probs_fail, sizeof(__pyx_k_probs_fail), 0, 0, 1, 1},
   {&__pyx_n_s_probs_nv, __pyx_k_probs_nv, sizeof(__pyx_k_probs_nv), 0, 0, 1, 1},
   {&__pyx_n_s_range, __pyx_k_range, sizeof(__pyx_k_range), 0, 0, 1, 1},
   {&__pyx_n_s_root_victims, __pyx_k_root_victims, sizeof(__pyx_k_root_victims), 0, 0, 1, 1},
   {&__pyx_n_s_roots, __pyx_k_roots, sizeof(__pyx_k_roots), 0, 0, 1, 1},
-  {&__pyx_n_s_send, __pyx_k_send, sizeof(__pyx_k_send), 0, 0, 1, 1},
+  {&__pyx_n_s_s, __pyx_k_s, sizeof(__pyx_k_s), 0, 0, 1, 1},
+  {&__pyx_n_s_seeds, __pyx_k_seeds, sizeof(__pyx_k_seeds), 0, 0, 1, 1},
   {&__pyx_n_s_successes, __pyx_k_successes, sizeof(__pyx_k_successes), 0, 0, 1, 1},
   {&__pyx_n_s_sum, __pyx_k_sum, sizeof(__pyx_k_sum), 0, 0, 1, 1},
   {&__pyx_n_s_t, __pyx_k_t, sizeof(__pyx_k_t), 0, 0, 1, 1},
   {&__pyx_n_s_test, __pyx_k_test, sizeof(__pyx_k_test), 0, 0, 1, 1},
   {&__pyx_n_s_thresh, __pyx_k_thresh, sizeof(__pyx_k_thresh), 0, 0, 1, 1},
-  {&__pyx_n_s_throw, __pyx_k_throw, sizeof(__pyx_k_throw), 0, 0, 1, 1},
   {&__pyx_kp_u_unknown_dtype_code_in_numpy_pxd, __pyx_k_unknown_dtype_code_in_numpy_pxd, sizeof(__pyx_k_unknown_dtype_code_in_numpy_pxd), 0, 1, 0, 0},
   {&__pyx_n_s_victims, __pyx_k_victims, sizeof(__pyx_k_victims), 0, 0, 1, 1},
   {&__pyx_n_s_w1, __pyx_k_w1, sizeof(__pyx_k_w1), 0, 0, 1, 1},
@@ -5282,9 +5362,8 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
   {0, 0, 0, 0, 0, 0, 0}
 };
 static int __Pyx_InitCachedBuiltins(void) {
-  __pyx_builtin_enumerate = __Pyx_GetBuiltinName(__pyx_n_s_enumerate); if (!__pyx_builtin_enumerate) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 49; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_builtin_sum = __Pyx_GetBuiltinName(__pyx_n_s_sum); if (!__pyx_builtin_sum) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 55; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_builtin_max = __Pyx_GetBuiltinName(__pyx_n_s_max); if (!__pyx_builtin_max) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 59; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_builtin_enumerate = __Pyx_GetBuiltinName(__pyx_n_s_enumerate); if (!__pyx_builtin_enumerate) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 43; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_builtin_sum = __Pyx_GetBuiltinName(__pyx_n_s_sum); if (!__pyx_builtin_sum) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 49; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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   __pyx_builtin_range = __Pyx_GetBuiltinName(__pyx_n_s_range); if (!__pyx_builtin_range) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 231; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __pyx_builtin_RuntimeError = __Pyx_GetBuiltinName(__pyx_n_s_RuntimeError); if (!__pyx_builtin_RuntimeError) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 802; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "ml.pyx":83
+  /* "ml.pyx":80
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  *     # iterate over all victim nodes to find the optimal threshold
- *     for beta in np.arange(0.001, .2, .002):             # <<<<<<<<<<<<<<
+ *     for beta in np.arange(0.01, 1., .01):             # <<<<<<<<<<<<<<
  *         thresh = log(beta/(3012.*(1.-beta)))
- *         # print 'beta:', beta, 'thresh:', thresh, 'infected:', len(probs[probs>=thresh])
+ *         seeds = probs<thresh
  */
-  __pyx_tuple_ = PyTuple_Pack(3, __pyx_float_0_001, __pyx_float__2, __pyx_float__002); if (unlikely(!__pyx_tuple_)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 83; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple_ = PyTuple_Pack(3, __pyx_float_0_01, __pyx_float_1_, __pyx_float__01); if (unlikely(!__pyx_tuple_)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple_);
   __Pyx_GIVEREF(__pyx_tuple_);
 
@@ -5374,17 +5453,17 @@ static int __Pyx_InitCachedConstants(void) {
   __Pyx_GOTREF(__pyx_tuple__7);
   __Pyx_GIVEREF(__pyx_tuple__7);
 
-  /* "ml.pyx":35
+  /* "ml.pyx":27
  *     return result
  * 
  * def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age,             # <<<<<<<<<<<<<<
  *        DTYPE_t alpha, DTYPE_t delta):
  *     cdef:
  */
-  __pyx_tuple__8 = PyTuple_Pack(33, __pyx_n_s_root_victims, __pyx_n_s_victims, __pyx_n_s_non_victims, __pyx_n_s_age, __pyx_n_s_alpha, __pyx_n_s_delta, __pyx_n_s_n_roots, __pyx_n_s_n_victims, __pyx_n_s_n_nodes, __pyx_n_s_roots, __pyx_n_s_i, __pyx_n_s_dist, __pyx_n_s_dt, __pyx_n_s_t, __pyx_n_s_l, __pyx_n_s_beta, __pyx_n_s_ll, __pyx_n_s_beta_add, __pyx_n_s_max_beta, __pyx_n_s_max_beta_add, __pyx_n_s_parents, __pyx_n_s_failures, __pyx_n_s_successes, __pyx_n_s_probs, __pyx_n_s_probs_fail, __pyx_n_s_probs_nv, __pyx_n_s_thresh, __pyx_n_s_max_roots, __pyx_n_s_w1, __pyx_n_s_w2, __pyx_n_s_w3, __pyx_n_s_genexpr, __pyx_n_s_genexpr); if (unlikely(!__pyx_tuple__8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__8 = PyTuple_Pack(40, __pyx_n_s_root_victims, __pyx_n_s_victims, __pyx_n_s_non_victims, __pyx_n_s_age, __pyx_n_s_alpha, __pyx_n_s_delta, __pyx_n_s_n_roots, __pyx_n_s_n_victims, __pyx_n_s_roots, __pyx_n_s_i, __pyx_n_s_dist, __pyx_n_s_dt, __pyx_n_s_t, __pyx_n_s_l, __pyx_n_s_beta, __pyx_n_s_ll, __pyx_n_s_beta_add, __pyx_n_s_max_beta, __pyx_n_s_max_beta_add, __pyx_n_s_parents, __pyx_n_s_failures, __pyx_n_s_successes, __pyx_n_s_n_nodes, __pyx_n_s_probs, __pyx_n_s_probs_fail, __pyx_n_s_parent_dists, __pyx_n_s_parent_dts, __pyx_n_s_probs_nv, __pyx_n_s_dists, __pyx_n_s_dts, __pyx_n_s_s, __pyx_n_s_prob, __pyx_n_s_thresh, __pyx_n_s_seeds, __pyx_n_s_non_seeds, __pyx_n_s_max_roots, __pyx_n_s_prnt, __pyx_n_s_w1, __pyx_n_s_w2, __pyx_n_s_w3); if (unlikely(!__pyx_tuple__8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 27; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__8);
   __Pyx_GIVEREF(__pyx_tuple__8);
-  __pyx_codeobj__9 = (PyObject*)__Pyx_PyCode_New(6, 0, 33, 0, 0, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__8, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Users_ben_Documents_Cascade_Pro, __pyx_n_s_ml, 35, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__9)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_codeobj__9 = (PyObject*)__Pyx_PyCode_New(6, 0, 40, 0, 0, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__8, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Users_ben_Documents_Cascade_Pro, __pyx_n_s_ml, 27, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__9)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 27; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_RefNannyFinishContext();
   return 0;
   __pyx_L1_error:;
@@ -5394,9 +5473,10 @@ static int __Pyx_InitCachedConstants(void) {
 
 static int __Pyx_InitGlobals(void) {
   if (__Pyx_InitStrings(__pyx_string_tab) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
-  __pyx_float__2 = PyFloat_FromDouble(.2); if (unlikely(!__pyx_float__2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_float__002 = PyFloat_FromDouble(.002); if (unlikely(!__pyx_float__002)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_float_0_001 = PyFloat_FromDouble(0.001); if (unlikely(!__pyx_float_0_001)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_float_1_ = PyFloat_FromDouble(1.); if (unlikely(!__pyx_float_1_)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_float__01 = PyFloat_FromDouble(.01); if (unlikely(!__pyx_float__01)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_float_0_01 = PyFloat_FromDouble(0.01); if (unlikely(!__pyx_float_0_01)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_int_148152 = PyInt_FromLong(148152L); if (unlikely(!__pyx_int_148152)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   return 0;
   __pyx_L1_error:;
   return -1;
@@ -5483,12 +5563,6 @@ PyMODINIT_FUNC PyInit_ml(void)
   /*--- Variable export code ---*/
   /*--- Function export code ---*/
   /*--- Type init code ---*/
-  if (PyType_Ready(&__pyx_type_2ml___pyx_scope_struct__ml) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_type_2ml___pyx_scope_struct__ml.tp_print = 0;
-  __pyx_ptype_2ml___pyx_scope_struct__ml = &__pyx_type_2ml___pyx_scope_struct__ml;
-  if (PyType_Ready(&__pyx_type_2ml___pyx_scope_struct_1_genexpr) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 59; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_type_2ml___pyx_scope_struct_1_genexpr.tp_print = 0;
-  __pyx_ptype_2ml___pyx_scope_struct_1_genexpr = &__pyx_type_2ml___pyx_scope_struct_1_genexpr;
   /*--- Type import code ---*/
   __pyx_ptype_7cpython_4type_type = __Pyx_ImportType(__Pyx_BUILTIN_MODULE_NAME, "type", 
   #if CYTHON_COMPILING_IN_PYPY
@@ -5532,16 +5606,16 @@ PyMODINIT_FUNC PyInit_ml(void)
   if (PyDict_SetItem(__pyx_d, __pyx_n_s_DTYPE, __pyx_t_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 6; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
 
-  /* "ml.pyx":35
+  /* "ml.pyx":27
  *     return result
  * 
  * def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age,             # <<<<<<<<<<<<<<
  *        DTYPE_t alpha, DTYPE_t delta):
  *     cdef:
  */
-  __pyx_t_2 = PyCFunction_NewEx(&__pyx_mdef_2ml_1ml, NULL, __pyx_n_s_ml); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_2 = PyCFunction_NewEx(&__pyx_mdef_2ml_1ml, NULL, __pyx_n_s_ml); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 27; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_2);
-  if (PyDict_SetItem(__pyx_d, __pyx_n_s_ml, __pyx_t_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 35; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_d, __pyx_n_s_ml, __pyx_t_2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 27; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
 
   /* "ml.pyx":1
@@ -5780,88 +5854,6 @@ static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, in
     return 0;
 }
 
-static CYTHON_INLINE void __Pyx_RaiseClosureNameError(const char *varname) {
-    PyErr_Format(PyExc_NameError, "free variable '%s' referenced before assignment in enclosing scope", varname);
-}
-
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
-    PyObject *r;
-    if (!j) return NULL;
-    r = PyObject_GetItem(o, j);
-    Py_DECREF(j);
-    return r;
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
-                                                              int wraparound, int boundscheck) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    if (wraparound & unlikely(i < 0)) i += PyList_GET_SIZE(o);
-    if ((!boundscheck) || likely((0 <= i) & (i < PyList_GET_SIZE(o)))) {
-        PyObject *r = PyList_GET_ITEM(o, i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
-                                                              int wraparound, int boundscheck) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    if (wraparound & unlikely(i < 0)) i += PyTuple_GET_SIZE(o);
-    if ((!boundscheck) || likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) {
-        PyObject *r = PyTuple_GET_ITEM(o, i);
-        Py_INCREF(r);
-        return r;
-    }
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-#else
-    return PySequence_GetItem(o, i);
-#endif
-}
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
-                                                     int is_list, int wraparound, int boundscheck) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    if (is_list || PyList_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
-        if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) {
-            PyObject *r = PyList_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    }
-    else if (PyTuple_CheckExact(o)) {
-        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
-        if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) {
-            PyObject *r = PyTuple_GET_ITEM(o, n);
-            Py_INCREF(r);
-            return r;
-        }
-    } else {
-        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
-        if (likely(m && m->sq_item)) {
-            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
-                Py_ssize_t l = m->sq_length(o);
-                if (likely(l >= 0)) {
-                    i += l;
-                } else {
-                    if (PyErr_ExceptionMatches(PyExc_OverflowError))
-                        PyErr_Clear();
-                    else
-                        return NULL;
-                }
-            }
-            return m->sq_item(o, i);
-        }
-    }
-#else
-    if (is_list || PySequence_Check(o)) {
-        return PySequence_GetItem(o, i);
-    }
-#endif
-    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-}
-
 static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) {
     PyObject *result;
 #if CYTHON_COMPILING_IN_CPYTHON
@@ -6740,57 +6732,135 @@ static CYTHON_INLINE int __Pyx_dict_iter_next(PyObject* iter_obj, Py_ssize_t ori
         if (unlikely(!next_item)) {
             return __Pyx_IterFinish();
         }
-    }
-    if (pitem) {
-        *pitem = next_item;
-    } else if (pkey && pvalue) {
-        if (__Pyx_unpack_tuple2(next_item, pkey, pvalue, source_is_dict, source_is_dict, 1))
-            return -1;
-    } else if (pkey) {
-        *pkey = next_item;
+    }
+    if (pitem) {
+        *pitem = next_item;
+    } else if (pkey && pvalue) {
+        if (__Pyx_unpack_tuple2(next_item, pkey, pvalue, source_is_dict, source_is_dict, 1))
+            return -1;
+    } else if (pkey) {
+        *pkey = next_item;
+    } else {
+        *pvalue = next_item;
+    }
+    return 1;
+}
+
+static double __Pyx__PyObject_AsDouble(PyObject* obj) {
+    PyObject* float_value;
+#if CYTHON_COMPILING_IN_PYPY
+    float_value = PyNumber_Float(obj);
+#else
+    PyNumberMethods *nb = Py_TYPE(obj)->tp_as_number;
+    if (likely(nb) && likely(nb->nb_float)) {
+        float_value = nb->nb_float(obj);
+        if (likely(float_value) && unlikely(!PyFloat_Check(float_value))) {
+            PyErr_Format(PyExc_TypeError,
+                "__float__ returned non-float (type %.200s)",
+                Py_TYPE(float_value)->tp_name);
+            Py_DECREF(float_value);
+            goto bad;
+        }
+    } else if (PyUnicode_CheckExact(obj) || PyBytes_CheckExact(obj)) {
+#if PY_MAJOR_VERSION >= 3
+        float_value = PyFloat_FromString(obj);
+#else
+        float_value = PyFloat_FromString(obj, 0);
+#endif
+    } else {
+        PyObject* args = PyTuple_New(1);
+        if (unlikely(!args)) goto bad;
+        PyTuple_SET_ITEM(args, 0, obj);
+        float_value = PyObject_Call((PyObject*)&PyFloat_Type, args, 0);
+        PyTuple_SET_ITEM(args, 0, 0);
+        Py_DECREF(args);
+    }
+#endif
+    if (likely(float_value)) {
+        double value = PyFloat_AS_DOUBLE(float_value);
+        Py_DECREF(float_value);
+        return value;
+    }
+bad:
+    return (double)-1;
+}
+
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
+    PyObject *r;
+    if (!j) return NULL;
+    r = PyObject_GetItem(o, j);
+    Py_DECREF(j);
+    return r;
+}
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
+                                                              int wraparound, int boundscheck) {
+#if CYTHON_COMPILING_IN_CPYTHON
+    if (wraparound & unlikely(i < 0)) i += PyList_GET_SIZE(o);
+    if ((!boundscheck) || likely((0 <= i) & (i < PyList_GET_SIZE(o)))) {
+        PyObject *r = PyList_GET_ITEM(o, i);
+        Py_INCREF(r);
+        return r;
+    }
+    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+#else
+    return PySequence_GetItem(o, i);
+#endif
+}
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
+                                                              int wraparound, int boundscheck) {
+#if CYTHON_COMPILING_IN_CPYTHON
+    if (wraparound & unlikely(i < 0)) i += PyTuple_GET_SIZE(o);
+    if ((!boundscheck) || likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) {
+        PyObject *r = PyTuple_GET_ITEM(o, i);
+        Py_INCREF(r);
+        return r;
+    }
+    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+#else
+    return PySequence_GetItem(o, i);
+#endif
+}
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
+                                                     int is_list, int wraparound, int boundscheck) {
+#if CYTHON_COMPILING_IN_CPYTHON
+    if (is_list || PyList_CheckExact(o)) {
+        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
+        if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) {
+            PyObject *r = PyList_GET_ITEM(o, n);
+            Py_INCREF(r);
+            return r;
+        }
+    }
+    else if (PyTuple_CheckExact(o)) {
+        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
+        if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) {
+            PyObject *r = PyTuple_GET_ITEM(o, n);
+            Py_INCREF(r);
+            return r;
+        }
     } else {
-        *pvalue = next_item;
-    }
-    return 1;
-}
-
-static double __Pyx__PyObject_AsDouble(PyObject* obj) {
-    PyObject* float_value;
-#if CYTHON_COMPILING_IN_PYPY
-    float_value = PyNumber_Float(obj);
-#else
-    PyNumberMethods *nb = Py_TYPE(obj)->tp_as_number;
-    if (likely(nb) && likely(nb->nb_float)) {
-        float_value = nb->nb_float(obj);
-        if (likely(float_value) && unlikely(!PyFloat_Check(float_value))) {
-            PyErr_Format(PyExc_TypeError,
-                "__float__ returned non-float (type %.200s)",
-                Py_TYPE(float_value)->tp_name);
-            Py_DECREF(float_value);
-            goto bad;
+        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
+        if (likely(m && m->sq_item)) {
+            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
+                Py_ssize_t l = m->sq_length(o);
+                if (likely(l >= 0)) {
+                    i += l;
+                } else {
+                    if (PyErr_ExceptionMatches(PyExc_OverflowError))
+                        PyErr_Clear();
+                    else
+                        return NULL;
+                }
+            }
+            return m->sq_item(o, i);
         }
-    } else if (PyUnicode_CheckExact(obj) || PyBytes_CheckExact(obj)) {
-#if PY_MAJOR_VERSION >= 3
-        float_value = PyFloat_FromString(obj);
+    }
 #else
-        float_value = PyFloat_FromString(obj, 0);
-#endif
-    } else {
-        PyObject* args = PyTuple_New(1);
-        if (unlikely(!args)) goto bad;
-        PyTuple_SET_ITEM(args, 0, obj);
-        float_value = PyObject_Call((PyObject*)&PyFloat_Type, args, 0);
-        PyTuple_SET_ITEM(args, 0, 0);
-        Py_DECREF(args);
+    if (is_list || PySequence_Check(o)) {
+        return PySequence_GetItem(o, i);
     }
 #endif
-    if (likely(float_value)) {
-        double value = PyFloat_AS_DOUBLE(float_value);
-        Py_DECREF(float_value);
-        return value;
-    }
-bad:
-    return (double)-1;
+    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
 }
 
 static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) {
@@ -7737,639 +7807,6 @@ raise_neg_overflow:
     return (long) -1;
 }
 
-static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) {
-    PyObject* fake_module;
-    PyTypeObject* cached_type = NULL;
-    fake_module = PyImport_AddModule((char*) "_cython_" CYTHON_ABI);
-    if (!fake_module) return NULL;
-    Py_INCREF(fake_module);
-    cached_type = (PyTypeObject*) PyObject_GetAttrString(fake_module, type->tp_name);
-    if (cached_type) {
-        if (!PyType_Check((PyObject*)cached_type)) {
-            PyErr_Format(PyExc_TypeError,
-                "Shared Cython type %.200s is not a type object",
-                type->tp_name);
-            goto bad;
-        }
-        if (cached_type->tp_basicsize != type->tp_basicsize) {
-            PyErr_Format(PyExc_TypeError,
-                "Shared Cython type %.200s has the wrong size, try recompiling",
-                type->tp_name);
-            goto bad;
-        }
-    } else {
-        if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad;
-        PyErr_Clear();
-        if (PyType_Ready(type) < 0) goto bad;
-        if (PyObject_SetAttrString(fake_module, type->tp_name, (PyObject*) type) < 0)
-            goto bad;
-        Py_INCREF(type);
-        cached_type = type;
-    }
-done:
-    Py_DECREF(fake_module);
-    return cached_type;
-bad:
-    Py_XDECREF(cached_type);
-    cached_type = NULL;
-    goto done;
-}
-
-static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyThreadState *tstate = PyThreadState_GET();
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = *type;
-    tstate->exc_value = *value;
-    tstate->exc_traceback = *tb;
-#else
-    PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb);
-    PyErr_SetExcInfo(*type, *value, *tb);
-#endif
-    *type = tmp_type;
-    *value = tmp_value;
-    *tb = tmp_tb;
-}
-
-static PyObject* __Pyx_PyObject_CallMethod1(PyObject* obj, PyObject* method_name, PyObject* arg) {
-    PyObject *method, *result = NULL;
-    method = __Pyx_PyObject_GetAttrStr(obj, method_name);
-    if (unlikely(!method)) goto bad;
-#if CYTHON_COMPILING_IN_CPYTHON
-    if (likely(PyMethod_Check(method))) {
-        PyObject *self = PyMethod_GET_SELF(method);
-        if (likely(self)) {
-            PyObject *args;
-            PyObject *function = PyMethod_GET_FUNCTION(method);
-            args = PyTuple_New(2);
-            if (unlikely(!args)) goto bad;
-            Py_INCREF(self);
-            PyTuple_SET_ITEM(args, 0, self);
-            Py_INCREF(arg);
-            PyTuple_SET_ITEM(args, 1, arg);
-            Py_INCREF(function);
-            Py_DECREF(method); method = NULL;
-            result = __Pyx_PyObject_Call(function, args, NULL);
-            Py_DECREF(args);
-            Py_DECREF(function);
-            return result;
-        }
-    }
-#endif
-    result = __Pyx_PyObject_CallOneArg(method, arg);
-bad:
-    Py_XDECREF(method);
-    return result;
-}
-
-static PyObject *__Pyx_Generator_Next(PyObject *self);
-static PyObject *__Pyx_Generator_Send(PyObject *self, PyObject *value);
-static PyObject *__Pyx_Generator_Close(PyObject *self);
-static PyObject *__Pyx_Generator_Throw(PyObject *gen, PyObject *args);
-static PyTypeObject *__pyx_GeneratorType = 0;
-#define __Pyx_Generator_CheckExact(obj) (Py_TYPE(obj) == __pyx_GeneratorType)
-#define __Pyx_Generator_Undelegate(gen) Py_CLEAR((gen)->yieldfrom)
-#if 1 || PY_VERSION_HEX < 0x030300B0
-static int __Pyx_PyGen_FetchStopIterationValue(PyObject **pvalue) {
-    PyObject *et, *ev, *tb;
-    PyObject *value = NULL;
-    __Pyx_ErrFetch(&et, &ev, &tb);
-    if (!et) {
-        Py_XDECREF(tb);
-        Py_XDECREF(ev);
-        Py_INCREF(Py_None);
-        *pvalue = Py_None;
-        return 0;
-    }
-    if (unlikely(et != PyExc_StopIteration) &&
-            unlikely(!PyErr_GivenExceptionMatches(et, PyExc_StopIteration))) {
-        __Pyx_ErrRestore(et, ev, tb);
-        return -1;
-    }
-    if (likely(et == PyExc_StopIteration)) {
-        if (likely(!ev) || !PyObject_IsInstance(ev, PyExc_StopIteration)) {
-            if (!ev) {
-                Py_INCREF(Py_None);
-                ev = Py_None;
-            }
-            Py_XDECREF(tb);
-            Py_DECREF(et);
-            *pvalue = ev;
-            return 0;
-        }
-    }
-    PyErr_NormalizeException(&et, &ev, &tb);
-    if (unlikely(!PyObject_IsInstance(ev, PyExc_StopIteration))) {
-        __Pyx_ErrRestore(et, ev, tb);
-        return -1;
-    }
-    Py_XDECREF(tb);
-    Py_DECREF(et);
-#if PY_VERSION_HEX >= 0x030300A0
-    value = ((PyStopIterationObject *)ev)->value;
-    Py_INCREF(value);
-    Py_DECREF(ev);
-#else
-    {
-        PyObject* args = PyObject_GetAttr(ev, __pyx_n_s_args);
-        Py_DECREF(ev);
-        if (likely(args)) {
-            value = PyObject_GetItem(args, 0);
-            Py_DECREF(args);
-        }
-        if (unlikely(!value)) {
-            __Pyx_ErrRestore(NULL, NULL, NULL);
-            Py_INCREF(Py_None);
-            value = Py_None;
-        }
-    }
-#endif
-    *pvalue = value;
-    return 0;
-}
-#endif
-static CYTHON_INLINE
-void __Pyx_Generator_ExceptionClear(__pyx_GeneratorObject *self) {
-    PyObject *exc_type = self->exc_type;
-    PyObject *exc_value = self->exc_value;
-    PyObject *exc_traceback = self->exc_traceback;
-    self->exc_type = NULL;
-    self->exc_value = NULL;
-    self->exc_traceback = NULL;
-    Py_XDECREF(exc_type);
-    Py_XDECREF(exc_value);
-    Py_XDECREF(exc_traceback);
-}
-static CYTHON_INLINE
-int __Pyx_Generator_CheckRunning(__pyx_GeneratorObject *gen) {
-    if (unlikely(gen->is_running)) {
-        PyErr_SetString(PyExc_ValueError,
-                        "generator already executing");
-        return 1;
-    }
-    return 0;
-}
-static CYTHON_INLINE
-PyObject *__Pyx_Generator_SendEx(__pyx_GeneratorObject *self, PyObject *value) {
-    PyObject *retval;
-    assert(!self->is_running);
-    if (unlikely(self->resume_label == 0)) {
-        if (unlikely(value && value != Py_None)) {
-            PyErr_SetString(PyExc_TypeError,
-                            "can't send non-None value to a "
-                            "just-started generator");
-            return NULL;
-        }
-    }
-    if (unlikely(self->resume_label == -1)) {
-        PyErr_SetNone(PyExc_StopIteration);
-        return NULL;
-    }
-    if (value) {
-#if CYTHON_COMPILING_IN_PYPY
-#else
-        if (self->exc_traceback) {
-            PyThreadState *tstate = PyThreadState_GET();
-            PyTracebackObject *tb = (PyTracebackObject *) self->exc_traceback;
-            PyFrameObject *f = tb->tb_frame;
-            Py_XINCREF(tstate->frame);
-            assert(f->f_back == NULL);
-            f->f_back = tstate->frame;
-        }
-#endif
-        __Pyx_ExceptionSwap(&self->exc_type, &self->exc_value,
-                            &self->exc_traceback);
-    } else {
-        __Pyx_Generator_ExceptionClear(self);
-    }
-    self->is_running = 1;
-    retval = self->body((PyObject *) self, value);
-    self->is_running = 0;
-    if (retval) {
-        __Pyx_ExceptionSwap(&self->exc_type, &self->exc_value,
-                            &self->exc_traceback);
-#if CYTHON_COMPILING_IN_PYPY
-#else
-        if (self->exc_traceback) {
-            PyTracebackObject *tb = (PyTracebackObject *) self->exc_traceback;
-            PyFrameObject *f = tb->tb_frame;
-            Py_CLEAR(f->f_back);
-        }
-#endif
-    } else {
-        __Pyx_Generator_ExceptionClear(self);
-    }
-    return retval;
-}
-static CYTHON_INLINE
-PyObject *__Pyx_Generator_FinishDelegation(__pyx_GeneratorObject *gen) {
-    PyObject *ret;
-    PyObject *val = NULL;
-    __Pyx_Generator_Undelegate(gen);
-    __Pyx_PyGen_FetchStopIterationValue(&val);
-    ret = __Pyx_Generator_SendEx(gen, val);
-    Py_XDECREF(val);
-    return ret;
-}
-static PyObject *__Pyx_Generator_Next(PyObject *self) {
-    __pyx_GeneratorObject *gen = (__pyx_GeneratorObject*) self;
-    PyObject *yf = gen->yieldfrom;
-    if (unlikely(__Pyx_Generator_CheckRunning(gen)))
-        return NULL;
-    if (yf) {
-        PyObject *ret;
-        gen->is_running = 1;
-        ret = Py_TYPE(yf)->tp_iternext(yf);
-        gen->is_running = 0;
-        if (likely(ret)) {
-            return ret;
-        }
-        return __Pyx_Generator_FinishDelegation(gen);
-    }
-    return __Pyx_Generator_SendEx(gen, Py_None);
-}
-static PyObject *__Pyx_Generator_Send(PyObject *self, PyObject *value) {
-    __pyx_GeneratorObject *gen = (__pyx_GeneratorObject*) self;
-    PyObject *yf = gen->yieldfrom;
-    if (unlikely(__Pyx_Generator_CheckRunning(gen)))
-        return NULL;
-    if (yf) {
-        PyObject *ret;
-        gen->is_running = 1;
-        if (__Pyx_Generator_CheckExact(yf)) {
-            ret = __Pyx_Generator_Send(yf, value);
-        } else {
-            if (value == Py_None)
-                ret = PyIter_Next(yf);
-            else
-                ret = __Pyx_PyObject_CallMethod1(yf, __pyx_n_s_send, value);
-        }
-        gen->is_running = 0;
-        if (likely(ret)) {
-            return ret;
-        }
-        return __Pyx_Generator_FinishDelegation(gen);
-    }
-    return __Pyx_Generator_SendEx(gen, value);
-}
-static int __Pyx_Generator_CloseIter(__pyx_GeneratorObject *gen, PyObject *yf) {
-    PyObject *retval = NULL;
-    int err = 0;
-    if (__Pyx_Generator_CheckExact(yf)) {
-        retval = __Pyx_Generator_Close(yf);
-        if (!retval)
-            return -1;
-    } else {
-        PyObject *meth;
-        gen->is_running = 1;
-        meth = PyObject_GetAttr(yf, __pyx_n_s_close);
-        if (unlikely(!meth)) {
-            if (!PyErr_ExceptionMatches(PyExc_AttributeError)) {
-                PyErr_WriteUnraisable(yf);
-            }
-            PyErr_Clear();
-        } else {
-            retval = PyObject_CallFunction(meth, NULL);
-            Py_DECREF(meth);
-            if (!retval)
-                err = -1;
-        }
-        gen->is_running = 0;
-    }
-    Py_XDECREF(retval);
-    return err;
-}
-static PyObject *__Pyx_Generator_Close(PyObject *self) {
-    __pyx_GeneratorObject *gen = (__pyx_GeneratorObject *) self;
-    PyObject *retval, *raised_exception;
-    PyObject *yf = gen->yieldfrom;
-    int err = 0;
-    if (unlikely(__Pyx_Generator_CheckRunning(gen)))
-        return NULL;
-    if (yf) {
-        Py_INCREF(yf);
-        err = __Pyx_Generator_CloseIter(gen, yf);
-        __Pyx_Generator_Undelegate(gen);
-        Py_DECREF(yf);
-    }
-    if (err == 0)
-        PyErr_SetNone(PyExc_GeneratorExit);
-    retval = __Pyx_Generator_SendEx(gen, NULL);
-    if (retval) {
-        Py_DECREF(retval);
-        PyErr_SetString(PyExc_RuntimeError,
-                        "generator ignored GeneratorExit");
-        return NULL;
-    }
-    raised_exception = PyErr_Occurred();
-    if (!raised_exception
-        || raised_exception == PyExc_StopIteration
-        || raised_exception == PyExc_GeneratorExit
-        || PyErr_GivenExceptionMatches(raised_exception, PyExc_GeneratorExit)
-        || PyErr_GivenExceptionMatches(raised_exception, PyExc_StopIteration))
-    {
-        if (raised_exception) PyErr_Clear();
-        Py_INCREF(Py_None);
-        return Py_None;
-    }
-    return NULL;
-}
-static PyObject *__Pyx_Generator_Throw(PyObject *self, PyObject *args) {
-    __pyx_GeneratorObject *gen = (__pyx_GeneratorObject *) self;
-    PyObject *typ;
-    PyObject *tb = NULL;
-    PyObject *val = NULL;
-    PyObject *yf = gen->yieldfrom;
-    if (!PyArg_UnpackTuple(args, (char *)"throw", 1, 3, &typ, &val, &tb))
-        return NULL;
-    if (unlikely(__Pyx_Generator_CheckRunning(gen)))
-        return NULL;
-    if (yf) {
-        PyObject *ret;
-        Py_INCREF(yf);
-        if (PyErr_GivenExceptionMatches(typ, PyExc_GeneratorExit)) {
-            int err = __Pyx_Generator_CloseIter(gen, yf);
-            Py_DECREF(yf);
-            __Pyx_Generator_Undelegate(gen);
-            if (err < 0)
-                return __Pyx_Generator_SendEx(gen, NULL);
-            goto throw_here;
-        }
-        gen->is_running = 1;
-        if (__Pyx_Generator_CheckExact(yf)) {
-            ret = __Pyx_Generator_Throw(yf, args);
-        } else {
-            PyObject *meth = PyObject_GetAttr(yf, __pyx_n_s_throw);
-            if (unlikely(!meth)) {
-                Py_DECREF(yf);
-                if (!PyErr_ExceptionMatches(PyExc_AttributeError)) {
-                    gen->is_running = 0;
-                    return NULL;
-                }
-                PyErr_Clear();
-                __Pyx_Generator_Undelegate(gen);
-                gen->is_running = 0;
-                goto throw_here;
-            }
-            ret = PyObject_CallObject(meth, args);
-            Py_DECREF(meth);
-        }
-        gen->is_running = 0;
-        Py_DECREF(yf);
-        if (!ret) {
-            ret = __Pyx_Generator_FinishDelegation(gen);
-        }
-        return ret;
-    }
-throw_here:
-    __Pyx_Raise(typ, val, tb, NULL);
-    return __Pyx_Generator_SendEx(gen, NULL);
-}
-static int __Pyx_Generator_traverse(PyObject *self, visitproc visit, void *arg) {
-    __pyx_GeneratorObject *gen = (__pyx_GeneratorObject *) self;
-    Py_VISIT(gen->closure);
-    Py_VISIT(gen->classobj);
-    Py_VISIT(gen->yieldfrom);
-    Py_VISIT(gen->exc_type);
-    Py_VISIT(gen->exc_value);
-    Py_VISIT(gen->exc_traceback);
-    return 0;
-}
-static int __Pyx_Generator_clear(PyObject *self) {
-    __pyx_GeneratorObject *gen = (__pyx_GeneratorObject *) self;
-    Py_CLEAR(gen->closure);
-    Py_CLEAR(gen->classobj);
-    Py_CLEAR(gen->yieldfrom);
-    Py_CLEAR(gen->exc_type);
-    Py_CLEAR(gen->exc_value);
-    Py_CLEAR(gen->exc_traceback);
-    Py_CLEAR(gen->gi_name);
-    Py_CLEAR(gen->gi_qualname);
-    return 0;
-}
-static void __Pyx_Generator_dealloc(PyObject *self) {
-    __pyx_GeneratorObject *gen = (__pyx_GeneratorObject *) self;
-    PyObject_GC_UnTrack(gen);
-    if (gen->gi_weakreflist != NULL)
-        PyObject_ClearWeakRefs(self);
-    if (gen->resume_label > 0) {
-        PyObject_GC_Track(self);
-#if PY_VERSION_HEX >= 0x030400a1
-        if (PyObject_CallFinalizerFromDealloc(self))
-#else
-        Py_TYPE(gen)->tp_del(self);
-        if (self->ob_refcnt > 0)
-#endif
-        {
-            return;
-        }
-        PyObject_GC_UnTrack(self);
-    }
-    __Pyx_Generator_clear(self);
-    PyObject_GC_Del(gen);
-}
-static void __Pyx_Generator_del(PyObject *self) {
-    PyObject *res;
-    PyObject *error_type, *error_value, *error_traceback;
-    __pyx_GeneratorObject *gen = (__pyx_GeneratorObject *) self;
-    if (gen->resume_label <= 0)
-        return ;
-#if PY_VERSION_HEX < 0x030400a1
-    assert(self->ob_refcnt == 0);
-    self->ob_refcnt = 1;
-#endif
-    __Pyx_ErrFetch(&error_type, &error_value, &error_traceback);
-    res = __Pyx_Generator_Close(self);
-    if (res == NULL)
-        PyErr_WriteUnraisable(self);
-    else
-        Py_DECREF(res);
-    __Pyx_ErrRestore(error_type, error_value, error_traceback);
-#if PY_VERSION_HEX < 0x030400a1
-    assert(self->ob_refcnt > 0);
-    if (--self->ob_refcnt == 0) {
-        return;
-    }
-    {
-        Py_ssize_t refcnt = self->ob_refcnt;
-        _Py_NewReference(self);
-        self->ob_refcnt = refcnt;
-    }
-#if CYTHON_COMPILING_IN_CPYTHON
-    assert(PyType_IS_GC(self->ob_type) &&
-           _Py_AS_GC(self)->gc.gc_refs != _PyGC_REFS_UNTRACKED);
-    _Py_DEC_REFTOTAL;
-#endif
-#ifdef COUNT_ALLOCS
-    --Py_TYPE(self)->tp_frees;
-    --Py_TYPE(self)->tp_allocs;
-#endif
-#endif
-}
-static PyObject *
-__Pyx_Generator_get_name(__pyx_GeneratorObject *self)
-{
-    Py_INCREF(self->gi_name);
-    return self->gi_name;
-}
-static int
-__Pyx_Generator_set_name(__pyx_GeneratorObject *self, PyObject *value)
-{
-    PyObject *tmp;
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value))) {
-#else
-    if (unlikely(value == NULL || !PyString_Check(value))) {
-#endif
-        PyErr_SetString(PyExc_TypeError,
-                        "__name__ must be set to a string object");
-        return -1;
-    }
-    tmp = self->gi_name;
-    Py_INCREF(value);
-    self->gi_name = value;
-    Py_XDECREF(tmp);
-    return 0;
-}
-static PyObject *
-__Pyx_Generator_get_qualname(__pyx_GeneratorObject *self)
-{
-    Py_INCREF(self->gi_qualname);
-    return self->gi_qualname;
-}
-static int
-__Pyx_Generator_set_qualname(__pyx_GeneratorObject *self, PyObject *value)
-{
-    PyObject *tmp;
-#if PY_MAJOR_VERSION >= 3
-    if (unlikely(value == NULL || !PyUnicode_Check(value))) {
-#else
-    if (unlikely(value == NULL || !PyString_Check(value))) {
-#endif
-        PyErr_SetString(PyExc_TypeError,
-                        "__qualname__ must be set to a string object");
-        return -1;
-    }
-    tmp = self->gi_qualname;
-    Py_INCREF(value);
-    self->gi_qualname = value;
-    Py_XDECREF(tmp);
-    return 0;
-}
-static PyGetSetDef __pyx_Generator_getsets[] = {
-    {(char *) "__name__", (getter)__Pyx_Generator_get_name, (setter)__Pyx_Generator_set_name,
-     (char*) PyDoc_STR("name of the generator"), 0},
-    {(char *) "__qualname__", (getter)__Pyx_Generator_get_qualname, (setter)__Pyx_Generator_set_qualname,
-     (char*) PyDoc_STR("qualified name of the generator"), 0},
-    {0, 0, 0, 0, 0}
-};
-static PyMemberDef __pyx_Generator_memberlist[] = {
-    {(char *) "gi_running", T_BOOL, offsetof(__pyx_GeneratorObject, is_running), READONLY, NULL},
-    {0, 0, 0, 0, 0}
-};
-static PyMethodDef __pyx_Generator_methods[] = {
-    {"send", (PyCFunction) __Pyx_Generator_Send, METH_O, 0},
-    {"throw", (PyCFunction) __Pyx_Generator_Throw, METH_VARARGS, 0},
-    {"close", (PyCFunction) __Pyx_Generator_Close, METH_NOARGS, 0},
-    {0, 0, 0, 0}
-};
-static PyTypeObject __pyx_GeneratorType_type = {
-    PyVarObject_HEAD_INIT(0, 0)
-    "generator",
-    sizeof(__pyx_GeneratorObject),
-    0,
-    (destructor) __Pyx_Generator_dealloc,
-    0,
-    0,
-    0,
-#if PY_MAJOR_VERSION < 3
-    0,
-#else
-    0,
-#endif
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_HAVE_FINALIZE,
-    0,
-    (traverseproc) __Pyx_Generator_traverse,
-    0,
-    0,
-    offsetof(__pyx_GeneratorObject, gi_weakreflist),
-    0,
-    (iternextfunc) __Pyx_Generator_Next,
-    __pyx_Generator_methods,
-    __pyx_Generator_memberlist,
-    __pyx_Generator_getsets,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-    0,
-#if PY_VERSION_HEX >= 0x030400a1
-    0,
-#else
-    __Pyx_Generator_del,
-#endif
-    0,
-#if PY_VERSION_HEX >= 0x030400a1
-    __Pyx_Generator_del,
-#endif
-};
-static __pyx_GeneratorObject *__Pyx_Generator_New(__pyx_generator_body_t body,
-                                                  PyObject *closure, PyObject *name, PyObject *qualname) {
-    __pyx_GeneratorObject *gen =
-        PyObject_GC_New(__pyx_GeneratorObject, &__pyx_GeneratorType_type);
-    if (gen == NULL)
-        return NULL;
-    gen->body = body;
-    gen->closure = closure;
-    Py_XINCREF(closure);
-    gen->is_running = 0;
-    gen->resume_label = 0;
-    gen->classobj = NULL;
-    gen->yieldfrom = NULL;
-    gen->exc_type = NULL;
-    gen->exc_value = NULL;
-    gen->exc_traceback = NULL;
-    gen->gi_weakreflist = NULL;
-    Py_XINCREF(qualname);
-    gen->gi_qualname = qualname;
-    Py_XINCREF(name);
-    gen->gi_name = name;
-    PyObject_GC_Track(gen);
-    return gen;
-}
-static int __pyx_Generator_init(void) {
-    __pyx_GeneratorType_type.tp_getattro = PyObject_GenericGetAttr;
-    __pyx_GeneratorType_type.tp_iter = PyObject_SelfIter;
-    __pyx_GeneratorType = __Pyx_FetchCommonType(&__pyx_GeneratorType_type);
-    if (__pyx_GeneratorType == NULL) {
-        return -1;
-    }
-    return 0;
-}
-
 static int __Pyx_check_binary_version(void) {
     char ctversion[4], rtversion[4];
     PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
diff --git a/experiments/ml.pyx b/experiments/ml.pyx
index c8c99cc..858207f 100644
--- a/experiments/ml.pyx
+++ b/experiments/ml.pyx
@@ -10,14 +10,9 @@ cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,
                             DTYPE_t w1, DTYPE_t w2, DTYPE_t w3):
     """weight for successful infection, exponential time model"""
     cdef DTYPE_t structural, temporal, result
-    structural = dist * log(delta)
-    # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta)
-    temporal = log(exp(alpha)-1.) - alpha*dt
-    # temporal = 1 - exp(-alpha*dt)
-    # if exp(-alpha*dt)==0.: print 'UNDERFLOW ERROR'
-    # temporal = 1. / (1. + (dt - 1.)/alpha)**0.01 - 1. / (1. + dt/alpha)**0.01
-    result = structural + temporal
-    # print 'st', structural, temporal
+    structural = delta ** dist
+    temporal = log(exp(alpha)-1.) - alpha*dt/7.
+    result = log(structural) + temporal
     return result
 
 cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,
@@ -25,17 +20,14 @@ cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,
     """weight for failed infection, exponential time model"""
     cdef DTYPE_t structural, temporal, result
     structural = delta ** dist
-    # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta)
-    temporal = exp(-alpha * dt)
-    # temporal = 1.  - 1. / (1. + dt/alpha)**0.01
+    temporal = exp(-alpha * dt/7.)
     result = log(1. - structural + structural * temporal)
-    # print 'stnv', structural, temporal
     return result
 
 def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
        DTYPE_t alpha, DTYPE_t delta):
     cdef:
-        int n_roots, n_victims, n_nodes, roots, i, dist, dt, t, l
+        int n_roots, n_victims, roots, i, dist, dt, t, l
         DTYPE_t beta, ll, beta_add, max_beta, max_beta_add
         list parents, failures, successes
     n_roots, n_victims = len(root_victims), len(victims)
@@ -43,6 +35,8 @@ def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
     cdef:
         np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE)
         np.ndarray[DTYPE_t] probs_fail = np.zeros(n_victims, dtype=DTYPE)
+        np.ndarray[DTYPE_t] parent_dists = np.zeros(n_victims, dtype=DTYPE)
+        np.ndarray[DTYPE_t] parent_dts = np.zeros(n_victims, dtype=DTYPE)
         np.ndarray[DTYPE_t] probs_nv = np.zeros(len(non_victims), dtype=DTYPE)
 
     # loop through victims
@@ -51,43 +45,47 @@ def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
         # fail to infect it, also computes the probability that its most
         # likely parent infects it
         failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3)
-                    for (dist, dt, w1, w2, w3) in parents]
+                    for (prnt, dist, dt, w1, w2, w3) in parents]
         probs_fail[i] = sum(failures)
         successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3)
-                     for (dist, dt, w1, w2, w3) in parents]
+                     for (prnt, dist, dt, w1, w2, w3) in parents]
+        dists = [dist for (prnt, dist, dt, w1, w2, w3) in parents]
+        dts = [dt for (prnt, dist, dt, w1, w2, w3) in parents]
         # find parent that maximizes log(p) - log(\tilde{p})
-        probs[i] = max(s - failures[l] for l, s in enumerate(successes)) 
+        # probs[i] = max(s - failures[l] for l, s in enumerate(successes)) 
+        probs[i] = float("-inf")
+        for l, s in enumerate(successes):
+            prob = s - failures[l]
+            if prob > probs[i]:
+                probs[i] = prob
+                parent_dists[i] = dists[l]
+                parent_dts[i] = dts[l]
+                # probs_fail[i] = failures[l]
 
     # loop through non-victims
     for i, parents in enumerate(non_victims.itervalues()):
         # for each non victim node, compute the probability that all its
         # parents fail to infect it
         failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3)
-                    for (dist, dt, w1, w2, w3) in parents]
+                    for (prnt, dist, dt, w1, w2, w3) in parents]
         probs_nv[i] = sum(failures)
 
-    # print successes
-    # print failures
-    # print probs
-
     # calculate log likelihood
-    # probs.sort(); probs = probs[::-1] # sort probs in descending order
-    # cdef:
-    #     np.ndarray[DTYPE_t] cums = probs.cumsum()
-    ll =  probs_fail.sum() # add probability that all edges to victims fail
+    ll =  probs_fail.sum() # add probability that all edges to all victims fail
     ll += probs_nv.sum() # add probability that all edges to non_victims fail
 
     # print 'probs', probs
     max_beta_add = float('-inf')
     # iterate over all victim nodes to find the optimal threshold
-    for beta in np.arange(0.001, .2, .002):
+    for beta in np.arange(0.01, 1., .01):
         thresh = log(beta/(3012.*(1.-beta)))
-        # print 'beta:', beta, 'thresh:', thresh, 'infected:', len(probs[probs>=thresh])
-        roots = n_roots + len(probs[probs<thresh])
+        seeds = probs<thresh
+        non_seeds = probs>=thresh
+        roots = n_roots + sum(seeds)
 
         beta_add = 0.
         # add probability for realized edges and subtract probability these edges fail
-        beta_add += (probs[probs>=thresh]).sum()
+        beta_add += (probs[non_seeds]).sum()
         # add probability for the seeds and non-seeds
         beta_add += roots * log(beta/3012.) + (n_nodes-roots) * log(1. - beta)
 
diff --git a/experiments/ml.so b/experiments/ml.so
index 7832e67..a08d635 100755
Binary files a/experiments/ml.so and b/experiments/ml.so differ
diff --git a/experiments/ml3.c b/experiments/ml3.c
index 59de174..7ad3edb 100644
--- a/experiments/ml3.c
+++ b/experiments/ml3.c
@@ -695,7 +695,7 @@ typedef npy_longdouble __pyx_t_5numpy_longdouble_t;
  * DTYPE = np.float64
  * ctypedef np.float_t DTYPE_t             # <<<<<<<<<<<<<<
  * 
- * cdef DTYPE_t weight_success(int dist, DTYPE_t dt, DTYPE_t alpha, DTYPE_t delta,
+ * cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,
  */
 typedef __pyx_t_5numpy_float_t __pyx_t_3ml3_DTYPE_t;
 #if CYTHON_CCOMPLEX
@@ -1169,8 +1169,8 @@ static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, cha
 /* Module declarations from 'libc.math' */
 
 /* Module declarations from 'ml3' */
-static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_success(int, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t); /*proto*/
-static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_failure(int, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t); /*proto*/
+static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_success(int, int, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t); /*proto*/
+static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_failure(int, int, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t); /*proto*/
 static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_3ml3_DTYPE_t = { "DTYPE_t", NULL, sizeof(__pyx_t_3ml3_DTYPE_t), { 0 }, 0, 'R', 0, 0 };
 #define __Pyx_MODULE_NAME "ml3"
 int __pyx_module_is_main_ml3 = 0;
@@ -1217,7 +1217,7 @@ static char __pyx_k_sum[] = "sum";
 static char __pyx_k_beta[] = "beta";
 static char __pyx_k_dist[] = "dist";
 static char __pyx_k_main[] = "__main__";
-static char __pyx_k_ones[] = "ones";
+static char __pyx_k_prnt[] = "prnt";
 static char __pyx_k_prob[] = "prob";
 static char __pyx_k_test[] = "__test__";
 static char __pyx_k_DTYPE[] = "DTYPE";
@@ -1233,7 +1233,6 @@ static char __pyx_k_seeds[] = "seeds";
 static char __pyx_k_zeros[] = "zeros";
 static char __pyx_k_arange[] = "arange";
 static char __pyx_k_import[] = "__import__";
-static char __pyx_k_isSeed[] = "isSeed";
 static char __pyx_k_thresh[] = "thresh";
 static char __pyx_k_float64[] = "float64";
 static char __pyx_k_n_roots[] = "n_roots";
@@ -1287,7 +1286,6 @@ static PyObject *__pyx_n_s_float64;
 static PyObject *__pyx_n_s_i;
 static PyObject *__pyx_n_s_import;
 static PyObject *__pyx_kp_s_inf;
-static PyObject *__pyx_n_s_isSeed;
 static PyObject *__pyx_n_s_itervalues;
 static PyObject *__pyx_n_s_l;
 static PyObject *__pyx_n_s_ll;
@@ -1304,10 +1302,10 @@ static PyObject *__pyx_n_s_non_seeds;
 static PyObject *__pyx_n_s_non_victims;
 static PyObject *__pyx_n_s_np;
 static PyObject *__pyx_n_s_numpy;
-static PyObject *__pyx_n_s_ones;
 static PyObject *__pyx_n_s_parent_dists;
 static PyObject *__pyx_n_s_parent_dts;
 static PyObject *__pyx_n_s_parents;
+static PyObject *__pyx_n_s_prnt;
 static PyObject *__pyx_n_s_prob;
 static PyObject *__pyx_n_s_probs;
 static PyObject *__pyx_n_s_probs_fail;
@@ -1327,9 +1325,8 @@ static PyObject *__pyx_n_s_w1;
 static PyObject *__pyx_n_s_w2;
 static PyObject *__pyx_n_s_w3;
 static PyObject *__pyx_n_s_zeros;
-static PyObject *__pyx_float_1_;
-static PyObject *__pyx_float_0_00001;
-static PyObject *__pyx_int_0;
+static PyObject *__pyx_float__01;
+static PyObject *__pyx_float_0_01;
 static PyObject *__pyx_int_1;
 static PyObject *__pyx_tuple_;
 static PyObject *__pyx_tuple__2;
@@ -1344,12 +1341,12 @@ static PyObject *__pyx_codeobj__9;
 /* "ml3.pyx":9
  * ctypedef np.float_t DTYPE_t
  * 
- * cdef DTYPE_t weight_success(int dist, DTYPE_t dt, DTYPE_t alpha, DTYPE_t delta,             # <<<<<<<<<<<<<<
+ * cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,             # <<<<<<<<<<<<<<
  *                             DTYPE_t w1, DTYPE_t w2, DTYPE_t w3):
  *     """weight for successful infection, exponential time model"""
  */
 
-static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_success(int __pyx_v_dist, __pyx_t_3ml3_DTYPE_t __pyx_v_dt, __pyx_t_3ml3_DTYPE_t __pyx_v_alpha, __pyx_t_3ml3_DTYPE_t __pyx_v_delta, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w1, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w2, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w3) {
+static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_success(int __pyx_v_dist, int __pyx_v_dt, __pyx_t_3ml3_DTYPE_t __pyx_v_alpha, __pyx_t_3ml3_DTYPE_t __pyx_v_delta, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w1, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w2, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w3) {
   __pyx_t_3ml3_DTYPE_t __pyx_v_structural;
   __pyx_t_3ml3_DTYPE_t __pyx_v_temporal;
   __pyx_t_3ml3_DTYPE_t __pyx_v_result;
@@ -1389,7 +1386,7 @@ static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_success(int __pyx_v_dist, __pyx_
  *     result = log(structural) + temporal
  *     return result             # <<<<<<<<<<<<<<
  * 
- * cdef DTYPE_t weight_failure(int dist, DTYPE_t dt, DTYPE_t alpha, DTYPE_t delta,
+ * cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,
  */
   __pyx_r = __pyx_v_result;
   goto __pyx_L0;
@@ -1397,7 +1394,7 @@ static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_success(int __pyx_v_dist, __pyx_
   /* "ml3.pyx":9
  * ctypedef np.float_t DTYPE_t
  * 
- * cdef DTYPE_t weight_success(int dist, DTYPE_t dt, DTYPE_t alpha, DTYPE_t delta,             # <<<<<<<<<<<<<<
+ * cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,             # <<<<<<<<<<<<<<
  *                             DTYPE_t w1, DTYPE_t w2, DTYPE_t w3):
  *     """weight for successful infection, exponential time model"""
  */
@@ -1411,12 +1408,12 @@ static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_success(int __pyx_v_dist, __pyx_
 /* "ml3.pyx":18
  *     return result
  * 
- * cdef DTYPE_t weight_failure(int dist, DTYPE_t dt, DTYPE_t alpha, DTYPE_t delta,             # <<<<<<<<<<<<<<
+ * cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,             # <<<<<<<<<<<<<<
  *                             DTYPE_t w1, DTYPE_t w2, DTYPE_t w3):
  *     """weight for failed infection, exponential time model"""
  */
 
-static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_failure(int __pyx_v_dist, __pyx_t_3ml3_DTYPE_t __pyx_v_dt, __pyx_t_3ml3_DTYPE_t __pyx_v_alpha, __pyx_t_3ml3_DTYPE_t __pyx_v_delta, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w1, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w2, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w3) {
+static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_failure(int __pyx_v_dist, int __pyx_v_dt, __pyx_t_3ml3_DTYPE_t __pyx_v_alpha, __pyx_t_3ml3_DTYPE_t __pyx_v_delta, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w1, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w2, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w3) {
   __pyx_t_3ml3_DTYPE_t __pyx_v_structural;
   __pyx_t_3ml3_DTYPE_t __pyx_v_temporal;
   __pyx_t_3ml3_DTYPE_t __pyx_v_result;
@@ -1464,7 +1461,7 @@ static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_failure(int __pyx_v_dist, __pyx_
   /* "ml3.pyx":18
  *     return result
  * 
- * cdef DTYPE_t weight_failure(int dist, DTYPE_t dt, DTYPE_t alpha, DTYPE_t delta,             # <<<<<<<<<<<<<<
+ * cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,             # <<<<<<<<<<<<<<
  *                             DTYPE_t w1, DTYPE_t w2, DTYPE_t w3):
  *     """weight for failed infection, exponential time model"""
  */
@@ -1608,20 +1605,18 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
   PyArrayObject *__pyx_v_probs_fail = 0;
   PyArrayObject *__pyx_v_parent_dists = 0;
   PyArrayObject *__pyx_v_parent_dts = 0;
-  PyArrayObject *__pyx_v_isSeed = 0;
   PyObject *__pyx_v_dists = NULL;
   PyObject *__pyx_v_dts = NULL;
   PyObject *__pyx_v_s = NULL;
   PyObject *__pyx_v_prob = NULL;
-  CYTHON_UNUSED double __pyx_v_thresh;
+  double __pyx_v_thresh;
   PyObject *__pyx_v_seeds = NULL;
   PyObject *__pyx_v_non_seeds = NULL;
   int __pyx_v_max_roots;
+  CYTHON_UNUSED PyObject *__pyx_v_prnt = NULL;
   PyObject *__pyx_v_w1 = NULL;
   PyObject *__pyx_v_w2 = NULL;
   PyObject *__pyx_v_w3 = NULL;
-  __Pyx_LocalBuf_ND __pyx_pybuffernd_isSeed;
-  __Pyx_Buffer __pyx_pybuffer_isSeed;
   __Pyx_LocalBuf_ND __pyx_pybuffernd_parent_dists;
   __Pyx_Buffer __pyx_pybuffer_parent_dists;
   __Pyx_LocalBuf_ND __pyx_pybuffernd_parent_dts;
@@ -1642,11 +1637,11 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
   PyArrayObject *__pyx_t_8 = NULL;
   PyArrayObject *__pyx_t_9 = NULL;
   PyArrayObject *__pyx_t_10 = NULL;
-  PyArrayObject *__pyx_t_11 = NULL;
+  int __pyx_t_11;
   int __pyx_t_12;
   int __pyx_t_13;
-  int __pyx_t_14;
-  Py_ssize_t __pyx_t_15;
+  Py_ssize_t __pyx_t_14;
+  PyObject *__pyx_t_15 = NULL;
   PyObject *__pyx_t_16 = NULL;
   PyObject *__pyx_t_17 = NULL;
   PyObject *__pyx_t_18 = NULL;
@@ -1686,10 +1681,6 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
   __pyx_pybuffer_parent_dts.refcount = 0;
   __pyx_pybuffernd_parent_dts.data = NULL;
   __pyx_pybuffernd_parent_dts.rcbuffer = &__pyx_pybuffer_parent_dts;
-  __pyx_pybuffer_isSeed.pybuffer.buf = NULL;
-  __pyx_pybuffer_isSeed.refcount = 0;
-  __pyx_pybuffernd_isSeed.data = NULL;
-  __pyx_pybuffernd_isSeed.rcbuffer = &__pyx_pybuffer_isSeed;
 
   /* "ml3.pyx":33
  *         DTYPE_t beta, ll, beta_add, max_beta, max_beta_add
@@ -1804,7 +1795,7 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
  *         np.ndarray[DTYPE_t] probs_fail = np.zeros(n_victims, dtype=DTYPE)
  *         np.ndarray[DTYPE_t] parent_dists = np.zeros(n_victims, dtype=DTYPE)             # <<<<<<<<<<<<<<
  *         np.ndarray[DTYPE_t] parent_dts = np.zeros(n_victims, dtype=DTYPE)
- *         np.ndarray[DTYPE_t] isSeed = np.ones(n_victims, dtype=DTYPE)
+ * 
  */
   __pyx_t_4 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 37; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_4);
@@ -1847,8 +1838,8 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
  *         np.ndarray[DTYPE_t] probs_fail = np.zeros(n_victims, dtype=DTYPE)
  *         np.ndarray[DTYPE_t] parent_dists = np.zeros(n_victims, dtype=DTYPE)
  *         np.ndarray[DTYPE_t] parent_dts = np.zeros(n_victims, dtype=DTYPE)             # <<<<<<<<<<<<<<
- *         np.ndarray[DTYPE_t] isSeed = np.ones(n_victims, dtype=DTYPE)
  * 
+ *     # loop through victims
  */
   __pyx_t_3 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 38; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_3);
@@ -1887,134 +1878,93 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
   __pyx_v_parent_dts = ((PyArrayObject *)__pyx_t_6);
   __pyx_t_6 = 0;
 
-  /* "ml3.pyx":39
- *         np.ndarray[DTYPE_t] parent_dists = np.zeros(n_victims, dtype=DTYPE)
- *         np.ndarray[DTYPE_t] parent_dts = np.zeros(n_victims, dtype=DTYPE)
- *         np.ndarray[DTYPE_t] isSeed = np.ones(n_victims, dtype=DTYPE)             # <<<<<<<<<<<<<<
- * 
- *     # loop through victims
- */
-  __pyx_t_6 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 39; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_6);
-  __pyx_t_3 = __Pyx_PyObject_GetAttrStr(__pyx_t_6, __pyx_n_s_ones); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 39; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_3);
-  __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
-  __pyx_t_6 = __Pyx_PyInt_From_int(__pyx_v_n_victims); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 39; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_6);
-  __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 39; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_5);
-  PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_6);
-  __Pyx_GIVEREF(__pyx_t_6);
-  __pyx_t_6 = 0;
-  __pyx_t_6 = PyDict_New(); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 39; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_6);
-  __pyx_t_4 = __Pyx_GetModuleGlobalName(__pyx_n_s_DTYPE); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 39; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_4);
-  if (PyDict_SetItem(__pyx_t_6, __pyx_n_s_dtype, __pyx_t_4) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 39; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
-  __pyx_t_4 = __Pyx_PyObject_Call(__pyx_t_3, __pyx_t_5, __pyx_t_6); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 39; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_4);
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-  __pyx_builtin_enumerate = __Pyx_GetBuiltinName(__pyx_n_s_enumerate); if (!__pyx_builtin_enumerate) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 42; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_builtin_sum = __Pyx_GetBuiltinName(__pyx_n_s_sum); if (!__pyx_builtin_sum) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 48; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_builtin_enumerate = __Pyx_GetBuiltinName(__pyx_n_s_enumerate); if (!__pyx_builtin_enumerate) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 41; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_builtin_sum = __Pyx_GetBuiltinName(__pyx_n_s_sum); if (!__pyx_builtin_sum) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 47; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __pyx_builtin_ValueError = __Pyx_GetBuiltinName(__pyx_n_s_ValueError); if (!__pyx_builtin_ValueError) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 218; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __pyx_builtin_range = __Pyx_GetBuiltinName(__pyx_n_s_range); if (!__pyx_builtin_range) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 231; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __pyx_builtin_RuntimeError = __Pyx_GetBuiltinName(__pyx_n_s_RuntimeError); if (!__pyx_builtin_RuntimeError) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 802; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
@@ -5099,14 +5070,14 @@ static int __Pyx_InitCachedConstants(void) {
   __Pyx_RefNannyDeclarations
   __Pyx_RefNannySetupContext("__Pyx_InitCachedConstants", 0);
 
-  /* "ml3.pyx":70
+  /* "ml3.pyx":69
  *     max_beta_add = float('-inf')
  *     # iterate over all victim nodes to find the optimal threshold
- *     for beta in np.arange(0.00001, 1., 1.):             # <<<<<<<<<<<<<<
+ *     for beta in np.arange(0.01, 1, .01):             # <<<<<<<<<<<<<<
  *         thresh = log(beta/(3012.*(1.-beta)))
- *         seeds = isSeed==1
+ *         seeds = probs<thresh
  */
-  __pyx_tuple_ = PyTuple_Pack(3, __pyx_float_0_00001, __pyx_float_1_, __pyx_float_1_); if (unlikely(!__pyx_tuple_)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 70; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple_ = PyTuple_Pack(3, __pyx_float_0_01, __pyx_int_1, __pyx_float__01); if (unlikely(!__pyx_tuple_)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 69; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple_);
   __Pyx_GIVEREF(__pyx_tuple_);
 
@@ -5183,7 +5154,7 @@ static int __Pyx_InitCachedConstants(void) {
  *        DTYPE_t alpha, DTYPE_t delta):
  *     cdef:
  */
-  __pyx_tuple__8 = PyTuple_Pack(38, __pyx_n_s_root_victims, __pyx_n_s_victims, __pyx_n_s_non_victims, __pyx_n_s_age, __pyx_n_s_alpha, __pyx_n_s_delta, __pyx_n_s_n_roots, __pyx_n_s_n_victims, __pyx_n_s_roots, __pyx_n_s_i, __pyx_n_s_dist, __pyx_n_s_dt, __pyx_n_s_t, __pyx_n_s_l, __pyx_n_s_beta, __pyx_n_s_ll, __pyx_n_s_beta_add, __pyx_n_s_max_beta, __pyx_n_s_max_beta_add, __pyx_n_s_parents, __pyx_n_s_failures, __pyx_n_s_successes, __pyx_n_s_probs, __pyx_n_s_probs_fail, __pyx_n_s_parent_dists, __pyx_n_s_parent_dts, __pyx_n_s_isSeed, __pyx_n_s_dists, __pyx_n_s_dts, __pyx_n_s_s, __pyx_n_s_prob, __pyx_n_s_thresh, __pyx_n_s_seeds, __pyx_n_s_non_seeds, __pyx_n_s_max_roots, __pyx_n_s_w1, __pyx_n_s_w2, __pyx_n_s_w3); if (unlikely(!__pyx_tuple__8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 27; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple__8 = PyTuple_Pack(38, __pyx_n_s_root_victims, __pyx_n_s_victims, __pyx_n_s_non_victims, __pyx_n_s_age, __pyx_n_s_alpha, __pyx_n_s_delta, __pyx_n_s_n_roots, __pyx_n_s_n_victims, __pyx_n_s_roots, __pyx_n_s_i, __pyx_n_s_dist, __pyx_n_s_dt, __pyx_n_s_t, __pyx_n_s_l, __pyx_n_s_beta, __pyx_n_s_ll, __pyx_n_s_beta_add, __pyx_n_s_max_beta, __pyx_n_s_max_beta_add, __pyx_n_s_parents, __pyx_n_s_failures, __pyx_n_s_successes, __pyx_n_s_probs, __pyx_n_s_probs_fail, __pyx_n_s_parent_dists, __pyx_n_s_parent_dts, __pyx_n_s_dists, __pyx_n_s_dts, __pyx_n_s_s, __pyx_n_s_prob, __pyx_n_s_thresh, __pyx_n_s_seeds, __pyx_n_s_non_seeds, __pyx_n_s_max_roots, __pyx_n_s_prnt, __pyx_n_s_w1, __pyx_n_s_w2, __pyx_n_s_w3); if (unlikely(!__pyx_tuple__8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 27; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple__8);
   __Pyx_GIVEREF(__pyx_tuple__8);
   __pyx_codeobj__9 = (PyObject*)__Pyx_PyCode_New(6, 0, 38, 0, 0, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__8, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Users_ben_Documents_Cascade_Pro, __pyx_n_s_ml3, 27, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__9)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 27; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
@@ -5196,9 +5167,8 @@ static int __Pyx_InitCachedConstants(void) {
 
 static int __Pyx_InitGlobals(void) {
   if (__Pyx_InitStrings(__pyx_string_tab) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
-  __pyx_float_1_ = PyFloat_FromDouble(1.); if (unlikely(!__pyx_float_1_)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_float_0_00001 = PyFloat_FromDouble(0.00001); if (unlikely(!__pyx_float_0_00001)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_int_0 = PyInt_FromLong(0); if (unlikely(!__pyx_int_0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_float__01 = PyFloat_FromDouble(.01); if (unlikely(!__pyx_float__01)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_float_0_01 = PyFloat_FromDouble(0.01); if (unlikely(!__pyx_float_0_01)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __pyx_int_1 = PyInt_FromLong(1); if (unlikely(!__pyx_int_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   return 0;
   __pyx_L1_error:;
diff --git a/experiments/ml3.pyx b/experiments/ml3.pyx
index da993bc..6e031ef 100644
--- a/experiments/ml3.pyx
+++ b/experiments/ml3.pyx
@@ -6,7 +6,7 @@ from libc.math cimport log, exp
 DTYPE = np.float64
 ctypedef np.float_t DTYPE_t
 
-cdef DTYPE_t weight_success(int dist, DTYPE_t dt, DTYPE_t alpha, DTYPE_t delta,
+cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,
                             DTYPE_t w1, DTYPE_t w2, DTYPE_t w3):
     """weight for successful infection, exponential time model"""
     cdef DTYPE_t structural, temporal, result
@@ -15,7 +15,7 @@ cdef DTYPE_t weight_success(int dist, DTYPE_t dt, DTYPE_t alpha, DTYPE_t delta,
     result = log(structural) + temporal
     return result
 
-cdef DTYPE_t weight_failure(int dist, DTYPE_t dt, DTYPE_t alpha, DTYPE_t delta,
+cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,
                             DTYPE_t w1, DTYPE_t w2, DTYPE_t w3):
     """weight for failed infection, exponential time model"""
     cdef DTYPE_t structural, temporal, result
@@ -36,20 +36,19 @@ def ml3(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
         np.ndarray[DTYPE_t] probs_fail = np.zeros(n_victims, dtype=DTYPE)
         np.ndarray[DTYPE_t] parent_dists = np.zeros(n_victims, dtype=DTYPE)
         np.ndarray[DTYPE_t] parent_dts = np.zeros(n_victims, dtype=DTYPE)
-        np.ndarray[DTYPE_t] isSeed = np.ones(n_victims, dtype=DTYPE)
 
     # loop through victims
     for i, parents in enumerate(victims.itervalues()):
         # for each victim node i, compute the probability that all its parents
         # fail to infect it, also computes the probability that its most
         # likely parent infects it
-        failures = [weight_failure(dist, dt/100., alpha, delta, w1, w2, w3)
-                    for (dist, dt, w1, w2, w3) in parents]
+        failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3)
+                    for (prnt, dist, dt, w1, w2, w3) in parents]
         probs_fail[i] = sum(failures)
-        successes = [weight_success(dist, dt/100., alpha, delta, w1, w2, w3)
-                     for (dist, dt, w1, w2, w3) in parents]
-        dists = [dist for (dist, dt, w1, w2, w3) in parents]
-        dts = [dt for (dist, dt, w1, w2, w3) in parents]
+        successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3)
+                     for (prnt, dist, dt, w1, w2, w3) in parents]
+        dists = [dist for (prnt, dist, dt, w1, w2, w3) in parents]
+        dts = [dt for (prnt, dist, dt, w1, w2, w3) in parents]
         # find parent that maximizes log(p) - log(\tilde{p})
         # probs[i] = max(s - failures[l] for l, s in enumerate(successes)) 
         probs[i] = float("-inf")
@@ -62,15 +61,15 @@ def ml3(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
                 # probs_fail[i] = failures[l]
 
     # calculate log likelihood
-    ll = probs_fail.sum() # add probability that all edges to all victims fail
+    ll =  probs_fail.sum() # add probability that all edges to all victims fail
 
     # print 'probs', probs
     max_beta_add = float('-inf')
     # iterate over all victim nodes to find the optimal threshold
-    for beta in np.arange(0.00001, 1., 1.):
+    for beta in np.arange(0.01, 1, .01):
         thresh = log(beta/(3012.*(1.-beta)))
-        seeds = isSeed==1
-        non_seeds = isSeed==0
+        seeds = probs<thresh
+        non_seeds = probs>=thresh
         roots = n_roots + sum(seeds)
 
         beta_add = 0.
@@ -89,4 +88,4 @@ def ml3(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
     roots = max_roots
     beta = max_beta
     # print n_nodes, n_roots, n_victims, max_i, roots
-    return (beta, roots, ll)
+    return (beta, roots, ll)
\ No newline at end of file
diff --git a/experiments/ml3.so b/experiments/ml3.so
index d2b6898..fb8afc9 100755
Binary files a/experiments/ml3.so and b/experiments/ml3.so differ
diff --git a/experiments/out.log b/experiments/out.log
index 98ddbb1..a718511 100644
--- a/experiments/out.log
+++ b/experiments/out.log
@@ -1,30 +1,100 @@
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+0.0599484250319	0.0129154966501	0.06	8304	-125604.769419
+0.0599484250319	0.0215443469003	0.04	6187	-124909.433209
+0.0599484250319	0.035938136638	0.03	4809	-123357.362127
+0.0599484250319	0.0599484250319	0.02	2564	-123821.010849
+0.0599484250319	0.1	0.01	1603	-131308.788018
+0.1	0.001	0.07	10122	-129407.183852
+0.1	0.0016681005372	0.07	9858	-128826.387421
+0.1	0.00278255940221	0.06	9551	-128164.459489
+0.1	0.00464158883361	0.06	9323	-127449.154213
+0.1	0.00774263682681	0.06	9138	-126798.726378
+0.1	0.0129154966501	0.06	8941	-126366.966378
+0.1	0.0215443469003	0.05	6820	-125456.586416
+0.1	0.035938136638	0.04	5608	-124150.372812
+0.1	0.0599484250319	0.02	2771	-124505.31667
+0.1	0.1	0.01	1909	-132157.286741
diff --git a/experiments/process.py b/experiments/process.py
index 0115bd4..a0c9b98 100644
--- a/experiments/process.py
+++ b/experiments/process.py
@@ -15,29 +15,29 @@ if __name__ == "__main__":
         sys.exit("usage: {0} <file>".format(sys.argv[0]))
     root_victims, victims, non_victims, age = load(open(sys.argv[1]))
     
-    # # alphas = np.arange(1e-3, 1e-2, 8e-4)  # parameter of the time component
-    # alphas = np.logspace(-4,-1,num=15)
-    # # deltas = np.arange(0.001, 0.3, 0.008)  # parameter of the structural component
-    # deltas = np.logspace(-4,-1.7,num=15)
-    # with open("out.log", "w") as fh:
-    #     for alpha, delta in product(alphas, deltas):
-    #         beta, roots, ll = ml3(root_victims, victims, non_victims, age, alpha, delta)
-    #         print "\t".join(map(str, [alpha, delta, beta, roots, ll]))
-    #         fh.write("\t".join(map(str, [alpha, delta, beta, roots, ll])) + "\n")
-    #         fh.flush()
-
-    alphas = np.arange(.15, .25, .01)  # parameter of the time component
-    # alphas = np.logspace(-4,-.5,num=15)
-    deltas = np.arange(.08, .1, .01)  # parameter of the structural component
-    # deltas = np.logspace(-2,-.1,num=15)
-    lmbda = 0.10#np.logspace(-12,-3,num=10)
+    # alphas = np.arange(.02, .1, .01)  # parameter of the time component
+    alphas = np.logspace(-3,-1,num=10)
+    # deltas = np.arange(.02, .1, .01)  # parameter of the structural component
+    deltas = np.logspace(-3,-1,num=10)
     with open("out.log", "w") as fh:
         for alpha, delta in product(alphas, deltas):
-            lmbda, roots, ll = ml2(root_victims, victims, non_victims, alpha, delta, lmbda)
-            print "\t".join(map(str, [alpha, delta, lmbda, roots, ll]))
-            fh.write("\t".join(map(str, [alpha, delta, lmbda, roots, ll])) + "\n")
+            beta, roots, ll = ml(root_victims, victims, non_victims, age, alpha, delta)
+            print "\t".join(map(str, [alpha, delta, beta, roots, ll]))
+            fh.write("\t".join(map(str, [alpha, delta, beta, roots, ll])) + "\n")
             fh.flush()
 
+    # alphas = np.arange(.15, .25, .01)  # parameter of the time component
+    # # alphas = np.logspace(-4,-.5,num=15)
+    # deltas = np.arange(.08, .1, .01)  # parameter of the structural component
+    # # deltas = np.logspace(-2,-.1,num=15)
+    # lmbda = 0.10#np.logspace(-12,-3,num=10)
+    # with open("out.log", "w") as fh:
+    #     for alpha, delta in product(alphas, deltas):
+    #         lmbda, roots, ll = ml2(root_victims, victims, non_victims, alpha, delta, lmbda)
+    #         print "\t".join(map(str, [alpha, delta, lmbda, roots, ll]))
+    #         fh.write("\t".join(map(str, [alpha, delta, lmbda, roots, ll])) + "\n")
+    #         fh.flush()
+
     # alpha = .016
     # delta = 0.077
     # beta, roots, ll = ml2(root_victims, victims, non_victims, alpha, delta, 1.)
-- 
cgit v1.2.3-70-g09d2


From 110069d77815a3d62e3526f18b2a34fb79beff1e Mon Sep 17 00:00:00 2001
From: Ben Green <ben@SEASITs-MacBook-Pro.local>
Date: Thu, 2 Jul 2015 23:10:54 -0400
Subject: just some minor tests with ml

---
 R Scripts/generate-network.R                       |  16 +-
 experiments/build/temp.macosx-10.6-x86_64-2.7/ml.o | Bin 306196 -> 306268 bytes
 experiments/build_network.py                       |   2 +-
 experiments/ml.c                                   | 180 +++++++++----------
 experiments/ml.pyx                                 |  11 +-
 experiments/ml.so                                  | Bin 119284 -> 119276 bytes
 experiments/out.log                                | 200 ++++++++++-----------
 experiments/process.py                             |   4 +-
 8 files changed, 207 insertions(+), 206 deletions(-)

diff --git a/R Scripts/generate-network.R b/R Scripts/generate-network.R
index dab81a4..db7012d 100644
--- a/R Scripts/generate-network.R	
+++ b/R Scripts/generate-network.R	
@@ -3,9 +3,9 @@ setwd("~/Documents/Cascade Project/")
 source('criminal_cascades/R Scripts/temporal.R')
 source('criminal_cascades/R Scripts/structural.R')
 
-alpha = 1/10
-beta = 0.01
-delta = 0.25
+alpha = 0.1
+beta = 0.25
+delta = 0.1
 # lmbda = 1/10
 t_max = 1000
 
@@ -16,7 +16,7 @@ plot(g, vertex.size=3, vertex.label=NA)
 V(g)$seed = runif(vcount(g))<beta
 seeds = which(V(g)$seed)
 V(g)$vic = V(g)$seed
-V(g)$vic.day[V(g)$seed] = 1#sample(1:t_max, sum(V(g)$seed))
+V(g)$vic.day[V(g)$seed] = sample(1:t_max, sum(V(g)$seed), replace=T) #1 if testing ml2
 V(g)$spawn.date = 0
 V(g)$infector = NA
 
@@ -31,9 +31,11 @@ for (day in 1:t_max){
     inf.days = day + ceiling(rexp(length(infected),alpha))
     realized = ((inf.days <= V(g)$vic.day[infected]) %in% c(NA,T)) & (inf.days<=t_max)
     infected = infected[realized]
-    V(g)$vic[infected] = TRUE
-    V(g)$vic.day[infected] = inf.days[realized]
-    V(g)$infector[infected] = vic
+    if(sum(realized)){
+      V(g)$vic[infected] = TRUE
+      V(g)$vic.day[infected] = inf.days[realized]
+      V(g)$infector[infected] = vic
+    }
   }
 }
 
diff --git a/experiments/build/temp.macosx-10.6-x86_64-2.7/ml.o b/experiments/build/temp.macosx-10.6-x86_64-2.7/ml.o
index 8667998..8cabba1 100644
Binary files a/experiments/build/temp.macosx-10.6-x86_64-2.7/ml.o and b/experiments/build/temp.macosx-10.6-x86_64-2.7/ml.o differ
diff --git a/experiments/build_network.py b/experiments/build_network.py
index a481a90..c58d5cf 100644
--- a/experiments/build_network.py
+++ b/experiments/build_network.py
@@ -8,7 +8,7 @@ def build_network(filename):
     victims = {}
     non_victims = {}
     age = 0
-    t_max = 3012
+    t_max = 1000
     with open(filename) as fh:
         reader = DictReader(fh)
         for row in reader:
diff --git a/experiments/ml.c b/experiments/ml.c
index 92e313f..4b9c2ef 100644
--- a/experiments/ml.c
+++ b/experiments/ml.c
@@ -1332,7 +1332,7 @@ static PyObject *__pyx_n_s_zeros;
 static PyObject *__pyx_float_1_;
 static PyObject *__pyx_float__01;
 static PyObject *__pyx_float_0_01;
-static PyObject *__pyx_int_148152;
+static PyObject *__pyx_int_5000;
 static PyObject *__pyx_tuple_;
 static PyObject *__pyx_tuple__2;
 static PyObject *__pyx_tuple__3;
@@ -1363,7 +1363,7 @@ static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_success(int __pyx_v_dist, int __py
  *     """weight for successful infection, exponential time model"""
  *     cdef DTYPE_t structural, temporal, result
  *     structural = delta ** dist             # <<<<<<<<<<<<<<
- *     temporal = log(exp(alpha)-1.) - alpha*dt/7.
+ *     temporal = log(exp(alpha)-1.) - alpha*dt/1.
  *     result = log(structural) + temporal
  */
   __pyx_v_structural = pow(__pyx_v_delta, ((__pyx_t_2ml_DTYPE_t)__pyx_v_dist));
@@ -1371,15 +1371,15 @@ static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_success(int __pyx_v_dist, int __py
   /* "ml.pyx":14
  *     cdef DTYPE_t structural, temporal, result
  *     structural = delta ** dist
- *     temporal = log(exp(alpha)-1.) - alpha*dt/7.             # <<<<<<<<<<<<<<
+ *     temporal = log(exp(alpha)-1.) - alpha*dt/1.             # <<<<<<<<<<<<<<
  *     result = log(structural) + temporal
  *     return result
  */
-  __pyx_v_temporal = (log((exp(__pyx_v_alpha) - 1.)) - ((__pyx_v_alpha * __pyx_v_dt) / 7.));
+  __pyx_v_temporal = (log((exp(__pyx_v_alpha) - 1.)) - ((__pyx_v_alpha * __pyx_v_dt) / 1.));
 
   /* "ml.pyx":15
  *     structural = delta ** dist
- *     temporal = log(exp(alpha)-1.) - alpha*dt/7.
+ *     temporal = log(exp(alpha)-1.) - alpha*dt/1.
  *     result = log(structural) + temporal             # <<<<<<<<<<<<<<
  *     return result
  * 
@@ -1387,7 +1387,7 @@ static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_success(int __pyx_v_dist, int __py
   __pyx_v_result = (log(__pyx_v_structural) + __pyx_v_temporal);
 
   /* "ml.pyx":16
- *     temporal = log(exp(alpha)-1.) - alpha*dt/7.
+ *     temporal = log(exp(alpha)-1.) - alpha*dt/1.
  *     result = log(structural) + temporal
  *     return result             # <<<<<<<<<<<<<<
  * 
@@ -1430,7 +1430,7 @@ static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_failure(int __pyx_v_dist, int __py
  *     """weight for failed infection, exponential time model"""
  *     cdef DTYPE_t structural, temporal, result
  *     structural = delta ** dist             # <<<<<<<<<<<<<<
- *     temporal = exp(-alpha * dt/7.)
+ *     temporal = exp(-alpha * dt/1.)
  *     result = log(1. - structural + structural * temporal)
  */
   __pyx_v_structural = pow(__pyx_v_delta, ((__pyx_t_2ml_DTYPE_t)__pyx_v_dist));
@@ -1438,15 +1438,15 @@ static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_failure(int __pyx_v_dist, int __py
   /* "ml.pyx":23
  *     cdef DTYPE_t structural, temporal, result
  *     structural = delta ** dist
- *     temporal = exp(-alpha * dt/7.)             # <<<<<<<<<<<<<<
+ *     temporal = exp(-alpha * dt/1.)             # <<<<<<<<<<<<<<
  *     result = log(1. - structural + structural * temporal)
  *     return result
  */
-  __pyx_v_temporal = exp((((-__pyx_v_alpha) * __pyx_v_dt) / 7.));
+  __pyx_v_temporal = exp((((-__pyx_v_alpha) * __pyx_v_dt) / 1.));
 
   /* "ml.pyx":24
  *     structural = delta ** dist
- *     temporal = exp(-alpha * dt/7.)
+ *     temporal = exp(-alpha * dt/1.)
  *     result = log(1. - structural + structural * temporal)             # <<<<<<<<<<<<<<
  *     return result
  * 
@@ -1454,7 +1454,7 @@ static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_failure(int __pyx_v_dist, int __py
   __pyx_v_result = log(((1. - __pyx_v_structural) + (__pyx_v_structural * __pyx_v_temporal)));
 
   /* "ml.pyx":25
- *     temporal = exp(-alpha * dt/7.)
+ *     temporal = exp(-alpha * dt/1.)
  *     result = log(1. - structural + structural * temporal)
  *     return result             # <<<<<<<<<<<<<<
  * 
@@ -1701,7 +1701,7 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
  *         DTYPE_t beta, ll, beta_add, max_beta, max_beta_add
  *         list parents, failures, successes
  *     n_roots, n_victims = len(root_victims), len(victims)             # <<<<<<<<<<<<<<
- *     n_nodes = 148152
+ *     n_nodes = 5000
  *     cdef:
  */
   if (unlikely(__pyx_v_root_victims == Py_None)) {
@@ -1720,15 +1720,15 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
   /* "ml.pyx":34
  *         list parents, failures, successes
  *     n_roots, n_victims = len(root_victims), len(victims)
- *     n_nodes = 148152             # <<<<<<<<<<<<<<
+ *     n_nodes = 5000             # <<<<<<<<<<<<<<
  *     cdef:
  *         np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE)
  */
-  __Pyx_INCREF(__pyx_int_148152);
-  __pyx_v_n_nodes = __pyx_int_148152;
+  __Pyx_INCREF(__pyx_int_5000);
+  __pyx_v_n_nodes = __pyx_int_5000;
 
   /* "ml.pyx":36
- *     n_nodes = 148152
+ *     n_nodes = 5000
  *     cdef:
  *         np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE)             # <<<<<<<<<<<<<<
  *         np.ndarray[DTYPE_t] probs_fail = np.zeros(n_victims, dtype=DTYPE)
@@ -2858,7 +2858,7 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
  *     ll =  probs_fail.sum() # add probability that all edges to all victims fail
  *     ll += probs_nv.sum() # add probability that all edges to non_victims fail             # <<<<<<<<<<<<<<
  * 
- *     # print 'probs', probs
+ *     max_beta_add = float('-inf')
  */
   __pyx_t_4 = PyFloat_FromDouble(__pyx_v_ll); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 75; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_4);
@@ -2890,38 +2890,38 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
   __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
   __pyx_v_ll = __pyx_t_27;
 
-  /* "ml.pyx":78
+  /* "ml.pyx":77
+ *     ll += probs_nv.sum() # add probability that all edges to non_victims fail
  * 
- *     # print 'probs', probs
  *     max_beta_add = float('-inf')             # <<<<<<<<<<<<<<
  *     # iterate over all victim nodes to find the optimal threshold
  *     for beta in np.arange(0.01, 1., .01):
  */
-  __pyx_t_29 = __Pyx_PyObject_AsDouble(__pyx_kp_s_inf); if (unlikely(__pyx_t_29 == ((double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 78; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_29 = __Pyx_PyObject_AsDouble(__pyx_kp_s_inf); if (unlikely(__pyx_t_29 == ((double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 77; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __pyx_v_max_beta_add = __pyx_t_29;
 
-  /* "ml.pyx":80
+  /* "ml.pyx":79
  *     max_beta_add = float('-inf')
  *     # iterate over all victim nodes to find the optimal threshold
  *     for beta in np.arange(0.01, 1., .01):             # <<<<<<<<<<<<<<
- *         thresh = log(beta/(3012.*(1.-beta)))
+ *         thresh = log(beta/(1000.*(1.-beta)))
  *         seeds = probs<thresh
  */
-  __pyx_t_5 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_5 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_5);
-  __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_t_5, __pyx_n_s_arange); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_t_5, __pyx_n_s_arange); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_6);
   __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
-  __pyx_t_5 = __Pyx_PyObject_Call(__pyx_t_6, __pyx_tuple_, NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_5 = __Pyx_PyObject_Call(__pyx_t_6, __pyx_tuple_, NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_5);
   __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
   if (likely(PyList_CheckExact(__pyx_t_5)) || PyTuple_CheckExact(__pyx_t_5)) {
     __pyx_t_6 = __pyx_t_5; __Pyx_INCREF(__pyx_t_6); __pyx_t_2 = 0;
     __pyx_t_36 = NULL;
   } else {
-    __pyx_t_2 = -1; __pyx_t_6 = PyObject_GetIter(__pyx_t_5); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_2 = -1; __pyx_t_6 = PyObject_GetIter(__pyx_t_5); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __Pyx_GOTREF(__pyx_t_6);
-    __pyx_t_36 = Py_TYPE(__pyx_t_6)->tp_iternext; if (unlikely(!__pyx_t_36)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_36 = Py_TYPE(__pyx_t_6)->tp_iternext; if (unlikely(!__pyx_t_36)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   }
   __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
   for (;;) {
@@ -2929,16 +2929,16 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
       if (likely(PyList_CheckExact(__pyx_t_6))) {
         if (__pyx_t_2 >= PyList_GET_SIZE(__pyx_t_6)) break;
         #if CYTHON_COMPILING_IN_CPYTHON
-        __pyx_t_5 = PyList_GET_ITEM(__pyx_t_6, __pyx_t_2); __Pyx_INCREF(__pyx_t_5); __pyx_t_2++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __pyx_t_5 = PyList_GET_ITEM(__pyx_t_6, __pyx_t_2); __Pyx_INCREF(__pyx_t_5); __pyx_t_2++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
         #else
-        __pyx_t_5 = PySequence_ITEM(__pyx_t_6, __pyx_t_2); __pyx_t_2++; if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __pyx_t_5 = PySequence_ITEM(__pyx_t_6, __pyx_t_2); __pyx_t_2++; if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
         #endif
       } else {
         if (__pyx_t_2 >= PyTuple_GET_SIZE(__pyx_t_6)) break;
         #if CYTHON_COMPILING_IN_CPYTHON
-        __pyx_t_5 = PyTuple_GET_ITEM(__pyx_t_6, __pyx_t_2); __Pyx_INCREF(__pyx_t_5); __pyx_t_2++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __pyx_t_5 = PyTuple_GET_ITEM(__pyx_t_6, __pyx_t_2); __Pyx_INCREF(__pyx_t_5); __pyx_t_2++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
         #else
-        __pyx_t_5 = PySequence_ITEM(__pyx_t_6, __pyx_t_2); __pyx_t_2++; if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __pyx_t_5 = PySequence_ITEM(__pyx_t_6, __pyx_t_2); __pyx_t_2++; if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
         #endif
       }
     } else {
@@ -2947,79 +2947,79 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
         PyObject* exc_type = PyErr_Occurred();
         if (exc_type) {
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+    /* "ml.pyx":91
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@@ -3142,17 +3142,17 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
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  *     # iterate over all victim nodes to find the optimal threshold
  *     for beta in np.arange(0.01, 1., .01):             # <<<<<<<<<<<<<<
- *         thresh = log(beta/(3012.*(1.-beta)))
+ *         thresh = log(beta/(1000.*(1.-beta)))
  *         seeds = probs<thresh
  */
   }
   __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
 
-  /* "ml.pyx":98
+  /* "ml.pyx":97
  *             # print 'beta:', max_beta, 'add:', max_beta_add, 'roots:', max_roots
  * 
  *     ll += max_beta_add             # <<<<<<<<<<<<<<
@@ -3161,7 +3161,7 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
  */
   __pyx_v_ll = (__pyx_v_ll + __pyx_v_max_beta_add);
 
-  /* "ml.pyx":99
+  /* "ml.pyx":98
  * 
  *     ll += max_beta_add
  *     roots = max_roots             # <<<<<<<<<<<<<<
@@ -3170,7 +3170,7 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
  */
   __pyx_v_roots = __pyx_v_max_roots;
 
-  /* "ml.pyx":100
+  /* "ml.pyx":99
  *     ll += max_beta_add
  *     roots = max_roots
  *     beta = max_beta             # <<<<<<<<<<<<<<
@@ -3179,19 +3179,19 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
  */
   __pyx_v_beta = __pyx_v_max_beta;
 
-  /* "ml.pyx":102
+  /* "ml.pyx":101
  *     beta = max_beta
  *     # print n_nodes, n_roots, n_victims, max_i, roots
  *     return (beta, roots, ll)             # <<<<<<<<<<<<<<
  */
   __Pyx_XDECREF(__pyx_r);
-  __pyx_t_6 = PyFloat_FromDouble(__pyx_v_beta); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 102; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_6 = PyFloat_FromDouble(__pyx_v_beta); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 101; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_6);
-  __pyx_t_20 = __Pyx_PyInt_From_int(__pyx_v_roots); if (unlikely(!__pyx_t_20)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 102; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_20 = __Pyx_PyInt_From_int(__pyx_v_roots); if (unlikely(!__pyx_t_20)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 101; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_20);
-  __pyx_t_4 = PyFloat_FromDouble(__pyx_v_ll); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 102; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_4 = PyFloat_FromDouble(__pyx_v_ll); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 101; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_4);
-  __pyx_t_21 = PyTuple_New(3); if (unlikely(!__pyx_t_21)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 102; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_21 = PyTuple_New(3); if (unlikely(!__pyx_t_21)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 101; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_21);
   PyTuple_SET_ITEM(__pyx_t_21, 0, __pyx_t_6);
   __Pyx_GIVEREF(__pyx_t_6);
@@ -5376,14 +5376,14 @@ static int __Pyx_InitCachedConstants(void) {
   __Pyx_RefNannyDeclarations
   __Pyx_RefNannySetupContext("__Pyx_InitCachedConstants", 0);
 
-  /* "ml.pyx":80
+  /* "ml.pyx":79
  *     max_beta_add = float('-inf')
  *     # iterate over all victim nodes to find the optimal threshold
  *     for beta in np.arange(0.01, 1., .01):             # <<<<<<<<<<<<<<
- *         thresh = log(beta/(3012.*(1.-beta)))
+ *         thresh = log(beta/(1000.*(1.-beta)))
  *         seeds = probs<thresh
  */
-  __pyx_tuple_ = PyTuple_Pack(3, __pyx_float_0_01, __pyx_float_1_, __pyx_float__01); if (unlikely(!__pyx_tuple_)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_tuple_ = PyTuple_Pack(3, __pyx_float_0_01, __pyx_float_1_, __pyx_float__01); if (unlikely(!__pyx_tuple_)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_tuple_);
   __Pyx_GIVEREF(__pyx_tuple_);
 
@@ -5476,7 +5476,7 @@ static int __Pyx_InitGlobals(void) {
   __pyx_float_1_ = PyFloat_FromDouble(1.); if (unlikely(!__pyx_float_1_)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __pyx_float__01 = PyFloat_FromDouble(.01); if (unlikely(!__pyx_float__01)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __pyx_float_0_01 = PyFloat_FromDouble(0.01); if (unlikely(!__pyx_float_0_01)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_int_148152 = PyInt_FromLong(148152L); if (unlikely(!__pyx_int_148152)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_int_5000 = PyInt_FromLong(5000); if (unlikely(!__pyx_int_5000)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   return 0;
   __pyx_L1_error:;
   return -1;
diff --git a/experiments/ml.pyx b/experiments/ml.pyx
index 858207f..0d72c44 100644
--- a/experiments/ml.pyx
+++ b/experiments/ml.pyx
@@ -11,7 +11,7 @@ cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,
     """weight for successful infection, exponential time model"""
     cdef DTYPE_t structural, temporal, result
     structural = delta ** dist
-    temporal = log(exp(alpha)-1.) - alpha*dt/7.
+    temporal = log(exp(alpha)-1.) - alpha*dt/1.
     result = log(structural) + temporal
     return result
 
@@ -20,7 +20,7 @@ cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,
     """weight for failed infection, exponential time model"""
     cdef DTYPE_t structural, temporal, result
     structural = delta ** dist
-    temporal = exp(-alpha * dt/7.)
+    temporal = exp(-alpha * dt/1.)
     result = log(1. - structural + structural * temporal)
     return result
 
@@ -31,7 +31,7 @@ def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
         DTYPE_t beta, ll, beta_add, max_beta, max_beta_add
         list parents, failures, successes
     n_roots, n_victims = len(root_victims), len(victims)
-    n_nodes = 148152
+    n_nodes = 5000
     cdef:
         np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE)
         np.ndarray[DTYPE_t] probs_fail = np.zeros(n_victims, dtype=DTYPE)
@@ -74,11 +74,10 @@ def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
     ll =  probs_fail.sum() # add probability that all edges to all victims fail
     ll += probs_nv.sum() # add probability that all edges to non_victims fail
 
-    # print 'probs', probs
     max_beta_add = float('-inf')
     # iterate over all victim nodes to find the optimal threshold
     for beta in np.arange(0.01, 1., .01):
-        thresh = log(beta/(3012.*(1.-beta)))
+        thresh = log(beta/(1000.*(1.-beta)))
         seeds = probs<thresh
         non_seeds = probs>=thresh
         roots = n_roots + sum(seeds)
@@ -87,7 +86,7 @@ def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
         # add probability for realized edges and subtract probability these edges fail
         beta_add += (probs[non_seeds]).sum()
         # add probability for the seeds and non-seeds
-        beta_add += roots * log(beta/3012.) + (n_nodes-roots) * log(1. - beta)
+        beta_add += roots * log(beta/1000.) + (n_nodes-roots) * log(1. - beta)
 
         if beta_add > max_beta_add:
             max_beta = beta
diff --git a/experiments/ml.so b/experiments/ml.so
index a08d635..d4cd7b4 100755
Binary files a/experiments/ml.so and b/experiments/ml.so differ
diff --git a/experiments/out.log b/experiments/out.log
index a718511..895f69f 100644
--- a/experiments/out.log
+++ b/experiments/out.log
@@ -1,100 +1,100 @@
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diff --git a/experiments/process.py b/experiments/process.py
index a0c9b98..6c71934 100644
--- a/experiments/process.py
+++ b/experiments/process.py
@@ -16,9 +16,9 @@ if __name__ == "__main__":
     root_victims, victims, non_victims, age = load(open(sys.argv[1]))
     
     # alphas = np.arange(.02, .1, .01)  # parameter of the time component
-    alphas = np.logspace(-3,-1,num=10)
+    alphas = np.logspace(-1,-.5,num=10)
     # deltas = np.arange(.02, .1, .01)  # parameter of the structural component
-    deltas = np.logspace(-3,-1,num=10)
+    deltas = np.logspace(-2,-1,num=10)
     with open("out.log", "w") as fh:
         for alpha, delta in product(alphas, deltas):
             beta, roots, ll = ml(root_victims, victims, non_victims, age, alpha, delta)
-- 
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