Worked on synthetic data recovery so we can tell how high the actual

infector is ranked among all potential parents. Cleaned up code for the
predicting victims benchmarking test.

13 files changed, 659 insertions, 1125 deletions

diff --git a/R Scripts/benchmarking.R b/R Scripts/benchmarking.R
deleted file mode 100644
index 1cf99e0..0000000
--- a/R Scripts/benchmarking.R
+++ /dev/null
@@ -1,130 +0,0 @@
-library(igraph)
-setwd('~/Documents/Cascade Project')
-load('Raw Data/lcc.RData')
-load('Results/hyper-lcc.RData')
-load('Results/dag_dat_all.RData')
-source('Scripts/temporal.R')
-source('Scripts/structural.R')
-
-##### Initialize data
-formula = vic ~ sex + race + age + gang.member + gang.name
-lcc_verts$sex = as.factor(lcc_verts$sex)
-lcc_verts$race = as.factor(lcc_verts$race)
-lcc_verts$age = as.numeric(lcc_verts$age)
-lcc_verts$gang.name = as.factor(lcc_verts$gang.name)
-# sum(hyp_lcc_verts$vic)/length(days)
-
-##### Loop through days
-alpha = 1/100
-gamma = 0.18
-days = 70:max(hyp_lcc_verts$vic.day, na.rm=T)
-lambdas = 0#c(0, exp(seq(log(0.0000001), log(.0005), length.out=150)), 1)
-nvics = sum(hyp_lcc_verts$vic.day %in% days)
-correct_rank = matrix(nrow=nvics, ncol=length(lambdas))
-# correct_rank1 = correct_rank2 = correct_rank3 = c()
-edges_all = dag_dat_all[dag_dat_all$dist<2,]
-ptm = proc.time()
-for (day in days){
-  if (day %% 100 == 0) print(day)
-  
-  ##### Demographics model
-#   vics = match(unique(hyp_lcc_verts$ir_no[which(hyp_lcc_verts$vic.day<day)]),lcc_verts$name)
-#   victims = lcc_verts[,c('vic','sex','race','age','gang.member','gang.name')]
-#   victims$vic[vics] = TRUE
-#   victims$vic[-vics] = FALSE
-# #   glm.fit = glm(formula, data=victims, family=binomial)
-#   glm.fit = lm(formula, data=victims)
-#   glm.probs = predict(glm.fit, newdata=lcc_verts, type='response')
-  
-  ##### Cascade Model
-  edges = edges_all[which(edges_all$t1<day),]
-  f = temporal(edges$t1, day, alpha)
-  h = structural(gamma,edges$dist)
-  weights = f*h
-  ids = edges$to
-  irs = hyp_lcc_verts$ir_no[ids]
-  risk = data.frame(id=ids, ir=irs, weight=weights)
-  risk = risk[order(weights, decreasing=T),]
-  risk = risk[match(unique(risk$ir),risk$ir),]
-# maybe need to change this to reflect new algorithm that accounts for \tilde{p}
-
-  ##### Combined Model
-#   combined = data.frame(ir=attr(glm.probs,'name'), dem=as.numeric(glm.probs), cas=0, comb=0)
-  combined$cas[match(risk$ir, attr(glm.probs,'name'))] = risk$weight
-  
-  ##### Gather results
-  infected_irs = hyp_lcc_verts$ir_no[which(hyp_lcc_verts$vic.day==day)]
-  for (lambda in lambdas){
-    combined$comb = combined$cas#lambda*combined$dem + (1-lambda)*(1-combined$dem)*combined$cas
-    c_idx = which(lambdas==lambda)
-    r_idx = head(which(is.na(correct_rank[,c_idx])),length(infected_irs))
-    # !! order should be first: rank of (3,5,5,7) should be (1,2,2,4), may need to do n-rank
-    correct_rank[r_idx,c_idx] = match(infected_irs, combined$ir[order(combined$comb, decreasing=T)])
-    # maybe should also mark down vic/nonvic status of each?
-  }
-  
-}
-print(proc.time()-ptm)
-
-
-##### Plot results
-hist(correct_rank3,150,xlim=c(0,vcount(lcc)),col=rgb(0,0,1,1/8),
-     xlab='Risk Ranking of Victims',main='')
-hist(correct_rank1,150,xlim=c(0,vcount(lcc)),col=rgb(1,0,1,1/8),add=T)
-hist(correct_rank2,150,xlim=c(0,vcount(lcc)),col=rgb(1,0,1,1/8),add=T)
-legend("topright", c("Demographics Model", "Cascade Model"), 
-       fill=c(rgb(1,0,1,1/8), rgb(0,0,1,1/8)))
-
-counts = matrix(c(colSums(correct_rank<(vcount(lcc)/1000))*100/nvics,
-                  colSums(correct_rank<(vcount(lcc)/200))*100/nvics,
-                  colSums(correct_rank<(vcount(lcc)/100))*100/nvics),
-                nrow=3, byrow=T)
-plot(lambdas,counts[1,],log='x',type='l')
-
-correct_rank1 = correct_rank[,length(lambdas)]
-correct_rank2 = correct_rank[,1]
-correct_rank3 = correct_rank[,which.min(colMeans(correct_rank))]
-counts = matrix(c(sum(correct_rank1<(vcount(lcc)*0.001)),
-               sum(correct_rank1<(vcount(lcc)*0.005)),
-               sum(correct_rank1<(vcount(lcc)*0.01)),
-               sum(correct_rank2<(vcount(lcc)*0.001)),
-               sum(correct_rank2<(vcount(lcc)*0.005)),
-               sum(correct_rank2<(vcount(lcc)*0.01)),
-               sum(correct_rank3<(vcount(lcc)*0.001)),
-               sum(correct_rank3<(vcount(lcc)*0.005)),
-               sum(correct_rank3<(vcount(lcc)*0.01))),
-               nrow=3, byrow=T)
-counts = counts*100/nvics
-barplot(counts, 
-        xlab="Size of High-Risk Population",
-        ylab="Percent of Victims Predicted",
-        names.arg=c('0.1%','0.5%','1%'),ylim=c(0,max(counts)*1.1),
-        col=c(rgb(0,0,1,1/2),rgb(1,0,0,1/2),rgb(0,1,0,1/2)),
-        beside=TRUE)
-legend("topleft", inset=0.05, 
-       c("Demographics Model", "Cascade Model", "Combined Model"), 
-       fill=c(rgb(0,0,1,1/2),rgb(1,0,0,1/2),rgb(0,1,0,1/2)))
-box(which='plot')
-par(new=T)
-counts = counts/(100/nvics)
-barplot(counts, 
-        ylim=c(0,max(counts)*1.1),
-        col=c(rgb(0,0,1,0),rgb(1,0,0,0),rgb(0,1,0,0)),
-        beside=TRUE)
-axis(side = 4)
-mtext(side = 4, line = 3, "Number of Victims Predicted")
-
-popsizes = c(0.1, 0.5, 1)
-plot(popsizes,counts[1,],type='l',ylim=c(0,max(counts)))
-lines(popsizes,counts[2,])
-lines(popsizes,counts[3,])
-lines(c(0,1),c(0,1))
-
-#### Precision-Recall Curve
-plot(ecdf(correct_rank1),col='red',xlim=c(0,vcount(lcc)),lwd=2)
-plot(ecdf(correct_rank2),col='darkblue',lwd=2,add=T)
-plot(ecdf(correct_rank3),col='darkgreen',lwd=2,add=T)
-legend("bottomright", inset=0.05, 
-       c("Demographics Model", "Cascade Model", "Combined Model"), 
-       fill=c('red','darkblue','darkgreen'))
-lines(c(0,vcount(lcc)),c(0,1))
diff --git a/R Scripts/find-cascades.R b/R Scripts/find-cascades.R
index 0a69c5c..7673909 100644
--- a/R Scripts/find-cascades.R
+++ b/R Scripts/find-cascades.R
@@ -9,14 +9,14 @@ source('criminal_cascades/R Scripts/structural.R')
 vic_ids = which(V(hyp_lcc)$vic==TRUE)
 
 #### Initialize model parameters
-alpha = 1/21
+alpha = 0.041
 # gamma = 0.3
-delta = 0.001
-beta = 0.00796964464237
+delta = 0.1
+# beta = 0.00796964464237
 
 ##### Get weights
 # find max n days where infection possible given alpha
-edges = dag_dat_vics
+edges = dag_dat_test[!is.na(dag_dat_test$t2),]
 # edges = edges[(edges$t2-edges$t1)<300,]
 
 p_t = temporal(edges$t1, edges$t2, alpha)
@@ -31,7 +31,7 @@ weights = p/p_tilde
 # probs = as.numeric(lm.probs)
 # betas = probs[match(hyp_lcc_verts$ir_no[edges$to],names)]
 # betas = 0.055
-thresh = beta/(1-beta)
+# thresh = beta/(1-beta)
 
 realized = c()
 # edges = edges[weights>thresh,]
@@ -51,6 +51,7 @@ for (u in vic_ids){
   }
   realized = c(realized, max_edge)
 }
+edges[realized,c('from','to')]
 #   if (length(Ei)>0){
 #     max_edge = Ei[which.max(weights[Ei])] # how to deal with ties????
 #     realized = c(realized, max_edge)
diff --git a/R Scripts/find-parents.R b/R Scripts/find-parents.R
new file mode 100644
index 0000000..3ec8809
--- /dev/null
+++ b/R Scripts/find-parents.R
@@ -0,0 +1,40 @@
+# library(igraph)
+# setwd("~/Documents/Cascade Project/")
+# load('Results/hyper-lcc.RData')
+# load('Results/dag_dat_vics.RData')
+# source('criminal_cascades/R Scripts/temporal.R')
+# source('criminal_cascades/R Scripts/structural.R')
+
+##### Initialize parameters based on what ml2 found
+alpha = 0.061
+delta = 0.082
+
+##### Get weights
+edges = dag_dat_test[!is.na(dag_dat_test$t2),]
+
+dt = edges$t2 - edges$t1
+p_t = exp(-alpha*dt) * (exp(alpha)-1)
+p_s = structural(delta, edges$dist)
+p = p_s * p_t
+p_tilde = 1 - p_s + p_s * exp(-alpha*dt)
+weights = p/p_tilde
+edges$weight = weights
+
+##### Find most likely parents
+parents = data.frame(vic=0,Npars=0,par_rank=0)
+vics = setdiff(vic_ids,seeds)
+for (u in vics){
+  u_parents = edges[edges$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$from==infector)
+  parents[which(vics==u),] = c(u, Nparents, infectorID)
+}
+
+##### Get some summary statistics on how well 
+median(parents$par_rank[parents$Npars>9])
+median(parents$par_rank[parents$Npars>99])
+
+
+
diff --git a/R Scripts/generate-network.R b/R Scripts/generate-network.R
index dc4a4f8..3b40969 100644
--- a/R Scripts/generate-network.R
+++ b/R Scripts/generate-network.R
@@ -9,7 +9,6 @@ delta = 0.15
 # lmbda = 1/10
 t_max = 1000
 
-# g = watts.strogatz.game(1, 100, 3, 0.25)
 N = 5000
 g = forest.fire.game(nodes=N, fw.prob=0.3, ambs=1, directed=F)
 plot(g, vertex.size=5, vertex.label=NA)
@@ -31,7 +30,7 @@ for (day in 1:t_max){
     infected = setdiff(infected,seeds) # don't try to infect seeds
     inf.days = day + ceiling(alpha*rexp(length(infected),alpha))
     V(g)$vic[infected] = TRUE
-    infects = (inf.days < V(g)$vic.day[infected]) %in% c(NA,T)
+    infects = (inf.days <= V(g)$vic.day[infected]) %in% c(NA,T)
     V(g)$vic.day[infected[infects]] = inf.days[infects]
     V(g)$infector[infected[infects]] = vic
   }
@@ -76,3 +75,5 @@ infectors = cbind(setdiff(vic_ids,seeds),
                   V(g)$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
diff --git a/R Scripts/predict-victims-plots.R b/R Scripts/predict-victims-plots.R
new file mode 100644
index 0000000..8a93667
--- /dev/null
+++ b/R Scripts/predict-victims-plots.R
@@ -0,0 +1,61 @@
+##### Plot results
+hist(correct_rank3,150,xlim=c(0,vcount(lcc)),col=rgb(0,0,1,1/8),
+     xlab='Risk Ranking of Victims',main='')
+hist(correct_rank1,150,xlim=c(0,vcount(lcc)),col=rgb(1,0,1,1/8),add=T)
+hist(correct_rank2,150,xlim=c(0,vcount(lcc)),col=rgb(1,0,1,1/8),add=T)
+legend("topright", c("Demographics Model", "Cascade Model"), 
+       fill=c(rgb(1,0,1,1/8), rgb(0,0,1,1/8)))
+
+counts = matrix(c(colSums(correct_rank<(vcount(lcc)/1000))*100/nvics,
+                  colSums(correct_rank<(vcount(lcc)/200))*100/nvics,
+                  colSums(correct_rank<(vcount(lcc)/100))*100/nvics),
+                nrow=3, byrow=T)
+plot(lambdas,counts[1,],log='x',type='l')
+
+correct_rank1 = correct_rank[,length(lambdas)]
+correct_rank2 = correct_rank[,1]
+correct_rank3 = correct_rank[,which.min(colMeans(correct_rank))]
+counts = matrix(c(sum(correct_rank1<(vcount(lcc)*0.001)),
+                  sum(correct_rank1<(vcount(lcc)*0.005)),
+                  sum(correct_rank1<(vcount(lcc)*0.01)),
+                  sum(correct_rank2<(vcount(lcc)*0.001)),
+                  sum(correct_rank2<(vcount(lcc)*0.005)),
+                  sum(correct_rank2<(vcount(lcc)*0.01)),
+                  sum(correct_rank3<(vcount(lcc)*0.001)),
+                  sum(correct_rank3<(vcount(lcc)*0.005)),
+                  sum(correct_rank3<(vcount(lcc)*0.01))),
+                nrow=3, byrow=T)
+counts = counts*100/nvics
+barplot(counts, 
+        xlab="Size of High-Risk Population",
+        ylab="Percent of Victims Predicted",
+        names.arg=c('0.1%','0.5%','1%'),ylim=c(0,max(counts)*1.1),
+        col=c(rgb(0,0,1,1/2),rgb(1,0,0,1/2),rgb(0,1,0,1/2)),
+        beside=TRUE)
+legend("topleft", inset=0.05, 
+       c("Demographics Model", "Cascade Model", "Combined Model"), 
+       fill=c(rgb(0,0,1,1/2),rgb(1,0,0,1/2),rgb(0,1,0,1/2)))
+box(which='plot')
+par(new=T)
+counts = counts/(100/nvics)
+barplot(counts, 
+        ylim=c(0,max(counts)*1.1),
+        col=c(rgb(0,0,1,0),rgb(1,0,0,0),rgb(0,1,0,0)),
+        beside=TRUE)
+axis(side = 4)
+mtext(side = 4, line = 3, "Number of Victims Predicted")
+
+popsizes = c(0.1, 0.5, 1)
+plot(popsizes,counts[1,],type='l',ylim=c(0,max(counts)))
+lines(popsizes,counts[2,])
+lines(popsizes,counts[3,])
+lines(c(0,1),c(0,1))
+
+#### Precision-Recall Curve
+plot(ecdf(correct_rank1),col='red',xlim=c(0,vcount(lcc)),lwd=2)
+plot(ecdf(correct_rank2),col='darkblue',lwd=2,add=T)
+plot(ecdf(correct_rank3),col='darkgreen',lwd=2,add=T)
+legend("bottomright", inset=0.05, 
+       c("Demographics Model", "Cascade Model", "Combined Model"), 
+       fill=c('red','darkblue','darkgreen'))
+lines(c(0,vcount(lcc)),c(0,1))
diff --git a/R Scripts/predict-victims.R b/R Scripts/predict-victims.R
new file mode 100644
index 0000000..470815d
--- /dev/null
+++ b/R Scripts/predict-victims.R
@@ -0,0 +1,67 @@
+library(igraph)
+setwd('~/Documents/Cascade Project')
+load('Raw Data/lcc.RData')
+load('Results/hyper-lcc.RData')
+load('Results/dag_dat_all.RData')
+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
+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)
+
+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))
+edges_all = dag_dat_all
+
+##### Loop through days
+ptm = proc.time()
+for (day in days){
+  if (which(day==days) %% 100 == 0) print(day)
+  
+  ##### Demographics model
+  vics = match(unique(hyp_lcc_verts$ir_no[which(hyp_lcc_verts$vic.day<day)]),lcc_verts$name)
+  victims = lcc_verts[,c('vic','sex','race','age','gang.member','gang.name')]
+  victims$vic[vics] = TRUE
+  victims$vic[-vics] = FALSE
+#   glm.fit = glm(formula, data=victims, family=binomial)
+  glm.fit = lm(formula, data=victims)
+  glm.probs = predict(glm.fit, newdata=lcc_verts, type='response')
+  
+  ##### Cascade Model
+  edges = edges_all[which(edges_all$t1<day),]
+  f = temporal(edges$t1, day, alpha)
+  h = structural(delta,edges$dist)
+  weights = f*h
+  ids = edges$to
+  irs = hyp_lcc_verts$ir_no[ids]
+  risk = data.frame(id=ids, ir=irs, weight=weights)
+  risk = risk[order(weights, decreasing=T),]
+  risk = risk[match(unique(risk$ir),risk$ir),]
+# maybe need to change this to reflect new algorithm that accounts for \tilde{p}
+
+  ##### Combined Model
+  combined = data.frame(ir=attr(glm.probs,'name'), dem=as.numeric(glm.probs), cas=0, comb=0)
+  combined$cas[match(risk$ir, attr(glm.probs,'name'))] = risk$weight
+  
+  ##### Gather results
+  infected_irs = hyp_lcc_verts$ir_no[which(hyp_lcc_verts$vic.day==day)]
+  for (lambda in lambdas){
+    combined$comb = 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?
+  }
+  
+}
+print(proc.time()-ptm)
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 e928c51..8ac85ca 100644
--- 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
diff --git a/experiments/ml2.so b/experiments/ml2.so
index 162e007..515e455 100755
--- a/experiments/ml2.so
+++ b/experiments/ml2.so
diff --git a/experiments/ml3.c b/experiments/ml3.c
index 12d0e1d..59de174 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, int dt, DTYPE_t alpha, DTYPE_t delta,
+ * cdef DTYPE_t weight_success(int dist, DTYPE_t dt, DTYPE_t alpha, DTYPE_t delta,
  */
 typedef __pyx_t_5numpy_float_t __pyx_t_3ml3_DTYPE_t;
 #if CYTHON_CCOMPLEX
@@ -914,6 +914,18 @@ static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) {
 #endif
 
 #define __Pyx_BufPtrStrided1d(type, buf, i0, s0) (type)((char*)buf + i0 * s0)
+static double __Pyx__PyObject_AsDouble(PyObject* obj);
+#if CYTHON_COMPILING_IN_PYPY
+#define __Pyx_PyObject_AsDouble(obj) \
+(likely(PyFloat_CheckExact(obj)) ? PyFloat_AS_DOUBLE(obj) : \
+ likely(PyInt_CheckExact(obj)) ? \
+ PyFloat_AsDouble(obj) : __Pyx__PyObject_AsDouble(obj))
+#else
+#define __Pyx_PyObject_AsDouble(obj) \
+((likely(PyFloat_CheckExact(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) : \
@@ -935,18 +947,6 @@ 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 double __Pyx__PyObject_AsDouble(PyObject* obj);
-#if CYTHON_COMPILING_IN_PYPY
-#define __Pyx_PyObject_AsDouble(obj) \
-(likely(PyFloat_CheckExact(obj)) ? PyFloat_AS_DOUBLE(obj) : \
- likely(PyInt_CheckExact(obj)) ? \
- PyFloat_AsDouble(obj) : __Pyx__PyObject_AsDouble(obj))
-#else
-#define __Pyx_PyObject_AsDouble(obj) \
-((likely(PyFloat_CheckExact(obj))) ? \
- PyFloat_AS_DOUBLE(obj) : __Pyx__PyObject_AsDouble(obj))
-#endif
-
 static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb);
 static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb);
 
@@ -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, 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_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_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;
@@ -1329,7 +1329,6 @@ 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_float_neg_19_523275053840013;
 static PyObject *__pyx_int_0;
 static PyObject *__pyx_int_1;
 static PyObject *__pyx_tuple_;
@@ -1345,12 +1344,12 @@ static PyObject *__pyx_codeobj__9;
 /* "ml3.pyx":9
  * ctypedef np.float_t DTYPE_t
  * 
- * cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,             # <<<<<<<<<<<<<<
+ * cdef DTYPE_t weight_success(int dist, DTYPE_t 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, 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) {
+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) {
   __pyx_t_3ml3_DTYPE_t __pyx_v_structural;
   __pyx_t_3ml3_DTYPE_t __pyx_v_temporal;
   __pyx_t_3ml3_DTYPE_t __pyx_v_result;
@@ -1390,7 +1389,7 @@ static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_success(int __pyx_v_dist, int __
  *     result = log(structural) + temporal
  *     return result             # <<<<<<<<<<<<<<
  * 
- * cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,
+ * cdef DTYPE_t weight_failure(int dist, DTYPE_t dt, DTYPE_t alpha, DTYPE_t delta,
  */
   __pyx_r = __pyx_v_result;
   goto __pyx_L0;
@@ -1398,7 +1397,7 @@ static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_success(int __pyx_v_dist, int __
   /* "ml3.pyx":9
  * ctypedef np.float_t DTYPE_t
  * 
- * cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,             # <<<<<<<<<<<<<<
+ * cdef DTYPE_t weight_success(int dist, DTYPE_t dt, DTYPE_t alpha, DTYPE_t delta,             # <<<<<<<<<<<<<<
  *                             DTYPE_t w1, DTYPE_t w2, DTYPE_t w3):
  *     """weight for successful infection, exponential time model"""
  */
@@ -1412,12 +1411,12 @@ static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_success(int __pyx_v_dist, int __
 /* "ml3.pyx":18
  *     return result
  * 
- * cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,             # <<<<<<<<<<<<<<
+ * cdef DTYPE_t weight_failure(int dist, DTYPE_t 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, 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) {
+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) {
   __pyx_t_3ml3_DTYPE_t __pyx_v_structural;
   __pyx_t_3ml3_DTYPE_t __pyx_v_temporal;
   __pyx_t_3ml3_DTYPE_t __pyx_v_result;
@@ -1465,7 +1464,7 @@ static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_failure(int __pyx_v_dist, int __
   /* "ml3.pyx":18
  *     return result
  * 
- * cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,             # <<<<<<<<<<<<<<
+ * cdef DTYPE_t weight_failure(int dist, DTYPE_t dt, DTYPE_t alpha, DTYPE_t delta,             # <<<<<<<<<<<<<<
  *                             DTYPE_t w1, DTYPE_t w2, DTYPE_t w3):
  *     """weight for failed infection, exponential time model"""
  */
@@ -1660,12 +1659,13 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
   __pyx_t_3ml3_DTYPE_t __pyx_t_25;
   __pyx_t_3ml3_DTYPE_t __pyx_t_26;
   int __pyx_t_27;
-  int __pyx_t_28;
+  double __pyx_t_28;
   int __pyx_t_29;
   int __pyx_t_30;
   int __pyx_t_31;
-  double __pyx_t_32;
-  PyObject *(*__pyx_t_33)(PyObject *);
+  int __pyx_t_32;
+  int __pyx_t_33;
+  PyObject *(*__pyx_t_34)(PyObject *);
   int __pyx_lineno = 0;
   const char *__pyx_filename = NULL;
   int __pyx_clineno = 0;
@@ -1921,7 +1921,7 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
   __pyx_t_11 = ((PyArrayObject *)__pyx_t_4);
   {
     __Pyx_BufFmt_StackElem __pyx_stack[1];
-    if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_isSeed.rcbuffer->pybuffer, (PyObject*)__pyx_t_11, &__Pyx_TypeInfo_nn___pyx_t_3ml3_DTYPE_t, PyBUF_FORMAT| PyBUF_STRIDES| PyBUF_WRITABLE, 1, 0, __pyx_stack) == -1)) {
+    if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_isSeed.rcbuffer->pybuffer, (PyObject*)__pyx_t_11, &__Pyx_TypeInfo_nn___pyx_t_3ml3_DTYPE_t, PyBUF_FORMAT| PyBUF_STRIDES, 1, 0, __pyx_stack) == -1)) {
       __pyx_v_isSeed = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); __pyx_pybuffernd_isSeed.rcbuffer->pybuffer.buf = NULL;
       {__pyx_filename = __pyx_f[0]; __pyx_lineno = 39; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     } else {__pyx_pybuffernd_isSeed.diminfo[0].strides = __pyx_pybuffernd_isSeed.rcbuffer->pybuffer.strides[0]; __pyx_pybuffernd_isSeed.diminfo[0].shape = __pyx_pybuffernd_isSeed.rcbuffer->pybuffer.shape[0];
@@ -1963,7 +1963,7 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
     /* "ml3.pyx":46
  *         # 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)             # <<<<<<<<<<<<<<
+ *         failures = [weight_failure(dist, dt/100., alpha, delta, w1, w2, w3)             # <<<<<<<<<<<<<<
  *                     for (dist, dt, w1, w2, w3) in parents]
  *         probs_fail[i] = sum(failures)
  */
@@ -1972,10 +1972,10 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
 
     /* "ml3.pyx":47
  *         # likely parent infects it
- *         failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3)
+ *         failures = [weight_failure(dist, dt/100., alpha, delta, w1, w2, w3)
  *                     for (dist, dt, w1, w2, w3) in parents]             # <<<<<<<<<<<<<<
  *         probs_fail[i] = sum(failures)
- *         successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3)
+ *         successes = [weight_success(dist, dt/100., alpha, delta, w1, w2, w3)
  */
     if (unlikely(__pyx_v_parents == Py_None)) {
       PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
@@ -2071,24 +2071,24 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
       /* "ml3.pyx":46
  *         # 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)             # <<<<<<<<<<<<<<
+ *         failures = [weight_failure(dist, dt/100., alpha, delta, w1, w2, w3)             # <<<<<<<<<<<<<<
  *                     for (dist, dt, w1, w2, w3) in parents]
  *         probs_fail[i] = sum(failures)
  */
       __pyx_t_24 = __pyx_PyFloat_AsDouble(__pyx_v_w1); if (unlikely((__pyx_t_24 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 46; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __pyx_t_25 = __pyx_PyFloat_AsDouble(__pyx_v_w2); if (unlikely((__pyx_t_25 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 46; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __pyx_t_26 = __pyx_PyFloat_AsDouble(__pyx_v_w3); if (unlikely((__pyx_t_26 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 46; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __pyx_t_3 = PyFloat_FromDouble(__pyx_f_3ml3_weight_failure(__pyx_v_dist, __pyx_v_dt, __pyx_v_alpha, __pyx_v_delta, __pyx_t_24, __pyx_t_25, __pyx_t_26)); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 46; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_3 = PyFloat_FromDouble(__pyx_f_3ml3_weight_failure(__pyx_v_dist, (__pyx_v_dt / 100.), __pyx_v_alpha, __pyx_v_delta, __pyx_t_24, __pyx_t_25, __pyx_t_26)); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 46; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __Pyx_GOTREF(__pyx_t_3);
       if (unlikely(__Pyx_ListComp_Append(__pyx_t_6, (PyObject*)__pyx_t_3))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 46; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
 
       /* "ml3.pyx":47
  *         # likely parent infects it
- *         failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3)
+ *         failures = [weight_failure(dist, dt/100., alpha, delta, w1, w2, w3)
  *                     for (dist, dt, w1, w2, w3) in parents]             # <<<<<<<<<<<<<<
  *         probs_fail[i] = sum(failures)
- *         successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3)
+ *         successes = [weight_success(dist, dt/100., alpha, delta, w1, w2, w3)
  */
     }
     __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
@@ -2096,10 +2096,10 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
     __pyx_t_6 = 0;
 
     /* "ml3.pyx":48
- *         failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3)
+ *         failures = [weight_failure(dist, dt/100., alpha, delta, w1, w2, w3)
  *                     for (dist, dt, w1, w2, w3) in parents]
  *         probs_fail[i] = sum(failures)             # <<<<<<<<<<<<<<
- *         successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3)
+ *         successes = [weight_success(dist, dt/100., alpha, delta, w1, w2, w3)
  *                      for (dist, dt, w1, w2, w3) in parents]
  */
     __pyx_t_6 = PyTuple_New(1); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 48; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
@@ -2119,7 +2119,7 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
     /* "ml3.pyx":49
  *                     for (dist, dt, w1, w2, w3) in parents]
  *         probs_fail[i] = sum(failures)
- *         successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3)             # <<<<<<<<<<<<<<
+ *         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]
  */
@@ -2128,7 +2128,7 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
 
     /* "ml3.pyx":50
  *         probs_fail[i] = sum(failures)
- *         successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3)
+ *         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]
@@ -2227,21 +2227,21 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
       /* "ml3.pyx":49
  *                     for (dist, dt, w1, w2, w3) in parents]
  *         probs_fail[i] = sum(failures)
- *         successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3)             # <<<<<<<<<<<<<<
+ *         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]
  */
       __pyx_t_26 = __pyx_PyFloat_AsDouble(__pyx_v_w1); if (unlikely((__pyx_t_26 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 49; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __pyx_t_25 = __pyx_PyFloat_AsDouble(__pyx_v_w2); if (unlikely((__pyx_t_25 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 49; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __pyx_t_24 = __pyx_PyFloat_AsDouble(__pyx_v_w3); if (unlikely((__pyx_t_24 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 49; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __pyx_t_3 = PyFloat_FromDouble(__pyx_f_3ml3_weight_success(__pyx_v_dist, __pyx_v_dt, __pyx_v_alpha, __pyx_v_delta, __pyx_t_26, __pyx_t_25, __pyx_t_24)); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 49; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_3 = PyFloat_FromDouble(__pyx_f_3ml3_weight_success(__pyx_v_dist, (__pyx_v_dt / 100.), __pyx_v_alpha, __pyx_v_delta, __pyx_t_26, __pyx_t_25, __pyx_t_24)); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 49; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __Pyx_GOTREF(__pyx_t_3);
       if (unlikely(__Pyx_ListComp_Append(__pyx_t_5, (PyObject*)__pyx_t_3))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 49; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
 
       /* "ml3.pyx":50
  *         probs_fail[i] = sum(failures)
- *         successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3)
+ *         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]
@@ -2252,7 +2252,7 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
     __pyx_t_5 = 0;
 
     /* "ml3.pyx":51
- *         successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3)
+ *         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]
@@ -2467,14 +2467,26 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
     __Pyx_XDECREF_SET(__pyx_v_dts, ((PyObject*)__pyx_t_5));
     __pyx_t_5 = 0;
 
+    /* "ml3.pyx":55
+ *         # 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")             # <<<<<<<<<<<<<<
+ *         for l, s in enumerate(successes):
+ *             prob = s - failures[l]
+ */
+    __pyx_t_28 = __Pyx_PyObject_AsDouble(__pyx_kp_s_inf); if (unlikely(__pyx_t_28 == ((double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 55; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_27 = __pyx_v_i;
+    if (__pyx_t_27 < 0) __pyx_t_27 += __pyx_pybuffernd_probs.diminfo[0].shape;
+    *__Pyx_BufPtrStrided1d(__pyx_t_3ml3_DTYPE_t *, __pyx_pybuffernd_probs.rcbuffer->pybuffer.buf, __pyx_t_27, __pyx_pybuffernd_probs.diminfo[0].strides) = __pyx_t_28;
+
     /* "ml3.pyx":56
  *         # probs[i] = max(s - failures[l] for l, s in enumerate(successes))
- *         # probs[i] = 0.
+ *         probs[i] = float("-inf")
  *         for l, s in enumerate(successes):             # <<<<<<<<<<<<<<
  *             prob = s - failures[l]
- *             if prob > -19.523275053840013:
+ *             if prob > probs[i]:
  */
-    __pyx_t_27 = 0;
+    __pyx_t_14 = 0;
     __pyx_t_5 = __pyx_v_successes; __Pyx_INCREF(__pyx_t_5); __pyx_t_15 = 0;
     for (;;) {
       if (__pyx_t_15 >= PyList_GET_SIZE(__pyx_t_5)) break;
@@ -2485,15 +2497,15 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
       #endif
       __Pyx_XDECREF_SET(__pyx_v_s, __pyx_t_6);
       __pyx_t_6 = 0;
-      __pyx_v_l = __pyx_t_27;
-      __pyx_t_27 = (__pyx_t_27 + 1);
+      __pyx_v_l = __pyx_t_14;
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  *         if beta_add > max_beta_add:
  *             max_beta = beta             # <<<<<<<<<<<<<<
@@ -2842,7 +2848,7 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
  */
       __pyx_v_max_beta = __pyx_v_beta;
 
-      /* "ml3.pyx":85
+      /* "ml3.pyx":84
  *         if beta_add > max_beta_add:
  *             max_beta = beta
  *             max_roots = roots             # <<<<<<<<<<<<<<
@@ -2851,7 +2857,7 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
  */
       __pyx_v_max_roots = __pyx_v_roots;
 
-      /* "ml3.pyx":86
+      /* "ml3.pyx":85
  *             max_beta = beta
  *             max_roots = roots
  *             max_beta_add = beta_add             # <<<<<<<<<<<<<<
@@ -2863,7 +2869,7 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
     }
     __pyx_L26:;
 
-    /* "ml3.pyx":71
+    /* "ml3.pyx":70
  *     max_beta_add = float('-inf')
  *     # iterate over all victim nodes to find the optimal threshold
  *     for beta in np.arange(0.00001, 1., 1.):             # <<<<<<<<<<<<<<
@@ -2873,7 +2879,7 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
   }
   __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
 
-  /* "ml3.pyx":89
+  /* "ml3.pyx":88
  *             # print 'beta:', max_beta, 'add:', max_beta_add, 'roots:', max_roots
  * 
  *     ll += max_beta_add             # <<<<<<<<<<<<<<
@@ -2882,7 +2888,7 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
  */
   __pyx_v_ll = (__pyx_v_ll + __pyx_v_max_beta_add);
 
-  /* "ml3.pyx":90
+  /* "ml3.pyx":89
  * 
  *     ll += max_beta_add
  *     roots = max_roots             # <<<<<<<<<<<<<<
@@ -2891,7 +2897,7 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
  */
   __pyx_v_roots = __pyx_v_max_roots;
 
-  /* "ml3.pyx":91
+  /* "ml3.pyx":90
  *     ll += max_beta_add
  *     roots = max_roots
  *     beta = max_beta             # <<<<<<<<<<<<<<
@@ -2900,29 +2906,29 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject
  */
   __pyx_v_beta = __pyx_v_max_beta;
 
-  /* "ml3.pyx":93
+  /* "ml3.pyx":92
  *     beta = max_beta
  *     # print n_nodes, n_roots, n_victims, max_i, roots
  *     return (beta, roots, ll)             # <<<<<<<<<<<<<<
  */
   __Pyx_XDECREF(__pyx_r);
-  __pyx_t_5 = PyFloat_FromDouble(__pyx_v_beta); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 93; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_5 = PyFloat_FromDouble(__pyx_v_beta); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 92; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_5);
-  __pyx_t_17 = __Pyx_PyInt_From_int(__pyx_v_roots); if (unlikely(!__pyx_t_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 93; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_17 = __Pyx_PyInt_From_int(__pyx_v_roots); if (unlikely(!__pyx_t_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 92; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_17);
-  __pyx_t_3 = PyFloat_FromDouble(__pyx_v_ll); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 93; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_3);
-  __pyx_t_16 = PyTuple_New(3); if (unlikely(!__pyx_t_16)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 93; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_6 = PyFloat_FromDouble(__pyx_v_ll); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 92; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_6);
+  __pyx_t_16 = PyTuple_New(3); if (unlikely(!__pyx_t_16)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 92; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_16);
   PyTuple_SET_ITEM(__pyx_t_16, 0, __pyx_t_5);
   __Pyx_GIVEREF(__pyx_t_5);
   PyTuple_SET_ITEM(__pyx_t_16, 1, __pyx_t_17);
   __Pyx_GIVEREF(__pyx_t_17);
-  PyTuple_SET_ITEM(__pyx_t_16, 2, __pyx_t_3);
-  __Pyx_GIVEREF(__pyx_t_3);
+  PyTuple_SET_ITEM(__pyx_t_16, 2, __pyx_t_6);
+  __Pyx_GIVEREF(__pyx_t_6);
   __pyx_t_5 = 0;
   __pyx_t_17 = 0;
-  __pyx_t_3 = 0;
+  __pyx_t_6 = 0;
   __pyx_r = __pyx_t_16;
   __pyx_t_16 = 0;
   goto __pyx_L0;
@@ -5093,14 +5099,14 @@ static int __Pyx_InitCachedConstants(void) {
   __Pyx_RefNannyDeclarations
   __Pyx_RefNannySetupContext("__Pyx_InitCachedConstants", 0);
 
-  /* "ml3.pyx":71
+  /* "ml3.pyx":70
  *     max_beta_add = float('-inf')
  *     # iterate over all victim nodes to find the optimal threshold
  *     for beta in np.arange(0.00001, 1., 1.):             # <<<<<<<<<<<<<<
  *         thresh = log(beta/(3012.*(1.-beta)))
  *         seeds = isSeed==1
  */
-  __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 = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __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_GOTREF(__pyx_tuple_);
   __Pyx_GIVEREF(__pyx_tuple_);
 
@@ -5192,7 +5198,6 @@ 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_float_neg_19_523275053840013 = PyFloat_FromDouble(-19.523275053840013); if (unlikely(!__pyx_float_neg_19_523275053840013)) {__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_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;
@@ -6464,6 +6469,45 @@ static CYTHON_INLINE int __Pyx_dict_iter_next(PyObject* iter_obj, Py_ssize_t ori
     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;
@@ -6542,45 +6586,6 @@ static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
     return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
 }
 
-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 void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) {
 #if CYTHON_COMPILING_IN_CPYTHON
     PyObject *tmp_type, *tmp_value, *tmp_tb;
diff --git a/experiments/ml3.pyx b/experiments/ml3.pyx
index faeda92..da993bc 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, int dt, DTYPE_t alpha, DTYPE_t delta,
+cdef DTYPE_t weight_success(int dist, DTYPE_t 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, int dt, DTYPE_t alpha, DTYPE_t delta,
     result = log(structural) + temporal
     return result
 
-cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,
+cdef DTYPE_t weight_failure(int dist, DTYPE_t 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
@@ -43,21 +43,20 @@ def ml3(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
         # 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, alpha, delta, w1, w2, w3)
+        failures = [weight_failure(dist, dt/100., alpha, delta, w1, w2, w3)
                     for (dist, dt, w1, w2, w3) in parents]
         probs_fail[i] = sum(failures)
-        successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3)
+        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]
         # find parent that maximizes log(p) - log(\tilde{p})
         # probs[i] = max(s - failures[l] for l, s in enumerate(successes)) 
-        # probs[i] = 0.
+        probs[i] = float("-inf")
         for l, s in enumerate(successes):
             prob = s - failures[l]
-            if prob > -19.523275053840013:
-                isSeed[i] = 0
-                probs[i] += prob
+            if prob > probs[i]:
+                probs[i] = prob
                 parent_dists[i] = dists[l]
                 parent_dts[i] = dts[l]
                 # probs_fail[i] = failures[l]
diff --git a/experiments/ml3.so b/experiments/ml3.so
index 2dd1f2d..d2b6898 100755
--- a/experiments/ml3.so
+++ b/experiments/ml3.so
diff --git a/experiments/out.log b/experiments/out.log
index b6cddf7..6ac961e 100644
--- a/experiments/out.log
+++ b/experiments/out.log
@@ -1,735 +1,225 @@
-0.03	0.09	0.1	8	-2524.16991366
-0.03	0.091	0.1	8	-2523.37447955
-0.03	0.092	0.1	8	-2522.66916324
-0.03	0.093	0.1	8	-2522.05302411
-0.03	0.094	0.1	8	-2521.52515842
-0.03	0.095	0.1	8	-2521.08469776
-0.03	0.096	0.1	8	-2520.73080755
-0.03	0.097	0.1	8	-2520.46268567
-0.03	0.098	0.1	8	-2520.27956114
-0.03	0.099	0.1	8	-2520.18069285
-0.03	0.1	0.1	8	-2520.16489903
-0.03	0.101	0.1	8	-2520.21363286
-0.03	0.102	0.1	8	-2520.34476449
-0.03	0.103	0.1	8	-2520.55765713
-0.03	0.104	0.1	8	-2520.85169858
-0.03	0.105	0.1	8	-2521.2263003
-0.03	0.106	0.1	8	-2521.68089651
-0.03	0.107	0.1	8	-2522.21494335
-0.03	0.108	0.1	8	-2522.82791806
-0.03	0.109	0.1	8	-2523.51931823
-0.03	0.11	0.1	8	-2524.28866105
-0.031	0.09	0.1	8	-2522.1436876
-0.031	0.091	0.1	8	-2521.35313396
-0.031	0.092	0.1	8	-2520.65274236
-0.031	0.093	0.1	8	-2520.02170745
-0.031	0.094	0.1	8	-2519.47855828
-0.031	0.095	0.1	8	-2519.02308655
-0.031	0.096	0.1	8	-2518.65445318
-0.031	0.097	0.1	8	-2518.37185167
-0.031	0.098	0.1	8	-2518.1745068
-0.031	0.099	0.1	8	-2518.06167338
-0.031	0.1	0.1	8	-2518.03263499
-0.031	0.101	0.1	8	-2518.08670293
-0.031	0.102	0.1	8	-2518.22321507
-0.031	0.103	0.1	8	-2518.44153485
-0.031	0.104	0.1	8	-2518.74105029
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-0.031	0.106	0.1	8	-2519.58133762
-0.031	0.107	0.1	8	-2520.12100029
-0.031	0.108	0.1	8	-2520.73963857
-0.031	0.109	0.1	8	-2521.43675024
-0.031	0.11	0.1	8	-2522.21185273
-0.032	0.09	0.1	8	-2520.29066878
-0.032	0.091	0.1	8	-2519.48374789
-0.032	0.092	0.1	8	-2518.76727387
-0.032	0.093	0.1	8	-2518.14030131
-0.032	0.094	0.1	8	-2517.60192185
-0.032	0.095	0.1	8	-2517.15126258
-0.032	0.096	0.1	8	-2516.78748463
-0.032	0.097	0.1	8	-2516.50978171
-0.032	0.098	0.1	8	-2516.3173788
-0.032	0.099	0.1	8	-2516.20953091
-0.032	0.1	0.1	8	-2516.18552186
-0.032	0.101	0.1	8	-2516.24466312
-0.032	0.102	0.1	8	-2516.38629278
-0.032	0.103	0.1	8	-2516.60977448
-0.032	0.104	0.1	8	-2516.91449646
-0.032	0.105	0.1	8	-2517.2998706
-0.032	0.106	0.1	8	-2517.76533154
-0.032	0.107	0.1	8	-2518.31033586
-0.032	0.108	0.1	8	-2518.93436121
-0.032	0.109	0.1	8	-2519.63690563
-0.032	0.11	0.1	8	-2520.41748673
-0.033	0.09	0.1	8	-2518.6656139
-0.033	0.091	0.1	8	-2517.8631188
-0.033	0.092	0.1	8	-2517.15111081
-0.033	0.093	0.1	8	-2516.52864471
-0.033	0.094	0.1	8	-2515.99481231
-0.033	0.095	0.1	8	-2515.54874094
-0.033	0.096	0.1	8	-2515.1895919
-0.033	0.097	0.1	8	-2514.91655909
-0.033	0.098	0.1	8	-2514.72886771
-0.033	0.099	0.1	8	-2514.62577295
-0.033	0.1	0.1	8	-2514.60655882
-0.033	0.101	0.1	8	-2514.670537
-0.033	0.102	0.1	8	-2514.81704578
-0.033	0.103	0.1	8	-2515.04544899
-0.033	0.104	0.1	8	-2515.35513507
-0.033	0.105	0.1	8	-2515.7455161
-0.033	0.106	0.1	8	-2516.21602692
-0.033	0.107	0.1	8	-2516.76612429
-0.033	0.108	0.1	8	-2517.39528608
-0.033	0.109	0.1	8	-2518.09594913
-0.033	0.11	0.1	8	-2518.86459803
-0.034	0.09	0.1	8	-2517.29524031
-0.034	0.091	0.1	8	-2516.49697398
-0.034	0.092	0.1	8	-2515.78923326
-0.034	0.093	0.1	8	-2515.1710731
-0.034	0.094	0.1	8	-2514.64158553
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-0.034	0.097	0.1	8	-2513.57660184
-0.034	0.098	0.1	8	-2513.39341262
-0.034	0.099	0.1	8	-2513.29485983
-0.034	0.1	0.1	8	-2513.28022765
-0.034	0.101	0.1	8	-2513.34754019
-0.034	0.102	0.1	8	-2513.48010785
-0.034	0.103	0.1	8	-2513.69480394
-0.034	0.104	0.1	8	-2513.99101336
-0.034	0.105	0.1	8	-2514.36814478
-0.034	0.106	0.1	8	-2514.82562974
-0.034	0.107	0.1	8	-2515.36292177
-0.034	0.108	0.1	8	-2515.97949564
-0.034	0.109	0.1	8	-2516.67484652
-0.034	0.11	0.1	8	-2517.44848931
-0.035	0.09	0.1	8	-2516.1651594
-0.035	0.091	0.1	8	-2515.37094245
-0.035	0.092	0.1	8	-2514.66728801
-0.035	0.093	0.1	8	-2514.05325122
-0.035	0.094	0.1	8	-2513.52473289
-0.035	0.095	0.1	8	-2513.0671733
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diff --git a/experiments/process.py b/experiments/process.py
index 5f3cff4..f31e0d4 100644
--- a/experiments/process.py
+++ b/experiments/process.py
@@ -15,20 +15,20 @@ 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(-3,0,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(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(.03, .065, .001)  # parameter of the time component
+    # alphas = np.arange(.04, .075, .003)  # parameter of the time component
     # # alphas = np.logspace(-4,-1,num=20)
-    # deltas = np.arange(.09, .11, .001)  # parameter of the structural component
+    # 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)
     # with open("out.log", "w") as fh:
@@ -38,7 +38,7 @@ if __name__ == "__main__":
     #         fh.write("\t".join(map(str, [alpha, delta, lmbda, roots, ll])) + "\n")
     #         fh.flush()
 
-    alpha = 0.041
-    delta = 0.01
-    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
+    # alpha = 0.061
+    # delta = 0.082
+    # 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