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-rw-r--r--R Scripts/generate-network.R16
-rw-r--r--experiments/build/temp.macosx-10.6-x86_64-2.7/ml.obin306196 -> 306268 bytes
-rw-r--r--experiments/build_network.py2
-rw-r--r--experiments/ml.c180
-rw-r--r--experiments/ml.pyx11
-rwxr-xr-xexperiments/ml.sobin119284 -> 119276 bytes
-rw-r--r--experiments/out.log200
-rw-r--r--experiments/process.py4
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
--- 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
Binary files 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) {
if (likely(exc_type == PyExc_StopIteration || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear();
- else {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ else {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
}
break;
}
__Pyx_GOTREF(__pyx_t_5);
}
- __pyx_t_27 = __pyx_PyFloat_AsDouble(__pyx_t_5); if (unlikely((__pyx_t_27 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_27 = __pyx_PyFloat_AsDouble(__pyx_t_5); if (unlikely((__pyx_t_27 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
__pyx_v_beta = __pyx_t_27;
- /* "ml.pyx":81
+ /* "ml.pyx":80
* # 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
*/
- __pyx_v_thresh = log((__pyx_v_beta / (3012. * (1. - __pyx_v_beta))));
+ __pyx_v_thresh = log((__pyx_v_beta / (1000. * (1. - __pyx_v_beta))));
- /* "ml.pyx":82
+ /* "ml.pyx":81
* 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)
*/
- __pyx_t_5 = PyFloat_FromDouble(__pyx_v_thresh); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 82; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_5 = PyFloat_FromDouble(__pyx_v_thresh); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 81; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_5);
- __pyx_t_4 = PyObject_RichCompare(((PyObject *)__pyx_v_probs), __pyx_t_5, Py_LT); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 82; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_4 = PyObject_RichCompare(((PyObject *)__pyx_v_probs), __pyx_t_5, Py_LT); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 81; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
__Pyx_XDECREF_SET(__pyx_v_seeds, __pyx_t_4);
__pyx_t_4 = 0;
- /* "ml.pyx":83
- * thresh = log(beta/(3012.*(1.-beta)))
+ /* "ml.pyx":82
+ * thresh = log(beta/(1000.*(1.-beta)))
* seeds = probs<thresh
* non_seeds = probs>=thresh # <<<<<<<<<<<<<<
* roots = n_roots + sum(seeds)
*
*/
- __pyx_t_4 = PyFloat_FromDouble(__pyx_v_thresh); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 83; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_4 = PyFloat_FromDouble(__pyx_v_thresh); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 82; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_4);
- __pyx_t_5 = PyObject_RichCompare(((PyObject *)__pyx_v_probs), __pyx_t_4, Py_GE); __Pyx_XGOTREF(__pyx_t_5); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 83; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_5 = PyObject_RichCompare(((PyObject *)__pyx_v_probs), __pyx_t_4, Py_GE); __Pyx_XGOTREF(__pyx_t_5); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 82; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
__Pyx_XDECREF_SET(__pyx_v_non_seeds, __pyx_t_5);
__pyx_t_5 = 0;
- /* "ml.pyx":84
+ /* "ml.pyx":83
* seeds = probs<thresh
* non_seeds = probs>=thresh
* roots = n_roots + sum(seeds) # <<<<<<<<<<<<<<
*
* beta_add = 0.
*/
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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
--- a/experiments/ml.so
+++ b/experiments/ml.so
Binary files 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)
generated by cgit v1.2.3-70-g09d2 (git 2.52.0) at 2025-12-21 13:24:00 +0000