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-rwxr-xr-xR Scripts/generate-dag-dat.R10
-rw-r--r--experiments/build/temp.macosx-10.6-x86_64-2.7/ml.obin298724 -> 304076 bytes
-rw-r--r--experiments/build_network.py2
-rw-r--r--experiments/ml.c470
-rw-r--r--experiments/ml.pyx26
-rwxr-xr-xexperiments/ml.sobin109636 -> 109372 bytes
-rw-r--r--experiments/out.log2550
-rw-r--r--experiments/process.py9
8 files changed, 2834 insertions, 233 deletions
diff --git a/R Scripts/generate-dag-dat.R b/R Scripts/generate-dag-dat.R
index b5f2c3a..a2df165 100755
--- a/R Scripts/generate-dag-dat.R
+++ b/R Scripts/generate-dag-dat.R
@@ -6,7 +6,7 @@ vic_ids = which(V(hyp_lcc)$vic==TRUE)
edgeWeights = function(eis){return(c(hyp_lcc_edges$weight[eis],Inf,Inf)[1:3])}
-dag_dat_all = data.frame(matrix(nrow=1,ncol=8))
+dag_dat_all = data.frame(matrix(nrow=1,ncol=10))
hyp_lcc2 = remove.edge.attribute(hyp_lcc,'weight')
ei = 1
ptm=proc.time()
@@ -28,15 +28,15 @@ for (u in vic_ids){
#will be faster to pre-allocate and fill in rather than rbind each time
dag_dat_all[ei:(ei+length(nbhd)-1),] = data.frame(rep(u,length(nbhd)), nbhd,
rep(tu,length(nbhd)), tvs, dists,
- weights, row.names=NULL)
+ weights, u_spawn, v_spawn, row.names=NULL)
ei = ei + length(nbhd)
}
print(proc.time()-ptm) #3.5 hours
-colnames(dag_dat_all) = c('from','to','t1','t2','dist','w1','w2','w3')
+colnames(dag_dat_all) = c('from','to','t1','t2','dist','w1','w2','w3','spawn1','spawn2')
rownames(dag_dat_all) = NULL
-dag_dat_all$spawn1 = hyp_lcc_verts$spawn.date[dag_dat_all$from]
-dag_dat_all$spawn2 = hyp_lcc_verts$spawn.date[dag_dat_all$to]
+# dag_dat_all$spawn1 = hyp_lcc_verts$spawn.date[dag_dat_all$from]
+# dag_dat_all$spawn2 = hyp_lcc_verts$spawn.date[dag_dat_all$to]
save(dag_dat_all, file='Results/dag_dat_all.RData')
write.csv(dag_dat_all, file='Results/dag_dat_all.csv')
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 cbe973b..8b0e648 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 0756ded..e34db1c 100644
--- a/experiments/build_network.py
+++ b/experiments/build_network.py
@@ -39,10 +39,12 @@ def build_network(filename):
age += int(row["t1"]) - int(row["spawn1"])
victims[from_] = []
root_victims = {}
+ print victims.keys()
for victim in victims.keys():
if not victims[victim]:
del victims[victim]
root_victims[victim] = []
+ print 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 cdf4064..4c3f024 100644
--- a/experiments/ml.c
+++ b/experiments/ml.c
@@ -1326,7 +1326,6 @@ static char __pyx_k_range[] = "range";
static char __pyx_k_roots[] = "roots";
static char __pyx_k_throw[] = "throw";
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";
@@ -1369,7 +1368,6 @@ static PyObject *__pyx_kp_s_Users_ben_Documents_Cascade_Pro;
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;
@@ -1426,9 +1424,7 @@ 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__001;
-static PyObject *__pyx_float_0_001;
+static PyObject *__pyx_float_0_13;
static PyObject *__pyx_tuple_;
static PyObject *__pyx_tuple__2;
static PyObject *__pyx_tuple__3;
@@ -1501,22 +1497,22 @@ static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_success(int __pyx_v_dist, int __py
* cdef DTYPE_t structural, temporal, result
* structural = delta ** dist # <<<<<<<<<<<<<<
* # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta)
- * # temporal = exp(-alpha*dt) * (exp(alpha)-1)
+ * temporal = exp(-alpha*dt) * (exp(alpha)-1.)
*/
__pyx_v_structural = pow(__pyx_v_delta, ((__pyx_t_2ml_DTYPE_t)__pyx_v_dist));
- /* "ml.pyx":19
+ /* "ml.pyx":18
+ * structural = delta ** dist
* # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta)
- * # temporal = exp(-alpha*dt) * (exp(alpha)-1)
- * temporal = 1 - exp(-alpha*dt) # <<<<<<<<<<<<<<
+ * temporal = exp(-alpha*dt) * (exp(alpha)-1.) # <<<<<<<<<<<<<<
+ * # 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
*/
- __pyx_v_temporal = (1.0 - exp(((-__pyx_v_alpha) * __pyx_v_dt)));
+ __pyx_v_temporal = (exp(((-__pyx_v_alpha) * __pyx_v_dt)) * (exp(__pyx_v_alpha) - 1.));
/* "ml.pyx":20
- * # temporal = exp(-alpha*dt) * (exp(alpha)-1)
- * temporal = 1 - exp(-alpha*dt)
+ * temporal = exp(-alpha*dt) * (exp(alpha)-1.)
+ * # 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 = log(structural * temporal)
@@ -1937,8 +1933,8 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
__pyx_t_2ml_DTYPE_t __pyx_t_23;
__pyx_t_2ml_DTYPE_t __pyx_t_24;
int __pyx_t_25;
- double __pyx_t_26;
- PyObject *(*__pyx_t_27)(PyObject *);
+ int __pyx_t_26;
+ double __pyx_t_27;
int __pyx_t_28;
int __pyx_lineno = 0;
const char *__pyx_filename = NULL;
@@ -1967,7 +1963,7 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
* DTYPE_t beta, ll
* list parents, failures, successes
* n_roots, n_victims = len(root_victims), len(victims) # <<<<<<<<<<<<<<
- * n_nodes = 11270
+ * n_nodes = 100
* cdef:
*/
if (unlikely(__pyx_v_root_victims == Py_None)) {
@@ -1986,14 +1982,14 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
/* "ml.pyx":45
* list parents, failures, successes
* n_roots, n_victims = len(root_victims), len(victims)
- * n_nodes = 11270 # <<<<<<<<<<<<<<
+ * n_nodes = 100 # <<<<<<<<<<<<<<
* cdef:
* np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE)
*/
- __pyx_v_n_nodes = 11270;
+ __pyx_v_n_nodes = 100;
/* "ml.pyx":47
- * n_nodes = 11270
+ * n_nodes = 100
* cdef:
* np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE) # <<<<<<<<<<<<<<
* np.ndarray[DTYPE_t] probs_fail = np.zeros(n_victims, dtype=DTYPE)
@@ -2119,7 +2115,7 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
__pyx_t_9 = ((PyArrayObject *)__pyx_t_3);
{
__Pyx_BufFmt_StackElem __pyx_stack[1];
- if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_probs_nv.rcbuffer->pybuffer, (PyObject*)__pyx_t_9, &__Pyx_TypeInfo_nn___pyx_t_2ml_DTYPE_t, PyBUF_FORMAT| PyBUF_STRIDES, 1, 0, __pyx_stack) == -1)) {
+ if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_probs_nv.rcbuffer->pybuffer, (PyObject*)__pyx_t_9, &__Pyx_TypeInfo_nn___pyx_t_2ml_DTYPE_t, PyBUF_FORMAT| PyBUF_STRIDES| PyBUF_WRITABLE, 1, 0, __pyx_stack) == -1)) {
__pyx_v_probs_nv = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); __pyx_pybuffernd_probs_nv.rcbuffer->pybuffer.buf = NULL;
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 49; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
} else {__pyx_pybuffernd_probs_nv.diminfo[0].strides = __pyx_pybuffernd_probs_nv.rcbuffer->pybuffer.strides[0]; __pyx_pybuffernd_probs_nv.diminfo[0].shape = __pyx_pybuffernd_probs_nv.rcbuffer->pybuffer.shape[0];
@@ -2478,186 +2474,337 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
}
__Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
- /* "ml.pyx":76
+ /* "ml.pyx":65
+ *
+ * # 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
+ */
+ __pyx_t_10 = 0;
+ __pyx_t_1 = 0;
+ if (unlikely(__pyx_v_non_victims == Py_None)) {
+ PyErr_Format(PyExc_AttributeError, "'NoneType' object has no attribute '%s'", "itervalues");
+ {__pyx_filename = __pyx_f[0]; __pyx_lineno = 65; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ }
+ __pyx_t_5 = __Pyx_dict_iterator(__pyx_v_non_victims, 1, __pyx_n_s_itervalues, (&__pyx_t_2), (&__pyx_t_11)); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 65; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_t_5);
+ __Pyx_XDECREF(__pyx_t_3);
+ __pyx_t_3 = __pyx_t_5;
+ __pyx_t_5 = 0;
+ while (1) {
+ __pyx_t_12 = __Pyx_dict_iter_next(__pyx_t_3, __pyx_t_2, &__pyx_t_1, NULL, &__pyx_t_5, NULL, __pyx_t_11);
+ if (unlikely(__pyx_t_12 == 0)) break;
+ if (unlikely(__pyx_t_12 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 65; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_t_5);
+ if (!(likely(PyList_CheckExact(__pyx_t_5))||((__pyx_t_5) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "list", Py_TYPE(__pyx_t_5)->tp_name), 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 65; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_XDECREF_SET(__pyx_v_parents, ((PyObject*)__pyx_t_5));
+ __pyx_t_5 = 0;
+ __pyx_v_i = __pyx_t_10;
+ __pyx_t_10 = (__pyx_t_10 + 1);
+
+ /* "ml.pyx":68
+ * # 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]
+ * probs_nv[i] = sum(failures)
+ */
+ __pyx_t_5 = PyList_New(0); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 68; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_t_5);
+
+ /* "ml.pyx":69
+ * # parents fail to infect it
+ * failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3)
+ * for (dist, dt, w1, w2, w3) in parents] # <<<<<<<<<<<<<<
+ * probs_nv[i] = sum(failures)
+ *
+ */
+ if (unlikely(__pyx_v_parents == Py_None)) {
+ PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
+ {__pyx_filename = __pyx_f[0]; __pyx_lineno = 69; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ }
+ __pyx_t_4 = __pyx_v_parents; __Pyx_INCREF(__pyx_t_4); __pyx_t_13 = 0;
+ for (;;) {
+ if (__pyx_t_13 >= PyList_GET_SIZE(__pyx_t_4)) break;
+ #if CYTHON_COMPILING_IN_CPYTHON
+ __pyx_t_6 = PyList_GET_ITEM(__pyx_t_4, __pyx_t_13); __Pyx_INCREF(__pyx_t_6); __pyx_t_13++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 69; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ #else
+ __pyx_t_6 = PySequence_ITEM(__pyx_t_4, __pyx_t_13); __pyx_t_13++; if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 69; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ #endif
+ if ((likely(PyTuple_CheckExact(__pyx_t_6))) || (PyList_CheckExact(__pyx_t_6))) {
+ PyObject* sequence = __pyx_t_6;
+ #if CYTHON_COMPILING_IN_CPYTHON
+ Py_ssize_t size = Py_SIZE(sequence);
+ #else
+ Py_ssize_t size = PySequence_Size(sequence);
+ #endif
+ if (unlikely(size != 5)) {
+ if (size > 5) __Pyx_RaiseTooManyValuesError(5);
+ else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size);
+ {__pyx_filename = __pyx_f[0]; __pyx_lineno = 69; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ }
+ #if CYTHON_COMPILING_IN_CPYTHON
+ if (likely(PyTuple_CheckExact(sequence))) {
+ __pyx_t_14 = PyTuple_GET_ITEM(sequence, 0);
+ __pyx_t_15 = PyTuple_GET_ITEM(sequence, 1);
+ __pyx_t_16 = PyTuple_GET_ITEM(sequence, 2);
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+ __pyx_t_18 = PyTuple_GET_ITEM(sequence, 4);
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+ __pyx_t_14 = PyList_GET_ITEM(sequence, 0);
+ __pyx_t_15 = PyList_GET_ITEM(sequence, 1);
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+ __pyx_t_17 = PyList_GET_ITEM(sequence, 3);
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+ }
+ __Pyx_INCREF(__pyx_t_14);
+ __Pyx_INCREF(__pyx_t_15);
+ __Pyx_INCREF(__pyx_t_16);
+ __Pyx_INCREF(__pyx_t_17);
+ __Pyx_INCREF(__pyx_t_18);
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+ {
+ Py_ssize_t i;
+ PyObject** temps[5] = {&__pyx_t_14,&__pyx_t_15,&__pyx_t_16,&__pyx_t_17,&__pyx_t_18};
+ for (i=0; i < 5; i++) {
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+ __Pyx_GOTREF(item);
+ *(temps[i]) = item;
+ }
+ }
+ #endif
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+ __Pyx_GOTREF(__pyx_t_19);
+ __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
+ __pyx_t_20 = Py_TYPE(__pyx_t_19)->tp_iternext;
+ for (index=0; index < 5; index++) {
+ PyObject* item = __pyx_t_20(__pyx_t_19); if (unlikely(!item)) goto __pyx_L17_unpacking_failed;
+ __Pyx_GOTREF(item);
+ *(temps[index]) = item;
+ }
+ if (__Pyx_IternextUnpackEndCheck(__pyx_t_20(__pyx_t_19), 5) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 69; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_20 = NULL;
+ __Pyx_DECREF(__pyx_t_19); __pyx_t_19 = 0;
+ goto __pyx_L18_unpacking_done;
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+ __Pyx_DECREF(__pyx_t_19); __pyx_t_19 = 0;
+ __pyx_t_20 = NULL;
+ if (__Pyx_IterFinish() == 0) __Pyx_RaiseNeedMoreValuesError(index);
+ {__pyx_filename = __pyx_f[0]; __pyx_lineno = 69; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_L18_unpacking_done:;
+ }
+ __pyx_t_12 = __Pyx_PyInt_As_int(__pyx_t_14); if (unlikely((__pyx_t_12 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 69; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_DECREF(__pyx_t_14); __pyx_t_14 = 0;
+ __pyx_t_26 = __Pyx_PyInt_As_int(__pyx_t_15); if (unlikely((__pyx_t_26 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 69; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_DECREF(__pyx_t_15); __pyx_t_15 = 0;
+ __pyx_v_dist = __pyx_t_12;
+ __pyx_v_dt = __pyx_t_26;
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+ __pyx_t_16 = 0;
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+ __pyx_t_17 = 0;
+ __Pyx_XDECREF_SET(__pyx_v_w3, __pyx_t_18);
+ __pyx_t_18 = 0;
+
+ /* "ml.pyx":68
+ * # 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]
+ * probs_nv[i] = sum(failures)
+ */
+ __pyx_t_22 = __pyx_PyFloat_AsDouble(__pyx_v_w1); if (unlikely((__pyx_t_22 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 68; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_23 = __pyx_PyFloat_AsDouble(__pyx_v_w2); if (unlikely((__pyx_t_23 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 68; __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 = 68; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_6 = PyFloat_FromDouble(__pyx_f_2ml_weight_failure(__pyx_v_dist, __pyx_v_dt, __pyx_v_alpha, __pyx_v_delta, __pyx_t_22, __pyx_t_23, __pyx_t_24)); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 68; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_t_6);
+ if (unlikely(__Pyx_ListComp_Append(__pyx_t_5, (PyObject*)__pyx_t_6))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 68; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
+
+ /* "ml.pyx":69
+ * # parents fail to infect it
+ * failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3)
+ * for (dist, dt, w1, w2, w3) in parents] # <<<<<<<<<<<<<<
+ * probs_nv[i] = sum(failures)
+ *
+ */
+ }
+ __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+ __Pyx_XGOTREF(__pyx_cur_scope->__pyx_v_failures);
+ __Pyx_XDECREF_SET(__pyx_cur_scope->__pyx_v_failures, ((PyObject*)__pyx_t_5));
+ __Pyx_GIVEREF(__pyx_t_5);
+ __pyx_t_5 = 0;
+
+ /* "ml.pyx":70
+ * failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3)
+ * for (dist, dt, w1, w2, w3) in parents]
+ * probs_nv[i] = sum(failures) # <<<<<<<<<<<<<<
+ *
+ * # print successes
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*/
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}
}
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__Pyx_XDECREF_SET(__pyx_v_beta_add, __pyx_t_4);
__pyx_t_4 = 0;
- /* "ml.pyx":90
+ /* "ml.pyx":94
* beta_add = (probs[probs>=thresh]).sum()
* # add probability for the seeds and non-seeds
- * beta_add += roots * log(beta) + (n_nodes-roots) * log(1 - beta) # <<<<<<<<<<<<<<
+ * beta_add += roots * log(beta) + (n_nodes-roots) * log(1. - beta) # <<<<<<<<<<<<<<
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* if beta_add > max_beta_add:
*/
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+ __pyx_t_6 = PyNumber_InPlaceAdd(__pyx_v_beta_add, __pyx_t_4); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 94; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__pyx_t_6 = 0;
- /* "ml.pyx":92
- * beta_add += roots * log(beta) + (n_nodes-roots) * log(1 - beta)
+ /* "ml.pyx":96
+ * beta_add += roots * log(beta) + (n_nodes-roots) * log(1. - beta)
*
* if beta_add > max_beta_add: # <<<<<<<<<<<<<<
* max_beta = beta
* max_roots = roots
*/
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__Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
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- /* "ml.pyx":93
+ /* "ml.pyx":97
*
* if beta_add > max_beta_add:
* max_beta = beta # <<<<<<<<<<<<<<
* max_roots = roots
* max_beta_add = beta_add
*/
- __pyx_t_6 = PyFloat_FromDouble(__pyx_v_beta); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 93; __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 = 97; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_XDECREF_SET(__pyx_v_max_beta, __pyx_t_6);
__pyx_t_6 = 0;
- /* "ml.pyx":94
+ /* "ml.pyx":98
* if beta_add > max_beta_add:
* max_beta = beta
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@@ -2729,7 +2876,7 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
*/
__pyx_v_max_roots = __pyx_v_roots;
- /* "ml.pyx":95
+ /* "ml.pyx":99
* max_beta = beta
* max_roots = roots
* max_beta_add = beta_add # <<<<<<<<<<<<<<
@@ -2738,37 +2885,37 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
*/
__Pyx_INCREF(__pyx_v_beta_add);
__Pyx_DECREF_SET(__pyx_v_max_beta_add, __pyx_v_beta_add);
- goto __pyx_L15;
+ goto __pyx_L21;
}
- __pyx_L15:;
+ __pyx_L21:;
- /* "ml.pyx":82
+ /* "ml.pyx":86
* max_beta_add = float('-inf')
* # iterate over all victim nodes to find the optimal threshold
- * for beta in np.arange(0.001, .1, .001): # <<<<<<<<<<<<<<
+ * for beta in [0.13]:#np.arange(0.001, .2, .002): # <<<<<<<<<<<<<<
* thresh = log(beta/(1.-beta))
* # print 'beta:', beta, 'thresh:', thresh, 'infected:', len(probs[probs>=thresh])
*/
}
__Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
- /* "ml.pyx":98
+ /* "ml.pyx":102
* # print 'beta:', max_beta, 'add:', max_beta_add, 'roots:', max_roots
*
* ll += max_beta_add # <<<<<<<<<<<<<<
* roots = max_roots
* beta = max_beta
*/
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+ __pyx_t_5 = PyFloat_FromDouble(__pyx_v_ll); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 102; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
- __pyx_v_ll = __pyx_t_22;
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- /* "ml.pyx":99
+ /* "ml.pyx":103
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* ll += max_beta_add
* roots = max_roots # <<<<<<<<<<<<<<
@@ -2777,30 +2924,30 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
*/
__pyx_v_roots = __pyx_v_max_roots;
- /* "ml.pyx":100
+ /* "ml.pyx":104
* ll += max_beta_add
* roots = max_roots
* beta = max_beta # <<<<<<<<<<<<<<
* # print n_nodes, n_roots, n_victims, max_i, roots
* return (beta, roots, ll)
*/
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- __pyx_t_22 = __pyx_PyFloat_AsDouble(__pyx_v_max_beta); if (unlikely((__pyx_t_22 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 100; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_v_beta = __pyx_t_22;
+ if (unlikely(!__pyx_v_max_beta)) { __Pyx_RaiseUnboundLocalError("max_beta"); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 104; __pyx_clineno = __LINE__; goto __pyx_L1_error;} }
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+ __pyx_v_beta = __pyx_t_24;
- /* "ml.pyx":102
+ /* "ml.pyx":106
* beta = max_beta
* # print n_nodes, n_roots, n_victims, max_i, roots
* return (beta, roots, ll) # <<<<<<<<<<<<<<
*/
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- __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 = 106; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- __pyx_t_5 = __Pyx_PyInt_From_int(__pyx_v_roots); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 102; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_5 = __Pyx_PyInt_From_int(__pyx_v_roots); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 106; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- __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 = 106; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_4);
- __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 102; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 106; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_3);
PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_6);
__Pyx_GIVEREF(__pyx_t_6);
@@ -5127,7 +5274,6 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
{&__pyx_n_s_ValueError, __pyx_k_ValueError, sizeof(__pyx_k_ValueError), 0, 0, 1, 1},
{&__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},
@@ -5202,14 +5348,14 @@ static int __Pyx_InitCachedConstants(void) {
__Pyx_RefNannyDeclarations
__Pyx_RefNannySetupContext("__Pyx_InitCachedConstants", 0);
- /* "ml.pyx":82
+ /* "ml.pyx":86
* max_beta_add = float('-inf')
* # iterate over all victim nodes to find the optimal threshold
- * for beta in np.arange(0.001, .1, .001): # <<<<<<<<<<<<<<
+ * for beta in [0.13]:#np.arange(0.001, .2, .002): # <<<<<<<<<<<<<<
* thresh = log(beta/(1.-beta))
* # print 'beta:', beta, 'thresh:', thresh, 'infected:', len(probs[probs>=thresh])
*/
- __pyx_tuple_ = PyTuple_Pack(3, __pyx_float_0_001, __pyx_float__1, __pyx_float__001); if (unlikely(!__pyx_tuple_)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 82; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple_ = PyTuple_Pack(1, __pyx_float_0_13); if (unlikely(!__pyx_tuple_)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 86; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple_);
__Pyx_GIVEREF(__pyx_tuple_);
@@ -5299,9 +5445,7 @@ 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__001 = PyFloat_FromDouble(.001); if (unlikely(!__pyx_float__001)) {__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_0_13 = PyFloat_FromDouble(0.13); if (unlikely(!__pyx_float_0_13)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
return 0;
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return -1;
diff --git a/experiments/ml.pyx b/experiments/ml.pyx
index 67b561c..e1ce4cf 100644
--- a/experiments/ml.pyx
+++ b/experiments/ml.pyx
@@ -15,8 +15,8 @@ cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,
cdef DTYPE_t structural, temporal, result
structural = delta ** dist
# structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta)
- # temporal = exp(-alpha*dt) * (exp(alpha)-1)
- temporal = 1 - exp(-alpha*dt)
+ temporal = exp(-alpha*dt) * (exp(alpha)-1.)
+ # 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 = log(structural * temporal)
@@ -42,7 +42,7 @@ def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
DTYPE_t beta, ll
list parents, failures, successes
n_roots, n_victims = len(root_victims), len(victims)
- n_nodes = 11270
+ n_nodes = 100
cdef:
np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE)
np.ndarray[DTYPE_t] probs_fail = np.zeros(n_victims, dtype=DTYPE)
@@ -62,12 +62,16 @@ def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
probs[i] = max(s - failures[l] for l, s in enumerate(successes))
# 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]
- # probs_nv[i] = sum(failures)
+ 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]
+ probs_nv[i] = sum(failures)
+
+ # print successes
+ # print failures
+ # print probs
# calculate log likelihood
# probs.sort(); probs = probs[::-1] # sort probs in descending order
@@ -79,7 +83,7 @@ def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
# 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, .1, .001):
+ for beta in [0.13]:#np.arange(0.001, .2, .002):
thresh = log(beta/(1.-beta))
# print 'beta:', beta, 'thresh:', thresh, 'infected:', len(probs[probs>=thresh])
roots = n_roots + len(probs[probs<thresh])
@@ -87,7 +91,7 @@ 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[probs>=thresh]).sum()
# add probability for the seeds and non-seeds
- beta_add += roots * log(beta) + (n_nodes-roots) * log(1 - beta)
+ beta_add += roots * log(beta) + (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 df7f087..da6fb1f 100755
--- a/experiments/ml.so
+++ b/experiments/ml.so
Binary files differ
diff --git a/experiments/out.log b/experiments/out.log
index 7a1f0ab..6b67669 100644
--- a/experiments/out.log
+++ b/experiments/out.log
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+0.794328234724 0.156270697655 0.13 20 -98.0706945468
+0.794328234724 0.175751062485 0.13 20 -107.314812599
+0.794328234724 0.19765980717 0.13 20 -118.850659032
+0.794328234724 0.222299648253 0.13 20 -133.366402969
+0.794328234724 0.250011038262 0.13 15 -151.489327103
+0.794328234724 0.281176869797 0.13 15 -174.415599288
+0.794328234724 0.316227766017 0.13 15 -204.283938855
diff --git a/experiments/process.py b/experiments/process.py
index b5e0d6d..ff722c8 100644
--- a/experiments/process.py
+++ b/experiments/process.py
@@ -11,9 +11,10 @@ if __name__ == "__main__":
sys.exit("usage: {0} <file>".format(sys.argv[0]))
root_victims, victims, non_victims, age = load(open(sys.argv[1]))
- # alphas = 1. / np.arange(1000., 10000., 50.) # parameter of the time component
- alphas = np.logspace(-2,2,num=5)
- deltas = np.logspace(-10,-1,num=10)#np.arange(0.000001, 0.005, 0.001) # parameter of the structural component
+ # alphas = 1. / np.arange(1., 2000., 30.) # parameter of the time component
+ alphas = np.logspace(-3,-.1,num=50)
+ # deltas = np.arange(0.005, 0.5, 0.005) # parameter of the structural component
+ deltas = np.logspace(-3,-.5,num=50)
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)
@@ -21,7 +22,7 @@ if __name__ == "__main__":
fh.write("\t".join(map(str, [alpha, delta, beta, roots, ll])) + "\n")
fh.flush()
- # alpha = 20000.
+ # alpha = 1/10.
# delta = .5
# beta, roots, ll = ml(root_victims, victims, non_victims, age, alpha, delta)
# print "\t".join(map(str, [1./alpha, delta, beta, roots, ll])) \ No newline at end of file
generated by cgit v1.2.3-70-g09d2 (git 2.52.0) at 2025-12-21 13:22:54 +0000