tried generating cascades and recovering parameters, still got the same

result where optimization minimizes alpha, delta as much as possible

7 files changed, 2829 insertions, 228 deletions

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
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;}
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@@ -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;
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+    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;}
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+    __pyx_t_5 = 0;
+    __pyx_v_i = __pyx_t_10;
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+ *         # for each non victim node, compute the probability that all its
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+ *         failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3)             # <<<<<<<<<<<<<<
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+ *         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;}
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+    /* "ml.pyx":69
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+ *                     for (dist, dt, w1, w2, w3) in parents]             # <<<<<<<<<<<<<<
+ *         probs_nv[i] = sum(failures)
+ * 
+ */
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+      PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 69; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    }
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  *     # iterate over all victim nodes to find the optimal threshold
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  *     # iterate over all victim nodes to find the optimal threshold
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  *         # print 'beta:', beta, 'thresh:', thresh, 'infected:', len(probs[probs>=thresh])
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-    __pyx_t_6 = PyObject_RichCompare(((PyObject *)__pyx_v_probs), __pyx_t_3, Py_GE); __Pyx_XGOTREF(__pyx_t_6); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 88; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_6 = PyObject_RichCompare(((PyObject *)__pyx_v_probs), __pyx_t_3, Py_GE); __Pyx_XGOTREF(__pyx_t_6); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 92; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    __pyx_t_3 = PyObject_GetItem(((PyObject *)__pyx_v_probs), __pyx_t_6); if (unlikely(__pyx_t_3 == NULL)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 92; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
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     __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
-    __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_sum); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 88; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_sum); 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_DECREF(__pyx_t_3); __pyx_t_3 = 0;
     __pyx_t_3 = NULL;
@@ -2671,56 +2818,56 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
       }
     }
     if (__pyx_t_3) {
-      __pyx_t_4 = __Pyx_PyObject_CallOneArg(__pyx_t_6, __pyx_t_3); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 88; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_4 = __Pyx_PyObject_CallOneArg(__pyx_t_6, __pyx_t_3); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 92; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
     } else {
-      __pyx_t_4 = __Pyx_PyObject_CallNoArg(__pyx_t_6); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 88; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_4 = __Pyx_PyObject_CallNoArg(__pyx_t_6); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 92; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     }
     __Pyx_GOTREF(__pyx_t_4);
     __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
     __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)             # <<<<<<<<<<<<<<
  * 
  *         if beta_add > max_beta_add:
  */
-    __pyx_t_4 = PyFloat_FromDouble(((__pyx_v_roots * log(__pyx_v_beta)) + ((__pyx_v_n_nodes - __pyx_v_roots) * log((1.0 - __pyx_v_beta))))); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 90; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_4 = PyFloat_FromDouble(((__pyx_v_roots * log(__pyx_v_beta)) + ((__pyx_v_n_nodes - __pyx_v_roots) * log((1. - __pyx_v_beta))))); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 94; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __Pyx_GOTREF(__pyx_t_4);
-    __pyx_t_6 = PyNumber_InPlaceAdd(__pyx_v_beta_add, __pyx_t_4); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 90; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __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;}
     __Pyx_GOTREF(__pyx_t_6);
     __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
     __Pyx_DECREF_SET(__pyx_v_beta_add, __pyx_t_6);
     __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
  */
-    __pyx_t_6 = PyObject_RichCompare(__pyx_v_beta_add, __pyx_v_max_beta_add, Py_GT); __Pyx_XGOTREF(__pyx_t_6); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 92; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __pyx_t_28 = __Pyx_PyObject_IsTrue(__pyx_t_6); if (unlikely(__pyx_t_28 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 92; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_6 = PyObject_RichCompare(__pyx_v_beta_add, __pyx_v_max_beta_add, Py_GT); __Pyx_XGOTREF(__pyx_t_6); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 96; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_28 = __Pyx_PyObject_IsTrue(__pyx_t_6); if (unlikely(__pyx_t_28 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 96; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
     if (__pyx_t_28) {
 
-      /* "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;}
       __Pyx_GOTREF(__pyx_t_6);
       __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
  *             max_roots = roots             # <<<<<<<<<<<<<<
@@ -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
  */
-  __pyx_t_5 = PyFloat_FromDouble(__pyx_v_ll); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 98; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __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;}
   __Pyx_GOTREF(__pyx_t_5);
-  __pyx_t_6 = PyNumber_InPlaceAdd(__pyx_t_5, __pyx_v_max_beta_add); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 98; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_6 = PyNumber_InPlaceAdd(__pyx_t_5, __pyx_v_max_beta_add); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 102; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_6);
   __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
-  __pyx_t_22 = __pyx_PyFloat_AsDouble(__pyx_t_6); if (unlikely((__pyx_t_22 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 98; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_24 = __pyx_PyFloat_AsDouble(__pyx_t_6); if (unlikely((__pyx_t_24 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 102; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
-  __pyx_v_ll = __pyx_t_22;
+  __pyx_v_ll = __pyx_t_24;
 
-  /* "ml.pyx":99
+  /* "ml.pyx":103
  * 
  *     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)
  */
-  if (unlikely(!__pyx_v_max_beta)) { __Pyx_RaiseUnboundLocalError("max_beta"); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 100; __pyx_clineno = __LINE__; goto __pyx_L1_error;} }
-  __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;} }
+  __pyx_t_24 = __pyx_PyFloat_AsDouble(__pyx_v_max_beta); if (unlikely((__pyx_t_24 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 104; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __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)             # <<<<<<<<<<<<<<
  */
   __Pyx_XDECREF(__pyx_r);
-  __pyx_t_6 = PyFloat_FromDouble(__pyx_v_beta); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 102; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_6 = PyFloat_FromDouble(__pyx_v_beta); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 106; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_6);
-  __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;}
   __Pyx_GOTREF(__pyx_t_5);
-  __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;
   __pyx_L1_error:;
   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
diff --git a/experiments/out.log b/experiments/out.log
index 7a1f0ab..6b67669 100644
--- a/experiments/out.log
+++ b/experiments/out.log
@@ -1,50 +1,2500 @@
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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