just some minor tests with ml

1 files changed, 90 insertions, 90 deletions

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;}
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+  __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;}
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+  __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;}
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     __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;}
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   __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
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@@ -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
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     } 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);
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-    __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;}
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     __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)
  */
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+    __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;}
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+    __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;}
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     __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)
  * 
  */
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     __Pyx_XDECREF_SET(__pyx_v_non_seeds, __pyx_t_5);
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-    /* "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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+    __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 83; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "ml.pyx":86
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-    /* "ml.pyx":88
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  *         # add probability for the seeds and non-seeds
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+    __pyx_t_5 = PyObject_GetItem(((PyObject *)__pyx_v_probs), __pyx_v_non_seeds); if (unlikely(__pyx_t_5 == NULL)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 87; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
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-    /* "ml.pyx":92
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-      /* "ml.pyx":93
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-      /* "ml.pyx":94
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-      /* "ml.pyx":95
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-    /* "ml.pyx":80
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  *     for beta in np.arange(0.01, 1., .01):             # <<<<<<<<<<<<<<
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-  /* "ml.pyx":98
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-  /* "ml.pyx":99
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-  /* "ml.pyx":102
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