testing new model, realizing we may be getting optimal beta wrong

1 files changed, 275 insertions, 103 deletions

diff --git a/experiments/ml.c b/experiments/ml.c
index 3c6c463..976accb 100644
--- a/experiments/ml.c
+++ b/experiments/ml.c
@@ -1287,7 +1287,7 @@ static char __pyx_k_d[] = "d";
 static char __pyx_k_f[] = "f";
 static char __pyx_k_g[] = "g";
 static char __pyx_k_h[] = "h";
-static char __pyx_k_i[] = "i";
+static char __pyx_k_i[] = "i:";
 static char __pyx_k_l[] = "l";
 static char __pyx_k_q[] = "q";
 static char __pyx_k_t[] = "t";
@@ -1298,11 +1298,14 @@ static char __pyx_k_dt[] = "dt";
 static char __pyx_k_ll[] = "ll";
 static char __pyx_k_ml[] = "ml";
 static char __pyx_k_np[] = "np";
+static char __pyx_k_st[] = "st";
 static char __pyx_k_w1[] = "w1";
 static char __pyx_k_w2[] = "w2";
 static char __pyx_k_w3[] = "w3";
+static char __pyx_k_add[] = "add:";
 static char __pyx_k_age[] = "age";
 static char __pyx_k_end[] = "end";
+static char __pyx_k_i_2[] = "i";
 static char __pyx_k_inf[] = "-inf";
 static char __pyx_k_max[] = "max";
 static char __pyx_k_sum[] = "sum";
@@ -1312,6 +1315,7 @@ static char __pyx_k_dist[] = "dist";
 static char __pyx_k_file[] = "file";
 static char __pyx_k_main[] = "__main__";
 static char __pyx_k_send[] = "send";
+static char __pyx_k_stnv[] = "stnv";
 static char __pyx_k_test[] = "__test__";
 static char __pyx_k_DTYPE[] = "DTYPE";
 static char __pyx_k_alpha[] = "alpha";
@@ -1324,21 +1328,23 @@ static char __pyx_k_numpy[] = "numpy";
 static char __pyx_k_print[] = "print";
 static char __pyx_k_probs[] = "probs";
 static char __pyx_k_range[] = "range";
-static char __pyx_k_roots[] = "roots";
+static char __pyx_k_roots[] = "roots:";
 static char __pyx_k_throw[] = "throw";
 static char __pyx_k_zeros[] = "zeros";
 static char __pyx_k_import[] = "__import__";
-static char __pyx_k_thresh[] = "thresh";
+static char __pyx_k_thresh[] = "thresh:";
 static char __pyx_k_xrange[] = "xrange";
 static char __pyx_k_float64[] = "float64";
 static char __pyx_k_genexpr[] = "genexpr";
 static char __pyx_k_n_nodes[] = "n_nodes";
 static char __pyx_k_n_roots[] = "n_roots";
 static char __pyx_k_parents[] = "parents";
+static char __pyx_k_roots_2[] = "roots";
 static char __pyx_k_victims[] = "victims";
 static char __pyx_k_beta_add[] = "beta_add";
 static char __pyx_k_failures[] = "failures";
 static char __pyx_k_probs_nv[] = "probs_nv";
+static char __pyx_k_thresh_2[] = "thresh";
 static char __pyx_k_enumerate[] = "enumerate";
 static char __pyx_k_n_victims[] = "n_victims";
 static char __pyx_k_successes[] = "successes";
@@ -1351,6 +1357,7 @@ static char __pyx_k_max_beta_add[] = "max_beta_add";
 static char __pyx_k_root_victims[] = "root_victims";
 static char __pyx_k_UNDERFLOW_ERROR[] = "UNDERFLOW ERROR";
 static char __pyx_k_ml_locals_genexpr[] = "ml.<locals>.genexpr";
+static char __pyx_k_len_probs_probs_thresh[] = "len(probs[probs>=thresh]):";
 static char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous";
 static char __pyx_k_Users_ben_Documents_Cascade_Pro[] = "/Users/ben/Documents/Cascade Project/criminal_cascades/experiments/ml.pyx";
 static char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)";
@@ -1366,6 +1373,7 @@ static PyObject *__pyx_n_s_RuntimeError;
 static PyObject *__pyx_kp_s_UNDERFLOW_ERROR;
 static PyObject *__pyx_kp_s_Users_ben_Documents_Cascade_Pro;
 static PyObject *__pyx_n_s_ValueError;
+static PyObject *__pyx_kp_s_add;
 static PyObject *__pyx_n_s_age;
 static PyObject *__pyx_n_s_alpha;
 static PyObject *__pyx_n_s_args;
@@ -1383,11 +1391,13 @@ static PyObject *__pyx_n_s_failures;
 static PyObject *__pyx_n_s_file;
 static PyObject *__pyx_n_s_float64;
 static PyObject *__pyx_n_s_genexpr;
-static PyObject *__pyx_n_s_i;
+static PyObject *__pyx_kp_s_i;
+static PyObject *__pyx_n_s_i_2;
 static PyObject *__pyx_n_s_import;
 static PyObject *__pyx_kp_s_inf;
 static PyObject *__pyx_n_s_itervalues;
 static PyObject *__pyx_n_s_l;
+static PyObject *__pyx_kp_s_len_probs_probs_thresh;
 static PyObject *__pyx_n_s_ll;
 static PyObject *__pyx_n_s_main;
 static PyObject *__pyx_n_s_max;
@@ -1410,13 +1420,17 @@ static PyObject *__pyx_n_s_probs_fail;
 static PyObject *__pyx_n_s_probs_nv;
 static PyObject *__pyx_n_s_range;
 static PyObject *__pyx_n_s_root_victims;
-static PyObject *__pyx_n_s_roots;
+static PyObject *__pyx_kp_s_roots;
+static PyObject *__pyx_n_s_roots_2;
 static PyObject *__pyx_n_s_send;
+static PyObject *__pyx_n_s_st;
+static PyObject *__pyx_n_s_stnv;
 static PyObject *__pyx_n_s_successes;
 static PyObject *__pyx_n_s_sum;
 static PyObject *__pyx_n_s_t;
 static PyObject *__pyx_n_s_test;
-static PyObject *__pyx_n_s_thresh;
+static PyObject *__pyx_kp_s_thresh;
+static PyObject *__pyx_n_s_thresh_2;
 static PyObject *__pyx_n_s_throw;
 static PyObject *__pyx_kp_u_unknown_dtype_code_in_numpy_pxd;
 static PyObject *__pyx_n_s_victims;
@@ -1487,6 +1501,9 @@ static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_success(int __pyx_v_dist, int __py
   __pyx_t_2ml_DTYPE_t __pyx_r;
   __Pyx_RefNannyDeclarations
   int __pyx_t_1;
+  PyObject *__pyx_t_2 = NULL;
+  PyObject *__pyx_t_3 = NULL;
+  PyObject *__pyx_t_4 = NULL;
   int __pyx_lineno = 0;
   const char *__pyx_filename = NULL;
   int __pyx_clineno = 0;
@@ -1528,14 +1545,39 @@ static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_success(int __pyx_v_dist, int __py
  *     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)             # <<<<<<<<<<<<<<
- *     # print 'st', structural, temporal
+ *     print 'st', structural, temporal
  *     return result
  */
   __pyx_v_result = log((__pyx_v_structural * __pyx_v_temporal));
 
+  /* "ml.pyx":22
+ *     # temporal = 1. / (1. + (dt - 1.)/alpha)**0.01 - 1. / (1. + dt/alpha)**0.01
+ *     result = log(structural * temporal)
+ *     print 'st', structural, temporal             # <<<<<<<<<<<<<<
+ *     return result
+ * 
+ */
+  __pyx_t_2 = PyFloat_FromDouble(__pyx_v_structural); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 22; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_t_3 = PyFloat_FromDouble(__pyx_v_temporal); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 22; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_3);
+  __pyx_t_4 = PyTuple_New(3); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 22; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_4);
+  __Pyx_INCREF(__pyx_n_s_st);
+  PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_n_s_st);
+  __Pyx_GIVEREF(__pyx_n_s_st);
+  PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_t_2);
+  __Pyx_GIVEREF(__pyx_t_2);
+  PyTuple_SET_ITEM(__pyx_t_4, 2, __pyx_t_3);
+  __Pyx_GIVEREF(__pyx_t_3);
+  __pyx_t_2 = 0;
+  __pyx_t_3 = 0;
+  if (__Pyx_Print(0, __pyx_t_4, 1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 22; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+
   /* "ml.pyx":23
  *     result = log(structural * temporal)
- *     # print 'st', structural, temporal
+ *     print 'st', structural, temporal
  *     return result             # <<<<<<<<<<<<<<
  * 
  * cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,
@@ -1553,6 +1595,9 @@ static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_success(int __pyx_v_dist, int __py
 
   /* function exit code */
   __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_XDECREF(__pyx_t_4);
   __Pyx_WriteUnraisable("ml.weight_success", __pyx_clineno, __pyx_lineno, __pyx_filename, 0);
   __pyx_r = 0;
   __pyx_L0:;
@@ -1574,6 +1619,12 @@ static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_failure(int __pyx_v_dist, int __py
   __pyx_t_2ml_DTYPE_t __pyx_v_result;
   __pyx_t_2ml_DTYPE_t __pyx_r;
   __Pyx_RefNannyDeclarations
+  PyObject *__pyx_t_1 = NULL;
+  PyObject *__pyx_t_2 = NULL;
+  PyObject *__pyx_t_3 = NULL;
+  int __pyx_lineno = 0;
+  const char *__pyx_filename = NULL;
+  int __pyx_clineno = 0;
   __Pyx_RefNannySetupContext("weight_failure", 0);
 
   /* "ml.pyx":29
@@ -1598,14 +1649,39 @@ static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_failure(int __pyx_v_dist, int __py
  *     temporal = exp(-alpha * dt)
  *     # temporal = 1.  - 1. / (1. + dt/alpha)**0.01
  *     result = log(1. - structural + structural * temporal)             # <<<<<<<<<<<<<<
- *     # print 'stnv', structural, temporal
+ *     print 'stnv', structural, temporal
  *     return result
  */
   __pyx_v_result = log(((1. - __pyx_v_structural) + (__pyx_v_structural * __pyx_v_temporal)));
 
+  /* "ml.pyx":34
+ *     # temporal = 1.  - 1. / (1. + dt/alpha)**0.01
+ *     result = log(1. - structural + structural * temporal)
+ *     print 'stnv', structural, temporal             # <<<<<<<<<<<<<<
+ *     return result
+ * 
+ */
+  __pyx_t_1 = PyFloat_FromDouble(__pyx_v_structural); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 34; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_1);
+  __pyx_t_2 = PyFloat_FromDouble(__pyx_v_temporal); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 34; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 34; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_3);
+  __Pyx_INCREF(__pyx_n_s_stnv);
+  PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_n_s_stnv);
+  __Pyx_GIVEREF(__pyx_n_s_stnv);
+  PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_1);
+  __Pyx_GIVEREF(__pyx_t_1);
+  PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_t_2);
+  __Pyx_GIVEREF(__pyx_t_2);
+  __pyx_t_1 = 0;
+  __pyx_t_2 = 0;
+  if (__Pyx_Print(0, __pyx_t_3, 1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 34; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+
   /* "ml.pyx":35
  *     result = log(1. - structural + structural * temporal)
- *     # print 'stnv', structural, temporal
+ *     print 'stnv', structural, temporal
  *     return result             # <<<<<<<<<<<<<<
  * 
  * def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
@@ -1622,6 +1698,12 @@ static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_failure(int __pyx_v_dist, int __py
  */
 
   /* function exit code */
+  __pyx_L1_error:;
+  __Pyx_XDECREF(__pyx_t_1);
+  __Pyx_XDECREF(__pyx_t_2);
+  __Pyx_XDECREF(__pyx_t_3);
+  __Pyx_WriteUnraisable("ml.weight_failure", __pyx_clineno, __pyx_lineno, __pyx_filename, 0);
+  __pyx_r = 0;
   __pyx_L0:;
   __Pyx_RefNannyFinishContext();
   return __pyx_r;
@@ -1963,7 +2045,7 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
  *         DTYPE_t beta, ll, beta2
  *         list parents, failures, successes
  *     n_roots, n_victims = len(root_victims), len(victims)             # <<<<<<<<<<<<<<
- *     n_nodes = 148152
+ *     n_nodes = 4#148152
  *     cdef:
  */
   if (unlikely(__pyx_v_root_victims == Py_None)) {
@@ -1982,14 +2064,14 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
   /* "ml.pyx":44
  *         list parents, failures, successes
  *     n_roots, n_victims = len(root_victims), len(victims)
- *     n_nodes = 148152             # <<<<<<<<<<<<<<
+ *     n_nodes = 4#148152             # <<<<<<<<<<<<<<
  *     cdef:
  *         np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE)
  */
-  __pyx_v_n_nodes = 148152;
+  __pyx_v_n_nodes = 4;
 
   /* "ml.pyx":46
- *     n_nodes = 148152
+ *     n_nodes = 4#148152
  *     cdef:
  *         np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE)             # <<<<<<<<<<<<<<
  *         np.ndarray[DTYPE_t] probs_fail = np.zeros(n_victims, dtype=DTYPE)
@@ -2776,8 +2858,8 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
  *     max_beta_add = float('-inf')
  *     # iterate over all victim nodes to find the optimal threshold
  *     for i in xrange(0, n_victims+1, 1):             # <<<<<<<<<<<<<<
+ *         print
  *         roots = n_roots + n_victims - i
- *         beta = float(roots)/float(n_nodes)
  */
   __pyx_t_28 = (__pyx_v_n_victims + 1);
   for (__pyx_t_10 = 0; __pyx_t_10 < __pyx_t_28; __pyx_t_10+=1) {
@@ -2786,208 +2868,291 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
     /* "ml.pyx":83
  *     # iterate over all victim nodes to find the optimal threshold
  *     for i in xrange(0, n_victims+1, 1):
+ *         print             # <<<<<<<<<<<<<<
+ *         roots = n_roots + n_victims - i
+ *         beta = 1. / (1. + exp(-probs[i]))
+ */
+    if (__Pyx_Print(0, __pyx_empty_tuple, 1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 83; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+
+    /* "ml.pyx":84
+ *     for i in xrange(0, n_victims+1, 1):
+ *         print
  *         roots = n_roots + n_victims - i             # <<<<<<<<<<<<<<
- *         beta = float(roots)/float(n_nodes)
+ *         beta = 1. / (1. + exp(-probs[i]))
  *         thresh = log(beta/(1.-beta))
  */
     __pyx_v_roots = ((__pyx_v_n_roots + __pyx_v_n_victims) - __pyx_v_i);
 
-    /* "ml.pyx":84
- *     for i in xrange(0, n_victims+1, 1):
+    /* "ml.pyx":85
+ *         print
  *         roots = n_roots + n_victims - i
- *         beta = float(roots)/float(n_nodes)             # <<<<<<<<<<<<<<
+ *         beta = 1. / (1. + exp(-probs[i]))             # <<<<<<<<<<<<<<
  *         thresh = log(beta/(1.-beta))
- *         # print 'thresh:', thresh
+ *         print 'thresh:', thresh
  */
-    __pyx_v_beta = (((double)__pyx_v_roots) / ((double)__pyx_v_n_nodes));
+    __pyx_t_11 = __pyx_v_i;
+    if (__pyx_t_11 < 0) __pyx_t_11 += __pyx_pybuffernd_probs.diminfo[0].shape;
+    __pyx_v_beta = (1. / (1. + exp((-(*__Pyx_BufPtrStrided1d(__pyx_t_2ml_DTYPE_t *, __pyx_pybuffernd_probs.rcbuffer->pybuffer.buf, __pyx_t_11, __pyx_pybuffernd_probs.diminfo[0].strides))))));
 
-    /* "ml.pyx":85
+    /* "ml.pyx":86
  *         roots = n_roots + n_victims - i
- *         beta = float(roots)/float(n_nodes)
+ *         beta = 1. / (1. + exp(-probs[i]))
  *         thresh = log(beta/(1.-beta))             # <<<<<<<<<<<<<<
- *         # print 'thresh:', thresh
+ *         print 'thresh:', thresh
  * 
  */
     __pyx_v_thresh = log((__pyx_v_beta / (1. - __pyx_v_beta)));
 
-    /* "ml.pyx":89
+    /* "ml.pyx":87
+ *         beta = 1. / (1. + exp(-probs[i]))
+ *         thresh = log(beta/(1.-beta))
+ *         print 'thresh:', thresh             # <<<<<<<<<<<<<<
  * 
  *         # add probability for realized edges and subtract probability these edges fail
- *         beta_add = (probs[probs>thresh]).sum()             # <<<<<<<<<<<<<<
- *         # print 'len(probs[probs>thresh]):', len(probs[probs>thresh])
- *         # add probability for the seeds and non-seeds
  */
-    __pyx_t_4 = PyFloat_FromDouble(__pyx_v_thresh); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 89; __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 = 87; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_5);
+    __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 87; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __Pyx_GOTREF(__pyx_t_4);
-    __pyx_t_3 = PyObject_RichCompare(((PyObject *)__pyx_v_probs), __pyx_t_4, Py_GT); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 89; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_INCREF(__pyx_kp_s_thresh);
+    PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_kp_s_thresh);
+    __Pyx_GIVEREF(__pyx_kp_s_thresh);
+    PyTuple_SET_ITEM(__pyx_t_4, 1, __pyx_t_5);
+    __Pyx_GIVEREF(__pyx_t_5);
+    __pyx_t_5 = 0;
+    if (__Pyx_Print(0, __pyx_t_4, 1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 87; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    __pyx_t_4 = PyObject_GetItem(((PyObject *)__pyx_v_probs), __pyx_t_3); if (unlikely(__pyx_t_4 == NULL)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 89; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
-    __Pyx_GOTREF(__pyx_t_4);
+
+    /* "ml.pyx":90
+ * 
+ *         # add probability for realized edges and subtract probability these edges fail
+ *         beta_add = (probs[probs>=thresh]).sum()             # <<<<<<<<<<<<<<
+ *         print 'len(probs[probs>=thresh]):', len(probs[probs>=thresh])
+ *         # add probability for the seeds and non-seeds
+ */
+    __pyx_t_5 = PyFloat_FromDouble(__pyx_v_thresh); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 90; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_5);
+    __pyx_t_3 = PyObject_RichCompare(((PyObject *)__pyx_v_probs), __pyx_t_5, Py_GE); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 90; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
+    __pyx_t_5 = PyObject_GetItem(((PyObject *)__pyx_v_probs), __pyx_t_3); if (unlikely(__pyx_t_5 == NULL)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 90; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
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+  __pyx_t_4 = __Pyx_PyInt_From_int(__pyx_v_roots); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 106; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_5 = PyFloat_FromDouble(__pyx_v_ll); 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);
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-  __Pyx_GIVEREF(__pyx_t_5);
-  PyTuple_SET_ITEM(__pyx_t_6, 2, __pyx_t_4);
+  PyTuple_SET_ITEM(__pyx_t_6, 1, __pyx_t_4);
   __Pyx_GIVEREF(__pyx_t_4);
+  PyTuple_SET_ITEM(__pyx_t_6, 2, __pyx_t_5);
+  __Pyx_GIVEREF(__pyx_t_5);
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-  __pyx_t_5 = 0;
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@@ -3583,7 +3748,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P
  *                 elif t == NPY_LONG:        f = "l"
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-      __pyx_v_f = __pyx_k_i;
+      __pyx_v_f = __pyx_k_i_2;
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   {&__pyx_n_s_ValueError, __pyx_k_ValueError, sizeof(__pyx_k_ValueError), 0, 0, 1, 1},
+  {&__pyx_kp_s_add, __pyx_k_add, sizeof(__pyx_k_add), 0, 0, 1, 0},
   {&__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},
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-  {&__pyx_n_s_i, __pyx_k_i, sizeof(__pyx_k_i), 0, 0, 1, 1},
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+  {&__pyx_n_s_i_2, __pyx_k_i_2, sizeof(__pyx_k_i_2), 0, 0, 1, 1},
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   {&__pyx_n_s_l, __pyx_k_l, sizeof(__pyx_k_l), 0, 0, 1, 1},
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-  {&__pyx_n_s_roots, __pyx_k_roots, sizeof(__pyx_k_roots), 0, 0, 1, 1},
+  {&__pyx_kp_s_roots, __pyx_k_roots, sizeof(__pyx_k_roots), 0, 0, 1, 0},
+  {&__pyx_n_s_roots_2, __pyx_k_roots_2, sizeof(__pyx_k_roots_2), 0, 0, 1, 1},
   {&__pyx_n_s_send, __pyx_k_send, sizeof(__pyx_k_send), 0, 0, 1, 1},
+  {&__pyx_n_s_st, __pyx_k_st, sizeof(__pyx_k_st), 0, 0, 1, 1},
+  {&__pyx_n_s_stnv, __pyx_k_stnv, sizeof(__pyx_k_stnv), 0, 0, 1, 1},
   {&__pyx_n_s_successes, __pyx_k_successes, sizeof(__pyx_k_successes), 0, 0, 1, 1},
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-  {&__pyx_n_s_thresh, __pyx_k_thresh, sizeof(__pyx_k_thresh), 0, 0, 1, 1},
+  {&__pyx_kp_s_thresh, __pyx_k_thresh, sizeof(__pyx_k_thresh), 0, 0, 1, 0},
+  {&__pyx_n_s_thresh_2, __pyx_k_thresh_2, sizeof(__pyx_k_thresh_2), 0, 0, 1, 1},
   {&__pyx_n_s_throw, __pyx_k_throw, sizeof(__pyx_k_throw), 0, 0, 1, 1},
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