diff options
Diffstat (limited to 'experiments/ml.c')
| -rw-r--r-- | experiments/ml.c | 180 |
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;} __Pyx_GOTREF(__pyx_t_5); - __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_t_5, __pyx_n_s_arange); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_6 = __Pyx_PyObject_GetAttrStr(__pyx_t_5, __pyx_n_s_arange); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; - __pyx_t_5 = __Pyx_PyObject_Call(__pyx_t_6, __pyx_tuple_, NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = __Pyx_PyObject_Call(__pyx_t_6, __pyx_tuple_, NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; if (likely(PyList_CheckExact(__pyx_t_5)) || PyTuple_CheckExact(__pyx_t_5)) { __pyx_t_6 = __pyx_t_5; __Pyx_INCREF(__pyx_t_6); __pyx_t_2 = 0; __pyx_t_36 = NULL; } else { - __pyx_t_2 = -1; __pyx_t_6 = PyObject_GetIter(__pyx_t_5); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = -1; __pyx_t_6 = PyObject_GetIter(__pyx_t_5); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __pyx_t_36 = Py_TYPE(__pyx_t_6)->tp_iternext; if (unlikely(!__pyx_t_36)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_36 = Py_TYPE(__pyx_t_6)->tp_iternext; if (unlikely(!__pyx_t_36)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;} } __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; for (;;) { @@ -2929,16 +2929,16 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_ if (likely(PyList_CheckExact(__pyx_t_6))) { if (__pyx_t_2 >= PyList_GET_SIZE(__pyx_t_6)) break; #if CYTHON_COMPILING_IN_CPYTHON - __pyx_t_5 = PyList_GET_ITEM(__pyx_t_6, __pyx_t_2); __Pyx_INCREF(__pyx_t_5); __pyx_t_2++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyList_GET_ITEM(__pyx_t_6, __pyx_t_2); __Pyx_INCREF(__pyx_t_5); __pyx_t_2++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;} #else - __pyx_t_5 = PySequence_ITEM(__pyx_t_6, __pyx_t_2); __pyx_t_2++; if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PySequence_ITEM(__pyx_t_6, __pyx_t_2); __pyx_t_2++; if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;} #endif } else { if (__pyx_t_2 >= PyTuple_GET_SIZE(__pyx_t_6)) break; #if CYTHON_COMPILING_IN_CPYTHON - __pyx_t_5 = PyTuple_GET_ITEM(__pyx_t_6, __pyx_t_2); __Pyx_INCREF(__pyx_t_5); __pyx_t_2++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyTuple_GET_ITEM(__pyx_t_6, __pyx_t_2); __Pyx_INCREF(__pyx_t_5); __pyx_t_2++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;} #else - __pyx_t_5 = PySequence_ITEM(__pyx_t_6, __pyx_t_2); __pyx_t_2++; if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PySequence_ITEM(__pyx_t_6, __pyx_t_2); __pyx_t_2++; if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;} #endif } } else { @@ -2947,79 +2947,79 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_ PyObject* exc_type = PyErr_Occurred(); if (exc_type) { if (likely(exc_type == PyExc_StopIteration || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration))) PyErr_Clear(); - else {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + else {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;} } break; } __Pyx_GOTREF(__pyx_t_5); } - __pyx_t_27 = __pyx_PyFloat_AsDouble(__pyx_t_5); if (unlikely((__pyx_t_27 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_27 = __pyx_PyFloat_AsDouble(__pyx_t_5); if (unlikely((__pyx_t_27 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __pyx_v_beta = __pyx_t_27; - /* "ml.pyx":81 + /* "ml.pyx":80 * # iterate over all victim nodes to find the optimal threshold * for beta in np.arange(0.01, 1., .01): - * thresh = log(beta/(3012.*(1.-beta))) # <<<<<<<<<<<<<< + * thresh = log(beta/(1000.*(1.-beta))) # <<<<<<<<<<<<<< * seeds = probs<thresh * non_seeds = probs>=thresh */ - __pyx_v_thresh = log((__pyx_v_beta / (3012. * (1. - __pyx_v_beta)))); + __pyx_v_thresh = log((__pyx_v_beta / (1000. * (1. - __pyx_v_beta)))); - /* "ml.pyx":82 + /* "ml.pyx":81 * for beta in np.arange(0.01, 1., .01): - * thresh = log(beta/(3012.*(1.-beta))) + * thresh = log(beta/(1000.*(1.-beta))) * seeds = probs<thresh # <<<<<<<<<<<<<< * non_seeds = probs>=thresh * roots = n_roots + sum(seeds) */ - __pyx_t_5 = PyFloat_FromDouble(__pyx_v_thresh); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 82; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyFloat_FromDouble(__pyx_v_thresh); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 81; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); - __pyx_t_4 = PyObject_RichCompare(((PyObject *)__pyx_v_probs), __pyx_t_5, Py_LT); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 82; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyObject_RichCompare(((PyObject *)__pyx_v_probs), __pyx_t_5, Py_LT); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 81; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_XDECREF_SET(__pyx_v_seeds, __pyx_t_4); __pyx_t_4 = 0; - /* "ml.pyx":83 - * thresh = log(beta/(3012.*(1.-beta))) + /* "ml.pyx":82 + * thresh = log(beta/(1000.*(1.-beta))) * seeds = probs<thresh * non_seeds = probs>=thresh # <<<<<<<<<<<<<< * roots = n_roots + sum(seeds) * */ - __pyx_t_4 = PyFloat_FromDouble(__pyx_v_thresh); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 83; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyFloat_FromDouble(__pyx_v_thresh); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 82; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); - __pyx_t_5 = PyObject_RichCompare(((PyObject *)__pyx_v_probs), __pyx_t_4, Py_GE); __Pyx_XGOTREF(__pyx_t_5); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 83; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyObject_RichCompare(((PyObject *)__pyx_v_probs), __pyx_t_4, Py_GE); __Pyx_XGOTREF(__pyx_t_5); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 82; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_XDECREF_SET(__pyx_v_non_seeds, __pyx_t_5); __pyx_t_5 = 0; - /* "ml.pyx":84 + /* "ml.pyx":83 * seeds = probs<thresh * non_seeds = probs>=thresh * roots = n_roots + sum(seeds) # <<<<<<<<<<<<<< * * beta_add = 0. */ - __pyx_t_5 = __Pyx_PyInt_From_int(__pyx_v_n_roots); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 84; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = __Pyx_PyInt_From_int(__pyx_v_n_roots); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 83; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); - __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 84; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __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;} __Pyx_GOTREF(__pyx_t_4); __Pyx_INCREF(__pyx_v_seeds); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_v_seeds); __Pyx_GIVEREF(__pyx_v_seeds); - __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_sum, __pyx_t_4, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 84; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_sum, __pyx_t_4, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 83; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; - __pyx_t_4 = PyNumber_Add(__pyx_t_5, __pyx_t_3); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 84; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyNumber_Add(__pyx_t_5, __pyx_t_3); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 83; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __pyx_t_12 = __Pyx_PyInt_As_int(__pyx_t_4); if (unlikely((__pyx_t_12 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 84; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_12 = __Pyx_PyInt_As_int(__pyx_t_4); if (unlikely((__pyx_t_12 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 83; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __pyx_v_roots = __pyx_t_12; - /* "ml.pyx":86 + /* "ml.pyx":85 * roots = n_roots + sum(seeds) * * beta_add = 0. # <<<<<<<<<<<<<< @@ -3028,18 +3028,18 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_ */ __pyx_v_beta_add = 0.; - /* "ml.pyx":88 + /* "ml.pyx":87 * beta_add = 0. * # add probability for realized edges and subtract probability these edges fail * beta_add += (probs[non_seeds]).sum() # <<<<<<<<<<<<<< * # add probability for the seeds and non-seeds - * beta_add += roots * log(beta/3012.) + (n_nodes-roots) * log(1. - beta) + * beta_add += roots * log(beta/1000.) + (n_nodes-roots) * log(1. - beta) */ - __pyx_t_4 = PyFloat_FromDouble(__pyx_v_beta_add); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 88; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyFloat_FromDouble(__pyx_v_beta_add); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 87; 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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 = 101; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __pyx_t_20 = __Pyx_PyInt_From_int(__pyx_v_roots); if (unlikely(!__pyx_t_20)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 102; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_20 = __Pyx_PyInt_From_int(__pyx_v_roots); if (unlikely(!__pyx_t_20)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 101; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_20); - __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 = 101; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); - __pyx_t_21 = PyTuple_New(3); if (unlikely(!__pyx_t_21)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 102; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_21 = PyTuple_New(3); if (unlikely(!__pyx_t_21)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 101; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_21); PyTuple_SET_ITEM(__pyx_t_21, 0, __pyx_t_6); __Pyx_GIVEREF(__pyx_t_6); @@ -5376,14 +5376,14 @@ static int __Pyx_InitCachedConstants(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_InitCachedConstants", 0); - /* "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_tuple_ = PyTuple_Pack(3, __pyx_float_0_01, __pyx_float_1_, __pyx_float__01); if (unlikely(!__pyx_tuple_)) {__pyx_filename = __pyx_f[0]; 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__pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_int_5000 = PyInt_FromLong(5000); if (unlikely(!__pyx_int_5000)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} return 0; __pyx_L1_error:; return -1; |