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-rw-r--r--experiments/README.txt9
-rw-r--r--experiments/build/temp.macosx-10.6-x86_64-2.7/ml.obin304288 -> 308728 bytes
-rw-r--r--experiments/ml.c772
-rw-r--r--experiments/ml.pyx26
-rwxr-xr-xexperiments/ml.sobin109300 -> 109612 bytes
-rw-r--r--experiments/out.log3873
-rw-r--r--experiments/plot3d.py2
-rw-r--r--experiments/process.py13
8 files changed, 468 insertions, 4227 deletions
diff --git a/experiments/README.txt b/experiments/README.txt
index a3f2e22..80f6aa0 100644
--- a/experiments/README.txt
+++ b/experiments/README.txt
@@ -16,7 +16,7 @@ Description of the files
(time component) and delta (structural component). The likelihood (and a few
other things) for each of the values is printed in the file "out.log".
Takes as argument the name of .pickle file computed by build_network.py
- Prints columns as alpha, delta, beta, #roots, likelihood
+ Prints columns as alpha, delta, beta, number of roots, likelihood
* plot3d.py: code to obtain a 3d plot of the log likelihood as a function of
alpha and delta. Can also be easily modified to obtain 2d plots along
@@ -38,3 +38,10 @@ complicated, it is also possible to compile .pyx files using python distutils
(which I would assume is more standard on MacOS). This only requires distutils
and a simple setup.py file. More details here:
http://docs.cython.org/src/reference/compilation.html#configuring-the-c-build
+
+Running Code
+============
+python build_network.py ../../Results/dag_dat_all.csv
+python setup.py build_ext --inplace
+python process.py ../../Results/dag_dat_all.pickle
+python plot3d.py
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 0f6640f..5fbcfe9 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/ml.c b/experiments/ml.c
index 67e5cd1..1ae9098 100644
--- a/experiments/ml.c
+++ b/experiments/ml.c
@@ -773,12 +773,12 @@ struct __pyx_obj_2ml___pyx_scope_struct__ml {
};
-/* "ml.pyx":71
- * successes = [weight_success(dist, dt, alpha, delta, gamma)
- * for (dist, dt) in parents]
+/* "ml.pyx":74
+ * for (dist, dt, w1, w2, w3) in parents]
+ * # find parent that maximizes p/\tilde{p}
* probs[i] = max(s - failures[l] for l, s in enumerate(successes)) # <<<<<<<<<<<<<<
- * probs_fail[i] = all_failures
*
+ * # loop through non-victims
*/
struct __pyx_obj_2ml___pyx_scope_struct_1_genexpr {
PyObject_HEAD
@@ -1284,6 +1284,9 @@ 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_w1[] = "w1";
+static char __pyx_k_w2[] = "w2";
+static char __pyx_k_w3[] = "w3";
static char __pyx_k_age[] = "age";
static char __pyx_k_end[] = "end";
static char __pyx_k_max[] = "max";
@@ -1331,7 +1334,6 @@ static char __pyx_k_itervalues[] = "itervalues";
static char __pyx_k_probs_fail[] = "probs_fail";
static char __pyx_k_non_victims[] = "non_victims";
static char __pyx_k_RuntimeError[] = "RuntimeError";
-static char __pyx_k_all_failures[] = "all_failures";
static char __pyx_k_root_victims[] = "root_victims";
static char __pyx_k_ml_locals_genexpr[] = "ml.<locals>.genexpr";
static char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous";
@@ -1350,7 +1352,6 @@ static PyObject *__pyx_n_s_RuntimeError;
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_all_failures;
static PyObject *__pyx_n_s_alpha;
static PyObject *__pyx_kp_s_alpha_0_delta_1_Everyone_is_a_ro;
static PyObject *__pyx_n_s_args;
@@ -1405,6 +1406,9 @@ static PyObject *__pyx_n_s_test;
static PyObject *__pyx_n_s_throw;
static PyObject *__pyx_kp_u_unknown_dtype_code_in_numpy_pxd;
static PyObject *__pyx_n_s_victims;
+static PyObject *__pyx_n_s_w1;
+static PyObject *__pyx_n_s_w2;
+static PyObject *__pyx_n_s_w3;
static PyObject *__pyx_n_s_xrange;
static PyObject *__pyx_n_s_zeros;
static PyObject *__pyx_int_neg_1;
@@ -1815,12 +1819,12 @@ static PyObject *__pyx_pw_2ml_1ml(PyObject *__pyx_self, PyObject *__pyx_args, Py
}
static PyObject *__pyx_gb_2ml_2ml_2generator(__pyx_GeneratorObject *__pyx_generator, PyObject *__pyx_sent_value); /* proto */
-/* "ml.pyx":71
- * successes = [weight_success(dist, dt, alpha, delta, gamma)
- * for (dist, dt) in parents]
+/* "ml.pyx":74
+ * for (dist, dt, w1, w2, w3) in parents]
+ * # find parent that maximizes p/\tilde{p}
* probs[i] = max(s - failures[l] for l, s in enumerate(successes)) # <<<<<<<<<<<<<<
- * probs_fail[i] = all_failures
*
+ * # loop through non-victims
*/
static PyObject *__pyx_pf_2ml_2ml_genexpr(PyObject *__pyx_self) {
@@ -1841,7 +1845,7 @@ static PyObject *__pyx_pf_2ml_2ml_genexpr(PyObject *__pyx_self) {
__Pyx_INCREF(((PyObject *)__pyx_cur_scope->__pyx_outer_scope));
__Pyx_GIVEREF(__pyx_cur_scope->__pyx_outer_scope);
{
- __pyx_GeneratorObject *gen = __Pyx_Generator_New((__pyx_generator_body_t) __pyx_gb_2ml_2ml_2generator, (PyObject *) __pyx_cur_scope, __pyx_n_s_genexpr, __pyx_n_s_ml_locals_genexpr); if (unlikely(!gen)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_GeneratorObject *gen = __Pyx_Generator_New((__pyx_generator_body_t) __pyx_gb_2ml_2ml_2generator, (PyObject *) __pyx_cur_scope, __pyx_n_s_genexpr, __pyx_n_s_ml_locals_genexpr); if (unlikely(!gen)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 74; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_DECREF(__pyx_cur_scope);
__Pyx_RefNannyFinishContext();
return (PyObject *) gen;
@@ -1879,16 +1883,16 @@ static PyObject *__pyx_gb_2ml_2ml_2generator(__pyx_GeneratorObject *__pyx_genera
return NULL;
}
__pyx_L3_first_run:;
- if (unlikely(!__pyx_sent_value)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (unlikely(!__pyx_sent_value)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 74; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_t_1 = 0;
- if (unlikely(!__pyx_cur_scope->__pyx_outer_scope->__pyx_v_successes)) { __Pyx_RaiseClosureNameError("successes"); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;} }
+ if (unlikely(!__pyx_cur_scope->__pyx_outer_scope->__pyx_v_successes)) { __Pyx_RaiseClosureNameError("successes"); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 74; __pyx_clineno = __LINE__; goto __pyx_L1_error;} }
__pyx_t_2 = __pyx_cur_scope->__pyx_outer_scope->__pyx_v_successes; __Pyx_INCREF(__pyx_t_2); __pyx_t_3 = 0;
for (;;) {
if (__pyx_t_3 >= PyList_GET_SIZE(__pyx_t_2)) break;
#if CYTHON_COMPILING_IN_CPYTHON
- __pyx_t_4 = PyList_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_4); __pyx_t_3++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_4 = PyList_GET_ITEM(__pyx_t_2, __pyx_t_3); __Pyx_INCREF(__pyx_t_4); __pyx_t_3++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 74; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
#else
- __pyx_t_4 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_4 = PySequence_ITEM(__pyx_t_2, __pyx_t_3); __pyx_t_3++; if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 74; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
#endif
__Pyx_XGOTREF(__pyx_cur_scope->__pyx_v_s);
__Pyx_XDECREF_SET(__pyx_cur_scope->__pyx_v_s, __pyx_t_4);
@@ -1896,14 +1900,14 @@ static PyObject *__pyx_gb_2ml_2ml_2generator(__pyx_GeneratorObject *__pyx_genera
__pyx_t_4 = 0;
__pyx_cur_scope->__pyx_v_l = __pyx_t_1;
__pyx_t_1 = (__pyx_t_1 + 1);
- if (unlikely(!__pyx_cur_scope->__pyx_outer_scope->__pyx_v_failures)) { __Pyx_RaiseClosureNameError("failures"); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;} }
+ if (unlikely(!__pyx_cur_scope->__pyx_outer_scope->__pyx_v_failures)) { __Pyx_RaiseClosureNameError("failures"); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 74; __pyx_clineno = __LINE__; goto __pyx_L1_error;} }
if (unlikely(__pyx_cur_scope->__pyx_outer_scope->__pyx_v_failures == Py_None)) {
PyErr_SetString(PyExc_TypeError, "'NoneType' object is not subscriptable");
- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ {__pyx_filename = __pyx_f[0]; __pyx_lineno = 74; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
}
- __pyx_t_4 = __Pyx_GetItemInt_List(__pyx_cur_scope->__pyx_outer_scope->__pyx_v_failures, __pyx_cur_scope->__pyx_v_l, Py_ssize_t, 1, PyInt_FromSsize_t, 1, 1, 0); if (unlikely(__pyx_t_4 == NULL)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
+ __pyx_t_4 = __Pyx_GetItemInt_List(__pyx_cur_scope->__pyx_outer_scope->__pyx_v_failures, __pyx_cur_scope->__pyx_v_l, Py_ssize_t, 1, PyInt_FromSsize_t, 1, 1, 0); if (unlikely(__pyx_t_4 == NULL)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 74; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
__Pyx_GOTREF(__pyx_t_4);
- __pyx_t_5 = PyNumber_Subtract(__pyx_cur_scope->__pyx_v_s, __pyx_t_4); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_5 = PyNumber_Subtract(__pyx_cur_scope->__pyx_v_s, __pyx_t_4); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 74; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_5);
__Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
__pyx_r = __pyx_t_5;
@@ -1923,7 +1927,7 @@ static PyObject *__pyx_gb_2ml_2ml_2generator(__pyx_GeneratorObject *__pyx_genera
__pyx_cur_scope->__pyx_t_1 = 0;
__Pyx_XGOTREF(__pyx_t_2);
__pyx_t_3 = __pyx_cur_scope->__pyx_t_2;
- if (unlikely(!__pyx_sent_value)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (unlikely(!__pyx_sent_value)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 74; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
}
__Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
@@ -1961,13 +1965,15 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
int __pyx_v_dist;
int __pyx_v_dt;
__pyx_t_2ml_DTYPE_t __pyx_v_beta;
- __pyx_t_2ml_DTYPE_t __pyx_v_all_failures;
__pyx_t_2ml_DTYPE_t __pyx_v_ll;
PyObject *__pyx_v_parents = 0;
PyArrayObject *__pyx_v_probs = 0;
PyArrayObject *__pyx_v_probs_fail = 0;
PyArrayObject *__pyx_v_probs_nv = 0;
PyArrayObject *__pyx_v_cums = 0;
+ CYTHON_UNUSED PyObject *__pyx_v_w1 = NULL;
+ CYTHON_UNUSED PyObject *__pyx_v_w2 = NULL;
+ CYTHON_UNUSED PyObject *__pyx_v_w3 = NULL;
__Pyx_LocalBuf_ND __pyx_pybuffernd_cums;
__Pyx_Buffer __pyx_pybuffer_cums;
__Pyx_LocalBuf_ND __pyx_pybuffernd_probs;
@@ -1994,16 +2000,19 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
PyObject *__pyx_t_14 = NULL;
PyObject *__pyx_t_15 = NULL;
PyObject *__pyx_t_16 = NULL;
- PyObject *(*__pyx_t_17)(PyObject *);
- int __pyx_t_18;
- __pyx_t_2ml_DTYPE_t __pyx_t_19;
- int __pyx_t_20;
+ PyObject *__pyx_t_17 = NULL;
+ PyObject *__pyx_t_18 = NULL;
+ PyObject *__pyx_t_19 = NULL;
+ PyObject *(*__pyx_t_20)(PyObject *);
int __pyx_t_21;
- PyObject *__pyx_t_22 = NULL;
- PyObject *__pyx_t_23 = NULL;
- PyObject *__pyx_t_24 = NULL;
- PyArrayObject *__pyx_t_25 = NULL;
- int __pyx_t_26;
+ __pyx_t_2ml_DTYPE_t __pyx_t_22;
+ int __pyx_t_23;
+ int __pyx_t_24;
+ PyObject *__pyx_t_25 = NULL;
+ PyObject *__pyx_t_26 = NULL;
+ PyObject *__pyx_t_27 = NULL;
+ PyArrayObject *__pyx_t_28 = NULL;
+ int __pyx_t_29;
int __pyx_lineno = 0;
const char *__pyx_filename = NULL;
int __pyx_clineno = 0;
@@ -2032,10 +2041,10 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
__pyx_pybuffernd_cums.rcbuffer = &__pyx_pybuffer_cums;
/* "ml.pyx":56
- * DTYPE_t beta, all_failures, ll, beta2
+ * DTYPE_t beta, ll, beta2
* list parents, failures, successes
* n_roots, n_victims = len(root_victims), len(victims) # <<<<<<<<<<<<<<
- * n_nodes = n_victims + len(non_victims) + n_roots
+ * n_nodes = n_roots + n_victims + len(non_victims)
* cdef:
*/
if (unlikely(__pyx_v_root_victims == Py_None)) {
@@ -2054,7 +2063,7 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
/* "ml.pyx":57
* list parents, failures, successes
* n_roots, n_victims = len(root_victims), len(victims)
- * n_nodes = n_victims + len(non_victims) + n_roots # <<<<<<<<<<<<<<
+ * n_nodes = n_roots + n_victims + len(non_victims) # <<<<<<<<<<<<<<
* cdef:
* np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE)
*/
@@ -2063,10 +2072,10 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 57; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
}
__pyx_t_2 = PyDict_Size(__pyx_v_non_victims); if (unlikely(__pyx_t_2 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 57; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_v_n_nodes = ((__pyx_v_n_victims + __pyx_t_2) + __pyx_v_n_roots);
+ __pyx_v_n_nodes = ((__pyx_v_n_roots + __pyx_v_n_victims) + __pyx_t_2);
/* "ml.pyx":59
- * n_nodes = n_victims + len(non_victims) + n_roots
+ * n_nodes = n_roots + n_victims + len(non_victims)
* cdef:
* np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE) # <<<<<<<<<<<<<<
* np.ndarray[DTYPE_t] probs_fail = np.zeros(n_victims, dtype=DTYPE)
@@ -2114,7 +2123,7 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
* np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE)
* np.ndarray[DTYPE_t] probs_fail = np.zeros(n_victims, dtype=DTYPE) # <<<<<<<<<<<<<<
* np.ndarray[DTYPE_t] probs_nv = np.zeros(len(non_victims), dtype=DTYPE)
- * for i, parents in enumerate(victims.itervalues()):
+ *
*/
__pyx_t_6 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 60; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_6);
@@ -2157,8 +2166,8 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
* np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE)
* np.ndarray[DTYPE_t] probs_fail = np.zeros(n_victims, dtype=DTYPE)
* np.ndarray[DTYPE_t] probs_nv = np.zeros(len(non_victims), dtype=DTYPE) # <<<<<<<<<<<<<<
- * for i, parents in enumerate(victims.itervalues()):
- * # for each victim node i, compute the probability that all its parents
+ *
+ * # loop through victims
*/
__pyx_t_4 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 61; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_4);
@@ -2202,9 +2211,9 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
__pyx_v_probs_nv = ((PyArrayObject *)__pyx_t_3);
__pyx_t_3 = 0;
- /* "ml.pyx":62
- * np.ndarray[DTYPE_t] probs_fail = np.zeros(n_victims, dtype=DTYPE)
- * np.ndarray[DTYPE_t] probs_nv = np.zeros(len(non_victims), dtype=DTYPE)
+ /* "ml.pyx":64
+ *
+ * # loop through victims
* for i, parents in enumerate(victims.itervalues()): # <<<<<<<<<<<<<<
* # for each victim node i, compute the probability that all its parents
* # fail to infect it, also computes the probability that its most
@@ -2213,9 +2222,9 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
__pyx_t_2 = 0;
if (unlikely(__pyx_v_victims == Py_None)) {
PyErr_Format(PyExc_AttributeError, "'NoneType' object has no attribute '%s'", "itervalues");
- {__pyx_filename = __pyx_f[0]; __pyx_lineno = 62; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ {__pyx_filename = __pyx_f[0]; __pyx_lineno = 64; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
}
- __pyx_t_4 = __Pyx_dict_iterator(__pyx_v_victims, 1, __pyx_n_s_itervalues, (&__pyx_t_1), (&__pyx_t_11)); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 62; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_4 = __Pyx_dict_iterator(__pyx_v_victims, 1, __pyx_n_s_itervalues, (&__pyx_t_1), (&__pyx_t_11)); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 64; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_4);
__Pyx_XDECREF(__pyx_t_3);
__pyx_t_3 = __pyx_t_4;
@@ -2223,42 +2232,42 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
while (1) {
__pyx_t_12 = __Pyx_dict_iter_next(__pyx_t_3, __pyx_t_1, &__pyx_t_2, NULL, &__pyx_t_4, NULL, __pyx_t_11);
if (unlikely(__pyx_t_12 == 0)) break;
- if (unlikely(__pyx_t_12 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 62; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (unlikely(__pyx_t_12 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 64; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_t_4);
- if (!(likely(PyList_CheckExact(__pyx_t_4))||((__pyx_t_4) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "list", Py_TYPE(__pyx_t_4)->tp_name), 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 62; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_4 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_probs_fail), __pyx_n_s_sum); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 88; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -2795,27 +2853,27 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
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+ /* "ml.pyx":89
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* for i in xrange(n_victims - 1, 0, -1):
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+ __pyx_t_5 = __Pyx_PyObject_GetAttrStr(((PyObject *)__pyx_v_probs_nv), __pyx_n_s_sum); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 89; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -2828,22 +2886,22 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
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+ __pyx_t_4 = __Pyx_PyObject_CallNoArg(__pyx_t_5); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 89; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_22 = __pyx_PyFloat_AsDouble(__pyx_t_5); if (unlikely((__pyx_t_22 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 89; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
- __pyx_v_ll = __pyx_t_19;
+ __pyx_v_ll = __pyx_t_22;
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+ /* "ml.pyx":91
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*
* for i in xrange(n_victims - 1, 0, -1): # <<<<<<<<<<<<<<
@@ -2853,38 +2911,50 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
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__pyx_v_i = __pyx_t_10;
- /* "ml.pyx":89
+ /* "ml.pyx":93
* for i in xrange(n_victims - 1, 0, -1):
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* roots = n_roots + n_victims - 1 - i # <<<<<<<<<<<<<<
- * beta = 1. / (1. + exp(-probs[i]))
- * if beta > float(roots) / age:
+ * beta = exp(probs[i])#1. / (1. + exp(-probs[i]))
+ * print beta
*/
__pyx_v_roots = (((__pyx_v_n_roots + __pyx_v_n_victims) - 1) - __pyx_v_i);
- /* "ml.pyx":90
+ /* "ml.pyx":94
* # iterate over all victim nodes to find the optimal threshold
* roots = n_roots + n_victims - 1 - i
- * beta = 1. / (1. + exp(-probs[i])) # <<<<<<<<<<<<<<
+ * beta = exp(probs[i])#1. / (1. + exp(-probs[i])) # <<<<<<<<<<<<<<
+ * print beta
* if beta > float(roots) / age:
- * break
*/
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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))))));
+ __pyx_v_beta = 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":91
+ /* "ml.pyx":95
* roots = n_roots + n_victims - 1 - i
- * beta = 1. / (1. + exp(-probs[i]))
+ * beta = exp(probs[i])#1. / (1. + exp(-probs[i]))
+ * print beta # <<<<<<<<<<<<<<
+ * if beta > float(roots) / age:
+ * break
+ */
+ __pyx_t_5 = PyFloat_FromDouble(__pyx_v_beta); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 95; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_t_5);
+ if (__Pyx_PrintOne(0, __pyx_t_5) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 95; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
+
+ /* "ml.pyx":96
+ * beta = exp(probs[i])#1. / (1. + exp(-probs[i]))
+ * print beta
* if beta > float(roots) / age: # <<<<<<<<<<<<<<
* break
* else:
*/
- __pyx_t_26 = ((__pyx_v_beta > (((double)__pyx_v_roots) / __pyx_v_age)) != 0);
- if (__pyx_t_26) {
+ __pyx_t_29 = ((__pyx_v_beta > (((double)__pyx_v_roots) / __pyx_v_age)) != 0);
+ if (__pyx_t_29) {
- /* "ml.pyx":92
- * beta = 1. / (1. + exp(-probs[i]))
+ /* "ml.pyx":97
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* if beta > float(roots) / age:
* break # <<<<<<<<<<<<<<
* else:
@@ -2895,50 +2965,50 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
}
/*else*/ {
- /* "ml.pyx":94
+ /* "ml.pyx":99
* break
* else:
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* i = -1
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__Pyx_GOTREF(__pyx_t_6);
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- __pyx_t_15 = PyMethod_GET_SELF(__pyx_t_4);
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PyObject* function = PyMethod_GET_FUNCTION(__pyx_t_4);
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- PyTuple_SET_ITEM(__pyx_t_14, 0, __pyx_t_15); __Pyx_GIVEREF(__pyx_t_15); __pyx_t_15 = NULL;
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__Pyx_GOTREF(__pyx_t_5);
- __Pyx_DECREF(__pyx_t_14); __pyx_t_14 = 0;
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__Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
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+ if (__Pyx_PrintOne(0, __pyx_t_5) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 99; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
- /* "ml.pyx":95
+ /* "ml.pyx":100
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- /* "ml.pyx":96
+ /* "ml.pyx":101
* print "alpha: {0}, delta: {1}. Everyone is a root".format(alpha, delta)
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* i = -1 # <<<<<<<<<<<<<<
@@ -2958,7 +3028,7 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
}
__pyx_L20_break:;
- /* "ml.pyx":97
+ /* "ml.pyx":102
* roots = n_victims + n_roots
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*/
__pyx_v_beta = (((double)__pyx_v_roots) / __pyx_v_age);
- /* "ml.pyx":98
+ /* "ml.pyx":103
* i = -1
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for (__pyx_t_10 = (__pyx_v_n_victims - 1); __pyx_t_10 > 0; __pyx_t_10-=1) {
__pyx_v_i = __pyx_t_10;
- /* "ml.pyx":99
+ /* "ml.pyx":104
* beta = float(roots) / age
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* break
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- __pyx_t_26 = (((*__Pyx_BufPtrStrided1d(__pyx_t_2ml_DTYPE_t *, __pyx_pybuffernd_probs.rcbuffer->pybuffer.buf, __pyx_t_20, __pyx_pybuffernd_probs.diminfo[0].strides)) >= log((__pyx_v_beta / (1. - __pyx_v_beta)))) != 0);
- if (__pyx_t_26) {
+ __pyx_t_12 = __pyx_v_i;
+ if (__pyx_t_12 < 0) __pyx_t_12 += __pyx_pybuffernd_probs.diminfo[0].shape;
+ __pyx_t_29 = (((*__Pyx_BufPtrStrided1d(__pyx_t_2ml_DTYPE_t *, __pyx_pybuffernd_probs.rcbuffer->pybuffer.buf, __pyx_t_12, __pyx_pybuffernd_probs.diminfo[0].strides)) >= log((__pyx_v_beta / (1. - __pyx_v_beta)))) != 0);
+ if (__pyx_t_29) {
- /* "ml.pyx":100
+ /* "ml.pyx":105
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}
__pyx_L23_break:;
- /* "ml.pyx":101
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*/
__pyx_v_ll = (__pyx_v_ll + (__pyx_v_age * log((1.0 - __pyx_v_beta))));
- /* "ml.pyx":102
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* if roots > 0:
*/
- __pyx_t_26 = ((__pyx_v_i >= 0) != 0);
- if (__pyx_t_26) {
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- /* "ml.pyx":103
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__Pyx_XDECREF(__pyx_t_14);
__Pyx_XDECREF(__pyx_t_15);
__Pyx_XDECREF(__pyx_t_16);
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__Pyx_XDECREF((PyObject *)__pyx_v_probs_nv);
__Pyx_XDECREF((PyObject *)__pyx_v_cums);
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{&__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},
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{&__pyx_n_s_alpha, __pyx_k_alpha, sizeof(__pyx_k_alpha), 0, 0, 1, 1},
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{&__pyx_n_s_victims, __pyx_k_victims, sizeof(__pyx_k_victims), 0, 0, 1, 1},
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{&__pyx_n_s_xrange, __pyx_k_xrange, sizeof(__pyx_k_xrange), 0, 0, 1, 1},
{&__pyx_n_s_zeros, __pyx_k_zeros, sizeof(__pyx_k_zeros), 0, 0, 1, 1},
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+ __pyx_builtin_xrange = __Pyx_GetBuiltinName(__pyx_n_s_range); if (!__pyx_builtin_xrange) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- __pyx_builtin_xrange = __Pyx_GetBuiltinName(__pyx_n_s_xrange); if (!__pyx_builtin_xrange) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 87; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_builtin_xrange = __Pyx_GetBuiltinName(__pyx_n_s_xrange); if (!__pyx_builtin_xrange) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 91; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
#endif
__pyx_builtin_ValueError = __Pyx_GetBuiltinName(__pyx_n_s_ValueError); if (!__pyx_builtin_ValueError) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 218; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_builtin_range = __Pyx_GetBuiltinName(__pyx_n_s_range); if (!__pyx_builtin_range) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 231; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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__Pyx_RefNannySetupContext("__Pyx_InitCachedConstants", 0);
- /* "ml.pyx":81
- * probs_nv[i] = sum(failures)
- * probs.sort()
- * probs = probs[::-1] # <<<<<<<<<<<<<<
+ /* "ml.pyx":85
+ *
+ * # calculate log likelihood
+ * probs.sort(); probs = probs[::-1] # sort probs in descending order # <<<<<<<<<<<<<<
* cdef:
* np.ndarray[DTYPE_t] cums = probs.cumsum()
*/
- __pyx_slice_ = PySlice_New(Py_None, Py_None, __pyx_int_neg_1); if (unlikely(!__pyx_slice_)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 81; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_slice_ = PySlice_New(Py_None, Py_None, __pyx_int_neg_1); if (unlikely(!__pyx_slice_)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 85; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_slice_);
__Pyx_GIVEREF(__pyx_slice_);
@@ -5550,10 +5628,10 @@ static int __Pyx_InitCachedConstants(void) {
* DTYPE_t alpha, DTYPE_t delta, DTYPE_t gamma=10):
* cdef:
*/
- __pyx_tuple__8 = PyTuple_Pack(29, __pyx_n_s_root_victims, __pyx_n_s_victims, __pyx_n_s_non_victims, __pyx_n_s_age, __pyx_n_s_alpha, __pyx_n_s_delta, __pyx_n_s_gamma, __pyx_n_s_n_roots, __pyx_n_s_n_victims, __pyx_n_s_n_nodes, __pyx_n_s_roots, __pyx_n_s_i, __pyx_n_s_dist, __pyx_n_s_dt, __pyx_n_s_t, __pyx_n_s_l, __pyx_n_s_beta, __pyx_n_s_all_failures, __pyx_n_s_ll, __pyx_n_s_beta2, __pyx_n_s_parents, __pyx_n_s_failures, __pyx_n_s_successes, __pyx_n_s_probs, __pyx_n_s_probs_fail, __pyx_n_s_probs_nv, __pyx_n_s_cums, __pyx_n_s_genexpr, __pyx_n_s_genexpr); if (unlikely(!__pyx_tuple__8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 50; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_tuple__8 = PyTuple_Pack(31, __pyx_n_s_root_victims, __pyx_n_s_victims, __pyx_n_s_non_victims, __pyx_n_s_age, __pyx_n_s_alpha, __pyx_n_s_delta, __pyx_n_s_gamma, __pyx_n_s_n_roots, __pyx_n_s_n_victims, __pyx_n_s_n_nodes, __pyx_n_s_roots, __pyx_n_s_i, __pyx_n_s_dist, __pyx_n_s_dt, __pyx_n_s_t, __pyx_n_s_l, __pyx_n_s_beta, __pyx_n_s_ll, __pyx_n_s_beta2, __pyx_n_s_parents, __pyx_n_s_failures, __pyx_n_s_successes, __pyx_n_s_probs, __pyx_n_s_probs_fail, __pyx_n_s_probs_nv, __pyx_n_s_cums, __pyx_n_s_w1, __pyx_n_s_w2, __pyx_n_s_w3, __pyx_n_s_genexpr, __pyx_n_s_genexpr); if (unlikely(!__pyx_tuple__8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 50; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__Pyx_GOTREF(__pyx_tuple__8);
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+ __pyx_codeobj__9 = (PyObject*)__Pyx_PyCode_New(7, 0, 31, 0, 0, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__8, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_Users_ben_Documents_Cascade_Pro, __pyx_n_s_ml, 50, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__9)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 50; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -5653,7 +5731,7 @@ PyMODINIT_FUNC PyInit_ml(void)
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__pyx_type_2ml___pyx_scope_struct__ml.tp_print = 0;
__pyx_ptype_2ml___pyx_scope_struct__ml = &__pyx_type_2ml___pyx_scope_struct__ml;
- if (PyType_Ready(&__pyx_type_2ml___pyx_scope_struct_1_genexpr) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ if (PyType_Ready(&__pyx_type_2ml___pyx_scope_struct_1_genexpr) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 74; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
__pyx_type_2ml___pyx_scope_struct_1_genexpr.tp_print = 0;
__pyx_ptype_2ml___pyx_scope_struct_1_genexpr = &__pyx_type_2ml___pyx_scope_struct_1_genexpr;
/*--- Type import code ---*/
diff --git a/experiments/ml.pyx b/experiments/ml.pyx
index 74e5be3..c1a5616 100644
--- a/experiments/ml.pyx
+++ b/experiments/ml.pyx
@@ -51,34 +51,38 @@ def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
DTYPE_t alpha, DTYPE_t delta, DTYPE_t gamma=10):
cdef:
int n_roots, n_victims, n_nodes, roots, i, dist, dt, t, l
- DTYPE_t beta, all_failures, ll, beta2
+ DTYPE_t beta, ll, beta2
list parents, failures, successes
n_roots, n_victims = len(root_victims), len(victims)
- n_nodes = n_victims + len(non_victims) + n_roots
+ n_nodes = n_roots + n_victims + len(non_victims)
cdef:
np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE)
np.ndarray[DTYPE_t] probs_fail = np.zeros(n_victims, dtype=DTYPE)
np.ndarray[DTYPE_t] probs_nv = np.zeros(len(non_victims), dtype=DTYPE)
+
+ # loop through victims
for i, parents in enumerate(victims.itervalues()):
# for each victim node i, compute the probability that all its parents
# fail to infect it, also computes the probability that its most
# likely parent infects it
failures = [weight_failure(dist, dt, alpha, delta, gamma)
- for (dist, dt) in parents]
- all_failures = sum(failures)
+ for (dist, dt, w1, w2, w3) in parents]
+ probs_fail[i] = sum(failures)
successes = [weight_success(dist, dt, alpha, delta, gamma)
- for (dist, dt) in parents]
- probs[i] = max(s - failures[l] for l, s in enumerate(successes))
- probs_fail[i] = all_failures
+ for (dist, dt, w1, w2, w3) in parents]
+ # find parent that maximizes p/\tilde{p}
+ 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, gamma)
- for (dist, dt) in parents]
+ for (dist, dt, w1, w2, w3) in parents]
probs_nv[i] = sum(failures)
- probs.sort()
- probs = probs[::-1]
+
+ # calculate log likelihood
+ probs.sort(); probs = probs[::-1] # sort probs in descending order
cdef:
np.ndarray[DTYPE_t] cums = probs.cumsum()
ll = probs_fail.sum()
@@ -87,7 +91,7 @@ def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
for i in xrange(n_victims - 1, 0, -1):
# iterate over all victim nodes to find the optimal threshold
roots = n_roots + n_victims - 1 - i
- beta = 1. / (1. + exp(-probs[i]))
+ beta = 1. / (1. + exp(-probs[i]))#exp(probs[i])#
if beta > float(roots) / age:
break
else:
diff --git a/experiments/ml.so b/experiments/ml.so
index 571865b..6dcab82 100755
--- a/experiments/ml.so
+++ b/experiments/ml.so
Binary files differ
diff --git a/experiments/out.log b/experiments/out.log
index 407eefe..f6caa9b 100644
--- a/experiments/out.log
+++ b/experiments/out.log
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-0.00100908173562 0.64 8.76338000871e-07 2792 -161374.432009
-0.00100908173562 0.67 8.76338000871e-07 2792 -166435.448913
-0.00100908173562 0.7 8.76338000871e-07 2792 -172010.22098
-0.00100908173562 0.73 8.76338000871e-07 2792 -178158.525769
-0.00100908173562 0.76 8.76338000871e-07 2792 -184955.814528
-0.00100908173562 0.79 8.76338000871e-07 2792 -192498.843186
-0.00100908173562 0.82 8.76338000871e-07 2792 -200914.440526
-0.00100908173562 0.85 8.76338000871e-07 2792 -210373.73309
-0.00100908173562 0.88 8.76338000871e-07 2792 -221117.175084
+1.0 0.01 3.82496925296e-05 10927 -123894.745252
+1.0 0.11 3.69615176554e-05 10559 -146433.341002
+1.0 0.21 3.66569763128e-05 10472 -191235.823611
+1.0 0.31 3.64469478007e-05 10412 -260465.254415
+1.0 0.41 3.61669097846e-05 10332 -358572.455236
+1.0 0.51 3.61669097846e-05 10332 -493346.776777
+1.0 0.61 3.5879870818e-05 10250 -678702.795812
+1.0 0.71 3.5879870818e-05 10250 -941793.902677
+1.0 0.81 3.56243361283e-05 10177 -1345861.15169
+0.00990099009901 0.01 3.45181859646e-05 9861 -123434.443377
+0.00990099009901 0.11 1.80484501403e-05 5156 -138449.306153
+0.00990099009901 0.21 1.45689777897e-05 4162 -175475.261761
+0.00990099009901 0.31 1.32527991139e-05 3786 -237994.762027
+0.00990099009901 0.41 1.26192131023e-05 3605 -329318.903778
+0.00990099009901 0.51 1.21921551277e-05 3483 -456389.667272
diff --git a/experiments/plot3d.py b/experiments/plot3d.py
index 64c144d..2158588 100644
--- a/experiments/plot3d.py
+++ b/experiments/plot3d.py
@@ -7,7 +7,7 @@ with open("out.log") as fh:
values = [map(float, line.strip().split()) for line in fh]
#values = [(b, a, l) for (b, a, l) in values if b >= 0.04]
am = max(values, key=lambda x: x[4])
- am[0] = 1./am[0]
+ # am[0] = 1./am[0]
print am
alpha, delta, beta, _ , l = zip(*values)
alpha = 1./np.array(alpha)
diff --git a/experiments/process.py b/experiments/process.py
index ec25d8e..5c4c215 100644
--- a/experiments/process.py
+++ b/experiments/process.py
@@ -6,21 +6,16 @@ from itertools import product
from math import exp
-def print_ll(alpha, delta):
- beta, roots, ll = ml(root_victims, victims, non_victims, alpha, delta)
- print "\t".join(map(str, [alpha, delta, beta, roots, ll, exp(ll)])) + "\n"
-
-
if __name__ == "__main__":
if len(sys.argv) < 2:
sys.exit("usage: {0} <file>".format(sys.argv[0]))
root_victims, victims, non_victims, age = load(open(sys.argv[1]))
- alpha = 1. / np.arange(1., 1000., 10.) # parameter of the time component
- delta = np.arange(0.01, 0.9, 0.03) # parameter of the structural component
- with open("out.log", "a") as fh:
+ alpha = 1. / np.arange(1., 1000., 100.) # parameter of the time component
+ delta = np.arange(0.01, 0.9, 0.1) # parameter of the structural component
+ with open("out.log", "w") as fh:
for a, d in product(alpha, delta):
beta, roots, ll = ml(root_victims, victims, non_victims, age, a, d)
- print beta
+ print "\t".join(map(str, [a, d, beta, roots, ll, exp(ll)]))
fh.write("\t".join(map(str, [a, d, beta, roots, ll])) + "\n")
fh.flush()
generated by cgit v1.2.3-70-g09d2 (git 2.52.0) at 2025-12-21 15:04:03 +0000