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Diffstat (limited to 'experiments/ml.c')
-rw-r--r--experiments/ml.c470
1 files changed, 307 insertions, 163 deletions
diff --git a/experiments/ml.c b/experiments/ml.c
index cdf4064..4c3f024 100644
--- a/experiments/ml.c
+++ b/experiments/ml.c
@@ -1326,7 +1326,6 @@ static char __pyx_k_range[] = "range";
static char __pyx_k_roots[] = "roots";
static char __pyx_k_throw[] = "throw";
static char __pyx_k_zeros[] = "zeros";
-static char __pyx_k_arange[] = "arange";
static char __pyx_k_import[] = "__import__";
static char __pyx_k_thresh[] = "thresh";
static char __pyx_k_float64[] = "float64";
@@ -1369,7 +1368,6 @@ static PyObject *__pyx_kp_s_Users_ben_Documents_Cascade_Pro;
static PyObject *__pyx_n_s_ValueError;
static PyObject *__pyx_n_s_age;
static PyObject *__pyx_n_s_alpha;
-static PyObject *__pyx_n_s_arange;
static PyObject *__pyx_n_s_args;
static PyObject *__pyx_n_s_beta;
static PyObject *__pyx_n_s_beta_add;
@@ -1426,9 +1424,7 @@ static PyObject *__pyx_n_s_w1;
static PyObject *__pyx_n_s_w2;
static PyObject *__pyx_n_s_w3;
static PyObject *__pyx_n_s_zeros;
-static PyObject *__pyx_float__1;
-static PyObject *__pyx_float__001;
-static PyObject *__pyx_float_0_001;
+static PyObject *__pyx_float_0_13;
static PyObject *__pyx_tuple_;
static PyObject *__pyx_tuple__2;
static PyObject *__pyx_tuple__3;
@@ -1501,22 +1497,22 @@ static __pyx_t_2ml_DTYPE_t __pyx_f_2ml_weight_success(int __pyx_v_dist, int __py
* cdef DTYPE_t structural, temporal, result
* structural = delta ** dist # <<<<<<<<<<<<<<
* # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta)
- * # temporal = exp(-alpha*dt) * (exp(alpha)-1)
+ * temporal = exp(-alpha*dt) * (exp(alpha)-1.)
*/
__pyx_v_structural = pow(__pyx_v_delta, ((__pyx_t_2ml_DTYPE_t)__pyx_v_dist));
- /* "ml.pyx":19
+ /* "ml.pyx":18
+ * structural = delta ** dist
* # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta)
- * # temporal = exp(-alpha*dt) * (exp(alpha)-1)
- * temporal = 1 - exp(-alpha*dt) # <<<<<<<<<<<<<<
+ * temporal = exp(-alpha*dt) * (exp(alpha)-1.) # <<<<<<<<<<<<<<
+ * # temporal = 1 - exp(-alpha*dt)
* if exp(-alpha*dt)==0.: print 'UNDERFLOW ERROR'
- * # temporal = 1. / (1. + (dt - 1.)/alpha)**0.01 - 1. / (1. + dt/alpha)**0.01
*/
- __pyx_v_temporal = (1.0 - exp(((-__pyx_v_alpha) * __pyx_v_dt)));
+ __pyx_v_temporal = (exp(((-__pyx_v_alpha) * __pyx_v_dt)) * (exp(__pyx_v_alpha) - 1.));
/* "ml.pyx":20
- * # temporal = exp(-alpha*dt) * (exp(alpha)-1)
- * temporal = 1 - exp(-alpha*dt)
+ * temporal = exp(-alpha*dt) * (exp(alpha)-1.)
+ * # temporal = 1 - exp(-alpha*dt)
* if exp(-alpha*dt)==0.: print 'UNDERFLOW ERROR' # <<<<<<<<<<<<<<
* # temporal = 1. / (1. + (dt - 1.)/alpha)**0.01 - 1. / (1. + dt/alpha)**0.01
* result = log(structural * temporal)
@@ -1937,8 +1933,8 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
__pyx_t_2ml_DTYPE_t __pyx_t_23;
__pyx_t_2ml_DTYPE_t __pyx_t_24;
int __pyx_t_25;
- double __pyx_t_26;
- PyObject *(*__pyx_t_27)(PyObject *);
+ int __pyx_t_26;
+ double __pyx_t_27;
int __pyx_t_28;
int __pyx_lineno = 0;
const char *__pyx_filename = NULL;
@@ -1967,7 +1963,7 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
* DTYPE_t beta, ll
* list parents, failures, successes
* n_roots, n_victims = len(root_victims), len(victims) # <<<<<<<<<<<<<<
- * n_nodes = 11270
+ * n_nodes = 100
* cdef:
*/
if (unlikely(__pyx_v_root_victims == Py_None)) {
@@ -1986,14 +1982,14 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
/* "ml.pyx":45
* list parents, failures, successes
* n_roots, n_victims = len(root_victims), len(victims)
- * n_nodes = 11270 # <<<<<<<<<<<<<<
+ * n_nodes = 100 # <<<<<<<<<<<<<<
* cdef:
* np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE)
*/
- __pyx_v_n_nodes = 11270;
+ __pyx_v_n_nodes = 100;
/* "ml.pyx":47
- * n_nodes = 11270
+ * n_nodes = 100
* cdef:
* np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE) # <<<<<<<<<<<<<<
* np.ndarray[DTYPE_t] probs_fail = np.zeros(n_victims, dtype=DTYPE)
@@ -2119,7 +2115,7 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
__pyx_t_9 = ((PyArrayObject *)__pyx_t_3);
{
__Pyx_BufFmt_StackElem __pyx_stack[1];
- if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_probs_nv.rcbuffer->pybuffer, (PyObject*)__pyx_t_9, &__Pyx_TypeInfo_nn___pyx_t_2ml_DTYPE_t, PyBUF_FORMAT| PyBUF_STRIDES, 1, 0, __pyx_stack) == -1)) {
+ if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_probs_nv.rcbuffer->pybuffer, (PyObject*)__pyx_t_9, &__Pyx_TypeInfo_nn___pyx_t_2ml_DTYPE_t, PyBUF_FORMAT| PyBUF_STRIDES| PyBUF_WRITABLE, 1, 0, __pyx_stack) == -1)) {
__pyx_v_probs_nv = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); __pyx_pybuffernd_probs_nv.rcbuffer->pybuffer.buf = NULL;
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 49; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
} else {__pyx_pybuffernd_probs_nv.diminfo[0].strides = __pyx_pybuffernd_probs_nv.rcbuffer->pybuffer.strides[0]; __pyx_pybuffernd_probs_nv.diminfo[0].shape = __pyx_pybuffernd_probs_nv.rcbuffer->pybuffer.shape[0];
@@ -2478,186 +2474,337 @@ static PyObject *__pyx_pf_2ml_ml(CYTHON_UNUSED PyObject *__pyx_self, PyObject *_
}
__Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
- /* "ml.pyx":76
+ /* "ml.pyx":65
+ *
+ * # loop through non-victims
+ * for i, parents in enumerate(non_victims.itervalues()): # <<<<<<<<<<<<<<
+ * # for each non victim node, compute the probability that all its
+ * # parents fail to infect it
+ */
+ __pyx_t_10 = 0;
+ __pyx_t_1 = 0;
+ if (unlikely(__pyx_v_non_victims == Py_None)) {
+ PyErr_Format(PyExc_AttributeError, "'NoneType' object has no attribute '%s'", "itervalues");
+ {__pyx_filename = __pyx_f[0]; __pyx_lineno = 65; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ }
+ __pyx_t_5 = __Pyx_dict_iterator(__pyx_v_non_victims, 1, __pyx_n_s_itervalues, (&__pyx_t_2), (&__pyx_t_11)); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 65; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_t_5);
+ __Pyx_XDECREF(__pyx_t_3);
+ __pyx_t_3 = __pyx_t_5;
+ __pyx_t_5 = 0;
+ while (1) {
+ __pyx_t_12 = __Pyx_dict_iter_next(__pyx_t_3, __pyx_t_2, &__pyx_t_1, NULL, &__pyx_t_5, NULL, __pyx_t_11);
+ if (unlikely(__pyx_t_12 == 0)) break;
+ if (unlikely(__pyx_t_12 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 65; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_t_5);
+ if (!(likely(PyList_CheckExact(__pyx_t_5))||((__pyx_t_5) == Py_None)||(PyErr_Format(PyExc_TypeError, "Expected %.16s, got %.200s", "list", Py_TYPE(__pyx_t_5)->tp_name), 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 65; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_XDECREF_SET(__pyx_v_parents, ((PyObject*)__pyx_t_5));
+ __pyx_t_5 = 0;
+ __pyx_v_i = __pyx_t_10;
+ __pyx_t_10 = (__pyx_t_10 + 1);
+
+ /* "ml.pyx":68
+ * # for each non victim node, compute the probability that all its
+ * # parents fail to infect it
+ * failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3) # <<<<<<<<<<<<<<
+ * for (dist, dt, w1, w2, w3) in parents]
+ * probs_nv[i] = sum(failures)
+ */
+ __pyx_t_5 = PyList_New(0); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 68; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_t_5);
+
+ /* "ml.pyx":69
+ * # parents fail to infect it
+ * failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3)
+ * for (dist, dt, w1, w2, w3) in parents] # <<<<<<<<<<<<<<
+ * probs_nv[i] = sum(failures)
+ *
+ */
+ if (unlikely(__pyx_v_parents == Py_None)) {
+ PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable");
+ {__pyx_filename = __pyx_f[0]; __pyx_lineno = 69; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ }
+ __pyx_t_4 = __pyx_v_parents; __Pyx_INCREF(__pyx_t_4); __pyx_t_13 = 0;
+ for (;;) {
+ if (__pyx_t_13 >= PyList_GET_SIZE(__pyx_t_4)) break;
+ #if CYTHON_COMPILING_IN_CPYTHON
+ __pyx_t_6 = PyList_GET_ITEM(__pyx_t_4, __pyx_t_13); __Pyx_INCREF(__pyx_t_6); __pyx_t_13++; if (unlikely(0 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 69; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ #else
+ __pyx_t_6 = PySequence_ITEM(__pyx_t_4, __pyx_t_13); __pyx_t_13++; if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 69; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ #endif
+ if ((likely(PyTuple_CheckExact(__pyx_t_6))) || (PyList_CheckExact(__pyx_t_6))) {
+ PyObject* sequence = __pyx_t_6;
+ #if CYTHON_COMPILING_IN_CPYTHON
+ Py_ssize_t size = Py_SIZE(sequence);
+ #else
+ Py_ssize_t size = PySequence_Size(sequence);
+ #endif
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+ else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size);
+ {__pyx_filename = __pyx_f[0]; __pyx_lineno = 69; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ }
+ #if CYTHON_COMPILING_IN_CPYTHON
+ if (likely(PyTuple_CheckExact(sequence))) {
+ __pyx_t_14 = PyTuple_GET_ITEM(sequence, 0);
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+ __pyx_t_17 = PyList_GET_ITEM(sequence, 3);
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+ }
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+ __Pyx_INCREF(__pyx_t_16);
+ __Pyx_INCREF(__pyx_t_17);
+ __Pyx_INCREF(__pyx_t_18);
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+ {
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+ __Pyx_GOTREF(__pyx_t_19);
+ __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
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+ for (index=0; index < 5; index++) {
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+ __Pyx_DECREF(__pyx_t_19); __pyx_t_19 = 0;
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+ __pyx_t_20 = NULL;
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+ __pyx_t_18 = 0;
+
+ /* "ml.pyx":68
+ * # for each non victim node, compute the probability that all its
+ * # parents fail to infect it
+ * failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3) # <<<<<<<<<<<<<<
+ * for (dist, dt, w1, w2, w3) in parents]
+ * probs_nv[i] = sum(failures)
+ */
+ __pyx_t_22 = __pyx_PyFloat_AsDouble(__pyx_v_w1); if (unlikely((__pyx_t_22 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 68; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __pyx_t_24 = __pyx_PyFloat_AsDouble(__pyx_v_w3); if (unlikely((__pyx_t_24 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 68; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __pyx_t_6 = PyFloat_FromDouble(__pyx_f_2ml_weight_failure(__pyx_v_dist, __pyx_v_dt, __pyx_v_alpha, __pyx_v_delta, __pyx_t_22, __pyx_t_23, __pyx_t_24)); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 68; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_GOTREF(__pyx_t_6);
+ if (unlikely(__Pyx_ListComp_Append(__pyx_t_5, (PyObject*)__pyx_t_6))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 68; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+ __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0;
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+ /* "ml.pyx":69
+ * # parents fail to infect it
+ * failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3)
+ * for (dist, dt, w1, w2, w3) in parents] # <<<<<<<<<<<<<<
+ * probs_nv[i] = sum(failures)
+ *
+ */
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+ __pyx_t_5 = 0;
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+ /* "ml.pyx":70
+ * failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3)
+ * for (dist, dt, w1, w2, w3) in parents]
+ * probs_nv[i] = sum(failures) # <<<<<<<<<<<<<<
+ *
+ * # print successes
+ */
+ __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 70; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ __Pyx_INCREF(__pyx_cur_scope->__pyx_v_failures);
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+ __Pyx_GIVEREF(__pyx_cur_scope->__pyx_v_failures);
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+ __Pyx_GOTREF(__pyx_t_4);
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+ __pyx_t_26 = __pyx_v_i;
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+ }
+ __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+
+ /* "ml.pyx":80
* # cdef:
* # np.ndarray[DTYPE_t] cums = probs.cumsum()
* ll = probs_fail.sum() # add probability that all edges to victims fail # <<<<<<<<<<<<<<
* ll += probs_nv.sum() # add probability that all edges to non_victims fail
*
*/
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- if (likely(__pyx_t_4)) {
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- __Pyx_INCREF(__pyx_t_4);
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+ __Pyx_GOTREF(__pyx_t_4);
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generated by cgit v1.2.3-70-g09d2 (git 2.52.0) at 2025-12-21 16:22:20 +0000