diff options
Diffstat (limited to 'experiments')
| -rw-r--r-- | experiments/build/temp.macosx-10.6-x86_64-2.7/ml3.o | bin | 277036 -> 285944 bytes | |||
| -rwxr-xr-x | experiments/ml2.so | bin | 123652 -> 123652 bytes | |||
| -rw-r--r-- | experiments/ml3.c | 463 | ||||
| -rw-r--r-- | experiments/ml3.pyx | 15 | ||||
| -rwxr-xr-x | experiments/ml3.so | bin | 106764 -> 110884 bytes | |||
| -rw-r--r-- | experiments/out.log | 960 | ||||
| -rw-r--r-- | experiments/process.py | 32 |
7 files changed, 482 insertions, 988 deletions
diff --git a/experiments/build/temp.macosx-10.6-x86_64-2.7/ml3.o b/experiments/build/temp.macosx-10.6-x86_64-2.7/ml3.o Binary files differindex e928c51..8ac85ca 100644 --- a/experiments/build/temp.macosx-10.6-x86_64-2.7/ml3.o +++ b/experiments/build/temp.macosx-10.6-x86_64-2.7/ml3.o diff --git a/experiments/ml2.so b/experiments/ml2.so Binary files differindex 162e007..515e455 100755 --- a/experiments/ml2.so +++ b/experiments/ml2.so diff --git a/experiments/ml3.c b/experiments/ml3.c index 12d0e1d..59de174 100644 --- a/experiments/ml3.c +++ b/experiments/ml3.c @@ -695,7 +695,7 @@ typedef npy_longdouble __pyx_t_5numpy_longdouble_t; * DTYPE = np.float64 * ctypedef np.float_t DTYPE_t # <<<<<<<<<<<<<< * - * cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, + * cdef DTYPE_t weight_success(int dist, DTYPE_t dt, DTYPE_t alpha, DTYPE_t delta, */ typedef __pyx_t_5numpy_float_t __pyx_t_3ml3_DTYPE_t; #if CYTHON_CCOMPLEX @@ -914,6 +914,18 @@ static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { #endif #define __Pyx_BufPtrStrided1d(type, buf, i0, s0) (type)((char*)buf + i0 * s0) +static double __Pyx__PyObject_AsDouble(PyObject* obj); +#if CYTHON_COMPILING_IN_PYPY +#define __Pyx_PyObject_AsDouble(obj) \ +(likely(PyFloat_CheckExact(obj)) ? PyFloat_AS_DOUBLE(obj) : \ + likely(PyInt_CheckExact(obj)) ? \ + PyFloat_AsDouble(obj) : __Pyx__PyObject_AsDouble(obj)) +#else +#define __Pyx_PyObject_AsDouble(obj) \ +((likely(PyFloat_CheckExact(obj))) ? \ + PyFloat_AS_DOUBLE(obj) : __Pyx__PyObject_AsDouble(obj)) +#endif + #define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \ __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) : \ @@ -935,18 +947,6 @@ static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, int wraparound, int boundscheck); -static double __Pyx__PyObject_AsDouble(PyObject* obj); -#if CYTHON_COMPILING_IN_PYPY -#define __Pyx_PyObject_AsDouble(obj) \ -(likely(PyFloat_CheckExact(obj)) ? PyFloat_AS_DOUBLE(obj) : \ - likely(PyInt_CheckExact(obj)) ? \ - PyFloat_AsDouble(obj) : __Pyx__PyObject_AsDouble(obj)) -#else -#define __Pyx_PyObject_AsDouble(obj) \ -((likely(PyFloat_CheckExact(obj))) ? \ - PyFloat_AS_DOUBLE(obj) : __Pyx__PyObject_AsDouble(obj)) -#endif - static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); @@ -1169,8 +1169,8 @@ static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, cha /* Module declarations from 'libc.math' */ /* Module declarations from 'ml3' */ -static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_success(int, int, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t); /*proto*/ -static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_failure(int, int, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t); /*proto*/ +static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_success(int, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t); /*proto*/ +static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_failure(int, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t, __pyx_t_3ml3_DTYPE_t); /*proto*/ static __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_3ml3_DTYPE_t = { "DTYPE_t", NULL, sizeof(__pyx_t_3ml3_DTYPE_t), { 0 }, 0, 'R', 0, 0 }; #define __Pyx_MODULE_NAME "ml3" int __pyx_module_is_main_ml3 = 0; @@ -1329,7 +1329,6 @@ static PyObject *__pyx_n_s_w3; static PyObject *__pyx_n_s_zeros; static PyObject *__pyx_float_1_; static PyObject *__pyx_float_0_00001; -static PyObject *__pyx_float_neg_19_523275053840013; static PyObject *__pyx_int_0; static PyObject *__pyx_int_1; static PyObject *__pyx_tuple_; @@ -1345,12 +1344,12 @@ static PyObject *__pyx_codeobj__9; /* "ml3.pyx":9 * ctypedef np.float_t DTYPE_t * - * cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, # <<<<<<<<<<<<<< + * cdef DTYPE_t weight_success(int dist, DTYPE_t dt, DTYPE_t alpha, DTYPE_t delta, # <<<<<<<<<<<<<< * DTYPE_t w1, DTYPE_t w2, DTYPE_t w3): * """weight for successful infection, exponential time model""" */ -static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_success(int __pyx_v_dist, int __pyx_v_dt, __pyx_t_3ml3_DTYPE_t __pyx_v_alpha, __pyx_t_3ml3_DTYPE_t __pyx_v_delta, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w1, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w2, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w3) { +static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_success(int __pyx_v_dist, __pyx_t_3ml3_DTYPE_t __pyx_v_dt, __pyx_t_3ml3_DTYPE_t __pyx_v_alpha, __pyx_t_3ml3_DTYPE_t __pyx_v_delta, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w1, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w2, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w3) { __pyx_t_3ml3_DTYPE_t __pyx_v_structural; __pyx_t_3ml3_DTYPE_t __pyx_v_temporal; __pyx_t_3ml3_DTYPE_t __pyx_v_result; @@ -1390,7 +1389,7 @@ static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_success(int __pyx_v_dist, int __ * result = log(structural) + temporal * return result # <<<<<<<<<<<<<< * - * cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, + * cdef DTYPE_t weight_failure(int dist, DTYPE_t dt, DTYPE_t alpha, DTYPE_t delta, */ __pyx_r = __pyx_v_result; goto __pyx_L0; @@ -1398,7 +1397,7 @@ static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_success(int __pyx_v_dist, int __ /* "ml3.pyx":9 * ctypedef np.float_t DTYPE_t * - * cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, # <<<<<<<<<<<<<< + * cdef DTYPE_t weight_success(int dist, DTYPE_t dt, DTYPE_t alpha, DTYPE_t delta, # <<<<<<<<<<<<<< * DTYPE_t w1, DTYPE_t w2, DTYPE_t w3): * """weight for successful infection, exponential time model""" */ @@ -1412,12 +1411,12 @@ static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_success(int __pyx_v_dist, int __ /* "ml3.pyx":18 * return result * - * cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, # <<<<<<<<<<<<<< + * cdef DTYPE_t weight_failure(int dist, DTYPE_t dt, DTYPE_t alpha, DTYPE_t delta, # <<<<<<<<<<<<<< * DTYPE_t w1, DTYPE_t w2, DTYPE_t w3): * """weight for failed infection, exponential time model""" */ -static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_failure(int __pyx_v_dist, int __pyx_v_dt, __pyx_t_3ml3_DTYPE_t __pyx_v_alpha, __pyx_t_3ml3_DTYPE_t __pyx_v_delta, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w1, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w2, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w3) { +static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_failure(int __pyx_v_dist, __pyx_t_3ml3_DTYPE_t __pyx_v_dt, __pyx_t_3ml3_DTYPE_t __pyx_v_alpha, __pyx_t_3ml3_DTYPE_t __pyx_v_delta, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w1, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w2, CYTHON_UNUSED __pyx_t_3ml3_DTYPE_t __pyx_v_w3) { __pyx_t_3ml3_DTYPE_t __pyx_v_structural; __pyx_t_3ml3_DTYPE_t __pyx_v_temporal; __pyx_t_3ml3_DTYPE_t __pyx_v_result; @@ -1465,7 +1464,7 @@ static __pyx_t_3ml3_DTYPE_t __pyx_f_3ml3_weight_failure(int __pyx_v_dist, int __ /* "ml3.pyx":18 * return result * - * cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, # <<<<<<<<<<<<<< + * cdef DTYPE_t weight_failure(int dist, DTYPE_t dt, DTYPE_t alpha, DTYPE_t delta, # <<<<<<<<<<<<<< * DTYPE_t w1, DTYPE_t w2, DTYPE_t w3): * """weight for failed infection, exponential time model""" */ @@ -1660,12 +1659,13 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject __pyx_t_3ml3_DTYPE_t __pyx_t_25; __pyx_t_3ml3_DTYPE_t __pyx_t_26; int __pyx_t_27; - int __pyx_t_28; + double __pyx_t_28; int __pyx_t_29; int __pyx_t_30; int __pyx_t_31; - double __pyx_t_32; - PyObject *(*__pyx_t_33)(PyObject *); + int __pyx_t_32; + int __pyx_t_33; + PyObject *(*__pyx_t_34)(PyObject *); int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; @@ -1921,7 +1921,7 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject __pyx_t_11 = ((PyArrayObject *)__pyx_t_4); { __Pyx_BufFmt_StackElem __pyx_stack[1]; - if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_isSeed.rcbuffer->pybuffer, (PyObject*)__pyx_t_11, &__Pyx_TypeInfo_nn___pyx_t_3ml3_DTYPE_t, PyBUF_FORMAT| PyBUF_STRIDES| PyBUF_WRITABLE, 1, 0, __pyx_stack) == -1)) { + if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_isSeed.rcbuffer->pybuffer, (PyObject*)__pyx_t_11, &__Pyx_TypeInfo_nn___pyx_t_3ml3_DTYPE_t, PyBUF_FORMAT| PyBUF_STRIDES, 1, 0, __pyx_stack) == -1)) { __pyx_v_isSeed = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); __pyx_pybuffernd_isSeed.rcbuffer->pybuffer.buf = NULL; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 39; __pyx_clineno = __LINE__; goto __pyx_L1_error;} } else {__pyx_pybuffernd_isSeed.diminfo[0].strides = __pyx_pybuffernd_isSeed.rcbuffer->pybuffer.strides[0]; __pyx_pybuffernd_isSeed.diminfo[0].shape = __pyx_pybuffernd_isSeed.rcbuffer->pybuffer.shape[0]; @@ -1963,7 +1963,7 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject /* "ml3.pyx":46 * # fail to infect it, also computes the probability that its most * # likely parent infects it - * failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3) # <<<<<<<<<<<<<< + * failures = [weight_failure(dist, dt/100., alpha, delta, w1, w2, w3) # <<<<<<<<<<<<<< * for (dist, dt, w1, w2, w3) in parents] * probs_fail[i] = sum(failures) */ @@ -1972,10 +1972,10 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject /* "ml3.pyx":47 * # likely parent infects it - * failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3) + * failures = [weight_failure(dist, dt/100., alpha, delta, w1, w2, w3) * for (dist, dt, w1, w2, w3) in parents] # <<<<<<<<<<<<<< * probs_fail[i] = sum(failures) - * successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3) + * successes = [weight_success(dist, dt/100., alpha, delta, w1, w2, w3) */ if (unlikely(__pyx_v_parents == Py_None)) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); @@ -2071,24 +2071,24 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject /* "ml3.pyx":46 * # fail to infect it, also computes the probability that its most * # likely parent infects it - * failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3) # <<<<<<<<<<<<<< + * failures = [weight_failure(dist, dt/100., alpha, delta, w1, w2, w3) # <<<<<<<<<<<<<< * for (dist, dt, w1, w2, w3) in parents] * probs_fail[i] = sum(failures) */ __pyx_t_24 = __pyx_PyFloat_AsDouble(__pyx_v_w1); if (unlikely((__pyx_t_24 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 46; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_25 = __pyx_PyFloat_AsDouble(__pyx_v_w2); if (unlikely((__pyx_t_25 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 46; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_26 = __pyx_PyFloat_AsDouble(__pyx_v_w3); if (unlikely((__pyx_t_26 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 46; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_t_3 = PyFloat_FromDouble(__pyx_f_3ml3_weight_failure(__pyx_v_dist, __pyx_v_dt, __pyx_v_alpha, __pyx_v_delta, __pyx_t_24, __pyx_t_25, __pyx_t_26)); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 46; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyFloat_FromDouble(__pyx_f_3ml3_weight_failure(__pyx_v_dist, (__pyx_v_dt / 100.), __pyx_v_alpha, __pyx_v_delta, __pyx_t_24, __pyx_t_25, __pyx_t_26)); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 46; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); if (unlikely(__Pyx_ListComp_Append(__pyx_t_6, (PyObject*)__pyx_t_3))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 46; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "ml3.pyx":47 * # likely parent infects it - * failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3) + * failures = [weight_failure(dist, dt/100., alpha, delta, w1, w2, w3) * for (dist, dt, w1, w2, w3) in parents] # <<<<<<<<<<<<<< * probs_fail[i] = sum(failures) - * successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3) + * successes = [weight_success(dist, dt/100., alpha, delta, w1, w2, w3) */ } __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; @@ -2096,10 +2096,10 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject __pyx_t_6 = 0; /* "ml3.pyx":48 - * failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3) + * failures = [weight_failure(dist, dt/100., alpha, delta, w1, w2, w3) * for (dist, dt, w1, w2, w3) in parents] * probs_fail[i] = sum(failures) # <<<<<<<<<<<<<< - * successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3) + * successes = [weight_success(dist, dt/100., alpha, delta, w1, w2, w3) * for (dist, dt, w1, w2, w3) in parents] */ __pyx_t_6 = PyTuple_New(1); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 48; __pyx_clineno = __LINE__; goto __pyx_L1_error;} @@ -2119,7 +2119,7 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject /* "ml3.pyx":49 * for (dist, dt, w1, w2, w3) in parents] * probs_fail[i] = sum(failures) - * successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3) # <<<<<<<<<<<<<< + * successes = [weight_success(dist, dt/100., alpha, delta, w1, w2, w3) # <<<<<<<<<<<<<< * for (dist, dt, w1, w2, w3) in parents] * dists = [dist for (dist, dt, w1, w2, w3) in parents] */ @@ -2128,7 +2128,7 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject /* "ml3.pyx":50 * probs_fail[i] = sum(failures) - * successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3) + * successes = [weight_success(dist, dt/100., alpha, delta, w1, w2, w3) * for (dist, dt, w1, w2, w3) in parents] # <<<<<<<<<<<<<< * dists = [dist for (dist, dt, w1, w2, w3) in parents] * dts = [dt for (dist, dt, w1, w2, w3) in parents] @@ -2227,21 +2227,21 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject /* "ml3.pyx":49 * for (dist, dt, w1, w2, w3) in parents] * probs_fail[i] = sum(failures) - * successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3) # <<<<<<<<<<<<<< + * successes = [weight_success(dist, dt/100., alpha, delta, w1, w2, w3) # <<<<<<<<<<<<<< * for (dist, dt, w1, w2, w3) in parents] * dists = [dist for (dist, dt, w1, w2, w3) in parents] */ __pyx_t_26 = __pyx_PyFloat_AsDouble(__pyx_v_w1); if (unlikely((__pyx_t_26 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 49; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_25 = __pyx_PyFloat_AsDouble(__pyx_v_w2); if (unlikely((__pyx_t_25 == (npy_double)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 49; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __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 = 49; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_t_3 = PyFloat_FromDouble(__pyx_f_3ml3_weight_success(__pyx_v_dist, __pyx_v_dt, __pyx_v_alpha, __pyx_v_delta, __pyx_t_26, __pyx_t_25, __pyx_t_24)); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 49; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyFloat_FromDouble(__pyx_f_3ml3_weight_success(__pyx_v_dist, (__pyx_v_dt / 100.), __pyx_v_alpha, __pyx_v_delta, __pyx_t_26, __pyx_t_25, __pyx_t_24)); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 49; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); if (unlikely(__Pyx_ListComp_Append(__pyx_t_5, (PyObject*)__pyx_t_3))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 49; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; /* "ml3.pyx":50 * probs_fail[i] = sum(failures) - * successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3) + * successes = [weight_success(dist, dt/100., alpha, delta, w1, w2, w3) * for (dist, dt, w1, w2, w3) in parents] # <<<<<<<<<<<<<< * dists = [dist for (dist, dt, w1, w2, w3) in parents] * dts = [dt for (dist, dt, w1, w2, w3) in parents] @@ -2252,7 +2252,7 @@ static PyObject *__pyx_pf_3ml3_ml3(CYTHON_UNUSED PyObject *__pyx_self, PyObject __pyx_t_5 = 0; /* "ml3.pyx":51 - * successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3) + * successes = [weight_success(dist, dt/100., alpha, delta, w1, w2, w3) * for (dist, dt, w1, w2, w3) in parents] * dists = [dist for (dist, dt, w1, w2, w3) in parents] # <<<<<<<<<<<<<< * dts = [dt for (dist, dt, w1, w2, w3) in parents] @@ 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__LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_16); PyTuple_SET_ITEM(__pyx_t_16, 0, __pyx_t_5); __Pyx_GIVEREF(__pyx_t_5); PyTuple_SET_ITEM(__pyx_t_16, 1, __pyx_t_17); __Pyx_GIVEREF(__pyx_t_17); - PyTuple_SET_ITEM(__pyx_t_16, 2, __pyx_t_3); - __Pyx_GIVEREF(__pyx_t_3); + PyTuple_SET_ITEM(__pyx_t_16, 2, __pyx_t_6); + __Pyx_GIVEREF(__pyx_t_6); __pyx_t_5 = 0; __pyx_t_17 = 0; - __pyx_t_3 = 0; + __pyx_t_6 = 0; __pyx_r = __pyx_t_16; __pyx_t_16 = 0; goto __pyx_L0; @@ -5093,14 +5099,14 @@ static int __Pyx_InitCachedConstants(void) { __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("__Pyx_InitCachedConstants", 0); - /* "ml3.pyx":71 + /* "ml3.pyx":70 * max_beta_add = float('-inf') * # iterate over all victim nodes to find the optimal threshold * for beta in np.arange(0.00001, 1., 1.): # <<<<<<<<<<<<<< * thresh = log(beta/(3012.*(1.-beta))) * seeds = isSeed==1 */ - __pyx_tuple_ = PyTuple_Pack(3, __pyx_float_0_00001, __pyx_float_1_, __pyx_float_1_); if (unlikely(!__pyx_tuple_)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_tuple_ = PyTuple_Pack(3, __pyx_float_0_00001, __pyx_float_1_, __pyx_float_1_); if (unlikely(!__pyx_tuple_)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 70; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple_); __Pyx_GIVEREF(__pyx_tuple_); @@ -5192,7 +5198,6 @@ static int __Pyx_InitGlobals(void) { if (__Pyx_InitStrings(__pyx_string_tab) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; __pyx_float_1_ = PyFloat_FromDouble(1.); if (unlikely(!__pyx_float_1_)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_float_0_00001 = PyFloat_FromDouble(0.00001); if (unlikely(!__pyx_float_0_00001)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_float_neg_19_523275053840013 = PyFloat_FromDouble(-19.523275053840013); if (unlikely(!__pyx_float_neg_19_523275053840013)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_int_0 = PyInt_FromLong(0); if (unlikely(!__pyx_int_0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_int_1 = PyInt_FromLong(1); if (unlikely(!__pyx_int_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} return 0; @@ -6464,6 +6469,45 @@ static CYTHON_INLINE int __Pyx_dict_iter_next(PyObject* iter_obj, Py_ssize_t ori return 1; } +static double __Pyx__PyObject_AsDouble(PyObject* obj) { + PyObject* float_value; +#if CYTHON_COMPILING_IN_PYPY + float_value = PyNumber_Float(obj); +#else + PyNumberMethods *nb = Py_TYPE(obj)->tp_as_number; + if (likely(nb) && likely(nb->nb_float)) { + float_value = nb->nb_float(obj); + if (likely(float_value) && unlikely(!PyFloat_Check(float_value))) { + PyErr_Format(PyExc_TypeError, + "__float__ returned non-float (type %.200s)", + Py_TYPE(float_value)->tp_name); + Py_DECREF(float_value); + goto bad; + } + } else if (PyUnicode_CheckExact(obj) || PyBytes_CheckExact(obj)) { +#if PY_MAJOR_VERSION >= 3 + float_value = PyFloat_FromString(obj); +#else + float_value = PyFloat_FromString(obj, 0); +#endif + } else { + PyObject* args = PyTuple_New(1); + if (unlikely(!args)) goto bad; + PyTuple_SET_ITEM(args, 0, obj); + float_value = PyObject_Call((PyObject*)&PyFloat_Type, args, 0); + PyTuple_SET_ITEM(args, 0, 0); + Py_DECREF(args); + } +#endif + if (likely(float_value)) { + double value = PyFloat_AS_DOUBLE(float_value); + Py_DECREF(float_value); + return value; + } +bad: + return (double)-1; +} + static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { PyObject *r; if (!j) return NULL; @@ -6542,45 +6586,6 @@ static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); } -static double __Pyx__PyObject_AsDouble(PyObject* obj) { - PyObject* float_value; -#if CYTHON_COMPILING_IN_PYPY - float_value = PyNumber_Float(obj); -#else - PyNumberMethods *nb = Py_TYPE(obj)->tp_as_number; - if (likely(nb) && likely(nb->nb_float)) { - float_value = nb->nb_float(obj); - if (likely(float_value) && unlikely(!PyFloat_Check(float_value))) { - PyErr_Format(PyExc_TypeError, - "__float__ returned non-float (type %.200s)", - Py_TYPE(float_value)->tp_name); - Py_DECREF(float_value); - goto bad; - } - } else if (PyUnicode_CheckExact(obj) || PyBytes_CheckExact(obj)) { -#if PY_MAJOR_VERSION >= 3 - float_value = PyFloat_FromString(obj); -#else - float_value = PyFloat_FromString(obj, 0); -#endif - } else { - PyObject* args = PyTuple_New(1); - if (unlikely(!args)) goto bad; - PyTuple_SET_ITEM(args, 0, obj); - float_value = PyObject_Call((PyObject*)&PyFloat_Type, args, 0); - PyTuple_SET_ITEM(args, 0, 0); - Py_DECREF(args); - } -#endif - if (likely(float_value)) { - double value = PyFloat_AS_DOUBLE(float_value); - Py_DECREF(float_value); - return value; - } -bad: - return (double)-1; -} - static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { #if CYTHON_COMPILING_IN_CPYTHON PyObject *tmp_type, *tmp_value, *tmp_tb; diff --git a/experiments/ml3.pyx b/experiments/ml3.pyx index faeda92..da993bc 100644 --- a/experiments/ml3.pyx +++ b/experiments/ml3.pyx @@ -6,7 +6,7 @@ from libc.math cimport log, exp DTYPE = np.float64 ctypedef np.float_t DTYPE_t -cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, +cdef DTYPE_t weight_success(int dist, DTYPE_t dt, DTYPE_t alpha, DTYPE_t delta, DTYPE_t w1, DTYPE_t w2, DTYPE_t w3): """weight for successful infection, exponential time model""" cdef DTYPE_t structural, temporal, result @@ -15,7 +15,7 @@ cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, result = log(structural) + temporal return result -cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, +cdef DTYPE_t weight_failure(int dist, DTYPE_t dt, DTYPE_t alpha, DTYPE_t delta, DTYPE_t w1, DTYPE_t w2, DTYPE_t w3): """weight for failed infection, exponential time model""" cdef DTYPE_t structural, temporal, result @@ -43,21 +43,20 @@ def ml3(dict root_victims, dict victims, dict non_victims, DTYPE_t age, # 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, w1, w2, w3) + failures = [weight_failure(dist, dt/100., alpha, delta, w1, w2, w3) for (dist, dt, w1, w2, w3) in parents] probs_fail[i] = sum(failures) - successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3) + successes = [weight_success(dist, dt/100., alpha, delta, w1, w2, w3) for (dist, dt, w1, w2, w3) in parents] dists = [dist for (dist, dt, w1, w2, w3) in parents] dts = [dt for (dist, dt, w1, w2, w3) in parents] # find parent that maximizes log(p) - log(\tilde{p}) # probs[i] = max(s - failures[l] for l, s in enumerate(successes)) - # probs[i] = 0. + probs[i] = float("-inf") for l, s in enumerate(successes): prob = s - failures[l] - if prob > -19.523275053840013: - isSeed[i] = 0 - probs[i] += prob + if prob > probs[i]: + probs[i] = prob parent_dists[i] = dists[l] parent_dts[i] = dts[l] # probs_fail[i] = failures[l] diff --git a/experiments/ml3.so b/experiments/ml3.so Binary files differindex 2dd1f2d..d2b6898 100755 --- a/experiments/ml3.so +++ b/experiments/ml3.so diff --git a/experiments/out.log b/experiments/out.log index b6cddf7..6ac961e 100644 --- a/experiments/out.log +++ b/experiments/out.log @@ -1,735 +1,225 @@ -0.03 0.09 0.1 8 -2524.16991366 -0.03 0.091 0.1 8 -2523.37447955 -0.03 0.092 0.1 8 -2522.66916324 -0.03 0.093 0.1 8 -2522.05302411 -0.03 0.094 0.1 8 -2521.52515842 -0.03 0.095 0.1 8 -2521.08469776 -0.03 0.096 0.1 8 -2520.73080755 -0.03 0.097 0.1 8 -2520.46268567 -0.03 0.098 0.1 8 -2520.27956114 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-220155.435246 diff --git a/experiments/process.py b/experiments/process.py index 5f3cff4..f31e0d4 100644 --- a/experiments/process.py +++ b/experiments/process.py @@ -15,20 +15,20 @@ if __name__ == "__main__": sys.exit("usage: {0} <file>".format(sys.argv[0])) root_victims, victims, non_victims, age = load(open(sys.argv[1])) - # # alphas = np.arange(1e-3, 1e-2, 8e-4) # parameter of the time component - # alphas = np.logspace(-3,0,num=15) - # # deltas = np.arange(0.001, 0.3, 0.008) # parameter of the structural component - # deltas = np.logspace(-4,-1.7,num=15) - # with open("out.log", "w") as fh: - # for alpha, delta in product(alphas, deltas): - # beta, roots, ll = ml3(root_victims, victims, non_victims, age, alpha, delta) - # print "\t".join(map(str, [alpha, delta, beta, roots, ll])) - # fh.write("\t".join(map(str, [alpha, delta, beta, roots, ll])) + "\n") - # fh.flush() + # alphas = np.arange(1e-3, 1e-2, 8e-4) # parameter of the time component + alphas = np.logspace(-4,-1,num=15) + # deltas = np.arange(0.001, 0.3, 0.008) # parameter of the structural component + deltas = np.logspace(-4,-1.7,num=15) + with open("out.log", "w") as fh: + for alpha, delta in product(alphas, deltas): + beta, roots, ll = ml3(root_victims, victims, non_victims, age, alpha, delta) + print "\t".join(map(str, [alpha, delta, beta, roots, ll])) + fh.write("\t".join(map(str, [alpha, delta, beta, roots, ll])) + "\n") + fh.flush() - # alphas = np.arange(.03, .065, .001) # parameter of the time component + # alphas = np.arange(.04, .075, .003) # parameter of the time component # # alphas = np.logspace(-4,-1,num=20) - # deltas = np.arange(.09, .11, .001) # parameter of the structural component + # deltas = np.arange(.07, .11, .003) # parameter of the structural component # # deltas = np.logspace(-2,-.5,num=20) # lmbda = 0.10#np.logspace(-12,-3,num=10) # with open("out.log", "w") as fh: @@ -38,7 +38,7 @@ if __name__ == "__main__": # fh.write("\t".join(map(str, [alpha, delta, lmbda, roots, ll])) + "\n") # fh.flush() - alpha = 0.041 - delta = 0.01 - beta, roots, ll = ml2(root_victims, victims, non_victims, alpha, delta, 1.) - print "\t".join(map(str, [alpha, delta, beta, roots, ll]))
\ No newline at end of file + # alpha = 0.061 + # delta = 0.082 + # beta, roots, ll = ml2(root_victims, victims, non_victims, alpha, delta, 1.) + # print "\t".join(map(str, [alpha, delta, beta, roots, ll]))
\ No newline at end of file |