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cpython/Modules/_statisticsmodule.c at pyfunction_getannotations · larryhastings/cpython · GitHub
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pyfunction_getannotations
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cpython
/
Modules
/
_statisticsmodule.c
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151 lines (137 loc) · 4.81 KB
pyfunction_getannotations
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cpython
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Modules
/
_statisticsmodule.c
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/* statistics accelerator C extension: _statistics module. */
#include
"Python.h"
#include
"clinic/_statisticsmodule.c.h"
/*[clinic input]
module _statistics
[clinic start generated code]*/
/*[clinic end generated code: output=da39a3ee5e6b4b0d input=864a6f59b76123b2]*/
/*
* There is no closed-form solution to the inverse CDF for the normal
* distribution, so we use a rational approximation instead:
* Wichura, M.J. (1988). "Algorithm AS241: The Percentage Points of the
* Normal Distribution". Applied Statistics. Blackwell Publishing. 37
* (3): 477–484. doi:10.2307/2347330. JSTOR 2347330.
*/
/*[clinic input]
_statistics._normal_dist_inv_cdf -> double
p: double
mu: double
sigma: double
/
[clinic start generated code]*/
static
double
_statistics__normal_dist_inv_cdf_impl
(
PyObject
*
module
,
double
p
,
double
mu
,
double
sigma
)
/*[clinic end generated code: output=02fd19ddaab36602 input=24715a74be15296a]*/
{
double
q
,
num
,
den
,
r
,
x
;
if
(
p
<=
0.0
||
p
>=
1.0
) {
goto
error
;
}
q
=
p
-
0.5
;
if
(
fabs
(
q
) <=
0.425
) {
r
=
0.180625
-
q
*
q
;
// Hash sum-55.8831928806149014439
num
=
(((((((
2.5090809287301226727e+3
*
r
+
3.3430575583588128105e+4
)
*
r
+
6.7265770927008700853e+4
)
*
r
+
4.5921953931549871457e+4
)
*
r
+
1.3731693765509461125e+4
)
*
r
+
1.9715909503065514427e+3
)
*
r
+
1.3314166789178437745e+2
)
*
r
+
3.3871328727963666080e+0
)
*
q
;
den
=
(((((((
5.2264952788528545610e+3
*
r
+
2.8729085735721942674e+4
)
*
r
+
3.9307895800092710610e+4
)
*
r
+
2.1213794301586595867e+4
)
*
r
+
5.3941960214247511077e+3
)
*
r
+
6.8718700749205790830e+2
)
*
r
+
4.2313330701600911252e+1
)
*
r
+
1.0
);
if
(
den
==
0.0
) {
goto
error
;
}
x
=
num
/
den
;
return
mu
+
(
x
*
sigma
);
}
r
=
(
q
<=
0.0
) ?
p
: (
1.0
-
p
);
if
(
r
<=
0.0
||
r
>=
1.0
) {
goto
error
;
}
r
=
sqrt
(
-
log
(
r
));
if
(
r
<=
5.0
) {
r
=
r
-
1.6
;
// Hash sum-49.33206503301610289036
num
=
(((((((
7.74545014278341407640e-4
*
r
+
2.27238449892691845833e-2
)
*
r
+
2.41780725177450611770e-1
)
*
r
+
1.27045825245236838258e+0
)
*
r
+
3.64784832476320460504e+0
)
*
r
+
5.76949722146069140550e+0
)
*
r
+
4.63033784615654529590e+0
)
*
r
+
1.42343711074968357734e+0
);
den
=
(((((((
1.05075007164441684324e-9
*
r
+
5.47593808499534494600e-4
)
*
r
+
1.51986665636164571966e-2
)
*
r
+
1.48103976427480074590e-1
)
*
r
+
6.89767334985100004550e-1
)
*
r
+
1.67638483018380384940e+0
)
*
r
+
2.05319162663775882187e+0
)
*
r
+
1.0
);
}
else
{
r
-=
5.0
;
// Hash sum-47.52583317549289671629
num
=
(((((((
2.01033439929228813265e-7
*
r
+
2.71155556874348757815e-5
)
*
r
+
1.24266094738807843860e-3
)
*
r
+
2.65321895265761230930e-2
)
*
r
+
2.96560571828504891230e-1
)
*
r
+
1.78482653991729133580e+0
)
*
r
+
5.46378491116411436990e+0
)
*
r
+
6.65790464350110377720e+0
);
den
=
(((((((
2.04426310338993978564e-15
*
r
+
1.42151175831644588870e-7
)
*
r
+
1.84631831751005468180e-5
)
*
r
+
7.86869131145613259100e-4
)
*
r
+
1.48753612908506148525e-2
)
*
r
+
1.36929880922735805310e-1
)
*
r
+
5.99832206555887937690e-1
)
*
r
+
1.0
);
}
if
(
den
==
0.0
) {
goto
error
;
}
x
=
num
/
den
;
if
(
q
<
0.0
) {
x
=
-
x
;
}
return
mu
+
(
x
*
sigma
);
error
:
PyErr_SetString
(
PyExc_ValueError
,
"inv_cdf undefined for these parameters"
);
return
-1.0
;
}
static
PyMethodDef
statistics_methods
[]
=
{
_STATISTICS__NORMAL_DIST_INV_CDF_METHODDEF
{
NULL
,
NULL
,
0
,
NULL
}
};
PyDoc_STRVAR
(
statistics_doc
,
"Accelerators for the statistics module.\n"
);
static
struct
PyModuleDef_Slot
_statisticsmodule_slots
[]
=
{
{
0
,
NULL
}
};
static
struct
PyModuleDef
statisticsmodule
=
{
PyModuleDef_HEAD_INIT
,
"_statistics"
,
statistics_doc
,
0
,
statistics_methods
,
_statisticsmodule_slots
,
NULL
,
NULL
,
NULL
};
PyMODINIT_FUNC
PyInit__statistics
(
void
)
{
return
PyModuleDef_Init
(
&
statisticsmodule
);
}
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