bpo-37798: Add C fastpath for statistics.NormalDist.inv_cdf() (GH-15266)

Lib/statistics.py

@@ -824,6 +824,81 @@ def pstdev(data, mu=None):
 
 ## Normal Distribution #####################################################
 
+
+def _normal_dist_inv_cdf(p, mu, sigma):
+    # 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.
+    q = p - 0.5
+    if fabs(q) <= 0.425:
+        r = 0.180625 - q * q
+        # Hash sum: 55.88319_28806_14901_4439
+        num = (((((((2.50908_09287_30122_6727e+3 * r +
+                     3.34305_75583_58812_8105e+4) * r +
+                     6.72657_70927_00870_0853e+4) * r +
+                     4.59219_53931_54987_1457e+4) * r +
+                     1.37316_93765_50946_1125e+4) * r +
+                     1.97159_09503_06551_4427e+3) * r +
+                     1.33141_66789_17843_7745e+2) * r +
+                     3.38713_28727_96366_6080e+0) * q
+        den = (((((((5.22649_52788_52854_5610e+3 * r +
+                     2.87290_85735_72194_2674e+4) * r +
+                     3.93078_95800_09271_0610e+4) * r +
+                     2.12137_94301_58659_5867e+4) * r +
+                     5.39419_60214_24751_1077e+3) * r +
+                     6.87187_00749_20579_0830e+2) * r +
+                     4.23133_30701_60091_1252e+1) * r +
+                     1.0)
+        x = num / den
+        return mu + (x * sigma)
+    r = p if q <= 0.0 else 1.0 - p
+    r = sqrt(-log(r))
+    if r <= 5.0:
+        r = r - 1.6
+        # Hash sum: 49.33206_50330_16102_89036
+        num = (((((((7.74545_01427_83414_07640e-4 * r +
+                     2.27238_44989_26918_45833e-2) * r +
+                     2.41780_72517_74506_11770e-1) * r +
+                     1.27045_82524_52368_38258e+0) * r +
+                     3.64784_83247_63204_60504e+0) * r +
+                     5.76949_72214_60691_40550e+0) * r +
+                     4.63033_78461_56545_29590e+0) * r +
+                     1.42343_71107_49683_57734e+0)
+        den = (((((((1.05075_00716_44416_84324e-9 * r +
+                     5.47593_80849_95344_94600e-4) * r +
+                     1.51986_66563_61645_71966e-2) * r +
+                     1.48103_97642_74800_74590e-1) * r +
+                     6.89767_33498_51000_04550e-1) * r +
+                     1.67638_48301_83803_84940e+0) * r +
+                     2.05319_16266_37758_82187e+0) * r +
+                     1.0)
+    else:
+        r = r - 5.0
+        # Hash sum: 47.52583_31754_92896_71629
+        num = (((((((2.01033_43992_92288_13265e-7 * r +
+                     2.71155_55687_43487_57815e-5) * r +
+                     1.24266_09473_88078_43860e-3) * r +
+                     2.65321_89526_57612_30930e-2) * r +
+                     2.96560_57182_85048_91230e-1) * r +
+                     1.78482_65399_17291_33580e+0) * r +
+                     5.46378_49111_64114_36990e+0) * r +
+                     6.65790_46435_01103_77720e+0)
+        den = (((((((2.04426_31033_89939_78564e-15 * r +
+                     1.42151_17583_16445_88870e-7) * r +
+                     1.84631_83175_10054_68180e-5) * r +
+                     7.86869_13114_56132_59100e-4) * r +
+                     1.48753_61290_85061_48525e-2) * r +
+                     1.36929_88092_27358_05310e-1) * r +
+                     5.99832_20655_58879_37690e-1) * r +
+                     1.0)
+    x = num / den
+    if q < 0.0:
+        x = -x
+    return mu + (x * sigma)
+
+
 class NormalDist:
     "Normal distribution of a random variable"
     # https://en.wikipedia.org/wiki/Normal_distribution
@@ -882,79 +957,7 @@ class NormalDist:
             raise StatisticsError('p must be in the range 0.0 < p < 1.0')
         if self._sigma <= 0.0:
             raise StatisticsError('cdf() not defined when sigma at or below zero')
-
+        return _normal_dist_inv_cdf(p, self._mu, self._sigma)
-        # 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.
-
-        q = p - 0.5
-        if fabs(q) <= 0.425:
-            r = 0.180625 - q * q
-            # Hash sum: 55.88319_28806_14901_4439
-            num = (((((((2.50908_09287_30122_6727e+3 * r +
-                         3.34305_75583_58812_8105e+4) * r +
-                         6.72657_70927_00870_0853e+4) * r +
-                         4.59219_53931_54987_1457e+4) * r +
-                         1.37316_93765_50946_1125e+4) * r +
-                         1.97159_09503_06551_4427e+3) * r +
-                         1.33141_66789_17843_7745e+2) * r +
-                         3.38713_28727_96366_6080e+0) * q
-            den = (((((((5.22649_52788_52854_5610e+3 * r +
-                         2.87290_85735_72194_2674e+4) * r +
-                         3.93078_95800_09271_0610e+4) * r +
-                         2.12137_94301_58659_5867e+4) * r +
-                         5.39419_60214_24751_1077e+3) * r +
-                         6.87187_00749_20579_0830e+2) * r +
-                         4.23133_30701_60091_1252e+1) * r +
-                         1.0)
-            x = num / den
-            return self._mu + (x * self._sigma)
-        r = p if q <= 0.0 else 1.0 - p
-        r = sqrt(-log(r))
-        if r <= 5.0:
-            r = r - 1.6
-            # Hash sum: 49.33206_50330_16102_89036
-            num = (((((((7.74545_01427_83414_07640e-4 * r +
-                         2.27238_44989_26918_45833e-2) * r +
-                         2.41780_72517_74506_11770e-1) * r +
-                         1.27045_82524_52368_38258e+0) * r +
-                         3.64784_83247_63204_60504e+0) * r +
-                         5.76949_72214_60691_40550e+0) * r +
-                         4.63033_78461_56545_29590e+0) * r +
-                         1.42343_71107_49683_57734e+0)
-            den = (((((((1.05075_00716_44416_84324e-9 * r +
-                         5.47593_80849_95344_94600e-4) * r +
-                         1.51986_66563_61645_71966e-2) * r +
-                         1.48103_97642_74800_74590e-1) * r +
-                         6.89767_33498_51000_04550e-1) * r +
-                         1.67638_48301_83803_84940e+0) * r +
-                         2.05319_16266_37758_82187e+0) * r +
-                         1.0)
-        else:
-            r = r - 5.0
-            # Hash sum: 47.52583_31754_92896_71629
-            num = (((((((2.01033_43992_92288_13265e-7 * r +
-                         2.71155_55687_43487_57815e-5) * r +
-                         1.24266_09473_88078_43860e-3) * r +
-                         2.65321_89526_57612_30930e-2) * r +
-                         2.96560_57182_85048_91230e-1) * r +
-                         1.78482_65399_17291_33580e+0) * r +
-                         5.46378_49111_64114_36990e+0) * r +
-                         6.65790_46435_01103_77720e+0)
-            den = (((((((2.04426_31033_89939_78564e-15 * r +
-                         1.42151_17583_16445_88870e-7) * r +
-                         1.84631_83175_10054_68180e-5) * r +
-                         7.86869_13114_56132_59100e-4) * r +
-                         1.48753_61290_85061_48525e-2) * r +
-                         1.36929_88092_27358_05310e-1) * r +
-                         5.99832_20655_58879_37690e-1) * r +
-                         1.0)
-        x = num / den
-        if q < 0.0:
-            x = -x
-        return self._mu + (x * self._sigma)
 
     def overlap(self, other):
         """Compute the overlapping coefficient (OVL) between two normal distributions.
@@ -1078,6 +1081,12 @@ class NormalDist:
     def __repr__(self):
         return f'{type(self).__name__}(mu={self._mu!r}, sigma={self._sigma!r})'
 
+# If available, use C implementation
+try:
+    from _statistics import _normal_dist_inv_cdf
+except ImportError:
+    pass
+
 
 if __name__ == '__main__':
 

Misc/NEWS.d/next/Library/2019-08-14-13-51-24.bpo-37798.AmXrik.rst

@@ -0,0 +1 @@
+Add C fastpath for statistics.NormalDist.inv_cdf() Patch by Dong-hee Na

Modules/Setup

@@ -182,6 +182,7 @@ _symtable symtablemodule.c
 #_heapq _heapqmodule.c	# Heap queue algorithm
 #_asyncio _asynciomodule.c  # Fast asyncio Future
 #_json -I$(srcdir)/Include/internal -DPy_BUILD_CORE_BUILTIN _json.c	# _json speedups
+#_statistics _statisticsmodule.c # statistics accelerator
 
 #unicodedata unicodedata.c    # static Unicode character database
 

Modules/_statisticsmodule.c

@@ -0,0 +1,122 @@
+/* statistics accelerator C extensor: _statistics module. */
+
+#include "Python.h"
+#include "structmember.h"
+#include "clinic/_statisticsmodule.c.h"
+
+/*[clinic input]
+module _statistics
+
+[clinic start generated code]*/
+/*[clinic end generated code: output=da39a3ee5e6b4b0d input=864a6f59b76123b2]*/
+
+
+static PyMethodDef speedups_methods[] = {
+    _STATISTICS__NORMAL_DIST_INV_CDF_METHODDEF
+    {NULL, NULL, 0, NULL}
+};
+
+/*[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;
+    q = p - 0.5;
+    // Algorithm AS 241: The Percentage Points of the Normal Distribution
+    if(fabs(q) <= 0.425) {
+        r = 0.180625 - q * q;
+        // Hash sum AB: 55.88319 28806 14901 4439
+        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);
+        x = num / den;
+        return mu + (x * sigma);
+    }
+    r = q <= 0.0? p : 1.0-p;
+    r = sqrt(-log(r));
+    if (r <= 5.0) {
+        r = r - 1.6;
+        // Hash sum CD: 49.33206 50330 16102 89036
+        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 EF: 47.52583 31754 92896 71629
+        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);
+    }
+    x = num / den;
+    if (q < 0.0) x = -x;
+    return mu + (x * sigma);
+}
+
+static struct PyModuleDef statisticsmodule = {
+        PyModuleDef_HEAD_INIT,
+        "_statistics",
+        _statistics__normal_dist_inv_cdf__doc__,
+        -1,
+        speedups_methods,
+        NULL,
+        NULL,
+        NULL,
+        NULL
+};
+
+
+PyMODINIT_FUNC
+PyInit__statistics(void)
+{
+    PyObject *m = PyModule_Create(&statisticsmodule);
+    if (!m) return NULL;
+    return m;
+}

Modules/clinic/_statisticsmodule.c.h

@@ -0,0 +1,50 @@
+/*[clinic input]
+preserve
+[clinic start generated code]*/
+
+PyDoc_STRVAR(_statistics__normal_dist_inv_cdf__doc__,
+"_normal_dist_inv_cdf($module, p, mu, sigma, /)\n"
+"--\n"
+"\n");
+
+#define _STATISTICS__NORMAL_DIST_INV_CDF_METHODDEF    \
+    {"_normal_dist_inv_cdf", (PyCFunction)(void(*)(void))_statistics__normal_dist_inv_cdf, METH_FASTCALL, _statistics__normal_dist_inv_cdf__doc__},
+
+static double
+_statistics__normal_dist_inv_cdf_impl(PyObject *module, double p, double mu,
+                                      double sigma);
+
+static PyObject *
+_statistics__normal_dist_inv_cdf(PyObject *module, PyObject *const *args, Py_ssize_t nargs)
+{
+    PyObject *return_value = NULL;
+    double p;
+    double mu;
+    double sigma;
+    double _return_value;
+
+    if (!_PyArg_CheckPositional("_normal_dist_inv_cdf", nargs, 3, 3)) {
+        goto exit;
+    }
+    p = PyFloat_AsDouble(args[0]);
+    if (PyErr_Occurred()) {
+        goto exit;
+    }
+    mu = PyFloat_AsDouble(args[1]);
+    if (PyErr_Occurred()) {
+        goto exit;
+    }
+    sigma = PyFloat_AsDouble(args[2]);
+    if (PyErr_Occurred()) {
+        goto exit;
+    }
+    _return_value = _statistics__normal_dist_inv_cdf_impl(module, p, mu, sigma);
+    if ((_return_value == -1.0) && PyErr_Occurred()) {
+        goto exit;
+    }
+    return_value = PyFloat_FromDouble(_return_value);
+
+exit:
+    return return_value;
+}
+/*[clinic end generated code: output=ba6af124acd34732 input=a9049054013a1b77]*/

PC/config.c

@@ -23,6 +23,7 @@ extern PyObject* PyInit__sha1(void);
 extern PyObject* PyInit__sha256(void);
 extern PyObject* PyInit__sha512(void);
 extern PyObject* PyInit__sha3(void);
+extern PyObject* PyInit__statistics(void);
 extern PyObject* PyInit__blake2(void);
 extern PyObject* PyInit_time(void);
 extern PyObject* PyInit__thread(void);
@@ -103,6 +104,7 @@ struct _inittab _PyImport_Inittab[] = {
     {"_blake2", PyInit__blake2},
     {"time", PyInit_time},
     {"_thread", PyInit__thread},
+    {"_statistics", PyInit__statistics},
 #ifdef WIN32
     {"msvcrt", PyInit_msvcrt},
     {"_locale", PyInit__locale},

PCbuild/pythoncore.vcxproj

@@ -333,6 +333,7 @@
     <ClCompile Include="..\Modules\sha256module.c" />
     <ClCompile Include="..\Modules\sha512module.c" />
     <ClCompile Include="..\Modules\signalmodule.c" />
+    <ClCompile Include="..\Modules\_statisticsmodule.c" />
     <ClCompile Include="..\Modules\symtablemodule.c" />
     <ClCompile Include="..\Modules\_threadmodule.c" />
     <ClCompile Include="..\Modules\_tracemalloc.c" />

PCbuild/pythoncore.vcxproj.filters

@@ -611,6 +611,9 @@
     <ClCompile Include="..\Modules\_sre.c">
       <Filter>Modules</Filter>
     </ClCompile>
+    <ClCompile Include="..\Modules\_statisticsmodule.c">
+      <Filter>Modules</Filter>
+    </ClCompile>
     <ClCompile Include="..\Modules\_struct.c">
       <Filter>Modules</Filter>
     </ClCompile>

setup.py

@@ -785,6 +785,8 @@ class PyBuildExt(build_ext):
         self.add(Extension("_abc", ["_abc.c"]))
         # _queue module
         self.add(Extension("_queue", ["_queuemodule.c"]))
+        # _statistics module
+        self.add(Extension("_statistics", ["_statisticsmodule.c"]))
 
         # Modules with some UNIX dependencies -- on by default:
         # (If you have a really backward UNIX, select and socket may not be