Restoring authorship annotation for <shadchin@yandex-team.ru>. Commit 2 of 2.

1 files changed, 151 insertions, 151 deletions

diff --git a/contrib/tools/python3/src/Modules/_statisticsmodule.c b/contrib/tools/python3/src/Modules/_statisticsmodule.c
index fd65b659b4..78c0676a01 100644
--- a/contrib/tools/python3/src/Modules/_statisticsmodule.c
+++ b/contrib/tools/python3/src/Modules/_statisticsmodule.c
@@ -1,151 +1,151 @@
-/* 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 || sigma <= 0.0) { 
-        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); 
-} 
+/* 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 || sigma <= 0.0) {
+        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);
+}