bpo-37798: Minor code formatting and comment clean-ups. (GH-15526)
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@ -1,4 +1,4 @@
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/* statistics accelerator C extensor: _statistics module. */
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/* statistics accelerator C extension: _statistics module. */
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#include "Python.h"
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#include "structmember.h"
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@ -10,11 +10,13 @@ module _statistics
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[clinic start generated code]*/
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/*[clinic end generated code: output=da39a3ee5e6b4b0d input=864a6f59b76123b2]*/
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static PyMethodDef speedups_methods[] = {
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_STATISTICS__NORMAL_DIST_INV_CDF_METHODDEF
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{NULL, NULL, 0, NULL}
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};
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/*
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* There is no closed-form solution to the inverse CDF for the normal
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* distribution, so we use a rational approximation instead:
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* Wichura, M.J. (1988). "Algorithm AS241: The Percentage Points of the
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* Normal Distribution". Applied Statistics. Blackwell Publishing. 37
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* (3): 477–484. doi:10.2307/2347330. JSTOR 2347330.
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*/
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/*[clinic input]
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_statistics._normal_dist_inv_cdf -> double
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@ -34,7 +36,7 @@ _statistics__normal_dist_inv_cdf_impl(PyObject *module, double p, double mu,
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// Algorithm AS 241: The Percentage Points of the Normal Distribution
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if(fabs(q) <= 0.425) {
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r = 0.180625 - q * q;
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// Hash sum AB: 55.88319 28806 14901 4439
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// Hash sum-55.8831928806149014439
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num = (((((((2.5090809287301226727e+3 * r +
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3.3430575583588128105e+4) * r +
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6.7265770927008700853e+4) * r +
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@ -54,11 +56,11 @@ _statistics__normal_dist_inv_cdf_impl(PyObject *module, double p, double mu,
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x = num / den;
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return mu + (x * sigma);
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}
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r = q <= 0.0? p : 1.0-p;
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r = (q <= 0.0) ? p : (1.0 - p);
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r = sqrt(-log(r));
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if (r <= 5.0) {
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r = r - 1.6;
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// Hash sum CD: 49.33206 50330 16102 89036
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// Hash sum-49.33206503301610289036
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num = (((((((7.74545014278341407640e-4 * r +
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2.27238449892691845833e-2) * r +
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2.41780725177450611770e-1) * r +
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@ -77,7 +79,7 @@ _statistics__normal_dist_inv_cdf_impl(PyObject *module, double p, double mu,
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1.0);
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} else {
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r -= 5.0;
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// Hash sum EF: 47.52583 31754 92896 71629
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// Hash sum-47.52583317549289671629
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num = (((((((2.01033439929228813265e-7 * r +
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2.71155556874348757815e-5) * r +
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1.24266094738807843860e-3) * r +
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@ -96,23 +98,30 @@ _statistics__normal_dist_inv_cdf_impl(PyObject *module, double p, double mu,
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1.0);
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}
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x = num / den;
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if (q < 0.0) x = -x;
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if (q < 0.0) {
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x = -x;
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}
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return mu + (x * sigma);
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}
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static PyMethodDef statistics_methods[] = {
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_STATISTICS__NORMAL_DIST_INV_CDF_METHODDEF
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{NULL, NULL, 0, NULL}
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};
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static struct PyModuleDef statisticsmodule = {
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PyModuleDef_HEAD_INIT,
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"_statistics",
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_statistics__normal_dist_inv_cdf__doc__,
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-1,
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speedups_methods,
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statistics_methods,
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NULL,
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NULL,
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NULL,
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NULL
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};
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PyMODINIT_FUNC
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PyInit__statistics(void)
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{
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