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	Only define fallback implementations when needed. It avoids producing deadcode when the system provides required math functions.
		
			
				
	
	
		
			266 lines
		
	
	
	
		
			7.3 KiB
		
	
	
	
		
			C
		
	
	
	
	
	
			
		
		
	
	
			266 lines
		
	
	
	
		
			7.3 KiB
		
	
	
	
		
			C
		
	
	
	
	
	
/* Definitions of some C99 math library functions, for those platforms
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   that don't implement these functions already. */
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#include "Python.h"
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#include <float.h>
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#include "_math.h"
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/* The following copyright notice applies to the original
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   implementations of acosh, asinh and atanh. */
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/*
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 * ====================================================
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 * Copyright (C) 1993 by Sun Microsystems, Inc. All rights reserved.
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 *
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 * Developed at SunPro, a Sun Microsystems, Inc. business.
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 * Permission to use, copy, modify, and distribute this
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 * software is freely granted, provided that this notice
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 * is preserved.
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 * ====================================================
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 */
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#if !defined(HAVE_ACOSH) || !defined(HAVE_ASINH)
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static const double ln2 = 6.93147180559945286227E-01;
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static const double two_pow_p28 = 268435456.0; /* 2**28 */
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#endif
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#if !defined(HAVE_ASINH) || !defined(HAVE_ATANH)
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static const double two_pow_m28 = 3.7252902984619141E-09; /* 2**-28 */
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#endif
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#if !defined(HAVE_ATANH) && !defined(Py_NAN)
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static const double zero = 0.0;
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#endif
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#ifndef HAVE_ACOSH
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/* acosh(x)
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 * Method :
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 *      Based on
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 *            acosh(x) = log [ x + sqrt(x*x-1) ]
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 *      we have
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 *            acosh(x) := log(x)+ln2, if x is large; else
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 *            acosh(x) := log(2x-1/(sqrt(x*x-1)+x)) if x>2; else
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 *            acosh(x) := log1p(t+sqrt(2.0*t+t*t)); where t=x-1.
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 *
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 * Special cases:
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 *      acosh(x) is NaN with signal if x<1.
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 *      acosh(NaN) is NaN without signal.
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 */
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double
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_Py_acosh(double x)
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{
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    if (Py_IS_NAN(x)) {
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        return x+x;
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    }
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    if (x < 1.) {                       /* x < 1;  return a signaling NaN */
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        errno = EDOM;
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#ifdef Py_NAN
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        return Py_NAN;
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#else
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        return (x-x)/(x-x);
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#endif
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    }
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    else if (x >= two_pow_p28) {        /* x > 2**28 */
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        if (Py_IS_INFINITY(x)) {
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            return x+x;
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        }
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        else {
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            return log(x) + ln2;          /* acosh(huge)=log(2x) */
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        }
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    }
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    else if (x == 1.) {
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        return 0.0;                     /* acosh(1) = 0 */
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    }
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    else if (x > 2.) {                  /* 2 < x < 2**28 */
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        double t = x * x;
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        return log(2.0 * x - 1.0 / (x + sqrt(t - 1.0)));
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    }
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    else {                              /* 1 < x <= 2 */
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        double t = x - 1.0;
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        return m_log1p(t + sqrt(2.0 * t + t * t));
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    }
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}
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#endif   /* HAVE_ACOSH */
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#ifndef HAVE_ASINH
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/* asinh(x)
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 * Method :
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 *      Based on
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 *              asinh(x) = sign(x) * log [ |x| + sqrt(x*x+1) ]
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 *      we have
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 *      asinh(x) := x  if  1+x*x=1,
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 *               := sign(x)*(log(x)+ln2)) for large |x|, else
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 *               := sign(x)*log(2|x|+1/(|x|+sqrt(x*x+1))) if|x|>2, else
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 *               := sign(x)*log1p(|x| + x^2/(1 + sqrt(1+x^2)))
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 */
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double
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_Py_asinh(double x)
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{
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    double w;
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    double absx = fabs(x);
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    if (Py_IS_NAN(x) || Py_IS_INFINITY(x)) {
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        return x+x;
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    }
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    if (absx < two_pow_m28) {           /* |x| < 2**-28 */
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        return x;                       /* return x inexact except 0 */
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    }
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    if (absx > two_pow_p28) {           /* |x| > 2**28 */
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        w = log(absx) + ln2;
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    }
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    else if (absx > 2.0) {              /* 2 < |x| < 2**28 */
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        w = log(2.0 * absx + 1.0 / (sqrt(x * x + 1.0) + absx));
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    }
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    else {                              /* 2**-28 <= |x| < 2= */
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        double t = x*x;
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        w = m_log1p(absx + t / (1.0 + sqrt(1.0 + t)));
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    }
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    return copysign(w, x);
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}
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#endif   /* HAVE_ASINH */
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#ifndef HAVE_ATANH
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/* atanh(x)
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 * Method :
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 *    1.Reduced x to positive by atanh(-x) = -atanh(x)
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 *    2.For x>=0.5
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 *                  1              2x                          x
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 *      atanh(x) = --- * log(1 + -------) = 0.5 * log1p(2 * -------)
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 *                  2             1 - x                      1 - x
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 *
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 *      For x<0.5
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 *      atanh(x) = 0.5*log1p(2x+2x*x/(1-x))
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 *
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 * Special cases:
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 *      atanh(x) is NaN if |x| >= 1 with signal;
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 *      atanh(NaN) is that NaN with no signal;
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 *
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 */
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double
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_Py_atanh(double x)
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{
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    double absx;
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    double t;
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    if (Py_IS_NAN(x)) {
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        return x+x;
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    }
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    absx = fabs(x);
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    if (absx >= 1.) {                   /* |x| >= 1 */
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        errno = EDOM;
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#ifdef Py_NAN
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        return Py_NAN;
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#else
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        return x / zero;
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#endif
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    }
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    if (absx < two_pow_m28) {           /* |x| < 2**-28 */
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        return x;
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    }
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    if (absx < 0.5) {                   /* |x| < 0.5 */
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        t = absx+absx;
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        t = 0.5 * m_log1p(t + t*absx / (1.0 - absx));
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    }
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    else {                              /* 0.5 <= |x| <= 1.0 */
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        t = 0.5 * m_log1p((absx + absx) / (1.0 - absx));
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    }
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    return copysign(t, x);
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}
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#endif   /* HAVE_ATANH */
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#ifndef HAVE_EXPM1
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/* Mathematically, expm1(x) = exp(x) - 1.  The expm1 function is designed
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   to avoid the significant loss of precision that arises from direct
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   evaluation of the expression exp(x) - 1, for x near 0. */
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double
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_Py_expm1(double x)
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{
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    /* For abs(x) >= log(2), it's safe to evaluate exp(x) - 1 directly; this
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       also works fine for infinities and nans.
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       For smaller x, we can use a method due to Kahan that achieves close to
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       full accuracy.
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    */
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    if (fabs(x) < 0.7) {
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        double u;
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        u = exp(x);
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        if (u == 1.0)
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            return x;
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        else
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            return (u - 1.0) * x / log(u);
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    }
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    else
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        return exp(x) - 1.0;
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}
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#endif   /* HAVE_EXPM1 */
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/* log1p(x) = log(1+x).  The log1p function is designed to avoid the
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   significant loss of precision that arises from direct evaluation when x is
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   small. */
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double
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_Py_log1p(double x)
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{
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#ifdef HAVE_LOG1P
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    /* Some platforms supply a log1p function but don't respect the sign of
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       zero:  log1p(-0.0) gives 0.0 instead of the correct result of -0.0.
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       To save fiddling with configure tests and platform checks, we handle the
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       special case of zero input directly on all platforms.
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    */
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    if (x == 0.0) {
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        return x;
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    }
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    else {
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        return log1p(x);
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    }
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#else
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    /* For x small, we use the following approach.  Let y be the nearest float
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       to 1+x, then
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         1+x = y * (1 - (y-1-x)/y)
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       so log(1+x) = log(y) + log(1-(y-1-x)/y).  Since (y-1-x)/y is tiny, the
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       second term is well approximated by (y-1-x)/y.  If abs(x) >=
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       DBL_EPSILON/2 or the rounding-mode is some form of round-to-nearest
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       then y-1-x will be exactly representable, and is computed exactly by
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       (y-1)-x.
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       If abs(x) < DBL_EPSILON/2 and the rounding mode is not known to be
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       round-to-nearest then this method is slightly dangerous: 1+x could be
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       rounded up to 1+DBL_EPSILON instead of down to 1, and in that case
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       y-1-x will not be exactly representable any more and the result can be
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       off by many ulps.  But this is easily fixed: for a floating-point
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       number |x| < DBL_EPSILON/2., the closest floating-point number to
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       log(1+x) is exactly x.
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    */
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    double y;
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    if (fabs(x) < DBL_EPSILON / 2.) {
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        return x;
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    }
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    else if (-0.5 <= x && x <= 1.) {
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        /* WARNING: it's possible that an overeager compiler
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           will incorrectly optimize the following two lines
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           to the equivalent of "return log(1.+x)". If this
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           happens, then results from log1p will be inaccurate
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           for small x. */
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        y = 1.+x;
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        return log(y) - ((y - 1.) - x) / y;
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    }
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    else {
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        /* NaNs and infinities should end up here */
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        return log(1.+x);
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    }
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#endif /* ifdef HAVE_LOG1P */
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}
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