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📄 bdtrl.c

📁 linux下用PCMCIA无线网卡虚拟无线AP的程序源码
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/*							bdtrl.c * *	Binomial distribution * * * * SYNOPSIS: * * int k, n; * long double p, y, bdtrl(); * * y = bdtrl( k, n, p ); * * * * DESCRIPTION: * * Returns the sum of the terms 0 through k of the Binomial * probability density: * *   k *   --  ( n )   j      n-j *   >   (   )  p  (1-p) *   --  ( j ) *  j=0 * * The terms are not summed directly; instead the incomplete * beta integral is employed, according to the formula * * y = bdtr( k, n, p ) = incbet( n-k, k+1, 1-p ). * * The arguments must be positive, with p ranging from 0 to 1. * * * * ACCURACY: * * Tested at random points (k,n,p) with a and b between 0 * and 10000 and p between 0 and 1. *    Relative error: * arithmetic   domain     # trials      peak         rms *    IEEE      0,10000      3000       1.6e-14     2.2e-15 * * ERROR MESSAGES: * *   message         condition      value returned * bdtrl domain        k < 0            0.0 *                     n < k *                     x < 0, x > 1 * *//*							bdtrcl() * *	Complemented binomial distribution * * * * SYNOPSIS: * * int k, n; * long double p, y, bdtrcl(); * * y = bdtrcl( k, n, p ); * * * * DESCRIPTION: * * Returns the sum of the terms k+1 through n of the Binomial * probability density: * *   n *   --  ( n )   j      n-j *   >   (   )  p  (1-p) *   --  ( j ) *  j=k+1 * * The terms are not summed directly; instead the incomplete * beta integral is employed, according to the formula * * y = bdtrc( k, n, p ) = incbet( k+1, n-k, p ). * * The arguments must be positive, with p ranging from 0 to 1. * * * * ACCURACY: * * See incbet.c. * * ERROR MESSAGES: * *   message         condition      value returned * bdtrcl domain     x<0, x>1, n<k       0.0 *//*							bdtril() * *	Inverse binomial distribution * * * * SYNOPSIS: * * int k, n; * long double p, y, bdtril(); * * p = bdtril( k, n, y ); * * * * DESCRIPTION: * * Finds the event probability p such that the sum of the * terms 0 through k of the Binomial probability density * is equal to the given cumulative probability y. * * This is accomplished using the inverse beta integral * function and the relation * * 1 - p = incbi( n-k, k+1, y ). * * ACCURACY: * * See incbi.c. * Tested at random k, n between 1 and 10000.  The "domain" refers to p: *                      Relative error: * arithmetic   domain     # trials      peak         rms *    IEEE       0,1        3500       2.0e-15     8.2e-17 * * ERROR MESSAGES: * *   message         condition      value returned * bdtril domain     k < 0, n <= k         0.0 *                  x < 0, x > 1 *//*								bdtr() *//*Cephes Math Library Release 2.3:  March, 1995Copyright 1984, 1995 by Stephen L. Moshier*/#include <math.h>#ifdef ANSIPROTextern long double incbetl ( long double, long double, long double );extern long double incbil ( long double, long double, long double );extern long double powl ( long double, long double );extern long double expm1l ( long double );extern long double log1pl ( long double );#elselong double incbetl(), incbil(), powl(), expm1l(), log1pl();#endiflong double bdtrcl( k, n, p )int k, n;long double p;{long double dk, dn;if( (p < 0.0L) || (p > 1.0L) )	goto domerr;if( k < 0 )	return( 1.0L );if( n < k )	{domerr:	mtherr( "bdtrcl", DOMAIN );	return( 0.0L );	}if( k == n )	return( 0.0L );dn = n - k;if( k == 0 )	{	if( p < .01L )		dk = -expm1l( dn * log1pl(-p) );	else		dk = 1.0L - powl( 1.0L-p, dn );	}else	{	dk = k + 1;	dk = incbetl( dk, dn, p );	}return( dk );}long double bdtrl( k, n, p )int k, n;long double p;{long double dk, dn, q;if( (p < 0.0L) || (p > 1.0L) )	goto domerr;if( (k < 0) || (n < k) )	{domerr:	mtherr( "bdtrl", DOMAIN );	return( 0.0L );	}if( k == n )	return( 1.0L );q = 1.0L - p;dn = n - k;if( k == 0 )	{	dk = powl( q, dn );	}else	{	dk = k + 1;	dk = incbetl( dn, dk, q );	}return( dk );}long double bdtril( k, n, y )int k, n;long double y;{long double dk, dn, p;if( (y < 0.0L) || (y > 1.0L) )	goto domerr;if( (k < 0) || (n <= k) )	{domerr:	mtherr( "bdtril", DOMAIN );	return( 0.0L );	}dn = n - k;if( k == 0 )	{	if( y > 0.8L )		p = -expm1l( log1pl(y-1.0L) / dn );	else		p = 1.0L - powl( y, 1.0L/dn );	}else	{	dk = k + 1;	p = incbetl( dn, dk, y );	if( p > 0.5 )		p = incbil( dk, dn, 1.0L-y );	else		p = 1.0 - incbil( dn, dk, y );	}return( p );}

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