scauchy.cc

c++编写的并行拉马克遗传算法的程序。实现分析对接程序

/* scauchy.cc */

/***** RCS INFO **********************************************************

$Id: scauchy.cc,v 3.0 1996/03/11 05:40:00 halliday Exp $
$Source: /tmp_mnt/mgl/apps/src/autodock/3.0/autodock/RCS/scauchy.cc,v $
$Log: scauchy.cc,v $
// Revision 3.0  1996/03/11  05:40:00  halliday
// The function definition for the GA/LS hybrid.
//
Revision 1.1  1993/02/05  15:42:11  whart
Initial revision


****** RCS INFO *********************************************************/

/* cauchy.c
 *
 * Code for generating deviates from the Cauchy/Lorentzian distribution.
 *
 * Comments regarding this code are included at the end of the file.
 *
 * Code suggested by Lester Ingber <[e-mail]> and
 *	Numerical Recipies in C by Press et. al.
 */

#include <math.h>
#include "ranlib.h"


#define EPS     1.e-12

float scauchy1()
{
float x, y;
 
			/* These four lines generate the tangent of a random
			 *	angle;  this is equivalent to 
			 *	y/x = tan(PI * ranf())
			 */
do {
   x = 2.0 * ranf() - 1.0;
   y = 2.0 * ranf() - 1.0;
   } while (x * x + y * y > 1.0);

if (fabs(x) < EPS)
   x = (x < 0.0 ? x - EPS : x + EPS);
 
return (y / x);
}




/*******

Here's the summary of responses that I received concerning the
generation of Cauchy deviates:

Pretty much everyone who replied pointed out that the Cauchy distribution
can be generated by generating two standard normal deviates and taking
their quotient:  N(0,1)/N(0,1).  Barrett P. Eynon <[e-mail]>
noted that the t distribution with 1 degree of freedom is equivalent to
the Cauchy distribution.  Finally, B. Narasimhan <[e-mail]>
noted that 

	the ratio X/Y is Cauchy any time the pair (X,Y) is radially
	distributed. See Devroye, "Non Uniform Random Variate
	Generation", for more. Thus you can even use ratio of two
	coordinates of a point generated uniformly in the unit disk in
	2D.

Code which uses this last observation was provided by Lester Ingber
<[e-mail]> and is included at the end of this article.

Mark Plutowski <[e-mail]> noted that the Cauchy distribution
is also called the Lorentzian distribution, and is discussed as such in
Numerical Recipies.  Both NR and Ping Chou <[e-mail]> noted
that you can generate a Cauchy deviate by taking a uniform deviate
on x \in [0,1] and computing tan(PI*x).  However, in NR they later
compute the Cauchy deviates using the same method used in Ingber's code,
so presumably this is more efficient.

Robert E George <[e-mail]> also noted that
there is a routine in IMSL for generating Cauchy deviates.

Thanks for everyone's replies!

--Bill

*****/