osd-contiki/tools/cooja/apps/arm/java/statistics/CDF_Normal.java

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package statistics;
//Gaussian CDF Taylor approximation
//Code borrowed from http://www1.fpl.fs.fed.us/distributions.html 19/9 2006
/**
*
*This class contains routines to calculate the
*normal cumulative distribution function (CDF) and
*its inverse.
*
*@version .5 --- June 7, 1996
*@version .6 --- January 10, 2001 (normcdf added)
*
*/
public class CDF_Normal extends Object {
/**
*
*This method calculates the normal cdf inverse function.
*<p>
*Let PHI(x) be the normal cdf. Suppose that Q calculates
*1.0 - PHI(x), and that QINV calculates QINV(p) for p in (0.0,.5].
*Then for p .le. .5, x = PHIINV(p) = -QINV(p).
*For p .gt. .5, x = PHIINV(p) = QINV(1.0 - p).
*The formula for approximating QINV is taken from Abramowitz and Stegun,
*Handbook of Mathematical Functions, Dover, 9th printing,
*formula 26.2.3, page 933. The error in x is claimed to
*be less than 4.5e-4 in absolute value.
*
*@param p p must lie between 0 and 1. xnormi returns
* the normal cdf inverse evaluated at p.
*
*@author Steve Verrill
*@version .5 --- June 7, 1996
*
*/
// FIX: Eventually I should build in a check that p lies in (0,1)
public static double xnormi(double p) {
double arg,t,t2,t3,xnum,xden,qinvp,x,pc;
final double c[] = {2.515517,
.802853,
.010328};
final double d[] = {1.432788,
.189269,
.001308};
if (p <= .5) {
arg = -2.0*Math.log(p);
t = Math.sqrt(arg);
t2 = t*t;
t3 = t2*t;
xnum = c[0] + c[1]*t + c[2]*t2;
xden = 1.0 + d[0]*t + d[1]*t2 + d[2]*t3;
qinvp = t - xnum/xden;
x = -qinvp;
return x;
}
else {
pc = 1.0 - p;
arg = -2.0*Math.log(pc);
t = Math.sqrt(arg);
t2 = t*t;
t3 = t2*t;
xnum = c[0] + c[1]*t + c[2]*t2;
xden = 1.0 + d[0]*t + d[1]*t2 + d[2]*t3;
x = t - xnum/xden;
return x;
}
}
/**
*
*This method calculates the normal cumulative distribution function.
*<p>
*It is based upon algorithm 5666 for the error function, from:<p>
*<pre>
* Hart, J.F. et al, 'Computer Approximations', Wiley 1968
*</pre>
*<p>
*The FORTRAN programmer was Alan Miller. The documentation
*in the FORTRAN code claims that the function is "accurate
*to 1.e-15."<p>
*Steve Verrill
*translated the FORTRAN code (the March 30, 1986 version)
*into Java. This translation was performed on January 10, 2001.
*
*@param z The method returns the value of the normal
* cumulative distribution function at z.
*
*@version .5 --- January 10, 2001
*
*/
/*
Here is a copy of the documentation in the FORTRAN code:
SUBROUTINE NORMP(Z, P, Q, PDF)
C
C Normal distribution probabilities accurate to 1.e-15.
C Z = no. of standard deviations from the mean.
C P, Q = probabilities to the left & right of Z. P + Q = 1.
C PDF = the probability density.
C
C Based upon algorithm 5666 for the error function, from:
C Hart, J.F. et al, 'Computer Approximations', Wiley 1968
C
C Programmer: Alan Miller
C
C Latest revision - 30 March 1986
C
*/
public static double normp(double z) {
double zabs;
double p;
double expntl,pdf;
final double p0 = 220.2068679123761;
final double p1 = 221.2135961699311;
final double p2 = 112.0792914978709;
final double p3 = 33.91286607838300;
final double p4 = 6.373962203531650;
final double p5 = .7003830644436881;
final double p6 = .3526249659989109E-01;
final double q0 = 440.4137358247522;
final double q1 = 793.8265125199484;
final double q2 = 637.3336333788311;
final double q3 = 296.5642487796737;
final double q4 = 86.78073220294608;
final double q5 = 16.06417757920695;
final double q6 = 1.755667163182642;
final double q7 = .8838834764831844E-1;
final double cutoff = 7.071;
final double root2pi = 2.506628274631001;
zabs = Math.abs(z);
// |z| > 37
if (z > 37.0) {
p = 1.0;
return p;
}
if (z < -37.0) {
p = 0.0;
return p;
}
// |z| <= 37.
expntl = Math.exp(-.5*zabs*zabs);
pdf = expntl/root2pi;
// |z| < cutoff = 10/sqrt(2).
if (zabs < cutoff) {
p = expntl*((((((p6*zabs + p5)*zabs + p4)*zabs + p3)*zabs +
p2)*zabs + p1)*zabs + p0)/(((((((q7*zabs + q6)*zabs +
q5)*zabs + q4)*zabs + q3)*zabs + q2)*zabs + q1)*zabs +
q0);
} else {
p = pdf/(zabs + 1.0/(zabs + 2.0/(zabs + 3.0/(zabs + 4.0/
(zabs + 0.65)))));
}
if (z < 0.0) {
return p;
} else {
p = 1.0 - p;
return p;
}
}
}