section r of routines in random.i

Home Manual Packages Global Index Keywords Quick Reference | # functions in random.i - r
random_ipq |
                 random_ipq(ipq_model, dimlist)

         returns an array of double values with the given DIMLIST (see array
         function, nil for a scalar result).  The numbers are distributed
         according to a piecewise linear function (possibly with power law
         or exponential tails) specified by the IPQ_MODEL.  The "IPQ" stands
         for "inverse piecewise quadratic", which the type of function
         required to transform a uniform random deviate into the piecewise
         linear distribution.  Use the ipq_setup function to compute
         IPQ_MODEL.

    interpreted function, defined at i/random.i   line 178
SEE ALSO: | random, random_x, random_u, random_n, random_rej, ipq_setup
random_n |
                 random_n(dimlist)

         returns an array of normally distributed random double values with
         the given DIMLIST (see array function, nil for a scalar result).
         The mean is 0.0 and the standard deviation is 1.0.
         The algorithm follows the Box-Muller method (see Numerical Recipes
         by Press et al.).

    interpreted function, defined at i/random.i   line 129
SEE ALSO: | random, random_x, random_u, random_ipq, random_rej
random_rej |
                 random_rej(target_dist, ipq_model, dimlist)

              or random_rej(target_dist, bounding_dist, bounding_rand, dimlist)

         returns an array of double values with the given DIMLIST (see array
         function, nil for a scalar result).  The numbers are distributed
         according to the TARGET_DIST function:
            func target_dist(x)
         returning u(x)>=0 of same number and dimensionality as x, normalized
         so that the integral of target_dist(x) from -infinity to +infinity
         is 1.0.  The BOUNDING_DIST function must have the same calling
         sequence as TARGET_DIST:
            func bounding_dist(x)
         returning b(x)>=u(x) everywhere.  Since u(x) is normalized, the
         integral of b(x) must be >=1.0.  Finally, BOUNDING_RAND is a
         function which converts an array of uniformly distributed random
         numbers on (0,1) -- as returned by random -- into an array
         distributed according to BOUNDING_DIST:
            func bounding_rand(uniform_x_01)
         Mathematically, BOUNDING_RAND is the inverse of the integral of
         BOUNDING_DIST from -infinity to x, with its input scaled to (0,1).
         If BOUNDING_DIST is not a function, then it must be an IPQ_MODEL
         returned by the ipq_setup function.  In this case BOUNDING_RAND is
         omitted -- ipq_compute will be used automatically.

    interpreted function, defined at i/random.i   line 198
SEE ALSO: | random, random_x, random_u, random_n, random_ipq, ipq_setup
random_u |
                 random_u(a, b, dimlist)

         return uniformly distributed random numbers between A and B.
         (Will never exactly equal A or B.)  The DIMLIST is as for the
         array function.  Same as (b-a)*random(dimlist)+a.  If A==0,
         you are better off just writing B*random(dimlist).

    interpreted function, defined at i/random.i   line 112
SEE ALSO: | random, random_x, random_n, random_ipq, random_rej
random_x |
                 random_x(dimlist)

         same as random(DIMLIST), except that random_x calls random
         twice at each point, to avoid the defect that random only
         can produce about 2.e9 numbers on the interval (0.,1.) (see
         random for an explanation of these bins).
         You may set random=random_x to get these "better" random
         numbers in every call to random.
         Unlike random, there is a chance in 1.e15 or so that random_x
         may return exactly 1.0 or 0.0 (the latter may not be possible
         with IEEE standard arithmetic, while the former apparently is).
         Since cosmic rays are far more likely, you may as well not
         worry about this.  Also, because of rounding errors, some bit
         patterns may still be more likely than others, but the 0.5e-9
         wide bins of random will be absent.

    interpreted function, defined at i/random.i   line 73
SEE ALSO: | random