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Top Forums Programming how to create random no between 10 to 40 in C Post 302128069 by jgt on Saturday 21st of July 2007 03:49:22 PM
Old 07-21-2007
Divide the random number by 31 ( 10 through 40) take the remainder and add 10.
Create an array of 31 entries, run 1,000,000 iterations and count how many times each number appears to see if you have 25000 of each number, or what the deviation is.
 

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simulation::random(n)					       Tcl Simulation Tools					     simulation::random(n)

__________________________________________________________________________________________________________________________________________________

NAME
simulation::random - Pseudo-random number generators SYNOPSIS
package require Tcl ?8.4? package require simulation::random 0.1 ::simulation::random::prng_Bernoulli p ::simulation::random::prng_Discrete n ::simulation::random::prng_Poisson lambda ::simulation::random::prng_Uniform min max ::simulation::random::prng_Exponential min mean ::simulation::random::prng_Normal mean stdev ::simulation::random::prng_Pareto min steep ::simulation::random::prng_Gumbel min f ::simulation::random::prng_chiSquared df ::simulation::random::prng_Disk rad ::simulation::random::prng_Sphere rad ::simulation::random::prng_Ball rad ::simulation::random::prng_Rectangle length width ::simulation::random::prng_Block length width depth _________________________________________________________________ DESCRIPTION
This package consists of commands to generate pseudo-random number generators. These new commands deliver o numbers that are distributed normally, uniformly, according to a Pareto or Gumbel distribution and so on o coordinates of points uniformly spread inside a sphere or a rectangle For example: set p [::simulation::random::prng_Normal -1.0 10.0] produces a new command (whose name is stored in the variable "p") that generates normally distributed numbers with a mean of -1.0 and a standard deviation of 10.0. PROCEDURES
The package defines the following public procedures for discrete distributions: ::simulation::random::prng_Bernoulli p Create a command (PRNG) that generates numbers with a Bernoulli distribution: the value is either 1 or 0, with a chance p to be 1 float p Chance the outcome is 1 ::simulation::random::prng_Discrete n Create a command (PRNG) that generates numbers 0 to n-1 with equal probability. int n Number of different values (ranging from 0 to n-1) ::simulation::random::prng_Poisson lambda Create a command (PRNG) that generates numbers according to the Poisson distribution. float lambda Mean number per time interval The package defines the following public procedures for continuous distributions: ::simulation::random::prng_Uniform min max Create a command (PRNG) that generates uniformly distributed numbers between "min" and "max". float min Minimum number that will be generated float max Maximum number that will be generated ::simulation::random::prng_Exponential min mean Create a command (PRNG) that generates exponentially distributed numbers with a given minimum value and a given mean value. float min Minimum number that will be generated float mean Mean value for the numbers ::simulation::random::prng_Normal mean stdev Create a command (PRNG) that generates normally distributed numbers with a given mean value and a given standard deviation. float mean Mean value for the numbers float stdev Standard deviation ::simulation::random::prng_Pareto min steep Create a command (PRNG) that generates numbers distributed according to Pareto with a given minimum value and a given distribution steepness. float min Minimum number that will be generated float steep Steepness of the distribution ::simulation::random::prng_Gumbel min f Create a command (PRNG) that generates numbers distributed according to Gumbel with a given minimum value and a given scale factor. The probability density function is: P(v) = exp( -exp(f*(v-min))) float min Minimum number that will be generated float f Scale factor for the values ::simulation::random::prng_chiSquared df Create a command (PRNG) that generates numbers distributed according to the chi-squared distribution with df degrees of freedom. The mean is 0 and the standard deviation is 1. float df Degrees of freedom The package defines the following public procedures for random point sets: ::simulation::random::prng_Disk rad Create a command (PRNG) that generates (x,y)-coordinates for points uniformly spread over a disk of given radius. float rad Radius of the disk ::simulation::random::prng_Sphere rad Create a command (PRNG) that generates (x,y,z)-coordinates for points uniformly spread over the surface of a sphere of given radius. float rad Radius of the disk ::simulation::random::prng_Ball rad Create a command (PRNG) that generates (x,y,z)-coordinates for points uniformly spread within a ball of given radius. float rad Radius of the ball ::simulation::random::prng_Rectangle length width Create a command (PRNG) that generates (x,y)-coordinates for points uniformly spread over a rectangle. float length Length of the rectangle (x-direction) float width Width of the rectangle (y-direction) ::simulation::random::prng_Block length width depth Create a command (PRNG) that generates (x,y)-coordinates for points uniformly spread over a block float length Length of the block (x-direction) float width Width of the block (y-direction) float depth Depth of the block (z-direction) KEYWORDS
math, random numbers, simulation, statistical distribution COPYRIGHT
Copyright (c) 2004 Arjen Markus <arjenmarkus@users.sourceforge.net> simulation 0.1 simulation::random(n)
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