# Probability distributions library in Ruby

Hello,

I would extract some numbers according to a given
probability distribution (at least, Normal, Exponential,
Poisson and Bernoulli).

Do you know if a similar library already exists in Ruby? I
only found pseudo-random number generator.

TIA

Libra

If you don’t find any library, you can always write your own using the
technique outlined in section 1.3 of this paper.
http://eeyore.ucdavis.edu/stat141/RNG.pdf

Basically given the CDF F of some distribution (say, Poisson), you can
generate random numbers from the Poisson distribution by doing:
u = rand # uniformely generated random number
y = Finv(u)

-Szymon

On 13 Apr 2007, at 06:55, Libra wrote:

Libra
If you are comfortable with R, you can use RSRuby (http://
web.kuicr.kyoto-u.ac.jp/~alexg/rsruby/) to generate numbers from any
its distribution functions. For example, extracting 10 numbers from a
normal distribution with mean 0:

[[email protected]]/Users/alexg(1): irb -rubygems
irb(main):001:0> require ‘rsruby’
=> true
irb(main):002:0> r = RSRuby.instance
=> #RSRuby:0x1c1510
irb(main):003:0> r.rnorm(10)
=> [-0.418488870801272, 3.8442920287686, -2.23915973195997,
-1.1852964455658, 0.292187794718865, -0.0538355720349994,
-0.682675725085902, -0.179593328583021, 0.617238096715829,
-0.353627801112474]

The default R stats package has the following distributions built in
and you can use any R library to add more (I couldn’t see Bernoulli
in this list, but maybe it has an alternative name?):

Beta(stats) The Beta Distribution
Binomial(stats) The Binomial Distribution
Cauchy(stats) The Cauchy Distribution
Chisquare(stats) The (non-central) Chi-Squared Distribution
Exponential(stats) The Exponential Distribution
FDist(stats) The F Distribution
Geometric(stats) The Geometric Distribution
Hypergeometric(stats) The Hypergeometric Distribution
Logistic(stats) The Logistic Distribution
Lognormal(stats) The Log Normal Distribution
Multinomial(stats) The Multinomial Distribution
NegBinomial(stats) The Negative Binomial Distribution
Normal(stats) The Normal Distribution
Poisson(stats) The Poisson Distribution
SignRank(stats) Distribution of the Wilcoxon Signed Rank
Statistic
TDist(stats) The Student t Distribution
Tukey(stats) The Studentized Range Distribution
Uniform(stats) The Uniform Distribution
Weibull(stats) The Weibull Distribution
Wilcoxon(stats) Distribution of the Wilcoxon Rank Sum Statistic
ecdf(stats) Empirical Cumulative Distribution Function
survreg.distributions(survival)
Parametric Survival Distributions

Alex G.

Bioinformatics Center
Kyoto University

Libra wrote:

Libra
R is great! maybe too great for your requirements
rb-gsl is easy

for example this will print you a nice gauss-shape graph
from rb-gsl-1.8.3/samples/histogram/gauss.rb

sigma, mean, height, = h.fit_gaussian

x = GSL::Vector.linspace(-MAX, MAX, 100)
y = height*Ran::gaussian_pdf(x-mean, sigma)
GSL::graph(h, [x, y], “-T X -C -g 3”)

NICE!

cauchy exponential power poisson etc. also available
ruby - making maths easy

On 13 Apr 2007, at 09:35, Alex G. wrote:

TIA

Libra

If you are comfortable with R, you can use RSRuby (http://
web.kuicr.kyoto-u.ac.jp/~alexg/rsruby/) to generate numbers from
any its distribution functions.

A quick bit of googling found the missing Bernoulli distribution
function:

It seems to be part of a CRAN package called Rlab:

http://cran.r-project.org/src/contrib/Descriptions/Rlab.html

You should be able to install and then access this library through
RSRuby to get your complete set of functions.

Alex G.

Bioinformatics Center
Kyoto University

Roger P. wrote:

u = rand

Libra wrote:

GSL::graph(h, [x, y], “-T X -C -g 3”)
NICE!

cauchy exponential power poisson etc. also available
ruby - making maths easy

There is also RSRuby, a way of calling the R libraries, including their
probability distribution routines, from Ruby. I don’t know anything
about the ones in GSL, but I know the algorithms in R are first-rate,
and I wouldn’t code my own no matter how bored I got.

From wikipedia : algorithm for generating pseudo poissons:

def poisson(lambda)

# init

l = Math.exp(-lambda)
k = 0
p = 1
while p >= l
k += 1
u = rand
p = p *u
end
return k -1
end

only returns ints, but hey, they center around lambda
gl.
-Roger

maw wrote:

Libra wrote:

Libra
R is great! maybe too great for your requirements
rb-gsl is easy

for example this will print you a nice gauss-shape graph
from rb-gsl-1.8.3/samples/histogram/gauss.rb

sigma, mean, height, = h.fit_gaussian

x = GSL::Vector.linspace(-MAX, MAX, 100)
y = height*Ran::gaussian_pdf(x-mean, sigma)
GSL::graph(h, [x, y], “-T X -C -g 3”)

NICE!

cauchy exponential power poisson etc. also available
ruby - making maths easy

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