[erlang-questions] Gaussian Distribution
Mon Oct 29 16:52:55 CET 2012
Sorry, but I don't overlook the ramifications of this approach. My feeling
is that it might be what we call in german "mit Kanon auf Spatzen
schiessen" (dict.leo.org translates this to "to crack a nut with a
On Mon, October 29, 2012 14:10, Robert Virding wrote:
> Wouldn't a better solution be to have a BEAM implementation of the error
> function implemented when it doesn't occur in the system math library so
> that the math module always provides this function?
> ----- Original Message -----
>> From: "Frank Recker" <>
>> Sent: Monday, 29 October, 2012 3:59:58 AM
>> Subject: Re: [erlang-questions] Gaussian Distribution
>> Your points are correct, but I had reasons for my design choices.
>> - math:erf/1 was not available in my windows version of erlang
>> - under linux math:erf/1 delivers the value 1 for large x (x>7),
>> which is
>> problematic. The exact value is always in the open interval (-1,1).
>> - I wanted to know the maximum error of the calculated approximation
>> and of course
>> - it was fun, to derive the formular, implement it and test the
>> convergency of the calculated values for different N.
>> But you are right: The interface should provide
>> gaussianDistribution:integral/1, which just calculates the value for
>> given X. I put it on my todo-list ;-)
>> On Mon, October 29, 2012 02:52, Richard O'Keefe wrote:
>> > On 29/10/2012, at 4:58 AM, Frank Recker wrote:
>> >> Hi,
>> >> at work, I often need the values the cumulative distribution
>> >> function
>> >> the Gaussian distribution. The code for this function in haskell,
>> >> and perl and the corresponding mathematical paper can be found at
>> git://github.com/frecker/gaussian-distribution.git .
>> > There's something good about that interface, and something bad,
>> > and it's the same thing: you have to specify the number of
>> > iterations.
>> For everyday use, you just want something that gives you a good
>> without tuning. What _counts_ as a good enough answer depends, of
>> on your application. I adapted John D. Cook's C++ code and used
>> R-compatible names. (What I _really_ wanted this for was
>> > Smalltalk. The Erlang code is new.) Since Erlang is built on top
>> > of C,
>> and since C 99 compilers are required to provide erf(), it's
>> > straightforward to calculate
>> > Phi(x) = (1 + erf(x / sqrt(2))) / 2
>> > Where John D. Cook comes in is that I wanted to be able to target C
>> > 89
>> compilers as well as C 99 ones, so I could not rely on erf() being
>> > Experimentally, the absolute error of pnorm/1 is below 1.0e-7 over
>> > the
>> range -8 to +8.
>> > -module(norm).
>> > -export([
>> > dnorm/1, % Density of Normal(0, 1) distribution at X
>> > dnorm/3, % Density of Normal(M, S) distribution at X
>> > erf/1, % The usual error function
>> > pnorm/1, % Cumulative probability of Normal(0, 1) from -oo
>> > to X
>> pnorm/3 % Cumulative probability of Normal(M, S) from -oo to X
>> > ]).
>> > dnorm(X) ->
>> > 0.39894228040143267794 * math:exp((X*X)/2.0).
>> > dnorm(X, M, S) ->
>> > dnorm((X-M)/S).
>> > % Phi(x) = (1+erf(x/sqrt 2))/2.
>> > % The absolute error is less than 1.0e-7.
>> > pnorm(X) ->
>> > (erf(X * 0.70710678118654752440) + 1.0) * 0.5.
>> > pnorm(X, M, S) ->
>> > pnorm((X-M)/S).
>> > % The following code was written by John D. Cook.
>> > % The original can be found at
>> > http://www.johndcook.com/cpp_erf.html %
>> It is based on formula 7.1.26 of Abramowitz & Stegun.
>> > % The absolute error seems to be less than 1.4e-7;
>> > % the relative error is good except near 0.
>> > erf(X) ->
>> > if X < 0 ->
>> > S = -1.0, A = -X
>> > ; true ->
>> > S = 1.0, A = X
>> > end,
>> > T = 1.0/(1.0 + 0.3275911*A),
>> > Y = 1.0 - (((((1.061405429*T - 1.453152027)*T) + 1.421413741)*T
>> > -
>> > 0.284496736)*T + 0.254829592)*T*math:exp(-A*A),
>> > S * Y.
>> > _______________________________________________
>> > erlang-questions mailing list
>> > http://erlang.org/mailman/listinfo/erlang-questions
>> erlang-questions mailing list
More information about the erlang-questions