| | | Scilab Reference Manual | |
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grand — Random number generator(s)
Y=grand(m, n, dist_type [,p1,...,pk]) Y=grand(X, dist_type [,p1,...,pk]) Y=grand(n, dist_type [,p1,...,pk]) S=grand(action [,q1,....,ql])
| m, n | : integers, size of the wanted matrix Y |
| X | : a matrix whom only the dimensions (say m x n) are used |
| dist_type | : a string given the distribution which (independants) variates are to be generated ('bin', 'nor', 'poi', etc ...) |
| p1, ..., pk | : the parameters (reals or integers) required to define completly the distribution dist_type |
| Y | : the resulting m x n random matrix |
| action | : a string given the action onto the base generator(s) ('setgen' to change the current base generator, 'getgen' to retrieve the current base generator name, 'getsd' to retrieve the state (seeds) of the current base generator, etc ...) |
| q1, ..., ql | : the parameters (generally one string) needed to define the action |
| S | : output of the action (generaly a string or a real column vector) |
This function may be used to generate random numbers from various distributions. In this case you must apply one of the three first forms of the possible calling sequences to get an m x n matrix. The two firsts are equivalent if X is a m x n matrix, and the third form corresponds to 'multivalued' distributions (e.g. multinomial, multivariate gaussian, etc...) where a sample is a column vector (says of dim m) and you get then n such random vectors (as an m x n matrix). The last form is used to undertake various manipulations onto the base generators like changing the base generator (since v 2.7 you may choose between several base generators), changing or retrieving its internal state (seeds), etc ... These base generators give random integers following a uniform distribution on a large integer interval (lgi), all the others distributions being gotten from it (in general via a scheme lgi -> U([0,1)) -> wanted distribution).
| beta | : Y=grand(m,n,'bet',A,B) generates random variates from the beta distribution with parameters A and B. The density of the beta is (0 < x < 1) :
A-1 B-1
x (1-x) / beta(A,B) ( beta(A,B) = gamma(A+B) / (gamma(A) gamma(B)) )
A and B must be reals > 10^(-37). Related function(s) : cdfbet. |
| binomial | : Y=grand(m,n,'bin',N,p) generates random variates from the binomial distribution with parameters N (positive integer) and p (real in [0,1]) : number of successes in N independant Bernouilli trials with probability p of success. Related function(s) : binomial, cdfbin. |
| negative binomial | : Y=grand(m,n,'nbn',N,p) generates random variates from the negative binomial distribution with parameters N (positive integer) and p (real in (0,1)) : number of failures occurring before N successes in independant Bernouilli trials with probability p of success. Related function(s) : cdfnbn. |
| chisquare | : Y=grand(m,n,'chi', Df) generates random variates from the chisquare distribution with Df (real > 0.0) degrees of freedom. Related function(s) : cdfchi. |
| non central chisquare | : Y=grand(m,n,'nch',Df,Xnon) generates random variates from the non central chisquare distribution with Df degrees of freedom (real >= 1.0) and noncentrality parameter Xnonc (real >= 0.0). Related function(s) : cdfchn. |
| exponential | : Y=grand(m,n,'exp',Av) generates random variates from the exponential distribution with mean Av (real >= 0.0). |
| F variance ratio | : Y=grand(m,n,'f',Dfn,Dfd) generates random variates from the F (variance ratio) distribution with Dfn (real > 0.0) degrees of freedom in the numerator and Dfd (real > 0.0) degrees of freedom in the denominator. Related function(s) : cdff. |
| non central F variance ratio | : Y=grand(m,n,'nf',Dfn,Dfd,Xnon) generates random variates from the noncentral F (variance ratio) distribution with Dfn (real >= 1) degrees of freedom in the numerator, and Dfd (real > 0) degrees of freedom in the denominator, and noncentrality parameter Xnonc (real >= 0). Related function(s) : cdffnc. |
| gamma | : Y=grand(m,n,'gam',shape,scale) generates random variates from the gamma distribution with parameters shape (real > 0) and scale (real > 0). The density of the gamma is :
shape (shape-1) -scale x
scale x e / gamma(shape)
Related function(s) : gamma, cdfgam. |
| Gauss Laplace (normal) | : Y=grand(m,n,'nor',Av,Sd) generates random variates from the normal distribution with mean Av (real) and standard deviation Sd (real >= 0). Related function(s) : cdfnor. |
| multivariate gaussian (multivariate normal) | : Y=grand(n,'mn',Mean,Cov) generates n multivariate normal random variates ; Mean must be a m x 1 matrix and Cov a m x m symetric positive definite matrix (Y is then a m x n matrix). |
| geometric | : Y=grand(m,n,'geom', p) generates random variates from the geometric distribution with parameter p : number of Bernouilli trials (with probability succes of p) until a succes is met. p must be in [pmin,1] (with pmin = 1.3 10^(-307)). |
| markov | : Y=grand(n,'markov',P,x0) generate n successive states of a Markov chain described by the transition matrix P. Initial state is given by x0. If x0 is a matrix of size m=size(x0,'*') then Y is a matrix of size m x n. Y(i,:) is the sample path obtained from initial state x0(i). |
| multinomial | : Y=grand(n,'mul',nb,P) generates n observations from the Multinomial distribution : class nb events in m categories (put nb "balls" in m "boxes"). P(i) is the probability that an event will be classified into category i. P the vector of probabilities is of size m-1 (the probability of category m being 1-sum(P)). Y is of size m x n, each column Y(:,j) being an observation from multinomial distribution and Y(i,j) the number of events falling in category i (for the j th observation) (sum(Y(:,j)) = nb). |
| Poisson | : Y=grand(m,n,'poi',mu) generates random variates from the Poisson distribution with mean mu (real >= 0.0). |
| random permutations | : Y=grand(n,'prm',vect) generate n random permutations of the column vector (m x 1) vect. |
| uniform (def) | : Y=grand(m,n,'def') generates random variates from the uniform distribution over [0,1) (1 is never return). |
| uniform (unf) | : Y=grand(m,n,'unf',Low,High) generates random reals uniformly distributed in [Low, High). |
| uniform (uin) | : Y=grand(m,n,'uin',Low,High) generates random integers uniformly distributed between Low and High (included). High and Low must be integers such that (High-Low+1) > 2,147,483,561. |
| uniform (lgi) | : Y=grand(m,n,'lgi') returns the basic output of the current generator : random integers following a uniform distribution over : |
Since Scilab-2.7 you have the possibility to choose between different base generators (which give random integers following the 'lgi' distribution, the others being gotten from it) :
| mt | : the Mersenne-Twister of M. Matsumoto and T. Nishimura, period about 2^19937, state given by an array of 624 integers (plus an index onto this array); this is the default generator. |
| kiss | : The Keep It Simple Stupid of G. Marsaglia, period about 2^123, state given by 4 integers. |
| clcg2 | : a Combined 2 Linear Congruential Generator of P. L'Ecuyer, period about 2^61, state given by 2 integers ; this was the only generator previously used by grand (but slightly modified) |
| clcg4 | : a Combined 4 Linear Congruential Generator of P. L'Ecuyer, period about 2^121, state given by 4 integers ; this one is splitted in 101 different virtual (non over-lapping) generators which may be useful for different tasks (see 'Actions specific to clcg4' and 'Test example for clcg4'). |
| urand | : the generator used by the scilab function rand, state given by 1 integer, period of 2^31 (based on theory and suggestions given in d.e. knuth (1969), vol 2. State). This is the faster of this list but a little outdated (don't use it for serious simulations). |
| fsultra | : Arif Zaman (arif@stat.fsu.edu) and George Marsaglia (geo@stat.fsu.edu). State given by 2 integers. |
| action= 'getgen' | : S=grand('getgen') returns the current base generator ( S is a string among 'mt', 'kiss', 'clcg2', 'clcg4', 'urand', 'fsultra'. |
| action= 'setgen' | : grand('setgen',gen) sets the current base generator to be gen a string among 'mt', 'kiss', 'clcg2', 'clcg4', 'urand', 'fsultra' (notes that this call returns the new current generator, ie gen). |
| action= 'getsd' | : S=grand('getsd') gets the current state (the current seeds) of the current base generator ; S is given as a column vector (of integers) of dimension 625 for mt (the first being an index in [1,624]), 4 for kiss, 2 for clcg2 and fsultra, 4 for clcg4 (for this last one you get the current state of the current virtual generator) and 1 for urand. |
| action= 'setsd' | : grand('setsd',S), grand('setsd',s1[,s2,s3,s4]) sets the state of the current base generator (the new seeds) : |
| action= 'phr2sd' | : Sd=grand('phr2sd', phrase) given a phrase (character string) generates a 1 x 2 vector Sd which may be used as seeds to change the state of a base generator (initialy suited for clcg2). |
The clcg4 generator may be used as the others generators but it offers the advantage to be splitted in several (101) virtual generators with non over-lapping sequences (when you use a classic generator you may change the initial state (seeds) in order to get another sequence but you are not warranty to get a complete different one). Each virtual generator corresponds to a sequence of 2^72 values which is further split into V=2^31 segments (or blocks) of length W=2^41. For a given virtual generator you have the possibility to return at the beginning of the sequence or at the beginning of the current segment or to go directly at the next segment. You may also change the initial state (seed) of the generator 0 with the 'setall' option which then change also the initial state of the other virtual generators so as to get synchronisation (ie in function of the new initial state of gen 0 the initial state of gen 1..100 are recomputed so as to get 101 non over-lapping sequences.
| action= 'setcgn' | : grand('setcgn',G) sets the current virtual generator for clcg4 (when clcg4 is set, this is the virtual (clcg4) generator number G which is used); the virtual clcg4 generators are numbered from 0,1,..,100 (and so G must be an integer in [0,100]) ; by default the current virtual generator is 0. |
| action= 'getcgn' | : S=grand('getcgn') returns the number of the current virtual clcg4 generator. |
| action= 'initgn' | : grand('initgn',I) reinitializes the state of the current virtual generator |
| action= 'setall' | : grand('setall',s1,s2,s3,s4) sets the initial state of generator 0 to s1,s2,s3,s4. The initial seeds of the other generators are set accordingly to have synchronisation. For constraints on s1, s2, s3, s4 see the 'setsd' action. |
| action= 'advnst' | : grand('advnst',K) advances the state of the current generator by 2^K values and resets the initial seed to that value. |
An example of the need of the splitting capabilities of clcg4 is as follows. Two statistical techniques are being compared on data of different sizes. The first technique uses bootstrapping and is thought to be as accurate using less data than the second method which employs only brute force. For the first method, a data set of size uniformly distributed between 25 and 50 will be generated. Then the data set of the specified size will be generated and analyzed. The second method will choose a data set size between 100 and 200, generate the data and analyze it. This process will be repeated 1000 times. For variance reduction, we want the random numbers used in the two methods to be the same for each of the 1000 comparisons. But method two will use more random numbers than method one and without this package, synchronization might be difficult. With clcg4, it is a snap. Use generator 0 to obtain the sample size for method one and generator 1 to obtain the data. Then reset the state to the beginning of the current block and do the same for the second method. This assures that the initial data for method two is that used by method one. When both have concluded, advance the block for both generators.
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