setrandvar — A class to manage a set of random variables.
tokenmatrix = setrandvar_tokens ()
nb = setrandvar_size ()
srv = setrandvar_new ( )
srv = setrandvar_new ( n )
srv = setrandvar_new ( file )
setrandvar_destroy ( srv )
setrandvar_setsample ( srv , name , np )
setrandvar_setsample ( srv , k , i , value )
setrandvar_setsample ( srv , k , value )
setrandvar_setsample ( srv , value )
setrandvar_save ( srv , file )
np = setrandvar_getsize ( srv )
sample = setrandvar_getsample ( srv , k , i )
sample = setrandvar_getsample ( srv , k )
sample = setrandvar_getsample ( srv )
setrandvar_getlog ( srv )
nx = setrandvar_getdimension ( srv )
setrandvar_freememory ( srv )
setrandvar_buildsample ( srv , srv2 )
setrandvar_buildsample ( srv , name , np )
setrandvar_buildsample ( srv , name , np , ne )
setrandvar_addrandvar ( srv , rv )
The setrandvar_new function allows to create a new set of random variables. The setrandvar_destroy function allows to destroy an existing set of random variables.
The setrandvar_new function returns a token which is a unique identifier for the new set of random variables. Indeed, when a new set is created, a counter is updated which corresponds to the returned token. This way, each token is unique and can correspond only to one single set.
The functions setrandvar_tokens and setrandvar_size allow to manage the sets which have already been created. The function setrandvar_size returns the number of sets, while the setrandvar_tokens function returns the list of current sets.
The following functions are available.
Returns a 1x(n) matrix containing the tokens of current random variables, where n is the number of random variables.
Returns a 1x(n) matrix containing the tokens of current random variables, where n is the number of random variables.
Creates a new set of random variables.
Creates a new set of n uniformly distributed random variables.
Creates a new set of random variables from an existing file.
Destroys the current object.
Initialize the number of simulation to np and stores the name of the sampling.
Set the value of the the sample input #i with 1≤ i≤ nx where nx is the number of input random variables of the sample #k with 1≤ k≤ np, where np is the number of simulations.
Set the sample #k with 1≤ k≤ np, where np is the number of simulations and value is a matrix with size 1 x (nx) where nx is the number of input random variables.
Set the experiment matrix, where value is a matrix with size (np) x (nx), where np is the number of experiments and nx is the number of input random variables.
Save the set into the file.
Returns the number of experiments of the set random variable
returns the sample input #i=1,...nx where nx is the number of random variables of the sample #k, where 1≤ k≤ np, where np is the number of experiments.
Returns the sample #k as a matrix with size 1 x (nx), where 1≤ k≤ np, np is the number of experiments and nx is the number of input random variables.
Returns the sampling matrix with size (np) x (nx), where np is the number of experiments and nx is the number of input random variables.
Prints out a log of the set of random variables.
Returns the number of input random variables.
Free the memory of the current set.
Build a sampling from the other set srv2. Uses density transformation methods to produce the sampling for srv, given the sampling of srv2.
Build a sampling with sampling method "name" and np experiments. (TODO : clarify the difference between np as experiments and np as degree)
The list of possible values for the input argument "name" is the following.
In that case, np is the number of experiments.
In that case, np is the number of experiments.
In that case, np is the number of experiments.
The random variables must be normalized.
In that case, np is the degree of the polynomial which must be lower or equal to 40. The random variables must be normalized.
Build a sampling with sampling method "name" and np experiments, with selected ne samples.
Add the random variable "rv" to the set.
In the following example, we build a Monte-Carlo design of experiments, with 2 input random variables. The first variable is associated with a Normal distribution function and the second variable is associated with a Uniform distribution function. The simulation is based on 1000 experiments.
The function nisp_initseed is used to set the value of the seed to zero, so that the results can be reproduced. The setrandvar_new function is used to create a new set of random variables. Then we create two new random variables with the randvar_new function. These two variables are added to the set with the setrandvar_addrandvar function. The setrandvar_buildsample allows to build the design of experiments, which can be retrieved as matrix with the setrandvar_getsample function. The sampling matrix has np=1000 rows and 2 columns (one for each input variable).
// Test setrandvar_buildsample with ( name , np ) and with MonteCarlo
nisp_initseed ( 0 );
srv = setrandvar_new ( );
rv1 = randvar_new("Normale",1.0,0.5);
rv2 = randvar_new("Uniforme",1.0,2.5);
setrandvar_addrandvar ( srv , rv1 );
setrandvar_addrandvar ( srv , rv2 );
np = 1000;
name = "MonteCarlo";
setrandvar_buildsample ( srv , name , np );
sampling = setrandvar_getsample ( srv );
// Check sampling of random variable #1
mean(sampling(1:np,1))
// Check sampling of random variable #2
mean(sampling(1:np,2))
setrandvar_destroy(srv);
randvar_destroy(rv1);
randvar_destroy(rv2);