#LATIN HYPERCUBE SAMPLING SCRIPT SIMULATOR#
for parameters that cannot easily be iterated or for parameters that are not set via the openCARP simulator (e.g. You can write your own python function to generate a polling file e.g.It is checked automatically if the polling-param is actually set by the user in the run script or in any.Each parameter requires a polling-range and the number of samples has to be the same. An arbitrary number of parameters can be added to -polling-param., and to modify and augment existing designs./run.py -polling-file stimulus.poll \ -polling-param stimulus.start stimulus.start \ -polling-range 20:60:25 50:70:25 \ -sampling-type lhs ,, ,Īnd to generate Latin Hypercube Samples. (1987) Large Sample Properties of Simulations Using Latin Hypercube Sampling.
(2005) A method to improve design reliability using optimal Latin hypercube samplingĬomputer Assisted Mechanics and Engineering Sciences 12, 87–105. The mutation is accomplished by swtching two elements in a columnĪn n by k Latin Hypercube Sample matrix with values uniformly distributed on Take a random column out of the best matrix and place it in a random column of each of the other matricies, and take a random column out of each of the other matricies and put it in copies of the best matrix thereby causing the progenyįor each of the progeny, cause a genetic mutation pMut percent of the time. Keep the best design in the first position and throw away half of the rest of the population Generate pop random latin hypercube designs of size n by kĬalculate the S optimality measure of each design The other points in the design, so the points are as spread out as possible. S-optimality seeks to maximize the mean distance from each design point to all The uniform sample from a column can be transformed to any distribution by Then sampled from within each of the n sections. Integers into n sections of a standard uniform distribution. Of the first n integers in each of k columns and then transforming those This program generates a Latin Hypercube Sample by creating random permutations Latin Hypercube sampling generates more efficientĮstimates of desired parameters than simple Monte Carlo sampling. n sample points are then drawn such that a Sampling a function of k variables, the range of each variable is divided Generalisation of this concept to an arbitrary number of dimensions. Is only one sample in each row and each column. Of collections of parameter values from a multidimensional distribution.Ī square grid containing possible sample points is a Latin square iff there Latin hypercube sampling (LHS) was developed to generate a distribution The optimality criterium of the algorithm. The probability with which a mutation occurs in a column of the progeny The number of generations over which the algorithm is applied The number of designs in the initial population The number of replications (variables or columns) The number of partitions (simulations or design points or rows) GeneticLHS ( n = 10, k = 2, pop = 100, gen = 4, pMut = 0.1, criterium = "S", verbose = FALSE )
create_oalhs: Create an orthogonal array Latin hypercube.create_galois_field: Create a Galois field.createBusht: Create an orthogonal array using the Bush algorithm with.createBush: Create an orthogonal array using the Bush algorithm.createBoseBushl: Create an orthogonal array using the Bose-Bush algorithm with.createBoseBush: Create an orthogonal array using the Bose-Bush algorithm.createBose: Create an orthogonal array using the Bose algorithm.createAddelKempN: Create an orthogonal array using the Addelman-Kempthorne.createAddelKemp3: Create an orthogonal array using the Addelman-Kempthorne.createAddelKemp: Create an orthogonal array using the Addelman-Kempthorne.augmentLHS: Augment a Latin Hypercube Design.
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