Full Monte for Microsoft Project Add to compare View comparison. Project Management. Oracle's Primavera P6 Professional Project Management, a recognized standard for project management software, is designed to handle large-scale, highly sophisticated and multifaceted projects. Latin HyperCube Sampling Process Capability (6 Sigma. A User’s Guide to Sandia’s Latin Hypercube Sampling Software: LHS UNIX Library/Standalone Version. Abstract This document is a reference guide for the UNIX Library/Standalone version of the Latin Hypercube Sampling Software. This software has been developed to generate Latin hypercube. Concepts involved in the Latin hypercube sampling. Latin Hypercube of discrete and continuous. Learn more about latin_hypercube, latin hypercube, sampling, discrete, continuous, doe, correlation. Latin Hypercube of discrete and continuous variables. Asked by Rashmi B. Rashmi B (view profile) 1 question asked. I want to use Latin Hypercube to sample these variables together preserving. Latin Hypercube Sampling is sampling utility. This is sampling utility implementing Latin hypercube sampling. This document is a reference guide for the UNIX Library/Standalone version of the Latin Hypercube Sampling Software. Latin Hypercube Sampling Theory. The Free Encyclopedia. Latin hypercube sampling of Gaussian random fields. Real-time latin-hypercube sampling-based Monte Carlo ERror Propagation - tisimst/mcerp. Skip to content. Join GitHub today. GitHub is home to over 28 million developers working together to host and review code, manage projects, and build software together.

1. Latin Hypercube Sampling Matlab
2. Latin Hypercube Sampling Example
3. Latin Hypercube Design
4. Latin Hypercube Sampling R
5. Latin Hypercube Sampling Software Mac Free
6. Latin Hypercube Sampling Software Machine
7. Python Latin Hypercube Sampling

I am running a simulation on a Linux server:

1. Is it possible to run a simulation (experiment) on batch mode?
2. The only document I found was this, but it says nothing about running experiments on batch mode.

Here an example of what I am trying to do:

• I set a parameter variation experiment using a Latin hypercube sampling (240 parameter combinations):

• I am saving text files of each run; I am running 10 replications per iteration.

I can run this using a Linux server, after running the application I get a window (XQuartz, I am using a Mac) from which I can manipulate and run the experiment:

• As far as I keep the window of the experiment open, the simulation will run. If I lose the connection with the server, the simulation will stop, unless I run this in a background mode (i.e., batch mode).

Any ideas? Examples?

1 Answer

To run AnyLogic (multi- or single-run) experiments in 'batch', the normal method is to export the experiment to a standalone Java application, which can then be run as needed from the command line. This is available only in the Professional edition; looks like you are using the Personal Learning Edition (PLE).

Otherwise, AnyLogic is fundamentally a 'client-based' application. You might find some clever tricks to run it remotely as you suggest, but there is no 'intended' method, and you may be skirting along the edges of the license conditions.

Not the answer you're looking for? Browse other questions tagged javaanylogic or ask your own question.

Latin Hypercube Sampling LHC

JOSE LOPEZ-COLLADO

JULY, 2015

After searching fow a while, I finally found a paper describing the LHC sampling (Swiler and Wyss 2004). LHC is a re-scaling function in the domain of a random uniform variate so to have a better dispersion of the input numbers used to generate the pdf deviates. The paper of Swiler & Wyss presents a detailed example of the algorithm so anybody can check the results and the algorithm itself (pages 2-9 in the paper).

In essence, the sample size ss serves to divide the sampling space into ss categories and then the U values are re-scaled to the new limits:

(* HERE IS THE MATHEMATICA CODE WITH COMMENTS *)

SetDirectory[NotebookDirectory[]];

wdist = WeibullDistribution[1.5, 3];

dname = wdist;

(* ss is the sample size*)

Latin Hypercube Sampling Matlab

ss= 1500;

(*scaleu is the LHS re-scaling, very simple indeed! *)

scaleu[u_, i_, ss_] := u (1/ss) + ((i - 1)/ss);

(* set function as listable, capable of handling lists*)

Latin Hypercube Sampling Example

SetAttributes[scaleu, Listable];

(*get a list of uniform random numbers*)

un = RandomVariate[UniformDistribution[{0, 1}], ss];

(*Get a sequence of integers, 1,2,3,... ,ss*)

strata = Range[ss];

(*Re-distribute the random numbers using LHC*)

usc = scaleu[un, strata, ss];

(*Obtain a random number from the list above, in this example is wdist, a Weibull Distribution with shape and scale parameters of 1.5 and 3 respectively*)

(*Obtain a list of random numbers using LHC, check that we use the inverse of the cumulative density function CDF to translate from the re-scaled U values to their pdf values *)

pvL = Map[InverseCDF[dname, #] &, usc];

(* get some statistics, mean and standard deviation *)

mL = Mean[pvL]

2.70744

sdL = StandardDeviation[pvL]

Latin Hypercube Design

1.83532

Latin Hypercube Sampling R

(*The next call is is the conventional random sampling, NOT the LHC, it uses the built-in Mathematica function RandomVariate and the distribution name and sample size as arguments *)

pvR = RandomVariate[dname, ss];

(* get the same statistics: mean and standard deviation*)

mR = Mean[pvR]

Latin Hypercube Sampling Software Mac Free

2.62812

Latin Hypercube Sampling Software Machine

sdR = StandardDeviation[pvR]

Python Latin Hypercube Sampling

1.7902

(* Draw the distributions, Latin Hypercube on the left, regular sampling on the right *)

GraphicsRow[{Histogram[pvL], Histogram[pvR]}]