https://github.com/sejas/muia-simulation-methods-random-generators

Study of random generators
https://github.com/sejas/muia-simulation-methods-random-generators

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Study of random generators

• Host: GitHub
• URL: https://github.com/sejas/muia-simulation-methods-random-generators
• Owner: sejas
• Created: 2019-10-02T14:46:02.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-12-20T12:33:29.000Z (about 5 years ago)
• Last Synced: 2025-01-01T15:42:14.781Z (2 days ago)
• Language: Python
• Size: 5.37 MB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# RANDOM GENERATORS AND CONTRASTS [MUIA - SIMULATION METHODS]

Implementation of congruential random generator. IMSL.

Using Schrage method to avoid overflow.

> Park, Stephen & Miller, Keith. (1988). Random Number Generators: Good Ones Are Hard to Find. Commun. ACM. 31. 1192-1201. 10.1145/63039.63042.

**Authors**

- Antonio Sejas

- Danielle Pellegrino

- Sergio Cavero

## ======= CREATE A CONGRUENTIAL RANDOM GENERATOR IN PYTHON =======

> You can find our custom development in python in the folder: `/generator-in-python`

## Execute

```

python main.py

```

It will create `random-numbers-sample.txt` with the random numbers list

## Test algorithm

```

python imsl_test.py

```

## ======= STUDY OF RANDOM GENERATOR CONTRASTS WITH TESTU01 =======

> You can find our constrast study in the folder: `/contrasts-with-testu01`

## Requirements

You need to have installed the TestU01 library to be able to compile the code.

http://simul.iro.umontreal.ca/testu01/tu01.html

If you are on Unix or MacOS system, you can try to directly execute the `./muia` application.

## Compilation

After installing the code

```

➜ clang muia.c  -lmylib -lprobdist -ltestu01 -o muia

```

## How to execute

```bin

➜ ./muia

```

---

MUIA 2019/2020

Authors: Antonio Sejas, Danielle Pellegrino, Sergio Cavero