Generating random numbers

While getting truly random numbers is a difficult task, most of the Monte Carlo methods perform well with pseudo-random numbers and this makes it easier to re-run simulations based on a seed. Practically, all the modern programming languages include basic random sequences, at least good enough to make good simulations.

Python includes the random library. In the following code we can see the basic usage of the library:

• Importing the random library as rnd:

  import random as rnd

• Getting a random float between 0 and 1:

  >>>rnd.random()
  0.254587458742659

• Getting a random number between 1 and 100:

  >>>rnd.randint(1,100)
  56

• Getting a random float between 10 and 100 using a uniform distribution:

  >>>rnd.uniform(10,100)
  15.2542689537156

Tip

For a detailed list of methods of the random library, follow the link http://docs.python.org/3.2/library/random.html.

In the case of JavaScript, a more basic random function is included with the Math.random() function, however...