Randomness

We often would like the behavior of a program to change or be unpredictable - to not be exactly the same every time we run it.

In other cases, randomness helps eliminate unwanted artifacts, such as obvious, excessively regular or repeated patterns. These include patterns in the appearance and in the movement of objects.






Randomness

Things that can be randomized include:






Random Number Functions

Randomness is added to programs by using random numbers. These are generated by random number functions.

Python has a package called random.
Two of the functions it provides are:

random()
Returns a random floating point number between 0 and 1.
uniform(a,b)
Returns a random floating point number between a and b.





Distributions

Random numbers can come in different distributions - how frequently the various numbers occur.

e.g. rolling a fair die many times will yield roughly the same number of 1s, 2s, 3s, 4s, 5s, and 6s.
A loaded die can produce one number more frequently.

random() & uniform() return uniform distributions - each possible number is equally likely to be returned.
If you call the function a large number of times, then you can expect each possible number to be returned about (but not exactly) the same number of times.
(Flipping a coin 1,000,000 times, you would expect to get roughly 500,000 heads and 500,000 tails.)

This plot is from calling int(random() * 100) 100,000 times. It shows how many times each of the possible values (from 0 to 99) was returned.






Distributions

Sometimes we want a different distribution.

A common distribution is the bell curve (or gaussian distribution). In this distribution, one small range of numbers is returned more frequently than others, with values further from the center of the distribution returned less and less frequently.






Gaussian Distribution

The Python function random.gauss(center, deviation) returns random numbers in a gaussian distribution.

The first argument (center) is the central number that the return values will be clustered around. The second argument (deviation) is the standard deviation of the distribution - this measures how broad the bell curve is; roughly 2/3 of all returned values will be within +/- deviation of the center value.

Example: a gaussian distribution lets you place objects randomly, but clustered about a center.

UniformGaussian





choice

Python's random.choice(list) randomly chooses one element from a list.

>>> letters = ['a', 'b', 'c', 'd', 'e', 'f']
>>> random.choice(letters)
'a'
>>> random.choice(letters)
'e'
>>> random.choice(letters)
'b'
>>> random.choice(letters)
'b'





shuffle

Python's random.shuffle(list) randomly re-orders a list (in place).

>>> letters = ['a', 'b', 'c', 'd', 'e', 'f']
>>> letters
['a', 'b', 'c', 'd', 'e', 'f']
>>> random.shuffle(letters)
>>> letters
['f', 'e', 'd', 'c', 'b', 'a']
>>> random.shuffle(letters)
>>> letters
['c', 'b', 'a', 'd', 'e', 'f']


Creative Commons License
This document is by Dave Pape, and is released under a Creative Commons License.