Learning Processing: A Beginner's Guide to Programming Images ...

204 Learning Processing
Example 13-2: Random number distribution
// An array to keep track of how often random numbers are picked.
float[] randomCounts;
void setup() {
size(200,200);
randomCounts = new float[20];
}
void draw() {
background(255);
// Pick a random number and increase the count
int index = int(random(randomCounts.length));
randomCounts[index] + + ;
// Draw a rectangle to graph results
stroke(0);
fill(175);
for (int x = 0; x < randomCounts.length; x + + ) {
rect(x*10,0,9,randomCounts[x]);
}
}
With a few tricks, we can change the way we use random( ) to produce a nonuniform distribution of
random numbers and generate probabilities for certain events to occur. For example, what if we wanted
to create a sketch where the background color had a 10% chance of being green and a 90% chance of
being blue?
13.4
Probability Review
Let’s review the basic principles of probability, fi rst looking at single event probability, that is, the
likelihood of something to occur.
Given a system with a certain number of possible outcomes, the probability of any given event occurring
is the number of outcomes which qualify as that event divided by total number of possible outcomes. Th e
simplest example is a coin toss. Th ere are a total of two possible outcomes (heads or tails). Th ere is only
one way to fl ip heads, therefore the probability of heads is one divided by two, that is, 1/2 or 50%.
Consider a deck of 52 cards. Th e probability of drawing an ace from that deck is:
number of aces/number of cards � 4/52 � 0.077 � � 8%
Th e probability of drawing a diamond is:
number of diamonds/number of cards � 13/52 � 0.25 � 25%
fi g. 13.1
We can also calculate the probability of multiple events occurring in sequence as the product of the
individual probabilities of each event.