Is there a better way to simulate randomly choosing from a weighted set?

[I originally posted this to comp.lang.c++ since that is the language
I'm working in, but then I realized that my problem isn't language
specific and this would probably be a better group.]

Here is my problem: suppose there are, say, five events with these
probabilities:

event1 0.7
event2 0.1
event3 0.1
event4 0.05
event5 0.05

Note that sum of the probabilities is 1.0. I would like a function that
simulates these events and returns an int to indicate which event
occurred: the function should statistically return 1 about 70% of the
time, 2 about 10% of the time, and so on.

I have figured out a way to do this, but I suspect my way is
suboptimal.

I build a vector of five elements that looks like:

( 0.05, 0.05+0.05, 0.05+0.05+0.1, 0.05+0.05+0.1+0.1,
0.05+0.05+0.1+0.1+0.7 )
= ( 0.05, 0.1, 0.2, 0.3, 1.0 )

I then generate a random float in the interval 0.0 ... 1.0, and if the
random float is in the range 0 to 0.05, I return event 5, and if the
random float is in the range 0.05-0.1, I return event 4, and so on.
(Actually, I should test for event 1 first since it is most common, but
I'm too lazy to re-type my example vector above.)

For my real problem, I have to deal with many different cases where the
number of events to consider constantly varies, and I suspect there has
to be a better way than building a vector to represent the different
ranges a random variable can fall in and then seeing which range it
falls in.

So is there a better way?

.