RandomGenerator
As the Stanford course uses the ACM libraries, professor Sahami had us use the RandomGenerator class. I've come to learn that the use of the ACM libraries is somewhat looked down upon and they aren't used very much outside of the academic setting, but this worked fine as an introduction. I believe this was also one of the earlier instances we had of learning about constructors for new objects.
The RandomGenerator methods include nextInt() for integers, nextDouble() for decimal values, nextBoolean() for true or false (1 or 0) values, and, unique to the ACM version, nextColor() for a randomly generated opaque color, with values 0-255.
The first example used was to simulate a six-sided die roll with the code below. As someone who has had an interest in dice, from the six-sided up to the twenty-sided dice used in role playing games, this was a fun application.
Of course from a programming perspective, it's good that, though seemingly random to the user, the sequence is determined and will always be the same, given the same seed. That's for the purpose of debugging. If there was a problem in your program and it only occurred when a certain "random" number was used, you might have a very hard time tracking down the bug. If, on the other, hand, you can set the seed used and so always produce the same sequence of numbers, it will be much easier to reproduce the problem and find the bug that is causing it.
In a future post I want to write about a virtual dice throwing app that I just installed. Rather than just generating random number results, it uses a physics engine to throw the dice and the dice will even interact with each other, affecting the way that they land, just like with real dice.
The RandomGenerator methods include nextInt() for integers, nextDouble() for decimal values, nextBoolean() for true or false (1 or 0) values, and, unique to the ACM version, nextColor() for a randomly generated opaque color, with values 0-255.
int dieRoll = rgen.nextInt(1,6);
PseudoRandomness
But are the numbers produced by computers through random number generators really random? No, they are actually determined by a complex algorithm, based on an initial value. Given the same initial value, the seed, they will always produce the same sequence of "random" numbers. For most practical needs, however, pseudorandom numbers are sufficient because the user does not know the what the seed is and so cannot determine the sequence of values that will be produced. The ACM RandomGenerator uses the system time in milliseconds, which no one will know, for the seed.A process (or data produced by a process) is said to be pseudorandom when the outcome is deterministic, yet also effectively random, as long as the internal action of the process is hidden from observation. (NIST.gov)
Of course from a programming perspective, it's good that, though seemingly random to the user, the sequence is determined and will always be the same, given the same seed. That's for the purpose of debugging. If there was a problem in your program and it only occurred when a certain "random" number was used, you might have a very hard time tracking down the bug. If, on the other, hand, you can set the seed used and so always produce the same sequence of numbers, it will be much easier to reproduce the problem and find the bug that is causing it.
In a future post I want to write about a virtual dice throwing app that I just installed. Rather than just generating random number results, it uses a physics engine to throw the dice and the dice will even interact with each other, affecting the way that they land, just like with real dice.
