We want to use H V U to nest a function which adds a random step to an ‘(x,y)’ coordinate. The function is a bit long, but otherwise the problem is quite straightforward.
2: [0, 0] 1: [ [ 0, 0 ]
1: 50 [ 0.4288, -0.1695 ]
. [ -0.4787, -0.9027 ]
...
[0,0] 50 H V U ' <# + [random(2.0)-1, random(2.0)-1]> RET
Just as the text recommended, we used ‘< >’ nameless function
notation to keep the two random calls from being evaluated
before nesting even begins.
We now have a vector of ‘[x, y]’ sub-vectors, which by Calc’s rules acts like a matrix. We can transpose this matrix and unpack to get a pair of vectors, ‘x’ and ‘y’, suitable for graphing.
2: [ 0, 0.4288, -0.4787, ... ]
1: [ 0, -0.1696, -0.9027, ... ]
.
v t v u g f
Incidentally, because the ‘x’ and ‘y’ are completely independent in this case, we could have done two separate commands to create our ‘x’ and ‘y’ vectors of numbers directly.
To make a random walk of unit steps, we note that sincos of
a random direction exactly gives us an ‘[x, y]’ step of unit
length; in fact, the new nesting function is even briefer, though
we might want to lower the precision a bit for it.
2: [0, 0] 1: [ [ 0, 0 ]
1: 50 [ 0.1318, 0.9912 ]
. [ -0.5965, 0.3061 ]
...
[0,0] 50 m d p 6 RET H V U ' <# + sincos(random(360.0))> RET
Another v t v u g f sequence will graph this new random walk.
An interesting twist on these random walk functions would be to use complex numbers instead of 2-vectors to represent points on the plane. In the first example, we’d use something like ‘random + random*(0,1)’, and in the second we could use polar complex numbers with random phase angles. (This exercise was first suggested in this form by Randal Schwartz.)