Let's say you have a function that takes a number as an input and outputs a vector. However, the output vector size depends on the input and you can't calculate it before the function.
For example, take the 3N+1 famous algorithm. A simple implementation of that algorithm, returning the whole path until 1 could look like this:
compute <- function(x) {
if (x %% 2 == 0)
return(x / 2)
return(3*x + 1)
}
algo <- function(x) {
if (x == 1)
return(1)
output <- x
while(x != 1) {
x <- compute(x)
output <- c(output, x)
}
return(output)
}
The algo function returns the whole path of an input X to 1, according to the function. As you can tell, the output variable grows dynamically, using the c() (combine) function.
Are there any alternatives to this? Is growing a list faster? Should I adopt some classic dynamic vector logic, such as initializing an empty N-sized vector and double it everytime it goes full?
EDIT: Please don't mind trying to optimize the way my helper functions are structured. I get it, but that's not the point here! I am only concerned about the c() function and an alternative to it.
As per your edit, maybe you can check the following solution
algo_TIC2 <- function(x) {
res <- x
repeat {
u <- tail(res, 1)
if (u != 1) {
res[length(res) + 1] <- if (u %% 2) 3 * u + 1 else u / 2
} else {
return(res)
}
}
}
You can use recursions like below
compute <- function(x) if (x %% 2) 3*x + 1 else x / 2
algo_TIC1 <- function(x) {
if (x == 1) {
return(1)
}
c(x, algo_TIC1(compute(x)))
}
and you will see
> algo_TIC1(3000)
[1] 3000 1500 750 375 1126 563 1690 845 2536 1268 634 317 952 476 238
[16] 119 358 179 538 269 808 404 202 101 304 152 76 38 19 58
[31] 29 88 44 22 11 34 17 52 26 13 40 20 10 5 16
[46] 8 4 2 1
If you don't want any helper function, i.e., compute, you can try
algo_TIC1 <- function(x) {
if (x == 1) {
return(1)
}
c(x, algo_TIC1(if (x %% 2) 3*x + 1 else x / 2))
}
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