I tried to write a function to calculate the nth Fibonnaci number in R. I can do this recursively.
fibonacci = function(n){
if (n == 1) {return(1)}
if (n == 2) {return(2)}
return(fibonacci(n - 1) + fibonacci(n - 2))
}
I couldn't find any examples in R but from guide in other languages I came up with the following. However it doesn't seem to run any faster.
fibonacci = function(n, lookup = NULL){
if (is.null(lookup)) {
lookup = integer(n + 1)
}
if (n == 1) {return(1)}
if (n == 2) {return(2)}
lookup[1] = 1
lookup[2] = 2
if (lookup[n - 1] == 0) {
lookup[n - 1] = fibonacci(n - 1, lookup)
}
if (lookup[n - 2] == 0) {
lookup[n - 2] = fibonacci(n - 2, lookup)
}
return(lookup[n - 1] + lookup[n - 2])
}
The problem with your solution is that your lookup vector is always local to the call frame environment and new solutions are not propagated up to the callers, i.e., changes to the lookup vector are lost when the function returns. In order to make a persistent variable a la static variables in C, you may create an attribute to the function that acts as a memoizer. Here is one solution:
fibonaccid = function(n, init=T){
if (init) {
lookup <- integer(n + 1)
lookup[1] <- 1
lookup[2] <- 2
} else {
lookup <- attr(fibonaccid, ".lookup")
}
# ... calculate lookup as before, recurse with fibonaccid(...,init=F)
attr(fibonaccid, ".lookup") <<- lookup
return(lookup[n - 1] + lookup[n - 2])
}
This indeed runs much faster:
R> system.time(print(fibonacci(35)))
[1] 14930352
user system elapsed
20.923 0.140 21.446
R> system.time(print(fibonaccid(35)))
[1] 14930352
user system elapsed
0.202 0.006 0.209
See this post for more information.
Related
I'm trying to solve the problem #14 of Project Euler.
So the main objective is finding length of Collatz sequence.
Firstly I solved problem with regular loop:
compute <- function(n) {
result <- 0
max_chain <- 0
hashmap <- 1
for (i in 1:n) {
chain <- 1
number <- i
while (number > 1) {
if (!is.na(hashmap[number])) {
chain <- chain + hashmap[number]
break
}
if (number %% 2 == 0) {
chain <- chain + 1
number <- number / 2
} else {
chain <- chain + 2
number <- (3 * number + 1) / 2
}
}
hashmap[i] <- chain
if (chain > max_chain) {
max_chain <- chain
result <- i
}
}
return(result)
}
Only 2 seconds for n = 1000000.
I decided to replace while loop to recursion
len_collatz_chain <- function(n, hashmap) {
get_len <- function(n) {
if (is.na(hashmap[n])) {
hashmap[n] <<- ifelse(n %% 2 == 0, 1 + get_len(n / 2), 2 + get_len((3 * n + 1) / 2))
}
return(hashmap[n])
}
get_len(n)
return(hashmap)
}
compute <- function(n) {
result <- 0
max_chain <- 0
hashmap <- 1
for (i in 1:n) {
hashmap <- len_collatz_chain(i, hashmap)
print(length(hashmap))
if (hashmap[i] > max_chain) {
max_chain <- hashmap[i]
result <- i
}
}
return(result)
}
This solution works but works so slow. Almost 1 min for n = 10000.
I suppose that one of the reasons is R creates hashmap object each time when call function len_collatz_chain.
I know about Rcpp packages and yes, the first solution works fine but I can't understand where I'm wrong.
Any tips?
For example, my Python recursive solution works in 1 second with n = 1000000
def len_collatz_chain(n: int, hashmap: dict) -> int:
if n not in hashmap:
hashmap[n] = 1 + len_collatz_chain(n // 2, hashmap) if n % 2 == 0 else 2 + len_collatz_chain((3 * n + 1) // 2, hashmap)
return hashmap[n]
def compute(n: int) -> int:
result, max_chain, hashmap = 0, 0, {1: 1}
for i in range(2, n):
chain = len_collatz_chain(i, hashmap)
if chain > max_chain:
result, max_chain = i, chain
return result
The main difference between your R and Python code is that in R you use a vector for the hashmap, while in Python you use a dictionary and that hashmap is transferred many times as function argument.
In Python, if you have a Dictionary as function argument, only a reference to the actual data is transfered to the called function. This is fast. The called function works on the same data as the caller.
In R, a vector is copied when used as function argument. This is potentially slow, but safer in the sense that the called function cannot alter the data of the caller.
This the main reason that Python is so much faster in your code.
You can however alter the R code slightly, such that the hashmap is not transfered as function argument anymore:
len_collatz_chain <- local({
hashmap <- 1L
get_len <- function(n) {
if (is.na(hashmap[n])) {
hashmap[n] <<- ifelse(n %% 2 == 0, 1 + get_len(n / 2), 2 + get_len((3 * n + 1) / 2))
}
hashmap[n]
}
get_len
})
compute <- function(n) {
result <- rep(NA_integer_, n)
for (i in seq_len(n)) {
result[i] <- len_collatz_chain(i)
}
result
}
compute(n=10000)
This makes the R code much faster. (Python will probably still be faster though).
Note that I have also removed the return statements in the R code, as they are not needed and add one level to the call stack.
Hi I was wondering if someone knows how to realize this sequence in R?
Consider a sequence with following requirement.
a1=1
an=an-1+3 (If n is a even number)
an=2×an-1-5 (If n is a odd number)
e.g. 1,4,3,6,7,10,15,...
a30=?
Try the following.
It will return the entire sequence, not just the last element.
seq_chih_peng <- function(n){
a <- integer(n)
a[1] <- 1
for(i in seq_along(a)[-1]){
if(i %% 2 == 0){
a[i] <- a[i - 1] + 3
}else{
a[i] <- 2*a[i - 1] - 5
}
}
a
}
seq_chih_peng(30)
Note that I do not include code to check for input errors such as passing n = 0 or a negative number.
If you want to do it recursively, you just have the write the equations in your function as follows:
sequence <- function(n) {
if (n == 1) return(1)
else if (n > 1) {
if (n %% 2 == 1) {
return(2 * sequence(n - 1) - 5)
}else{
return(sequence(n - 1) + 3)
}
}else{
stop("n must be stricly positive")
}
}
sequence(30)
# returns 32770
Is there any implementation of functionality in R, such that it is possible to get the next representable floating point number from a given floating point number. This would be similar to the nextafter function in the C standard library. Schemes such as number + .Machine$double.eps don't work in general.
No, but there are two ways you can make it:
Using C
If you want the exact functionality of the nextafter() function, you can write a C function that works as an interface to the function such that the following two constraints are met:
The function does not return a value. All work is accomplished as a "side effect" (changing the values of arguments).
All the arguments are pointers. Even scalars are vectors (of length one) in R.
That function should then be compiled as a shared library:
R CMD SHLIB foo.c
for UNIX-like OSs. The shared library can be called using dyn.load("foo.so"). You can then call the function from inside R using the .C() function
.C("foo", ...)
A more in depth treatment of calling C from R is here.
Using R
number + .Machine$double.eps is the way to go but you have to consider edge cases, such as if x - y < .Machine$double.eps or if x == y. I would write the function like this:
nextafter <- function(x, y){
# Appropriate type checking and bounds checking goes here
delta = y - x
if(x > 0){
factor = 2^floor(log2(x)) + ifelse(x >= 4, 1, 0)
} else if (x < 0) {
factor = 65
}
if (delta > .Machine$double.eps){
return(x + factor * .Machine$double.eps)
} else if (delta < .Machine$double.eps){
return(x - factor * .Machine$double.eps)
} else {
return(x)
}
}
Now, unlike C, if you want to check integers, you can do so in the same function but you need to change the increment based on the type.
UPDATE
The previous code did not perform as expected for numbers larger than 2. There is a factor that needs to be multiplied by the .Machine$double.eps to make it large enough to cause the numbers to be different. It is related to the nearest power of 2 plus one. You can get an idea of how this works with the below code:
n <- -100
factor <- vector('numeric', 100)
for(i in 1:n){
j = 0
while(TRUE){
j = j + 1
if(i - j * .Machine$double.eps != i) break()
}
factor[i] = j
}
If you prefer Rcpp:
#include <Rcpp.h>
using namespace Rcpp;
// [[Rcpp::export]]
double nextAfter(double x, double y) {
return nextafter(x, y);
}
Then in R:
sprintf("%.20f", 1)
#[1] "1.00000000000000000000"
sprintf("%.20f", nextAfter(1, 2))
#[1] "1.00000000000000022204"
I'm not sure if Christopher Louden's answer works for all values, but here's a pure R version of the classic approach (increments/decrements the integer bits). R does not make it easy to convert between doubles and integers, nor does it have a 64-bit integer type, so there's quite a lot of code for this.
doubleToRaw <- function(d) writeBin(d, raw());
rawToDouble <- function(r) readBin(r, numeric());
int64inc <- function(lo, hi) {
if (lo == 0xffffffff) { hi <- hi + 1; lo <- 0; } else { lo <- lo + 1; }
return(c(lo, hi));
}
int64dec <- function(lo, hi) {
if (lo == 0) { hi <- hi - 1; lo <- 0xffffffff; } else { lo <- lo - 1; }
return(c(lo, hi));
}
nextafter <- function(x, y) {
if (is.nan(x + y))
return(NaN);
if (x == y)
return(x);
if (x == 0)
return(sign(y) * rawToDouble(as.raw(c(0, 0, 0, 0, 0, 0, 0, 1))));
ints <- packBits(rawToBits(doubleToRaw(x)), "integer")
if ((y > x) == (x > 0))
ints <- int64inc(ints[1], ints[2])
else
ints <- int64dec(ints[1], ints[2]);
return(rawToDouble(packBits(intToBits(ints), "raw")))
}
I have made a script to calculate the line integral in a matrix of cartesian coordinates points:
# Line Integral (Discrete)
# Input dataSet(as a matrix)
LineInt <- function(dataSet)
{
Dist = 0;
L = nrow(dataSet)
for (idx in 1: L - 1)
{
Dist = Dist + ( ( (dataSet[idx + 1, 1] - dataSet[idx ,1])^2) + ((dataSet[idx + 1, 2] - dataSet[idx, 2])^2) )^(1/2)
}
return(Dist)
}
The script just have to get the diference between next point and actual point, calculate the module of the distance and storage in a variable, then the variable is returned. But for some reason I am getting numeric(0) for all the inputs. I am a beginner and I still cannot find the solution in other topics.
Indexing of idx to Dist variable should be done, hope you may get the solution
LineInt <- function(dataSet)
{
Dist = 0;
L = nrow(dataSet)
for (idx in 1: (L - 1))
{
Dist[idx] = Dist + ( ( (dataSet[idx + 1, 1] - dataSet[idx ,1])^2) + ((dataSet[idx + 1, 2] - dataSet[idx, 2])^2) )^(1/2)
}
return(Dist)
}
This question already has answers here:
Why is my recursive function so slow in R?
(7 answers)
Closed 7 years ago.
I have R running function:
f <- function (n) {
if ( n == 1) return (0)
if ( n == 2) return (1)
else return ( f(n-1) + f(n-2))
}
f(50) takes very long time to calculate. f(35) takes approx 30 seconds. Is there any way to make it faster in R?
Edit. Thanks for help! I used this and it gave me f(50) instantly.
> f <- local({
+ memo <- c(1, 1, rep(NA,100))
+ f <- function(x) {
+ if(x == 1) return(0)
+ if(x == 2) return(1)
+ if(x > length(memo))
+ stop("’x’ too big for implementation")
+ if(!is.na(memo[x])) return(memo[x])
+ ans <- f(x-1) + f(x-2)
+ memo[x] <<- ans
+ ans
+ }
+ })
This is a comment problem with recursive functions that can be solved by tail-recursion. Unfortunately, it appears that R does not support tail call optimization. Fortunately, you can always use the iterative solution, which you should be fairly fast since it does not have to allocate stack frames:
fIter <- function(n) {
if ( n < 2 )
n
else {
f <- c(0, 1)
for (i in 2:n) {
t <- f[2]
f[2] <- sum(f)
f[1] <- t
}
f[2]
}
}
fIter(100)'
This program runs in ~0.45 seconds on ideone. I don't know R personally, source of program: http://rosettacode.org/wiki/Fibonacci_numbers#R