Multiple outputs used repeatedly in for loop Julia - julia

I am using Julia and I've designed a for loop that takes the outputs of a function in one loop and uses them as the input of that function in the next loop (and over and over). When I run this code, Julia flags an "undefined" error, however, if I run the code in debug mode, it executes perfectly. For example, the code looks like this:
function do_command(a,b,c,d)
a = a + 1
b = split(b, keepempty=false)[1]
c = split(b, keepempty=false)[1]
if a == 1000
d = true
else
d = false
end
return a, b, c, d
end
for ii in 1:length(x)
if ii == 1
a = 0
b = "string something"
c = ""
d = false
end
a,b,c,d = do_command(a,b,c,d)
if d == true
print(string(b))
break
end
end
What am I doing wrong here?

An issue with your code is that for introduces a new scope for each iteration of the loop. That is to say: variable a created within the loop body at iteration 1 is not the same as variable a created within the loop body at iteration 2.
In order to fix your problem, you should declare variables outside the loop, so that at each iteration, references to them from within the loop body would actually refer to the same variables from the enclosing scope.
I'd go with something like this:
function do_command(a,b,c,d)
a = a + 1
b = split(b, keepempty=false)[1]
c = split(b, keepempty=false)[1]
if a == 1000
d = true
else
d = false
end
return a, b, c, d
end
# Let's create a local scope: it's good practice to avoid global variables
let
# All these variables are declared in the scope introduced by `let`
a = 0
b = "string something"
c = ""
d = false
for ii in 1:10 #length(x)
# now these names refer to the variables declared in the enclosing scope
a,b,c,d = do_command(a,b,c,d)
if d == true
print(string(b))
break
end
end
end

Related

Execution time orders of magnitude longer depending upon global definition location?

I finished writing the following program and began to do some cleanup after the debugging stage:
using BenchmarkTools
function main()
global solution = 0
global a = big"1"
global b = big"1"
global c = big"0"
global total = 0
while a < 100
while b < 100
c = a^b
s = string(c)
total = 0
for i in 1:length(s)
total = total + Int(s[i]) - 48
end
if total > solution
global solution = total
end
global b = b + 1
end
global b = 1
global a = a + 1
end
end
#elapsed begin
main()
end
#run #elapsed twice to ignore compilation overhead
t = #elapsed main()
print("Solution: ", solution)
t = t * 1000;
print("\n\nProgram completed in ", round.(t; sigdigits=5), " milliseconds.")
The runtime for my machine was around 150ms.
I decided to rearrange the globals to better match the typical layout of program, where globals are defined at the top:
using BenchmarkTools
global solution = 0
global a = big"1"
global b = big"1"
global c = big"0"
global total = 0
function main()
while a < 100
while b < 100
c = a^b
s = string(c)
total = 0
for i in 1:length(s)
total = total + Int(s[i]) - 48
end
if total > solution
global solution = total
end
global b = b + 1
end
global b = 1
global a = a + 1
end
end
#elapsed begin
main()
end
#run #elapsed twice to ignore compilation overhead
t = #elapsed main()
print("Solution: ", solution)
t = t * 1000;
print("\n\nProgram completed in ", round.(t; sigdigits=5), " milliseconds.")
Making that one change for where the globals were defined reduced the runtime on my machine to 0.0042ms.
Why is the runtime so drastically reduced?
Don't use globals.
Don't. Use. Globals. They are bad.
When you define your globals outside the main function, then the second time you run your function, a already equals 100, and main() bails out before doing anything at all.
Global variables are a bad idea, not just in Julia, but in programming in general. You can use them when defining proper constants, like π, and maybe some other specialized cases, but not for things like this.
Let me rewrite your function without globals:
function main_locals()
solution = 0
a = 1
while a < 100
b = 1
c = big(1)
while b < 100
c *= a
s = string(c)
total = sum(Int, s) - 48 * length(s)
solution = max(solution, total)
b += 1
end
a += 1
end
return solution
end
On my laptop this is >20x faster than your version with globals defined inside the function, that is, the version that actually works. The other one doesn't work as it should, so the comparison is not relevant.
Edit: I have even complicated this too much. The only thing you need to do is to remove all the globals from your first function, and return the solution, then it will work fine, and be almost as fast as the code I wrote:
function main_with_globals_removed()
solution = 0
a = big"1"
b = big"1"
c = big"0"
total = 0
while a < 100
while b < 100
c = a^b
s = string(c)
total = 0
for i in 1:length(s)
total = total + Int(s[i]) - 48
end
if total > solution
solution = total
end
b = b + 1
end
b = 1
a = a + 1
end
return solution # remember return!
end
Don't use globals.
In the first case, you are always assigning the globals and possibly changing types. Hence compiler needs to do extra work. I assume that the two programs generate different answers after the 2nd run because of the failure to reset globals…
Globals are discouraged in Julia for performance reasons because of potential type instability.

How to resolve "UndefVarError: consume not defined" in JULIA

When I call consume(generator) I get this error. Is this a version problem?
function fib()
a = 0
produce(a)
b = 1
produce(b)
while true
a , b = b , a+b
produce(b)
end
end
generator = Task(fib)
consume(generator)
Here a way to do something similar using a Channel
global channel = Channel{Int}(1)
function fib()
# global here is not needed because we aren't modifying the channel handle, just the contents
# global channel
a = 0
put!(channel, a)
b = 1
put!(channel, b)
while true
a , b = b , a+b
put!(channel,b)
sleep(1)
end
end
#async while true; item = take!(channel); println("item: $item"); end
#async fib()
Note that #async will hide errors, so you may want to do a try catch with showerror(stderr, e, catch_backtrace()) if things are not running.
Each #async produces a Task handle.
Also the put! and take! will block when the channel if filled up. You may want to expand the channel size to handle a larger buffer.

Populating an array using a FOR loop and a function

I was expecting that the following code would populate E with random 1's and 0's, but that does not happen. I cannot figure out why.
Pkg.add("StatsBase")
using StatsBase
function randomSample(items,weights)
sample(items, Weights(weights))
end
n = 10
periods = 100
p = [ones(n,periods)*0.5]
E = fill(NaN, (n,periods))
for i in 1:periods
for ii in 1:n
E(ii,i) = randomSample([1 0],[(p(ii,i)), 1 - p(ii,i)])
end
end
E
The statement:
E(ii,i) = randomSample([1 0],[(p(ii,i)), 1 - p(ii,i)])
defines a local function E and is not an assignment operation to a matrix E. Use
E[ii,i] = randomSample([1, 0],[p[ii,i], 1 - p[ii,i]])
(I have fixed additional errors in your code so please check out the differences)
and for it to run you should also write:
p = ones(n,periods)*0.5

Pass function arguments into Julia non-interactively

I have a Julia function in a file. Let's say it is the below. Now I want to pass arguments into this function. I tried doing
julia filename.jl randmatstat(5)
but this gives an error that '(' token is unexpected. Not sure what the solution would be. I am also a little torn on if there is a main function / how to write a full solution using Julia. For example what is the starting / entry point of a Julia Program?
function randmatstat(t)
n = 5
v = zeros(t)
w = zeros(t)
for i = 1:t
a = randn(n,n)
b = randn(n,n)
c = randn(n,n)
d = randn(n,n)
P = [a b c d]
Q = [a b; c d]
v[i] = trace((P.'*P)^4)
w[i] = trace((Q.'*Q)^4)
end
std(v)/mean(v), std(w)/mean(w)
end
Julia doesn't have an "entry point" as such.
When you call julia myscript.jl from the terminal, you're essentially asking julia to execute the script and exit. As such, it needs to be a script. If all you have in your script is a function definition, then it won't do much unless you later call that function from your script.
As for arguments, if you call julia myscript.jl 1 2 3 4, all the remaining arguments (i.e. in this case, 1, 2, 3 and 4) become an array of strings with the special name ARGS. You can use this special variable to access the input arguments.
e.g. if you have a julia script which simply says:
# in julia mytest.jl
show(ARGS)
Then calling this from the linux terminal will give this result:
<bashprompt> $ julia mytest.jl 1 two "three and four"
UTF8String["1","two","three and four"]
EDIT: So, from what I understand from your program, you probably want to do something like this (note: in julia, the function needs to be defined before it's called).
# in file myscript.jl
function randmatstat(t)
n = 5
v = zeros(t)
w = zeros(t)
for i = 1:t
a = randn(n,n)
b = randn(n,n)
c = randn(n,n)
d = randn(n,n)
P = [a b c d]
Q = [a b; c d]
v[i] = trace((P.'*P)^4)
w[i] = trace((Q.'*Q)^4)
end
std(v)/mean(v), std(w)/mean(w)
end
t = parse(Int64, ARGS[1])
(a,b) = randmatstat(t)
print("a is $a, and b is $b\n")
And then call this from your linux terminal like so:
julia myscript.jl 5
You can try running like so:
julia -L filename.jl -E 'randmatstat(5)'
Add the following to your Julia file:
### original file
function randmatstat...
...
end
### new stuff
if length(ARGS)>0
ret = eval(parse(join(ARGS," ")))
end
println(ret)
Now, you can run:
julia filename.jl "randmatstat(5)"
As attempted originally. Note the additional quotes added to make sure the parenthesis don't mess up the command.
Explanation: The ARGS variable is defined by Julia to hold the parameters to the command running the file. Since Julia is an interpreter, we can join these parameters to a string, parse it as Julia code, run it and print the result (the code corresponds to this description).

Lua : recursive multiple numbers GCD Euclide calculation -- everlasting loop

I try to calculate recusively the GCD of several numbers.
Here my try. with more than numbers two is an erverlasting loop.
I'm not sure about the pgcd( unpack(arg)) part, but I have no idea of something else.
Edit In fact, it seems to be the arg.n >2 that is not efficient...
function pgcd ( ... )
local arg = table.pack(...)
if arg.n >2
then
local tmp = table.remove(arg,1)
return pgcd (tmp, pgcd( unpack(arg) ))
else
a,b = unpack(arg)
repeat
a , b = b , a % b
until a % b == 0
return b
end
end
print (pgcd(18,12)) -- works fine
print (pgcd(18,12,9)) -- everlasting loop
In fact the endding test was testing once too far.
function pgcd ( ... )
local arg = table.pack(...)
if arg.n > 2
then
local tmp = table.remove(arg,1)
return pgcd (tmp, pgcd( unpack(arg) ) )
else
a,b = unpack(arg)
repeat
a , b = b , math.fmod(a,b)
until b == 0 -- test was once too far
return a
end
end
print (pgcd(18,12)) -- works fine
print (pgcd(18,12,6)) -- works fine

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