I have a dictionary that has 3 key, each key has arbitrary 2d arrays. And I want to assign each of these values(2d arrays) to newly created arrays. So I wrote this :
levels = (:easy, :medium, :hard)
easy = []; medium = []; hard = [];
curriculum = Dict((k=>rand(3,3) for k in levels)...); # dictionary with 3 keys - each key is a symbol
for level in levels
eval(level) = curriculum[level]
end
There is no problem during execution but when I check the easy, medium or hard arrays after, there are no matrices inside.
What is the proper way of doing this?
B.R.
(edited)
What I understand is that during the loop iteration eval(level) behaves like local binding. Thus, at the end of the iteration it does not remember anything. But why? And how to convert to global again ?
There is no problem during execution but when I check the easy, medium or hard arrays after, there are no matrices inside.
What I understand is that during the loop iteration eval(level) behaves like local binding. Thus, at the end of the iteration it does not remember anything. But why? And how to convert to global again ?
The problem is that you are defining and then redefining an eval function in each iteration inside the for loop, instead of doing assignments as you believe.
easy = []; medium = []; hard = []
1.- There is no need to predefine this variables.
eval(level) = curriculum[level]
2.- Warning: This is being interpreted as a short style function definition!
julia> eval(level) = curriculum[level]
eval (generic function with 1 method)
julia> levels = :easy, :medium, :hard
(:easy, :medium, :hard)
julia> curriculum = Dict((k => rand(3, 3) for k in levels)...)
Dict{Symbol,Array{Float64,2}} with 3 entries:
:medium => [0.230877 0.244128 0.205712; 0.649405 0.887568 0.957849; 0.245076 0.476088 0.160561]
:hard => [0.0424943 0.157261 0.798607; 0.590535 0.78125 0.704322; 0.555827 0.675355 0.74026]
:easy => [0.715595 0.914093 0.517973; 0.750787 0.489784 0.491666; 0.1966 0.133281 0.249144]
julia> for level in levels
#eval $level = curriculum[$(Meta.quot(level))]
end
julia> easy
3×3 Array{Float64,2}:
0.715595 0.914093 0.517973
0.750787 0.489784 0.491666
0.1966 0.133281 0.249144
3.- Inside the for loop, in the first iteration, $level is interpreted as the easy variable identifier, while $(Meta.quot(level)) is interpreted as the symbol :easy, for which the extra level of quoting is needed.
julia> Meta.quot(:foo)
:(:foo)
julia> Expr(:quote, :foo)
:(:foo)
Edit there is a better way as pointed out in the accepted answer and comment by Ismael. Namely that:
global level = level is not necessary, see my answer, the issue is
that if you want to interpolate a symbol into an expression as a
symbol instead of as an identifier, then you need to wrap the symbol
into another quote expression.
Warning: this is (almost certainly) not the best way of setting out to accomplish whatever you want to do. Metaprogramming in julia is super powerful and useful but you should very rarely need to use eval. You can read more about it here. If you ask another question with the details of your end-goal you might get a better answer :)
Nonetheless:
If you want to do the assignment this way you'll have to create an Expression Expr that assigns the matrix object to the variable with the name you want. But because eval always executes the statement in the global scope you'll need to make the variable you want to use available in the global scope.
for level in levels
global level = level
eval(:($level = curriculum[level]))
end
or using the #eval macro:
for level in levels
global level = level
#eval $level = curriculum[level]
end
Related
I'm a little new to Julia and am trying to use the fill! method to improve code performance on Julia. Currently, I read a 2d array from a file say read_array and perform row-operations on it to get a processed_array as follows:
function preprocess(matrix)
# Initialise
processed_array= Array{Float64,2}(undef, size(matrix));
#first row of processed_array is the difference of first two row of matrix
processed_array[1,:] = (matrix[2,:] .- matrix[1,:]) ;
#last row of processed_array is difference of last two rows of matrix
processed_array[end,:] = (matrix[end,:] .- matrix[end-1,:]);
#all other rows of processed_array is the mean-difference of other two rows
processed_array[2:end-1,:] = (matrix[3:end,:] .- matrix[1:end-2,:]) .*0.5 ;
return processed_array
end
However, when I try using the fill! method I get a MethodError.
processed_array = copy(matrix)
fill!(processed_array [1,:],d[2,:]-d[1,:])
MethodError: Cannot convert an object of type Matrix{Float64} to an object of type Float64
I'll be glad if someone can tell me what I'm missing and also suggest a method to optimize the code. Thanks in advance!
fill!(A, x) is used to fill the array A with a unique value x, so it's not what you want anyway.
What you could do for a little performance gain is to broadcast the assignments. That is, use .= instead of =. If you want, you can also use the #. macro to automatically add dots everywhere for you (for maybe cleaner/easier-to-read code):
function preprocess(matrix)
out = Array{Float64,2}(undef, size(matrix))
#views #. out[1,:] = matrix[2,:] - matrix[1,:]
#views #. out[end,:] = matrix[end,:] - matrix[end-1,:]
#views #. out[2:end-1,:] = 0.5 * (matrix[3:end,:] - matrix[1:end-2,:])
return out
end
For optimal performance, I think you probably want to write the loops explicitly and use multithreading with a package like LoopVectorization.jl for example.
PS: Note that in your code comments you wrote "cols" instead of "rows", and you wrote "mean" but take a difference. (Not sure it was intentional.)
I have been banging my head against a wall trying to use static arrays in julia.
https://github.com/JuliaArrays/StaticArrays.jl
They are fast but updating them is a pain. This is no surprise, they are meant to be immutable!
But it is continuously recommended to me that I use static arrays even though I have to update them. In my case, the static arrays are small, just length 3, and i have a vector of them, but I only update 1 length three SVector at a time.
Option 1
There is a really neat package called Setfield that allows you to do inplace updates of SVectors in Julia.
https://github.com/jw3126/Setfield.jl
The catch... it updates the local copy. So if you are in a nested function, it updates the local copy. So it comes with some book keeping since you have to inplace update the local copy and then return that copy and update the actual array of interest. You can't pass in your desired array and update it in place, at least, not that I can figure out! Now, I do not mind bookeeping, but I feel like updating a local copy, then returning the value, updating another local copy, and then returning the values and finally updating the actual array must come with a speed penalty. I could be wrong.
Option 2
It bugs me that in order to do an update a static array I must
exampleSVector::SVector{3,Float64} <-- just to make clear its type and size
exampleSVector = [value1, value2, value3]
This will update the desired array even if it is inside a function, which is nice and the goal, but if you do this inside a function it creates a temporary array. And this kills me because my function is in a loop that gets called 4+ million times, so this creates a ton of allocations and slows things down.
How do I update an SVector for the Option 2 scenario without creating a temporary array?
For the Option 1 scenario, can I update the actual array of interest rather than the local copy?
If this requires a simple example code, please say so in the comments, and I will make one. My thinking is that it is answerable without one, but I will make one if it is needed.
EDIT:
MCVE code - Option 1 works, option 2 does not.
using Setfield
using StaticArrays
struct Keep
dreaming::Vector{SVector{3,Float64}}
end
function INNER!(vec::SVector{3,Float64},pre::SVector{3,Float64})
# pretend series of calculations
for i = 1:3 # illustrate use of Setfield (used in real code for this)
pre = #set pre[i] = rand() * i * 1000
end
# more pretend calculations
x = 25.0 # assume more calculations equals x
################## OPTION 1 ########################
vec = #set vec = x * [ pre[1], pre[2], pre[3] ] # UNCOMMENT FOR FOR OPTION 1
return vec # UNCOMMENT FOR FOR OPTION 1
################## OPTION 2 ########################
#vec = x * [ pre[1], pre[2], pre[3] ] # UNCOMMENT FOR FOR OPTION 2
#nothing # UNCOMMENT FOR FOR OPTION 2
end
function OUTER!(always::Keep)
preAllocate = SVector{3}(0.0,0.0,0.0)
for i=1:length(always.dreaming)
always.dreaming[i] = INNER!(always.dreaming[i], preAllocate) # UNCOMMENT FOR FOR OPTION 1
#INNER!(always.dreaming[i], preAllocate) # UNCOMMENT FOR FOR OPTION 2
end
end
code = Keep([zero(SVector{3}) for i=1:5])
OUTER!(code)
println(code.dreaming)
I hope that I have understood your question correctly. It's a bit hard with a MWE like this, that does a lot of things that are mostly redundant and a bit confusing.
There seems to be two alternative interpretations here: Either you really need to update ('mutate') an SVector, but your MWE fails to demonstrate why. Or, you have convinced yourself that you need to mutate, but you actually don't.
I have decided to focus on alternative 2: You don't really need to 'mutate'. Rewriting your code from that point of view simplifies it greatly.
I couldn't find any reason for you to mutate any static vectors here, so I just removed that. The behaviour of the INNER! function with the inputs was very confusing. You provide two inputs but don't use either of them, so I removed those inputs.
function inner()
pre = #SVector [rand() * 1000i for i in 1:3]
x = 25
return pre .* x
end
function outer!(always::Keep)
always.dreaming .= inner.() # notice the dot in inner.()
end
code = Keep([zero(SVector{3}) for i in 1:5])
outer!(code)
display(code.dreaming)
This runs fast and with zero allocations. In general with StaticArrays, don't try to mutate things, just create new instances.
Even though it's not clear from your MWE, there may be some legitimate reason why you may want to 'mutate' an SVector. In that case you can use the setindex method of StaticArrays, you don't need Setfield.jl:
julia> v = rand(SVector{3})
3-element SArray{Tuple{3},Float64,1,3}:
0.4730258499237898
0.23658547518737905
0.9140206579322541
julia> v = setindex(v, -3.1, 2)
3-element SArray{Tuple{3},Float64,1,3}:
0.4730258499237898
-3.1
0.9140206579322541
To clarify: setindex (without a !) does not mutate its input, but creates a new instance with one index value changed.
If you really do need to 'mutate', perhaps you can make a new MWE that shows this. I would recommend that you try to simplify it a bit, because it is quite confusing now. For example, the inclusion of the type Keep seems entirely unnecessary and distracting. Just make a Vector of SVectors and show what you want to do with that.
Edit: Here's an attempt based on the comments below. As far as I understand it now, the question is about modifying a vector of SVectors. You cannot really mutate the SVectors, but you can replace them using a convenient syntax, setindex, where you can keep some of the elements and change some of the others:
oldvec = [zero(SVector{3}) for _ in 1:5]
replacevec = [rand(SVector{3}) for _ in 1:5]
Now we replace the second element of each element of oldvec with the corresponding one in replacevec. First a one-liner:
oldvec .= setindex.(oldvec, getindex.(replacevec, 2), 2)
Then an even faster one with a loop:
for i in eachindex(oldvec, replacevec)
#inbounds oldvec[i] = setindex(oldvec[i], replacevec[i][2], 2)
end
There are two types of static arrays - mutable (starting with M in type name) and immutable ones (starting with S) - just use the mutable ones! have a look at the example below:
julia> mut = MVector{3,Int64}(1:3);
julia> mut[1]=55
55
julia> mut
3-element MArray{Tuple{3},Int64,1,3}:
55
2
3
julia> immut = SVector{3,Int64}(1:3);
julia> inmut[1]=55
ERROR: setindex!(::SArray{Tuple{3},Int64,1,3}, value, ::Int) is not defined.
Let us see some simple benchmark (ordinary array, vs mutable static vs immutable static):
using BenchmarkTools
julia> ord = [1,2,3];
julia> #btime $ord.*$ord;
39.680 ns (1 allocation: 112 bytes)
3-element Array{Int64,1}:
1
4
9
julia> #btime $mut.*$mut
8.533 ns (1 allocation: 32 bytes)
3-element MArray{Tuple{3},Int64,1,3}:
3025
4
9
julia> #btime $immut.*$immut
2.133 ns (0 allocations: 0 bytes)
3-element SArray{Tuple{3},Int64,1,3}:
1
4
9
I have a variable as follows
local armies = {
[1] = "ARMY_1",
[2] = "ARMY_3",
[3] = "ARMY_6",
[4] = "ARMY_7",
}
Now I want to do an action for each value. What is the best way to loop over the values? The typical thing I'm finding on the internet is this:
for i, armyName in pairs(armies) do
doStuffWithArmyName(armyName)
end
I don't like that as it results in an unused variable i. The following approach avoids that and is what I am currently using:
for i in pairs(armies) do
doStuffWithArmyName(armies[i])
end
However this is still not as readable and simple as I'd like, since this is iterating over the keys and then getting the value using the key (rather imperatively). Another boon I have with both approaches is that pairs is needed. The value being looped over here is one I have control over, and I'd prefer that it can be looped over as easily as possible.
Is there a better way to do such a loop if I only care about the values? Is there a way to address the concerns I listed?
I'm using Lua 5.0 (and am quite new to the language)
The idiomatic way to iterate over an array is:
for _, armyName in ipairs(armies) do
doStuffWithArmyName(armyName)
end
Note that:
Use ipairs over pairs for arrays
If the key isn't what you are interested, use _ as placeholder.
If, for some reason, that _ placeholder still concerns you, make your own iterator. Programming in Lua provides it as an example:
function values(t)
local i = 0
return function() i = i + 1; return t[i] end
end
Usage:
for v in values(armies) do
print(v)
end
I need to create an array filled within a range in Matlab
e.g.
from=2
to=6
increment=1
result
[2,3,4,5,6]
e.g.
from=15
to=25
increment=2
result
[15,17,19,21,23,25]
Obviously I can create a loop to perform this action from scratch but I wondering if there is a coincise and efficent way to do this with built-in matlab commands since seems a very common operation
EDIT
If I use linspace the operation is weird since the spacing between the points is (x2-x1)/(n-1).
This can be handled simply by the : operator in the following notation
array = from:increment:to
Note that the increment defaults to 1 if written with only one colon seperator
array = from:to
Example
array1 = 2:6 %Produces [2,3,4,5,6]
array2 = 15:2:25 %Produces [15,17,19,21,23,25]
So I would like to optimize my code such that I can look through an array such as
{'one','two','three'}
and create corresponding variables defined as tables or arrays
such as
one = table()
two = table()
three = table()
I am aware of the eval function however I would like to use this function in a loop s.t I allocate values to the new variable right after i create it
If I am understanding your question properly, given a cell array consisting only of strings, you wish to create variables in your workspace where each variable is declared as a string using the names from this cell array.
You could use eval, but I'm going to recommend something other than eval. eval should be avoided and instead of iterating those reasons, you can read Loren Shure's article on eval.
In any case, I would recommend you place these variables as dynamic fields inside a structure instead. Do something like this:
s = struct()
strs = {'one', 'two', 'three'};
for idx = 1 : numel(strs)
s.(strs{idx}) = table();
end
In this case, s would be a structure, and you can access the variable by the dot operator. In this case, you can access the corresponding variables by:
d = s.one; %// or
d2 = s.two; %// or
d3 = s.three;
If you want to place this into a function per se, you can place this into a function like so:
function [s] = createVariables(strs)
s = struct();
for idx = 1 : numel(strs)
s.(strs{idx}) = table();
end
This function will take in a cell array of strings, and outputs a structure that contains fields that correspond to the cell array of strings you put in. As such, you'd call it like so:
strs = {'one', 'two', 'three'};
s = createVariables(strs);
However, if you really really... I mean really... want to use eval, you can create your workspace variables like so:
strs = {'one', 'two', 'three'};
for idx = 1 : numel(strs)
eval([strs{idx} ' = table();']);
end
To place this into a function, do:
function [] = createVariables(strs)
for idx = 1 : numel(strs)
eval([strs{idx} ' = table();']);
end
However, be warned that if you run the function above, these variables will only be defined in the scope that the function was run in. You will not see these variables when the function exits. If you want to run a function so that the variables get defined in the workspace after you run the function, then eval is not the right solution for you. You should thus stick with the dynamic field approach that I talked about at the beginning of this post.
In any case, this will create one, two and three as workspace variables that are initialized to empty tables. However, I will argue with you that the first line of code is easier to read than the second piece of code, which is one of the main arguing points as to why eval should be avoided. If you stare at the second piece of code long enough, then you can certainly see what we're trying to achieve, but if you read the first piece of code, you can ascertain its purpose more clearly.