I have just started using Julia. To my understanding Julia allows you to declare multiple for loops on a single line.
For example, this:
for i = 1:2, j = [-1,-2]
println((i, j))
end
Will result in this:
(1,-1)
(1,-2)
(2,-1)
(2,-2)
I am now doing something similar but while looping over dictionaries. I declare the following:
rename = function(x)
x["num"] = -x["num"]
x
end
players1 = [["num" => 1],["num" => 2]]
players2 = map(rename, copy(players1)) # = [["num" => -1],["num" => -2]]
Oddly, to me, when I do this:
for i=players1, j=players2
println(i, j)
end
Why don't I get this output?
["num"=>1]["num"=>-1]
["num"=>1]["num"=>-2]
["num"=>2]["num"=>-1]
["num"=>2]["num"=>-2]
Ah. The map functions still needs a deepcopy in this case.
This snippet of code does seem to work.
rename = function(x)
x["num"] = -x["num"]
x
end
players1 = [["num" => 1],["num" => 2]]
players2 = map(rename, deepcopy(players1))
for i=players1, j=players2
println(i, j)
end
Related
I have the following function that uses symbolics in Julia. Everything works fine until the moment of plotting
using Distributions
using Plots
using Symbolics
using SymbolicUtils
function BinomialMeasure(iter::Int64, p::Float64, current_level = nothing)
#variables m0 m1
if current_level == nothing
current_level = [1]
end
next_level = []
for item in current_level
append!(next_level, m0*item)
append!(next_level, m1*item)
end
If iter != 0
current_level = next_level
return BinomialMeasure(iter - 1, p , current_level)
else
return [substitute(i, Dict([m0 => p, m1 => 1 - p])) for i in next_level]
end
end
y = BinomialMeasure(10, 0.4)
x = [( i + 1 ) / length(y) for i = 1:length(y) ]
append!(x, 0)
append!(y,0)
plot(x,y)
Then it returns the following:
MethodError: no method matching AbstractFloat(::Num)
Closest candidates are:
AbstractFloat(::Real, !Matched::RoundingMode) where T<:AbstractFloat at rounding.jl:200
AbstractFloat(::T) where T<:Number at boot.jl:716
AbstractFloat(!Matched::Bool) at float.jl:258
y is an Array{Num,1} and x is an Array{Float64,1}.
I tried map(float, y), convert(float,y) and float(y), but I think it not possible to convert a type Num to a Float64 or at least I don't know how to do it.
you can access the field val without using string and parse
y_val = [i.val for i in y]
this will of course have way better performance than parsing a string
I'm trying to run a loop over different functions with different number of arguments. The variables are created at runtime inside the loop, and I want to use eval at each iteration to instantiate a Struct using the variable :symbol. However, I can't do this since eval only works in the global scope. This is the MWE for the case that works:
function f1(x); return x; end
function f2(x1,x2); return x1+x2; end
handles = [f1,f2]
args =[:(x1),:(x1,x2)]
x1 = 1; x2 = 1;
for (i,f) in enumerate(handles)
params = eval(args[i])
#show f(params...)
end
f(params...) = 1
f(params...) = 2
However, if I move the variable definitions inside the loop, which is what I actually want, it doesn't work after restarting Julia to clear the workspace.
function f1(x); return x; end
function f2(x1,x2); return x1+x2; end
handles = [f1,f2]
args =[:(x1),:(x1,x2)]
for (i,f) in enumerate(handles)
x1 = 1; x2 = 1;
params = eval(args[i])
#show f(params...)
end
ERROR: UndefVarError: x1 not defined
I've tried several of the answers, such as this one, but I can't seem to make it work. I could write a custom dispatch function that takes[x1,x2] and calls f1 or f2 with the correct arguments. But still, is there any way to do this with eval or with an alternative elegant solution?
EDIT: here are more details as to what I'm trying to do in my code. I have a config struct for each algorithm, and in this I want to define beforehand the arguments it takes
KMF_config = AlgConfig(
name = "KMF",
constructor = KMC.KMF,
parameters = :(mu,N,L,p),
fit = KMC.fit!)
MF_config = AlgConfig(
name = "MF",
constructor = KMC.MF,
parameters = :(mu,N,L),
fit = KMC.fit!)
alg_config_list = [KMF_config, MF_config]
for (i,alg_config) in enumerate(alg_config_list)
mu,N,L,p,A,B,C,D,data = gen_vars() #this returns a bunch of variables that are used in different algorithms
method = alg_config.constructor(eval(method.parameters)...)
method.fit(data)
end
One possible solution is to have a function take all the variables and method, and return a tuple with a subset of variables according to method.name. But I'm not sure if it's the best way to do it.
Here's an approach using multiple dispatch rather than eval:
run_a(x, y) = x + 10*y
run_b(x, y, z) = x + 10*y + 100*z
extract(p, ::typeof(run_a)) = (p.x, p.y)
extract(p, ::typeof(run_b)) = (p.x, p.y, p.z)
genvars() = (x=1, y=2, z=3)
function doall()
todo = [
run_a,
run_b,
]
for runalg in todo
v = genvars()
p = extract(v, runalg)
#show runalg(p...)
end
end
In your example you would replace run_a and run_b with KMC.KMF and KMC.MF.
Edit: Cleaned up example to avoid structs that don't exist in your example.
Can I add type information to arguments that are functions?
Consider the following example:
function f{T} (func, x::Int)
output = Dict{Int, Any}()
output[x] = func(x)
return output
end
I don't like that I have to say Any for the value type of the dictionary. I'd much rather do the following:
function f{T} (func::Function{Int->T}, x::Int)
output = Dict{Int, T}()
output[x] = func(x)
return output
end
Can I provide type hints of functions like this? I kind of want to say the following
f :: (Int -> T), Int -> Dict{Int, T}
Not currently. We may add something along those lines in the future, however.
This is not an answer to the main question, but more a really ugly workaround the Any in the Dict issue:
function f(func, x::Int)
T = code_typed(func, (Int,))[1].args[3].typ
output = Dict{Int, T}()
output[x] = func(x)
return output
end
That is probably not efficient and will probably work only on simple cases (which do not even include anonymous functions) like
>>> g(x) = x*2
>>> typeof(f(g, 1234))
Dict{Int64,Int64}
>>> h(x) = x > zero(x) ? x : nothing
>>> typeof(f(h, 1234))
Dict{Int64,Union(Int64,Nothing)}
EDIT:
This works better:
function f(func, x::Int)
[x => func(x)]
end
>>> dump( f(x->2x, 3) )
Dict{Int64,Int64} len 1
3: Int64 6
Suppose I have a Dict defined as follows:
x = Dict{AbstractString,Array{Integer,1}}("A" => [1,2,3], "B" => [4,5,6])
I want to convert this to a DataFrame object (from the DataFrames module). Constructing a DataFrame has a similar syntax to constructing a dictionary. For example, the above dictionary could be manually constructed as a data frame as follows:
DataFrame(A = [1,2,3], B = [4,5,6])
I haven't found a direct way to get from a dictionary to a data frame but I figured one could exploit the syntactic similarity and write a macro to do this. The following doesn't work at all but it illustrates the approach I had in mind:
macro dict_to_df(x)
typeof(eval(x)) <: Dict || throw(ArgumentError("Expected Dict"))
return quote
DataFrame(
for k in keys(eval(x))
#eval ($k) = $(eval(x)[$k])
end
)
end
end
I also tried writing this as a function, which does work when all dictionary values have the same length:
function dict_to_df(x::Dict)
s = "DataFrame("
for k in keys(x)
v = x[k]
if typeof(v) <: AbstractString
v = string('"', v, '"')
end
s *= "$(k) = $(v),"
end
s = chop(s) * ")"
return eval(parse(s))
end
Is there a better, faster, or more idiomatic approach to this?
Another method could be
DataFrame(Any[values(x)...],Symbol[map(symbol,keys(x))...])
It was a bit tricky to get the types in order to access the right constructor. To get a list of the constructors for DataFrames I used methods(DataFrame).
The DataFrame(a=[1,2,3]) way of creating a DataFrame uses keyword arguments. To use splatting (...) for keyword arguments the keys need to be symbols. In the example x has strings, but these can be converted to symbols. In code, this is:
DataFrame(;[Symbol(k)=>v for (k,v) in x]...)
Finally, things would be cleaner if x had originally been with symbols. Then the code would go:
x = Dict{Symbol,Array{Integer,1}}(:A => [1,2,3], :B => [4,5,6])
df = DataFrame(;x...)
I am having trouble figuring out how to get the length of a matrix within a matrix within a matrix (nested depth of 3). So what the code is doing in short is... looks to see if the publisher is already in the array, then it either adds a new column in the array with a new publisher and the corresponding system, or adds the new system to the existing array publisher
output[k][1] is the publisher array
output[k][2][l] is the system
where the first [] is the amount of different publishers
and the second [] is the amount of different systems within the same publisher
So how would I find out what the length of the third deep array is?
function reviewPubCount()
local output = {}
local k = 0
for i = 1, #keys do
if string.find(tostring(keys[i]), '_') then
key = Split(tostring(keys[i]), '_')
for j = 1, #reviewer_code do
if key[1] == reviewer_code[j] and key[1] ~= '' then
k = k + 1
output[k] = {}
-- output[k] = reviewer_code[j]
for l = 1, k do
if output[l][1] == reviewer_code[j] then
ltable = output[l][2]
temp = table.getn(ltable)
output[l][2][temp+1] = key[2]
else
output[k][1] = reviewer_code[j]
output[k][2][1] = key[2]
end
end
end
end
end
end
return output
end
The code has been fixed here for future reference: http://codepad.org/3di3BOD2#output
You should be able to replace table.getn(t) with #t (it's deprecated in Lua 5.1 and removed in Lua 5.2); instead of this:
ltable = output[l][2]
temp = table.getn(ltable)
output[l][2][temp+1] = key[2]
try this:
output[l][2][#output[l][2]+1] = key[2]
or this:
table.insert(output[l][2], key[2])