I want to create an empty lsit and gardually fill that out with tuples. I've tried the following and each returns an error. My question is: how to append or add and element to an empty array?
My try:
A = []
A.append((2,5)) # return Error type Array has no field append
append(A, (2,5)) # ERROR: UndefVarError: append not defined
B = Vector{Tuple{String, String}}
# same error occues
You do not actually want to append, you want to push elements into your vector. To do that use the function push! (the trailing ! indicates that the function modifies one of its input arguments. It's a naming convention only, the ! doesn't do anything).
I would also recommend creating a typed vector instead of A = [], which is a Vector{Any} with poor performance.
julia> A = Tuple{Int, Int}[]
Tuple{Int64, Int64}[]
julia> push!(A, (2,3))
1-element Vector{Tuple{Int64, Int64}}:
(2, 3)
julia> push!(A, (11,3))
2-element Vector{Tuple{Int64, Int64}}:
(2, 3)
(11, 3)
For the vector of string tuples, do this:
julia> B = Tuple{String, String}[]
Tuple{String, String}[]
julia> push!(B, ("hi", "bye"))
1-element Vector{Tuple{String, String}}:
("hi", "bye")
This line in your code is wrong, btw:
B = Vector{Tuple{String, String}}
It does not create a vector, but a type variable. To create an instance you can write e.g. one of these:
B = Tuple{String, String}[]
B = Vector{Tuple{String,String}}() # <- parens necessary to construct an instance
It can also be convenient to use the NTuple notation:
julia> NTuple{2, String} === Tuple{String, String}
true
julia> NTuple{3, String} === Tuple{String, String, String}
true
Related
I'm trying construct an array of structs where each instance contains a different function. I want to add these to an array in a loop.
Here's an example:
struct mystruc{F}
σ::F
end
a = [mystruc(relu)]
for i in 1:3
append!(a, [mystruc(identity), ])
end
As a side note, I have the option to preallocate the array I just couldn't figure out how to do with this type of struct.
Each function has a type, which is exclusive to that function:
julia> typeof(x -> x) == typeof(x -> x)
false
Here we created the function x -> x twice, they are two different functions, so their types are not the same.
In your construction of a, you create an Array of that specific type:
julia> a = [mystruc(relu)]
1-element Array{mystruc{typeof(relu)},1}:
mystruc{typeof(relu)}(relu)
julia> typeof(a)
Array{mystruc{typeof(relu)},1}
So when you push another function, we get an error, because this array can only contain objects of the type mystruc{typeof(relu)}.
julia> push!(a, mystruc(x -> 2x))
ERROR: MethodError: Cannot `convert` an object of type
mystruc{var"#3#4"} to an object of type
mystruc{typeof(relu)}
Closest candidates are:
convert(::Type{T}, ::T) where T at essentials.jl:171
mystruc{typeof(relu)}(::Any) where F at REPL[2]:2
Solution
When you construct a, tell Julia that the array will contain mystruc with any function:
julia> a = mystruc{<:Function}[mystruc(relu)]
and now it works!
julia> push!(a, mystruc(x -> 2x))
2-element Array{mystruc{#s1} where #s1<:Function,1}:
mystruc{typeof(relu)}(relu)
mystruc{var"#5#6"}(var"#5#6"())
What I am trying to do is
i = occursin("ENTITIES\n", lines)
i != 0 || error("ENTITIES section not found")
The error information is
ERROR: LoadError: LoadError: MethodError: no method matching occursin(::String, ::Array{String,1})
Closest candidates are:
occursin(::Union{AbstractChar, AbstractString}, ::AbstractString) at strings/search.jl:452
This is a piece of julia v0.6 code. I am using v1.1 now. I am new to julia and don't know what's the proper subsititute function for this. Please help.
You can broadcast orrursin like this (add a . after function name):
julia> x = "abc"
"abc"
julia> y = ["abc", "xyz"]
2-element Array{String,1}:
"abc"
"xyz"
julia> b = occursin.(x, y)
2-element BitArray{1}:
true
false
julia> findall(b)
1-element Array{Int64,1}:
1
julia> findfirst(b)
1
Note that although String can be iterated over it is treated by broadcast as a scalar.
Also it is worth to remember that occursin returns Bool value so that you can use it directly in logical tests e.g. i || error("ENTITIES section not found") in the code from your question.
In order to locate the index in the collection of the occurrence of true in the return value of broadcasted occursin use findall or findfirst functions (there is also findlast). The difference is that findall returns a vector of entries where true is encountered in the collection, while findfirst returns the first such entry only. Also note the difference when you pass all falses to it. findall will return an empty vector and findfirst will return nothing.
If you do not want to retain the vector b in the code above, you can get the indices directly (this should be faster) by passing a predicate as a first argument to findall/findfirst:
julia> findall(t -> occursin(x, t), y)
1-element Array{Int64,1}:
1
julia> findfirst(t -> occursin(x, t), y)
1
I want to push a SVector (provided by JuliaArrays/StaticArrays.jl) into a vector of SVector. The following code is my trial:
using StaticArrays
lst = Vector{SVector{2, Float64}}[]
a = SVector(1, 2)
push!(lst, a)
But it causes the following error:
ERROR: LoadError: MethodError: Cannot `convert` an object of type Int64 to an object of type SArray{Tuple{2},Float64,1,2}
How can I fix it?
The mistake you are making is that you create an Array of Array of an SVector. T[] creates an empty array of type T.
# This creates an empty array of type Float64
julia> lst = Float64[]
0-element Array{Float64,1}
# This creates an empty array of a Float64 array
julia> lst = Vector{Float64}[]
0-element Array{Array{Float64,1},1}
So you need to redefine your array as an array of SVector.
julia> lst = SVector{2, Float64}[] # an empty 1D array(i.e. Vector) of `SVector`
0-element Array{SArray{Tuple{2},Float64,1,2},1}
julia> a = SVector(1, 2)
2-element SArray{Tuple{2},Int64,1,2}:
1
2
julia> push!(lst, a)
1-element Array{SArray{Tuple{2},Float64,1,2},1}:
[1.0, 2.0]
You can also use this instead of your way of empty array definition:
lst = Vector{SVector{2, Float64}}(undef, 0) # this creates a `Vector` of `SVector` of size 0 (empty)
Hello i trying create converter method from Disct to Vector in Julia language.
But i receive error, with i can't understand
ERROR: TypeError: Tuple: in parameter, expected Type{T}, got Dict{AbstractString,Int64}
My code
type Family
name::UTF8String
value::Int
end
function convertToVector(a1::Dict{AbstractString, Int64}())
A::Vector{Node}
for k in sort(collect(keys(a1)))
push!(A, Family(a1[k] , k))
end
return A
end
Any idea hot to change convertToVector method ?
There were several typos in the above code, but I think this should work:
# No () after the type of a1
# Also, see comment, better to parameterize function, use concrete type for Dict
function convertToVector{T<:AbstractString}(a1::Dict{T, Int64})
# This is how you create an empty vector to hold Family objects
A = Vector{Family}()
for k in sort(collect(keys(a1)))
# The values passed to the Family constructor were backwards
push!(A, Family(k, a1[k]))
end
A
end
Another way (probably not very quick):
julia> dict = Dict("fred" => 3, "jim" => 4)
Dict{ASCIIString,Int64} with 2 entries:
"fred" => 3
"jim" => 4
julia> Vector{Family}(map(f -> Family(f...), map(x -> collect(x), dict)))
2-element Array{Family,1}:
Family("fred",3)
Family("jim",4)
Perhaps I've been using too much Lisp recently...
I'd like to check if var is an Array or a Dict.
typeof(var) == Dict
typeof(var) == Array
But it doesn't work because typeof is too precise: Dict{ASCIIString,Int64}.
What's the best way ?
If you need a "less precise" check, you may want to consider using the isa() function, like this:
julia> d = Dict([("A", 1), ("B", 2)])
julia> isa(d, Dict)
true
julia> isa(d, Array)
false
julia> a = rand(1,2,3);
julia> isa(a, Dict)
false
julia> isa(a, Array)
true
The isa() function could then be used in control flow constructs, like this:
julia> if isa(d, Dict)
println("I'm a dictionary!")
end
I'm a dictionary!
julia> if isa(a, Array)
println("I'm an array!")
end
I'm an array!
Note: Tested with Julia 0.4.3
Instead of checking for a particular concrete type, such as Array, or Dict, you might do better by checking for the abstract types, and gain a lot of flexibility.
For example:
julia> x = [1,2,3]
3-element Array{Int64,1}:
1
2
3
julia> d = Dict(:a=>1,:b=>2)
Dict(:a=>1,:b=>2)
julia> isa(d, Associative)
true
julia> isa(x, AbstractArray)
true
There are many different types of arrays in Julia, so checking for Array is likely to be too restrictive, you won't get sparse matrices, for example.
There are also a number of different types of associative structures, Dict, ObjectIdDict, SortedDict, OrderedDict.