I have this error
ERROR: MethodError: vcat(::Array{Real,2}, ::TrackedArray{…,Array{Float32,2}}) is ambiguous. Candidates:
vcat(364::AbstractArray, x::Union{TrackedArray, TrackedReal}, xs::Union{Number, AbstractArray}...) in Tracker at C:\Users\Henri\.julia\packages\Tracker\6wcYJ\src\lib\array.jl:167
vcat(A::Union{AbstractArray{T,2}, AbstractArray{T,1}} where T...) in Base at abstractarray.jl:1296
Possible fix, define
vcat(::Union{AbstractArray{T,2}, AbstractArray{T,1}} where T, ::Union{TrackedArray{T,1,A} where A<:AbstractArray{T,1} where T, TrackedArray{T,2,A} where A<:AbstractArray{T,2} where T}, ::Vararg{Union{AbstractArray{T,2}, AbstractArray{T,1}} where T,N} where N)
Telling me that two vcat() functions are ambiguous. I want to use the Base.vcat() function but using it explicitly throws the same error. Why is that ? And what is this "possible fix" proposed by the error throw?
Moreover, when I call manually each line in the REPL no error is thrown. I do not understand this behavior. This only happens when vcat() is in a function called inside another function. Like in my example below.
Here is a code that reproduces the error:
using Flux
function loss(a, b, net, net2)
net2(vcat(net(a),a))
end
function test()
opt = ADAM()
net = Chain(Dense(3,3))
net2 = Chain(Dense(6,1))
L(a, b) = loss(a, b, net, net2)
data = tuple(rand(3,1), rand(3,1))
xs = Flux.params(net)
gs = Tracker.gradient(() -> L(data...), xs)
Tracker.update!(opt, xs, gs)
end
As mentionned in comments with Henri.D, we've managed to fix it by being carreful with the type of a which was an Array of Float64, default type returned by rand whereas net(a) returned a TrackedArray of Float32 and made impossible to vcat it with a.
I've managed to fix vcat by changing your loss function with this: net2(vcat(net(a),Float32.(a))) because vcat couldn't concatenate as net(a) was a Float32 Array and a a Float64 one. Then L(data...) is a TrackedArray of 1 element whereas I think you need a Float32 that's why I finally replace loss function by net2(vcat(net(a),Float32.(a)))[1]
Related
I'm very new to Julia, and I'm trying to just pass an array of numbers into a function and count the number of zeros in it. I keep getting the error:
ERROR: UndefVarError: array not defined
I really don't understand what I am doing wrong, so I'm sorry if this seems like such an easy task that I can't do.
function number_of_zeros(lst::array[])
count = 0
for e in lst
if e == 0
count + 1
end
end
println(count)
end
lst = [0,1,2,3,0,4]
number_of_zeros(lst)
There are two issues with your function definition:
As noted in Shayan's answer and Dan's comment, the array type in Julia is called Array (capitalized) rather than array. To see:
julia> array
ERROR: UndefVarError: array not defined
julia> Array
Array
Empty square brackets are used to instantiate an array, and if preceded by a type, they specifically instantiate an array holding objects of that type:
julia> x = Int[]
Int64[]
julia> push!(x, 3); x
1-element Vector{Int64}:
3
julia> push!(x, "test"); x
ERROR: MethodError: Cannot `convert` an object of type String to an object of type Int64
Thus when you do Array[] you are actually instantiating an empty vector of Arrays:
julia> y = Array[]
Array[]
julia> push!(y, rand(2)); y
1-element Vector{Array}:
[0.10298669573927233, 0.04327245960128345]
Now it is important to note that there's a difference between a type and an object of a type, and if you want to restrict the types of input arguments to your functions, you want to do this by specifying the type that the function should accept, not an instance of this type. To see this, consider what would happen if you had fixed your array typo and passed an Array[] instead:
julia> f(x::Array[])
ERROR: TypeError: in typeassert, expected Type, got a value of type Vector{Array}
Here Julia complains that you have provided a value of the type Vector{Array} in the type annotation, when I should have provided a type.
More generally though, you should think about why you are adding any type restrictions to your functions. If you define a function without any input types, Julia will still compile a method instance specialised for the type of input provided when first call the function, and therefore generate (most of the time) machine code that is optimal with respect to the specific types passed.
That is, there is no difference between
number_of_zeros(lst::Vector{Int64})
and
number_of_zeros(lst)
in terms of runtime performance when the second definition is called with an argument of type Vector{Int64}. Some people still like type annotations as a form of error check, but you also need to consider that adding type annotations makes your methods less generic and will often restrict you from using them in combination with code other people have written. The most common example of this are Julia's excellent autodiff capabilities - they rely on running your code with dual numbers, which are a specific numerical type enabling automatic differentiation. If you strictly type your functions as suggested (Vector{Int}) you preclude your functions from being automatically differentiated in this way.
Finally just a note of caution about the Array type - Julia's array's can be multidimensional, which means that Array{Int} is not a concrete type:
julia> isconcretetype(Array{Int})
false
to make it concrete, the dimensionality of the array has to be provided:
julia> isconcretetype(Array{Int, 1})
true
First, it might be better to avoid variable names similar to function names. count is a built-in function of Julia. So if you want to use the count function in the number_of_zeros function, you will undoubtedly face a problem.
Second, consider returning the value instead of printing it (Although you didn't write the print function in the correct place).
Third, You can update the value by += not just a +!
Last but not least, Types in Julia are constantly introduced with the first capital letter! So we don't have an array standard type. It's an Array.
Here is the correction of your code.
function number_of_zeros(lst::Array{Int64})
counter = 0
for e in lst
if e == 0
counter += 1
end
end
return counter
end
lst = [0,1,2,3,0,4]
number_of_zeros(lst)
would result in 2.
Additional explanation
First, it might be better to avoid variable names similar to function names. count is a built-in function of Julia. So if you want to use the count function in the number_of_zeros function, you will undoubtedly face a problem.
Check this example:
function number_of_zeros(lst::Array{Int64})
count = 0
for e in lst
if e == 0
count += 1
end
end
return count, count(==(1), lst)
end
number_of_zeros(lst)
This code will lead to this error:
ERROR: MethodError: objects of type Int64 are not callable
Maybe you forgot to use an operator such as *, ^, %, / etc. ?
Stacktrace:
[1] number_of_zeros(lst::Vector{Int64})
# Main \t.jl:10
[2] top-level scope
# \t.jl:16
Because I overwrote the count variable on the count function! It's possible to avoid such problems by calling the function from its module:
function number_of_zeros(lst::Array{Int64})
count = 0
for e in lst
if e == 0
count += 1
end
end
return count, Base.count(==(1), lst)
The point is I used Base.count, then the compiler knows which count I mean by Base.count.
I need to attach an action to the row in the list. The code below displays a window with a list, but the activation of a row (double click on it) leads to an error:
ls = GtkListStore(String, Int)
push!(ls,("Peter",20))
push!(ls,("Paul",30))
push!(ls,("Mary",25))
tv = GtkTreeView(GtkTreeModel(ls))
rTxt = GtkCellRendererText()
c1 = GtkTreeViewColumn("Name", rTxt, Dict([("text",0)]))
c2 = GtkTreeViewColumn("Age", rTxt, Dict([("text",1)]))
push!(tv, c1, c2)
signal_connect(tv_row_activated, tv, "row-activated")
win = GtkWindow(tv, "List View")
showall(win)
function tv_row_activated(w)
println("Works")
end
As the error message says, it is expecting the callback method to be tv_row_activated(::GtkTreeViewLeaf, ::Gtk.GLib.GBoxedUnknown, GtkTreeViewColumnLeaf) i.e. the callback method should accept three arguments. Currently yours accepts a single argument w (mentioned under "Closest candidates" as tv_row_activated(::Any)).
Looking at the signature of "row-activated" in the underlying C Gtk library,
void
row_activated (
GtkTreeView* self,
GtkTreePath* path,
GtkTreeViewColumn* column,
gpointer user_data
)
it seems that the arguments passed to the callback in Julia might be the first three arguments mentioned here (though I'm not familiar enough with the library to say that for sure).
Gtk is attempting to call your tv_row_activated function with three arguments:
An argument of type GtkTreeViewLeaf
An argument of type Gtk.Glib.BoxedUnknown
An argument of type GtkTreeViewColumnLeaf
As defined your function tv_row_activated takes a single argument, w.
Since you appear to be trying to debug or explore, I suggest redefining tv_row_activated to take any number of arguments as follows:
function tv_row_activated(w...)
println("Works")
end
Let's test this in the REPL:
julia> function tv_row_activated(w...)
println(w)
println(typeof.(w))
println("Works")
end
tv_row_activated (generic function with 1 method)
julia> tv_row_activated(1,2,3)
(1, 2, 3)
(Int64, Int64, Int64)
Works
julia> tv_row_activated(nothing)
(nothing,)
(Nothing,)
Works
julia> tv_row_activated(5.0)
(5.0,)
(Float64,)
Works
v06
I want to write a signature that expect 2 to 3 arguments. The first one is either an Integer or Vector of Integer. The second one is either a Vector of Integer or Matrix of Integer. The third one is either a Vector of Integer or not specified.
I first tried it like this
function foo(
a::Union{Integer, Vector{Integer}},
b::Union{Vector{Integer}, Matrix{Integer}},
c::Union{Void, Vector{Integer}} = nothing)
When I call this like this foo(3, [0o7, 0o5]) I get an error telling me that it is unable to match.
ERROR: LoadError: MethodError: no method matching foo(::Int64, ::Array{UInt8,1})
Closest candidates are:
foo(::Union{Array{Integer,1}, Integer}, !Matched::Union{Array{Integer,1}, Array{Integer,2}}) at ...
foo(::Union{Array{Integer,1}, Integer}, !Matched::Union{Array{Integer,1}, Array{Integer,2}}, !Matched::Union{Array{Integer,1}, Void}) at ...
Now I understand why julia is unable to match this Array{UInt8} <: Array{Integer} == false, but this just seems to be not smart of julia.
Then I tried this
foo(a::Union{Z1, Vector{Z1}},
b::Union{Vector{Z2}, Matrix{Z2}},
c::Union{Void, Vector{Z3}} = nothing
) where {Z1 <: Integer, Z2 <: Integer, Z3 <: Integer}
Now julia does not even tell me what does not match!
ERROR: LoadError: MethodError: no method matching foo(::Int64, ::Array{UInt8,1}, ::Void)
Closest candidates are:
foo(::Union{Array{Z1<:Integer,1}, Z1<:Integer}, ::Union{Array{Z2<:Integer,1}, Array{Z2<:Integer,2}}, ::Union{Array{Z3<:Integer,1}, Void}) where {Z1<:Integer, Z2<:Integer, Z3<:Integer} at ...
foo(::Union{Array{Z1<:Integer,1}, Z1<:Integer}, ::Union{Array{Z2<:Integer,1}, Array{Z2<:Integer,2}}) where {Z1<:Integer, Z2<:Integer} at ...
Yes, Array{UInt8} <: Array{Integer} == false. This is called "parametric invariance." Lots of other questions have discussed this topic.
The other problem you're running into, though, is that when you use a static function parameter — that is, f(…) where T — the T must match something since it's available for use in the body of the function. This causes trouble in Unions where T isn't available in every option. I believe there's an open issue about changing this behavior to allow matching Union elements that don't contain T, which would turn that binding into an undefined variable if you tried to access it.
The workaround right now is to use type vars that aren't static parameters of the function. E.g.,
foo(a::Union{Integer, Vector{<:Integer}},
b::Union{Vector{<:Integer}, Matrix{<:Integer}},
c::Union{Void, Vector{<:Integer}} = nothing) = 1
Best be explained by an example:
I define a type
type myType
α::Float64
β::Float64
end
z = myType( 1., 2. )
Then suppose that I want to pass this type as an argument to a function:
myFunc( x::Vector{Float64}, m::myType ) =
x[1].^m.α+x[2].^m.β
Is there a way to pass myType so that I can actually use it in the body of the function in a "cleaner" fashion as follows:
x[1].^α+x[2].^β
Thanks for any answer.
One way is to use dispatch to a more general function:
myFunc( x::Vector{Float64}, α::Float64, β::Float64) = x[1].^α+x[2].^β
myFunc( x::Vector{Float64}, m::myType = myFunc(x,m.α,m.β)
Or if your functions are longer, you may want to use Parameters.jl's #unpack:
function myFunc( x::Vector{Float64}, m::myType )
#unpack m: α,β #now those are defined
x[1].^α+x[2].^β
end
The overhead of unpacking is small because you're not copying, it's just doing a bunch of α=m.α which is just making an α which points to m.α. For longer equations, this can be a much nicer form if you have many fields and use them in long calculations (for reference, I use this a lot in DifferentialEquations.jl).
Edit
There's another way as noted in the comments. Let me show this. You can define your type (with optional kwargs) using the #with_kw macro from Parameters.jl. For example:
using Parameters
#with_kw type myType
α::Float64 = 1.0 # Give a default value
β::Float64 = 2.0
end
z = myType() # Generate with the default values
Then you can use the #unpack_myType macro which is automatically made by the #with_kw macro:
function myFunc( x::Vector{Float64}, m::myType )
#unpack_myType m
x[1].^α+x[2].^β
end
Again, this only has the overhead of making the references α and β without copying, so it's pretty lightweight.
You could add this to the body of your function:
(α::Float64, β::Float64) = (m.α, m.β)
UPDATE: My original answer was wrong for a subtle reason, but I thought it was a very interesting bit of information so rather than delete it altogether, I'm leaving it with an explanation on why it's wrong. Many thanks to Fengyang for pointing out the global scope of eval! (as well as the use of $ in an Expr context!)
The original answer suggested that:
[eval( parse( string( i,"=",getfield( m,i)))) for i in fieldnames( m)]
would return a list comprehension which had assignment side-effects, since it conceptually would result in something like [α=1., β=2., etc]. The assumption was that this assignment would be within local scope. However, as pointed out, eval is always assessed at global scope, therefore the above one-liner does not do what it's meant to. Example:
julia> type MyType
α::Float64
β::Float64
end
julia> function myFunc!(x::Vector{Float64}, m::MyType)
α=5.; β=6.;
[eval( parse( string( i,"=",getfield( m,i)))) for i in fieldnames( m)]
x[1] = α; x[2] = β; return x
end;
julia> myFunc!([0.,0.],MyType(1., 2.))
2-element Array{Float64,1}:
5.0
6.0
julia> whos()
MyType 124 bytes DataType
myFunc 0 bytes #myFunc
α 8 bytes Float64
β 8 bytes Float64
I.e. as you can see, the intention was for the local variables α and β to be overwritten, but they didn't; eval placed α and β variables at global scope instead. As a matlab programmer I naively assumed that eval() was conceptually equivalent to Matlab, without actually checking. Turns out it's more similar to the evalin('base',...) command.
Thanks again to Fengyand for giving another example of why the phrase "parse and eval" seems to have about the same effect on Julia programmers as the word "it" on the knights who until recently said "NI". :)
This snippet of F# code
let rec reformat = new EventHandler(fun _ _ ->
b.TextChanged.RemoveHandler reformat
b |> ScrollParser.rewrite_contents_of_rtb
b.TextChanged.AddHandler reformat
)
b.TextChanged.AddHandler reformat
results in the following warning:
traynote.fs(62,41): warning FS0040: This and other recursive references to the object(s) being defined will be checked for initialization-soundness at runtime through the use of a delayed reference. This is because you are defining one or more recursive objects, rather than recursive functions. This warning may be suppressed by using '#nowarn "40"' or '--nowarn:40'.
Is there a way in which the code can be rewritten to avoid this warning? Or is there no kosher way of having recursive objects in F#?
Your code is a perfectly fine way to construct a recursive object. The compiler emits a warning, because it cannot guarantee that the reference won't be accessed before it is initialized (which would cause a runtime error). However, if you know that EventHandler does not call the provided lambda function during the construction (it does not), then you can safely ignore the warning.
To give an example where the warning actually shows a problem, you can try the following code:
type Evil(f) =
let n = f()
member x.N = n + 1
let rec e = Evil(fun () ->
printfn "%d" (e:Evil).N; 1)
The Evil class takes a function in a constructor and calls it during the construction. As a result, the recursive reference in the lambda function tries to access e before it is set to a value (and you'll get a runtime error). However, especially when working with event handlers, this is not an issue (and you get the warnning when you're using recursive objects correctly).
If you want to get rid of the warning, you can rewrite the code using explicit ref values and using null, but then you'll be in the same danger of a runtime error, just without the warning and with uglier code:
let foo (evt:IEvent<_, _>) =
let eh = ref null
eh := new EventHandler(fun _ _ ->
evt.RemoveHandler(!eh) )
evt.AddHandler(!eh)