I'm studying Standard ML and one of the exercices I have to do is to write a function called opPairs that receives a list of tuples of type int, and returns a list with the sum of each pair.
Example:
input: opPairs [(1, 2), (3, 4)]
output: val it = [3, 7]
These were my attempts, which are not compiling:
ATTEMPT 1
type T0 = int * int;
fun opPairs ((h:TO)::t) = let val aux =(#1 h + #2 h) in
aux::(opPairs(t))
end;
The error message is:
Error: unbound type constructor: TO
Error: operator and operand don't agree [type mismatch]
operator domain: {1:'Y; 'Z}
operand: [E]
in expression:
(fn {1=1,...} => 1) h
ATTEMPT 2
fun opPairs2 l = map (fn x => #1 x + #2 x ) l;
The error message is: Error: unresolved flex record (need to know the names of ALL the fields
in this context)
type: {1:[+ ty], 2:[+ ty]; 'Z}
The first attempt has a typo: type T0 is defined, where 0 is zero, but then type TO is referenced in the pattern, where O is the letter O. This gets rid of the "operand and operator do not agree" error, but there is a further problem. The pattern ((h:T0)::t) does not match an empty list, so there is a "match nonexhaustive" warning with the corrected type identifier. This manifests as an exception when the function is used, because the code needs to match an empty list when it reaches the end of the input.
The second attempt needs to use a type for the tuples. This is because the tuple accessor #n needs to know the type of the tuple it accesses. To fix this problem, provide the type of the tuple argument to the anonymous function:
fun opPairs2 l = map (fn x:T0 => #1 x + #2 x) l;
But, really it is bad practice to use #1, #2, etc. to access tuple fields; use pattern matching instead. Here is a cleaner approach, more like the first attempt, but taking full advantage of pattern matching:
fun opPairs nil = nil
| opPairs ((a, b)::cs) = (a + b)::(opPairs cs);
Here, opPairs returns an empty list when the input is an empty list, otherwise pattern matching provides the field values a and b to be added and consed recursively onto the output. When the last tuple is reached, cs is the empty list, and opPairs cs is then also the empty list: the individual tuple sums are then consed onto this empty list to create the output list.
To extend on exnihilo's answer, once you have achieved familiarity with the type of solution that uses explicit recursion and pattern matching (opPairs ((a, b)::cs) = ...), you can begin to generalise the solution using list combinators:
val opPairs = map op+
I'm writing a genetic program in order to test the fitness of randomly generated expressions. Shown here is the function to generate the expression as well a the main function. DIV and GT are defined elsewhere in the code:
function create_single_full_tree(depth, fs, ts)
"""
Creates a single AST with full depth
Inputs
depth Current depth of tree. Initially called from main() with max depth
fs Function Set - Array of allowed functions
ts Terminal Set - Array of allowed terminal values
Output
Full AST of typeof()==Expr
"""
# If we are at the bottom
if depth == 1
# End of tree, return function with two terminal nodes
return Expr(:call, fs[rand(1:length(fs))], ts[rand(1:length(ts))], ts[rand(1:length(ts))])
else
# Not end of expression, recurively go back through and create functions for each new node
return Expr(:call, fs[rand(1:length(fs))], create_single_full_tree(depth-1, fs, ts), create_single_full_tree(depth-1, fs, ts))
end
end
function main()
"""
Main function
"""
# Define functional and terminal sets
fs = [:+, :-, :DIV, :GT]
ts = [:x, :v, -1]
# Create the tree
ast = create_single_full_tree(4, fs, ts)
#println(typeof(ast))
#println(ast)
#println(dump(ast))
x = 1
v = 1
eval(ast) # Error out unless x and v are globals
end
main()
I am generating a random expression based on certain allowed functions and variables. As seen in the code, the expression can only have symbols x and v, as well as the value -1. I will need to test the expression with a variety of x and v values; here I am just using x=1 and v=1 to test the code.
The expression is being returned correctly, however, eval() can only be used with global variables, so it will error out when run unless I declare x and v to be global (ERROR: LoadError: UndefVarError: x not defined). I would like to avoid globals if possible. Is there a better way to generate and evaluate these generated expressions with locally defined variables?
Here is an example for generating an (anonymous) function. The result of eval can be called as a function and your variable can be passed as parameters:
myfun = eval(Expr(:->,:x, Expr(:block, Expr(:call,:*,3,:x) )))
myfun(14)
# returns 42
The dump function is very useful to inspect the expression that the parsers has created. For two input arguments you would use a tuple for example as args[1]:
julia> dump(parse("(x,y) -> 3x + y"))
Expr
head: Symbol ->
args: Array{Any}((2,))
1: Expr
head: Symbol tuple
args: Array{Any}((2,))
1: Symbol x
2: Symbol y
typ: Any
2: Expr
[...]
Does this help?
In the Metaprogramming part of the Julia documentation, there is a sentence under the eval() and effects section which says
Every module has its own eval() function that evaluates expressions in its global scope.
Similarly, the REPL help ?eval will give you, on Julia 0.6.2, the following help:
Evaluate an expression in the given module and return the result. Every Module (except those defined with baremodule) has its own 1-argument definition of eval, which evaluates expressions in that module.
I assume, you are working in the Main module in your example. That's why you need to have the globals defined there. For your problem, you can use macros and interpolate the values of x and y directly inside the macro.
A minimal working example would be:
macro eval_line(a, b, x)
isa(a, Real) || (warn("$a is not a real number."); return :(throw(DomainError())))
isa(b, Real) || (warn("$b is not a real number."); return :(throw(DomainError())))
return :($a * $x + $b) # interpolate the variables
end
Here, #eval_line macro does the following:
Main> #macroexpand #eval_line(5, 6, 2)
:(5 * 2 + 6)
As you can see, the values of macro's arguments are interpolated inside the macro and the expression is given to the user accordingly. When the user does not behave,
Main> #macroexpand #eval_line([1,2,3], 7, 8)
WARNING: [1, 2, 3] is not a real number.
:((Main.throw)((Main.DomainError)()))
a user-friendly warning message is provided to the user at parse-time, and a DomainError is thrown at run-time.
Of course, you can do these things within your functions, again by interpolating the variables --- you do not need to use macros. However, what you would like to achieve in the end is to combine eval with the output of a function that returns Expr. This is what the macro functionality is for. Finally, you would simply call your macros with an # sign preceding the macro name:
Main> #eval_line(5, 6, 2)
16
Main> #eval_line([1,2,3], 7, 8)
WARNING: [1, 2, 3] is not a real number.
ERROR: DomainError:
Stacktrace:
[1] eval(::Module, ::Any) at ./boot.jl:235
EDIT 1. You can take this one step further, and create functions accordingly:
macro define_lines(linedefs)
for (name, a, b) in eval(linedefs)
ex = quote
function $(Symbol(name))(x) # interpolate name
return $a * x + $b # interpolate a and b here
end
end
eval(ex) # evaluate the function definition expression in the module
end
end
Then, you can call this macro to create different line definitions in the form of functions to be called later on:
#define_lines([
("identity_line", 1, 0);
("null_line", 0, 0);
("unit_shift", 0, 1)
])
identity_line(5) # returns 5
null_line(5) # returns 0
unit_shift(5) # returns 1
EDIT 2. You can, I guess, achieve what you would like to achieve by using a macro similar to that below:
macro random_oper(depth, fs, ts)
operations = eval(fs)
oper = operations[rand(1:length(operations))]
terminals = eval(ts)
ts = terminals[rand(1:length(terminals), 2)]
ex = :($oper($ts...))
for d in 2:depth
oper = operations[rand(1:length(operations))]
t = terminals[rand(1:length(terminals))]
ex = :($oper($ex, $t))
end
return ex
end
which will give the following, for instance:
Main> #macroexpand #random_oper(1, [+, -, /], [1,2,3])
:((-)([3, 3]...))
Main> #macroexpand #random_oper(2, [+, -, /], [1,2,3])
:((+)((-)([2, 3]...), 3))
Thanks Arda for the thorough response! This helped, but part of me thinks there may be a better way to do this as it seems too roundabout. Since I am writing a genetic program, I will need to create 500 of these ASTs, all with random functions and terminals from a set of allowed functions and terminals (fs and ts in the code). I will also need to test each function with 20 different values of x and v.
In order to accomplish this with the information you have given, I have come up with the following macro:
macro create_function(defs)
for name in eval(defs)
ex = quote
function $(Symbol(name))(x,v)
fs = [:+, :-, :DIV, :GT]
ts = [x,v,-1]
return create_single_full_tree(4, fs, ts)
end
end
eval(ex)
end
end
I can then supply a list of 500 random function names in my main() function, such as ["func1, func2, func3,.....". Which I can eval with any x and v values in my main function. This has solved my issue, however, this seems to be a very roundabout way of doing this, and may make it difficult to evolve each AST with each iteration.
I define the type
type mytype
e1:: Real
e2:: Real
end
I want to have a vector of mytype:
Vmtype = Array{mytype}(10)
when I ask julia for the 10 e1. I get an error
Vmtype[1:2].e1
ERROR: type Array has no field e1
How can I access the Vector Vmtype[1:10]?
First, you have to fill in the values of Vmtype. What you are doing is creating an "empty" array of type mytype.
Vmtype = Array{mytype}(10)
e1s = collect(1:10)
e2s = collect(91:100)
for i in 1:10
Vmtype[i] = mytype(e1s[i], e2s[i])
end
then you can access the fields as
Vmtype[1].e1
Notice that one thing is an object of type mytype and another is an array with elements of type mytype. See http://docs.julialang.org/en/latest/manual/types/#man-parametric-types
EDIT:
To create another array with the e1s of Vmtype you can use
Ae1 = map(x -> x.e1, Vmtype)
Then you can use Ae1 in plot((1:10), Ae1).
The following code complains
ERROR: `setindex!` has no method matching setindex!(::Type{Array{Int32,32}}, ::Int32, ::Int64)
Should I be able to do this? The problem, I think, is that the loop variable has the wrong type to be used as an array index?
n = parseint(readline(STDIN))
A = Array{Int32, n}
for i in 1:n-1
ai = parseint(Int32, readuntil(STDIN, ' '))
A[i] = ai #The error happens here!
end
A[n] = parseint(Int32, readline(STDIN))
Your assignment of A is legal, but it doesn't do what you think it does.
A = Array{Int32,n}
julia> typeof(A)
DataType
This declares an A to be the type representing an array of n dimensions. What you want instead, probably is A to be a variable of type Array{Int32,1} that contains n elements. So instead try the following:
A = Array(Int32,n);
julia> typeof(A)
Array{Int32,1}
To declare a new composite type, we use the following syntax
type foo
a::Int64
b::Int64
end
and instantiate like such
x = foo(1,3)
Is there some way to have type attributes that always just a function of other attributes? For example, is there some way to do the following (which is invalid syntax)...
type foo
a::Int64
b::Int64
c = a + b
end
My current workaround is just to define a function which calculates c and returns an instance of the type, like so...
type foo
a::Int64
b::Int64
c::Int64
end
function foo_maker(a, b)
return foo(a, b, a+b)
end
Is there a more elegant solution? Possibly one that can be contained within the type definition?
EDIT - 3/7/14
With Cristóvão's suggestion in mind, I've ended up declaring constructors like the following to allow for keyword args and attributes calculated upon instantiation
# Type with optional keyword argument structure
type LargeType
# Declare all the attributes in order up top
q::Int64
w::Int64
e::Int64
r::Int64
t::Int64
y::Int64
a::Number
b::Number
c::Number
# Declare Longer constructor with stuff going on in the body
LargeType(;q=1,w=1,e=1,r=1,t=1,y=1) = begin
# Large Constructor Example
a = round(r^t - log(pi))
b = a % t
c = a*b
# Return new instance with correctly ordered arguments
return new(q,w,e,r,t,y,a,b,c)
end
end
println(LargeType(r=2,t=5))
Try this:
julia> type foo
a::Int64
b::Int64
c::Int64
foo(a::Int64, b::Int64) = new(a, b, a+b)
end
julia> foo(1,2)
foo(1,2,3)
julia> foo(4,5,6)
no method foo(Int64, Int64, Int64)
However, that won't prevent one from manually changing a, b or c and rendering c inconsistent. To prevent that, if it presents no other problems, you can make foo immutable:
julia> immutable foo
...
There isn't any way to do this currently, but there might be in the future:
https://github.com/JuliaLang/julia/issues/1974