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I have a pure function that takes 18 arguments process them and returns an answer.
Inside this function I call many other pure functions and those functions call other pure functions within them as deep as 6 levels.
This way of composition is cumbersome to test as the top level functions,in addition to their logic,have to gather parameters for inner functions.
# Minimal conceptual example
main_function(a, b, c, d, e) = begin
x = pure_function_1(a, b, d)
y = pure_function_2(a, c, e, x)
z = pure_function_3(b, c, y, x)
answer = pure_function_4(x,y,z)
return answer
end
# real example
calculate_time_dependant_losses(
Ap,
u,
Ac,
e,
Ic,
Ep,
Ecm_t,
fck,
RH,
T,
cementClass::Char,
ρ_1000,
σ_p_start,
f_pk,
t0,
ts,
t_start,
t_end,
) = begin
μ = σ_p_start / f_pk
fcm = fck + 8
Fr = σ_p_start * Ap
_σ_pb = σ_pb(Fr, Ac, e, Ic)
_ϵ_cs_t_start_t_end = ϵ_cs_ti_tj(ts, t_start, t_end, Ac, u, fck, RH, cementClass)
_ϕ_t0_t_start_t_end = ϕ_t0_ti_tj(RH, fcm, Ac, u, T, cementClass, t0, t_start, t_end)
_Δσ_pr_t_start_t_end =
Δσ_pr(σ_p_start, ρ_1000, t_end, μ) - Δσ_pr(σ_p_start, ρ_1000, t_start, μ)
denominator =
1 +
(1 + 0.8 * _ϕ_t0_t_start_t_end) * (1 + (Ac * e^2) / Ic) * ((Ep * Ap) / (Ecm_t * Ac))
shrinkageLoss = (_ϵ_cs_t_start_t_end * Ep) / denominator
relaxationLoss = (0.8 * _Δσ_pr_t_start_t_end) / denominator
creepLoss = (Ep * _ϕ_t0_t_start_t_end * _σ_pb) / Ecm_t / denominator
return shrinkageLoss + relaxationLoss + creepLoss
end
I see examples of functional composition (dot chaining,pipe operator etc) with single argument functions.
Is it practical to compose the above function using functional programming?If yes, how?
The standard and simple way is to recast your example so that it can be written as
# Minimal conceptual example, re-cast
main_function(a, b, c, d, e) = begin
x = pure_function_1'(a, b, d)()
y = pure_function_2'(a, c, e)(x)
z = pure_function_3'(b, c)(y) // I presume you meant `y` here
answer = pure_function_4(z) // and here, z
return answer
end
Meaning, we use functions that return functions of one argument. Now these functions can be easily composed, using e.g. a forward-composition operator (f >>> g)(x) = g(f(x)) :
# Minimal conceptual example, re-cast, composed
main_function(a, b, c, d, e) = begin
composed_calculation =
pure_function_1'(a, b, d) >>>
pure_function_2'(a, c, e) >>>
pure_function_3'(b, c, y) >>>
pure_function_4
answer = composed_calculation()
return answer
end
If you really need the various x y and z at differing points in time during the composed computation, you can pass them around in a compound, record-like data structure. We can avoid the coupling of this argument handling if we have extensible records:
# Minimal conceptual example, re-cast, composed, args packaged
main_function(a, b, c, d, e) = begin
composed_calculation =
pure_function_1'(a, b, d) >>> put('x') >>>
get('x') >>> pure_function_2'(a, c, e) >>> put('y') >>>
get('x') >>> pure_function_3'(b, c, y) >>> put('z') >>>
get({'x';'y';'z'}) >>> pure_function_4
answer = composed_calculation(empty_initial_state)
return value(answer)
end
The passed around "state" would be comprised of two fields: a value and an extensible record. The functions would accept this state, use the value as their additional input, and leave the record unchanged. get would take the specified field out of the record and put it in the "value" field in the state. put would mutate the extensible record in the state:
put(field_name) = ( {value:v ; record:r} =>
{v ; put_record_field( r, field_name, v)} )
get(field_name) = ( {value:v ; record:r} =>
{get_record_field( r, field_name) ; r} )
pure_function_2'(a, c, e) = ( {value:v ; record:r} =>
{pure_function_2(a, c, e, v); r} )
value(r) = get_record_field( r, value)
empty_initial_state = { novalue ; empty_record }
All in pseudocode.
Augmented function application, and hence composition, is one way of thinking about "what monads are". Passing around the pairing of a produced/expected argument and a state is known as State Monad. The coder focuses on dealing with the values while treating the state as if "hidden" "under wraps", as we do here through the get/put etc. facilities. Under this illusion/abstraction, we do get to "simply" compose our functions.
I can make a small start at the end:
sum $ map (/ denominator)
[ _ϵ_cs_t_start_t_end * Ep
, 0.8 * _Δσ_pr_t_start_t_end
, (Ep * _ϕ_t0_t_start_t_end * _σ_pb) / Ecm_t
]
As mentioned in the comments (repeatedly), the function composition operator does indeed accept multiple argument functions. Cite: https://docs.julialang.org/en/v1/base/base/#Base.:%E2%88%98
help?> ∘
"∘" can be typed by \circ<tab>
search: ∘
f ∘ g
Compose functions: i.e. (f ∘ g)(args...; kwargs...) means f(g(args...; kwargs...)). The ∘ symbol
can be entered in the Julia REPL (and most editors, appropriately configured) by typing
\circ<tab>.
Function composition also works in prefix form: ∘(f, g) is the same as f ∘ g. The prefix form
supports composition of multiple functions: ∘(f, g, h) = f ∘ g ∘ h and splatting ∘(fs...) for
composing an iterable collection of functions.
The challenge is chaining the operations together, because any function can only pass on a tuple to the next function in the composed chain. The solution could be making sure your chained functions 'splat' the input tuples into the next function.
Example:
# splat to turn max into a tuple-accepting function
julia> f = (x->max(x...)) ∘ minmax;
julia> f(3,5)
5
Using this will in no way help make your function cleaner, though, in fact it will probably make a horrible mess.
Your problems do not at all seem to me to be related to how you call, chain or compose your functions, but are entirely due to not organizing the inputs in reasonable types with clean interfaces.
Edit: Here's a custom composition operator that splats arguments, to avoid the tuple output issue, though I don't see how it can help picking the right arguments, it just passes everything on:
⊕(f, g) = (args...) -> f(g(args...)...)
⊕(f, g, h...) = ⊕(f, ⊕(g, h...))
Example:
julia> myrev(x...) = reverse(x);
julia> (myrev ⊕ minmax)(5,7)
(7, 5)
julia> (minmax ⊕ myrev ⊕ minmax)(5,7)
(5, 7)
After doing some Prolog in uni and doing some exercises I decided to go along somewhat further although I got to admit I don't understand recursion that well, I get the concept and idea but how to code it, is still a question for me. So that's why I was curious if anyone knows how to help tackle this problem.
The idea is given a number e.g. 45, check whether it is possible to make a list starting with 1 going n+1 into the list and if the sum of the list is the same as the given number.
So for 45, [1,2,3,4,5,6,7,8,9] would be correct.
So far I tried looking at the [sum_list/2][1] implemented in Prolog itself but that only checks whether a list is the same as the number it follows.
So given a predicate lijstSom(L,S) (dutch for listSum), given
?- lijstSom(L, 45)
L = [1,2,3,4,5,6,7,8,9];
False
My Idea was something along the line of for example if S = 45, doing steps of the numbers (increasing by 1) and subtracting it of S, if 0 is the remainder, return the list, else return false.
But for that you need counters and I find it rather hard to grasp that in recursion.
EDIT:
Steps in recursion.
Base case empty list, 0 (counter nr, that is minus S), 45 (S, the remainder)
[1], 1, 44
[1,2], 2, 42
[1,2,3], 3, 39
I'm not sure how to read the example
?- lijstSom(L, 45)
L = [1,2,3,4,5,6,7,8,9],
False
...but think of the predicate lijstSom(List, Sum) as relating certain lists of integers to their sum, as opposed to computing the sum of lists of integers. Why "certain lists"? Because we have the constraint that the integers in the list of integers must be monotonically increasing in increments of 1, starting from 1.
You can thus ask the Prolog Processor the following:
"Say something about the relationship between the first argument of lijstSom/2 and the second argument lijstSom/2 (assuming the first is a list of monotonically increasing integers, and the second an integer):
lijstSom([1,2,3], Sum)
... should return true (because yes, there is at least one solution) and give Sum = 6 (because it constructs the solution, too ... we are some corner of Construtivism here.
lijstSom(L, 6)
... should return true (because yes, there is at least one solution) and give the solution [1,2,3].
lijstSom([1,2,3], 6)
... should return true (because yes, [1,2,3] has a sum 6); no further information is needed.
lijstSom(L, S)
... should an infinite series of true and pairs of solution ("generate the solutions").
L = [1], S = 1;
L = [1,2], S = 3;
L = [1,2,3], S = 6;
...
lijstSom([1,2,3], 7)
...should return false ("fail") because 7 is not in a relation lijstSom with [1,2,3] as 7 =/= 1+2+3.
One might even want things to have Prolog Processor say something interesting about:
lijstSom([1,2,X], 6)
X = 3
or even
lijstSom([1,2,X], S)
X = 3
S = 6
In fact, lijstSom/2 as near to mathematically magical as physically possible, which is to say:
Have unrestricted access to the full table of list<->sum relationships floating somewhere in Platonic Math Space.
Be able to find the correct entry in seriously less than infinite number of steps.
And output it.
Of course we are restricted to polynomial algorithms of low exponent and finite number of dstinguishable symbols for eminently practical reasons. Sucks!
So, first define lijstSom(L,S) using an inductive definition:
lijstSom([a list with final value N],S) ... is true if ... lijstSom([a list],S-N and
lijstSom([],0) because the empty list has sum 0.
This is nice because it gives the recipe to reduce a list of arbitrary length down to a list of size 0 eventually while keeping full knowledge its sum!
Prolog is not good at working with the tail of lists, but good with working with the head, so we cheat & change our definition of lijstSom/2 to state that the list is given in reverse order:
lijstSom([3,2,1], 6)
Now some code.
#= is the "constain to be equal" operator from library(clpfd). To employ it, we need to issue use_module(library(clpfd)). command first.
lijstSom([],0).
lijstSom([K|Rest],N) :- lijstSom([Rest],T), T+K #= N.
The above follows the mathematical desiderate of lijstSom and allows the Prolog Processor to perform its computation: in the second clause, it can compute the values for a list of size A from the values of a list of size A-1, "falling down" the staircase of always decreasing list length until it reaches the terminating case of lijstSom([],0)..
But we haven't said anything about the monotonically decreasing-by-1 list.
Let's be more precise:
lijstSom([],0) :- !.
lijstSom([1],1) :- ! .
lijstSom([K,V|Rest],N) :- K #= V+1, T+K #= N, lijstSom([V|Rest],T).
Better!
(We have also added '!' to tell the Prolog Processor to not look for alternate solutions past this point, because we know more about the algorithm than it will ever do. Additionally, the 3rd line works, but only because I got it right after running the tests below and having them pass.)
If the checks fail, the Prolog Processor will says "false" - no solution for your input. This is exactly what we want.
But does it work? How far can we go in the "mathematic-ness" of this eminently physical machine?
Load library(clpfd) for constraints and use library(plunit) for unit tests:
Put this into a file x.pl that you can load with [x] alias consult('x') or reload with make on the Prolog REPL:
:- use_module(library(clpfd)).
lijstSom([],0) :-
format("Hit case ([],0)\n"),!.
lijstSom([1],1) :-
format("Hit case ([1],1)\n"),!.
lijstSom([K,V|Rest],N) :-
format("Called with K=~w, V=~w, Rest=~w, N=~w\n", [K,V,Rest,N]),
K #= V+1,
T+K #= N,
T #> 0, V #> 0, % needed to avoid infinite descent
lijstSom([V|Rest],T).
:- begin_tests(listsom).
test("0 verify") :- lijstSom([],0).
test("1 verify") :- lijstSom([1],1).
test("3 verify") :- lijstSom([2,1],3).
test("6 verify") :- lijstSom([3,2,1],6).
test("0 construct") :- lijstSom(L,0) , L = [].
test("1 construct") :- lijstSom(L,1) , L = [1].
test("3 construct") :- lijstSom(L,3) , L = [2,1].
test("6 construct") :- lijstSom(L,6) , L = [3,2,1].
test("0 sum") :- lijstSom([],S) , S = 0.
test("1 sum") :- lijstSom([1],S) , S = 1.
test("3 sum") :- lijstSom([2,1],S) , S = 3.
test("6 sum") :- lijstSom([3,2,1],S) , S = 6.
test("1 partial") :- lijstSom([X],1) , X = 1.
test("3 partial") :- lijstSom([X,1],3) , X = 2.
test("6 partial") :- lijstSom([X,2,1],6) , X = 3.
test("1 extreme partial") :- lijstSom([X],S) , X = 1, S = 1.
test("3 extreme partial") :- lijstSom([X,1],S) , X = 2, S = 3.
test("6 extreme partial") :- lijstSom([X,2,1],S) , X = 3, S = 6.
test("6 partial list") :- lijstSom([X|L],6) , X = 3, L = [2,1].
% Important to test the NOPES
test("bad list", fail) :- lijstSom([3,1],_).
test("bad sum", fail) :- lijstSom([3,2,1],5).
test("reversed list", fail) :- lijstSom([1,2,3],6).
test("infinite descent from 2", fail) :- lijstSom(_,2).
test("infinite descent from 9", fail) :- lijstSom(_,9).
:- end_tests(listsom).
Then
?- run_tests(listsom).
% PL-Unit: listsom ...................... done
% All 22 tests passed
What would Dijkstra say? Yeah, he would probably bitch about something.
How do I evaluate the function in only one of its variables, that is, I hope to obtain another function after evaluating the function. I have the following piece of code.
deff ('[F] = fun (x, y)', 'F = x ^ 2-3 * y ^ 2 + x * y ^ 3');
fun (4, y)
I hope to get 16-3y ^ 2 + 4y ^ 3
If what you want to do is to write x = f(4,y), and later just do x(2) to get -36, that is called partial application:
Intuitively, partial function application says "if you fix the first arguments of the function, you get a function of the remaining arguments".
This is a very useful feature, and very common Functional Programming Languages, such as Haskell, but even JS and Python now are able to do it. It is also possible to do this in MATLAB and GNU/Octave using anonymous functions (see this answer). In Scilab, however, this feature is not available.
Workround
Nonetheless, Scilab itself uses a workarounds to carry a function with its arguments without fully evaluating. You see this being used in ode(), fsolve(), optim(), and others:
Create a list containing the function and the arguments to partial evaluation: list(f,arg1,arg2,...,argn)
Use another function to evaluate such list and the last argument: evalPartList(list(...),last_arg)
The implementation of evalPartList() can be something like this:
function y = evalPartList(fList,last_arg)
//fList: list in which the first element is a function
//last_arg: last argument to be applied to the function
func = fList(1); //extract function from the list
y = func(fList(2:$),last_arg); //each element of the list, from second
//to last, becomes an argument
endfunction
You can test it on Scilab's console:
--> deff ('[F] = fun (x, y)', 'F = x ^ 2-3 * y ^ 2 + x * y ^ 3');
--> x = list(fun,4)
x =
x(1)
[F]= x(1)(x,y)
x(2)
4.
--> evalPartList(x,2)
ans =
36.
This is a very simple implementation for evalPartList(), and you have to be careful not to exceed or be short on the number of arguments.
In the way you're asking, you can't.
What you're looking is called symbolic (or formal) computational mathematics, because you don't pass actual numerical values to functions.
Scilab is numerical software so it can't do such thing. But there is a toolbox scimax (installation guide) that rely on a the free formal software wxmaxima.
BUT
An ugly, stupid but still sort of working solution is to takes advantages of strings :
function F = fun (x, y) // Here we define a function that may return a constant or string depending on the input
fmt = '%10.3E'
if (type(x)==type('')) & (type(y)==type(0)) // x is string is
ys = msprintf(fmt,y)
F = x+'^2 - 3*'+ys+'^2 + '+x+'*'+ys+'^3'
end
if (type(y)==type('')) & (type(x)==type(0)) // y is string so is F
xs = msprintf(fmt,x)
F = xs+'^2 - 3*'+y+'^2 + '+xs+'*'+y+'^3'
end
if (type(y)==type('')) & (type(x)==type('')) // x&y are strings so is F
F = x+'^2 - 3*'+y+'^2 + '+x+'*'+y+'^3'
end
if (type(y)==type(0)) & (type(x)==type(0)) // x&y are constant so is F
F = x^2 - 3*y^2 + x*y^3
end
endfunction
// Then we can use this 'symbolic' function
deff('F2 = fun2(y)',' F2 = '+fun(4,'y'))
F2=fun2(2) // does compute fun(4,2)
disp(F2)
Right now I have an SML function:
method([1,1,1,1,2,2,2,3,3,3]);
returns:
val it = [[2,2,2],[3,3,3]] : int list list
but I need it to return:
val it = [[1,1,1,1],[2,2,2],[3,3,3]] : int list list
This is my current code:
- fun method2(L: int list) =
= if tl(L) = [] then [hd(L)] else
= if hd(tl(L)) = hd(L) then hd(L)::method(tl(L)) else [hd(L)];
- fun method(L: int list) =
= if tl(L) = [] then [] else
= if hd(tl(L)) = hd(L) then method(tl(L)) else
= method2(tl(L))::method(tl(L));
As you can see it misses the first method2 call. Any ideas on how I can fix this? I am completely stumped.
Your problem is here if hd(tl(L)) = hd(L) then method(tl(L)) else. This is saying if the head of the tail is equal to the head, then continue processing, but don't add it to the result list. this will skip the first contiguous chunk of equal values. I would suggest separating the duties of these functions a bit more. The way to do this is to have method2 strip off the next contiguous chunk of values, and return a pair, where the first element will have the contiguous chunk removed, and the second element will have the remaining list. For example, method2([1, 1, 1, 2, 2, 3, 3]) = ([1, 1, 1], [2, 2, 3, 3]) and method2([2, 2, 3, 3]) = ([2, 2], [3, 3]). Now, you can just keep calling method2 until the second part of the pair is nil.
I'm not quite sure what you are trying to do with your code. I would recommend creating a tail recursive helper function which is passed three arguments:
1) The list of lists you are trying to build up
2) The current list you are building up
3) The list you are processing
In your example, a typical call somewhere in the middle of the computation would look like:
helper([[1,1,1,1]], [2,2],[2,3,3,3])
The recursion would work by looking at the head of the last argument ([2,3,3,3]) as well as the head of the list which is currently being built up ([2,2]) and, since they are the same -- the 2 at the end of the last argument is shunted to the list being built up:
helper([[1,1,1,1]], [2,2,2],[3,3,3])
in the next step in the recursion the heads are then compared and found to be different (2 != 3), so the helper function will put the middle list at the front of the list of lists:
helper([[2,2,2], [1,1,1,1]], [3],[3,3])
the middle list is re-initialized to [3] so it will start growing
eventually you reach something like this:
helper([[2,2,2], [1,1,1,1]], [3,3,3],[])
the [3,3,3] is then tacked onto the list of lists and the reverse of this list is returned.
Once such a helper function is defined, the main method checks for an empty list and, if not empty, initializes the first call to the helper function. The following code fleshes out theses ideas -- using pattern-matching style rather than hd and tl (I am not a big fan of using those functions explicitly -- it makes the code too Lisp-like). If this is homework then you should probably thoroughly understand how it works and then translate it to code involving hd and tl since your professor would regard it as plagiarized if you use things you haven't yet studied and haven't made it your own work:
fun helper (xs, ys, []) = rev (ys::xs)
| helper (xs, y::ys, z::zs) =
if y = z
then helper(xs, z :: y :: ys, zs)
else helper((y::ys)::xs,[z],zs);
fun method [] = []
| method (x::xs) = helper([],[x],xs);
I'm trying to define the division in prolog using the remainder theorem and the well-ordering principle.
I've got thus far:
less(0, s(0)).
less(0, s(B)) :- less(0, B).
less(s(A), s(s(B))) :- less(A, s(B)).
add(A,0,A) :- nat(A).
add(A,s(B),s(C)) :- add(A,B,C). % add(A,B+1,C+1) = add(A,B,C)
add2(A,0,A).
add2(A,s(B),s(C)) :- add2(A,B,C). % add(A,B+1,C+1) = add(A,B,C)
times(A,0,0).
times(A,s(B),X) :- times(A,B,X1),
add(A,X1,X).
eq(0,0).
eq(s(A), s(B)) :- eq(A, B).
% A / B = Q (R) => A = B * Q + R
div(A, B, Q, R) :- less(R, B), eq(A, add(times(Q, R), R)).
But the definition of div is somehow wrong. Could someone please give me a hint?
PS: I shouldn't be using eq, but I couldn't get is or = to work.
In SWI-Prolog, you can try ?- gtrace, your_goal. to use the graphical tracer and see what goes wrong. Instead of eq(A, add(times(Q, R), R)), you should write for example: times(Q, R, T), add(T, R, A), since you want to use the "times/3" and "add/3" predicates, instead of just calling the "eq/2" predicate with a compound term consisting of "add/2" and "times/2" as its second argument. There are other problems with the code as well, for example, the definition of nat/1 is missing, but I hope this helps somewhat.