Trying to modify a value already pushed to the stack of a PLY/Yacc parser. I'm using ply on python 3.
Basically I want to invert the previous two values when a token SWAP is used.
Imagine we have this stack:
1, 2, 3, 4, SWAP
I need it to reduce to:
1, 2, 4, 3
the value you write to p[0] will be pushed to the stack, but how can I push more then one value?
# this fails because it consume two values and pushes only one
# results into: `1`, `2`, `4`
def p_swap(p):
'value : value value SWAP'
p[0] = p[2]
# this was just a try... fails as well
def p_swap(p):
'value : value value SWAP'
p[0] = p[2]
p[1] = p[1]
# this locked as a good idea since consumes only only value and modify the second in place
# it fails because the stack (negative indexes) are immutable:
# https://github.com/dabeaz/ply/blob/master/ply/yacc.py#L234
# results into: `1`, `2`, `3`, `3`
def p_swap(p):
'value : value SWAP'
p[0] = p[-1]
p[-1] = p[1] # this is a NOP
p is an instance of this class
I guess it was designed to be immutable to enforce the parsing to be done a certain way (the correct way), but I'm missing it: what's the correct way to modify the stack or to design a parser?
It sounds like you're trying to create a stack-based language like Forth or Joy. If so, you shouldn't need a bottom-up parser, and you shouldn't be surprised that a bottom-up parser-generator doesn't work the way you want it to.
Stack-based languages are mostly simply streams of tokens. Each token has some kind of stack effect, and they are just applied in sequence; there's usually little or no syntactic structure beyond that. Consequently, the languages really aren't parsed; at best, they are tokenised.
Most stack-based languages contain some kind of nested control structures which are not strictly conformant with the above (but not all; see Postscript, for example). But even these are so simple that a real parser is unnecessary.
Of course, nothing stops you from using a generated parser to parse a trivial language. But if you do that, you should certainly not expect to be able to gain access to the parser's internal datastructures. The parser stack is used by the parser in ways which might not be fully obvious, and which certainly must not be interfered with. If you want to implement a stack-based language interpreter, you need to use your own value stack. (Or stacks; many stack-based languages have several different stacks, each with its own semantics.)
Related
I am searching some SWI-Prolog function which is able to make some set union with variables as parameters inside. My aim is to make the union first and define the parameters at further on in source code.
Means eg. I have some function union and the call union(A, B, A_UNION_B) makes sense. Means further more the call:
union(A, [1,2], C), A=[3].
would give me as result
C = [3, 1, 2].
(What you call union/3 is most probably just concatenation, so I will use append/3 for keeping this answer short.)
What you expect is impossible without delayed goals or constraints. To see this, consider the following failure-slice
?- append(A, [1,2], C), false, A=[3].
loops, unexpected. % observed, but for us unexpected
false. % expected, but not the case
This query must terminate, in order to make the entire question useful. But there are infinitely many lists of different length for A. So in order to describe all possible solutions, we would need infinitely many answer substitutions, like
?- append(A, [1,2], C).
A = [], C = [1,2]
; A = [_A], C = [_A,1,2]
; A = [_A,_B], C = [_A,_B,1,2]
; A = [_A,_B,_C], C = [_A,_B,_C,1,2]
; ... .
The only way around is to describe that set of solutions with finitely many answers. One possibility could be:
?- when((ground(A);ground(C)), append(A,B,C)).
when((ground(A);ground(C)),append(A,B,C)).
Essentially it reads: Yes, the query is true, provided the query is true.
While this solves your exact problem, it will now delay many otherwise succeeding goals, think of A = [X], B = [].
A more elaborate version could provide more complex tests. But it would require a somehow different definition than append/3 is. Some systems like sicstus-prolog provide block declarations to make this more smoothly (SWI has a coarse emulation for that).
So it is possible to make this even better, but the question remains whether or not this makes much sense. After all, debugging delayed goals becomes more and more difficult with larger programs.
In many situations it is preferable to prevent this and produce an instantiation error in its stead as iwhen/2 does:
?- iwhen((ground(A);ground(C)),append(A,B,C)).
error(instantiation_error,iwhen/2).
That error is not the nicest answer possible, but at least it is not incorrect. It says: You need to provide more instantiations.
If you really want to solve this problem for the general case you have to delve into E-unification. That is an area with most trivial problem statements and extremely evolved answers. Often, just decidability is non-trivial let alone an effective algorithm. For your particular question, either ACI (for sets) or ANlr (for concatenation) are of interest. Where ACI requires solving Diophantine Equations and associative unification alone is even more complex than that. I am unaware of any such implementation for a Prolog system that solves the general problem.
Prolog IV offered an associative infix operator for concatenation but simply delayed more complex cases. So debugging these remains non-trivial.
I am new to Functional programming.
The challenge I have is regarding the mental map of how a binary search tree works in Haskell.
In other programs (C,C++) we have something called root. We store it in a variable. We insert elements into it and do balancing etc..
The program takes a break does other things (may be process user inputs, create threads) and then figures out it needs to insert a new element in the already created tree. It knows the root (stored as a variable) and invokes the insert function with the root and the new value.
So far so good in other languages. But how do I mimic such a thing in Haskell, i.e.
I see functions implementing converting a list to a Binary Tree, inserting a value etc.. That's all good
I want this functionality to be part of a bigger program and so i need to know what the root is so that i can use it to insert it again. Is that possible? If so how?
Note: Is it not possible at all because data structures are immutable and so we cannot use the root at all to insert something. in such a case how is the above situation handled in Haskell?
It all happens in the same way, really, except that instead of mutating the existing tree variable we derive a new tree from it and remember that new tree instead of the old one.
For example, a sketch in C++ of the process you describe might look like:
int main(void) {
Tree<string> root;
while (true) {
string next;
cin >> next;
if (next == "quit") exit(0);
root.insert(next);
doSomethingWith(root);
}
}
A variable, a read action, and loop with a mutate step. In haskell, we do the same thing, but using recursion for looping and a recursion variable instead of mutating a local.
main = loop Empty
where loop t = do
next <- getLine
when (next /= "quit") $ do
let t' = insert next t
doSomethingWith t'
loop t'
If you need doSomethingWith to be able to "mutate" t as well as read it, you can lift your program into State:
main = loop Empty
where loop t = do
next <- getLine
when (next /= "quit") $ do
loop (execState doSomethingWith (insert next t))
Writing an example with a BST would take too much time but I give you an analogous example using lists.
Let's invent a updateListN which updates the n-th element in a list.
updateListN :: Int -> a -> [a] -> [a]
updateListN i n l = take (i - 1) l ++ n : drop i l
Now for our program:
list = [1,2,3,4,5,6,7,8,9,10] -- The big data structure we might want to use multiple times
main = do
-- only for shows
print $ updateListN 3 30 list -- [1,2,30,4,5,6,7,8,9,10]
print $ updateListN 8 80 list -- [1,2,3,4,5,6,7,80,9,10]
-- now some illustrative complicated processing
let list' = foldr (\i l -> updateListN i (i*10) l) list list
-- list' = [10,20,30,40,50,60,70,80,90,100]
-- Our crazily complicated illustrative algorithm still needs `list`
print $ zipWith (-) list' list
-- [9,18,27,36,45,54,63,72,81,90]
See how we "updated" list but it was still available? Most data structures in Haskell are persistent, so updates are non-destructive. As long as we have a reference of the old data around we can use it.
As for your comment:
My program is trying the following a) Convert a list to a Binary Search Tree b) do some I/O operation c) Ask for a user input to insert a new value in the created Binary Search Tree d) Insert it into the already created list. This is what the program intends to do. Not sure how to get this done in Haskell (or) is am i stuck in the old mindset. Any ideas/hints welcome.
We can sketch a program:
data BST
readInt :: IO Int; readInt = undefined
toBST :: [Int] -> BST; toBST = undefined
printBST :: BST -> IO (); printBST = undefined
loop :: [Int] -> IO ()
loop list = do
int <- readInt
let newList = int : list
let bst = toBST newList
printBST bst
loop newList
main = loop []
"do balancing" ... "It knows the root" nope. After re-balancing the root is new. The function balance_bst must return the new root.
Same in Haskell, but also with insert_bst. It too will return the new root, and you will use that new root from that point forward.
Even if the new root's value is the same, in Haskell it's a new root, since one of its children has changed.
See ''How to "think functional"'' here.
Even in C++ (or other imperative languages), it would usually be considered a poor idea to have a single global variable holding the root of the binary search tree.
Instead code that needs access to a tree should normally be parameterised on the particular tree it operates on. That's a fancy way of saying: it should be a function/method/procedure that takes the tree as an argument.
So if you're doing that, then it doesn't take much imagination to figure out how several different sections of code (or one section, on several occasions) could get access to different versions of an immutable tree. Instead of passing the same tree to each of these functions (with modifications in between), you just pass a different tree each time.
It's only a little more work to imagine what your code needs to do to "modify" an immutable tree. Obviously you won't produce a new version of the tree by directly mutating it, you'll instead produce a new value (probably by calling methods on the class implementing the tree for you, but if necessary by manually assembling new nodes yourself), and then you'll return it so your caller can pass it on - by returning it to its own caller, by giving it to another function, or even calling you again.
Putting that all together, you can have your whole program manipulate (successive versions of) this binary tree without ever having it stored in a global variable that is "the" tree. An early function (possibly even main) creates the first version of the tree, passes it to the first thing that uses it, gets back a new version of the tree and passes it to the next user, and so on. And each user of the tree can call other subfunctions as needed, with possibly many of new versions of the tree produced internally before it gets returned to the top level.
Note that I haven't actually described any special features of Haskell here. You can do all of this in just about any programming language, including C++. This is what people mean when they say that learning other types of programming makes them better programmers even in imperative languages they already knew. You can see that your habits of thought are drastically more limited than they need to be; you could not imagine how you could deal with a structure "changing" over the course of your program without having a single variable holding a structure that is mutated, when in fact that is just a small part of the tools that even C++ gives you for approaching the problem. If you can only imagine this one way of dealing with it then you'll never notice when other ways would be more helpful.
Haskell also has a variety of tools it can bring to this problem that are less common in imperative languages, such as (but not limited to):
Using the State monad to automate and hide much of the boilerplate of passing around successive versions of the tree.
Function arguments allow a function to be given an unknown "tree-consumer" function, to which it can give a tree, without any one place both having the tree and knowing which function it's passing it to.
Lazy evaluation sometimes negates the need to even have successive versions of the tree; if the modifications are expanding branches of the tree as you discover they are needed (like a move-tree for a game, say), then you could alternatively generate "the whole tree" up front even if it's infinite, and rely on lazy evaluation to limit how much work is done generating the tree to exactly the amount you need to look at.
Haskell does in fact have mutable variables, it just doesn't have functions that can access mutable variables without exposing in their type that they might have side effects. So if you really want to structure your program exactly as you would in C++ you can; it just won't really "feel like" you're writing Haskell, won't help you learn Haskell properly, and won't allow you to benefit from many of the useful features of Haskell's type system.
I want to make a hash of sets. Well, SetHashes, since they need to be mutable.
In fact, I would like to initialize my Hash with multiple identical copies of the same SetHash.
I have an array containing the keys for the new hash: #keys
And I have my SetHash already initialized in a scalar variable: $set
I'm looking for a clean way to initialize the hash.
This works:
my %hash = ({ $_ => $set.clone } for #keys);
(The parens are needed for precedence; without them, the assignment to %hash is part of the body of the for loop. I could change it to a non-postfix for loop or make any of several other minor changes to get the same result in a slightly different way, but that's not what I'm interested in here.)
Instead, I was kind of hoping I could use one of Raku's nifty hyper-operators, maybe like this:
my %hash = #keys »=>» $set;
That expression works a treat when $set is a simple string or number, but a SetHash?
Array >>=>>> SetHash can never work reliably: order of keys in SetHash is indeterminate
Good to know, but I don't want it to hyper over the RHS, in any order. That's why I used the right-pointing version of the hyperop: so it would instead replicate the RHS as needed to match it up to the LHS. In this sort of expression, is there any way to say "Yo, Raku, treat this as a scalar. No, really."?
I tried an explicit Scalar wrapper (which would make the values harder to get at, but it was an experiment):
my %map = #keys »=>» $($set,)
And that got me this message:
Lists on either side of non-dwimmy hyperop of infix:«=>» are not of the same length while recursing
left: 1 elements, right: 4 elements
So it has apparently recursed into the list on the left and found a single key and is trying to map it to a set on the right which has 4 elements. Which is what I want - the key mapped to the set. But instead it's mapping it to the elements of the set, and the hyperoperator is pointing the wrong way for that combination of sizes.
So why is it recursing on the right at all? I thought a Scalar container would prevent that. The documentation says it prevents flattening; how is this recursion not flattening? What's the distinction being drawn?
The error message says the version of the hyperoperator I'm using is "non-dwimmy", which may explain why it's not in fact doing what I mean, but is there maybe an even-less-dwimmy version that lets me be even more explicit? I still haven't gotten my brain aligned well enough with the way Raku works for it to be able to tell WIM reliably.
I'm looking for a clean way to initialize the hash.
One idiomatic option:
my %hash = #keys X=> $set;
See X metaoperator.
The documentation says ... a Scalar container ... prevents flattening; how is this recursion not flattening? What's the distinction being drawn?
A cat is an animal, but an animal is not necessarily a cat. Flattening may act recursively, but some operations that act recursively don't flatten. Recursive flattening stops if it sees a Scalar. But hyperoperation isn't flattening. I get where you're coming from, but this is not the real problem, or at least not a solution.
I had thought that hyperoperation had two tests controlling recursing:
Is it hyperoperating a nodal operation (eg .elems)? If so, just apply it like a parallel shallow map (so don't recurse). (The current doc quite strongly implies that nodal can only be usefully applied to a method, and only a List one (or augmentation thereof) rather than any routine that might get hyperoperated. That is much more restrictive than I was expecting, and I'm sceptical of its truth.)
Otherwise, is a value Iterable? If so, then recurse into that value. In general the value of a Scalar automatically behaves as the value it contains, and that applies here. So Scalars won't help.
A SetHash doesn't do the Iterable role. So I think this refusal to hyperoperate with it is something else.
I just searched the source and that yields two matches in the current Rakudo source, both in the Hyper module, with this one being the specific one we're dealing with:
multi method infix(List:D \left, Associative:D \right) {
die "{left.^name} $.name {right.^name} can never work reliably..."
}
For some reason hyperoperation explicitly rejects use of Associatives on either the right or left when coupled with the other side being a List value.
Having pursued the "blame" (tracking who made what changes) I arrived at the commit "Die on Associative <<op>> Iterable" which says:
This can never work due to the random order of keys in the Associative.
This used to die before, but with a very LTA error about a Pair.new()
not finding a suitable candidate.
Perhaps this behaviour could be refined so that the determining factor is, first, whether an operand does the Iterable role, and then if it does, and is Associative, it dies, but if it isn't, it's accepted as a single item?
A search for "can never work reliably" in GH/rakudo/rakudo issues yields zero matches.
Maybe file an issue? (Update I filed "RFC: Allow use of hyperoperators with an Associative that does not do Iterable role instead of dying with "can never work reliably".)
For now we need to find some other technique to stop a non-Iterable Associative being rejected. Here I use a Capture literal:
my %hash = #keys »=>» \($set);
This yields: {a => \(SetHash.new("b","a","c")), b => \(SetHash.new("b","a","c")), ....
Adding a custom op unwraps en passant:
sub infix:« my=> » ($lhs, $rhs) { $lhs => $rhs[0] }
my %hash = #keys »my=>» \($set);
This yields the desired outcome: {a => SetHash(a b c), b => SetHash(a b c), ....
my %hash = ({ $_ => $set.clone } for #keys);
(The parens seem to be needed so it can tell that the curlies are a block instead of a Hash literal...)
No. That particular code in curlies is a Block regardless of whether it's in parens or not.
More generally, Raku code of the form {...} in term position is almost always a Block.
For an explanation of when a {...} sequence is a Hash, and how to force it to be one, see my answer to the Raku SO Is that a Hash or a Block?.
Without the parens you've written this:
my %hash = { block of code } for #keys
which attempts to iterate #keys, running the code my %hash = { block of code } for each iteration. The code fails because you can't assign a block of code to a hash.
Putting parens around the ({ block of code } for #keys) part completely alters the meaning of the code.
Now it runs the block of code for each iteration. And it concatenates the result of each run into a list of results, each of which is a Pair generated by the code $_ => $set.clone. Then, when the for iteration has completed, that resulting list of pairs is assigned, once, to my %hash.
I am working on fairly large Mathematica projects and the problem arises that I have to intermittently check numerical results but want to easily revert to having all my constructs in analytical form.
The code is fairly fluid I don't want to use scoping constructs everywhere as they add work overhead. Is there an easy way for identifying and clearing all assignments that are numerical?
EDIT: I really do know that scoping is the way to do this correctly ;-). However, for my workflow I am really just looking for a dirty trick to nix all numerical assignments after the fact instead of having the foresight to put down a Block.
If your assignments are on the top level, you can use something like this:
a = 1;
b = c;
d = 3;
e = d + b;
Cases[DownValues[In],
HoldPattern[lhs_ = rhs_?NumericQ] |
HoldPattern[(lhs_ = rhs_?NumericQ;)] :> Unset[lhs],
3]
This will work if you have a sufficient history length $HistoryLength (defaults to infinity). Note however that, in the above example, e was assigned 3+c, and 3 here was not undone. So, the problem is really ambiguous in formulation, because some numbers could make it into definitions. One way to avoid this is to use SetDelayed for assignments, rather than Set.
Another alternative would be to analyze the names in say Global' context (if that is the context where your symbols live), and then say OwnValues and DownValues of the symbols, in a fashion similar to the above, and remove definitions with purely numerical r.h.s.
But IMO neither of these approaches are robust. I'd still use scoping constructs and try to isolate numerics. One possibility is to wrap you final code in Block, and assign numerical values inside this Block. This seems a much cleaner approach. The work overhead is minimal - you just have to remember which symbols you want to assign the values to. Block will automatically ensure that outside it, the symbols will have no definitions.
EDIT
Yet another possibility is to use local rules. For example, one could define rule[a] = a->1; rule[d]=d->3 instead of the assignments above. You could then apply these rules, extracting them as say
DownValues[rule][[All, 2]], whenever you want to test with some numerical arguments.
Building on Andrew Moylan's solution, one can construct a Block like function that would takes rules:
SetAttributes[BlockRules, HoldRest]
BlockRules[rules_, expr_] :=
Block ## Append[Apply[Set, Hold#rules, {2}], Unevaluated[expr]]
You can then save your numeric rules in a variable, and use BlockRules[ savedrules, code ], or even define a function that would apply a fixed set of rules, kind of like so:
In[76]:= NumericCheck =
Function[body, BlockRules[{a -> 3, b -> 2`}, body], HoldAll];
In[78]:= a + b // NumericCheck
Out[78]= 5.
EDIT In response to Timo's comment, it might be possible to use NotebookEvaluate (new in 8) to achieve the requested effect.
SetAttributes[BlockRules, HoldRest]
BlockRules[rules_, expr_] :=
Block ## Append[Apply[Set, Hold#rules, {2}], Unevaluated[expr]]
nb = CreateDocument[{ExpressionCell[
Defer[Plot[Sin[a x], {x, 0, 2 Pi}]], "Input"],
ExpressionCell[Defer[Integrate[Sin[a x^2], {x, 0, 2 Pi}]],
"Input"]}];
BlockRules[{a -> 4}, NotebookEvaluate[nb, InsertResults -> "True"];]
As the result of this evaluation you get a notebook with your commands evaluated when a was locally set to 4. In order to take it further, you would have to take the notebook
with your code, open a new notebook, evaluate Notebooks[] to identify the notebook of interest and then do :
BlockRules[variablerules,
NotebookEvaluate[NotebookPut[NotebookGet[nbobj]],
InsertResults -> "True"]]
I hope you can make this idea work.
Kindly correct me if my understanding about DLR operation resolving methodology is wrong
DLR has something call as call site caching. Given "a+b" (where a and b are both integers), the first time it will not be able to understand the +(plus) operator as wellas the operand. So it will resolve the operands and then the operators and will cache in level 1 after building a proper rule.
Next time onwards, if a similar kind of match is found (where the operand are of type integers and operator is of type +), it will look into the cache and will return the result and henceforth the performance will be enhanced.
If this understanding of mine is correct(if you fell that I am incorrect in the basic understanding kindly rectify that), I have the below questions for which I am looking answers
a) What kind of operations happen in level 1 , level 2
and level 3 call site caching in general
( i mean plus, minus..what kind)
b) DLR caches those results. Where it stores..i mean the cache location
c) How it makes the rules.. any algorithm.. if so could you please specify(any link or your own answer)
d) Sometime back i read somewhere that in level 1 caching , DLR has 10 rules,
level 2 it has 20 or something(may be) while level 3 has 100.
If a new operation comes, then I makes a new rule. Are these rules(level 1 , 2 ,3) predefined?
e) Where these rules are kept?
f) Instead of passing two integers (a and b in the example),
if we pass two strings or one string and one integer,
whether it will form a new rule?
Thanks
a) If I understand this correctly currently all of the levels work effectively the same - they run a delegate which tests the arguments which matter for the rule and then either performs an operation or notes the failure (the failure is noted by either setting a value on the call site or doing a tail call to the update method). Therefore every rule works like:
public object Rule(object x, object y) {
if(x is int && y is int) {
return (int)x + (int)y;
}
CallSiteOps.SetNotMatched(site);
return null;
}
And a delegate to this method is used in the L0, L1, and L2 caches. But the behavior here could change (and did change many times during development). For example at one point in time the L2 cache was a tree based upon the type of the arguments.
b) The L0 cache and the L1 caches are stored on the CallSite object. The L0 cache is a single delegate which is always the 1st thing to run. Initially this is set to a delegate which just updates the site for the 1st time. Additional calls attempt to perform the last operation the call site saw.
The L1 cache includes the last 10 actions the call site saw. If the L0 cache fails all the delegates in the L1 cache will be tried.
The L2 cache lives on the CallSiteBinder. The CallSiteBinder should be shared across multiple call sites. For example there should generally be one and only one additiona call site binder for the language assuming all additions are the same. If the L0 and L1 all of the available rules in the L2 cache will be searched. Currently the upper limit for the L2 cache is 128.
c) Rules can ultimately be produced in 2 ways. The general solution is to create a DynamicMetaObject which includes both the expression tree of the operation to be performed as well as the restrictions (the test) which determines if it's applicable. This is something like:
public DynamicMetaObject FallbackBinaryOperation(DynamicMetaObject target, DynamicMetaObject arg) {
return new DynamicMetaObject(
Expression.Add(Expression.Convert(target, typeof(int)), Expression.Convert(arg, typeof(int))),
BindingRestrictions.GetTypeRestriction(target, typeof(int)).Merge(
BindingRestrictions.GetTypeRestriction(arg, typeof(int))
)
);
}
This creates the rule which adds two integers. This isn't really correct because if you get something other than integers it'll loop infinitely - so usually you do a bunch of type checks, produce the addition for the things you can handle, and then produce an error if you can't handle the addition.
The other way to make a rule is provide the delegate directly. This is more advanced and enables some advanced optimizations to avoid compiling code all of the time. To do this you override BindDelegate on CallSiteBinder, inspect the arguments, and provide a delegate which looks like the Rule method I typed above.
d) Answered in b)
e) I believe this is the same question as b)
f) If you put the appropriate restrictions on then yes (which you're forced to do for the standard binders). Because the restrictions will fail because you don't have two ints the DLR will probe the caches and when it doesn't find a rule it'll call the binder to produce a new rule. That new rule will come back w/ a new set of restrictions, will be installed in the L0, L1, and L2 caches, and then will perform the operation for strings from then on out.