I've written a little recursive function, however, if i include the recursive bit in the let block it throws an unsuported binding error.
The code:
(defn total-weight [parcel cost]
"Calculate the total route cost"
(if (not(empty? parcel))
(let [ first-parcel (first parcel)
weight (:weight first-parcel)
parcel-two (rest parcel)
(total-weight parcel-two (+ cost weight))
cost])))
(total-weight task5 0)
The error:
CompilerException java.lang.Exception: Unsupported binding form: (total-weight parcel-two (+ cost weight)), compiling:(/private/var/folders/2h/7b4v1ls11mjf_n5hb6mwngl40000gn/T/form-init2186446380943426996.clj:4:6)
Any ideas?
While this is a fine exercise to practice recursive thinking, note as an aside that the "best" way to implement this as an experienced Clojure programmer would be:
(apply + (map :weight task5))
So simple it doesn't even need a function defined for it.
Your function should be like this:
(defn total-weight [parcel cost] "Calculate the total route cost"
(if (not (empty? parcel))
(let [first-parcel (first parcel)
weight (:weight first-parcel)
parcel-two (rest parcel)]
(total-weight parcel-two (+ cost weight))
cost)))
let in bind should be like
(let [x1 x2] (print x1))
your ] is in the wrong place.
Related
I am trying to compare the performance of a function and a macro.
EDIT: Why do I want to compare the two?
Paul Graham wrote in his ON LISP book that macros can be used to make a system more efficient because a lot of the computation can be done at compile time. so in the example below (length args) is dealt with at compile time in the macro case and at run time in the function case. So, I just wanted how much faster did (avg2 super-list) get computed relative to (avg super-list).
Here is the function and the macro:
(defun avg (args)
(/ (apply #'+ args) (length args)))
(defmacro avg2 (args)
`(/ (+ ,#args) ,(length args)))
I have looked at this question How to pass a list to macro in common lisp? and a few other ones but they do not help because their solutions do not work; for example, in one of the questions a user answered by saying to do this:
(avg2 (2 3 4 5))
instead of this:
(avg2 '(2 3 4))
This works but I want a list containg 100,000 items:
(defvar super-list (loop for i from 1 to 100000 collect i))
But this doesnt work.
So, how can I pass super-list to avg2?
First of all, it simply makes no sense to 'compare the performance of a function and a macro'. It only makes sense to compare the performance of the expansion of a macro with a function. So that's what I'll do.
Secondly, it only makes sense to compare the performance of a function with the expansion of a macro if that macro is equivalent to the function. In other words the only places this comparison is useful is where the macro is being used as a hacky way of inlining a function. It doesn't make sense to compare the performance of something which a function can't express, like if or and say. So we must rule out all the interesting uses of macros.
Thirdly it makes no sense to compare the performance of things which are broken: it is very easy to make programs which do not work be as fast as you like. So I'll successively modify both your function and macro so they're not broken.
Fourthly it makes no sense to compare the performance of things which use algorithms which are gratuitously terrible, so I'll modify both your function and your macro to use better algrorithms.
Finally it makes no sense to compare the performance of things without using the tools the language provides to encourage good performance, so I will do that as the last step.
So let's address the third point above: let's see how avg (and therefore avg2) is broken.
Here's the broken definition of avg from the question:
(defun avg (args)
(/ (apply #'+ args) (length args)))
So let's try it:
> (let ((l (make-list 1000000 :initial-element 0)))
(avg l))
Error: Last argument to apply is too long: 1000000
Oh dear, as other people have pointed out. So probably I need instead to make avg at least work. As other people have, again, pointed out, the way to do this is reduce:
(defun avg (args)
(/ (reduce #'+ args) (length args)))
And now a call to avg works, at least. avg is now non-buggy.
We need to make avg2 non-buggy as well. Well, first of all the (+ ,#args) thing is a non-starter: args is a symbol at macroexpansion time, not a list. So we could try this (apply #'+ ,args) (the expansion of the macro is now starting to look a bit like the body of the function, which is unsurprising!). So given
(defmacro avg2 (args)
`(/ (apply #'+ ,args) (length ,args)))
We get
> (let ((l (make-list 1000000 :initial-element 0)))
(avg2 l))
Error: Last argument to apply is too long: 1000000
OK, unsurprising again. let's fix it to use reduce again:
(defmacro avg2 (args)
`(/ (reduce #'+ ,args) (length ,args)))
So now it 'works'. Except it doesn't: it's not safe. Look at this:
> (macroexpand-1 '(avg2 (make-list 1000000 :initial-element 0)))
(/ (reduce #'+ (make-list 1000000 :initial-element 0))
(length (make-list 1000000 :initial-element 0)))
t
That definitely is not right: it will be enormously slow but also it will just be buggy. We need to fix the multiple-evaluation problem.
(defmacro avg2 (args)
`(let ((r ,args))
(/ (reduce #'+ r) (length r))))
This is safe in all sane cases. So this is now a reasonably safe 70s-style what-I-really-want-is-an-inline-function macro.
So, let's write a test-harness both for avg and avg2. You will need to recompile av2 each time you change avg2 and in fact you'll need to recompile av1 for a change we're going to make to avg as well. Also make sure everything is compiled!
(defun av0 (l)
l)
(defun av1 (l)
(avg l))
(defun av2 (l)
(avg2 l))
(defun test-avg-avg2 (nelements niters)
;; Return time per call in seconds per iteration per element
(let* ((l (make-list nelements :initial-element 0))
(lo (let ((start (get-internal-real-time)))
(dotimes (i niters (- (get-internal-real-time) start))
(av0 l)))))
(values
(let ((start (get-internal-real-time)))
(dotimes (i niters (float (/ (- (get-internal-real-time) start lo)
internal-time-units-per-second
nelements niters)))
(av1 l)))
(let ((start (get-internal-real-time)))
(dotimes (i niters (float (/ (- (get-internal-real-time) start lo)
internal-time-units-per-second
nelements niters)))
(av2 l))))))
So now we can test various combinations.
OK, so now the fouth point: both avg and avg2 use awful algorithms: they traverse the list twice. Well we can fix this:
(defun avg (args)
(loop for i in args
for c upfrom 0
summing i into s
finally (return (/ s c))))
and similarly
(defmacro avg2 (args)
`(loop for i in ,args
for c upfrom 0
summing i into s
finally (return (/ s c))))
These changes made a performance difference of about a factor of 4 for me.
OK so now the final point: we should use the tools the language gives us. As has been clear throughout this whole exercise only make sense if you're using a macro as a poor-person's inline function, as people had to do in the 1970s.
But it's not the 1970s any more: we have inline functions.
So:
(declaim (inline avg))
(defun avg (args)
(loop for i in args
for c upfrom 0
summing i into s
finally (return (/ s c))))
And now you will have to make sure you recompile avg and then av1. And when I look at av1 and av2 I can now see that they are the same code: the entire purpose of avg2 has now gone.
Indeed we can do even better than this:
(define-compiler-macro avg (&whole form l &environment e)
;; I can't imagine what other constant forms there might be in this
;; context, but, well, let's be safe
(if (and (constantp l e)
(listp l)
(eql (first l) 'quote))
(avg (second l))
form))
Now we have something which:
has the semantics of a function, so, say (funcall #'avg ...) will work;
isn't broken;
uses a non-terrible algorithm;
will be inlined on any competent implementation of the language (which I bet is 'all implementations' now) when it can be;
will detect (some?) cases where it can be compiled completely away and replaced by a compile-time constant.
Since the value of super-list is known, one can do all computation at macro expansion time:
(eval-when (:execute :compile-toplevel :load-toplevel)
(defvar super-list (loop for i from 1 to 100000 collect i)))
(defmacro avg2 (args)
(setf args (eval args))
(/ (reduce #'+ args) (length args)))
(defun test ()
(avg2 super-list))
Trying the compiled code:
CL-USER 10 > (time (test))
Timing the evaluation of (TEST)
User time = 0.000
System time = 0.000
Elapsed time = 0.000
Allocation = 0 bytes
0 Page faults
100001/2
Thus the runtime is near zero.
The generated code is just a number, the result number:
CL-USER 11 > (macroexpand '(avg2 super-list))
100001/2
Thus for known input this macro call in compiled code has a constant runtime of near zero.
I don't think you really want a list of 100,000 items. That would have terrible performance with all that cons'ing. You should consider a vector instead, e.g.
(avg2 #(2 3 4))
You didn't mention why it didn't work; if the function never returns, it's likely a memory issue from such a large list, or attempting to apply on such a large function argument list; there are implementation defined limits on how many arguments you can pass to a function.
Try reduce on a super-vector instead:
(reduce #'+ super-vector)
Hi i am looking for a bit of help with some Clojure code. I have written a function that will take in a list and calculate the qty*price for a list eg. '(pid3 6 9)
What i am looking for is to expand my current function so that it recursively does the qty*price calculation until it reaches the end of the list.
My current function is written like this:
(defn pid-calc [list] (* (nth list 1) (nth list 2)))
I have tried implementing it into a recursive function but have had no luck at all, i want to be able to call something like this:
(pid-calcc '( (pid1 8 5) (pid2 5 6))
return==> 70
Thats as close as i have came to an answer and cannot seem to find one. If anyone can help me find a solution i that would be great. As so far i am yet to find anything that will compile.
(defn pid-calc [list]
(if(empty? list)
nil
(* (nth list 1) (nth list 2)(+(pid-calc (rest list))))))
You don't need a recursive function. Just use + and map:
(defn pid-calc [list]
(letfn [(mul [[_ a b]] (* a b))]
(apply + (map mul list))))
#sloth's answer, suitably corrected, is a concise and fast enough way to solve your problem. It shows you a lot.
Your attempt at a recursive solution can be (a)mended to
(defn pid-calc [list]
(if (empty? list)
0
(let [x (first list)]
(+ (* (nth x 1) (nth x 2)) (pid-calc (next list))))))
This works on the example, but - being properly recursive - will run out of stack space on a long enough list. The limit is usually about 10K items.
We can get over this without being so concise as #sloth. You might find the following easier to understand:
(defn pid-calc [list]
(let [line-total (fn [item] (* (nth item 1) (nth item 2)))]
(apply + (map line-total list))))
reduce fits your scenario quite well:
(def your-list [[:x 1 2] [:x 1 3]])
(reduce #(+ %1 (* (nth %2 1) (nth %2 2))) 0 your-list)
(reduce #(+ %1 (let [[_ a b] %2] (* a b)) 0 your-list)
According to this document: http://cl-cookbook.sourceforge.net/functions.html
(defun adder (n)
(lambda (x) (+ x n)))
(funcall (adder 12) 1)
I have to use funcall to call (adder 12), And it is very ignoring to write funcall over and over, is there any way to write code like it in scheme:
((adder 12) 1)
No. There is none.
You can also see it as a feature: it makes calls of function objects explicit and improves understandability of source code.
However, you could use something like this (not sure why would you, but the number of characters typed would be the same as it is in Scheme):
(set-macro-character
#\[
#'(lambda (stream char)
(declare (ignore char))
(set-syntax-from-char #\] #\;)
(let ((forms (read-delimited-list #\] stream t)))
(set-syntax-from-char #\] #\x)
(append '(funcall) forms))))
(defun adder (n)
#'(lambda (x) (+ x n)))
(format t "sum: ~s~&" [(adder 12) #x128]) ;; 308
This may give you some problems if you will encounter a variable name with brackets in it. Sure, using it is up to you, consider yourself warned.
To set some context, I'm in the process of learning Clojure, and Lisp development more generally. On my path to Lisp, I'm currently working through the "Little" series in an effort to solidify a foundation in functional programming and recursive-based solution solving. In "The Little Schemer," I've worked through many of the exercises, however, I'm struggling a bit to convert some of them to Clojure. More specifically, I'm struggling to convert them to use "recur" so as to enable TCO. For example, here is a Clojure-based implementation to the "occurs*" function (from Little Schemer) which counts the number of occurrences of an atom appearing within a list of S-expressions:
(defn atom? [l]
(not (list? l)))
(defn occurs [a lst]
(cond
(empty? lst) 0
(atom? (first lst))
(cond
(= a (first lst)) (inc (occurs a (rest lst)))
true (occurs a (rest lst)))
true (+ (occurs a (first lst))
(occurs a (rest lst)))))
Basically, (occurs 'abc '(abc (def abc) (abc (abc def) (def (((((abc))))))))) will evaluate to 5. The obvious problem is that this definition consumes stack frames and will blow the stack if given a list of S-expressions too deep.
Now, I understand the option of refactoring recursive functions to use an accumulator parameter to enable putting the recursive call into the tail position (to allow for TCO), but I'm struggling if this option is even applicable to situations such as this one.
Here's how far I get if I try to refactor this using "recur" along with using an accumulator parameter:
(defn recur-occurs [a lst]
(letfn [(myoccurs [a lst count]
(cond
(empty? lst) 0
(atom? (first lst))
(cond
(= a (first lst)) (recur a (rest lst) (inc count))
true (recur a (rest lst) count))
true (+ (recur a (first lst) count)
(recur a (rest lst) count))))]
(myoccurs a lst 0)))
So, I feel like I'm almost there, but not quite. The obvious problem is my "else" clause in which the head of the list is not an atom. Conceptually, I want to sum the result of recurring over the first element in the list with the result of recurring over the rest of the list. I'm struggling in my head on how to refactor this such that the recurs can be moved to the tail position.
Are there additional techniques to the "accumulator" pattern to achieving getting your recursive calls put into the tail position that I should be applying here, or, is the issue simply more "fundamental" and that there isn't a clean Clojure-based solution due to the JVM's lack of TCO? If the latter, generally speaking, what should be the general pattern for Clojure programs to use that need to recur over a list of S-expressions? For what it's worth, I've seen the multi method w/lazy-seq technique used (page 151 of Halloway's "Programming Clojure" for reference) to "Replace Recursion with Laziness" - but I'm not sure how to apply that pattern to this example in which I'm not attempting to build a list, but to compute a single integer value.
Thank you in advance for any guidance on this.
Firstly, I must advise you to not worry much about implementation snags like stack overflows as you make your way through The Little Schemer. It is good to be conscientious of issues like the lack of tail call optimization when you're programming in anger, but the main point of the book is to teach you to think recursively. Converting the examples accumulator-passing style is certainly good practice, but it's essentially ditching recursion in favor of iteration.
However, and I must preface this with a spoiler warning, there is a way to keep the same recursive algorithm without being subject to the whims of the JVM stack. We can use continuation-passing style to make our own stack in the form of an extra anonymous function argument k:
(defn occurs-cps [a lst k]
(cond
(empty? lst) (k 0)
(atom? (first lst))
(cond
(= a (first lst)) (occurs-cps a (rest lst)
(fn [v] (k (inc v))))
:else (occurs-cps a (rest lst) k))
:else (occurs-cps a (first lst)
(fn [fst]
(occurs-cps a (rest lst)
(fn [rst] (k (+ fst rst))))))))
Instead of the stack being created implicitly by our non-tail function calls, we bundle up "what's left to do" after each call to occurs, and pass it along as the next continuation k. When we invoke it, we start off with a k that represents nothing left to do, the identity function:
scratch.core=> (occurs-cps 'abc
'(abc (def abc) (abc (abc def) (def (((((abc))))))))
(fn [v] v))
5
I won't go further into the details of how to do CPS, as that's for a later chapter of TLS. However, I will note that this of course doesn't yet work completely:
scratch.core=> (def ls (repeat 20000 'foo))
#'scratch.core/ls
scratch.core=> (occurs-cps 'foo ls (fn [v] v))
java.lang.StackOverflowError (NO_SOURCE_FILE:0)
CPS lets us move all of our non-trivial, stack-building calls to tail position, but in Clojure we need to take the extra step of replacing them with recur:
(defn occurs-cps-recur [a lst k]
(cond
(empty? lst) (k 0)
(atom? (first lst))
(cond
(= a (first lst)) (recur a (rest lst)
(fn [v] (k (inc v))))
:else (recur a (rest lst) k))
:else (recur a (first lst)
(fn [fst]
(recur a (rest lst) ;; Problem
(fn [rst] (k (+ fst rst))))))))
Alas, this goes wrong: java.lang.IllegalArgumentException: Mismatched argument count to recur, expected: 1 args, got: 3 (core.clj:39). The very last recur actually refers to the fn right above it, the one we're using to represent our continuations! We can get good behavior most of the time by changing just that recur to a call to occurs-cps-recur, but pathologically-nested input will still overflow the stack:
scratch.core=> (occurs-cps-recur 'foo ls (fn [v] v))
20000
scratch.core=> (def nested (reduce (fn [onion _] (list onion))
'foo (range 20000)))
#'scratch.core/nested
scratch.core=> (occurs-cps-recur 'foo nested (fn [v] v))
Java.lang.StackOverflowError (NO_SOURCE_FILE:0)
Instead of making the call to occurs-* and expecting it to give back an answer, we can have it return a thunk immediately. When we invoke that thunk, it'll go off and do some work right up until it does a recursive call, which in turn will return another thunk. This is trampolined style, and the function that "bounces" our thunks is trampoline. Returning a thunk each time we make a recursive call bounds our stack size to one call at a time, so our only limit is the heap:
(defn occurs-cps-tramp [a lst k]
(fn []
(cond
(empty? lst) (k 0)
(atom? (first lst))
(cond
(= a (first lst)) (occurs-cps-tramp a (rest lst)
(fn [v] (k (inc v))))
:else (occurs-cps-tramp a (rest lst) k))
:else (occurs-cps-tramp a (first lst)
(fn [fst]
(occurs-cps-tramp a (rest lst)
(fn [rst] (k (+ fst rst)))))))))
(declare done answer)
(defn my-trampoline [th]
(if done
answer
(recur (th))))
(defn empty-k [v]
(set! answer v)
(set! done true))
(defn run []
(binding [done false answer 'whocares]
(my-trampoline (occurs-cps-tramp 'foo nested empty-k))))
;; scratch.core=> (run)
;; 1
Note that Clojure has a built-in trampoline (with some limitations on the return type). Using that instead, we don't need a specialized empty-k:
scratch.core=> (trampoline (occurs-cps-tramp 'foo nested (fn [v] v)))
1
Trampolining is certainly a cool technique, but the prerequisite to trampoline a program is that it must contain only tail calls; CPS is the real star here. It lets you define your algorithm with the clarity of natural recursion, and through correctness-preserving transformations, express it efficiently on any host that has a single loop and a heap.
You can't do this with a fixed amount of memory. You can consume stack, or heap; that's the decision you get to make. If I were writing this in Clojure I would do it with map and reduce rather than with manual recursion:
(defn occurs [x coll]
(if (coll? coll)
(reduce + (map #(occurs x %) coll))
(if (= x coll)
1, 0)))
Note that shorter solutions exist if you use tree-seq or flatten, but at that point most of the problem is gone so there's not much to learn.
Edit
Here's a version that doesn't use any stack, instead letting its queue get larger and larger (using up heap).
(defn heap-occurs [item coll]
(loop [count 0, queue coll]
(if-let [[x & xs] (seq queue)]
(if (coll? x)
(recur count (concat x xs))
(recur (+ (if (= item x) 1, 0)
count)
xs))
count)))
I'm new to functional languages and clojure, so please bear with me...
I'm trying to construct a list of functions, with either random parameters or constants. The function that constructs the list of functions is already working, though it doesn't return the function itself. I verified this using println.
(edit: Okay, it isn't working correctly yet after all)
(edit: Now it's working, but it cannot be "eval"-ed. it seems I need to recur at least two times, to ensure there are at least two children nodes. Is this possible?)
Here is the snippet:
(def operations (list #(- %1 %2) #(+ %1 %2) #(* %1 %2) #(/ %1 %2)))
(def parameters (list \u \v \w \x \y \z))
(def parameterlistcount 6)
(def paramcount 2)
(def opcount 4)
(defn generate-function
([] (generate-function 2 4 0.5 0.6 () parameters))
([pc maxdepth fp pp function-list params]
(if (and (pos? maxdepth) (< (rand) fp))
(let [function-list
(conj function-list
(nth operations
(rand-int (count operations))))]
(recur pc (dec maxdepth) fp pp function-list params))
(if (and (< (rand) pp) (pos? pc))
(let [ params (pop parameters)
function-list
(conj function-list
(nth params
(rand-int (count params))))]
(if (contains? (set operations) (last function-list) )
(recur (dec pc) maxdepth fp pp function-list params)
nil))
(let [function-list
(conj function-list
(rand-int 100))]
(if (or (pos? maxdepth) (pos? pc))
(if (contains? (set operations) (last function-list) )
(recur pc maxdepth fp pp function-list params)
nil)
function-list))))))
Any help will be appreciated, thanks!
Here's my shot at rewriting your function (see comments below):
(defn generate-function
([] (generate-function 2 4 0.5 0.6 ()))
([pc maxdepth fp pp function-list]
(if (and (pos? maxdepth) (< (rand) fp))
(let [function-list
(conj function-list
{:op
(nth operations
(rand-int (count operations)))})]
(recur pc (dec maxdepth) fp pp function-list))
(if (and (< (rand) pp) (pos? pc))
(let [function-list
(conj function-list
{:param
(nth parameters
(rand-int (count parameters)))})]
(recur (dec pc) maxdepth fp pp function-list))
(let [function-list
(conj function-list
{:const
(rand-int 100)})]
(if (or (pos? maxdepth) (pos? pc))
(recur pc maxdepth fp pp function-list)
function-list))))))
And some examples of use from my REPL...
user> (generate-function)
({:const 63} {:op #<user$fn__4557 user$fn__4557#6cbb2d>} {:const 77} {:param \w} {:op #<user$fn__4559 user$fn__4559#8e68bd>} {:const 3} {:param \v} {:const 1} {:const 8} {:op #<user$fn__4559 user$fn__4559#8e68bd>} {:op #<user$fn__4555 user$fn__4555#6f0962>})
user> (generate-function)
({:const 27} {:param \y} {:param \v} {:op #<user$fn__4561 user$fn__4561#10463c3>} {:op #<user$fn__4561 user$fn__4561#10463c3>} {:op #<user$fn__4561 user$fn__4561#10463c3>} {:op #<user$fn__4561 user$fn__4561#10463c3>} {:const 61})
A couple of things to keep in mind, in pretty random order:
I used recur in the above to avoid consuming stack in the recursive self-calls. However, you have this dotimes statement which makes me wonder if you might be interested in constructing a bunch of function-lists in parallel with one generate-function call. If so, tail-recursion with recur might not be an option with simplistic code like this, so you could either settle for the regular self-calls (but do consider the possibility of hitting the recursion limit; if you're positive that you'll only generate smallish functions and this won't be a problem, go ahead with the self-calls) or investigate continuation-passing style and rewrite your function in that style.
The (do (dec pc) ...) thing in your code does nothing to the value of pc in the next recursive call, or indeed to its current value. Local variables (or locals, as they are most often called in the community) in Clojure are immutable; this includes function parameters. If you want to pass along a decremented pc to some function, you'll have to do just that, like you did with maxdepth in an earlier branch of your code.
I renamed your function to generate-function, because camel case in function names is quite unusual in Clojure land. Also, I renamed the parameter which you called function to function-list (so maybe I should have used a name like generate-function-list for the function... hm), because that's what it is for now.
Note that there's no point to keeping a separate opcount Var around; Clojure's persistent lists (as created by the list function) carry their count around, so (count some-list) is a constant-time operation (and very fast). Also, it would be idiomatic to use vectors for operations and parameters (and you can switch to vectors without changing anything in the rest of the code!). E.g. [\u \v \w \x \y \z].
In Clojure 1.2, you'll be able to use (rand-nth coll) for (nth coll (rand-int (count coll))).
If you want to generate actual Clojure functions from trees of items representing ops, params and constants, you'll want to use eval. That's discouraged in most scenarios, but not for evolutionary programming and similar stuff where it's the only way to go.
Personally, I'd use a different format of the op/param/constant maps: something like {:category foo, :content bar} where foo is :op, :param or :const and bar is something appropriate in connection to any given foo.
In general it is a better idea in Clojure to use (recur ...) for your recursive functions. From the docs: "Note that recur is the only non-stack-consuming looping construct in Clojure." link
One other thing to note is that you might want to call the randomizer outside the recursive function, so you can define the stop-condition inside the function.
So like this:
(let [n (rand-int 10)]
(println "Let's define f with n =" n)
(defn f [x]
(if (> x n)
"result"
(do (println x)
(recur (inc x))))))
It prints:
Let's define f with n = 4
user> (f 0)
0
1
2
3
4
"result"
where 4 is of course a random number between 0 (inclusive) and 10 (exclusive).
So okay, I discovered I was going about this the wrong way.
A recursive definition of a tree is non other than defining vertices, and trying to tie everything with it. So, I came up with this, in less than 15 minutes. >_<
(defn generate-branch
"Generate branches for tree"
([] (generate-branch 0.6 () (list \x \y \z)))
([pp branch params]
(loop [branch
(conj branch (nth operations (rand-int (count operations))))]
(if (= (count branch) 3)
branch
(if (and (< (rand) pp))
(recur (conj branch (nth params (rand-int (count params)))))
(recur (conj branch (rand-int 100))))))))
(defn build-vertex
"Generates a vertex from branches"
[]
(let [vertex (list (nth operations (rand-int (count operations))))]
(conj vertex (take 5 (repeatedly generate-branch)))))
THANKS EVERYONE!