Clojure: Multiple tasks in if - if-statement

As I've come to know it, the form of an if is (if [condition] [true] [false]). Similarly, cond is (cond [condition] [true] ... [condition] [true] [false]). Each true and false segment seems to only accept one action. If I want to represent the following logic:
if (i > 0)
{
a += 5;
b += 10;
}
I think I have to do:
(if (> i 0) (def a (+ a 5)))
(if (> i 0) (def b (+ b 10)))
Just so the second action isn't confused as a false result. Is this how it needs to be, or is there a way to create a larger body for an if?
p.s. I also suspect redefining a and b each time isn't the best way to increment, but also haven't seen a different way of doing that. I've had to also redefine lists when using conj.

The most direct transaction, using atoms instead of vars (def), would be
;; assuming something like (def a (atom 0)) (def b (atom 0))
(if (> i 0)
(do
(swap! a + 5)
(swap! b + 10)))
or
(when (> i 0)
(swap! a + 5)
(swap! b + 10))

You are looking through the wrong end of the telescope. Bear in mind that
Clojure has no local variables.
Clojure has no actions (usually called statements), only expressions returning a value.
There is no Clojure equivalent of the statement a += 5;.
However, expressions can have side effects: print and the like accomplish nothing else. The do form allows you to accomplish a series of side effects before returning a final value. For example,
(do (print a) (print b) (+ a b))
prints a,
prints b,
returns their sum.
That's why, as you write of the if form ...
Each true and false segment seems to only accept one action.
What Clojure does have is
local name bindings, with the let form and
a derived version of let called loop that implements a primitive
form of recursion that can replace simple uses of loops in
languages like C or Java.
Between them, let and its offspring loop allow you to write most simple control structures. to determine if this applies to your program fragment ...
if (i > 0)
{
a += 5;
b += 10;
}
... we'd have to see the context.
However, here's a greatest common divisor function in C (untested)
long gcd (long i, long j)
{
long m = i, n = j;
while (n != 0)
{
long t = n;
n = m % n;
m = t;
}
}
and in Clojure
(defn gcd [i j]
(loop [m i, n j]
(if (zero? n)
m
(recur n (mod m n)))))
Both of these can be abbreviated.

The other answer covered the explicit question about having more than one expression in the if branch (using do or by using when if there is no else branch as when wraps its nested expressions implicit do).
However, there is another issue in the question with using state which is usually local to the function. I don't think an atom stored in a global var is the best way to handle that, and as Clojure programs tend to minimise global state it's usually better to keep the state local.
We use let to define the local state and narrow its scope (NB it also supports destructuring):
(let [i 0
a 5
b 10]
(println i)
(println a)
(println b))
let assigns a value to a local variable and it cannot be redefined. If we need to update local state we can use recursion by calling recur directly on the function or by using loop and recur.
For example:
(defn plus [a b]
(if (> b 0)
(do
(println "Recurring...")
(recur (inc a) (dec b)))
(do
(println "Returning result")
a)))
Or:
(defn plus [a b]
(loop [a a
b b]
(if (> b 0)
(do
(println "Recurring...")
(recur (inc a) (dec b)))
(do
(println "Returning result")
a))))

Related

Clojure translate from Java

I'm starting to learn Clojure and have decided that doing some projects on HackerRank is a good way to do that. What I'm finding is that my Clojure solutions are horribly slow. I'm assuming that's because I'm still thinking imperatively or just don't know enough about how Clojure operates. The latest problem I wrote solutions for was Down To Zero II. Here's my Java code
import java.io.BufferedReader;
import java.io.InputStreamReader;
public class Solution {
private static final int MAX_NUMBER = 1000000;
private static final BufferedReader reader = new BufferedReader(new InputStreamReader(System.in));
public static int[] precompute() {
int[] values = new int[MAX_NUMBER];
values[0] = 0;
values[1] = 1;
for (int i = 1; i < MAX_NUMBER; i += 1) {
if ((values[i] == 0) || (values[i] > (values[i - 1] + 1))) {
values[i] = (values[i - 1] + 1);
}
for (int j = 1; j <= i && (i * j) < MAX_NUMBER; j += 1) {
int mult = i * j;
if ((values[mult] == 0) || (values[mult] > (values[i] + 1))) {
values[mult] = values[i] + 1;
}
}
}
return values;
}
public static void main(String[] args) throws Exception {
int numQueries = Integer.parseInt(reader.readLine());
int[] values = Solution.precompute();
for (int loop = 0; loop < numQueries; loop += 1) {
int query = Integer.parseInt(reader.readLine());
System.out.println(values[query]);
}
}
}
My Clojure implementation is
(def MAX-NUMBER 1000000)
(defn set-i [out i]
(cond
(= 0 i) (assoc out i 0)
(= 1 i) (assoc out i 1)
(or (= 0 (out i))
(> (out i) (inc (out (dec i)))))
(assoc out i (inc (out (dec i))))
:else out))
(defn set-j [out i j]
(let [mult (* i j)]
(if (or (= 0 (out mult)) (> (out mult) (inc (out i))))
(assoc out mult (inc (out i)))
out)))
;--------------------------------------------------
; Precompute the values for all possible inputs
;--------------------------------------------------
(defn precompute []
(loop [i 0 out (vec (repeat MAX-NUMBER 0))]
(if (< i MAX-NUMBER)
(recur (inc i) (loop [j 1 new-out (set-i out i)]
(if (and (<= j i) (< (* i j) MAX-NUMBER))
(recur (inc j) (set-j new-out i j))
new-out)))
out)))
;--------------------------------------------------
; Read the number of queries
;--------------------------------------------------
(def num-queries (Integer/parseInt (read-line)))
;--------------------------------------------------
; Precompute the solutions
;--------------------------------------------------
(def values (precompute))
;--------------------------------------------------
; Read and process each query
;--------------------------------------------------
(loop [iter 0]
(if (< iter num-queries)
(do
(println (values (Integer/parseInt (read-line))))
(recur (inc iter)))))
The Java code runs in about 1/10 of a second on my machine, while the Clojure code takes close to 2 seconds. Since it's the same machine, with the same JVM, it means I'm doing something wrong in Clojure.
How do people go about trying to translate this type of code? What are the gotchas that are causing it to be so much slower?
I'm going to do some transformations to your code (which might be slightly outside of what you were originally asking)
and then address your more specific questions.
I know it's almost two years later, but after running across your question and spending way too much time fighting with
HackerRank and its time limits, I thought I would post an answer. Does achieving a solution within HR's environment and
time limits make us better Clojure programmers? I didn't learn the answer to that. But I'll share what I did learn.
I found a slightly slimmer version of your same algorithm. It still has two loops, but the update only happens once in
the inner loop, and many of the conditions are handled in a min function. Here is my adaptation of it:
(defn compute
"Returns a vector of down-to-zero counts for all numbers from 0 to m."
[m]
(loop [i 2 out (vec (range (inc m)))]
(if (<= i m)
(recur (inc i)
(loop [j 1 out out]
(let [ij (* i j)]
(if (and (<= j i) (<= ij m))
(recur (inc j)
(assoc out ij (min (out ij) ;; current value
(inc (out (dec ij))) ;; steps from value just below
(inc (out i))))) ;; steps from a factor
out))))
out)))
Notice we're still using loop/recur (twice), we're still using a vector to hold the output. But some differences:
We initialize out to incrementing integers. This is the worst case number of steps for every value, and once
initialized, we don't have to test that a value equals 0 and we can skip indices 0 and 1 and start the outer loop at
index 2. (We also fix a bug in your original and make sure out contains MAX-NUMBER+1 values.)
All three tests happen inside a min function that encapsulates the original logic: a value will be
updated only if it's a shorter number of steps from the number just below it, or from one of it's factors.
The tests are now simple enough that we don't need to break them out into separate functions.
This code (along with your original) is fast enough to pass some of the test cases in HR, but not all. Here are some
things to speed this up:
Use int-array instead of vec. This means we'll use aset instead of assoc and aget instead of calling out
with an index. It also means that loop/recur isn't the best structure anymore (because we are no longer passing
around new versions of an immutable vector, but actually mutating a java.util.Array); instead we'll use doseq.
Type hints. This alone makes a huge speed difference. When testing your code, include a form at the top (set! *warn-on-reflection* true) and you'll see where Clojure is having to do extra work to figure out what types it is
dealing with.
Use custom I/O functions to read the input. HR's boilerplate I/O code is supposed to let you focus on solving the
challenge and not worry about I/O, but it is basically garbage, and often the culprit behind your program timing out.
Below is a version that incorporates the tips above and runs fast enough to pass all test cases. I've included my custom
I/O approach that I've been using for all my HR challenges. One nice benefit of using doseq is we can include a
:let and a :while clause within the binding form, removing some of the indentation within the body of doseq. Also
notice a few strategically placed type hints that really speed up the program.
(ns down-to-zero-int-array)
(set! *warn-on-reflection* true)
(defn compute
"Returns a vector of down-to-zero counts for all numbers from 0 to m."
^ints [m]
(let [out ^ints (int-array (inc m) (range (inc m)))]
(doseq [i (range 2 (inc m)) j (range 1 (inc i)) :let [ij (* i j)] :while (<= ij m)]
(aset out ij (min (aget out ij)
(inc (aget out (dec ij)))
(inc (aget out i)))))
out))
(let [tokens ^java.io.StreamTokenizer
(doto (java.io.StreamTokenizer. (java.io.BufferedReader. *in*))
(.parseNumbers))]
(defn next-int []
"Read next integer from input. As fast as `read-line` for a single value,
and _much_ faster than `read-line`+`split` for multiple values on same line."
(.nextToken tokens)
(int (.-nval tokens))))
(def MAX 1000000)
(let [q (next-int)
down-to-zero (compute MAX)]
(doseq [n (repeatedly q next-int)]
(println (aget down-to-zero n))))

creating a finite lazy sequence

I'm using the function iterate to create a lazy sequence. The sequence keeps producing new values on each item. At one point however the produced values "doesn't make sense" anymore, so they are useless. This should be the end of the lazy sequence. This is the intended behavior in a abstract form.
My approach was to let the sequence produce the values. And once detected that they are not useful anymore, the sequence would only emit nil values. Then, the sequence would be wrapped with a take-while, to make it finite.
simplified:
(take-while (comp not nil?)
(iterate #(let [v (myfunction1 %)]
(if (mypred? (myfunction2 v)) v nil)) start-value))
This works, but two questions arise here:
Is it generally a good idea to model a finite lazy sequence with a nil as a "stopper", or are there better ways?
The second question would be related to the way I implemented the mechanism above, especially inside the iterate.
The problem is: I need one function to get a value, then a predicate to test if it's valid, if yes: in needs to pass a second function, otherwise: return nil.
I'm looking for a less imperative way tho achieve this, more concretely omitting the let statement. Rather something like this:
(defn pass-if-true [pred v f]
(when (pred? v) (f v)))
#(pass-if-true mypred? (myfunction1 %) myfunction2)
For now, I'll go with this:
(comp #(when (mypred? %) (myfunction2 %)) myfunction1)
Is it generally a good idea to model a finite lazy sequence with a nil as a "stopper", or are there better ways?
nil is the idiomatic way to end a finite lazy sequence.
Regarding the second question, try writing it this way:
(def predicate (partial > 10))
(take-while predicate (iterate inc 0))
;; => (0 1 2 3 4 5 6 7 8 9)
Here inc takes the previous value and produces a next value, predicate tests whether or not a value is good. The first time predicate returns false, sequence is terminated.
Using a return value of nil can make a lazy sequence terminate.
For example, this code calculates the greatest common divisor of two integers:
(defn remainder-sequence [n d]
(let [[q r] ((juxt quot rem) n d)]
(if (= r 0) nil
(lazy-seq (cons r (remainder-sequence d r))))))
(defn gcd [n d]
(cond (< (Math/abs n) (Math/abs d)) (gcd d n)
(= 0 (rem n d)) d
:default (last (remainder-sequence n d))))
(gcd 100 32) ; returns 4

Clojure - sum up a bunch of numbers

Hey I'm doing a Project Euler question, and I'm looking to sum up all the numbers under 1000 that are multiplies of 3 or 5.
But being a clojure noob, my code just keeps returning zero.. and I'm not sure why.
(defn sum-of-multiples [max]
(let [result (atom 0)]
(for [i (range max)]
(if (or (= (rem i 3) 0) (= (rem i 5) 0))
(swap! result (+ #result i)))
)
#result))
(sum-of-multiples 1000)
Also the line (swap! result (+ #result i))) bugs me.. In C# I could do result += i, but I'm guessing there must be a better way to this in Clojure?
In Clojure - and at large in functional programming - we avoid assignment as it destroys state history and makes writing concurrent programs a whole lot harder. In fact, Clojure doesn't even support assignment. An atom is a reference type that is thread safe.
Another common trait of functional programming is that we try to solve problems as a series of data transformations. In your case you case some data, a list of numbers from 0 to 1000 exclusive, and you need to obtain the sum of all numbers that match a predicate. This can certainly be done by applying data transformations and completely removing the need for assignment. One such implementation is this:
(->> (range 1000)
(filter #(or (= (rem % 3) 0) (= (rem % 5) 0)))
(reduce +))
Please understand that a function such as the one you wrote isn't considered idiomatic code. Having said that, in the interest of learning, it can be made to work like so:
(defn sum-of-multiples [max]
(let [result (atom 0)]
(doseq [i (range max)]
(if (or (= (rem i 3) 0) (= (rem i 5) 0))
(swap! result #(+ % i)))
)
#result))
(sum-of-multiples 1000)
for returns a lazy sequence but since you're simply interested in the side-effects caused by swap! you need to use doseq to force the sequence. The other problem is that the second argument to swap! is a function, so you don't need to deref result again.
for is a list comprehension that return a lazy sequence, you have to traverse it for your code to work:
(defn sum-of-multiples [max]
(let [result (atom 0)]
(dorun
(for [i (range max)]
(if (or (= (rem i 3) 0) (= (rem i 5) 0))
(swap! result + i))))
#result))
An equivalent, more idiomatic implementation using for:
(defn sum-of-multiples [max]
(reduce +
(for [i (range max)
:when (or (zero? (rem i 3))
(zero? (rem i 5)))]
i)))
The other answers are good examples of what I alluded to in my comment. For the sake of completeness, here's a solution that uses loop/recur, so it may be easier to understand for someone who's still not comfortable with concepts like filter, map or reduce. It also happens to be about 30-40% faster, not that it really matters in this case.
(defn sum-of-multiples [max]
(loop [i 0
sum 0]
(if (> max i)
(recur (inc i)
(if (or (zero? (rem i 3)) (zero? (rem i 5)))
(+ sum i)
sum))
sum)))

Iteratively apply function to its result without generating a seq

This is one of those "Is there a built-in/better/idiomatic/clever way to do this?" questions.
I want a function--call it fn-pow--that will apply a function f to the result of applying f to an argument, then apply it to the result of applying it to its result, etc., n times. For example,
(fn-pow inc 0 3)
would be equivalent to
(inc (inc (inc 0)))
It's easy to do this with iterate:
(defn fn-pow-0
[f x n]
(nth (iterate f x) n))
but that creates and throws away an unnecessary lazy sequence.
It's not hard to write the function from scratch. Here is one version:
(defn fn-pow-1
[f x n]
(if (> n 0)
(recur f (f x) (dec n))
x))
I found this to be almost twice as fast as fn-pow-0, using Criterium on (fn-pow inc 0 10000000).
I don't consider the definition of fn-pow-1 to be unidiomatic, but fn-pow seems like something that might be a standard built-in function, or there may be some simple way to define it with a couple of higher-order functions in a clever arrangement. I haven't succeeded in discovering either. Am I missing something?
The built-in you are looking for is probably dotimes. I'll tell you why in a round-about fashion.
Time
What you are testing in your benchmark is mainly the overhead of a level of indirection. That (nth (iterate ...) n) is only twice as slow as what compiles to a loop when the body is a very fast function is rather surprising/encouraging. If f is a more costly function, the importance of that overhead diminishes. (Of course if your f is low-level and fast, then you should use a low-level loop construct.)
Say your function takes ~ 1 ms instead
(defn my-inc [x] (Thread/sleep 1) (inc x))
Then both of these will take about 1 second -- the difference is around 2% rather than 100%.
(bench (fn-pow-0 my-inc 0 1000))
(bench (fn-pow-1 my-inc 0 1000))
Space
The other concern is that iterate is creating an unnecessary sequence. But, if you are not holding onto the head, just doing an nth, then you aren't really creating a sequence per se but sequentially creating, using, and discarding LazySeq objects. In other words, you are using a constant amount of space, though generating garbage in proportion to n. However, unless your f is primitive or mutating its argument, then it is already producing garbage in proportion to n in producing its own intermediate results.
Reducing Garbage
An interesting compromise between fn-pow-0 and fn-pow-1 would be
(defn fn-pow-2 [f x n] (reduce (fn [x _] (f x)) x (range n)))
Since range objects know how to intelligently reduce themselves, this does not create additional garbage in proportion to n. It boils down to a loop as well. This is the reduce method of range:
public Object reduce(IFn f, Object start) {
Object ret = f.invoke(start,n);
for(int x = n+1;x < end;x++)
ret = f.invoke(ret, x);
return ret;
}
This was actually the fastest of the three (before adding primitive type-hints on n in the recur version, that is) with the slowed down my-inc.
Mutation
If you are iterating a function potentially expensive in time or space, such as matrix operations, then you may very well be wanting to use (in a contained manner) an f that mutates its argument to eliminate the garbage overhead. Since mutation is a side effect, and you want that side effect n times, dotimes is the natural choice.
For the sake of example, I'll use an atom as a stand-in, but imagine bashing on a mutable matrix instead.
(def my-x (atom 0))
(defn my-inc! [x] (Thread/sleep 1) (swap! x inc))
(defn fn-pow-3! [f! x n] (dotimes [i n] (f! x)))
That sounds just like composing functions n times.
(defn fn-pow [f p t]
((apply comp (repeat t f)) p))
Hmmm. I note that Ankur's version is around 10x slower than your original - possibly not the intent, no matter how idiomatic? :-)
Type hinting fn-pow-1 simply for the counter yields substantially faster results for me - around 3x faster.
(defn fn-pow-3 [f x ^long n]
(if (> n 0)
(recur f (f x) (dec n))
x))
This is around twice as slow as a version which uses inc directly, losing the variability (not hinting x to keep to the spirit of the test)...
(defn inc-pow [x ^long n]
(if (> n 0)
(recur (inc x) (dec n))
x))
I think that for any nontrivial f that fn-pow-3 is probably the best solution.
I haven't found a particularly "idiomatic" way of doing this as it does not feel like common use case outside of micro benchmarks (although would love to be contradicted).
Would be intrigued to hear of a real world example, if you have one?
To us benighted imperative programmers, a more general pattern is known as a while statement. We can capture it in a macro:
(defmacro while [bv ; binding vector
tf ; test form
recf ; recur form
retf ; return form
]
`(loop ~bv (if ~tf (recur ~#recf) ~retf)))
... in your case
(while [x 0, n 3] (pos? n)
[(inc x) (dec n)]
x)
; 3
I was hoping to type-hint the n, but it's illegal. Maybe it's
inferred.
Forgive me (re)using while.
This isn't quite right: it doesn't allow for computation prior to the recur-form.
We can adapt the macro to do things prior to the recur:
(defmacro while [bv ; binding vector
tf ; test form
bodyf ; body form
retf ; return form
]
(let [bodyf (vec bodyf)
recf (peek bodyf)
bodyf (seq (conj (pop bodyf) (cons `recur recf)))]
`(loop ~bv (if ~tf ~bodyf ~retf))))
For example
(while [x 0, n 3] (pos? n)
(let [x- (inc x) n- (dec n)] [x- n-])
x)
; 3
I find this quite expressive. YMMV.

How to call one defn function in another defn and how to debugging in Clojure

I am having a problem running my program in Clojure. I just start learning Clojure a couple of weeks ago. So I don't know the quick and easy way to debug a Clojure program. My func2 raises an exception at (adj(a b)) as followed:
ClassCastException java.lang.Long cannot be cast to clojure.lang.IFn
user/func2.
I don't know what is wrong with it. Can someone point out the problem with my coding?
And in func3, I call func2 recursively, but it throws:
ArityException Wrong number of args (0) passed to: PersistentVector
clojure.lan g.AFn.throwArity (AFn.java:437)
What is wrong with func3? Thank you.
(defn adj [value1 value2]
(def result (+ (/ value1 2) (/ value2 2)))
(if (= (mod result 2) 1)
(+ result 1)
result
)
)
(defn func2 [list]
(let [[a b c d] list]
(inc d)
([(adj c a) (adj a b) (adj b c) d]))
)
(defn func3 [list]
(loop [v list r []]
(if(= (v 0) (v 1) (v 2))
(conj list r)
(func3(func2(list)))
))
)
What's the intended result of these functions? We probably need to see some sample inputs and expected results to really be able to help you.
Here's my attempt at cleaning them up. I've noted the changes I made as comments. func3 has the most serious problem in that it's an infinite recursion - there's no end condition. What should cause it to stop working and return a result?
(defn adj [value1 value2]
;; don't use def within functions, use let
(let [result (+ (/ value1 2) (/ value2 2))]
(if (= (mod result 2) 1)
(+ result 1)
result)))
(defn func2 [list]
(let [[a b c d] list]
;; The extra parens around this vector were causing it
;; to be called as a function, which I don't think is
;; what you intended:
[(adj c a) (adj a b) (adj b c) d]))
;; This needs an end condition - it's an infinite recursion
(defn func3 [list]
(loop [v list r []]
(if (= (v 0) (v 1) (v 2))
(conj list r)
;; Removed extra parens around list
(func3 (func2 list)))))
The reason I say not to use def within functions is that it always creates a global function. For local bindings you want let.
Regarding the extra parens, the difference between [1 2 3] and ([1 2 3]) is that the former returns a vector containing the numbers 1, 2, and 3, whereas the latter tries to call that vector as a function. You had excess parens around the literal vector in func2 and around list in func3, which was causing exceptions.
As a style note, the name list isn't a good choice. For one thing, it's shadowing clojure.core/list, and for another you're probably using vectors rather than lists anyway. It would be more idiomatic to use coll (for collection) or s (for sequence) as the name.
This would suggest at least one other change. In func3 you use a vector-only feature (using the vector as a function to perform lookup by index), so to be more general (accept other data structures) you can convert to a vector with vec:
(defn func3 [coll]
(loop [v (vec coll) r []]
(if (= (v 0) (v 1) (v 2))
(conj v r)
(func3 (func2 v)))))
Oh, there is no need to debug that. I suggest you have a look at LightTable.
The first two functions are easily fixed:
(defn adj [value1 value2]
;(def result (+ (/ value1 2) (/ value2 2))) def creates a global binding in current namespace !!!
(let [result (+ (/ value1 2) (/ value2 2))]
(if
(= (mod result 2) 1)
(inc result)
result)))
(defn func2 [xx]
(let [[a b c d] xx]
[ (adj c a) (adj a b) (adj b c) (inc d)]
))
The third function is not clear to me. I don't read your intent. What I understand is: "Keep applying func2 to itself until the first three elements of its result are equal." But I'm afraid this condition is never met, so I replaced it with a true in order to see just one result without blowing the stack.
(defn func3 [xx]
(loop [ v (func2 xx) ]
(if
;(= (v 0) (v 1) (v 2))
true
v
(recur (func2 v))
)))
Useful link: http://clojure.org/cheatsheet
Cheers -