I just started to learn Haskell today and is completely overwhelmed by its syntax.
I am trying to apply math calculation to a list of items.
For example, lets say I want to square every item in the list using list comprehension.
My attempt
myfunc (n:lis) = [ k | k <-lis, k == k^k]
result_list = myfunc[1..]
take 10 result_list
My understand of my myfunc code: take a list and loop through elements that is stored in variable k and set k equals to its square.
after i execute the take command, and hit enter, apparently the process is running but does not do anything.
Note that i want to use list comprehension as a way to do it. I can use map do achieve my goal already.
You misunderstand the list comprehension.
[ k | k <- lis, k == k^k ]
The k == k^k clause is a filter –– it only keeps elements of the list that satisfy this equation. (== is a comparison operator that returns a bool, which is one hint). The reason you see no output is that there are no numbers in [1..] that satisfy this equation. But we get an infinite loop because we keep checking ever higher numbers to see if they satisfy it.
Something to experiment with
[ k | k <- lis, k < 100 ]
As for how to get a list of squares, use a comprehension like this
[ k^2 | k <- lis ]
If you want something more like your original phrasing, you can make let bindings within a list comprehension:
[ r | k <- lis, let r = k^2 ]
There are other issues with your code, but one baby step at a time! Good luck!
Related
I want to take five consecutive primes generated as an infinite list by primes and check them if they summed make another prime. I want to have something like this:
consecutivePrimes = [ a+b+c+d+e | a:b:c:d:e <- primes, prime a+b+c+d+e]
This a:b:c:d:e <- primes however doesn't work and I can't find any way as to get multiple elements at once in a list comprehension.
Since a list comprehension can be thought of as a map combined with a filter (at least for lists), you can only get one element at a time inside of it.
But you can still do this by making primes into a list of lists using tails, then taking 5 elements from each of the lists. The single element you map over (ps) in this case is a list.
import Data.List (tails)
consecutivePrimes = [ a+b+c+d+e | ps <- tails primes, let [a,b,c,d,e] = take 5 ps, prime a+b+c+d+e]
Pattern matching on the list of 5 elements will always succeed if your input list is infinite.
This is my solution which works but I find it quite ugly:
consecutivePrimes = [x | x <- consecutivePrimes' primes, prime x]
consecutivePrimes' (a:b:c:d:e:xs) | prime (a+b+c+d+e) = (a+b+c+d+e) : consecutivePrimes' (b:c:d:e:xs)
| otherwise = consecutivePrimes' (b:c:d:e:xs))
Can someone tell me the formal reason why list/arrays and such are considered more secure when it comes to incremental steps i.e (List.fold > loops).
Exampel code in F#
Functional way (list)
let rec sum lst =
match lst with
| [] -> 0
| x::xs -> x + sum xs
Imperative way (incremental)
let sum n m =
let mutable s = 0
for i=n to m do
s <- s + i
s
If by security you mean "safer" -- then I think this will explain it some. To begin with if you're summing a list, a fold should be somewhat safer as it removes the need for the programmer to correctly index the list:
let sum lst =
let mutable s = 0
for i=0 to (List.length lst - 1) do
s <- s + lst.[i]
s
You avoid a lot of pitfalls completely by using the library function:
let sum lst =
let folder acc element =
acc + element
List.fold folder 0 lst
The fold handles all the edge cases for you, in terms of indices, and list length. (note: this could also be done with a List.reduce (+) lst however that does not handle an empty list, where as a fold does).
The short of it all is that it keeps the programmer from making mistakes on silly index math, and keeps the focus on the actual logic of what is being done.
EDIT: I ironically messed up the index logic in my initial post
I have a quadratic Matrix of size n, say A, with non-negative real entries a_ij.
Furthermore I have a permutation tree. For n = 3 it looks like this: .
Now I would like to do a Depth-search (I don't know really, whether "Depth-search" is the correct description for this, but let's use it for now) along the branches of the tree in the following way:
On the first partial tree on the very left do the following starting with an "empty" Permutation (x,x,x):
If a_12 > a_21 set (1,2,x) and then check whether a_23 > a_32. If this is true as well, save (1,2,3) in a list, say P. Then go back to the first Level and check whether a_13 > a_31 and so on.
If a_21 > a_12 or a_32 > a_23 do not save the Permutation in P and go back to the first Level and check whether a_13 > a_31. If this is true set (1,3,x) and then check whether a_23 > a_32. If this is true save (1,3,2) in P and continue with the next partial tree. If a_31 > a_13 or a_32 > a_23 do not save the Permutation in P and continue with the same procedure for the next partial tree.
This procedure/algorithm I would like to implement for an arbitrary natural n > 0 with Input just the Matrix A and n and as an Output all permutations of size n that fullfill these conditions. By now I am not able to implement this in a general way.
Preferably in Python, but Pseudo Code would be nice as well. I also want to avoid functions like "itertools Permutation", because in the use case I Need to apply this for large n, for example n = 100, and then itertools Permutation is very slow.
If I understand correctly, this should get you what you want:
import numpy as np
from itertools import permutations
def fluboxing_permutations(a, n):
return [p for p in permutations(range(n))
if all(a[i, j] > a[j, i] for i, j in zip(p, p[1:]))]
n = 3
a = np.random.random([n, n])
fluboxing_permutations(a, n)
itertools.permutations will yield permutations in lexicographical order, which corresponds to your tree; then we check that for each consecutive pair of indices in the permutation, the element in the matrix is greater than the element at swapped indices. If so, we retain the permutation.
(No idea how to describe what the function does, so I made a new name. Hope you like it. If anyone knows a better way to describe it, please edit! :P )
EDIT: Here's a recursive function that should do the same, but with pruning:
def fluboxing_permutations_r(a, n):
nset = set(range(n))
def inner(p):
l = len(p)
if l > 1 and a[p[-2]][p[-1]] <= a[p[-1]][p[-2]]:
return []
if l == n:
return [p]
return [r for i in nset - set(p)
for r in inner(p + (i,))]
return inner(())
p starts as empty tuple, but it grows in recursion. Once there's at least two elements in the partial permutation, we can test the last two elements and see if it fails the test, and reject it if it does (pruning its subtree out of the search space). If it is a full permutation that wasn't rejected, we return it. If it's not full yet, we append to it all possible indices that are not already in there, and recurse.
tinyEDIT: BTW, parameter n is kind of redundant, because n = len(a) at the top of the function should take care of it.
I am trying to build a list of 0's using list comprehension. But i also want to make an index 1 where i choose in the list. For example myList 5 2 = [0,1,0,0,0] where 5 is the number of elements and 2 is the index.
myList el index = [0 | n <- [1..el], if n == index then 1 else 0]
but this results in an error.
The smallest change that fixes that is
myList el index = [if n == index then 1 else 0 | n <- [1..el]]
Note that what's at the left of | is what generates the list elements. A list comprehension of the form [ 0 | ...] will only generate zeros, and the ... part only decides how long is the resulting list.
Further, in your code the compiler complains because at the right of | we allow only generators (e.g. n <- someList), conditions (e.g. x > 23), or new definitions (let y = ...). In your code the if ... is interpreted to be a condition, and for that it should evaluate to a boolean, but then 1 makes the result a number, triggering a type error.
Another solution could be
myList el index = replicate (index-1) 0 ++ [1] ++ replicate (el-index) 0
where replicate m 0 generates a list with m zeros, and ++ concatenates.
Finally, note that your index is 1-based. In many programming languages, that's unconventional, since 0-based indexing is more frequently used.
all_nat x = [ls| sum ls == x]
I'd like to write a function that given an integer x it returns all the lists that the result of their elements when summed is the integer x but I always get the error "not in scope: 'ls' " for both times it apperas. I'm new to haskell. What's the syntax error here?
The problem is that you need to define all used variables somewhere, but ls is undefined. Moreover, it can't be defined automatically, because the compiler doesn't know about the task — how the list should be generated? Ho long can it be? Are terms positive or not, integral or not? Unfortunately your code definition of the problem is quite vague for modern non-AI languages.
Let's help the compiler. To solve such problems, it's often useful to involve some math and infer the algorithm inductively. For example, let's write an algorithm with ordered lists (where [2,1] and [1,2] are different solutions):
Start with a basis, where you know the output for some given input. For example, for 0 there is only an empty list of terms (if 0 could be a term, any number could be decomposed as a sum in infinitely many ways). So, let's define that:
allNats 0 = [[]] --One empty list
An inductive step. Assuming we can decompose a number n, we can decompose any number n+k for any positive k, by adding k as a term to all decompositions of n. In other words: for numbers greater than 0, we can take any number k from 1 to n, and make it the first term of all decompositions of (n-k):
allNats n = [ k:rest --Add k as a head to the rest, where
| k <- [1 .. n] --k is taken from 1 to n, and
, rest <- allNats (n - k)] --rest is taken from solutions for (n—k)
That's all! Let's test it:
ghci> allNat 4
[[1,1,1,1],[1,1,2],[1,2,1],[1,3],[2,1,1],[2,2],[3,1],[4]]
Let's break this up into two parts. If I've understood your question correctly, the first step is to generate all possible (sub)lists from a list. There's a function to do this, called subsequences.
The second step is to evaluate the sum of each subsequence, and keep the subsequences with the sum you want. So your list comprehension looks like this:
all_nat x = [ls| ls <- subsequences [1..x], sum ls == x]
What about
getAllSums x = [(l,r)| l <- partial_nat, r <- partial_nat, l + r == x ]
where partial_nat = [1..x]