I know this algorithm is for finding the majority element of any array if it has any. Can any one please explain the recursion calls?
if length(A) = 0 then
return null
end if
if length(A) = 1 then
return 1
end if
// "Command 7"
Call FIND-MAJORITY recursively on the first half of A, and let i be the result.
// "Command 8"
Call FIND-MAJORITY recursively on the second half of A, and let j be the result.
if i > 0 then
compare i to all objects in A(including itself);
let k be the number of times that equality holds;
if k > length(A)/2 then
return i.
end if
end if
if j > 0 then
compare j to all objects in A(including itself);
let k be the number of times that equality holds;
if k > length(A)/2 then
return j
end if
end if
return null
Is command 7 is executed until it get an single value ... and then command 8? I cannot understand these recursions. Please explain with example, thanks.
It depends what are inputs of this function.
If the array A is an input then we only search in the diminished array else if the array A is defined as global then you always search the whole array.
For example take the array A is 1,2,1,3,1,8,7,1
If the array is given as input to the function :
According to recursion we get A is 1,2,1,3 -> A is 1,2 -> A is 1
This returns i := 1.
Then A is 2, this returns j:=1.
Then we compare i to all elements of A i.e 1,2.
Then we compare j to all elements of A i.e. 1,2.
We return null from this recursive call.
After this we proceed to upper recursion i.e. 1,2,1,3 and up to the first call.
If the array is global:
According to recursion we get A is 1,2,1,3 -> A is 1,2 -> A is 1
This returns i := 1.
Then A is 2, this returns j:=1.
Then we compare i to all elements of A i.e. 1,2,1,3,1,8,7,1
we return according to the conditions.
**Remember even in this case we return all recursive calls and check the whole array for every recursive call which is not what you probably want.
Related
I am implementing a recursive program to calculate the certain values in the Schroder sequence, and I'm having two problems:
I need to calculate the number of calls in the program;
Past a certain number, the program will generate incorrect values (I think it's because the number is too big);
Here is the code:
let rec schroder n =
if n <= 0 then 1
else if n = 1 then 2
else 3 * schroder (n-1) + sum n 1
and sum n k =
if (k > n-2) then 0
else schroder k * schroder (n-k-1) + sum n (k+1)
When I try to return tuples (1.), the function sum stops working because it's trying to return int when it has type int * int;
Regarding 2., when I do schroder 15 it returns:
-357364258
when it should be returning
3937603038.
EDIT:
firstly thanks for the tips, secondly after some hours of deep struggle, i manage to create the function, now my problem is that i'm struggling to install zarith. I think I got it installed, but ..
in terminal when i do ocamlc -I +zarith test.ml i get an error saying Required module 'Z' is unavailable.
in utop after doing #load "zarith.cma";; and #install_printer Z.pp_print;; i can compile, run the function and it works. However i'm trying to implement a Scanf.scanf so that i can print different values of the sequence. With this being said whenever i try to run the scanf, i dont get a chance to write any number as i get a message saying that '\\n' is not a decimal digit.
With this being said i will most probably also have problems with printing the value, because i dont think that i'm going to be able to print such a big number with a %d. The let r1,c1 = in the following code, is a example of what i'm talking about.
Here's what i'm using :
(function)
..
let v1, v2 = Scanf.scanf "%d %d" (fun v1 v2-> v1,v2);;
let r1,c1 = schroder_a (Big_int_Z.of_int v1) in
Printf.printf "%d %d\n" (Big_int_Z.int_of_big_int r1) (Big_int_Z.int_of_big_int c1);
let r2,c2 = schroder_a v2 in
Printf.printf "%d %d\n" r2 c2;
P.S. 'r1' & 'r2' stands for result, and 'c1' and 'c2' stands for the number of calls of schroder's recursive function.
P.S.S. the prints are written differently because i was just testing, but i cant even pass through the scanf so..
This is the third time I've seen this problem here on StackOverflow, so I assume it's some kind of school assignment. As such, I'm just going to make some comments.
OCaml doesn't have a function named sum built in. If it's a function you've written yourself, the obvious suggestion would be to rewrite it so that it knows how to add up the tuples that you want to return. That would be one approach, at any rate.
It's true, ints in OCaml are subject to overflow. If you want to calculate larger values you need to use a "big number" package. The one to use with a modern OCaml is Zarith (I have linked to the description on ocaml.org).
However, none of the other people solving this assignment have mentioned overflow as a problem. It could be that you're OK if you just solve for representable OCaml int values.
3937603038 is larger than what a 32-bit int can hold, and will therefore overflow. You can fix this by using int64 instead (until you overflow that too). You'll have to use int64 literals, using the L suffix, and operations from the Int64 module. Here's your code converted to compute the value as an int64:
let rec schroder n =
if n <= 0 then 1L
else if n = 1 then 2L
else Int64.add (Int64.mul 3L (schroder (n-1))) (sum n 1)
and sum n k =
if (k > n-2) then 0L
else Int64.add (Int64.mul (schroder k) (schroder (n-k-1))) (sum n (k+1))
I need to calculate the number of calls in the program;
...
the function 'sum' stops working because it's trying to return 'int' when it has type 'int * int'
Make sure that you have updated all the recursive calls to shroder. Remember it is now returning a pair not a number, so you can't, for example, just to add it and you need to unpack the pair first. E.g.,
...
else
let r,i = schroder (n-1) (i+1) in
3 * r + sum n 1 and ...
and so on.
Past a certain number, the program will generate incorrect values (I think it's because the number is too big);
You need to use an arbitrary-precision numbers, e.g., zarith
Consider the problem of finding the k element subsets of a set with n elements. Write a recursive function that takes an array of integers representing the set, the number of integers in the set (n), and the required subset size (k) as input, and displays all subsets with k elements on the screen. You may assume that the elements in the array have unique values. For example, if the array (set) contains the elements [ 8 2 6 7 ], n is 4, and k is 2, then the output is 82 86 87 26 27 67.
Can you help me with this, at least tell what way should I follow?
The type of thing you're talking about is a **combination&&.
There's a recursive definition of the calculation tucked in the middle of the Wikipedia page.
$$\binom{n}{k}=\binom{n-1}{k-1}+\binom{n-1}{k}$$
Figuring out what your base cases are might be tricky, but I think everything you need is there.
I would've right something like this:
subset ( numbers, n, k, index)
{
if (index < n) // end for the recursion. passed through all elements
{
if (k == 0) // end for the recursion. no more elements needed
print ' '
else
{
print numbers[index]
subset(numbers, n, k-1, index+1) // uses the number in the current index
subset(numbers, n, k, index+1) // doesn't use the number in the current index
}
}
call subset(numbers, n, k, 0) to start
notice that because order doesn't play a role in sets, its enough to pass over the elements in one direction
I have a recursive function, as follows, where b >= 0
def multiply(a,b):
if b == 0:
return 0
elif b % 2 == 0:
return multiply(2*a, b/2)
else:
return a + multiply(a, b-1)
I would like to know how many times the function will run in terms of a and b.
Thanks.
If binary representation of b (call it B) ends with 1, like xxxx1 than next call to multiply has B = xxxx0.
If B ends with 0, like xxxx0 than next value of B is xxxx.
With that, digit of binary representation of b adds one call if it is 0, and two calls if it is 1. Summing that total number of calls equals to length of initial B + number of ones in initial B.
I might be wrong here, but I think your function does not work the way you intend it. In recursion the most important thing as a propper ending criteria, since it will run forever elseways.
Now your ending criteria is a==0, but with each recursive call you do not decrease a. Just make a pen & paper simulation with a=5 and check if it would stop at any point.
I see this code in the example of Elixir:
defmodule Recursion do
def print_multiple_times(msg, n) when n <= 1 do
IO.puts msg
end
def print_multiple_times(msg, n) do
IO.puts msg
print_multiple_times(msg, n - 1)
end
end
Recursion.print_multiple_times("Hello!", 3)
I see here the same function defined twice with different arguments, and I want to understand this technique.
Can I look at them as at overloaded functions?
Is it a single function with different behavior or are these two different functions, like print_only_once and print_multiple_times?
Are these functions linked anyhow or not?
Usually in functional languages a function is defined by clauses. For example, one way to implement Fibonacci in an imperative language would be the following code (not the best implementation):
def fibonacci(n):
if n < 0:
return None
if n == 0 or n == 1:
return 1
else:
return fibonacci(n - 1) + fibonacci(n - 2)
To define the function in Elixir you would do the following:
defmodule Test do
def fibonacci(0), do: 1
def fibonacci(1), do: 1
def fibonacci(n) when n > 1 do
fibonacci(n-1) + fibonacci(n - 2)
end
def fibonacci(_), do: nil
end
Test.fibonacci/1 is only one function. A function with four clauses and arity of 1.
The first clause matches only when the number is 0.
The second clause matches only when the number is 1.
The third clause matches with any number greater than 1.
The last clause matches anything (_ is used when the value of the variable is not going to be used inside the clause or is not relevant for the match).
The clauses are evaluated in the order they are declared, so for Test.fibonacci(2) will fail in the first 2 clauses and match the third one because 2 > 1.
Think of clauses as a more powerful if statement. The code looks cleaner this way. And is very useful for recursion. For example, a map implementation (the language already provide one in Enum.map/2):
defmodule Test do
def map([], _), do: []
def map([x | xs], f) when is_function(f) do
[f.(x) | map(xs, f)]
end
end
First clause matches an empty list. No need to apply a function.
Second clause matches a list where the first element (head) is x and the rest of the list (tail) is xs and f is a function. It applies the function to the first element and recursively calls map with the rest of the list.
Calling Test.map([1,2,3], fn x -> x * 2 end) will give you the following output [2, 4, 6]
So, a function in Elixir is defined with one or more clauses where every clause have the same arity as the rest.
I hope this answers your question.
In the example you posted both definitions of the function have the same number of arguments: 2, this "when" thing is a guard, but you can also have definitions with many arguments. First, guards -- they are uses to express what cannot be written as a mere matching, like the second line of the following:
def fac(0), do: 1
def fac(n), when n<0 do: "no factorial for negative numbers!"
def fac(n), do: n*fac(n-1)
-- since it's not possible to express being negative number by just equality/matching.
Btw this fac is a single definition, only with three cases. Notice the coolness of using constant "0" in the position of argument :)
You can think of this as it would be nicer way to write:
def fac(n) do
if n==0, do: 1, else: if n<0, do: "no factorial!", else: n*fac(n-1)
end
or a switch case (which even looks pretty close to the above):
def fa(n) do
case n do
0 -> 1
n when n>0 -> n*fa(n-1)
_ -> "no no no"
end
end
only "looks more fancy". Actually it turns out certain definitions (e.g. parsers, small interpreters) look much better in the former than latter style. Nb guard expressions are very limited (I think you can't use your own function in guard).
Now the real thing, varying number of arguments -- check this out!
def mutant(a), do: a*a
def mutant(a,b), do: a*b
def mutant(a,b,c), do: mutant(a,b)+mutant(c)
e.g.
iex(1)> Lol.mutant(2)
4
iex(2)> Lol.mutant(2,3)
6
iex(3)> Lol.mutant(2,3,4)
22
It works a bit similar like (lambda arg ...) in scheme -- think of mutant as taking all its arguments as a list and matching over it. But this time, elixir treats mutant as 3 functions, mutant/1, mutant/2, and mutant/3 and will refer to them as such.
So, to answer your question: these are not like overloaded functions, but rather scattered/fragmented definitions. You see similar ones in functional languages like miranda, haskell or sml.
Consider following recursive function.
int f(int n){
if(n<=0) return 1; return f(n-1)+f(n-1);
}
It is said that though there are 2^N (2 powered to N) calls but only N calls exist at a given time. Can someone explain how?
Sure. The complexity part is in the else portion:
return f(n-1)+f(n-1)
This spawns two calls at n-1 for each call at n. However, note the order of evaluation:
call f(n-1) ... left side
save the value as temp1
call f(n-1) ... right side
add the value to temp1
return that value
The entire left-side call tree has to complete before the right-side call tree can begin -- and that happens at every depth level. For each value of n, there will be only one call active at any point in time. For instance, f(2) gives us a sequence like this:
call f(2)
n isn't <= 0 ...
call f(1) ... left
n isn't <= 0 ...
call f(0) ... left | left
n <= 0; return 1
save the 1
call f(0) ... left | right
n <=0; return 1
add the 1 to the temp value;
return 2
save the 2
call f(1) ... right
call f(0) ... left | left
n <= 0; return 1
save the 1
call f(0) ... left | right
n <=0; return 1
add the 1 to the temp value;
return 2
add this 2 to the temp value, making 4
return the 4
done ...
At each point, there are no more than n+1 calls active (see indentation level), although we eventually execute 2^(n+1)-1 calls. We're off by one (n+1 instead of n) because we terminate at 0 instead of 1.
Does that clear things up?
For each n, f(n-1) is called twice. So if you draw a tree with n at root and add a branch for each call to f(n-1) and so on, you will see that you have a full binary tree of height n, which has 2^n nodes (total number of calls).
But it is important to note that the second call to f(n-1) cannot be initiated without first completing the first call and returning the result. The same holds recursively for f(n-2) calls inside f(n-1), ... .
The worst case (I assume that is what you are looking for), happens when the set of your function calls reach f(0) and you are at one of the leaves of the call tree. What you have in your call stack is a set of function calls starting from f(n-1), f(n-2), ... , f(1), f(0). So at each point of time you have at most O(n) functions in your stack. (Which is equal to the height of the call tree).