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my problem is that I'm given an array of with length l.
let's say this is my array: [1,5,4,2,9,3,6] let's call this A.
This array can have multiple sub arrays with nodes being adjacent to each other. so we can have [1,5,4] or [2,9,3,6] and so on. the length of each sub array does not matter.
But the trick is the sum part. we cannot just add all numbers, it works like flip flop. so for the sublist [2,9,3,6] the sum would be [2,-9,3,-6] which is: -10. and is pretty small.
what would be the sublist (or sub-array if you like) of this array A that produces the maximum sum?
one possible way would be (from intuition) that the sublist [4,2,9] will output a decent result : [4, -2, 9] = (add all the elements) = 11.
The question is, how to come up with a result like this?
what is the sub-array that gives us the maximum flip-flop sum?
and mainly, what is the algorithm that takes any array as an input and outputs a sub-array with all numbers being adjacent and with the maximum sum?
I haven't come up with anything but I'm pretty sure I should pick either dynamic programming or divide and conquer to solve this issue. again, I don't know, I may be totally wrong.
The problem can indeed be solved using dynamic programming, by keeping track of the maximum sum ending at each position.
However, since the current element can be either added to or subtracted from a sum (depending on the length of the subsequence), we will keep track of the maximum sums ending here, separately, for both even as well as odd subsequence lengths.
The code below (implemented in python) does that (please see comments in the code for additional details).
The time complexity is O(n).
a = [1, 5, 4, 2, 9, 3, 6]
# initialize the best sequences which end at element a[0]
# best sequence with odd length ending at the current position
best_ending_here_odd = a[0] # the sequence sum value
best_ending_here_odd_start_idx = 0
# best sequence with even length ending at the current position
best_ending_here_even = 0 # the sequence sum value
best_ending_here_even_start_idx = 1
best_sum = 0
best_start_idx = 0
best_end_idx = 0
for i in range(1, len(a)):
# add/subtract the current element to the best sequences that
# ended in the previous element
best_ending_here_even, best_ending_here_odd = \
best_ending_here_odd - a[i], best_ending_here_even + a[i]
# swap starting positions (since a sequence which had odd length when it
# was ending at the previous element has even length now, and vice-versa)
best_ending_here_even_start_idx, best_ending_here_odd_start_idx = \
best_ending_here_odd_start_idx, best_ending_here_even_start_idx
# we can always make a sequence of even length with sum 0 (empty sequence)
if best_ending_here_even < 0:
best_ending_here_even = 0
best_ending_here_even_start_idx = i + 1
# update the best known sub-sequence if it is the case
if best_ending_here_even > best_sum:
best_sum = best_ending_here_even
best_start_idx = best_ending_here_even_start_idx
best_end_idx = i
if best_ending_here_odd > best_sum:
best_sum = best_ending_here_odd
best_start_idx = best_ending_here_odd_start_idx
best_end_idx = i
print(best_sum, best_start_idx, best_end_idx)
For the example sequence in the question, the above code outputs the following flip-flop sub-sequence:
4 - 2 + 9 - 3 + 6 = 14
As quertyman wrote, we can use dynamic programming. This is similar to Kadane's algorithm but with a few twists. We need a second temporary variable to keep track of trying each element both as an addition and as a subtraction. Note that a subtraction must be preceded by an addition but not vice versa. O(1) space, O(n) time.
JavaScript code:
function f(A){
let prevAdd = [A[0], 1] // sum, length
let prevSubt = [0, 0]
let best = [0, -1, 0, null] // sum, idx, len, op
let add
let subt
for (let i=1; i<A.length; i++){
// Try adding
add = [A[i] + prevSubt[0], 1 + prevSubt[1]]
if (add[0] > best[0])
best = [add[0], i, add[1], ' + ']
// Try subtracting
if (prevAdd[0] - A[i] > 0)
subt = [prevAdd[0] - A[i], 1 + prevAdd[1]]
else
subt = [0, 0]
if (subt[0] > best[0])
best = [subt[0], i, subt[1], ' - ']
prevAdd = add
prevSubt = subt
}
return best
}
function show(A, sol){
let [sum, i, len, op] = sol
let str = A[i] + ' = ' + sum
for (let l=1; l<len; l++){
str = A[i-l] + op + str
op = op == ' + ' ? ' - ' : ' + '
}
return str
}
var A = [1, 5, 4, 2, 9, 3, 6]
console.log(JSON.stringify(A))
var sol = f(A)
console.log(JSON.stringify(sol))
console.log(show(A, sol))
Update
Per OP's request in the comments, here is some theoretical elaboration on the general recurrence (pseudocode): let f(i, subtract) represent the maximum sum up to and including the element indexed at i, where subtract indicates whether or not the element is subtracted or added. Then:
// Try subtracting
f(i, true) =
if f(i-1, false) - A[i] > 0
then f(i-1, false) - A[i]
otherwise 0
// Try adding
f(i, false) =
A[i] + f(i-1, true)
(Note that when f(i-1, true) evaluates
to zero, the best ending at
i as an addition is just A[i])
The recurrence only depends on the evaluation at the previous element, which means we can code it with O(1) space, just saving the very last evaluation after each iteration, and updating the best so far (including the sequence's ending index and length if we want).
This week is my first time doing recursion. One of the problems I was able to solve was Fibonacci's sequence to the nth number; it wasn't hard after messing with it for 5 minutes.
However, I am having trouble understanding why this works with the current return statement.
return array if num == 2
If I push to array, it doesn't work, if I make a new variable sequence and push to that, it returns the correct answer. I am cool with that, but my base case says return array, not sequence. I initially pushed the sequence to the array, the result was not fibs sequence. I only solved the problem when I tried seeing what would happen if I pushed to the sequence array.
Instead of just making it work I was hoping someone could explain what was happening under the hood, what the stacks might be and how the problem works.
I understand recursion to an extent and somehow intuitively can make it work by assuming things, but I feel funny not actually knowing all the whys behind it.
def fib_seq(num)
return [0] if num == 1
return [] if num == 0
array = [0, 1]
return array if num <= 2
seq = fib_seq(num - 1)
seq << seq[-2] + seq[-1]
end
The code can be simplified a bit by removing the temporary array variable. It's a distraction. It also only applies when num == 2; num < 2 will be handled by the other base cases. num < 0 is illegal and should be handled by an error check.
I've also added in an explicit return. Explicit returns make it very obvious what's being returned and that helps understand recursion. In this case it's seq. ("Explicit returns are evil!" all the Ruby style people cry. Tough cookies. Good style isn't an absolute.)
def fib_seq(num)
# Error check
if num < 0 then
raise ArgumentError, "The number must be a positive integer"
end
# Terminating base cases
return [] if num == 0
return [0] if num == 1
return [0,1] if num == 2
# Recursion
seq = fib_seq(num - 1)
# The recursive function
seq << seq[-2] + seq[-1]
return seq
end
Now it's a bit clearer that return [0,1] if num == 2 is one of three base cases for the recursion. These are the terminating conditions which stops the recursion. But processing doesn't end there. The result isn't [0,1] because after that first return the stack has to unwind.
Let's walk through fib_seq(4).
fib_seq(4) calls fib_seq(3)
fib_seq(3) calls fib_seq(2)
fib_seq(2) returns `[0,1]`
We've reached the base case, now we need to unwind that stack of calls.
The call to fib_seq(3) picks up where it left off. seq returned from fib_seq(2) is [0,1]. It adds seq[-2] + seq[-1] onto the end and returns [0,1,1].
fib_seq(4) picks up where it left off. seq returned from fib_seq(3) is [0,1,1]. It adds seq[-2] + seq[-1] to the end and returns [0,1,1,2].
The stack is unwound, so we get back [0,1,1,2].
As you can see, the actual calculation happens backwards. f(n) = f(n-1) + f(n-2) and f(2) = [0,1]. It recurses down to f(2), the base case, then unwinds back up doing f(3) using the result of f(2), and f(4) using the result of f(3) and so on.
Recursive functions need to have an exit condition to prevent them from running forever. The main part of your recursive method is the following:
seq = fib_seq(num - 1)
seq << seq[-2] + seq[-1]
In Ruby, the last expression of a method is considered to be the return value of that method, so the lines above are equivalent to:
seq = fib_seq(num - 1)
seq << seq[-2] + seq[-1]
return seq
Let's run down what would happen if the method only contained these two lines, with num = 4:
call fib_seq(4)
call fib_seq(3)
call fib_seq(2)
call fib_seq(1)
call fib_seq(0)
call fib_seq(-1)
...
Obviously this results in an infinite loop, since we have no exit condition. We always call fib_seq again on the first line, so the code has no chance of ever reaching the return statement at the end. To fix the problem, let's add in these two lines at the beginning:
array = [0, 1]
return array if num <= 2
These can be simplified down to just:
return [0, 1] if num <= 2
Now let's see what happens when we call the method with num = 4:
call fib_seq(4)
4 > 2, exit condition not triggered, calling fib_seq(n - 1)
call fib_seq(3)
3 > 2, exit condition not triggered, calling fib_seq(n - 1)
call fib_seq(2)
2 == 2, exit condition triggered, returning [0, 1]!
fib_seq(2) returned with seq = [0, 1]
add 0 + 1 together, push new value to seq
seq is now [0, 1, 1]
return seq
fib_seq(3) returned with seq = [0, 1, 1]
add 1 + 1 together, push new value to seq
seq is now [0, 1, 1, 2]
return seq
FINAL RESULT: [0, 1, 1, 2]
So it looks like this method is working for values of num that are >= 2:
def fib_seq(num)
return [0, 1] if num <= 2
seq = fib_seq(num - 1)
seq << seq[-2] + seq[-1]
end
There is one bug left: num = 0 and num = 1 both return [0, 1]. Let's fix that:
def fib_seq(num)
return [] if num == 0
return [0] if num == 1
return [0, 1] if num == 2
seq = fib_seq(num - 1)
seq << seq[-2] + seq[-1]
end
Clean it up a little:
def fib_seq(num)
return [0, 1].first(num) if num <= 2
seq = fib_seq(num - 1)
seq << seq[-2] + seq[-1]
end
I always find it confusing when people mix imperative style mutations with functional style recursion – if you're going to do all reassignment and manual array seeking, why bother with using recursion as the looping mechanism? just use a loop.
That's not to say this program can't be expressed in a more functional way, tho. Here, we separate concerns of computing fibonacci numbers and generating a sequence – the result is an extremely easy-to-understand program
def fib n
def aux m, a, b
m == 0 ? a : aux(m - 1, b, a + b)
end
aux n, 0, 1
end
def fib_seq n
(0..n).map &method(:fib)
end
fib_seq 10
#=> [0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55]
And another way that's a bit more efficient for generating the sequence specifically – Below, I define an axuiliary function aux that utilizes 4 state variables to generate the sequence in a relatively straightforward way.
Note the difference with the input 10 - this one is closer to your proposed function where 0 returns [] despite the 0th fibonacci number is actually 0
def fib_seq n
def aux acc, m, a, b
m == 0 ? acc << a : aux(acc << a, m - 1, b, a + b)
end
case n
when 0; []
when 1; [0]
when 2; [0,1]
else; aux [0,1], n - 3, 1, 2
end
end
fib_seq 10
# => [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]
This method seeks to express num as a product of elements in arr.
For e.g method1(37,[1,3,5]) returns [2,0,7]
// arr is an array of divisors sorted in asc order, e.g. [1,3,5]
def method1(num, arr)
newArr = Array.new(arr.size, 0)
count = arr.size - 1
while num > 0
div = arr[count]
if div <= num
arr[count] = num/div
num = num % div
end
count = count - 1
end
return newArr
end
Would really appreciate if you could give me some help to derive the complexity of the algorithm. Please also feel free to improve the efficiency of my algorithm
Here's a refactored version of your code :
def find_linear_combination(num, divisors)
results = divisors.map do |divisor|
q, num = num.divmod(divisor)
q
end
results if num == 0
end
puts find_linear_combination(37, [5, 3, 1]) == [7, 0, 2]
puts find_linear_combination(37, [1, 3, 5]) == [37, 0, 0]
puts find_linear_combination(37, [5]).nil?
With n being the size of divisors, this algorithm clearly appears to be O(n). There's only one loop (map) and there's only one integer division inside the loop.
Note that the divisors should be written in descending order. If no linear combination is found, the method returns nil.
If you want to sort divisors, the algorithm would be O(n*log n). It could also be a good idea to append 1 if necessary (O(1)).
Here's what you can do:
def method1(num, arr)
newArr = Array.new(arr.size, 0)
count = arr.size()-1
while num>0
div = arr[count]
if div <= num
arr[count] = num / div
num = num % div
end
count = count + 1
end
return arr
end
ar = Array.new(25000000) { rand(1...10000) }
t1 = Time.now
method1(37, ar)
t2 = Time.now
tdelta = t2 - t1
print tdelta.to_f
Output:
0.102611062
Now double the array size:
ar = Array.new(50000000) { rand(1...10) }
Output:
0.325793964
And double again:
ar = Array.new(100000000) { rand(1...10) }
Output:
0.973402568
So an n doubles, the duration roughly triples. Since O(3n) == O(n), the
whole algorithm runs in O(n) time, where n represents the size of the input
array.
Say you have a vertical game board of length n (being the number of spaces). And you have a three-sided die that has the options: go forward one, stay and go back one. If you go below or above the number of board game spaces it is an invalid game. The only valid move once you reach the end of the board is "stay". Given an exact number of die rolls t, is it possible to algorithmically work out the number of unique dice rolls that result in a winning game?
So far I've tried producing a list of every possible combination of (-1,0,1) for the given number of die rolls and sorting through the list to see if any add up to the length of the board and also meet all the requirements for being a valid game. But this is impractical for dice rolls above 20.
For example:
t=1, n=2; Output=1
t=3, n=2; Output=3
You can use a dynamic programming approach. The sketch of a recurrence is:
M(0, 1) = 1
M(t, n) = T(t-1, n-1) + T(t-1, n) + T(t-1, n+1)
Of course you have to consider the border cases (like going off the board or not allowing to exit the end of the board, but it's easy to code that).
Here's some Python code:
def solve(N, T):
M, M2 = [0]*N, [0]*N
M[0] = 1
for i in xrange(T):
M, M2 = M2, M
for j in xrange(N):
M[j] = (j>0 and M2[j-1]) + M2[j] + (j+1<N-1 and M2[j+1])
return M[N-1]
print solve(3, 2) #1
print solve(2, 1) #1
print solve(2, 3) #3
print solve(5, 20) #19535230
Bonus: fancy "one-liner" with list compreehension and reduce
def solve(N, T):
return reduce(
lambda M, _: [(j>0 and M[j-1]) + M[j] + (j<N-2 and M[j+1]) for j in xrange(N)],
xrange(T), [1]+[0]*N)[-1]
Let M[i, j] be an N by N matrix with M[i, j] = 1 if |i-j| <= 1 and 0 otherwise (and the special case for the "stay" rule of M[N, N-1] = 0)
This matrix counts paths of length 1 from position i to position j.
To find paths of length t, simply raise M to the t'th power. This can be performed efficiently by linear algebra packages.
The solution can be read off: M^t[1, N].
For example, computing paths of length 20 on a board of size 5 in an interactive Python session:
>>> import numpy
>>> M = numpy.matrix('1 1 0 0 0;1 1 1 0 0; 0 1 1 1 0; 0 0 1 1 1; 0 0 0 0 1')
>>> M
matrix([[1, 1, 0, 0, 0],
[1, 1, 1, 0, 0],
[0, 1, 1, 1, 0],
[0, 0, 1, 1, 1],
[0, 0, 0, 0, 1]])
>>> M ** 20
matrix([[31628466, 51170460, 51163695, 31617520, 19535230],
[51170460, 82792161, 82787980, 51163695, 31617520],
[51163695, 82787980, 82792161, 51170460, 31628465],
[31617520, 51163695, 51170460, 31628466, 19552940],
[ 0, 0, 0, 0, 1]])
So there's M^20[1, 5], or 19535230 paths of length 20 from start to finish on a board of size 5.
Try a backtracking algorithm. Recursively "dive down" into depth t and only continue with dice values that could still result in a valid state. Propably by passing a "remaining budget" around.
For example, n=10, t=20, when you reached depth 10 of 20 and your budget is still 10 (= steps forward and backwards seemed to cancelled), the next recursion steps until depth t would discontinue the 0 and -1 possibilities, because they could not result in a valid state at the end.
A backtracking algorithms for this case is still very heavy (exponential), but better than first blowing up a bubble with all possibilities and then filtering.
Since zeros can be added anywhere, we'll multiply those possibilities by the different arrangements of (-1)'s:
X (space 1) X (space 2) X (space 3) X (space 4) X
(-1)'s can only appear in spaces 1,2 or 3, not in space 4. I got help with the mathematical recurrence that counts the number of ways to place minus ones without skipping backwards.
JavaScript code:
function C(n,k){if(k==0||n==k)return 1;var p=n;for(var i=2;i<=k;i++)p*=(n+1-i)/i;return p}
function sumCoefficients(arr,cs){
var s = 0, i = -1;
while (arr[++i]){
s += cs[i] * arr[i];
}
return s;
}
function f(n,t){
var numMinusOnes = (t - (n-1)) >> 1
result = C(t,n-1),
numPlaces = n - 2,
cs = [];
for (var i=1; numPlaces-i>=i-1; i++){
cs.push(-Math.pow(-1,i) * C(numPlaces + 1 - i,i));
}
var As = new Array(cs.length),
An;
As[0] = 1;
for (var m=1; m<=numMinusOnes; m++){
var zeros = t - (n-1) - 2*m;
An = sumCoefficients(As,cs);
As.unshift(An);
As.pop();
result += An * C(zeros + 2*m + n-1,zeros);
}
return result;
}
Output:
console.log(f(5,20))
19535230
There is an array of size n (numbers are between 0 and n - 3) and only 2 numbers are repeated. Elements are placed randomly in the array.
E.g. in {2, 3, 6, 1, 5, 4, 0, 3, 5} n=9, and repeated numbers are 3 and 5.
What is the best way to find the repeated numbers?
P.S. [You should not use sorting]
There is a O(n) solution if you know what the possible domain of input is. For example if your input array contains numbers between 0 to 100, consider the following code.
bool flags[100];
for(int i = 0; i < 100; i++)
flags[i] = false;
for(int i = 0; i < input_size; i++)
if(flags[input_array[i]])
return input_array[i];
else
flags[input_array[i]] = true;
Of course there is the additional memory but this is the fastest.
OK, seems I just can't give it a rest :)
Simplest solution
int A[N] = {...};
int signed_1(n) { return n%2<1 ? +n : -n; } // 0,-1,+2,-3,+4,-5,+6,-7,...
int signed_2(n) { return n%4<2 ? +n : -n; } // 0,+1,-2,-3,+4,+5,-6,-7,...
long S1 = 0; // or int64, or long long, or some user-defined class
long S2 = 0; // so that it has enough bits to contain sum without overflow
for (int i=0; i<N-2; ++i)
{
S1 += signed_1(A[i]) - signed_1(i);
S2 += signed_2(A[i]) - signed_2(i);
}
for (int i=N-2; i<N; ++i)
{
S1 += signed_1(A[i]);
S2 += signed_2(A[i]);
}
S1 = abs(S1);
S2 = abs(S2);
assert(S1 != S2); // this algorithm fails in this case
p = (S1+S2)/2;
q = abs(S1-S2)/2;
One sum (S1 or S2) contains p and q with the same sign, the other sum - with opposite signs, all other members are eliminated.
S1 and S2 must have enough bits to accommodate sums, the algorithm does not stand for overflow because of abs().
if abs(S1)==abs(S2) then the algorithm fails, though this value will still be the difference between p and q (i.e. abs(p - q) == abs(S1)).
Previous solution
I doubt somebody will ever encounter such a problem in the field ;)
and I guess, I know the teacher's expectation:
Lets take array {0,1,2,...,n-2,n-1},
The given one can be produced by replacing last two elements n-2 and n-1 with unknown p and q (less order)
so, the sum of elements will be (n-1)n/2 + p + q - (n-2) - (n-1)
the sum of squares (n-1)n(2n-1)/6 + p^2 + q^2 - (n-2)^2 - (n-1)^2
Simple math remains:
(1) p+q = S1
(2) p^2+q^2 = S2
Surely you won't solve it as math classes teach to solve square equations.
First, calculate everything modulo 2^32, that is, allow for overflow.
Then check pairs {p,q}: {0, S1}, {1, S1-1} ... against expression (2) to find candidates (there might be more than 2 due to modulo and squaring)
And finally check found candidates if they really are present in array twice.
You know that your Array contains every number from 0 to n-3 and the two repeating ones (p & q). For simplicity, lets ignore the 0-case for now.
You can calculate the sum and the product over the array, resulting in:
1 + 2 + ... + n-3 + p + q = p + q + (n-3)(n-2)/2
So if you substract (n-3)(n-2)/2 from the sum of the whole array, you get
sum(Array) - (n-3)(n-2)/2 = x = p + q
Now do the same for the product:
1 * 2 * ... * n - 3 * p * q = (n - 3)! * p * q
prod(Array) / (n - 3)! = y = p * q
Your now got these terms:
x = p + q
y = p * q
=> y(p + q) = x(p * q)
If you transform this term, you should be able to calculate p and q
Insert each element into a set/hashtable, first checking if its are already in it.
You might be able to take advantage of the fact that sum(array) = (n-2)*(n-3)/2 + two missing numbers.
Edit: As others have noted, combined with the sum-of-squares, you can use this, I was just a little slow in figuring it out.
Check this old but good paper on the topic:
Finding Repeated Elements (PDF)
Some answers to the question: Algorithm to determine if array contains n…n+m? contain as a subproblem solutions which you can adopt for your purpose.
For example, here's a relevant part from my answer:
bool has_duplicates(int* a, int m, int n)
{
/** O(m) in time, O(1) in space (for 'typeof(m) == typeof(*a) == int')
Whether a[] array has duplicates.
precondition: all values are in [n, n+m) range.
feature: It marks visited items using a sign bit.
*/
assert((INT_MIN - (INT_MIN - 1)) == 1); // check n == INT_MIN
for (int *p = a; p != &a[m]; ++p) {
*p -= (n - 1); // [n, n+m) -> [1, m+1)
assert(*p > 0);
}
// determine: are there duplicates
bool has_dups = false;
for (int i = 0; i < m; ++i) {
const int j = abs(a[i]) - 1;
assert(j >= 0);
assert(j < m);
if (a[j] > 0)
a[j] *= -1; // mark
else { // already seen
has_dups = true;
break;
}
}
// restore the array
for (int *p = a; p != &a[m]; ++p) {
if (*p < 0)
*p *= -1; // unmark
// [1, m+1) -> [n, n+m)
*p += (n - 1);
}
return has_dups;
}
The program leaves the array unchanged (the array should be writeable but its values are restored on exit).
It works for array sizes upto INT_MAX (on 64-bit systems it is 9223372036854775807).
suppose array is
a[0], a[1], a[2] ..... a[n-1]
sumA = a[0] + a[1] +....+a[n-1]
sumASquare = a[0]*a[0] + a[1]*a[1] + a[2]*a[2] + .... + a[n]*a[n]
sumFirstN = (N*(N+1))/2 where N=n-3 so
sumFirstN = (n-3)(n-2)/2
similarly
sumFirstNSquare = N*(N+1)*(2*N+1)/6 = (n-3)(n-2)(2n-5)/6
Suppose repeated elements are = X and Y
so X + Y = sumA - sumFirstN;
X*X + Y*Y = sumASquare - sumFirstNSquare;
So on solving this quadratic we can get value of X and Y.
Time Complexity = O(n)
space complexity = O(1)
I know the question is very old but I suddenly hit it and I think I have an interesting answer to it.
We know this is a brainteaser and a trivial solution (i.e. HashMap, Sort, etc) no matter how good they are would be boring.
As the numbers are integers, they have constant bit size (i.e. 32). Let us assume we are working with 4 bit integers right now. We look for A and B which are the duplicate numbers.
We need 4 buckets, each for one bit. Each bucket contains numbers which its specific bit is 1. For example bucket 1 gets 2, 3, 4, 7, ...:
Bucket 0 : Sum ( x where: x & 2 power 0 == 0 )
...
Bucket i : Sum ( x where: x & 2 power i == 0 )
We know what would be the sum of each bucket if there was no duplicate. I consider this as prior knowledge.
Once above buckets are generated, a bunch of them would have values more than expected. By constructing the number from buckets we will have (A OR B for your information).
We can calculate (A XOR B) as follows:
A XOR B = Array[i] XOR Array[i-1] XOR ... 0, XOR n-3 XOR n-2 ... XOR 0
Now going back to buckets, we know exactly which buckets have both our numbers and which ones have only one (from the XOR bit).
For the buckets that have only one number we can extract the number num = (sum - expected sum of bucket). However, we should be good only if we can find one of the duplicate numbers so if we have at least one bit in A XOR B, we've got the answer.
But what if A XOR B is zero?
Well this case is only possible if both duplicate numbers are the same number, which then our number is the answer of A OR B.
Sorting the array would seem to be the best solution. A simple sort would then make the search trivial and would take a whole lot less time/space.
Otherwise, if you know the domain of the numbers, create an array with that many buckets in it and increment each as you go through the array. something like this:
int count [10];
for (int i = 0; i < arraylen; i++) {
count[array[i]]++;
}
Then just search your array for any numbers greater than 1. Those are the items with duplicates. Only requires one pass across the original array and one pass across the count array.
Here's implementation in Python of #eugensk00's answer (one of its revisions) that doesn't use modular arithmetic. It is a single-pass algorithm, O(log(n)) in space. If fixed-width (e.g. 32-bit) integers are used then it is requires only two fixed-width numbers (e.g. for 32-bit: one 64-bit number and one 128-bit number). It can handle arbitrary large integer sequences (it reads one integer at a time therefore a whole sequence doesn't require to be in memory).
def two_repeated(iterable):
s1, s2 = 0, 0
for i, j in enumerate(iterable):
s1 += j - i # number_of_digits(s1) ~ 2 * number_of_digits(i)
s2 += j*j - i*i # number_of_digits(s2) ~ 4 * number_of_digits(i)
s1 += (i - 1) + i
s2 += (i - 1)**2 + i**2
p = (s1 - int((2*s2 - s1**2)**.5)) // 2
# `Decimal().sqrt()` could replace `int()**.5` for really large integers
# or any function to compute integer square root
return p, s1 - p
Example:
>>> two_repeated([2, 3, 6, 1, 5, 4, 0, 3, 5])
(3, 5)
A more verbose version of the above code follows with explanation:
def two_repeated_seq(arr):
"""Return the only two duplicates from `arr`.
>>> two_repeated_seq([2, 3, 6, 1, 5, 4, 0, 3, 5])
(3, 5)
"""
n = len(arr)
assert all(0 <= i < n - 2 for i in arr) # all in range [0, n-2)
assert len(set(arr)) == (n - 2) # number of unique items
s1 = (n-2) + (n-1) # s1 and s2 have ~ 2*(k+1) and 4*(k+1) digits
s2 = (n-2)**2 + (n-1)**2 # where k is a number of digits in `max(arr)`
for i, j in enumerate(arr):
s1 += j - i
s2 += j*j - i*i
"""
s1 = (n-2) + (n-1) + sum(arr) - sum(range(n))
= sum(arr) - sum(range(n-2))
= sum(range(n-2)) + p + q - sum(range(n-2))
= p + q
"""
assert s1 == (sum(arr) - sum(range(n-2)))
"""
s2 = (n-2)**2 + (n-1)**2 + sum(i*i for i in arr) - sum(i*i for i in range(n))
= sum(i*i for i in arr) - sum(i*i for i in range(n-2))
= p*p + q*q
"""
assert s2 == (sum(i*i for i in arr) - sum(i*i for i in range(n-2)))
"""
s1 = p+q
-> s1**2 = (p+q)**2
-> s1**2 = p*p + 2*p*q + q*q
-> s1**2 - (p*p + q*q) = 2*p*q
s2 = p*p + q*q
-> p*q = (s1**2 - s2)/2
Let C = p*q = (s1**2 - s2)/2 and B = p+q = s1 then from Viete theorem follows
that p and q are roots of x**2 - B*x + C = 0
-> p = (B + sqrtD) / 2
-> q = (B - sqrtD) / 2
where sqrtD = sqrt(B**2 - 4*C)
-> p = (s1 + sqrt(2*s2 - s1**2))/2
"""
sqrtD = (2*s2 - s1**2)**.5
assert int(sqrtD)**2 == (2*s2 - s1**2) # perfect square
sqrtD = int(sqrtD)
assert (s1 - sqrtD) % 2 == 0 # even
p = (s1 - sqrtD) // 2
q = s1 - p
assert q == ((s1 + sqrtD) // 2)
assert sqrtD == (q - p)
return p, q
NOTE: calculating integer square root of a number (~ N**4) makes the above algorithm non-linear.
Since a range is specified, you can perform radix sort. This would sort your array in O(n). Searching for duplicates in a sorted array is then O(n)
You can use simple nested for loop
int[] numArray = new int[] { 1, 2, 3, 4, 5, 7, 8, 3, 7 };
for (int i = 0; i < numArray.Length; i++)
{
for (int j = i + 1; j < numArray.Length; j++)
{
if (numArray[i] == numArray[j])
{
//DO SOMETHING
}
}
*OR you can filter the array and use recursive function if you want to get the count of occurrences*
int[] array = { 1, 2, 3, 4, 5, 4, 4, 1, 8, 9, 23, 4, 6, 8, 9, 1,4 };
int[] myNewArray = null;
int a = 1;
void GetDuplicates(int[] array)
for (int i = 0; i < array.Length; i++)
{
for (int j = i + 1; j < array.Length; j++)
{
if (array[i] == array[j])
{
a += 1;
}
}
Console.WriteLine(" {0} occurred {1} time/s", array[i], a);
IEnumerable<int> num = from n in array where n != array[i] select n;
myNewArray = null;
a = 1;
myNewArray = num.ToArray() ;
break;
}
GetDuplicates(myNewArray);
answer to 18..
you are taking an array of 9 and elements are starting from 0..so max ele will be 6 in your array. Take sum of elements from 0 to 6 and take sum of array elements. compute their difference (say d). This is p + q. Now take XOR of elements from 0 to 6 (say x1). Now take XOR of array elements (say x2). x2 is XOR of all elements from 0 to 6 except two repeated elements since they cancel out each other. now for i = 0 to 6, for each ele of array, say p is that ele a[i] so you can compute q by subtracting this ele from the d. do XOR of p and q and XOR them with x2 and check if x1==x2. likewise doing for all elements you will get the elements for which this condition will be true and you are done in O(n). Keep coding!
check this out ...
O(n) time and O(1) space complexity
for(i=0;i< n;i++)
xor=xor^arr[i]
for(i=1;i<=n-3;i++)
xor=xor^i;
So in the given example you will get the xor of 3 and 5
xor=xor & -xor //Isolate the last digit
for(i = 0; i < n; i++)
{
if(arr[i] & xor)
x = x ^ arr[i];
else
y = y ^ arr[i];
}
for(i = 1; i <= n-3; i++)
{
if(i & xor)
x = x ^ i;
else
y = y ^ i;
}
x and y are your answers
For each number: check if it exists in the rest of the array.
Without sorting you're going to have a keep track of numbers you've already visited.
in psuedocode this would basically be (done this way so I'm not just giving you the answer):
for each number in the list
if number not already in unique numbers list
add it to the unique numbers list
else
return that number as it is a duplicate
end if
end for each
How about this:
for (i=0; i<n-1; i++) {
for (j=i+1; j<n; j++) {
if (a[i] == a[j]) {
printf("%d appears more than once\n",a[i]);
break;
}
}
}
Sure it's not the fastest, but it's simple and easy to understand, and requires
no additional memory. If n is a small number like 9, or 100, then it may well be the "best". (i.e. "Best" could mean different things: fastest to execute, smallest memory footprint, most maintainable, least cost to develop etc..)
In c:
int arr[] = {2, 3, 6, 1, 5, 4, 0, 3, 5};
int num = 0, i;
for (i=0; i < 8; i++)
num = num ^ arr[i] ^i;
Since x^x=0, the numbers that are repeated odd number of times are neutralized. Let's call the unique numbers a and b.We are left with a^b. We know a^b != 0, since a != b. Choose any 1 bit of a^b, and use that as a mask ie.choose x as a power of 2 so that x & (a^b) is nonzero.
Now split the list into two sublists -- one sublist contains all numbers y with y&x == 0, and the rest go in the other sublist. By the way we chose x, we know that the pairs of a and b are in different buckets. So we can now apply the same method used above to each bucket independently, and discover what a and b are.
I have written a small programme which finds out the number of elements not repeated, just go through this let me know your opinion, at the moment I assume even number of elements are even but can easily extended for odd numbers also.
So my idea is to first sort the numbers and then apply my algorithm.quick sort can be use to sort this elements.
Lets take an input array as below
int arr[] = {1,1,2,10,3,3,4,5,5,6,6};
the number 2,10 and 4 are not repeated ,but they are in sorted order, if not sorted use quick sort to first sort it out.
Lets apply my programme on this
using namespace std;
main()
{
//int arr[] = {2, 9, 6, 1, 1, 4, 2, 3, 5};
int arr[] = {1,1,2,10,3,3,4,5,5,6,6};
int i = 0;
vector<int> vec;
int var = arr[0];
for(i = 1 ; i < sizeof(arr)/sizeof(arr[0]); i += 2)
{
var = var ^ arr[i];
if(var != 0 )
{
//put in vector
var = arr[i-1];
vec.push_back(var);
i = i-1;
}
var = arr[i+1];
}
for(int i = 0 ; i < vec.size() ; i++)
printf("value not repeated = %d\n",vec[i]);
}
This gives the output:
value not repeated= 2
value not repeated= 10
value not repeated= 4
Its simple and very straight forward, just use XOR man.
for(i=1;i<=n;i++) {
if(!(arr[i] ^ arr[i+1]))
printf("Found Repeated number %5d",arr[i]);
}
Here is an algorithm that uses order statistics and runs in O(n).
You can solve this by repeatedly calling SELECT with the median as parameter.
You also rely on the fact that After a call to SELECT,
the elements that are less than or equal to the median are moved to the left of the median.
Call SELECT on A with the median as the parameter.
If the median value is floor(n/2) then the repeated values are right to the median. So you continue with the right half of the array.
Else if it is not so then a repeated value is left to the median. So you continue with the left half of the array.
You continue this way recursively.
For example:
When A={2, 3, 6, 1, 5, 4, 0, 3, 5} n=9, then the median should be the value 4.
After the first call to SELECT
A={3, 2, 0, 1, <3>, 4, 5, 6, 5} The median value is smaller than 4 so we continue with the left half.
A={3, 2, 0, 1, 3}
After the second call to SELECT
A={1, 0, <2>, 3, 3} then the median should be 2 and it is so we continue with the right half.
A={3, 3}, found.
This algorithm runs in O(n+n/2+n/4+...)=O(n).
What about using the https://en.wikipedia.org/wiki/HyperLogLog?
Redis does http://redis.io/topics/data-types-intro#hyperloglogs
A HyperLogLog is a probabilistic data structure used in order to count unique things (technically this is referred to estimating the cardinality of a set). Usually counting unique items requires using an amount of memory proportional to the number of items you want to count, because you need to remember the elements you have already seen in the past in order to avoid counting them multiple times. However there is a set of algorithms that trade memory for precision: you end with an estimated measure with a standard error, in the case of the Redis implementation, which is less than 1%. The magic of this algorithm is that you no longer need to use an amount of memory proportional to the number of items counted, and instead can use a constant amount of memory! 12k bytes in the worst case, or a lot less if your HyperLogLog (We'll just call them HLL from now) has seen very few elements.
Well using the nested for loop and assuming the question is to find the number occurred only twice in an array.
def repeated(ar,n):
count=0
for i in range(n):
for j in range(i+1,n):
if ar[i] == ar[j]:
count+=1
if count == 1:
count=0
print("repeated:",ar[i])
arr= [2, 3, 6, 1, 5, 4, 0, 3, 5]
n = len(arr)
repeated(arr,n)
Why should we try out doing maths ( specially solving quadratic equations ) these are costly op . Best way to solve this would be t construct a bitmap of size (n-3) bits , i.e, (n -3 ) +7 / 8 bytes . Better to do a calloc for this memory , so every single bit will be initialized to 0 . Then traverse the list & set the particular bit to 1 when encountered , if the bit is set to 1 already for that no then that is the repeated no .
This can be extended to find out if there is any missing no in the array or not.
This solution is O(n) in time complexity