From a given array (call it numbers[]), i want another array (results[]) which contains all sum possibilities between elements of the first array.
For example, if I have numbers[] = {1,3,5}, results[] will be {1,3,5,4,8,6,9,0}.
there are 2^n possibilities.
It doesn't matter if a number appears two times because results[] will be a set
I did it for sum of pairs or triplet, and it's very easy. But I don't understand how it works when we sum 0, 1, 2 or n numbers.
This is what I did for pairs :
std::unordered_set<int> pairPossibilities(std::vector<int> &numbers) {
std::unordered_set<int> results;
for(int i=0;i<numbers.size()-1;i++) {
for(int j=i+1;j<numbers.size();j++) {
results.insert(numbers.at(i)+numbers.at(j));
}
}
return results;
}
Also, assuming that the numbers[] is sorted, is there any possibility to sort results[] while we fill it ?
Thanks!
This can be done with Dynamic Programming (DP) in O(n*W) where W = sum{numbers}.
This is basically the same solution of Subset Sum Problem, exploiting the fact that the problem has optimal substructure.
DP[i, 0] = true
DP[-1, w] = false w != 0
DP[i, w] = DP[i-1, w] OR DP[i-1, w - numbers[i]]
Start by following the above solution to find DP[n, sum{numbers}].
As a result, you will get:
DP[n , w] = true if and only if w can be constructed from numbers
Following on from the Dynamic Programming answer, You could go with a recursive solution, and then use memoization to cache the results, top-down approach in contrast to Amit's bottom-up.
vector<int> subsetSum(vector<int>& nums)
{
vector<int> ans;
generateSubsetSum(ans,0,nums,0);
return ans;
}
void generateSubsetSum(vector<int>& ans, int sum, vector<int>& nums, int i)
{
if(i == nums.size() )
{
ans.push_back(sum);
return;
}
generateSubsetSum(ans,sum + nums[i],nums,i + 1);
generateSubsetSum(ans,sum,nums,i + 1);
}
Result is : {9 4 6 1 8 3 5 0} for the set {1,3,5}
This simply picks the first number at the first index i adds it to the sum and recurses. Once it returns, the second branch follows, sum, without the nums[i] added. To memoize this you would have a cache to store sum at i.
I would do something like this (seems easier) [I wanted to put this in comment but can't write the shifting and removing an elem at a time - you might need a linked list]
1 3 5
3 5
-----
4 8
1 3 5
5
-----
6
1 3 5
3 5
5
------
9
Add 0 to the list in the end.
Another way to solve this is create a subset arrays of vector of elements then sum up each array's vector's data.
e.g
1 3 5 = {1, 3} + {1,5} + {3,5} + {1,3,5} after removing sets of single element.
Keep in mind that it is always easier said than done. A single tiny mistake along the implemented algorithm would take a lot of time in debug to find it out. =]]
There has to be a binary chop version, as well. This one is a bit heavy-handed and relies on that set of answers you mention to filter repeated results:
Split the list into 2,
and generate the list of sums for each half
by recursion:
the minimum state is either
2 entries, with 1 result,
or 3 entries with 3 results
alternatively, take it down to 1 entry with 0 results, if you insist
Then combine the 2 halves:
All the returned entries from both halves are legitimate results
There are 4 additional result sets to add to the output result by combining:
The first half inputs vs the second half inputs
The first half outputs vs the second half inputs
The first half inputs vs the second half outputs
The first half outputs vs the second half outputs
Note that the outputs of the two halves may have some elements in common, but they should be treated separately for these combines.
The inputs can be scrubbed from the returned outputs of each recursion if the inputs are legitimate final results. If they are they can either be added back in at the top-level stage or returned by the bottom level stage and not considered again in the combining.
You could use a bitfield instead of a set to filter out the duplicates. There are reasonably efficient ways of stepping through a bitfield to find all the set bits. The max size of the bitfield is the sum of all the inputs.
There is no intelligence here, but lots of opportunity for parallel processing within the recursion and combine steps.
Related
I am looking for a least time-complex algorithm that would solve a variant of the perfect sum problem (initially: finding all variable size subset combinations from an array [*] of integers of size n that sum to a specific number x) where the subset combination size is of a fixed size k and return the possible combinations without direct and also indirect (when there's a combination containing the exact same elements from another in another order) duplicates.
I'm aware this problem is NP-hard, so I am not expecting a perfect general solution but something that could at least run in a reasonable time in my case, with n close to 1000 and k around 10
Things I have tried so far:
Finding a combination, then doing successive modifications on it and its modifications
Let's assume I have an array such as:
s = [1,2,3,3,4,5,6,9]
So I have n = 8, and I'd like x = 10 for k = 3
I found thanks to some obscure method (bruteforce?) a subset [3,3,4]
From this subset I'm finding other possible combinations by taking two elements out of it and replacing them with other elements that sum the same, i.e. (3, 3) can be replaced by (1, 5) since both got the same sum and the replacing numbers are not already in use. So I obtain another subset [1,5,4], then I repeat the process for all the obtained subsets... indefinitely?
The main issue as suggested here is that it's hard to determine when it's done and this method is rather chaotic. I imagined some variants of this method but they really are work in progress
Iterating through the set to list all k long combinations that sum to x
Pretty self explanatory. This is a naive method that do not work well in my case since I have a pretty large n and a k that is not small enough to avoid a catastrophically big number of combinations (the magnitude of the number of combinations is 10^27!)
I experimented several mechanism related to setting an area of research instead of stupidly iterating through all possibilities, but it's rather complicated and still work in progress
What would you suggest? (Snippets can be in any language, but I prefer C++)
[*] To clear the doubt about whether or not the base collection can contain duplicates, I used the term "array" instead of "set" to be more precise. The collection can contain duplicate integers in my case and quite much, with 70 different integers for 1000 elements (counts rounded), for example
With reasonable sum limit this problem might be solved using extension of dynamic programming approach for subset sum problem or coin change problem with predetermined number of coins. Note that we can count all variants in pseudopolynomial time O(x*n), but output size might grow exponentially, so generation of all variants might be a problem.
Make 3d array, list or vector with outer dimension x-1 for example: A[][][]. Every element A[p] of this list contains list of possible subsets with sum p.
We can walk through all elements (call current element item) of initial "set" (I noticed repeating elements in your example, so it is not true set).
Now scan A[] list from the last entry to the beginning. (This trick helps to avoid repeating usage of the same item).
If A[i - item] contains subsets with size < k, we can add all these subsets to A[i] appending item.
After full scan A[x] will contain subsets of size k and less, having sum x, and we can filter only those of size k
Example of output of my quick-made Delphi program for the next data:
Lst := [1,2,3,3,4,5,6,7];
k := 3;
sum := 10;
3 3 4
2 3 5 //distinct 3's
2 3 5
1 4 5
1 3 6
1 3 6 //distinct 3's
1 2 7
To exclude variants with distinct repeated elements (if needed), we can use non-first occurence only for subsets already containing the first occurence of item (so 3 3 4 will be valid while the second 2 3 5 won't be generated)
I literally translate my Delphi code into C++ (weird, I think :)
int main()
{
vector<vector<vector<int>>> A;
vector<int> Lst = { 1, 2, 3, 3, 4, 5, 6, 7 };
int k = 3;
int sum = 10;
A.push_back({ {0} }); //fictive array to make non-empty variant
for (int i = 0; i < sum; i++)
A.push_back({{}});
for (int item : Lst) {
for (int i = sum; i >= item; i--) {
for (int j = 0; j < A[i - item].size(); j++)
if (A[i - item][j].size() < k + 1 &&
A[i - item][j].size() > 0) {
vector<int> t = A[i - item][j];
t.push_back(item);
A[i].push_back(t); //add new variant including current item
}
}
}
//output needed variants
for (int i = 0; i < A[sum].size(); i++)
if (A[sum][i].size() == k + 1) {
for (int j = 1; j < A[sum][i].size(); j++) //excluding fictive 0
cout << A[sum][i][j] << " ";
cout << endl;
}
}
Here is a complete solution in Python. Translation to C++ is left to the reader.
Like the usual subset sum, generation of the doubly linked summary of the solutions is pseudo-polynomial. It is O(count_values * distinct_sums * depths_of_sums). However actually iterating through them can be exponential. But using generators the way I did avoids using a lot of memory to generate that list, even if it can take a long time to run.
from collections import namedtuple
# This is a doubly linked list.
# (value, tail) will be one group of solutions. (next_answer) is another.
SumPath = namedtuple('SumPath', 'value tail next_answer')
def fixed_sum_paths (array, target, count):
# First find counts of values to handle duplications.
value_repeats = {}
for value in array:
if value in value_repeats:
value_repeats[value] += 1
else:
value_repeats[value] = 1
# paths[depth][x] will be all subsets of size depth that sum to x.
paths = [{} for i in range(count+1)]
# First we add the empty set.
paths[0][0] = SumPath(value=None, tail=None, next_answer=None)
# Now we start adding values to it.
for value, repeats in value_repeats.items():
# Reversed depth avoids seeing paths we will find using this value.
for depth in reversed(range(len(paths))):
for result, path in paths[depth].items():
for i in range(1, repeats+1):
if count < i + depth:
# Do not fill in too deep.
break
result += value
if result in paths[depth+i]:
path = SumPath(
value=value,
tail=path,
next_answer=paths[depth+i][result]
)
else:
path = SumPath(
value=value,
tail=path,
next_answer=None
)
paths[depth+i][result] = path
# Subtle bug fix, a path for value, value
# should not lead to value, other_value because
# we already inserted that first.
path = SumPath(
value=value,
tail=path.tail,
next_answer=None
)
return paths[count][target]
def path_iter(paths):
if paths.value is None:
# We are the tail
yield []
else:
while paths is not None:
value = paths.value
for answer in path_iter(paths.tail):
answer.append(value)
yield answer
paths = paths.next_answer
def fixed_sums (array, target, count):
paths = fixed_sum_paths(array, target, count)
return path_iter(paths)
for path in fixed_sums([1,2,3,3,4,5,6,9], 10, 3):
print(path)
Incidentally for your example, here are the solutions:
[1, 3, 6]
[1, 4, 5]
[2, 3, 5]
[3, 3, 4]
You should first sort the so called array. Secondly, you should determine if the problem is actually solvable, to save time... So what you do is you take the last k elements and see if the sum of those is larger or equal to the x value, if it is smaller, you are done it is not possible to do something like that.... If it is actually equal yes you are also done there is no other permutations.... O(n) feels nice doesn't it?? If it is larger, than you got a lot of work to do..... You need to store all the permutations in an seperate array.... Then you go ahead and replace the smallest of the k numbers with the smallest element in the array.... If this is still larger than x then you do it for the second and third and so on until you get something smaller than x. Once you reach a point where you have the sum smaller than x, you can go ahead and start to increase the value of the last position you stopped at until you hit x.... Once you hit x that is your combination.... Then you can go ahead and get the previous element so if you had 1,1,5, 6 in your thingy, you can go ahead and grab the 1 as well, add it to your smallest element, 5 to get 6, next you check, can you write this number 6 as a combination of two values, you stop once you hit the value.... Then you can repeat for the others as well.... You problem can be solved in O(n!) time in the worst case.... I would not suggest that you 10^27 combinations, meaning you have more than 10^27 elements, mhmmm bad idea do you even have that much space??? That's like 3bits for the header and 8 bits for each integer you would need 9.8765*10^25 terabytes just to store that clossal array, more memory than a supercomputer, you should worry about whether your computer can even store this monster rather than if you can solve the problem, that many combinations even if you find a quadratic solution it would crash your computer, and you know what quadratic is a long way off from O(n!)...
A brute force method using recursion might look like this...
For example, given variables set, x, k, the following pseudo code might work:
setSumStructure find(int[] set, int x, int k, int setIdx)
{
int sz = set.length - setIdx;
if (sz < x) return null;
if (sz == x) check sum of set[setIdx] -> set[set.size] == k. if it does, return the set together with the sum, else return null;
for (int i = setIdx; i < set.size - (k - 1); i++)
filter(find (set, x - set[i], k - 1, i + 1));
return filteredSets;
}
I want to find out the number of all permutation of nnumber.Number will be from 1 to n.The given condition is that each ithposition can have number up to Si,where Si is given for each position of number.
1<=n<=10^6
1<=si<=n
For example:
n=5
then its all five element will be
1,2,3,4,5
and given Si for each position is as:
2,3,4,5,5
It shows that at:
1st position can have 1 to 2that is 1,2 but can not be number among 3 to 5.
Similarly,
At 2nd position can have number 1 to 3 only.
At 3rd position can have number 1 to 4 only.
At 4th position can have number 1 to 5 only.
At 5th position can have number 1 to 5 only.
Some of its permutation are:
1,2,3,4,5
2,3,1,4,5
2,3,4,1,5 etc.
But these can not be:
3,1,4,2,5 As 3 is present at 1st position.
1,2,5,3,4 As 5 is present at 3rd position.
I am not getting any idea to count all possible number of permutations with given condition.
Okay, if we have a guarantee that numbers si are given in not descending order then looks like it is possible to calculate the number of permutations in O(n).
The idea of straightforward algorithm is as follows:
At step i multiply the result by current value of si[i];
We chose some number for position i. As long as we need permutation, that number cannot be repeated, so decrement all the rest si[k] where k from i+1 to the end (e.g. n) by 1;
Increase i by 1, go back to (1).
To illustrate on example for si: 2 3 3 4:
result = 1;
current si is "2 3 3 4", result *= si[0] (= 1*2 == 2), decrease 3, 3 and 4 by 1;
current si is "..2 2 3", result *= si[1] (= 2*2 == 4), decrease last 2 and 3 by 1;
current si is "....1 2", result *= si[2] (= 4*1 == 4), decrease last number by 1;
current si is "..... 1", result *= si[3] (= 4*1 == 4), done.
Hovewer this straightforward approach would require O(n^2) due to decreasing steps. To optimize it we can easily observe that at the moment of result *= si[i] our si[i] was already decreased exactly i times (assuming we start from 0 of course).
Thus O(n) way:
unsigned int result = 1;
for (unsigned int i = 0; i < n; ++i)
{
result *= (si[i] - i);
}
for each si count the number of element in your array such that a[i] <= si using binary search, and store the value to an array count[i], now the answer is the product of all count[i], however we have decrease the number of redundancy from the answer ( as same number could be count twice ), for that you can sort si and check how many number is <= s[i], then decrease that number from each count,the complexity is O(nlog(n)), hope at least I give you an idea.
To complete Yuriy Ivaskevych answer, if you don't know if the sis are in increasing order, you can sort the sis and it will also works.
And the result will be null or negative if the permutations are impossible (ex: 1 1 1 1 1)
You can try backtracking, it's a little hardcore approach but will work.
try:
http://www.thegeekstuff.com/2014/12/backtracking-example/
or google backtracking tutorial C++
int i = 0;
for(; i<size-1; i++) {
int temp = arr[i];
arr[i] = arr[i+1];
arr[i+1] = temp;
}
Here I started with the fist position of array. What if after the loop I need to execute the for loop again where the for loop starts with the next position of array.
Like for first for loop starts from: Array[0]
Second iteration: Array[1]
Third iteration: Array[2]
Example:
For array: 1 2 3 4 5
for i=0: 2 1 3 4 5, 2 3 1 4 5, 2 3 4 1 5, 2 3 4 5 1
for i=1: 1 3 2 4 5, 1 3 4 2 5, 1 3 4 5 2 so on.
You can nest loops inside each other, including the ability for the inner loop to access the iterator value of the outer loop. Thus:
for(int start = 0; start < size-1; start++) {
for(int i = start; i < size-1; i++) {
// Inner code on 'i'
}
}
Would repeat your loop with an increasing start value, thus repeating with a higher initial value for i until you're gone through your list.
Suppose you have a routine to generate all possible permutations of the array elements for a given length n. Suppose the routine, after processing all n! permutations, leaves the n items of the array in their initial order.
Question: how can we build a routine to make all possible permutations of an array with (n+1) elements?
Answer:
Generate all permutations of the initial n elements, each time process the whole array; this way we have processed all n! permutations with the same last item.
Now, swap the (n+1)-st item with one of those n and repeat permuting n elements – we get another n! permutations with a new last item.
The n elements are left in their previous order, so put that last item back into its initial place and choose another one to put at the end of an array. Reiterate permuting n items.
And so on.
Remember, after each call the routine leaves the n-items array in its initial order. To retain this property at n+1 we need to make sure the same element gets finally placed at the end of an array after the (n+1)-st iteration of n! permutations.
This is how you can do that:
void ProcessAllPermutations(int arr[], int arrLen, int permLen)
{
if(permLen == 1)
ProcessThePermutation(arr, arrLen); // print the permutation
else
{
int lastpos = permLen - 1; // last item position for swaps
for(int pos = lastpos; pos >= 0; pos--) // pos of item to swap with the last
{
swap(arr[pos], arr[lastpos]); // put the chosen item at the end
ProcessAllPermutations(arr, arrLen, permLen - 1);
swap(arr[pos], arr[lastpos]); // put the chosen item back at pos
}
}
}
and here is an example of the routine running: https://ideone.com/sXp35O
Note, however, that this approach is highly ineffective:
It may work in a reasonable time for very small input size only. The number of permutations is a factorial function of the array length, and it grows faster than exponentially, which makes really BIG number of tests.
The routine has no short return. Even if the first or second permutation is the correct result, the routine will perform all the rest of n! unnecessary tests, too. Of course one can add a return path to break iteration, but that would make the code somewhat ugly. And it would bring no significant gain, because the routine will have to make n!/2 test on average.
Each generated permutation appears deep in the last level of the recursion. Testing for a correct result requires making a call to ProcessThePermutation from within ProcessAllPermutations, so it is difficult to replace the callee with some other function. The caller function must be modified each time you need another method of testing / procesing / whatever. Or one would have to provide a pointer to a processing function (a 'callback') and push it down through all the recursion, down to the place where the call will happen. This might be done indirectly by a virtual function in some context object, so it would look quite nice – but the overhead of passing additional data down the recursive calls can not be avoided.
The routine has yet another interesting property: it does not rely on the data values. Elements of the array are never compared. This may sometimes be an advantage: the routine can permute any kind of objects, even if they are not comparable. On the other hand it can not detect duplicates, so in case of equal items it will make repeated results. In a degenerate case of all n equal items the result will be n! equal sequences.
So if you ask how to generate all permutations to detect a sorted one, I must answer: DON'T.
Do learn effective sorting algorithms instead.
My objective is to iterate through all combinations of a given amount of 1's and 0's. Say, if I am given the number 5, what would be a sufficiently fast way to list
1110100100,
1011000101, etc.
(Each different combination of 5 1's and 5 0's)
I am attempting to avoid iterating through all possible permutations and checking if 5 1's exist as 2^n is much greater than (n choose n/2). Thanks.
UPDATE
The answer can be calculated efficiently (recurses 10 deep) with:
// call combo() to have calculate(b) called with every valid bitset combo exactly once
combo(int index = 0, int numones = 0) {
static bitset<10> b;
if( index == 10 ) {
calculate(b); // can't have too many zeroes or ones, it so must be 5 zero and 5 one
} else {
if( 10 - numones < 5 ) { // ignore paths with too many zeroes
b[index] = 0;
combo(b, index+1, numones);
}
if( numones < 5 ) { // ignore paths with too many ones
b[index] = 1;
combo(b, index+1, numones++);
}
}
}
(Above code is not tested)
You can transform the problem. If you fix the 1s (or vice versa) then it's simply a matter of where you put the 0s. For 5 1s, there are 5+1 bins, and you want to put 5 elements (0s) in the bins.
This can be solved with a recursion per bin and a loop for each bin (put 0...reaming elements in the bin - except for the last bin, where you have to put all the remaning elements).
Another way to think about it is as a variant of the the string permutation question - just build a vector of length 2n (e.g. 111000) and then use the same algorithm for string permutation to build the result.
Note that the naive algorithm will print duplicate results. However, the algorithm can be easily adapted to ignore such duplicates by keeping a bool array in the recursive function that keeps the values set for the specific bit.
I have a problem, I want to generate a table of 4 columns and 1 line, and with integers in the range 0 to 9, without repeating and are random each time it is run.
arrives to this, but I have a problem I always generates a 0 in the first element. And i dont know how to put a limit of 0-9
anyone who can help me?
Code of Function:
function [ n ] = generar( )
n = [-1 -1 -1 -1];
for i = 1:4
r=abs(i);
dig=floor((r-floor(r))*randn);
while find (n == dig)
r=r+1;
dig=dig+floor(r-randn);
end
n(i)=dig;
end
end
And the results:
generar()
ans =
0 3 9 6
generar()
ans =
0 2 4 8
I dont know if this post is a duplicate, but i need help with my specific problem.
So assuming you want matlab, because the code you supplied is matlab, you can simply do this:
randperm(10, 4) - 1
This will give you 4 unique random numbers from 0-9.
Another way of getting there is randsample(n, k) where n is an integer, then a random sample of size k will be drawn from the population 1:n (as a column vector). So for your case, you would get the result by:
randsample(10, 4)' - 1
It draws 4 random numbers from the population without replacement and all with same weights. This might be slower than randperm(10, 4) - 1 as its real strength comes with the ability to pass over population vectors for more sophisticated examples.
Alternatively one can call it with randsample(pop, k) where pop is the population-vector of which you want to draw a random sample of size k. So for your case, one would do:
randsample(0:9, 4)
The result will have the same singleton dimension as the population-vector, which in this case is a row vector.
Just to offer another solution and get you in touch with randsample().