Related
I have written this code using vector. Some case has been passed but others show timeout termination error.
The problem statement is:-
You have an identity permutation of N integers as an array initially. An identity permutation of N integers is [1,2,3,...N-1,N]. In this task, you have to perform M operations on the array and report the sum of the elements of the array after each operation.
The ith operation consists of an integer opi.
If the array contains opi, swap the first and last elements in the array.
Else, remove the last element of the array and push opi to the end of the array.
Input Format
The first line contains two space-separated integers N and M.
Then, M lines follow denoting the operations opi.
Constraints :
2<=N,M <= 10^5
1 <= op <= 5*10^5
Output Format
Print M lines, each containing a single integer denoting the answer to each of the M operations.
Sample Input 0
3 2
4
2
Sample Output 0
7
7
Explanation 0
Initially, the array is [1,2,3].
After the 1st operation, the array becomes[1,2,4] as opi = 4, as 4 is not present in the current array, we remove 3 and push 4 to the end of the array and hence, sum=7 .
After 2nd operation the array becomes [4,2,1] as opi = 2, as 2 is present in the current array, we swap 1 and 4 and hence, sum=7.
Here is my code:
#include <bits/stdc++.h>
using namespace std;
int main()
{
long int N,M,op,i,t=0;
vector<long int > g1;
cin>>N>>M;
if(N>=2 && M>=2) {
g1.reserve(N);
for(i = 1;i<=N;i++) {
g1.push_back(i);
}
while(M--) {
cin>>op;
auto it = find(g1.begin(), g1.end(), op);
if(it != (g1.end())) {
t = g1.front();
g1.front() = g1.back();
g1.back() = t;
cout<<accumulate(g1.begin(), g1.end(), 0);
cout<<endl;
}
else {
g1.back() = op;
cout<<accumulate(g1.begin(), g1.end(), 0);
cout<<endl;
}
}
}
return 0;
}
Please Suggest changes.
Looking carefully in question you will find that the operation are made only on the first and last element. So there is no need to involve a whole vector in it much less calculating the sum. we can calculate the whole sum of the elements except first and last by (n+1)(n-2)/2 and then we can manipulate the first and last element in the question. We can also shorten the search by using (1<op<n or op==first element or op == last element).
p.s. I am not sure it will work completely but it certainly is faster
my guess, let take N = 3, op = [4, 2]
N= [1,2,3]
sum = ((N-2) * (N+1)) / 2, it leave first and last element, give the sum of numbers between them.
we need to play with the first and last elements. it's big o(n).
function performOperations(N, op) {
let out = [];
let first = 1, last = N;
let sum = Math.ceil( ((N-2) * (N+1)) / 2);
for(let i =0;i<op.length;i++){
let not_between = !(op[i] >= 2 && op[i] <= N-1);
if( first!= op[i] && last != op[i] && not_between) {
last = op[i];
}else {
let t = first;
first = last;
last = t;
}
out.push(sum + first +last)
}
return out;
}
There are 2 i/p array's. They are identical when they have exactly same numbers in it. To make them identical, we can swap their elements. Swapping will have cost. If we are swapping a and b elements then cost = min(a, b).
While making array's identical, cost should be minimum.
If it is not possible to make array identical then print -1.
i/p:
3 6 6 2
2 7 7 3
o/p :
4
Here I have swapped (2,7) and (2,6). So min Cost = 2 + 2 = 4.
Logic :
Make 2 maps which will store frequency of i/p array's elements.
if element "a" in aMap is also present in bMap, then we have to consider number of swapping for a = abs(freq(a) in aMap - freq(a) in bMap)
if frequency of elements is "odd", then not possible to make them identical.
else , add total swaps from both maps and find cost using
cost = smallest element * total swaps
Here is the code
#include<iostream>
#include<algorithm>
#include<map>
using namespace std;
int main()
{
int t;
cin >> t;
while(t--)
{
int size;
long long int cost = 0;
cin >> size;
bool flag = false;
map<long long int, int> aMap;
map<long long int, int> bMap;
// storing frequency of elements of 1st input array in map
for( int i = 0 ; i < size; i++)
{
long long int no;
cin >> no;
aMap[no]++;
}
// storing frequency of elements of 2nd input array in map
for(int i = 0 ; i < size; i++)
{
long long int no;
cin >> no;
bMap[no]++;
}
// fetching smallest element (i.e. 1st element) from both map
long long int firstNo = aMap.begin()->first;
long long int secondNo = bMap.begin()->first;
long long int smallestNo;
// finding smallest element from both maps
if(firstNo < secondNo)
smallestNo = firstNo;
else
smallestNo = secondNo;
map<long long int, int> :: iterator itr;
// trying to find out total number of swaps we have to perform
int totalSwapsFromA = 0;
int totalSwapsFromB = 0;
// trversing a map
for(itr = aMap.begin(); itr != aMap.end(); itr++)
{
// if element "a" in aMap is also present in bMap, then we have to consider
// number of swapping = abs(freq(a) in aMap - freq(a) in bMap)
auto newItr = bMap.find(itr->first);
if(newItr != bMap.end())
{
if(itr->second >= newItr->second)
{
itr->second -= newItr->second;
newItr->second = 0;
}
else
{
newItr->second -= itr->second;
itr->second = 0;
}
}
// if freq is "odd" then, this input is invalid as it can not be swapped
if(itr->second & 1 )
{
flag = true;
break;
}
else
{
// if freq is even, then we need to swap only for freq(a)/ 2 times
itr->second /= 2;
// if swapping element is smallest element then we required 1 less swap
if(itr->first == smallestNo && itr->second != 0)
totalSwapsFromA += itr->second -1;
else
totalSwapsFromA += itr->second;
}
}
// traversing bMap to check whether there any number is present which is
// not in aMap.
if(!flag)
{
for(itr = bMap.begin(); itr != bMap.end(); itr++)
{
auto newItr = aMap.find(itr->first);
if( newItr == aMap.end())
{
// if frew is odd , then i/p is invalid
if(itr->second & 1)
{
flag = true;
break;
}
else
{
itr->second /= 2;
// if swapping element is smallest element then we required 1 less swap
if(itr->first == smallestNo && itr->second != 0)
totalSwapsFromB += itr->second -1;
else
totalSwapsFromB += itr->second;
}
}
}
}
if( !flag )
{
cost = smallestNo * (totalSwapsFromB + totalSwapsFromA);
cout<<"cost "<<cost <<endl;
}
else
cout<<"-1"<<endl;
}
return 0;
}
No error in the above code but giving wrong answer and not getting accepted.
Can anyone improve this code / logic ?
Suppose you have 2 arrays:
A: 1 5 5
B: 1 4 4
We know that we want to move a 5 down and a 4 up, so we have to options: swapping 4 by 5 (with cost min(4, 5) = 4) or using the minimum element to do achive the same result, making 2 swaps:
A: 1 5 5 swap 1 by 4 (cost 1)
B: 1 4 4
________
A: 4 5 5 swap 1 by 5 (cost 1)
B: 1 1 4
________
A: 4 1 5 total cost: 2
B: 5 1 4
So the question we do at every swap is this. Is it better to swap directly or swapping twice using the minimum element as pivot?
In a nutshell, let m be the minimum element in both arrays and you want to swap i for j. The cost of the swap will be
min( min(i,j), 2 * m )
So just find out all the swaps you need to do, apply this formula and sum the results to get your answer.
#user1745866 You can simplify your task of determining the answer -1 by using only variable:
let we have int x=0 and we will just do XOR of all the i/p integers like this:
int x = 0;
for(int i=0;i<n;i++){
cin>>a[i];
x = x^a[i];
}
for(int i=0;i<n;i++){
cin>>b[i];
x = x^b[i];
}
if(x!=0)
cout<<-1;
else{
...do code for remain 2 condition...
}
Now the point is how it will work because , as all the numbers of both array should occurs only even number of times and when we do XOR operation of any number which occured even number of times we will get 0.... otherwise they can't be identical arrays.
Now for 2nd condition(which gives answer 0) you should use multimap so you would be able to directly compare both arrays in O(n) time complexity as if all elements of both arrays are same you can output:0
(Notice: i am suggesting multimap because 1:You would have both array sorted and all elements would be there means also duplicates.
2: because they are sorted, if they consist of same element at same position we can output:0 otherwise you have to proceed further for your 3rd condition or have to swap the elements.)
For reducing the swap cost see Daniel's answer. For finding if the swap is actually possible, please do the following, the swaps are actually only possible if you have an even number of elements in total, so that you can split them out evenly, so if you have 2, 4 or 6 5's you are good, but if you have 1, 3, or 5 5's return -1. It is impossible if your number of duplicates of a number is odd. For actually solving the problem, there is a very simple solution I can think of, through it is a little bit expensive, you just need to make sure that there are the same number of elements on each side so the simple way to do that would be to declare a new array:
int temp[size of original arrays];
//Go through both arrays and store them in temp
Take half of each element, so something like:
int count[max element in array - min element in array];
for(int i = 0; i < temp.size(); i++){
count[temp[i]]++;
}
Take half of each element from temp. When you see an element that matches a element on your count array so whenever you see a 1 decrement the index on the count array by 1, so something like count[1]--; Assuming count starts at 0. If the index is at zero and the element is that one, that means a swap needs to be done, in this case find the next min in the other array and swap them. Albeit a little bit expensive, but it is the simplest way I can think of. So for example in your case:
i/p:
3 6 6 2
2 7 7 3
o/p :
4
We would need to store the min index as 2. Cause that is the smallest one. So we would have an array that looks like the following:
1 1 0 0 1 1
//one two one three zero four zero five 1 six and 1 seven
You would go through the first array, when you see the second six, your array index at 6 would be zero, so you know you need to swap it, you would find the min in the other array, which is 2 and then swap 6 with 2, after wards you can go through the array smoothly. Finally you go through the second array, afterwards when you see the last 7 it will look for the min on the other side swap them...., which is two, note that if you had 3 twos on one side and one two on the other, chances are the three twos will go to the other side, and 2 of them will come back, because we are always swapping the min, so there will always be an even number of ways we can rearrange the elements.
Problem link https://www.codechef.com/JULY20B/problems/CHFNSWPS
here for calculating minimum number of swap.we will having 2 cases
let say an example
l1=[1,2,2]
l2=[1,5,5]
case 1. swap each pair wrt to min(l1,l2)=1
step 1 swapping single 2 of a pair of 2 from l1-> [1,1,2]
[2,5,5] cost is 1
step 2 swapping single 5 of a pair of 5 from l1-> [1,5,2]
[2,1,5] cost is 1
total cost is 2
case 2. swap min of l1 with max of l2(repeat until both list end)
try to think if we sort 1st list in increasing order and other as decreasing order then we can minimize cost.
l1=[1,2,2]
l2=[5,5,1]
Trick is that we only need to store min(l1,l2) in variable say mn. Then remove all common element from both list.
now list became l1=[2,2]
l2=[5,5]
then swap each element from index 0 to len(l1)-1 with jump of 2 like 0,2,4,6..... because each odd neighbour wiil be same as previous number.
after perform swapping cost will be 2 and
l1=[5,2]
l2=[2,5] cost is 2
total cost is 2
Let say an other example
l1=[2,2,5,5]
l2=[3,3,4,4]
after solving wrt to min(l1,l2) total cost will be 2+2+2=6
but cost after sorting list will be swap of ((2,4) and (5,3)) is 2+3=5
so minimum swap to make list identical is min(5,6)=5
//code
l1.sort()
l2.sort(reverse=True)
sums=0
for i in range(len(l1)):
sums+=min(min(l1[i],l2[i]),2*minimum))
print(sums)
#print -1 if u get odd count of a key in total (means sums of count of key in both list)
I am struggling with a problem for hours. It is a constraint satisfaction problem. Let me describe it on a simple example:
Assume there is an array of integers with length 8. Every cell can take certain values. First 4 cells can take 0, 1 or 2 and the other half can take 0 or 1. These 3 arrays can be some examples.
{2,1,0,2,1,1,0,1}
{2,2,1,0,0,1,0,0}
{0,0,0,2,0,0,0,1}
However there are some constraints to construct the arrays as follows:
constraint1 = {1,-,-,-,-,1,-,-} // !(cell2=1 && cell6=1) cell2 and cell6 can not be in these format.
constraint2 = {0,-,-,-,-,-,-,0} // !(cell1=0 && cell8=0)
constraint3 = {-,-,-,2,1,1,-,-} // !(cell4=2 && cell5=1 && cell6=1)
constraint4 = {1,1,-,-,-,-,-,-} // !(cell1=1 && cell2=1)
For better understanding;
{0,1,1,2,0,1,0,0} // this is not valid, because it violates the constraint2
{1,1,2,2,1,1,0,1} // this is not valid, because it violates the constraint3 and constraint4
{1,1,0,0,0,1,0,0} // this is not valid, because it violates the constraint4
I need to generate an array of integers which does not violates any of the given constraints.
In my approach;
1) Create an array (called myArray) and initialize every cell to -1
2) Count the number of cells which are used in constraints. Above example, cell1 is used 3 times, cell2 is used 1 time, cell3 is not used, so on so forth.
3) Choose the cell which is used more in constraints (it is cell1, used 3 times)
4) Find the distribution of numbers in this cell. (In cell1, 1 is used 2 times and 0 is used 1 time)
5) Change this chosen cell in myArray to the number which is used less. (In cell1, since 0 is used less than 1, cell1 in myArray will be 0)
6) Delete all the constraints from the list which has 1 or 2 in their cell1.
7) Go to step 2 and do same steps until all constraints are eliminated
The idea of this algorithm is to chose the cell and its value in such a way that it will eliminate more constraints.
However, this algorithm is not working, when the number of constraints are higher.
Important Note: This is just a simple example. In normal case, length of the array is longer (averagely 100) and number of constraints is higher (more than 200). My input is length of the array, N constraints and the values each cell can take.
Is there anyone who has better idea to solve this problem?
Here is a code that I have written in C# to generate a random matrix and then to remove the constraint in the matrix.
class Program
{
static void Main(string[] args)
{
int[] inputData = new int[4] { 3, 7, 3, 3 };
int matrixRowSize = 6;
/////////////////////////// Constraints
int []constraint1 = new int[4] { 1, -1, -1, 2}; // here is the constaint that i want to remove.
// note the constaints could be more than 1, so there could be a generic method
Random r = new Random();
int[,] Random_matrix = new int[matrixRowSize, inputData.Length];
///////////// generate random matrix
for (int i = 0; i < inputData.Length; i++)
{
for (int j = 0; j < matrixRowSize; j++)
{
int k = r.Next(0, inputData[i]);
Random_matrix[j, i] = k;
}
}
}
Say I have a set of numbers from [0, ....., 499]. Combinations are currently being generated sequentially using the C++ std::next_permutation. For reference, the size of each tuple I am pulling out is 3, so I am returning sequential results such as [0,1,2], [0,1,3], [0,1,4], ... [497,498,499].
Now, I want to parallelize the code that this is sitting in, so a sequential generation of these combinations will no longer work. Are there any existing algorithms for computing the ith combination of 3 from 500 numbers?
I want to make sure that each thread, regardless of the iterations of the loop it gets, can compute a standalone combination based on the i it is iterating with. So if I want the combination for i=38 in thread 1, I can compute [1,2,5] while simultaneously computing i=0 in thread 2 as [0,1,2].
EDIT Below statement is irrelevant, I mixed myself up
I've looked at algorithms that utilize factorials to narrow down each individual element from left to right, but I can't use these as 500! sure won't fit into memory. Any suggestions?
Here is my shot:
int k = 527; //The kth combination is calculated
int N=500; //Number of Elements you have
int a=0,b=1,c=2; //a,b,c are the numbers you get out
while(k >= (N-a-1)*(N-a-2)/2){
k -= (N-a-1)*(N-a-2)/2;
a++;
}
b= a+1;
while(k >= N-1-b){
k -= N-1-b;
b++;
}
c = b+1+k;
cout << "["<<a<<","<<b<<","<<c<<"]"<<endl; //The result
Got this thinking about how many combinations there are until the next number is increased. However it only works for three elements. I can't guarantee that it is correct. Would be cool if you compare it to your results and give some feedback.
If you are looking for a way to obtain the lexicographic index or rank of a unique combination instead of a permutation, then your problem falls under the binomial coefficient. The binomial coefficient handles problems of choosing unique combinations in groups of K with a total of N items.
I have written a class in C# to handle common functions for working with the binomial coefficient. It performs the following tasks:
Outputs all the K-indexes in a nice format for any N choose K to a file. The K-indexes can be substituted with more descriptive strings or letters.
Converts the K-indexes to the proper lexicographic index or rank of an entry in the sorted binomial coefficient table. This technique is much faster than older published techniques that rely on iteration. It does this by using a mathematical property inherent in Pascal's Triangle and is very efficient compared to iterating over the set.
Converts the index in a sorted binomial coefficient table to the corresponding K-indexes. I believe it is also faster than older iterative solutions.
Uses Mark Dominus method to calculate the binomial coefficient, which is much less likely to overflow and works with larger numbers.
The class is written in .NET C# and provides a way to manage the objects related to the problem (if any) by using a generic list. The constructor of this class takes a bool value called InitTable that when true will create a generic list to hold the objects to be managed. If this value is false, then it will not create the table. The table does not need to be created in order to use the 4 above methods. Accessor methods are provided to access the table.
There is an associated test class which shows how to use the class and its methods. It has been extensively tested with 2 cases and there are no known bugs.
To read about this class and download the code, see Tablizing The Binomial Coeffieicent.
The following tested code will iterate through each unique combinations:
public void Test10Choose5()
{
String S;
int Loop;
int N = 500; // Total number of elements in the set.
int K = 3; // Total number of elements in each group.
// Create the bin coeff object required to get all
// the combos for this N choose K combination.
BinCoeff<int> BC = new BinCoeff<int>(N, K, false);
int NumCombos = BinCoeff<int>.GetBinCoeff(N, K);
// The Kindexes array specifies the indexes for a lexigraphic element.
int[] KIndexes = new int[K];
StringBuilder SB = new StringBuilder();
// Loop thru all the combinations for this N choose K case.
for (int Combo = 0; Combo < NumCombos; Combo++)
{
// Get the k-indexes for this combination.
BC.GetKIndexes(Combo, KIndexes);
// Verify that the Kindexes returned can be used to retrive the
// rank or lexigraphic order of the KIndexes in the table.
int Val = BC.GetIndex(true, KIndexes);
if (Val != Combo)
{
S = "Val of " + Val.ToString() + " != Combo Value of " + Combo.ToString();
Console.WriteLine(S);
}
SB.Remove(0, SB.Length);
for (Loop = 0; Loop < K; Loop++)
{
SB.Append(KIndexes[Loop].ToString());
if (Loop < K - 1)
SB.Append(" ");
}
S = "KIndexes = " + SB.ToString();
Console.WriteLine(S);
}
}
You should be able to port this class over fairly easily to C++. You probably will not have to port over the generic part of the class to accomplish your goals. Your test case of 500 choose 3 yields 20,708,500 unique combinations, which will fit in a 4 byte int. If 500 choose 3 is simply an example case and you need to choose combinations greater than 3, then you will have to use longs or perhaps fixed point int.
You can describe a particular selection of 3 out of 500 objects as a triple (i, j, k), where i is a number from 0 to 499 (the index of the first number), j ranges from 0 to 498 (the index of the second, skipping over whichever number was first), and k ranges from 0 to 497 (index of the last, skipping both previously-selected numbers). Given that, it's actually pretty easy to enumerate all the possible selections: starting with (0,0,0), increment k until it gets to its maximum value, then increment j and reset k to 0 and so on, until j gets to its maximum value, and so on, until j gets to its own maximum value; then increment i and reset both j and k and continue.
If this description sounds familiar, it's because it's exactly the same way that incrementing a base-10 number works, except that the base is much funkier, and in fact the base varies from digit to digit. You can use this insight to implement a very compact version of the idea: for any integer n from 0 to 500*499*498, you can get:
struct {
int i, j, k;
} triple;
triple AsTriple(int n) {
triple result;
result.k = n % 498;
n = n / 498;
result.j = n % 499;
n = n / 499;
result.i = n % 500; // unnecessary, any legal n will already be between 0 and 499
return result;
}
void PrintSelections(triple t) {
int i, j, k;
i = t.i;
j = t.j + (i <= j ? 1 : 0);
k = t.k + (i <= k ? 1 : 0) + (j <= k ? 1 : 0);
std::cout << "[" << i << "," << j << "," << k << "]" << std::endl;
}
void PrintRange(int start, int end) {
for (int i = start; i < end; ++i) {
PrintSelections(AsTriple(i));
}
}
Now to shard, you can just take the numbers from 0 to 500*499*498, divide them into subranges in any way you'd like, and have each shard compute the permutation for each value in its subrange.
This trick is very handy for any problem in which you need to enumerate subsets.
Write a function which has:
input: array of pairs (unique id and weight) length of N, K =< N
output: K random unique ids (from input array)
Note: being called many times frequency of appearing of some Id in the output should be greater the more weight it has.
Example: id with weight of 5 should appear in the output 5 times more often than id with weight of 1. Also, the amount of memory allocated should be known at compile time, i.e. no additional memory should be allocated.
My question is: how to solve this task?
EDIT
thanks for responses everybody!
currently I can't understand how weight of pair affects frequency of appearance of pair in the output, can you give me more clear, "for dummy" explanation of how it works?
Assuming a good enough random number generator:
Sum the weights (total_weight)
Repeat K times:
Pick a number between 0 and total_weight (selection)
Find the first pair where the sum of all the weights from the beginning of the array to that pair is greater than or equal to selection
Write the first part of the pair to the output
You need enough storage to store the total weight.
Ok so you are given input as follows:
(3, 7)
(1, 2)
(2, 5)
(4, 1)
(5, 2)
And you want to pick a random number so that the weight of each id is reflected in the picking, i.e. pick a random number from the following list:
3 3 3 3 3 3 3 1 1 2 2 2 2 2 4 5 5
Initially, I created a temporary array but this can be done in memory as well, you can calculate the size of the list by summing all the weights up = X, in this example = 17
Pick a random number between [0, X-1], and calculate which which id should be returned by looping through the list, doing a cumulative addition on the weights. Say I have a random number 8
(3, 7) total = 7 which is < 8
(1, 2) total = 9 which is >= 8 **boom** 1 is your id!
Now since you need K random unique ids you can create a hashtable from initial array passed to you to work with. Once you find an id, remove it from the hash and proceed with algorithm. Edit Note that you create the hashmap initially only once! You algorithm will work on this instead of looking through the array. I did not put in in the top to keep the answer clear
As long as your random calculation is not using any extra memory secretly, you will need to store K random pickings, which are <= N and a copy of the original array so max space requirements at runtime are O(2*N)
Asymptotic runtime is :
O(n) : create copy of original array into hastable +
(
O(n) : calculate sum of weights +
O(1) : calculate random between range +
O(n) : cumulative totals
) * K random pickings
= O(n*k) overall
This is a good question :)
This solution works with non-integer weights and uses constant space (ie: space complexity = O(1)). It does, however modify the input array, but the only difference in the end is that the elements will be in a different order.
Add the weight of each input to the weight of the following input, starting from the bottom working your way up. Now each weight is actually the sum of that input's weight and all of the previous weights.
sum_weights = the sum of all of the weights, and n = N.
K times:
Choose a random number r in the range [0,sum_weights)
binary search the first n elements for the first slot where the (now summed) weight is greater than or equal to r, i.
Add input[i].id to output.
Subtract input[i-1].weight from input[i].weight (unless i == 0). Now subtract input[i].weight from to following (> i) input weights and also sum_weight.
Move input[i] to position [n-1] (sliding the intervening elements down one slot). This is the expensive part, as it's O(N) and we do it K times. You can skip this step on the last iteration.
subtract 1 from n
Fix back all of the weights from n-1 down to 1 by subtracting the preceding input's weight
Time complexity is O(K*N). The expensive part (of the time complexity) is shuffling the chosen elements. I suspect there's a clever way to avoid that, but haven't thought of anything yet.
Update
It's unclear what the question means by "output: K random unique Ids". The solution above assumes that this meant that the output ids are supposed to be unique/distinct, but if that's not the case then the problem is even simpler:
Add the weight of each input to the weight of the following input, starting from the bottom working your way up. Now each weight is actually the sum of that input's weight and all of the previous weights.
sum_weights = the sum of all of the weights, and n = N.
K times:
Choose a random number r in the range [0,sum_weights)
binary search the first n elements for the first slot where the (now summed) weight is greater than or equal to r, i.
Add input[i].id to output.
Fix back all of the weights from n-1 down to 1 by subtracting the preceding input's weight
Time complexity is O(K*log(N)).
My short answer: in no way.
Just because the problem definition is incorrect. As Axn brilliantly noticed:
There is a little bit of contradiction going on in the requirement. It states that K <= N. But as K approaches N, the frequency requirement will be contradicted by the Uniqueness requirement. Worst case, if K=N, all elements will be returned (i.e appear with same frequency), irrespective of their weight.
Anyway, when K is pretty small relative to N, calculated frequencies will be pretty close to theoretical values.
The task may be splitted on two subtasks:
Generate random numbers with a given distribution (specified by weights)
Generate unique random numbers
Generate random numbers with a given distribution
Calculate sum of weights (sumOfWeights)
Generate random number from the range [1; sumOfWeights]
Find an array element where the sum of weights from the beginning of the array is greater than or equal to the generated random number
Code
#include <iostream>
#include <cstdlib>
#include <ctime>
// 0 - id, 1 - weight
typedef unsigned Pair[2];
unsigned Random(Pair* i_set, unsigned* i_indexes, unsigned i_size)
{
unsigned sumOfWeights = 0;
for (unsigned i = 0; i < i_size; ++i)
{
const unsigned index = i_indexes[i];
sumOfWeights += i_set[index][2];
}
const unsigned random = rand() % sumOfWeights + 1;
sumOfWeights = 0;
unsigned i = 0;
for (; i < i_size; ++i)
{
const unsigned index = i_indexes[i];
sumOfWeights += i_set[index][3];
if (sumOfWeights >= random)
{
break;
}
}
return i;
}
Generate unique random numbers
Well known Durstenfeld-Fisher-Yates algorithm may be used for generation unique random numbers. See this great explanation.
It requires N bytes of space, so if N value is defined at compiled time, we are able to allocate necessary space at compile time.
Now, we have to combine these two algorithms. We just need to use our own Random() function instead of standard rand() in unique numbers generation algorithm.
Code
template<unsigned N, unsigned K>
void Generate(Pair (&i_set)[N], unsigned (&o_res)[K])
{
unsigned deck[N];
for (unsigned i = 0; i < N; ++i)
{
deck[i] = i;
}
unsigned max = N - 1;
for (unsigned i = 0; i < K; ++i)
{
const unsigned index = Random(i_set, deck, max + 1);
std::swap(deck[max], deck[index]);
o_res[i] = i_set[deck[max]][0];
--max;
}
}
Usage
int main()
{
srand((unsigned)time(0));
const unsigned c_N = 5; // N
const unsigned c_K = 2; // K
Pair input[c_N] = {{0, 5}, {1, 3}, {2, 2}, {3, 5}, {4, 4}}; // input array
unsigned result[c_K] = {};
const unsigned c_total = 1000000; // number of iterations
unsigned counts[c_N] = {0}; // frequency counters
for (unsigned i = 0; i < c_total; ++i)
{
Generate<c_N, c_K>(input, result);
for (unsigned j = 0; j < c_K; ++j)
{
++counts[result[j]];
}
}
unsigned sumOfWeights = 0;
for (unsigned i = 0; i < c_N; ++i)
{
sumOfWeights += input[i][1];
}
for (unsigned i = 0; i < c_N; ++i)
{
std::cout << (double)counts[i]/c_K/c_total // empirical frequency
<< " | "
<< (double)input[i][1]/sumOfWeights // expected frequency
<< std::endl;
}
return 0;
}
Output
N = 5, K = 2
Frequencies
Empiricical | Expected
0.253813 | 0.263158
0.16584 | 0.157895
0.113878 | 0.105263
0.253582 | 0.263158
0.212888 | 0.210526
Corner case when weights are actually ignored
N = 5, K = 5
Frequencies
Empiricical | Expected
0.2 | 0.263158
0.2 | 0.157895
0.2 | 0.105263
0.2 | 0.263158
0.2 | 0.210526
I do assume that the ids in the output must be unique. This makes this problem a specific instance of random sampling problems.
The first approach that I can think of solves this in O(N^2) time, using O(N) memory (The input array itself plus constant memory).
I Assume that the weights are possitive.
Let A be the array of pairs.
1) Set N to be A.length
2) calculate the sum of all weights W.
3) Loop K times
3.1) r = rand(0,W)
3.2) loop on A and find the first index i such that A[1].w + ...+ A[i].w <= r < A[1].w + ... + A[i+1].w
3.3) add A[i].id to output
3.4) A[i] = A[N-1] (or swap if the array contents should be preserved)
3.5) N = N - 1
3.6) W = W - A[i].w