Related
Suppose I have an array of size 100.
Initially, let's assume that all the elements have a value of 0.
Now, let's say I want to insert 60 elements such that the elements get filled evenly.
I don't want all the elements to be filled from arr[0] to arr[59]. Rather, I want the whole array to be filled in such a way that the array looks filled. What algorithm can I use to achieve this?
For eg-
I have 5 elements to be filled (let's say with 1) in an array of size 10. Then the array should look like this:
[1,0,1,0,1,0,1,0,1,0]
In the case of 3 elements,
[1,0,0,0,1,0,0,0,1,0]
Is there any smart way to do this dynamically?
You would need to find the right gap length to evenly fill it. For this, we binary search the correct gap which most evenly fills the gap such that the no. of 1's to be supposedly filled doesn't go out of the array bounds.
In the case where no. of ones to be filled is greater than half of the array size, we fill it with as even gaps of length 2 as much as possible and fill the remaining ones adjacent to each other.
function solve(arraySize, oneCount) {
let newArray = Array(arraySize).fill(0);
let low = 0,
high = arraySize;
let gap = 0;
while (low <= high) {
let mid = (low + high) >> 1;
if (mid * (oneCount - 1) < arraySize) {
gap = mid;
low = mid + 1;
} else {
high = mid - 1;
}
}
let needsEvenDivision = oneCount > (arraySize >> 1) && gap === 1;
gap = needsEvenDivision ? 2 : gap;
let idx = 0;
while (idx < arraySize && oneCount > 0) {
newArray[idx] = 1;
oneCount--;
if (needsEvenDivision && arraySize - idx - 2 < oneCount) gap = 1;
idx += gap;
}
return newArray;
}
console.log(solve(4, 2));
console.log(solve(10, 4));
console.log(solve(10, 7));
The problem is like:
There is a 60mm long rubber with a scale every 1mm.
Where is the position of each scale when this rubber is stretched to a length of 100 mm?
int main(void)
{
constexpr int M= 10;
constexpr int n = 5;
//initially all 0
int Array[M] = { 0 };
//Calclate where the ith element of the n elements should go in the result array
for( int i=0; i<n; ++i )
{
int idx = (int)std::round( (double)i * M / n);
Array[idx] = 1;
}
//Show Result
for( auto a : Array ){ std::cout << a << " "; }
std::cout << std::endl;
return 0;
}
Here is something you can do
function populateArray(arraySize, arrayElements) {
const newArray = [];
if (arraySize >= arrayElements) {
const parts = Math.ceil(arraySize / arrayElements);
for (let i = 0; i < arraySize; i++) {
if (i % parts == 0) {
newArray.push(1);
} else {
newArray.push(0);
}
}
return newArray;
}
return "Invalid array Size";
}
console.log(populateArray(10, 3));
This question was asked in the Google programming interview. I thought of two approaches for the same:
Find all the subsequences of length. While doing so compute the sum and of the two elements and check if it is equal to k. If ye, print Yes, else keep searching. This is a brute Force approach.
Sort the array in non-decreasing order. Then start traversing the array from its right end. Say we have the sorted array, {3,5,7,10} and we want the sum to be 17. We will start from element 10, index=3, let's denote the index with 'j'. Then include the current element and compute required_sum= sum - current_element. After that, we can perform a binary or ternary search in array[0- (j-1)] to find if there is an element whose value is equal to the required_sum. If we find such an element, we can break as we have found a subsequence of length 2 whose sum is the given sum. If we don't find any such element, then decrease the index of j and repeat the above-mentioned steps for resulting subarray of length= length-1 i.e. by excluding the element at index 3 in this case.
Here we have considered that array could have negative as well as positive integers.
Can you suggest a better solution than this? A DP solution maybe? A solution that can further reduce it's time complexity.
This question can be easily solved with the help of set in O(N) time and space complexity.First add all the elements of array into set and then traverse each element of array and check whether K-ar[i] is present in set or not.
Here is the code in java with O(N) complexity :
boolean flag=false;
HashSet<Long> hashSet = new HashSet<>();
for(int i=0;i<n;i++){
if(hashSet.contains(k-ar[i]))flag=true;
hashSet.add(ar[i]);
}
if(flag)out.println("YES PRESENT");
else out.println("NOT PRESENT");
Here is a Java implementation with the same time complexity as the algorithm used to sort the array. Note that this is faster than your second idea because we do not need to search the entire array for a matching partner each time we examine a number.
public static boolean containsPairWithSum(int[] a, int x) {
Arrays.sort(a);
for (int i = 0, j = a.length - 1; i < j;) {
int sum = a[i] + a[j];
if (sum < x)
i++;
else if (sum > x)
j--;
else
return true;
}
return false;
}
Proof by induction:
Let a[0,n] be an array of length n+1 and p = (p1, p2) where p1, p2 are integers and p1 <= p2 (w.l.o.g.). Assume a[0,n] contains p1 and p2. In the case that it does not, the algorithm is obviously correct.
Base case (i = 0, j = n):
a[0,-1] does not contain p1 and a[n,n+1] does not contain p2.
Hypothesis:
a[0,i-1] does not contain a[i] and a[j+1,n] does not contain p2.
Step case (i to i + 1 or j to j - 1):
Assume p1 = a[i]. Then, since p1 + a[j] < p1 + p2, index j must be increased. But from the hypothesis we know that a[j+1,n-1] does not contain p2. Contradiction. It follows that p1 != a[i].
j to j - 1 analogously.
Because each iteration, a[0,i-1] and a[j+1,n], does not contain p1, and p2, a[i,j] does contain p1 and p2. Eventually, a[i] = p1 and a[j] = p2 and the algorithm returns true.
This is java implementation with O(n) Time complexity and O(n) space. The idea is have a HashMap which will contain complements of every array element w.r.t target. If the complement is found, we have 2 array elements which sum to the target.
public boolean twoSum(int[] nums, int target) {
if(nums.length == 0 || nums == null) return false;
Map<Integer, Integer> complementMap = new HashMap<>();
for (int i = 0; i < nums.length; i++) {
int curr = nums[i];
if(complementMap.containsKey(target - curr)){
return true;
}
complementMap.put(curr, i);
}
return false;
}
if you want to find pair count,
pairs = [3,5,7,10]
k = 17
counter = 0
for i in pairs:
if k - i in pairs:
counter += 1
print(counter//2)
Python Solution:
def FindPairs(arr, k):
for i in range(0, len(arr)):
if k - arr[i] in arr:
return True
return False
A = [1, 4, 45, 6, 10, 8]
n = 100
print(FindPairs(A, n))
Or
def findpair(list1, k):
for i in range(0, len(list1)):
for j in range(0, len(list1)):
if k == list1[i] + list1[j]:
return True
return False
nums = [10, 5, 6, 7, 3]
k = 100
print(findpair(nums, k))
Here is python's implementation
arr=[3,5,7,10]
k=17
flag=False
hashset = set()
for i in range(0,len(arr)):
if k-arr[i] in hashset:
flag=True
hashset.add(arr[i])
print( flag )
Javascript solution:
function hasSumK(arr, k) {
hashMap = {};
for (let value of arr) {
if (hashMap[value]) { return true;} else { hashMap[k - value] = true };
}
return false;
}
Using Scala, in a single pass with O(n) time and space complexity.
import collection.mutable.HashMap
def addUpToK(arr: Array[Int], k: Int): Option[Int] = {
val arrayHelper = new HashMap[Int,Int]()
def addUpToKHelper( i: Int): Option[Int] = {
if(i < arr.length){
if(arrayHelper contains k-arr(i) ){
Some(arr(i))
}else{
arrayHelper += (arr(i) -> (k-arr(i)) )
addUpToKHelper( i+1)
}
}else{
None
}
}
addUpToKHelper(0)
}
addUpToK(Array(10, 15, 3, 7), 17)
C++ solution:
int main(){
int n;
cin>>n;
int arr[n];
for(int i = 0; i < n; i++)
{
cin>>arr[i];
}
int k;
cin>>k;
int t = false;
for(int i = 0; i < n-1; i++)
{
int s = k-arr[i];
for(int j = i+1; j < n; j++)
{
if(s==arr[j])
t=true;
}
}
if (t){
cout<<"Thank you C++, very cool";
}
else{
cout<<"Damn it!";
}
return 0;
}
Python code:
L = list(map(int,input("Enter List: ").split()))
k = int(input("Enter value: "))
for i in L:
if (k - i) in L:
print("True",k-i,i)
Here is Swift solution:
func checkTwoSum(array: [Int], k: Int) -> Bool {
var foundPair = false
for n in array {
if array.contains(k - n) {
foundPair = true
break
} else {
foundPair = false
}
}
return foundPair
}
def sum_total(list, total):
dict = {}
for i in lista:
if (total - i) in dict:
return True
else:
dict[i] = i
return False
Here is a C implementationFor Sorting O(n2) time and space complexity.For Solving Problem We use
single pass with O(n) time and space complexity via Recursion.
/* Given a list of numbers and a number k , return weather any two numbers from the list add up to k.
For example, given [10,15,3,7] and k of 17 , return 10 + 7 is 17
Bonus: Can You Do in one pass ? */
#include<stdio.h>
int rec(int i , int j ,int k , int n,int array[])
{
int sum;
for( i = 0 ; i<j ;)
{
sum = array[i] + array[j];
if( sum > k)
{
j--;
}else if( sum < k)
{
i++;
}else if( sum == k )
{
printf("Value equal to sum of array[%d] = %d and array[%d] = %d",i,array[i],j,array[j]);
return 1;//True
}
}
return 0;//False
}
int main()
{
int n ;
printf("Enter The Value of Number of Arrays = ");
scanf("%d",&n);
int array[n],i,j,k,x;
printf("Enter the Number Which you Want to search in addition of Two Number = ");
scanf("%d",&x);
printf("Enter The Value of Array \n");
for( i = 0 ; i <=n-1;i++)
{
printf("Array[%d] = ",i);
scanf("%d",&array[i]);
}
//Sorting of Array
for( i = 0 ; i <=n-1;i++)
{
for( j = 0 ; j <=n-i-1;j++)
{
if( array[j]>array[j+1])
{
//swapping of two using bitwise operator
array[j] = array[j]^array[j+1];
array[j+1] = array[j]^array[j+1];
array[j] = array[j]^array[j+1];
}
}
}
k = x ;
j = n-1;
rec(i,j,k,n,array);
return 0 ;
}
OUTPUT
Enter The Value of Number of Arrays = 4
Enter the Number Which you Want to search in addition of Two Number = 17
Enter The Value of Array
Array[0] = 10
Array[1] = 15
Array[2] = 3
Array[3] = 7
Value equal to sum of array[1] = 7 and array[2] = 10
Process returned 0 (0x0) execution time : 54.206 s
Press any key to continue.
The solution can be found out in just one pass of the array. Initialise a hash Set and start iterating the array. If the current element in the array is found in the set then return true, else add the complement of this element (x - arr[i]) to the set. If the iteration of array ended without returning it means that there is no such pair whose sum is equal to x so return false.
public boolean containsPairWithSum(int[] a, int x) {
Set<Integer> set = new HashSet<>();
for (int i = 0; i< a.length; i++) {
if(set.contains(a[i]))
return true;
set.add(x - a[i]);
}
return false;
}
Here's Python. O(n). Need to remove the current element whilst looping because the list might not have duplicate numbers.
def if_sum_is_k(list, k):
i = 0
list_temp = list.copy()
match = False
for e in list:
list_temp.pop(i)
if k - e in list_temp:
match = True
i += 1
list_temp = list.copy()
return match
I came up with two solutions in C++. One was a naive brute force type which was in O(n^2) time.
int main() {
int N,K;
vector<int> list;
cin >> N >> K;
clock_t tStart = clock();
for(int i = 0;i<N;i++) {
list.push_back(i+1);
}
for(int i = 0;i<N;i++) {
for(int j = 0;j<N;j++) {
if(list[i] + list[j] == K) {
cout << list[i] << " " << list[j] << endl;
cout << "YES" << endl;
printf("Time taken: %.2fs\n", (double)(clock() - tStart)/CLOCKS_PER_SEC);
return 0;
}
}
}
cout << "NO" << endl;
printf("Time taken: %f\n", (double)(clock() - tStart)/CLOCKS_PER_SEC);
return 0;}
This solution as you could imagine will take a large amount of time on higher values of input.
My second solution I was able to implement in O(N) time. Using an unordered_set, much like the above solution.
#include <iostream>
#include <unordered_set>
#include <time.h>
using namespace std;
int main() {
int N,K;
int trig = 0;
int a,b;
time_t tStart = clock();
unordered_set<int> u;
cin >> N >> K;
for(int i = 1;i<=N;i++) {
if(u.find(abs(K - i)) != u.end()) {
trig = 1;
a = i;
b = abs(K - i);
}
u.insert(i);
}
trig ? cout << "YES" : cout << "NO";
cout << endl;
cout << a << " " << b << endl;
printf("Time taken %fs\n",(double) (clock() - tStart)/CLOCKS_PER_SEC);
return 0;
}
Python Implementation:
The code would execute in O(n) complexity with the use of dictionary. We would be storing the (desired_output - current_input) as the key in the dictionary. And then we would check if the number exists in the dictionary or not. Search in a dictionary has an average complexity as O(1).
def PairToSumK(numList,requiredSum):
dictionary={}
for num in numList:
if requiredSum-num not in dictionary:
dictionary[requiredSum-num]=0
if num in dictionary:
print(num,requiredSum-num)
return True
return False
arr=[10, 5, 3, 7, 3]
print(PairToSumK(arr,6))
Javascript
const findPair = (array, k) => {
array.sort((a, b) => a - b);
let left = 0;
let right = array.length - 1;
while (left < right) {
const sum = array[left] + array[right];
if (sum === k) {
return true;
} else if (sum < k) {
left += 1;
} else {
right -= 1;
}
}
return false;
}
Using HashSet in java we can do it in one go or with time complexity of O(n)
import java.util.Arrays;
import java.util.HashSet;
public class One {
public static void main(String[] args) {
sumPairsInOne(10, new Integer[]{8, 4, 3, 7});
}
public static void sumPairsInOne(int sum, Integer[] nums) {
HashSet<Integer> set = new HashSet<Integer>(Arrays.asList(nums));
//adding values to a hash set
for (Integer num : nums) {
if (set.contains(sum - num)) {
System.out.print("Found sum pair => ");
System.out.println(num + " + " + (sum - num) + " = " + sum);
return;
}
}
System.out.println("No matching pairs");
}
}
Python
def add(num, k):
for i in range(len(num)):
for j in range(len(num)):
if num[i] + num[j] == k:
return True
return False
C# solution:
bool flag = false;
var list = new List<int> { 10, 15, 3, 4 };
Console.WriteLine("Enter K");
int k = int.Parse(Console.ReadLine());
foreach (var item in list)
{
flag = list.Contains(k - item);
if (flag)
{
Console.WriteLine("Result: " + flag);
return;
}
}
Console.WriteLine(flag);
My C# Implementation:
bool isPairPresent(int[] numbers,int value)
{
for (int i = 0; i < numbers.Length; i++)
{
for (int j = 0; j < numbers.Length; j++)
{
if (value - numbers[i] == numbers[j])
return true;
}
}
return false;
}
Here's a javascript solution:
function ProblemOne_Solve()
{
const k = 17;
const values = [10, 15, 3, 8, 2];
for (i=0; i<values.length; i++) {
if (values.find((sum) => { return k-values[i] === sum} )) return true;
}
return false;
}
I implemented with Scala
def hasSome(xs: List[Int], k: Int): Boolean = {
def check(xs: List[Int], k: Int, expectedSet: Set[Int]): Boolean = {
xs match {
case List() => false
case head :: _ if expectedSet contains head => true
case head :: tail => check(tail, k, expectedSet + (k - head))
}
}
check(xs, k, Set())
}
I have tried the solution in Go Lang. However, it consumes O(n^2) time.
package main
import "fmt"
func twoNosAddUptoK(arr []int, k int) bool{
// O(N^2)
for i:=0; i<len(arr); i++{
for j:=1; j<len(arr);j++ {
if arr[i]+arr[j] ==k{
return true
}
}
}
return false
}
func main(){
xs := []int{10, 15, 3, 7}
fmt.Println(twoNosAddUptoK(xs, 17))
}
Here's two very quick Python implementations (which account for the case that inputs of [1,2] and 2 should return false; in other words, you can't just double a number, since it specifies "any two").
This first one loops through the list of terms and adds each term to all of the previously seen terms until it hits the desired sum.
def do_they_add(terms, result):
first_terms = []
for second_term in terms:
for first_term in first_terms:
if second_term + first_term == result:
return True
first_terms.append(second_term)
return False
This one subtracts each term from the result until it reaches a difference that is in the list of terms (using the rule that a+b=c -> c-a=b). The use of enumerate and the odd list indexing is to exclude the current value, per the first sentence in this answer.
def do_they_add_alt(terms, result):
for i, term in enumerate(terms):
diff = result - term
if diff in [*terms[:i - 1], *terms[i + 1:]]:
return True
return False
If you do allow adding a number to itself, then the second implementation could be simplified to:
def do_they_add_alt(terms, result):
for term in terms:
diff = result - term
if diff in terms:
return True
return False
solution in javascript
this function takes 2 parameters and loop through the length of list and inside the loop there is another loop which adds one number to other numbers in the list and check there sum if its equal to k or not
const list = [10, 15, 3, 7];
const k = 17;
function matchSum(list, k){
for (var i = 0; i < list.length; i++) {
list.forEach(num => {
if (num != list[i]) {
if (list[i] + num == k) {
console.log(`${num} + ${list[i]} = ${k} (true)`);
}
}
})
}
}
matchSum(list, k);
My answer to Daily Coding Problem
# Python 2.7
def pairSumK (arr, goal):
return any(map(lambda x: (goal - x) in arr, arr))
arr = [10, 15, 3, 7]
print pairSumK(arr, 17)
Here is the code in Python 3.7 with O(N) complexity :
def findsome(arr,k):
if len(arr)<2:
return False;
for e in arr:
if k>e and (k-e) in arr:
return True
return False
and also best case code in Python 3.7 with O(N^2) complexity :
def findsomen2 (arr,k):
if len(arr)>1:
j=0
if arr[j] <k:
while j<len(arr):
i =0
while i < len(arr):
if arr[j]+arr[i]==k:
return True
i +=1
j +=1
return False
Javascript Solution
function matchSum(arr, k){
for( var i=0; i < arr.length; i++ ){
for(var j= i+1; j < arr.length; j++){
if (arr[i] + arr[j] === k){
return true;
}
}
}
return false;
}
I need to generate a list of numbers to put into an array. These number of times that they occur in the list is based on how important a certain property is.
E.g.
// importance ranges from 0 (no importance) to 5 (utmost importance)
maxNumbersInArray = 10;
propAImportance = 5; --> will put 1 into the array
propBImportance = 3; --> will put 2 into the array
propCImportance = 2; --> will put 3 into the array
// Output will be
array = [1,1,1,1,1,2,2,2,3,3]
How can i create a list where the importance of a value means that value is added to an array a greater number of times, or more specifically, how to calculate how ofter a value will be added into an array based on a properties importance
First ideas: (language SQF scripting)
_max = 10;
_weight1 = 3;
_weight2 = 2;
_weight3 = 0;
_weight4 = 1;
_weights = [_weight1,_weight2,_weight3,_weight4];
_arr = [];
for "_i" from 0 to (_max - 1) do {
{
while {_i <= _x} do {
_arr set [(count _arr),_forEachIndex];
} forEach _weights;
};
_arr; // Output
I believe that this is the algorithm to use:
_index = [];
_max = 10;
_weights = [5,2,1,1];
_count = 0;
_countWeight = 0;
while {(count _index ) < _max } do {
if (_count >= (count _weights)) then {_count = 0};
_weight = _weights select _count;
_countWeight = _countWeight + 1;
if (_countWeight > _weight) then {
_countWeight = 0;
_count = _count + 1;
} else {
_index set [(count _index ),_count];
};
};
I have seen this question for other languages but not for AS3... and I'm having a hard time understanding it...
I need to generate 3 numbers, randomly, from 0 to 2, but they cannot repeat (as in 000, 001, 222, 212 etc) and they cannot be in the correct order (0,1,2)...
Im using
for (var u: int = 0; u < 3; u++)
{
mcCor = new CorDaCarta();
mcCor.x = larguraTrio + (mcCor.width + 5) * (u % 3);
mcCor.y = alturaTrio + (mcCor.height + 5) * (Math.floor(u / 3));
mcCor.gotoAndStop((Math.random() * (2 - u + 1) + u) | 0); // random w/ repeats
//mcCor.gotoAndStop(Math.floor(Math.random() * (2 - u + 1) + u)); // random w/ repeats
//mcCor.gotoAndStop((Math.random() * 3) | 0); // crap....
//mcCor.gotoAndStop(Math.round(Math.random()*u)); // 1,1,1
//mcCor.gotoAndStop(u + 1); // 1,2,3
mcCor.buttonMode = true;
mcCor.addEventListener(MouseEvent.CLICK, cliquetrio);
mcExplic.addChild(mcCor);
trio.push(mcCor);
}
those are the codes i've been trying.... best one so far is the active one (without the //), but it still gives me duplicates (as 1,1,1) and still has a small chance to come 0,1,2....
BTW, what I want is to mcCor to gotoAndStop on frames 1, 2 or 3....without repeating, so THE USER can put it on the right order (1,2,3 or (u= 0,1,2), thats why I add + 1 sometimes there)
any thoughts?? =)
I've found that one way to ensure random, unique numbers is to store the possible numbers in an array, and then sort them using a "random" sort:
// store the numbers 0, 1, 2 in an array
var sortedNumbers:Array = [];
for(var i:int = 0; i < 3; i++)
{
sortedNumbers.push(i);
}
var unsortedNumbers:Array = sortedNumbers.slice(); // make a copy of the sorted numbers
trace(sortedNumbers); // 0,1,2
trace(unsortedNumbers); // 0,1,2
// randomly sort array until it no longer matches the sorted array
while(sortedNumbers.join() == unsortedNumbers.join())
{
unsortedNumbers.sort(function (a:int, b:int):int { return Math.random() > .5 ? -1 : 1; });
}
trace(unsortedNumbers); // [1,0,2], [2,1,0], [0,1,2], etc
for (var u: int = 0; u < 3; u++)
{
mcCor = new CorDaCarta();
mcCor.x = larguraTrio + (mcCor.width + 5) * (u % 3);
mcCor.y = alturaTrio + (mcCor.height + 5) * (Math.floor(u / 3));
// grab the corresponding value from the unsorted array
mcCor.gotoAndStop(unsortedNumbers[u] + 1);
mcCor.buttonMode = true;
mcCor.addEventListener(MouseEvent.CLICK, cliquetrio);
mcExplic.addChild(mcCor);
trio.push(mcCor);
}
Marcela is right. Approach with an Array is widely used for such task. Of course, you will need to check 0, 1, 2 sequence and this will be ugly, but in common code to get the random sequence of integers can look like this:
function getRandomSequence(min:int, max:int):Array
{
if (min > max) throw new Error("Max value should be greater than Min value!");
if (min == max) return [min];
var values:Array = [];
for (var i:int = min; i <= max; i++) values.push(i);
var result:Array = [];
while (values.length > 0) result = result.concat(values.splice(Math.floor(Math.random() * values.length), 1));
return result;
}
for (var i:uint = 0; i < 10; i++)
{
trace(getRandomSequence(1, 10));
}
You will get something like that:
2,9,3,8,10,6,5,1,4,7
6,1,2,4,8,9,5,10,7,3
3,9,10,6,8,2,5,4,1,7
7,6,1,4,3,8,9,2,10,5
4,6,7,1,3,2,9,10,8,5
3,10,5,9,1,7,2,4,8,6
1,7,9,6,10,3,4,5,2,8
4,10,8,9,3,2,6,1,7,5
1,7,8,9,10,6,4,3,2,5
7,5,4,2,8,6,10,3,9,1
I created this for you. It is working but it can be optimized...
Hope is good for you.
var arr : Array = [];
var r : int;
for (var i: int = 0; i < 3; i++){
r=rand(0,2);
if(i == 1){
if(arr[0] == r){
i--;
continue;
}
if(arr[0] == 0){
if(r==1){
i--;
continue;
}
}
}else if(i==2){
if(arr[0] == r || arr[1] == r){
i--;
continue;
}
}
arr[i] = r;
}
trace(arr);
for(var i=0;i<3;i++){
mcCor = new CorDaCarta();
mcCor.x = larguraTrio + (mcCor.width + 5) * (i % 3);
mcCor.y = alturaTrio + (mcCor.height + 5) * (Math.floor(i / 3));
mcCor.gotoAndStop(arr[i]);
mcCor.buttonMode = true;
mcCor.addEventListener(MouseEvent.CLICK, cliquetrio);
mcExplic.addChild(mcCor);
trio.push(mcCor);
}
function rand(min:int, max:int):int {
return Math.round(Math.random() * (max - min) + min);
}
try this...
For an array of size N, what is the number of comparisons required?
The optimal algorithm uses n+log n-2 comparisons. Think of elements as competitors, and a tournament is going to rank them.
First, compare the elements, as in the tree
|
/ \
| |
/ \ / \
x x x x
this takes n-1 comparisons and each element is involved in comparison at most log n times. You will find the largest element as the winner.
The second largest element must have lost a match to the winner (he can't lose a match to a different element), so he's one of the log n elements the winner has played against. You can find which of them using log n - 1 comparisons.
The optimality is proved via adversary argument. See https://math.stackexchange.com/questions/1601 or http://compgeom.cs.uiuc.edu/~jeffe/teaching/497/02-selection.pdf or http://www.imada.sdu.dk/~jbj/DM19/lb06.pdf or https://www.utdallas.edu/~chandra/documents/6363/lbd.pdf
You can find the second largest value with at most 2ยท(N-1) comparisons and two variables that hold the largest and second largest value:
largest := numbers[0];
secondLargest := null
for i=1 to numbers.length-1 do
number := numbers[i];
if number > largest then
secondLargest := largest;
largest := number;
else
if number > secondLargest then
secondLargest := number;
end;
end;
end;
Use Bubble sort or Selection sort algorithm which sorts the array in descending order. Don't sort the array completely. Just two passes. First pass gives the largest element and second pass will give you the second largest element.
No. of comparisons for first pass: n-1
No. of comparisons for second pass: n-2
Total no. of comparison for finding second largest: 2n-3
May be you can generalize this algorithm. If you need the 3rd largest then you make 3 passes.
By above strategy you don't need any temporary variables as Bubble sort and Selection sort are in place sorting algorithms.
Here is some code that might not be optimal but at least actually finds the 2nd largest element:
if( val[ 0 ] > val[ 1 ] )
{
largest = val[ 0 ]
secondLargest = val[ 1 ];
}
else
{
largest = val[ 1 ]
secondLargest = val[ 0 ];
}
for( i = 2; i < N; ++i )
{
if( val[ i ] > secondLargest )
{
if( val[ i ] > largest )
{
secondLargest = largest;
largest = val[ i ];
}
else
{
secondLargest = val[ i ];
}
}
}
It needs at least N-1 comparisons if the largest 2 elements are at the beginning of the array and at most 2N-3 in the worst case (one of the first 2 elements is the smallest in the array).
case 1-->9 8 7 6 5 4 3 2 1
case 2--> 50 10 8 25 ........
case 3--> 50 50 10 8 25.........
case 4--> 50 50 10 8 50 25.......
public void second element()
{
int a[10],i,max1,max2;
max1=a[0],max2=a[1];
for(i=1;i<a.length();i++)
{
if(a[i]>max1)
{
max2=max1;
max1=a[i];
}
else if(a[i]>max2 &&a[i]!=max1)
max2=a[i];
else if(max1==max2)
max2=a[i];
}
}
Sorry, JS code...
Tested with the two inputs:
a = [55,11,66,77,72];
a = [ 0, 12, 13, 4, 5, 32, 8 ];
var first = Number.MIN_VALUE;
var second = Number.MIN_VALUE;
for (var i = -1, len = a.length; ++i < len;) {
var dist = a[i];
// get the largest 2
if (dist > first) {
second = first;
first = dist;
} else if (dist > second) { // && dist < first) { // this is actually not needed, I believe
second = dist;
}
}
console.log('largest, second largest',first,second);
largest, second largest 32 13
This should have a maximum of a.length*2 comparisons and only goes through the list once.
I know this is an old question, but here is my attempt at solving it, making use of the Tournament Algorithm. It is similar to the solution used by #sdcvvc , but I am using two-dimensional array to store elements.
To make things work, there are two assumptions:
1) number of elements in the array is the power of 2
2) there are no duplicates in the array
The whole process consists of two steps:
1. building a 2D array by comparing two by two elements. First row in the 2D array is gonna be the entire input array. Next row contains results of the comparisons of the previous row. We continue comparisons on the newly built array and keep building the 2D array until an array of only one element (the largest one) is reached.
2. we have a 2D-array where last row contains only one element: the largest one. We continue going from the bottom to the top, in each array finding the element that was "beaten" by the largest and comparing it to the current "second largest" value. To find the element beaten by the largest, and to avoid O(n) comparisons, we must store the index of the largest element in the previous row. That way we can easily check the adjacent elements. At any level (above root level),the adjacent elements are obtained as:
leftAdjacent = rootIndex*2
rightAdjacent = rootIndex*2+1,
where rootIndex is index of the largest(root) element at the previous level.
I know the question asks for C++, but here is my attempt at solving it in Java. (I've used lists instead of arrays, to avoid messy changing of the array size and/or unnecessary array size calculations)
public static Integer findSecondLargest(List<Integer> list) {
if (list == null) {
return null;
}
if (list.size() == 1) {
return list.get(0);
}
List<List<Integer>> structure = buildUpStructure(list);
System.out.println(structure);
return secondLargest(structure);
}
public static List<List<Integer>> buildUpStructure(List<Integer> list) {
List<List<Integer>> newList = new ArrayList<List<Integer>>();
List<Integer> tmpList = new ArrayList<Integer>(list);
newList.add(tmpList);
int n = list.size();
while (n>1) {
tmpList = new ArrayList<Integer>();
for (int i = 0; i<n; i=i+2) {
Integer i1 = list.get(i);
Integer i2 = list.get(i+1);
tmpList.add(Math.max(i1, i2));
}
n/= 2;
newList.add(tmpList);
list = tmpList;
}
return newList;
}
public static Integer secondLargest(List<List<Integer>> structure) {
int n = structure.size();
int rootIndex = 0;
Integer largest = structure.get(n-1).get(rootIndex);
List<Integer> tmpList = structure.get(n-2);
Integer secondLargest = Integer.MIN_VALUE;
Integer leftAdjacent = -1;
Integer rightAdjacent = -1;
for (int i = n-2; i>=0; i--) {
rootIndex*=2;
tmpList = structure.get(i);
leftAdjacent = tmpList.get(rootIndex);
rightAdjacent = tmpList.get(rootIndex+1);
if (leftAdjacent.equals(largest)) {
if (rightAdjacent > secondLargest) {
secondLargest = rightAdjacent;
}
}
if (rightAdjacent.equals(largest)) {
if (leftAdjacent > secondLargest) {
secondLargest = leftAdjacent;
}
rootIndex=rootIndex+1;
}
}
return secondLargest;
}
Suppose provided array is inPutArray = [1,2,5,8,7,3] expected O/P -> 7 (second largest)
take temp array
temp = [0,0], int dummmy=0;
for (no in inPutArray) {
if(temp[1]<no)
temp[1] = no
if(temp[0]<temp[1]){
dummmy = temp[0]
temp[0] = temp[1]
temp[1] = temp
}
}
print("Second largest no is %d",temp[1])
PHP version of the Gumbo algorithm: http://sandbox.onlinephpfunctions.com/code/51e1b05dac2e648fd13e0b60f44a2abe1e4a8689
$numbers = [10, 9, 2, 3, 4, 5, 6, 7];
$largest = $numbers[0];
$secondLargest = null;
for ($i=1; $i < count($numbers); $i++) {
$number = $numbers[$i];
if ($number > $largest) {
$secondLargest = $largest;
$largest = $number;
} else if ($number > $secondLargest) {
$secondLargest = $number;
}
}
echo "largest=$largest, secondLargest=$secondLargest";
Assuming space is irrelevant, this is the smallest I could get it. It requires 2*n comparisons in worst case, and n comparisons in best case:
arr = [ 0, 12, 13, 4, 5, 32, 8 ]
max = [ -1, -1 ]
for i in range(len(arr)):
if( arr[i] > max[0] ):
max.insert(0,arr[i])
elif( arr[i] > max[1] ):
max.insert(1,arr[i])
print max[1]
try this.
max1 = a[0].
max2.
for i = 0, until length:
if a[i] > max:
max2 = max1.
max1 = a[i].
#end IF
#end FOR
return min2.
it should work like a charm. low in complexity.
here is a java code.
int secondlLargestValue(int[] secondMax){
int max1 = secondMax[0]; // assign the first element of the array, no matter what, sorted or not.
int max2 = 0; // anything really work, but zero is just fundamental.
for(int n = 0; n < secondMax.length; n++){ // start at zero, end when larger than length, grow by 1.
if(secondMax[n] > max1){ // nth element of the array is larger than max1, if so.
max2 = max1; // largest in now second largest,
max1 = secondMax[n]; // and this nth element is now max.
}//end IF
}//end FOR
return max2;
}//end secondLargestValue()
Use counting sort and then find the second largest element, starting from index 0 towards the end. There should be at least 1 comparison, at most n-1 (when there's only one element!).
#include<stdio.h>
main()
{
int a[5] = {55,11,66,77,72};
int max,min,i;
int smax,smin;
max = min = a[0];
smax = smin = a[0];
for(i=0;i<=4;i++)
{
if(a[i]>max)
{
smax = max;
max = a[i];
}
if(max>a[i]&&smax<a[i])
{
smax = a[i];
}
}
printf("the first max element z %d\n",max);
printf("the second max element z %d\n",smax);
}
The accepted solution by sdcvvc in C++11.
#include <algorithm>
#include <iostream>
#include <vector>
#include <cassert>
#include <climits>
using std::vector;
using std::cout;
using std::endl;
using std::random_shuffle;
using std::min;
using std::max;
vector<int> create_tournament(const vector<int>& input) {
// make sure we have at least two elements, so the problem is interesting
if (input.size() <= 1) {
return input;
}
vector<int> result(2 * input.size() - 1, -1);
int i = 0;
for (const auto& el : input) {
result[input.size() - 1 + i] = el;
++i;
}
for (uint j = input.size() / 2; j > 0; j >>= 1) {
for (uint k = 0; k < 2 * j; k += 2) {
result[j - 1 + k / 2] = min(result[2 * j - 1 + k], result[2 * j + k]);
}
}
return result;
}
int second_smaller(const vector<int>& tournament) {
const auto& minimum = tournament[0];
int second = INT_MAX;
for (uint j = 0; j < tournament.size() / 2; ) {
if (tournament[2 * j + 1] == minimum) {
second = min(second, tournament[2 * j + 2]);
j = 2 * j + 1;
}
else {
second = min(second, tournament[2 * j + 1]);
j = 2 * j + 2;
}
}
return second;
}
void print_vector(const vector<int>& v) {
for (const auto& el : v) {
cout << el << " ";
}
cout << endl;
}
int main() {
vector<int> a;
for (int i = 1; i <= 2048; ++i)
a.push_back(i);
for (int i = 0; i < 1000; i++) {
random_shuffle(a.begin(), a.end());
const auto& v = create_tournament(a);
assert (second_smaller(v) == 2);
}
return 0;
}
I have gone through all the posts above but I am convinced that the implementation of the Tournament algorithm is the best approach. Let us consider the following algorithm posted by #Gumbo
largest := numbers[0];
secondLargest := null
for i=1 to numbers.length-1 do
number := numbers[i];
if number > largest then
secondLargest := largest;
largest := number;
else
if number > secondLargest then
secondLargest := number;
end;
end;
end;
It is very good in case we are going to find the second largest number in an array. It has (2n-1) number of comparisons. But what if you want to calculate the third largest number or some kth largest number. The above algorithm doesn't work. You got to another procedure.
So, I believe tournament algorithm approach is the best and here is the link for that.
The following solution would take 2(N-1) comparisons:
arr #array with 'n' elements
first=arr[0]
second=-999999 #large negative no
i=1
while i is less than length(arr):
if arr[i] greater than first:
second=first
first=arr[i]
else:
if arr[i] is greater than second and arr[i] less than first:
second=arr[i]
i=i+1
print second
It can be done in n + ceil(log n) - 2 comparison.
Solution:
it takes n-1 comparisons to get minimum.
But to get minimum we will build a tournament in which each element will be grouped in pairs. like a tennis tournament and winner of any round will go forward.
Height of this tree will be log n since we half at each round.
Idea to get second minimum is that it will be beaten by minimum candidate in one of previous round. So, we need to find minimum in potential candidates (beaten by minimum).
Potential candidates will be log n = height of tree
So, no. of comparison to find minimum using tournament tree is n-1
and for second minimum is log n -1
sums up = n + ceil(log n) - 2
Here is C++ code
#include <iostream>
#include <cstdio>
#include <cstdlib>
#include <cmath>
#include <vector>
using namespace std;
typedef pair<int,int> ii;
bool isPowerOfTwo (int x)
{
/* First x in the below expression is for the case when x is 0 */
return x && (!(x&(x-1)));
}
// modified
int log_2(unsigned int n) {
int bits = 0;
if (!isPowerOfTwo(n))
bits++;
if (n > 32767) {
n >>= 16;
bits += 16;
}
if (n > 127) {
n >>= 8;
bits += 8;
}
if (n > 7) {
n >>= 4;
bits += 4;
}
if (n > 1) {
n >>= 2;
bits += 2;
}
if (n > 0) {
bits++;
}
return bits;
}
int second_minima(int a[], unsigned int n) {
// build a tree of size of log2n in the form of 2d array
// 1st row represents all elements which fights for min
// candidate pairwise. winner of each pair moves to 2nd
// row and so on
int log_2n = log_2(n);
long comparison_count = 0;
// pair of ints : first element stores value and second
// stores index of its first row
ii **p = new ii*[log_2n];
int i, j, k;
for (i = 0, j = n; i < log_2n; i++) {
p[i] = new ii[j];
j = j&1 ? j/2+1 : j/2;
}
for (i = 0; i < n; i++)
p[0][i] = make_pair(a[i], i);
// find minima using pair wise fighting
for (i = 1, j = n; i < log_2n; i++) {
// for each pair
for (k = 0; k+1 < j; k += 2) {
// find its winner
if (++comparison_count && p[i-1][k].first < p[i-1][k+1].first) {
p[i][k/2].first = p[i-1][k].first;
p[i][k/2].second = p[i-1][k].second;
}
else {
p[i][k/2].first = p[i-1][k+1].first;
p[i][k/2].second = p[i-1][k+1].second;
}
}
// if no. of elements in row is odd the last element
// directly moves to next round (row)
if (j&1) {
p[i][j/2].first = p[i-1][j-1].first;
p[i][j/2].second = p[i-1][j-1].second;
}
j = j&1 ? j/2+1 : j/2;
}
int minima, second_minima;
int index;
minima = p[log_2n-1][0].first;
// initialize second minima by its final (last 2nd row)
// potential candidate with which its final took place
second_minima = minima == p[log_2n-2][0].first ? p[log_2n-2][1].first : p[log_2n-2][0].first;
// minima original index
index = p[log_2n-1][0].second;
for (i = 0, j = n; i <= log_2n - 3; i++) {
// if its last candidate in any round then there is
// no potential candidate
if (j&1 && index == j-1) {
index /= 2;
j = j/2+1;
continue;
}
// if minima index is odd, then it fighted with its index - 1
// else its index + 1
// this is a potential candidate for second minima, so check it
if (index&1) {
if (++comparison_count && second_minima > p[i][index-1].first)
second_minima = p[i][index-1].first;
}
else {
if (++comparison_count && second_minima > p[i][index+1].first)
second_minima = p[i][index+1].first;
}
index/=2;
j = j&1 ? j/2+1 : j/2;
}
printf("-------------------------------------------------------------------------------\n");
printf("Minimum : %d\n", minima);
printf("Second Minimum : %d\n", second_minima);
printf("comparison count : %ld\n", comparison_count);
printf("Least No. Of Comparisons (");
printf("n+ceil(log2_n)-2) : %d\n", (int)(n+ceil(log(n)/log(2))-2));
return 0;
}
int main()
{
unsigned int n;
scanf("%u", &n);
int a[n];
int i;
for (i = 0; i < n; i++)
scanf("%d", &a[i]);
second_minima(a,n);
return 0;
}
function findSecondLargeNumber(arr){
var fLargeNum = 0;
var sLargeNum = 0;
for(var i=0; i<arr.length; i++){
if(fLargeNum < arr[i]){
sLargeNum = fLargeNum;
fLargeNum = arr[i];
}else if(sLargeNum < arr[i]){
sLargeNum = arr[i];
}
}
return sLargeNum;
}
var myArray = [799, -85, 8, -1, 6, 4, 3, -2, -15, 0, 207, 75, 785, 122, 17];
Ref: http://www.ajaybadgujar.com/finding-second-largest-number-from-array-in-javascript/
A good way with O(1) time complexity would be to use a max-heap. Call the heapify twice and you have the answer.
int[] int_array = {4, 6, 2, 9, 1, 7, 4, 2, 9, 0, 3, 6, 1, 6, 8};
int largst=int_array[0];
int second=int_array[0];
for (int i=0; i<int_array.length; i++){
if(int_array[i]>largst) {
second=largst;
largst=int_array[i];
}
else if(int_array[i]>second && int_array[i]<largst) {
second=int_array[i];
}
}
I suppose, follow the "optimal algorithm uses n+log n-2 comparisons" from above, the code that I came up with that doesn't use binary tree to store the value would be the following:
During each recursive call, the array size is cut in half.
So the number of comparison is:
1st iteration: n/2 comparisons
2nd iteration: n/4 comparisons
3rd iteration: n/8 comparisons
...
Up to log n iterations?
Hence, total => n - 1 comparisons?
function findSecondLargestInArray(array) {
let winner = [];
if (array.length === 2) {
if (array[0] < array[1]) {
return array[0];
} else {
return array[1];
}
}
for (let i = 1; i <= Math.floor(array.length / 2); i++) {
if (array[2 * i - 1] > array[2 * i - 2]) {
winner.push(array[2 * i - 1]);
} else {
winner.push(array[2 * i - 2]);
}
}
return findSecondLargestInArray(winner);
}
Assuming array contain 2^n number of numbers.
If there are 6 numbers, then 3 numbers will move to the next level, which is not right.
Need like 8 numbers => 4 number => 2 number => 1 number => 2^n number of number
package com.array.orderstatistics;
import java.util.Arrays;
import java.util.Collections;
public class SecondLargestElement {
/**
* Total Time Complexity will be n log n + O(1)
* #param str
*/
public static void main(String str[]) {
Integer[] integerArr = new Integer[] { 5, 1, 2, 6, 4 };
// Step1 : Time Complexity will be n log(n)
Arrays.sort(integerArr, Collections.reverseOrder());
// Step2 : Array.get Second largestElement
int secondLargestElement = integerArr[1];
System.out.println(secondLargestElement);
}
}
Sort the array into ascending order then assign a variable to the (n-1)th term.