Max Priority Queue Implementation with Heap Data structure
Max Priority Queue is a data structure which manage a list of keys(values). And…
September 01, 2020
Given a non-empty array of integers, every element appears twice except for one. Find that single one.
Example
Input: [2,2,1]
Output: 1
Input: [4,1,2,1,2]
Output: 4
We can take help of HashMap
where we can save numbers and their counts.
HashMap
HashMap
, and find the number whose count is not 2public int singleNumber_extraMemory(int[] nums) {
int l = nums.length;
Map<Integer, Integer> map = new HashMap<Integer, Integer>();
for (int i=0; i<l; i++) {
Integer count = map.get(nums[i]);
if (count == null) {
count = 1;
}
else {
count ++;
}
map.put(nums[i], count);
}
for (Integer key : map.keySet()) {
if (map.get(key) != 2) {
return key;
}
}
return -1;
}
The complexity of above code is O(n)
If we can sort the numbers, then we will have all pair of numbers adjascent to each other.
Then, we can check pair of numbers where they are not equal.
public int singleNumber_sort(int[] nums) {
int l = nums.length;
Arrays.sort(nums);
int i=0;
while (i<l-1) {
if (nums[i] != nums[i+1]) {
return nums[i];
}
i += 2;
}
//check for last element
if (l % 2 == 1) {
return nums[l-1];
}
return -1;
}
Since we are sorting, and iterating over array once. The complexity is O(nlogn)
Assuming our array has all pairs. i.e. we don’t have a single number. Then, following is true:
2 (sum of unique numbers) = (sum of all numbers)
Now, if we know one of the number is missing in pairs. Following must be true:
2 (sum of unique numbers) - (sum of all numbers) = Missing Number
Lets take a look at example:
#input [2, 2, 1]
2 * (2 + 1) - (2 + 2 + 1)
= 6 - 5
= 1
Result = 1
#Another example
# input [4,1,2,1,2]
2 * (4 + 1 + 2) - (4+1+2+1+2)
= 14 - 10
= 4
Result = 4
public int singleNumber(int[] nums) {
int l = nums.length;
Set<Integer> set = new HashSet<>();
int sum = 0;
int uniqueElementSum = 0;
for (int i=0; i<l; i++) {
if (!set.contains(nums[i])) {
set.add(nums[i]);
uniqueElementSum += nums[i];
}
//all sum
sum += nums[i];
}
return 2 * uniqueElementSum - sum;
}
We require a HashSet
just to check for unique elements.
The complexity is O(n)
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