I have been trying to solve the below task:
You are given N counters, initially set to 0, and you have two possible operations on them:
increase(X) − counter X is increased by 1,
max_counter − all counters are set to the maximum value of any counter.
A non-empty zero-indexed array A of M integers is given. This array represents consecutive operations:
if A[K] = X, such that 1 ≤ X ≤ N, then operation K is increase(X),
if A[K] = N + 1 then operation K is max_counter.
For example, given integer N = 5 and array A such that:
A[0] = 3
A[1] = 4
A[2] = 4
A[3] = 6
A[4] = 1
A[5] = 4
A[6] = 4
the values of the counters after each consecutive operation will be:
(0, 0, 1, 0, 0)
(0, 0, 1, 1, 0)
(0, 0, 1, 2, 0)
(2, 2, 2, 2, 2)
(3, 2, 2, 2, 2)
(3, 2, 2, 3, 2)
(3, 2, 2, 4, 2)
The goal is to calculate the value of every counter after all operations.
struct Results {
int * C;
int L;
};
Write a function:
struct Results solution(int N, int A[], int M);
that, given an integer N and a non-empty zero-indexed array A consisting of M integers, returns a sequence of integers representing the values of the counters.
The sequence should be returned as:
a structure Results (in C), or
a vector of integers (in C++), or
a record Results (in Pascal), or
an array of integers (in any other programming language).
For example, given:
A[0] = 3
A[1] = 4
A[2] = 4
A[3] = 6
A[4] = 1
A[5] = 4
A[6] = 4
the function should return [3, 2, 2, 4, 2], as explained above.
Assume that:
N and M are integers within the range [1..100,000];
each element of array A is an integer within the range [1..N + 1].
Complexity:
expected worst-case time complexity is O(N+M);
expected worst-case space complexity is O(N), beyond input storage (not counting the storage required for input arguments).
Elements of input arrays can be modified.
Here is my solution:
import java.util.Arrays;
class Solution {
public int[] solution(int N, int[] A) {
final int condition = N + 1;
int currentMax = 0;
int countersArray[] = new int[N];
for (int iii = 0; iii < A.length; iii++) {
int currentValue = A[iii];
if (currentValue == condition) {
Arrays.fill(countersArray, currentMax);
} else {
int position = currentValue - 1;
int localValue = countersArray[position] + 1;
countersArray[position] = localValue;
if (localValue > currentMax) {
currentMax = localValue;
}
}
}
return countersArray;
}
}
Here is the code valuation: https://codility.com/demo/results/demo6AKE5C-EJQ/
Can you give me a hint what is wrong with this solution?
The problem comes with this piece of code:
for (int iii = 0; iii < A.length; iii++) {
...
if (currentValue == condition) {
Arrays.fill(countersArray, currentMax);
}
...
}
Imagine that every element of the array A
was initialized with the value N+1
. Since the function call Arrays.fill(countersArray, currentMax)
has a time complexity of O(N)
then overall your algorithm will have a time complexity O(M * N)
. A way to fix this, I think, instead of explicitly updating the whole array A
when the max_counter
operation is called you may keep the value of last update as a variable. When first operation (incrementation) is called you just see if the value you try to increment is larger than the last_update
. If it is you just update the value with 1 otherwise you initialize it to last_update + 1
. When the second operation is called you just update last_update
to current_max
. And finally, when you are finished and try to return the final values you again compare each value to last_update
. If it is greater you just keep the value otherwise you return last_update
class Solution {
public int[] solution(int N, int[] A) {
final int condition = N + 1;
int currentMax = 0;
int lastUpdate = 0;
int countersArray[] = new int[N];
for (int iii = 0; iii < A.length; iii++) {
int currentValue = A[iii];
if (currentValue == condition) {
lastUpdate = currentMax
} else {
int position = currentValue - 1;
if (countersArray[position] < lastUpdate)
countersArray[position] = lastUpdate + 1;
else
countersArray[position]++;
if (countersArray[position] > currentMax) {
currentMax = countersArray[position];
}
}
}
for (int iii = 0; iii < N; iii++) {
if (countersArray[iii] < lastUpdate)
countersArray[iii] = lastUpdate;
}
return countersArray;
}
}
The problem is that when you get lots of max_counter
operations you get lots of calls to Arrays.fill
which makes your solution slow.
You should keep a currentMax
and a currentMin
:
max_counter
you just set currentMin = currentMax
.i
:
i - 1
is smaller or equal to currentMin
you set it to currentMin + 1
.At the end just go through the counters array again and set everything less than currentMin
to currentMin
.
Another solution that I have developed and might be worth considering: http://codility.com/demo/results/demoM658NU-DYR/
This is the 100% solution of this question.
// you can also use imports, for example:
// import java.math.*;
class Solution {
public int[] solution(int N, int[] A) {
int counter[] = new int[N];
int n = A.length;
int max=-1,current_min=0;
for(int i=0;i<n;i++){
if(A[i]>=1 && A[i]<= N){
if(counter[A[i] - 1] < current_min) counter[A[i] - 1] = current_min;
counter[A[i] - 1] = counter[A[i] - 1] + 1;
if(counter[A[i] - 1] > max) max = counter[A[i] - 1];
}
else if(A[i] == N+1){
current_min = max;
}
}
for(int i=0;i<N;i++){
if(counter[i] < current_min) counter[i] = current_min;
}
return counter;
}
}
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