The task is:
A non-empty zero-indexed string S is given. String S consists of N characters from the set of upper-case English letters A, C, G, T.
This string actually represents a DNA sequence, and the upper-case letters represent single nucleotides.
You are also given non-empty zero-indexed arrays P and Q consisting of M integers. These arrays represent queries about minimal nucleotides. We represent the letters of string S as integers 1, 2, 3, 4 in arrays P and Q, where A = 1, C = 2, G = 3, T = 4, and we assume that A < C < G < T.
Query K requires you to find the minimal nucleotide from the range (P[K], Q[K]), 0 ≤ P[i] ≤ Q[i] < N.
For example, consider string S = GACACCATA and arrays P, Q such that:
P[0] = 0 Q[0] = 8
P[1] = 0 Q[1] = 2
P[2] = 4 Q[2] = 5
P[3] = 7 Q[3] = 7
The minimal nucleotides from these ranges are as follows:
(0, 8) is A identified by 1,
(0, 2) is A identified by 1,
(4, 5) is C identified by 2,
(7, 7) is T identified by 4.
Write a function:
class Solution { public int[] solution(String S, int[] P, int[] Q); }
that, given a non-empty zero-indexed string S consisting of N characters and two non-empty zero-indexed arrays P and Q consisting of M integers, returns an array consisting of M characters specifying the consecutive answers to all queries.
The sequence should be returned as:
a Results structure (in C), or
a vector of integers (in C++), or
a Results record (in Pascal), or
an array of integers (in any other programming language).
For example, given the string S = GACACCATA and arrays P, Q such that:
P[0] = 0 Q[0] = 8
P[1] = 0 Q[1] = 2
P[2] = 4 Q[2] = 5
P[3] = 7 Q[3] = 7
the function should return the values [1, 1, 2, 4], as explained above.
Assume that:
N is an integer within the range [1..100,000];
M is an integer within the range [1..50,000];
each element of array P, Q is an integer within the range [0..N − 1];
P[i] ≤ Q[i];
string S consists only of upper-case English letters A, C, G, T.
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.
My solution is:
class Solution {
public int[] solution(String S, int[] P, int[] Q) {
final char c[] = S.toCharArray();
final int answer[] = new int[P.length];
int tempAnswer;
char tempC;
for (int iii = 0; iii < P.length; iii++) {
tempAnswer = 4;
for (int zzz = P[iii]; zzz <= Q[iii]; zzz++) {
tempC = c[zzz];
if (tempC == 'A') {
tempAnswer = 1;
break;
} else if (tempC == 'C') {
if (tempAnswer > 2) {
tempAnswer = 2;
}
} else if (tempC == 'G') {
if (tempAnswer > 3) {
tempAnswer = 3;
}
}
}
answer[iii] = tempAnswer;
}
return answer;
}
}
It is not optimal, I believe it's supposed to be done within one loop, any hint how can I achieve it?
You can check quality of your solution here https://codility.com/train/ test name is Genomic-range-query.
Here is the solution that got 100 out of 100 in codility.com. Please read about prefix sums to understand the solution:
public static int[] solveGenomicRange(String S, int[] P, int[] Q) {
//used jagged array to hold the prefix sums of each A, C and G genoms
//we don't need to get prefix sums of T, you will see why.
int[][] genoms = new int[3][S.length()+1];
//if the char is found in the index i, then we set it to be 1 else they are 0
//3 short values are needed for this reason
short a, c, g;
for (int i=0; i<S.length(); i++) {
a = 0; c = 0; g = 0;
if ('A' == (S.charAt(i))) {
a=1;
}
if ('C' == (S.charAt(i))) {
c=1;
}
if ('G' == (S.charAt(i))) {
g=1;
}
//here we calculate prefix sums. To learn what's prefix sums look at here https://codility.com/media/train/3-PrefixSums.pdf
genoms[0][i+1] = genoms[0][i] + a;
genoms[1][i+1] = genoms[1][i] + c;
genoms[2][i+1] = genoms[2][i] + g;
}
int[] result = new int[P.length];
//here we go through the provided P[] and Q[] arrays as intervals
for (int i=0; i<P.length; i++) {
int fromIndex = P[i];
//we need to add 1 to Q[i],
//because our genoms[0][0], genoms[1][0] and genoms[2][0]
//have 0 values by default, look above genoms[0][i+1] = genoms[0][i] + a;
int toIndex = Q[i]+1;
if (genoms[0][toIndex] - genoms[0][fromIndex] > 0) {
result[i] = 1;
} else if (genoms[1][toIndex] - genoms[1][fromIndex] > 0) {
result[i] = 2;
} else if (genoms[2][toIndex] - genoms[2][fromIndex] > 0) {
result[i] = 3;
} else {
result[i] = 4;
}
}
return result;
}
Simple, elegant, domain specific, 100/100 solution in JS with comments!
function solution(S, P, Q) {
var N = S.length, M = P.length;
// dictionary to map nucleotide to impact factor
var impact = {A : 1, C : 2, G : 3, T : 4};
// nucleotide total count in DNA
var currCounter = {A : 0, C : 0, G : 0, T : 0};
// how many times nucleotide repeats at the moment we reach S[i]
var counters = [];
// result
var minImpact = [];
var i;
// count nucleotides
for(i = 0; i <= N; i++) {
counters.push({A: currCounter.A, C: currCounter.C, G: currCounter.G});
currCounter[S[i]]++;
}
// for every query
for(i = 0; i < M; i++) {
var from = P[i], to = Q[i] + 1;
// compare count of A at the start of query with count at the end of equry
// if counter was changed then query contains A
if(counters[to].A - counters[from].A > 0) {
minImpact.push(impact.A);
}
// same things for C and others nucleotides with higher impact factor
else if(counters[to].C - counters[from].C > 0) {
minImpact.push(impact.C);
}
else if(counters[to].G - counters[from].G > 0) {
minImpact.push(impact.G);
}
else { // one of the counters MUST be changed, so its T
minImpact.push(impact.T);
}
}
return minImpact;
}
Java, 100/100, but with no cumulative/prefix sums! I stashed the last occurrence index of lower 3 nucelotides in a array "map". Later I check if the last index is between P-Q. If so it returns the nuclotide, if not found, it's the top one (T):
class Solution {
int[][] lastOccurrencesMap;
public int[] solution(String S, int[] P, int[] Q) {
int N = S.length();
int M = P.length;
int[] result = new int[M];
lastOccurrencesMap = new int[3][N];
int lastA = -1;
int lastC = -1;
int lastG = -1;
for (int i = 0; i < N; i++) {
char c = S.charAt(i);
if (c == 'A') {
lastA = i;
} else if (c == 'C') {
lastC = i;
} else if (c == 'G') {
lastG = i;
}
lastOccurrencesMap[0][i] = lastA;
lastOccurrencesMap[1][i] = lastC;
lastOccurrencesMap[2][i] = lastG;
}
for (int i = 0; i < M; i++) {
int startIndex = P[i];
int endIndex = Q[i];
int minimum = 4;
for (int n = 0; n < 3; n++) {
int lastOccurence = getLastNucleotideOccurrence(startIndex, endIndex, n);
if (lastOccurence != 0) {
minimum = n + 1;
break;
}
}
result[i] = minimum;
}
return result;
}
int getLastNucleotideOccurrence(int startIndex, int endIndex, int nucleotideIndex) {
int[] lastOccurrences = lastOccurrencesMap[nucleotideIndex];
int endValueLastOccurenceIndex = lastOccurrences[endIndex];
if (endValueLastOccurenceIndex >= startIndex) {
return nucleotideIndex + 1;
} else {
return 0;
}
}
}
Here is the solution, supposing someone is still interested.
class Solution {
public int[] solution(String S, int[] P, int[] Q) {
int[] answer = new int[P.length];
char[] chars = S.toCharArray();
int[][] cumulativeAnswers = new int[4][chars.length + 1];
for (int iii = 0; iii < chars.length; iii++) {
if (iii > 0) {
for (int zzz = 0; zzz < 4; zzz++) {
cumulativeAnswers[zzz][iii + 1] = cumulativeAnswers[zzz][iii];
}
}
switch (chars[iii]) {
case 'A':
cumulativeAnswers[0][iii + 1]++;
break;
case 'C':
cumulativeAnswers[1][iii + 1]++;
break;
case 'G':
cumulativeAnswers[2][iii + 1]++;
break;
case 'T':
cumulativeAnswers[3][iii + 1]++;
break;
}
}
for (int iii = 0; iii < P.length; iii++) {
for (int zzz = 0; zzz < 4; zzz++) {
if ((cumulativeAnswers[zzz][Q[iii] + 1] - cumulativeAnswers[zzz][P[iii]]) > 0) {
answer[iii] = zzz + 1;
break;
}
}
}
return answer;
}
}
In case anyone cares about C:
#include <string.h>
struct Results solution(char *S, int P[], int Q[], int M) {
int i, a, b, N, *pA, *pC, *pG;
struct Results result;
result.A = malloc(sizeof(int) * M);
result.M = M;
// calculate prefix sums
N = strlen(S);
pA = malloc(sizeof(int) * N);
pC = malloc(sizeof(int) * N);
pG = malloc(sizeof(int) * N);
pA[0] = S[0] == 'A' ? 1 : 0;
pC[0] = S[0] == 'C' ? 1 : 0;
pG[0] = S[0] == 'G' ? 1 : 0;
for (i = 1; i < N; i++) {
pA[i] = pA[i - 1] + (S[i] == 'A' ? 1 : 0);
pC[i] = pC[i - 1] + (S[i] == 'C' ? 1 : 0);
pG[i] = pG[i - 1] + (S[i] == 'G' ? 1 : 0);
}
for (i = 0; i < M; i++) {
a = P[i] - 1;
b = Q[i];
if ((pA[b] - pA[a]) > 0) {
result.A[i] = 1;
} else if ((pC[b] - pC[a]) > 0) {
result.A[i] = 2;
} else if ((pG[b] - pG[a]) > 0) {
result.A[i] = 3;
} else {
result.A[i] = 4;
}
}
return result;
}
Here is my solution Using Segment Tree O(n)+O(log n)+O(M) time
public class DNAseq {
public static void main(String[] args) {
String S="CAGCCTA";
int[] P={2, 5, 0};
int[] Q={4, 5, 6};
int [] results=solution(S,P,Q);
System.out.println(results[0]);
}
static class segmentNode{
int l;
int r;
int min;
segmentNode left;
segmentNode right;
}
public static segmentNode buildTree(int[] arr,int l,int r){
if(l==r){
segmentNode n=new segmentNode();
n.l=l;
n.r=r;
n.min=arr[l];
return n;
}
int mid=l+(r-l)/2;
segmentNode le=buildTree(arr,l,mid);
segmentNode re=buildTree(arr,mid+1,r);
segmentNode root=new segmentNode();
root.left=le;
root.right=re;
root.l=le.l;
root.r=re.r;
root.min=Math.min(le.min,re.min);
return root;
}
public static int getMin(segmentNode root,int l,int r){
if(root.l>r || root.r<l){
return Integer.MAX_VALUE;
}
if(root.l>=l&& root.r<=r) {
return root.min;
}
return Math.min(getMin(root.left,l,r),getMin(root.right,l,r));
}
public static int[] solution(String S, int[] P, int[] Q) {
int[] arr=new int[S.length()];
for(int i=0;i<S.length();i++){
switch (S.charAt(i)) {
case 'A':
arr[i]=1;
break;
case 'C':
arr[i]=2;
break;
case 'G':
arr[i]=3;
break;
case 'T':
arr[i]=4;
break;
default:
break;
}
}
segmentNode root=buildTree(arr,0,S.length()-1);
int[] result=new int[P.length];
for(int i=0;i<P.length;i++){
result[i]=getMin(root,P[i],Q[i]);
}
return result;
} }
Here is a C# solution, the basic idea is pretty much the same as the other answers, but it may be cleaner:
using System;
class Solution
{
public int[] solution(string S, int[] P, int[] Q)
{
int N = S.Length;
int M = P.Length;
char[] chars = {'A','C','G','T'};
//Calculate accumulates
int[,] accum = new int[3, N+1];
for (int i = 0; i <= 2; i++)
{
for (int j = 0; j < N; j++)
{
if(S[j] == chars[i]) accum[i, j+1] = accum[i, j] + 1;
else accum[i, j+1] = accum[i, j];
}
}
//Get minimal nucleotides for the given ranges
int diff;
int[] minimums = new int[M];
for (int i = 0; i < M; i++)
{
minimums[i] = 4;
for (int j = 0; j <= 2; j++)
{
diff = accum[j, Q[i]+1] - accum[j, P[i]];
if (diff > 0)
{
minimums[i] = j+1;
break;
}
}
}
return minimums;
}
}
Here is my solution. Got %100 . Of course I needed to first check and study a little bit prefix sums.
public int[] solution(String S, int[] P, int[] Q){
int[] result = new int[P.length];
int[] factor1 = new int[S.length()];
int[] factor2 = new int[S.length()];
int[] factor3 = new int[S.length()];
int[] factor4 = new int[S.length()];
int factor1Sum = 0;
int factor2Sum = 0;
int factor3Sum = 0;
int factor4Sum = 0;
for(int i=0; i<S.length(); i++){
switch (S.charAt(i)) {
case 'A':
factor1Sum++;
break;
case 'C':
factor2Sum++;
break;
case 'G':
factor3Sum++;
break;
case 'T':
factor4Sum++;
break;
default:
break;
}
factor1[i] = factor1Sum;
factor2[i] = factor2Sum;
factor3[i] = factor3Sum;
factor4[i] = factor4Sum;
}
for(int i=0; i<P.length; i++){
int start = P[i];
int end = Q[i];
if(start == 0){
if(factor1[end] > 0){
result[i] = 1;
}else if(factor2[end] > 0){
result[i] = 2;
}else if(factor3[end] > 0){
result[i] = 3;
}else{
result[i] = 4;
}
}else{
if(factor1[end] > factor1[start-1]){
result[i] = 1;
}else if(factor2[end] > factor2[start-1]){
result[i] = 2;
}else if(factor3[end] > factor3[start-1]){
result[i] = 3;
}else{
result[i] = 4;
}
}
}
return result;
}
If someone is still interested in this exercise, I share my Python solution (100/100 in Codility)
def solution(S, P, Q):
count = []
for i in range(3):
count.append([0]*(len(S)+1))
for index, i in enumerate(S):
count[0][index+1] = count[0][index] + ( i =='A')
count[1][index+1] = count[1][index] + ( i =='C')
count[2][index+1] = count[2][index] + ( i =='G')
result = []
for i in range(len(P)):
start = P[i]
end = Q[i]+1
if count[0][end] - count[0][start]:
result.append(1)
elif count[1][end] - count[1][start]:
result.append(2)
elif count[2][end] - count[2][start]:
result.append(3)
else:
result.append(4)
return result
This is my JavaScript solution that got 100% across the board on Codility:
function solution(S, P, Q) {
let total = [];
let min;
for (let i = 0; i < P.length; i++) {
const substring = S.slice(P[i], Q[i] + 1);
if (substring.includes('A')) {
min = 1;
} else if (substring.includes('C')) {
min = 2;
} else if (substring.includes('G')) {
min = 3;
} else if (substring.includes('T')) {
min = 4;
}
total.push(min);
}
return total;
}
import java.util.Arrays;
import java.util.HashMap;
class Solution {
static HashMap<Character, Integer > characterMapping = new HashMap<Character, Integer>(){{
put('A',1);
put('C',2);
put('G',3);
put('T',4);
}};
public static int minimum(int[] arr) {
if (arr.length ==1) return arr[0];
int smallestIndex = 0;
for (int index = 0; index<arr.length; index++) {
if (arr[index]<arr[smallestIndex]) smallestIndex=index;
}
return arr[smallestIndex];
}
public int[] solution(String S, int[] P, int[] Q) {
final char[] characterInput = S.toCharArray();
final int[] integerInput = new int[characterInput.length];
for(int counter=0; counter < characterInput.length; counter++) {
integerInput[counter] = characterMapping.get(characterInput[counter]);
}
int[] result = new int[P.length];
//assuming P and Q have the same length
for(int index =0; index<P.length; index++) {
if (P[index]==Q[index]) {
result[index] = integerInput[P[index]];
break;
}
final int[] subArray = Arrays.copyOfRange(integerInput, P[index], Q[index]+1);
final int minimumValue = minimum(subArray);
result[index]= minimumValue;
}
return result;
}
}
Here's 100% Scala solution:
def solution(S: String, P: Array[Int], Q: Array[Int]): Array[Int] = {
val resp = for(ind <- 0 to P.length-1) yield {
val sub= S.substring(P(ind),Q(ind)+1)
var factor = 4
if(sub.contains("A")) {factor=1}
else{
if(sub.contains("C")) {factor=2}
else{
if(sub.contains("G")) {factor=3}
}
}
factor
}
return resp.toArray
}
And performance: https://codility.com/demo/results/trainingEUR4XP-425/
Hope this helps.
public int[] solution(String S, int[] P, int[] K) {
// write your code in Java SE 8
char[] sc = S.toCharArray();
int[] A = new int[sc.length];
int[] G = new int[sc.length];
int[] C = new int[sc.length];
int prevA =-1,prevG=-1,prevC=-1;
for(int i=0;i<sc.length;i++){
if(sc[i]=='A')
prevA=i;
else if(sc[i] == 'G')
prevG=i;
else if(sc[i] =='C')
prevC=i;
A[i] = prevA;
G[i] = prevG;
C[i] = prevC;
//System.out.println(A[i]+ " "+G[i]+" "+C[i]);
}
int[] result = new int[P.length];
for(int i=0;i<P.length;i++){
//System.out.println(A[P[i]]+ " "+A[K[i]]+" "+C[P[i]]+" "+C[K[i]]+" "+P[i]+" "+K[i]);
if(A[K[i]] >=P[i] && A[K[i]] <=K[i]){
result[i] =1;
}
else if(C[K[i]] >=P[i] && C[K[i]] <=K[i]){
result[i] =2;
}else if(G[K[i]] >=P[i] && G[K[i]] <=K[i]){
result[i] =3;
}
else{
result[i]=4;
}
}
return result;
}
Python Solution with explanation
The idea is to hold an auxiliary array per nucleotide X, with position i (ignoring zero) is how many times X has occurred as of now. And so if we need the number of occurrences of X from position f to position t, we could take the following equation:
aux(t) - aux(f)
Time complexity is:
O(N+M)
def solution(S, P, Q):
n = len(S)
m = len(P)
aux = [[0 for i in range(n+1)] for i in [0,1,2]]
for i,c in enumerate(S):
aux[0][i+1] = aux[0][i] + ( c == 'A' )
aux[1][i+1] = aux[1][i] + ( c == 'C' )
aux[2][i+1] = aux[2][i] + ( c == 'G' )
result = []
for i in range(m):
fromIndex , toIndex = P[i] , Q[i] +1
if aux[0][toIndex] - aux[0][fromIndex] > 0:
r = 1
elif aux[1][toIndex] - aux[1][fromIndex] > 0:
r = 2
elif aux[2][toIndex] - aux[2][fromIndex] > 0:
r = 3
else:
r = 4
result.append(r)
return result
This is a Swift 4 solution to the same problem. It is based on #codebusta's solution above:
public func solution(_ S : inout String, _ P : inout [Int], _ Q : inout [Int]) -> [Int] {
var impacts = [Int]()
var prefixSum = [[Int]]()
for _ in 0..<3 {
let array = Array(repeating: 0, count: S.count + 1)
prefixSum.append(array)
}
for (index, character) in S.enumerated() {
var a = 0
var c = 0
var g = 0
switch character {
case "A":
a = 1
case "C":
c = 1
case "G":
g = 1
default:
break
}
prefixSum[0][index + 1] = prefixSum[0][index] + a
prefixSum[1][index + 1] = prefixSum[1][index] + c
prefixSum[2][index + 1] = prefixSum[2][index] + g
}
for tuple in zip(P, Q) {
if prefixSum[0][tuple.1 + 1] - prefixSum[0][tuple.0] > 0 {
impacts.append(1)
}
else if prefixSum[1][tuple.1 + 1] - prefixSum[1][tuple.0] > 0 {
impacts.append(2)
}
else if prefixSum[2][tuple.1 + 1] - prefixSum[2][tuple.0] > 0 {
impacts.append(3)
}
else {
impacts.append(4)
}
}
return impacts
}
Here is python solution with little explanation hope it helps some one.
Python codility 100%
def solution(S, P, Q):
"""
https://app.codility.com/demo/results/training8QBVFJ-EQB/
100%
Idea is consider solution as single dimensional array and use concept of prefix some ie.
stores the value in array for p,c and g based on frequency
array stores the frequency of p,c and g for all positions
Example -
# [0, 0, 1, 1, 1, 1, 1, 2] - prefix some of A - represents the max occurrence of A as 2 in array
# [0, 1, 1, 1, 2, 3, 3, 3] - prefix some of C - represents the max occurrence of A as 3 in array
# [0, 0, 0, 1, 1, 1, 1, 1] - prefix some of G - represents the max occurrence of A as 1 in array
# To find the query answers we can just use prefix some and find the distance between position
S = CAGCCTA
P[0] = 2 Q[0] = 4
P[1] = 5 Q[1] = 5
P[2] = 0 Q[2] = 6
Given a non-empty zero-indexed string S consisting of N characters and two non-empty zero-indexed arrays P and Q consisting
of M integers, returns an array consisting of M integers specifying the consecutive answers to all queries.
The part of the DNA between positions 2 and 4 contains nucleotide G and C (twice), whose impact factors are 3 and 2 respectively, so the answer is 2.
The part between positions 5 and 5 contains a single nucleotide T, whose impact factor is 4, so the answer is 4.
The part between positions 0 and 6 (the whole string) contains all nucleotide, in particular nucleotide A whose impact factor is 1, so the answer is 1.
N is an integer within the range [1..100,000];
M is an integer within the range [1..50,000];
each element of arrays P, Q is an integer within the range [0..N − 1];
P[K] ≤ Q[K], where 0 ≤ K < M;
string S consists only of upper-case English letters A, C, G, T.
Ref - https://github.com/ghanan94/codility-lesson-solutions/blob/master/Lesson%2005%20-%20Prefix%20Sums/PrefixSums.pdf
:return: return the values [2, 4, 1]
"""
# two d array - column size is 3 for a,c,g - not taking size 4 since that will be part of else ie. don`t need to calculate
# row size is the length of DNA sequence
prefix_sum_two_d_array = [[0 for i in range(len(S) + 1)] for j in range(3)]
# find the prefix some of all nucleotide in given sequence
for i, nucleotide in enumerate(S):
# store prefix some of each
# nucleotide == 'A -> 1 if true 0 if false
# [0, 0, 1, 1, 1, 1, 1, 2] - prefix some of A - represents the max occurrence of A as 2 in array
prefix_sum_two_d_array[0][i + 1] = prefix_sum_two_d_array[0][i] + (nucleotide == 'A')
# store prefix some of c
# [0, 1, 1, 1, 2, 3, 3, 3] - prefix some of C - represents the max occurrence of A as 3 in array
prefix_sum_two_d_array[1][i + 1] = prefix_sum_two_d_array[1][i] + (nucleotide == 'C')
# store prefix some of g
# [0, 0, 0, 1, 1, 1, 1, 1] - prefix some of G - represents the max occurrence of A as 1 in array
prefix_sum_two_d_array[2][i + 1] = prefix_sum_two_d_array[2][i] + (nucleotide == 'G')
#print(prefix_sum_two_d_array)
# now to find the query answers we can just use prefix some and find the distance between position
query_answers = []
for position in range(len(P)):
# for each query of p
# find the start index from p
start_index = P[position]
# find the end index from Q
end_index = Q[position] + 1
# find the value from prefix some array - just subtract end index and start index to find the value
if prefix_sum_two_d_array[0][end_index] - prefix_sum_two_d_array[0][start_index]:
query_answers.append(1)
elif prefix_sum_two_d_array[1][end_index] - prefix_sum_two_d_array[1][start_index]:
query_answers.append(2)
elif prefix_sum_two_d_array[2][end_index] - prefix_sum_two_d_array[2][start_index]:
query_answers.append(3)
else:
query_answers.append(4)
return query_answers
result = solution("CAGCCTA", [2, 5, 0], [4, 5, 6])
print("Sol " + str(result))
# Sol [2, 4, 1]
My 100% JavaScript solution with O(N + M) time complexity and no use of advanced built-in methods such as .includes, .substring, etc:
function solution(S, P, Q) {
// initialize prefix sums for A, C, G (you don't need T)
const A = [0];
const C = [0];
const G = [0];
// calculate prefix sums for A, C, G
for (let i = 0, len = S.length; i < len; i++) {
A.push(A[i] + Number("A" === S[i]));
C.push(C[i] + Number("C" === S[i]));
G.push(G[i] + Number("G" === S[i]));
}
// calculate the result using prefix sums
const result = [];
for (let i = 0, len = P.length; i < len; i++) {
const from = P[i];
const to = Q[i] + 1;
if (A[to] - A[from] > 0) {
result.push(1);
} else if (C[to] - C[from] > 0) {
result.push(2);
} else if (G[to] - G[from] > 0) {
result.push(3);
} else {
result.push(4); // this is why you don't need T
}
}
return result;
}
pshemek's solution constrains itself to the space complexity (O(N)) - even with the 2-d array and the answer array because a constant (4) is used for the 2-d array. That solution also fits in with the computational complexity - whereas mine is O (N^2) - though the actual computational complexity is much lower because it skips over entire ranges that include minimal values.
I gave it a try - but mine ends up using more space - but makes more intuitive sense to me (C#):
public static int[] solution(String S, int[] P, int[] Q)
{
const int MinValue = 1;
Dictionary<char, int> stringValueTable = new Dictionary<char,int>(){ {'A', 1}, {'C', 2}, {'G', 3}, {'T', 4} };
char[] inputArray = S.ToCharArray();
int[,] minRangeTable = new int[S.Length, S.Length]; // The meaning of this table is [x, y] where x is the start index and y is the end index and the value is the min range - if 0 then it is the min range (whatever that is)
for (int startIndex = 0; startIndex < S.Length; ++startIndex)
{
int currentMinValue = 4;
int minValueIndex = -1;
for (int endIndex = startIndex; (endIndex < S.Length) && (minValueIndex == -1); ++endIndex)
{
int currentValue = stringValueTable[inputArray[endIndex]];
if (currentValue < currentMinValue)
{
currentMinValue = currentValue;
if (currentMinValue == MinValue) // We can stop iterating - because anything with this index in its range will always be minimal
minValueIndex = endIndex;
else
minRangeTable[startIndex, endIndex] = currentValue;
}
else
minRangeTable[startIndex, endIndex] = currentValue;
}
if (minValueIndex != -1) // Skip over this index - since it is minimal
startIndex = minValueIndex; // We would have a "+ 1" here - but the "auto-increment" in the for statement will get us past this index
}
int[] result = new int[P.Length];
for (int outputIndex = 0; outputIndex < result.Length; ++outputIndex)
{
result[outputIndex] = minRangeTable[P[outputIndex], Q[outputIndex]];
if (result[outputIndex] == 0) // We could avoid this if we initialized our 2-d array with 1's
result[outputIndex] = 1;
}
return result;
}
In pshemek's answer - the "trick" in the second loop is simply that once you've determined you've found a range with the minimal value - you don't need to continue iterating. Not sure if that helps.
The php 100/100 solution:
function solution($S, $P, $Q) {
$S = str_split($S);
$len = count($S);
$lep = count($P);
$arr = array();
$result = array();
$clone = array_fill(0, 4, 0);
for($i = 0; $i < $len; $i++){
$arr[$i] = $clone;
switch($S[$i]){
case 'A':
$arr[$i][0] = 1;
break;
case 'C':
$arr[$i][1] = 1;
break;
case 'G':
$arr[$i][2] = 1;
break;
default:
$arr[$i][3] = 1;
break;
}
}
for($i = 1; $i < $len; $i++){
for($j = 0; $j < 4; $j++){
$arr[$i][$j] += $arr[$i - 1][$j];
}
}
for($i = 0; $i < $lep; $i++){
$x = $P[$i];
$y = $Q[$i];
for($a = 0; $a < 4; $a++){
$sub = 0;
if($x - 1 >= 0){
$sub = $arr[$x - 1][$a];
}
if($arr[$y][$a] - $sub > 0){
$result[$i] = $a + 1;
break;
}
}
}
return $result;
}
This program has got score 100 and performance wise has got an edge over other java codes listed above!
The code can be found here.
public class GenomicRange {
final int Index_A=0, Index_C=1, Index_G=2, Index_T=3;
final int A=1, C=2, G=3, T=4;
public static void main(String[] args) {
GenomicRange gen = new GenomicRange();
int[] M = gen.solution( "GACACCATA", new int[] { 0,0,4,7 } , new int[] { 8,2,5,7 } );
System.out.println(Arrays.toString(M));
}
public int[] solution(String S, int[] P, int[] Q) {
int[] M = new int[P.length];
char[] charArr = S.toCharArray();
int[][] occCount = new int[3][S.length()+1];
int charInd = getChar(charArr[0]);
if(charInd!=3) {
occCount[charInd][1]++;
}
for(int sInd=1; sInd<S.length(); sInd++) {
charInd = getChar(charArr[sInd]);
if(charInd!=3)
occCount[charInd][sInd+1]++;
occCount[Index_A][sInd+1]+=occCount[Index_A][sInd];
occCount[Index_C][sInd+1]+=occCount[Index_C][sInd];
occCount[Index_G][sInd+1]+=occCount[Index_G][sInd];
}
for(int i=0;i<P.length;i++) {
int a,c,g;
if(Q[i]+1>=occCount[0].length) continue;
a = occCount[Index_A][Q[i]+1] - occCount[Index_A][P[i]];
c = occCount[Index_C][Q[i]+1] - occCount[Index_C][P[i]];
g = occCount[Index_G][Q[i]+1] - occCount[Index_G][P[i]];
M[i] = a>0? A : c>0 ? C : g>0 ? G : T;
}
return M;
}
private int getChar(char c) {
return ((c=='A') ? Index_A : ((c=='C') ? Index_C : ((c=='G') ? Index_G : Index_T)));
}
}
Here's a simple javascript solution which got 100%.
function solution(S, P, Q) {
var A = [];
var C = [];
var G = [];
var T = [];
var result = [];
var i = 0;
S.split('').forEach(function(a) {
if (a === 'A') {
A.push(i);
} else if (a === 'C') {
C.push(i);
} else if (a === 'G') {
G.push(i);
} else {
T.push(i);
}
i++;
});
function hasNucl(typeArray, start, end) {
return typeArray.some(function(a) {
return a >= P[j] && a <= Q[j];
});
}
for(var j=0; j<P.length; j++) {
if (hasNucl(A, P[j], P[j])) {
result.push(1)
} else if (hasNucl(C, P[j], P[j])) {
result.push(2);
} else if (hasNucl(G, P[j], P[j])) {
result.push(3);
} else {
result.push(4);
}
}
return result;
}
perl 100/100 solution:
sub solution {
my ($S, $P, $Q)=#_; my #P=#$P; my #Q=#$Q;
my #_A = (0), #_C = (0), #_G = (0), #ret =();
foreach (split //, $S)
{
push #_A, $_A[-1] + ($_ eq 'A' ? 1 : 0);
push #_C, $_C[-1] + ($_ eq 'C' ? 1 : 0);
push #_G, $_G[-1] + ($_ eq 'G' ? 1 : 0);
}
foreach my $i (0..$#P)
{
my $from_index = $P[$i];
my $to_index = $Q[$i] + 1;
if ( $_A[$to_index] - $_A[$from_index] > 0 )
{
push #ret, 1;
next;
}
if ( $_C[$to_index] - $_C[$from_index] > 0 )
{
push #ret, 2;
next;
}
if ( $_G[$to_index] - $_G[$from_index] > 0 )
{
push #ret, 3;
next;
}
push #ret, 4
}
return #ret;
}
Java 100/100
class Solution {
public int[] solution(String S, int[] P, int[] Q) {
int qSize = Q.length;
int[] answers = new int[qSize];
char[] sequence = S.toCharArray();
int[][] occCount = new int[3][sequence.length+1];
int[] geneImpactMap = new int['G'+1];
geneImpactMap['A'] = 0;
geneImpactMap['C'] = 1;
geneImpactMap['G'] = 2;
if(sequence[0] != 'T') {
occCount[geneImpactMap[sequence[0]]][0]++;
}
for(int i = 0; i < sequence.length; i++) {
occCount[0][i+1] = occCount[0][i];
occCount[1][i+1] = occCount[1][i];
occCount[2][i+1] = occCount[2][i];
if(sequence[i] != 'T') {
occCount[geneImpactMap[sequence[i]]][i+1]++;
}
}
for(int j = 0; j < qSize; j++) {
for(int k = 0; k < 3; k++) {
if(occCount[k][Q[j]+1] - occCount[k][P[j]] > 0) {
answers[j] = k+1;
break;
}
answers[j] = 4;
}
}
return answers;
}
}
In ruby (100/100)
def interval_sum x,y,p
p[y+1] - p[x]
end
def solution(s,p,q)
#Hash of arrays with prefix sums
p_sums = {}
respuesta = []
%w(A C G T).each do |letter|
p_sums[letter] = Array.new s.size+1, 0
end
(0...s.size).each do |count|
%w(A C G T).each do |letter|
p_sums[letter][count+1] = p_sums[letter][count]
end if count > 0
case s[count]
when 'A'
p_sums['A'][count+1] += 1
when 'C'
p_sums['C'][count+1] += 1
when 'G'
p_sums['G'][count+1] += 1
when 'T'
p_sums['T'][count+1] += 1
end
end
(0...p.size).each do |count|
x = p[count]
y = q[count]
if interval_sum(x, y, p_sums['A']) > 0 then
respuesta << 1
next
end
if interval_sum(x, y, p_sums['C']) > 0 then
respuesta << 2
next
end
if interval_sum(x, y, p_sums['G']) > 0 then
respuesta << 3
next
end
if interval_sum(x, y, p_sums['T']) > 0 then
respuesta << 4
next
end
end
respuesta
end
simple php 100/100 solution
function solution($S, $P, $Q) {
$result = array();
for ($i = 0; $i < count($P); $i++) {
$from = $P[$i];
$to = $Q[$i];
$length = $from >= $to ? $from - $to + 1 : $to - $from + 1;
$new = substr($S, $from, $length);
if (strpos($new, 'A') !== false) {
$result[$i] = 1;
} else {
if (strpos($new, 'C') !== false) {
$result[$i] = 2;
} else {
if (strpos($new, 'G') !== false) {
$result[$i] = 3;
} else {
$result[$i] = 4;
}
}
}
}
return $result;
}
Here's my Java (100/100) Solution:
class Solution {
private ImpactFactorHolder[] mHolder;
private static final int A=0,C=1,G=2,T=3;
public int[] solution(String S, int[] P, int[] Q) {
mHolder = createImpactHolderArray(S);
int queriesLength = P.length;
int[] result = new int[queriesLength];
for (int i = 0; i < queriesLength; ++i ) {
int value = 0;
if( P[i] == Q[i]) {
value = lookupValueForIndex(S.charAt(P[i])) + 1;
} else {
value = calculateMinImpactFactor(P[i], Q[i]);
}
result[i] = value;
}
return result;
}
public int calculateMinImpactFactor(int P, int Q) {
int minImpactFactor = 3;
for (int nucleotide = A; nucleotide <= T; ++nucleotide ) {
int qValue = mHolder[nucleotide].mOcurrencesSum[Q];
int pValue = mHolder[nucleotide].mOcurrencesSum[P];
// handling special cases when the less value is assigned on the P index
if( P-1 >= 0 ) {
pValue = mHolder[nucleotide].mOcurrencesSum[P-1] == 0 ? 0 : pValue;
} else if ( P == 0 ) {
pValue = mHolder[nucleotide].mOcurrencesSum[P] == 1 ? 0 : pValue;
}
if ( qValue - pValue > 0) {
minImpactFactor = nucleotide;
break;
}
}
return minImpactFactor + 1;
}
public int lookupValueForIndex(char nucleotide) {
int value = 0;
switch (nucleotide) {
case 'A' :
value = A;
break;
case 'C' :
value = C;
break;
case 'G':
value = G;
break;
case 'T':
value = T;
break;
default:
break;
}
return value;
}
public ImpactFactorHolder[] createImpactHolderArray(String S) {
int length = S.length();
ImpactFactorHolder[] holder = new ImpactFactorHolder[4];
holder[A] = new ImpactFactorHolder(1,'A', length);
holder[C] = new ImpactFactorHolder(2,'C', length);
holder[G] = new ImpactFactorHolder(3,'G', length);
holder[T] = new ImpactFactorHolder(4,'T', length);
int i =0;
for(char c : S.toCharArray()) {
int nucleotide = lookupValueForIndex(c);
++holder[nucleotide].mAcum;
holder[nucleotide].mOcurrencesSum[i] = holder[nucleotide].mAcum;
holder[A].mOcurrencesSum[i] = holder[A].mAcum;
holder[C].mOcurrencesSum[i] = holder[C].mAcum;
holder[G].mOcurrencesSum[i] = holder[G].mAcum;
holder[T].mOcurrencesSum[i] = holder[T].mAcum;
++i;
}
return holder;
}
private static class ImpactFactorHolder {
public ImpactFactorHolder(int impactFactor, char nucleotide, int length) {
mImpactFactor = impactFactor;
mNucleotide = nucleotide;
mOcurrencesSum = new int[length];
mAcum = 0;
}
int mImpactFactor;
char mNucleotide;
int[] mOcurrencesSum;
int mAcum;
}
}
Link: https://codility.com/demo/results/demoJFB5EV-EG8/
I'm looking forward to implement a Segment Tree similar to #Abhishek Kumar solution
My C++ solution
vector<int> solution(string &S, vector<int> &P, vector<int> &Q) {
vector<int> impactCount_A(S.size()+1, 0);
vector<int> impactCount_C(S.size()+1, 0);
vector<int> impactCount_G(S.size()+1, 0);
int lastTotal_A = 0;
int lastTotal_C = 0;
int lastTotal_G = 0;
for (int i = (signed)S.size()-1; i >= 0; --i) {
switch(S[i]) {
case 'A':
++lastTotal_A;
break;
case 'C':
++lastTotal_C;
break;
case 'G':
++lastTotal_G;
break;
};
impactCount_A[i] = lastTotal_A;
impactCount_C[i] = lastTotal_C;
impactCount_G[i] = lastTotal_G;
}
vector<int> results(P.size(), 0);
for (int i = 0; i < P.size(); ++i) {
int pIndex = P[i];
int qIndex = Q[i];
int numA = impactCount_A[pIndex]-impactCount_A[qIndex+1];
int numC = impactCount_C[pIndex]-impactCount_C[qIndex+1];
int numG = impactCount_G[pIndex]-impactCount_G[qIndex+1];
if (numA > 0) {
results[i] = 1;
}
else if (numC > 0) {
results[i] = 2;
}
else if (numG > 0) {
results[i] = 3;
}
else {
results[i] = 4;
}
}
return results;
}
/* 100/100 solution C++.
Using prefix sums. Firstly converting chars to integer in nuc variable. Then in a bi-dimensional vector we account the occurrence in S of each nucleoside x in it's respective prefix_sum[s][x]. After we just have to find out the lower nucluoside that occurred in each interval K.
*/
.
vector solution(string &S, vector &P, vector &Q) {
int n=S.size();
int m=P.size();
vector<vector<int> > prefix_sum(n+1,vector<int>(4,0));
int nuc;
//prefix occurrence sum
for (int s=0;s<n; s++) {
nuc = S.at(s) == 'A' ? 1 : (S.at(s) == 'C' ? 2 : (S.at(s) == 'G' ? 3 : 4) );
for (int u=0;u<4;u++) {
prefix_sum[s+1][u] = prefix_sum[s][u] + ((u+1)==nuc?1:0);
}
}
//find minimal impact factor in each interval K
int lower_impact_factor;
for (int k=0;k<m;k++) {
lower_impact_factor=4;
for (int u=2;u>=0;u--) {
if (prefix_sum[Q[k]+1][u] - prefix_sum[P[k]][u] != 0)
lower_impact_factor = u+1;
}
P[k]=lower_impact_factor;
}
return P;
}
static public int[] solution(String S, int[] P, int[] Q) {
// write your code in Java SE 8
int A[] = new int[S.length() + 1], C[] = new int[S.length() + 1], G[] = new int[S.length() + 1];
int last_a = 0, last_c = 0, last_g = 0;
int results[] = new int[P.length];
int p = 0, q = 0;
for (int i = S.length() - 1; i >= 0; i -= 1) {
switch (S.charAt(i)) {
case 'A': {
last_a += 1;
break;
}
case 'C': {
last_c += 1;
break;
}
case 'G': {
last_g += 1;
break;
}
}
A[i] = last_a;
G[i] = last_g;
C[i] = last_c;
}
for (int i = 0; i < P.length; i++) {
p = P[i];
q = Q[i];
if (A[p] - A[q + 1] > 0) {
results[i] = 1;
} else if (C[p] - C[q + 1] > 0) {
results[i] = 2;
} else if (G[p] - G[q + 1] > 0) {
results[i] = 3;
} else {
results[i] = 4;
}
}
return results;
}
scala solution 100/100
import scala.annotation.switch
import scala.collection.mutable
object Solution {
def solution(s: String, p: Array[Int], q: Array[Int]): Array[Int] = {
val n = s.length
def arr = mutable.ArrayBuffer.fill(n + 1)(0L)
val a = arr
val c = arr
val g = arr
val t = arr
for (i <- 1 to n) {
def inc(z: mutable.ArrayBuffer[Long]): Unit = z(i) = z(i - 1) + 1L
def shift(z: mutable.ArrayBuffer[Long]): Unit = z(i) = z(i - 1)
val char = s(i - 1)
(char: #switch) match {
case 'A' => inc(a); shift(c); shift(g); shift(t);
case 'C' => shift(a); inc(c); shift(g); shift(t);
case 'G' => shift(a); shift(c); inc(g); shift(t);
case 'T' => shift(a); shift(c); shift(g); inc(t);
}
}
val r = mutable.ArrayBuffer.fill(p.length)(0)
for (i <- p.indices) {
val start = p(i)
val end = q(i) + 1
r(i) =
if (a(start) != a(end)) 1
else if (c(start) != c(end)) 2
else if (g(start) != g(end)) 3
else if (t(start) != t(end)) 4
else 0
}
r.toArray
}
}