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I tried this Codility test: MinAbsSum.
https://codility.com/programmers/lessons/17-dynamic_programming/min_abs_sum/
I solved the problem by searching the whole tree of possibilities. The results were OK, however, my solution failed due to timeout for large input. In other words the time complexity was not as good as expected. My solution is O(nlogn), something normal with trees. But this coding test was in the section "Dynamic Programming", and there must be some way to improve it. I tried with summing the whole set first and then using this information, but always there is something missing in my solution. Does anybody have an idea on how to improve my solution using DP?
#include <vector>
using namespace std;
int sum(vector<int>& A, size_t i, int s)
{
if (i == A.size())
return s;
int tmpl = s + A[i];
int tmpr = s - A[i];
return min (abs(sum(A, i+1, tmpl)), abs(sum(A, i+1, tmpr)));
}
int solution(vector<int> &A) {
return sum(A, 0, 0);
}
I could not solve it. But here's the official answer.
Quoting it:
Notice that the range of numbers is quite small (maximum 100). Hence,
there must be a lot of duplicated numbers. Let count[i] denote the
number of occurrences of the value i. We can process all occurrences
of the same value at once. First we calculate values count[i] Then we
create array dp such that:
dp[j] = −1 if we cannot get the sum j,
dp[j] >= 0 if we can get sum j.
Initially, dp[j] = -1 for all of j (except dp[0] = 0). Then we scan
through all the values a appearing in A; we consider all a such
that count[a]>0. For every such a we update dp that dp[j] denotes
how many values a remain (maximally) after achieving sum j. Note
that if the previous value at dp[j] >= 0 then we can set dp[j] =
count[a] as no value a is needed to obtain the sum j. Otherwise we
must obtain sum j-a first and then use a number a to get sum j. In
such a situation dp[j] = dp[j-a]-1. Using this algorithm, we can
mark all the sum values and choose the best one (closest to half of S,
the sum of abs of A).
def MinAbsSum(A):
N = len(A)
M = 0
for i in range(N):
A[i] = abs(A[i])
M = max(A[i], M)
S = sum(A)
count = [0] * (M + 1)
for i in range(N):
count[A[i]] += 1
dp = [-1] * (S + 1)
dp[0] = 0
for a in range(1, M + 1):
if count[a] > 0:
for j in range(S):
if dp[j] >= 0:
dp[j] = count[a]
elif (j >= a and dp[j - a] > 0):
dp[j] = dp[j - a] - 1
result = S
for i in range(S // 2 + 1):
if dp[i] >= 0:
result = min(result, S - 2 * i)
return result
(note that since the final iteration only considers sums up until S // 2 + 1, we can save some space and time by only creating a DP Cache up until that value as well)
The Java answer provided by fladam returns wrong result for input [2, 3, 2, 2, 3], although it gets 100% score.
Java Solution
import java.util.Arrays;
public class MinAbsSum{
static int[] dp;
public static void main(String args[]) {
int[] array = {1, 5, 2, -2};
System.out.println(findMinAbsSum(array));
}
public static int findMinAbsSum(int[] A) {
int arrayLength = A.length;
int M = 0;
for (int i = 0; i < arrayLength; i++) {
A[i] = Math.abs(A[i]);
M = Math.max(A[i], M);
}
int S = sum(A);
dp = new int[S + 1];
int[] count = new int[M + 1];
for (int i = 0; i < arrayLength; i++) {
count[A[i]] += 1;
}
Arrays.fill(dp, -1);
dp[0] = 0;
for (int i = 1; i < M + 1; i++) {
if (count[i] > 0) {
for(int j = 0; j < S; j++) {
if (dp[j] >= 0) {
dp[j] = count[i];
} else if (j >= i && dp[j - i] > 0) {
dp[j] = dp[j - i] - 1;
}
}
}
}
int result = S;
for (int i = 0; i < Math.floor(S / 2) + 1; i++) {
if (dp[i] >= 0) {
result = Math.min(result, S - 2 * i);
}
}
return result;
}
public static int sum(int[] array) {
int sum = 0;
for(int i : array) {
sum += i;
}
return sum;
}
}
I invented another solution, better than the previous one. I do not use recursion any more.
This solution works OK (all logical tests passed), and also passed some of the performance tests, but not all. How else can I improve it?
#include <vector>
#include <set>
using namespace std;
int solution(vector<int> &A) {
if (A.size() == 0) return 0;
set<int> sums, tmpSums;
sums.insert(abs(A[0]));
for (auto it = begin(A) + 1; it != end(A); ++it)
{
for (auto s : sums)
{
tmpSums.insert(abs(s + abs(*it)));
tmpSums.insert(abs(s - abs(*it)));
}
sums = tmpSums;
tmpSums.clear();
}
return *sums.begin();
}
This solution (in Java) scored 100% for both (correctness and performance)
public int solution(int[] a){
if (a.length == 0) return 0;
if (a.length == 1) return a[0];
int sum = 0;
for (int i=0;i<a.length;i++){
sum += Math.abs(a[i]);
}
int[] indices = new int[a.length];
indices[0] = 0;
int half = sum/2;
int localSum = Math.abs(a[0]);
int minLocalSum = Integer.MAX_VALUE;
int placeIndex = 1;
for (int i=1;i<a.length;i++){
if (localSum<half){
if (Math.abs(2*minLocalSum-sum) > Math.abs(2*localSum - sum))
minLocalSum = localSum;
localSum += Math.abs(a[i]);
indices[placeIndex++] = i;
}else{
if (localSum == half)
return Math.abs(2*half - sum);
if (Math.abs(2*minLocalSum-sum) > Math.abs(2*localSum - sum))
minLocalSum = localSum;
if (placeIndex > 1) {
localSum -= Math.abs(a[indices[placeIndex--]]);
i = indices[placeIndex];
}
}
}
return (Math.abs(2*minLocalSum - sum));
}
this solution treats all elements like they are positive numbers and it's looking to reach as close as it can to the sum of all elements divided by 2 (in that case we know that the sum of all other elements will be the same delta far from the half too -> abs sum will be minimum possible ).
it does so by starting with the first element and successively adding others to the "local" sum (and recording indices of elements in the sum) until it reaches sum of x >= sumAll/2. if that x is equal to sumAll/2 we have an optimal solution. if not, we go step back in the indices array and continue picking other element where last iteration in that position ended. the result will be a "local" sum having abs((sumAll - sum) - sum) closest to 0;
fixed solution:
public static int solution(int[] a){
if (a.length == 0) return 0;
if (a.length == 1) return a[0];
int sum = 0;
for (int i=0;i<a.length;i++) {
a[i] = Math.abs(a[i]);
sum += a[i];
}
Arrays.sort(a);
int[] arr = a;
int[] arrRev = new int[arr.length];
int minRes = Integer.MAX_VALUE;
for (int t=0;t<=4;t++) {
arr = fold(arr);
int res1 = findSum(arr, sum);
if (res1 < minRes) minRes = res1;
rev(arr, arrRev);
int res2 = findSum(arrRev, sum);
if (res2 < minRes) minRes = res2;
arrRev = fold(arrRev);
int res3 = findSum(arrRev, sum);
if (res3 < minRes) minRes = res3;
}
return minRes;
}
private static void rev(int[] arr, int[] arrRev){
for (int i = 0; i < arrRev.length; i++) {
arrRev[i] = arr[arr.length - 1 - i];
}
}
private static int[] fold(int[] a){
int[] arr = new int[a.length];
for (int i=0;a.length/2+i/2 < a.length && a.length/2-i/2-1 >= 0;i+=2){
arr[i] = a[a.length/2+i/2];
arr[i+1] = a[a.length/2-i/2-1];
}
if (a.length % 2 > 0) arr[a.length-1] = a[a.length-1];
else{
arr[a.length-2] = a[0];
arr[a.length-1] = a[a.length-1];
}
return arr;
}
private static int findSum(int[] arr, int sum){
int[] indices = new int[arr.length];
indices[0] = 0;
double half = Double.valueOf(sum)/2;
int localSum = Math.abs(arr[0]);
int minLocalSum = Integer.MAX_VALUE;
int placeIndex = 1;
for (int i=1;i<arr.length;i++){
if (localSum == half)
return 2*localSum - sum;
if (Math.abs(2*minLocalSum-sum) > Math.abs(2*localSum - sum))
minLocalSum = localSum;
if (localSum<half){
localSum += Math.abs(arr[i]);
indices[placeIndex++] = i;
}else{
if (placeIndex > 1) {
localSum -= Math.abs(arr[indices[--placeIndex]]);
i = indices[placeIndex];
}
}
}
return Math.abs(2*minLocalSum - sum);
}
The following is a rendering of the official answer in C++ (scoring 100% in task, correctness, and performance):
#include <cmath>
#include <algorithm>
#include <numeric>
using namespace std;
int solution(vector<int> &A) {
// write your code in C++14 (g++ 6.2.0)
const int N = A.size();
int M = 0;
for (int i=0; i<N; i++) {
A[i] = abs(A[i]);
M = max(M, A[i]);
}
int S = accumulate(A.begin(), A.end(), 0);
vector<int> counts(M+1, 0);
for (int i=0; i<N; i++) {
counts[A[i]]++;
}
vector<int> dp(S+1, -1);
dp[0] = 0;
for (int a=1; a<M+1; a++) {
if (counts[a] > 0) {
for (int j=0; j<S; j++) {
if (dp[j] >= 0) {
dp[j] = counts[a];
} else if ((j >= a) && (dp[j-a] > 0)) {
dp[j] = dp[j-a]-1;
}
}
}
}
int result = S;
for (int i =0; i<(S/2+1); i++) {
if (dp[i] >= 0) {
result = min(result, S-2*i);
}
}
return result;
}
You are almost 90% to the actual solution. It seems you understand recursion very well. Now, You should apply dynamic programming here with your program.
Dynamic Programming is nothing but memoization to the recursion so that we will not calculate same sub problems again and again. If same sub problems encounter , we return the previously calculated and memorized value. Memorization can be done with the help of a 2D array , say dp[][], where first state represent current index of array and second state represent summation.
For this problem specific, instead of giving calls to both states from each state, you sometimes can greedily take decision to skip one call.
I would like to provide the algorithm and then my implementation in C++. Idea is more or less the same as the official codility solution with some constant optimisation added.
Calculate the maximum absolute element of the inputs.
Calculate the absolute sum of the inputs.
Count the number of occurrence of each number in the inputs. Store the results in a vector hash.
Go through each input.
For each input, goes through all possible sums of any number of inputs. It is a slight constant optimisation to go only up to half of the possible sums.
For each sum that has been made before, set the occurrence count of the current input.
Check for each potential sum equal to or greater than the current input whether this input has already been used before. Update the values at the current sum accordingly. We do not need to check for potential sums less than the current input in this iteration, since it is evident that it has not been used before.
The above nested loop will fill in each possible sum with a value greater than -1.
Go through this possible sum hash again to look for the closest sum to half that is possible to make. Eventually, the min abs sum will be the difference of this from the half multiplied by two as the difference will be added up in both groups as the difference from the median.
The runtime complexity of this algorithm is O(N * max(abs(A)) ^ 2), or simply O(N * M ^ 2). That is because the outer loop is iterating M times and the inner loop is iterating sum times. The sum is basically N * M in worst case. Therefore, it is O(M * N * M).
The space complexity of this solution is O(N * M) because we allocate a hash of N items for the counts and a hash of S items for the sums. S is N * M again.
int solution(vector<int> &A)
{
int M = 0, S = 0;
for (const int e : A) { M = max(abs(e), M); S += abs(e); }
vector<int> counts(M + 1, 0);
for (const int e : A) { ++counts[abs(e)]; }
vector<int> sums(S + 1, -1);
sums[0] = 0;
for (int ci = 1; ci < counts.size(); ++ci) {
if (!counts[ci]) continue;
for (int si = 0; si < S / 2 + 1; ++si) {
if (sums[si] >= 0) sums[si] = counts[ci];
else if (si >= ci and sums[si - ci] > 0) sums[si] = sums[si - ci] - 1;
}
}
int min_abs_sum = S;
for (int i = S / 2; i >= 0; --i) if (sums[i] >= 0) return S - 2 * i;
return min_abs_sum;
}
Let me add my 50 cent, how to come up with the score 100% solution.
For me it was hard to understand the ultimate solution, proposed earlier in this thread.
So I started with warm-up solution with score 63%, because its O(NxNxM),
and because it doesn't use the fact that M is quite small value, and there are many duplicates in big arrays
here the key part is to understand how array isSumPossible is filled and interpreted:
how to fill array isSumPossible using numbers in input array:
if isSumPossible[sum] >= 0, i.e. sum is already possible, even without current number, then let's set it's value to 1 - count of current number, that is left unused for this sum, it'll go to our "reserve", so we can use it later for greater sums.
if (isSumPossible[sum] >= 0) {
isSumPossible[sum] = 1;
}
if isSumPossible[sum] <= 0, i.e. sum is considered not yet possible, with all input numbers considered previously, then let's check maybe
smaller sum sum - number is already considered as possible, and we have in "reserve" our current number (isSumPossible[sum - number] == 1), then do following
else if (sum >= number && isSumPossible[sum - number] == 1) {
isSumPossible[sum] = 0;
}
here isSumPossible[sum] = 0 means that we have used number in composing sum and it's now considered as possible (>=0), but we have no number in "reserve", because we've used it ( =0)
how to interpret filled array isSumPossible after considering all numbers in input array:
if isSumPossible[sum] >= 0 then the sum is possible, i.e. it can be reached by summation of some numbers in given array
if isSumPossible[sum] < 0 then the sum can't be reached by summation of any numbers in given array
The more simple thing here is to understand why we are searching sums only in interval [0, maxSum/2]:
because if find a possible sum, that is very close to maxSum/2,
ideal case here if we've found possible sum = maxSum/2,
if so, then it's obvious, that we can somehow use the rest numbers in input array to make another maxSum/2, but now with negative sign, so as a result of annihilation we'll get solution = 0, because maxSum/2 + (-1)maxSum/2 = 0.
But 0 the best case solution, not always reachable.
But we, nevertheless, should seek for the minimal delta = ((maxSum - sum) - sum),
so this we seek for delta -> 0, that's why we have this:
int result = Integer.MAX_VALUE;
for (int sum = 0; sum < maxSum / 2 + 1; sum++) {
if (isSumPossible[sum] >= 0) {
result = Math.min(result, (maxSum - sum) - sum);
}
}
warm-up solution
public int solution(int[] A) {
if (A == null || A.length == 0) {
return 0;
}
if (A.length == 1) {
return A[0];
}
int maxSum = 0;
for (int i = 0; i < A.length; i++) {
A[i] = Math.abs(A[i]);
maxSum += A[i];
}
int[] isSumPossible = new int[maxSum + 1];
Arrays.fill(isSumPossible, -1);
isSumPossible[0] = 0;
for (int number : A) {
for (int sum = 0; sum < maxSum / 2 + 1; sum++) {
if (isSumPossible[sum] >= 0) {
isSumPossible[sum] = 1;
} else if (sum >= number && isSumPossible[sum - number] == 1) {
isSumPossible[sum] = 0;
}
}
}
int result = Integer.MAX_VALUE;
for (int sum = 0; sum < maxSum / 2 + 1; sum++) {
if (isSumPossible[sum] >= 0) {
result = Math.min(result, maxSum - 2 * sum);
}
}
return result;
}
and after this we can optimize it, using the fact that there are many duplicate numbers in big arrays, and we come up with the solution with 100% score, its O(Mx(NxM)), because maxSum = NxM at worst case
public int solution(int[] A) {
if (A == null || A.length == 0) {
return 0;
}
if (A.length == 1) {
return A[0];
}
int maxNumber = 0;
int maxSum = 0;
for (int i = 0; i < A.length; i++) {
A[i] = Math.abs(A[i]);
maxNumber = Math.max(maxNumber, A[i]);
maxSum += A[i];
}
int[] count = new int[maxNumber + 1];
for (int i = 0; i < A.length; i++) {
count[A[i]]++;
}
int[] isSumPossible = new int[maxSum + 1];
Arrays.fill(isSumPossible, -1);
isSumPossible[0] = 0;
for (int number = 0; number < maxNumber + 1; number++) {
if (count[number] > 0) {
for (int sum = 0; sum < maxSum / 2 + 1; sum++) {
if (isSumPossible[sum] >= 0) {
isSumPossible[sum] = count[number];
} else if (sum >= number && isSumPossible[sum - number] > 0) {
isSumPossible[sum] = isSumPossible[sum - number] - 1;
}
}
}
}
int result = Integer.MAX_VALUE;
for (int sum = 0; sum < maxSum / 2 + 1; sum++) {
if (isSumPossible[sum] >= 0) {
result = Math.min(result, maxSum - 2 * sum);
}
}
return result;
}
I hope I've made it at least a little clear
Kotlin solution
Time complexity: O(N * max(abs(A))**2)
Score: 100%
import kotlin.math.*
fun solution(A: IntArray): Int {
val N = A.size
var M = 0
for (i in 0 until N) {
A[i] = abs(A[i])
M = max(M, A[i])
}
val S = A.sum()
val counts = MutableList(M + 1) { 0 }
for (i in 0 until N) {
counts[A[i]]++
}
val dp = MutableList(S + 1) { -1 }
dp[0] = 0
for (a in 1 until M + 1) {
if (counts[a] > 0) {
for (j in 0 until S) {
if (dp[j] >= 0) {
dp[j] = counts[a]
} else if (j >= a && dp[j - a] > 0) {
dp[j] = dp[j - a] - 1
}
}
}
}
var result = S
for (i in 0 until (S / 2 + 1)) {
if (dp[i] >= 0) {
result = minOf(result, S - 2 * i)
}
}
return result
}
the thing is that we are tring to do for first time a binary search function for a vector class, but I dont really know the reason why this is not working. Does anyone know what could be wrong(Status acces violation when the number is not in the vector array)??
// size_ is de number of used elements i the array
int Vector::bsearch(int value) const
{
int first = 0;
int last = size_ - 1;
int Substraction = size_ /2;
while(last >= first)
{
Substraction = first + (last - first) / 2;
if(array_[Substraction] > value)
last = Substraction;
else if(array_[Substraction] < value)
first = Substraction;
else if(array_[Substraction] == value)
return Substraction;
}
return CS170::Vector::NO_INDEX;
}
//SOLVED
int Vector::bsearch(int value) const
{
unsigned first = 0;
unsigned last = size_ - 1;
unsigned int mid;
if(value < array_[0] || value > array_[size_ - 1])
return CS170::Vector::NO_INDEX;
while(last >= first)
{
mid = first + (last - first) / 2;
if(value < array_[mid])
last = mid - 1;
else if(array_[mid] < value)
first = mid + 1;
else
return mid;
}
return CS170::Vector::NO_INDEX;
}
You're not excluding the element you just tested before the next loop. And your choice of variable names, Subtraction is dreadful. Its a midpoint.
int first = 0;
int last = size_-1;
int mid = 0;
while(last >= first)
{
mid = first + (last - first) / 2;
if(value < array_[mid])
last = mid-1; // don't include element just tested
else if(array_[mid] < value)
first = mid+1; // don't include element just tested
else return mid;
}
return CS170::Vector::NO_INDEX
Your initialization is wrong -
int first = size_ - 1;
int last = first = 0;
should be -
int last = size_ - 1;
int first = 0;
Also, in while you should use the following condition -
while (last>first)
I need to place numbers within a grid such that it doesn't collide with each other. This number placement should be random and can be horizontal or vertical. The numbers basically indicate the locations of the ships. So the points for the ships should be together and need to be random and should not collide.
I have tried it:
int main()
{
srand(time(NULL));
int Grid[64];
int battleShips;
bool battleShipFilled;
for(int i = 0; i < 64; i++)
Grid[i]=0;
for(int i = 1; i <= 5; i++)
{
battleShips = 1;
while(battleShips != 5)
{
int horizontal = rand()%2;
if(horizontal == 0)
{
battleShipFilled = false;
while(!battleShipFilled)
{
int row = rand()%8;
int column = rand()%8;
while(Grid[(row)*8+(column)] == 1)
{
row = rand()%8;
column = rand()%8;
}
int j = 0;
if(i == 1) j= (i+1);
else j= i;
for(int k = -j/2; k <= j/2; k++)
{
int numberOfCorrectLocation = 0;
while(numberOfCorrectLocation != j)
{
if(row+k> 0 && row+k<8)
{
if(Grid[(row+k)*8+(column)] == 1) break;
numberOfCorrectLocation++;
}
}
if(numberOfCorrectLocation !=i) break;
}
for(int k = -j/2; k <= j/2; k++)
Grid[(row+k)*8+(column)] = 1;
battleShipFilled = true;
}
battleShips++;
}
else
{
battleShipFilled = false;
while(!battleShipFilled)
{
int row = rand()%8;
int column = rand()%8;
while(Grid[(row)*8+(column)] == 1)
{
row = rand()%8;
column = rand()%8;
}
int j = 0;
if(i == 1) j= (i+1);
else j= i;
for(int k = -j/2; k <= j/2; k++)
{
int numberOfCorrectLocation = 0;
while(numberOfCorrectLocation != i)
{
if(row+k> 0 && row+k<8)
{
if(Grid[(row)*8+(column+k)] == 1) break;
numberOfCorrectLocation++;
}
}
if(numberOfCorrectLocation !=i) break;
}
for(int k = -j/2; k <= j/2; k++)
Grid[(row)*8+(column+k)] = 1;
battleShipFilled = true;
}
battleShips++;
}
}
}
}
But the code i have written is not able to generate the numbers randomly in the 8x8 grid.
Need some guidance on how to solve this. If there is any better way of doing it, please tell me...
How it should look:
What My code is doing:
Basically, I am placing 5 ships, each of different size on a grid. For each, I check whether I want to place it horizontally or vertically randomly. After that, I check whether the surrounding is filled up or not. If not, I place them there. Or I repeat the process.
Important Point: I need to use just while, for loops..
You are much better of using recursion for that problem. This will give your algorithm unwind possibility. What I mean is that you can deploy each ship and place next part at random end of the ship, then check the new placed ship part has adjacent tiles empty and progress to the next one. if it happens that its touches another ship it will due to recursive nature it will remove the placed tile and try on the other end. If the position of the ship is not valid it should place the ship in different place and start over.
I have used this solution in a word search game, where the board had to be populated with words to look for. Worked perfect.
This is a code from my word search game:
bool generate ( std::string word, BuzzLevel &level, CCPoint position, std::vector<CCPoint> &placed, CCSize lSize )
{
std::string cPiece;
if ( word.size() == 0 ) return true;
if ( !level.inBounds ( position ) ) return false;
cPiece += level.getPiece(position)->getLetter();
int l = cPiece.size();
if ( (cPiece != " ") && (word[0] != cPiece[0]) ) return false;
if ( pointInVec (position, placed) ) return false;
if ( position.x >= lSize.width || position.y >= lSize.height || position.x < 0 || position.y < 0 ) return false;
placed.push_back(position);
bool used[6];
for ( int t = 0; t < 6; t++ ) used[t] = false;
int adj;
while ( (adj = HexCoord::getRandomAdjacentUnique(used)) != -1 )
{
CCPoint nextPosition = HexCoord::getAdjacentGridPositionInDirection((eDirection) adj, position);
if ( generate ( word.substr(1, word.size()), level, nextPosition, placed, lSize ) ) return true;
}
placed.pop_back();
return false;
}
CCPoint getRandPoint ( CCSize size )
{
return CCPoint ( rand() % (int)size.width, rand() % (int)size.height);
}
void generateWholeLevel ( BuzzLevel &level,
blockInfo* info,
const CCSize &levelSize,
vector<CCLabelBMFont*> wordList
)
{
for ( vector<CCLabelBMFont*>::iterator iter = wordList.begin();
iter != wordList.end(); iter++ )
{
std::string cWord = (*iter)->getString();
// CCLog("Curront word %s", cWord.c_str() );
vector<CCPoint> wordPositions;
int iterations = 0;
while ( true )
{
iterations++;
//CCLog("iteration %i", iterations );
CCPoint cPoint = getRandPoint(levelSize);
if ( generate (cWord, level, cPoint, wordPositions, levelSize ) )
{
//Place pieces here
for ( int t = 0; t < cWord.size(); t++ )
{
level.getPiece(wordPositions[t])->addLetter(cWord[t]);
}
break;
}
if ( iterations > 1500 )
{
level.clear();
generateWholeLevel(level, info, levelSize, wordList);
return;
}
}
}
}
I might add that shaped used in the game was a honeycomb. Letter could wind in any direction, so the code above is way more complex then what you are looking for I guess, but will provide a starting point.
I will provide something more suitable when I get back home as I don't have enough time now.
I can see a potential infinite loop in your code
int j = 0;
if(i == 1) j= (i+1);
else j= i;
for(int k = -j/2; k <= j/2; k++)
{
int numberOfCorrectLocation = 0;
while(numberOfCorrectLocation != i)
{
if(row+k> 0 && row+k<8)
{
if(Grid[(row)*8+(column+k)] == 1) break;
numberOfCorrectLocation++;
}
}
if(numberOfCorrectLocation !=i) break;
}
Here, nothing prevents row from being 0, as it was assignd rand%8 earlier, and k can be assigned a negative value (since j can be positive). Once that happens nothing will end the while loop.
Also, I would recommend re-approaching this problem in a more object oriented way (or at the very least breaking up the code in main() into multiple, shorter functions). Personally I found the code a little difficult to follow.
A very quick and probably buggy example of how you could really clean your solution up and make it more flexible by using some OOP:
enum Orientation {
Horizontal,
Vertical
};
struct Ship {
Ship(unsigned l = 1, bool o = Horizontal) : length(l), orientation(o) {}
unsigned char length;
bool orientation;
};
class Grid {
public:
Grid(const unsigned w = 8, const unsigned h = 8) : _w(w), _h(h) {
grid.resize(w * h);
foreach (Ship * sp, grid) {
sp = nullptr;
}
}
bool addShip(Ship * s, unsigned x, unsigned y) {
if ((x <= _w) && (y <= _h)) { // if in valid range
if (s->orientation == Horizontal) {
if ((x + s->length) <= _w) { // if not too big
int p = 0; //check if occupied
for (int c1 = 0; c1 < s->length; ++c1) if (grid[y * _w + x + p++]) return false;
p = 0; // occupy if not
for (int c1 = 0; c1 < s->length; ++c1) grid[y * _w + x + p++] = s;
return true;
} else return false;
} else {
if ((y + s->length) <= _h) {
int p = 0; // check
for (int c1 = 0; c1 < s->length; ++c1) {
if (grid[y * _w + x + p]) return false;
p += _w;
}
p = 0; // occupy
for (int c1 = 0; c1 < s->length; ++c1) {
grid[y * _w + x + p] = s;
p += _w;
}
return true;
} else return false;
}
} else return false;
}
void drawGrid() {
for (int y = 0; y < _h; ++y) {
for (int x = 0; x < _w; ++x) {
if (grid.at(y * w + x)) cout << "|S";
else cout << "|_";
}
cout << "|" << endl;
}
cout << endl;
}
void hitXY(unsigned x, unsigned y) {
if ((x <= _w) && (y <= _h)) {
if (grid[y * _w + x]) cout << "You sunk my battleship" << endl;
else cout << "Nothing..." << endl;
}
}
private:
QVector<Ship *> grid;
unsigned _w, _h;
};
The basic idea is create a grid of arbitrary size and give it the ability to "load" ships of arbitrary length at arbitrary coordinates. You need to check if the size is not too much and if the tiles aren't already occupied, that's pretty much it, the other thing is orientation - if horizontal then increment is +1, if vertical increment is + width.
This gives flexibility to use the methods to quickly populate the grid with random data:
int main() {
Grid g(20, 20);
g.drawGrid();
unsigned shipCount = 20;
while (shipCount) {
Ship * s = new Ship(qrand() % 8 + 2, qrand() %2);
if (g.addShip(s, qrand() % 20, qrand() % 20)) --shipCount;
else delete s;
}
cout << endl;
g.drawGrid();
for (int i = 0; i < 20; ++i) g.hitXY(qrand() % 20, qrand() % 20);
}
Naturally, you can extend it further, make hit ships sink and disappear from the grid, make it possible to move ships around and flip their orientation. You can even use diagonal orientation. A lot of flexibility and potential to harness by refining an OOP based solution.
Obviously, you will put some limits in production code, as currently you can create grids of 0x0 and ships of length 0. It's just a quick example anyway. I am using Qt and therefore Qt containers, but its just the same with std containers.
I tried to rewrite your program in Java, it works as required. Feel free to ask anything that is not clearly coded. I didn't rechecked it so it may have errors of its own. It can be further optimized and cleaned but as it is past midnight around here, I would rather not do that at the moment :)
public static void main(String[] args) {
Random generator = new Random();
int Grid[][] = new int[8][8];
for (int battleShips = 0; battleShips < 5; battleShips++) {
boolean isHorizontal = generator.nextInt(2) == 0 ? true : false;
boolean battleShipFilled = false;
while (!battleShipFilled) {
// Select a random row and column for trial
int row = generator.nextInt(8);
int column = generator.nextInt(8);
while (Grid[row][column] == 1) {
row = generator.nextInt(8);
column = generator.nextInt(8);
}
int lengthOfBattleship = 0;
if (battleShips == 0) // Smallest ship should be of length 2
lengthOfBattleship = (battleShips + 2);
else // Other 4 ships has the length of 2, 3, 4 & 5
lengthOfBattleship = battleShips + 1;
int numberOfCorrectLocation = 0;
for (int k = 0; k < lengthOfBattleship; k++) {
if (isHorizontal && row + k > 0 && row + k < 8) {
if (Grid[row + k][column] == 1)
break;
} else if (!isHorizontal && column + k > 0 && column + k < 8) {
if (Grid[row][column + k] == 1)
break;
} else {
break;
}
numberOfCorrectLocation++;
}
if (numberOfCorrectLocation == lengthOfBattleship) {
for (int k = 0; k < lengthOfBattleship; k++) {
if (isHorizontal)
Grid[row + k][column] = 1;
else
Grid[row][column + k] = 1;
}
battleShipFilled = true;
}
}
}
}
Some important points.
As #Kindread said in an another answer, the code has an infinite loop condition which must be eliminated.
This algorithm will use too much resources to find a solution, it should be optimized.
Code duplications should be avoided as it will result in more maintenance cost (which might not be a problem for this specific case), and possible bugs.
Hope this answer helps...
I have an array
Values array: 12 20 32 40 52
^ ^ ^ ^ ^
0 1 2 3 4
on which I have to perform binary search to find the index of the range in which the number lies. For example:
Given the number -> 19 (It lies between index 0 and 1), return 0
Given the number -> 22 (It lies between index 1 and 2), return 1
Given the number -> 40 (It lies between index 3 and 4), return 3
I implemented the binary search in the following manner, and this comes to be correct for case 1, and 3 but incorrect if we search for case 2 or 52, 55 32, etc.
#include <iostream>
using namespace std;
int findIndex(int values[], int number, unsigned first, unsigned last)
{
unsigned midPoint;
while(first<last)
{
unsigned midPoint = (first+last)/2;
if (number <= values[midPoint])
last = midPoint -1;
else if (number > values[midPoint])
first = midPoint + 1;
}
return midPoint;
}
int main()
{
int a[] = {12, 20, 32, 40, 52};
unsigned i = findIndex(a, 55, 0, 4);
cout << i;
}
Use of additional variables such as bool found is not allowed.
A range in C or C++ is normally given as the pointing directly to the lower bound, but one past the upper bound. Unless you're feeling extremely masochistic, you probably want to stick to that convention in your search as well.
Assuming you're going to follow that, your last = midpoint-1; is incorrect. Rather, you want to set last to one past the end of the range you're going to actually use, so it should be last = midpoint;
You also only really need one comparison, not two. In a binary search as long as the two bounds aren't equal, you're going to set either the lower or the upper bound to the center point, so you only need to do one comparison to decide which.
At least by convention, in C++, you do all your comparisons using < instead of <=, >, etc. Any of the above can work, but following the convention of using only < keeps from imposing extra (unnecessary) requirements on contained types.
Though most interviewers probably don't care, there's also a potential overflow when you do midpoint = (left + right)/2;. I'd generally prefer midpoint = left + (right - left)/2;
Taking those into account, code might look something like this:
template <class T>
T *lower_bound(T *left, T *right, T val) {
while (left < right) {
T *middle = left + (right - left) / 2;
if (*middle < val)
left = middle + 1;
else
right = middle;
}
return left;
}
template <class T>
T *upper_bound(T *left, T *right, T val) {
while (left < right) {
T *middle = left + (right - left) / 2;
if (val < *middle)
right = middle;
else
left = middle + 1;
}
return left;
}
Why not to use standard library functions?
#include <vector>
#include <algorithm>
#include <iostream>
using namespace std;
int main() {
for (int input = 10; input < 55; input++) {
cout << input << ": ";
// Your desire:
vector<int> v = { 12, 20, 32, 40, 52 };
if (input < v.front() || input > v.back()) {
cout << "Not found" << endl;
} else {
auto it = upper_bound(v.begin(), v.end(), input);
cout << it - v.begin() - 1 << endl;
}
}
}
Note: a pretty-cool site - http://en.cppreference.com/w/cpp/algorithm
This will work under the condition that min(A[i]) <= key <=max(A[i])
int binary_search(int A[],int key,int left, int right)
{
while (left <= right) {
int middle = left + (right - left) / 2;
if (A[middle] < key)
left = middle+1;
else if(A[middle] > key)
right = middle-1;
else
return middle;
}
return (left - 1);
}
For INPUT
4
1 3 8 10
4
OUTPUT
3 (the minimum of the 3 and 8)
#include <stdio.h>
int main()
{
int c, first, last, middle, n, search, array[100];
scanf("%d",&n);
for (c = 0; c < n; c++)
scanf("%d",&array[c]);
scanf("%d", &search);
first = 0;
last = n - 1;
middle = (first+last)/2;
while (first <= last) {
if (array[middle] < search)
{
first = middle + 1; }
else if (array[middle] == search) {
break;
}
else
{
last = middle - 1;
}
middle = (first + last)/2;
}
printf("%d\n",array[middle]);
return 0;
}
A regular binary search on success returns the index of the key. On failure to find the key it always stops at the index of the lowest key greater than the key we are searching. I guess following modified binary search algorithm will work.
Given sorted array A
Find a key using binary search and get an index.
If A[index] == key
return index;
else
while(index > 1 && A[index] == A[index -1]) index = index -1;
return index;
binsrch(array, num, low, high) {
if (num > array[high])
return high;
while(1) {
if (low == high-1)
return low;
if(low >= high)
return low-1;
mid = (low+high)/2
if (num < arr[mid])
high = mid;
else
low = mid+1;
}
}
here is a more specific answer
int findIndex(int values[],int key,int first, int last)
{
if(values[first]<=key && values[first+1]>=key)// stopping condition
{
return first;
}
int imid=first+(last-first)/2;
if(first==last || imid==first)
{
return -1;
}
if(values[imid]>key)
{
return findIndex(values,key,first,imid);
}
else if(values[imid]<=key)
{
return findIndex(values,key,imid,last);
}
}
I feel this is more inline to what you were looking for...and we won't crap out on the last value in this thing
/* binary_range.c (c) 2016 adolfo#di-mare.com */
/* http://stackoverflow.com/questions/10935635 */
/* This code is written to be easily translated to Fortran */
#include <stdio.h> /* printf() */
#include <assert.h> /* assert() */
/** Find the biggest index 'i' such that '*nSEED <= nVEC[i]'.
- nVEC[0..N-1] is an strict ascending order array.
- Returns and index in [0..N].
- Returns 'N' when '*nSEED>nVEC[N-1]'.
- Uses binary search to find the range for '*nSEED'.
*/
int binary_range( int *nSEED, int nVEC[] , int N ) {
int lo,hi, mid,plus;
if ( *nSEED > nVEC[N-1] ) {
return N;
}
for (;;) { /* lo = binary_range_search() */
lo = 0;
hi = N-1;
for (;;) {
plus = (hi-lo)>>1; /* mid = (hi+lo)/2; */
if ( plus == 0 ) { assert( hi-lo==1 );
if (*nSEED <= nVEC[lo]) {
hi = lo;
}
else {
lo = hi;
}
}
mid = lo + plus; /* mid = lo + (hi-lo)/2; */
if (*nSEED <= nVEC[mid]) {
hi = mid;
}
else {
lo = mid;
}
if (lo>=hi) { break; }
}
break;
} /* 'lo' is the index */
/* This implementation does not use division. */
/* ========================================= */
assert( *nSEED <= nVEC[lo] );
return lo;
}
/** Find the biggest index 'i' such that '*nSEED <= nVEC[i]'.
- nVEC[0..N-1] is an strict ascending order array.
- Returns and index in [0..N].
- Returns 'N' when '*nSEED>nVEC[N-1]'.
- Uses sequential search to find the range for '*nSEED'.
*/
int sequential_range( int* nSEED, int nVEC[] , int N ) {
int i;
if ( *nSEED > nVEC[N-1] ) {
return N;
}
i=0;
while ( i<N ) {
if ( *nSEED <= nVEC[i] ) { break; }
++i;
}
return i;
}
/** test->stackoverflow.10935635(). */
void test_10935635() {
{{ /* test.stackoverflow.10935635() */
/* http://stackoverflow.com/questions/10935635 */
/* binary_range search to find the range in which the number lies */
/* 0 1 2 3 4 */
int nVEC[] = { 12,20,32,40,52 }; int val;
int N = sizeof(nVEC)/sizeof(nVEC[0]); /* N = DIM(nVEC[]) */
val=19; val = binary_range( &val,nVEC,N );
/* 19 -> [12 < (19) <= 20] -> return 1 */
val=19; assert( binary_range( &val,nVEC,N ) == 1 );
/* 22 -> [20 < (22) <= 32] -> return 2 */
val=22; assert( binary_range( &val,nVEC,N ) == 2 );
/* 40 -> [32 < (40) <= 40] -> return 3 */
val=40; assert( binary_range( &val,nVEC,N ) == 3 );
/* Everything over 52 returns N */
val=53; assert( binary_range( &val,nVEC,N ) == N );
}}
}
/** Test program. */
int main() {
if (1) {
printf( "\ntest_10935635()" );
test_10935635();
}
printf( "\nEND" );
return 0;
}
/* Compiler: gcc.exe (tdm-1) 4.9.2 */
/* IDE: Code::Blocks 16.01 */
/* Language: C && C++ */
/* EOF: binary_range.c */
I know this is an old thread, but since I had to solve a similar problem I thought I would share it. Given a set of non-overlapping ranges of integers, I need to test if a given value lies in any of those ranges. The following (in Java), uses a modified binary search to test if a value lies within the sorted (lowest to highest) set of integer ranges.
/**
* Very basic Range representation for long values
*
*/
public class Range {
private long low;
private long high;
public Range(long low, long high) {
this.low = low;
this.high = high;
}
public boolean isInRange(long val) {
return val >= low && val <= high;
}
public long getLow() {
return low;
}
public void setLow(long low) {
this.low = low;
}
public long getHigh() {
return high;
}
public void setHigh(long high) {
this.high = high;
}
#Override
public String toString() {
return "Range [low=" + low + ", high=" + high + "]";
}
}
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
//Java implementation of iterative Binary Search over Ranges
class BinaryRangeSearch {
// Returns index of x if it is present in the list of Range,
// else return -1
int binarySearch(List<Range> ranges, int x)
{
Range[] arr = new Range[ranges.size()];
arr = ranges.toArray(arr);
int low = 0, high = arr.length - 1;
int iters = 0;
while (low <= high) {
int mid = low + (high - low) / 2; // find mid point
// Check if x is present a
if (arr[mid].getLow() == x) {
System.out.println(iters + " iterations");
return mid;
}
// If x greater, ignore left half
if (x > arr[mid].getHigh()) {
low = mid + 1;
}
else if (x >= arr[mid].getLow()) {
System.out.println(iters + " iterations");
return mid;
}
// If x is smaller, ignore right half of remaining Ranges
else
high = mid - 1;
iters++;
}
return -1; // not in any of the given Ranges
}
// Driver method to test above
public static void main(String args[])
{
BinaryRangeSearch ob = new BinaryRangeSearch();
// make a test list of long Range
int multiplier = 1;
List<Range> ranges = new ArrayList<>();
int high = 0;
for(int i = 0; i <7; i++) {
int low = i + high;
high = (i+10) * multiplier;
Range r = new Range(low, high);
multiplier *= 10;
ranges.add(r);
}
System.out.println(Arrays.toString(ranges.toArray()));
int result = ob.binarySearch(ranges, 11);
if (result == -1)
System.out.println("Element not present");
else
System.out.println("Element found at "
+ "index " + result);
}
}
My python implementation:
Time complexity: O(log(n))
Space complexity: O(log(n))
def searchForRange(array, target):
range = [-1, -1]
alteredBinarySerach(array, target, 0, len(array) -1, range, True)
alteredBinarySerach(array, target, 0, len(array) -1, range, False)
return range
def alteredBinarySerach(array, target, left, right, range, goLeft):
if left > right:
return
middle = (left+ right)//2
if array[middle] > target:
alteredBinarySerach(array, target, left, middle -1, range, goLeft)
elif array[middle] < target:
alteredBinarySerach(array, target, middle +1, right, range, goLeft)
else:
if goLeft:
if middle == 0 or array[middle -1] != target:
range[0] = middle
else:
alteredBinarySerach(array, target, left, middle -1 , range, goLeft)
else:
if middle == len(array) -1 or array[middle+1] != target:
range[1] = middle
else:
alteredBinarySerach(array, target, middle +1, right , range, goLeft)
Answering to another question, I wrote the program below to compare different search methods in a sorted array. Basically I compared two implementations of Interpolation search and one of binary search. I compared performance by counting cycles spent (with the same set of data) by the different variants.
However I'm sure there is ways to optimize these functions to make them even faster. Does anyone have any ideas on how can I make this search function faster? A solution in C or C++ is acceptable, but I need it to process an array with 100000 elements.
#include <stdlib.h>
#include <stdio.h>
#include <time.h>
#include <stdint.h>
#include <assert.h>
static __inline__ unsigned long long rdtsc(void)
{
unsigned long long int x;
__asm__ volatile (".byte 0x0f, 0x31" : "=A" (x));
return x;
}
int interpolationSearch(int sortedArray[], int toFind, int len) {
// Returns index of toFind in sortedArray, or -1 if not found
int64_t low = 0;
int64_t high = len - 1;
int64_t mid;
int l = sortedArray[low];
int h = sortedArray[high];
while (l <= toFind && h >= toFind) {
mid = low + (int64_t)((int64_t)(high - low)*(int64_t)(toFind - l))/((int64_t)(h-l));
int m = sortedArray[mid];
if (m < toFind) {
l = sortedArray[low = mid + 1];
} else if (m > toFind) {
h = sortedArray[high = mid - 1];
} else {
return mid;
}
}
if (sortedArray[low] == toFind)
return low;
else
return -1; // Not found
}
int interpolationSearch2(int sortedArray[], int toFind, int len) {
// Returns index of toFind in sortedArray, or -1 if not found
int low = 0;
int high = len - 1;
int mid;
int l = sortedArray[low];
int h = sortedArray[high];
while (l <= toFind && h >= toFind) {
mid = low + ((float)(high - low)*(float)(toFind - l))/(1+(float)(h-l));
int m = sortedArray[mid];
if (m < toFind) {
l = sortedArray[low = mid + 1];
} else if (m > toFind) {
h = sortedArray[high = mid - 1];
} else {
return mid;
}
}
if (sortedArray[low] == toFind)
return low;
else
return -1; // Not found
}
int binarySearch(int sortedArray[], int toFind, int len)
{
// Returns index of toFind in sortedArray, or -1 if not found
int low = 0;
int high = len - 1;
int mid;
int l = sortedArray[low];
int h = sortedArray[high];
while (l <= toFind && h >= toFind) {
mid = (low + high)/2;
int m = sortedArray[mid];
if (m < toFind) {
l = sortedArray[low = mid + 1];
} else if (m > toFind) {
h = sortedArray[high = mid - 1];
} else {
return mid;
}
}
if (sortedArray[low] == toFind)
return low;
else
return -1; // Not found
}
int order(const void *p1, const void *p2) { return *(int*)p1-*(int*)p2; }
int main(void) {
int i = 0, j = 0, size = 100000, trials = 10000;
int searched[trials];
srand(-time(0));
for (j=0; j<trials; j++) { searched[j] = rand()%size; }
while (size > 10){
int arr[size];
for (i=0; i<size; i++) { arr[i] = rand()%size; }
qsort(arr,size,sizeof(int),order);
unsigned long long totalcycles_bs = 0;
unsigned long long totalcycles_is_64 = 0;
unsigned long long totalcycles_is_float = 0;
unsigned long long totalcycles_new = 0;
int res_bs, res_is_64, res_is_float, res_new;
for (j=0; j<trials; j++) {
unsigned long long tmp, cycles = rdtsc();
res_bs = binarySearch(arr,searched[j],size);
tmp = rdtsc(); totalcycles_bs += tmp - cycles; cycles = tmp;
res_is_64 = interpolationSearch(arr,searched[j],size);
assert(res_is_64 == res_bs || arr[res_is_64] == searched[j]);
tmp = rdtsc(); totalcycles_is_64 += tmp - cycles; cycles = tmp;
res_is_float = interpolationSearch2(arr,searched[j],size);
assert(res_is_float == res_bs || arr[res_is_float] == searched[j]);
tmp = rdtsc(); totalcycles_is_float += tmp - cycles; cycles = tmp;
}
printf("----------------- size = %10d\n", size);
printf("binary search = %10llu\n", totalcycles_bs);
printf("interpolation uint64_t = %10llu\n", totalcycles_is_64);
printf("interpolation float = %10llu\n", totalcycles_is_float);
printf("new = %10llu\n", totalcycles_new);
printf("\n");
size >>= 1;
}
}
If you have some control over the in-memory layout of the data, you might want to look at Judy arrays.
Or to put a simpler idea out there: a binary search always cuts the search space in half. An optimal cut point can be found with interpolation (the cut point should NOT be the place where the key is expected to be, but the point which minimizes the statistical expectation of the search space for the next step). This minimizes the number of steps but... not all steps have equal cost. Hierarchical memories allow executing a number of tests in the same time as a single test, if locality can be maintained. Since a binary search's first M steps only touch a maximum of 2**M unique elements, storing these together can yield a much better reduction of search space per-cacheline fetch (not per comparison), which is higher performance in the real world.
n-ary trees work on that basis, and then Judy arrays add a few less important optimizations.
Bottom line: even "Random Access Memory" (RAM) is faster when accessed sequentially than randomly. A search algorithm should use that fact to its advantage.
Benchmarked on Win32 Core2 Quad Q6600, gcc v4.3 msys. Compiling with g++ -O3, nothing fancy.
Observation - the asserts, timing and loop overhead is about 40%, so any gains listed below should be divided by 0.6 to get the actual improvement in the algorithms under test.
Simple answers:
On my machine replacing the int64_t with int for "low", "high" and "mid" in interpolationSearch gives a 20% to 40% speed up. This is the fastest easy method I could find. It is taking about 150 cycles per look-up on my machine (for the array size of 100000). That's roughly the same number of cycles as a cache miss. So in real applications, looking after your cache is probably going to be the biggest factor.
Replacing binarySearch's "/2" with a ">>1" gives a 4% speed up.
Using STL's binary_search algorithm, on a vector containing the same data as "arr", is about the same speed as the hand coded binarySearch. Although on the smaller "size"s STL is much slower - around 40%.
I have an excessively complicated solution, which requires a specialized sorting function. The sort is slightly slower than a good quicksort, but all of my tests show that the search function is much faster than a binary or interpolation search. I called it a regression sort before I found out that the name was already taken, but didn't bother to think of a new name (ideas?).
There are three files to demonstrate.
The regression sort/search code:
#include <sstream>
#include <math.h>
#include <ctime>
#include "limits.h"
void insertionSort(int array[], int length) {
int key, j;
for(int i = 1; i < length; i++) {
key = array[i];
j = i - 1;
while (j >= 0 && array[j] > key) {
array[j + 1] = array[j];
--j;
}
array[j + 1] = key;
}
}
class RegressionTable {
public:
RegressionTable(int arr[], int s, int lower, int upper, double mult, int divs);
RegressionTable(int arr[], int s);
void sort(void);
int find(int key);
void printTable(void);
void showSize(void);
private:
void createTable(void);
inline unsigned int resolve(int n);
int * array;
int * table;
int * tableSize;
int size;
int lowerBound;
int upperBound;
int divisions;
int divisionSize;
int newSize;
double multiplier;
};
RegressionTable::RegressionTable(int arr[], int s) {
array = arr;
size = s;
multiplier = 1.35;
divisions = sqrt(size);
upperBound = INT_MIN;
lowerBound = INT_MAX;
for (int i = 0; i < size; ++i) {
if (array[i] > upperBound)
upperBound = array[i];
if (array[i] < lowerBound)
lowerBound = array[i];
}
createTable();
}
RegressionTable::RegressionTable(int arr[], int s, int lower, int upper, double mult, int divs) {
array = arr;
size = s;
lowerBound = lower;
upperBound = upper;
multiplier = mult;
divisions = divs;
createTable();
}
void RegressionTable::showSize(void) {
int bytes = sizeof(*this);
bytes = bytes + sizeof(int) * 2 * (divisions + 1);
}
void RegressionTable::createTable(void) {
divisionSize = size / divisions;
newSize = multiplier * double(size);
table = new int[divisions + 1];
tableSize = new int[divisions + 1];
for (int i = 0; i < divisions; ++i) {
table[i] = 0;
tableSize[i] = 0;
}
for (int i = 0; i < size; ++i) {
++table[((array[i] - lowerBound) / divisionSize) + 1];
}
for (int i = 1; i <= divisions; ++i) {
table[i] += table[i - 1];
}
table[0] = 0;
for (int i = 0; i < divisions; ++i) {
tableSize[i] = table[i + 1] - table[i];
}
}
int RegressionTable::find(int key) {
double temp = multiplier;
multiplier = 1;
int minIndex = table[(key - lowerBound) / divisionSize];
int maxIndex = minIndex + tableSize[key / divisionSize];
int guess = resolve(key);
double t;
while (array[guess] != key) {
// uncomment this line if you want to see where it is searching.
//cout << "Regression Guessing " << guess << ", not there." << endl;
if (array[guess] < key) {
minIndex = guess + 1;
}
if (array[guess] > key) {
maxIndex = guess - 1;
}
if (array[minIndex] > key || array[maxIndex] < key) {
return -1;
}
t = ((double)key - array[minIndex]) / ((double)array[maxIndex] - array[minIndex]);
guess = minIndex + t * (maxIndex - minIndex);
}
multiplier = temp;
return guess;
}
inline unsigned int RegressionTable::resolve(int n) {
float temp;
int subDomain = (n - lowerBound) / divisionSize;
temp = n % divisionSize;
temp /= divisionSize;
temp *= tableSize[subDomain];
temp += table[subDomain];
temp *= multiplier;
return (unsigned int)temp;
}
void RegressionTable::sort(void) {
int * out = new int[int(size * multiplier)];
bool * used = new bool[int(size * multiplier)];
int higher, lower;
bool placed;
for (int i = 0; i < size; ++i) {
/* Figure out where to put the darn thing */
higher = resolve(array[i]);
lower = higher - 1;
if (higher > newSize) {
higher = size;
lower = size - 1;
} else if (lower < 0) {
higher = 0;
lower = 0;
}
placed = false;
while (!placed) {
if (higher < size && !used[higher]) {
out[higher] = array[i];
used[higher] = true;
placed = true;
} else if (lower >= 0 && !used[lower]) {
out[lower] = array[i];
used[lower] = true;
placed = true;
}
--lower;
++higher;
}
}
int index = 0;
for (int i = 0; i < size * multiplier; ++i) {
if (used[i]) {
array[index] = out[i];
++index;
}
}
insertionSort(array, size);
}
And then there is the regular search functions:
#include <iostream>
using namespace std;
int binarySearch(int array[], int start, int end, int key) {
// Determine the search point.
int searchPos = (start + end) / 2;
// If we crossed over our bounds or met in the middle, then it is not here.
if (start >= end)
return -1;
// Search the bottom half of the array if the query is smaller.
if (array[searchPos] > key)
return binarySearch (array, start, searchPos - 1, key);
// Search the top half of the array if the query is larger.
if (array[searchPos] < key)
return binarySearch (array, searchPos + 1, end, key);
// If we found it then we are done.
if (array[searchPos] == key)
return searchPos;
}
int binarySearch(int array[], int size, int key) {
return binarySearch(array, 0, size - 1, key);
}
int interpolationSearch(int array[], int size, int key) {
int guess = 0;
double t;
int minIndex = 0;
int maxIndex = size - 1;
while (array[guess] != key) {
t = ((double)key - array[minIndex]) / ((double)array[maxIndex] - array[minIndex]);
guess = minIndex + t * (maxIndex - minIndex);
if (array[guess] < key) {
minIndex = guess + 1;
}
if (array[guess] > key) {
maxIndex = guess - 1;
}
if (array[minIndex] > key || array[maxIndex] < key) {
return -1;
}
}
return guess;
}
And then I wrote a simple main to test out the different sorts.
#include <iostream>
#include <iomanip>
#include <cstdlib>
#include <ctime>
#include "regression.h"
#include "search.h"
using namespace std;
void randomizeArray(int array[], int size) {
for (int i = 0; i < size; ++i) {
array[i] = rand() % size;
}
}
int main(int argc, char * argv[]) {
int size = 100000;
string arg;
if (argc > 1) {
arg = argv[1];
size = atoi(arg.c_str());
}
srand(time(NULL));
int * array;
cout << "Creating Array Of Size " << size << "...\n";
array = new int[size];
randomizeArray(array, size);
cout << "Sorting Array...\n";
RegressionTable t(array, size, 0, size*2.5, 1.5, size);
//RegressionTable t(array, size);
t.sort();
int trials = 10000000;
int start;
cout << "Binary Search...\n";
start = clock();
for (int i = 0; i < trials; ++i) {
binarySearch(array, size, i % size);
}
cout << clock() - start << endl;
cout << "Interpolation Search...\n";
start = clock();
for (int i = 0; i < trials; ++i) {
interpolationSearch(array, size, i % size);
}
cout << clock() - start << endl;
cout << "Regression Search...\n";
start = clock();
for (int i = 0; i < trials; ++i) {
t.find(i % size);
}
cout << clock() - start << endl;
return 0;
}
Give it a try and tell me if it's faster for you. It's super complicated, so it's really easy to break it if you don't know what you are doing. Be careful about modifying it.
I compiled the main with g++ on ubuntu.
Unless your data is known to have special properties, pure interpolation search has the risk of taking linear time. If you expect interpolation to help with most data but don't want it to hurt in the case of pathological data, I would use a (possibly weighted) average of the interpolated guess and the midpoint, ensuring a logarithmic bound on the run time.
One way of approaching this is to use a space versus time trade-off. There are any number of ways that could be done. The extreme way would be to simply make an array with the max size being the max value of the sorted array. Initialize each position with the index into sortedArray. Then the search would simply be O(1).
The following version, however, might be a little more realistic and possibly be useful in the real world. It uses a "helper" structure that is initialized on the first call. It maps the search space down to a smaller space by dividing by a number that I pulled out of the air without much testing. It stores the index of the lower bound for a group of values in sortedArray into the helper map. The actual search divides the toFind number by the chosen divisor and extracts the narrowed bounds of sortedArray for a normal binary search.
For example, if the sorted values range from 1 to 1000 and the divisor is 100, then the lookup array might contain 10 "sections". To search for value 250, it would divide it by 100 to yield integer index position 250/100=2. map[2] would contain the sortedArray index for values 200 and larger. map[3] would have the index position of values 300 and larger thus providing a smaller bounding position for a normal binary search. The rest of the function is then an exact copy of your binary search function.
The initialization of the helper map might be more efficient by using a binary search to fill in the positions rather than a simple scan, but it is a one time cost so I didn't bother testing that. This mechanism works well for the given test numbers which are evenly distributed. As written, it would not be as good if the distribution was not even. I think this method could be used with floating point search values too. However, extrapolating it to generic search keys might be harder. For example, I am unsure what the method would be for character data keys. It would need some kind of O(1) lookup/hash that mapped to a specific array position to find the index bounds. It's unclear to me at the moment what that function would be or if it exists.
I kludged the setup of the helper map in the following implementation pretty quickly. It is not pretty and I'm not 100% sure it is correct in all cases but it does show the idea. I ran it with a debug test to compare the results against your existing binarySearch function to be somewhat sure it works correctly.
The following are example numbers:
100000 * 10000 : cycles binary search = 10197811
100000 * 10000 : cycles interpolation uint64_t = 9007939
100000 * 10000 : cycles interpolation float = 8386879
100000 * 10000 : cycles binary w/helper = 6462534
Here is the quick-and-dirty implementation:
#define REDUCTION 100 // pulled out of the air
typedef struct {
int init; // have we initialized it?
int numSections;
int *map;
int divisor;
} binhelp;
int binarySearchHelp( binhelp *phelp, int sortedArray[], int toFind, int len)
{
// Returns index of toFind in sortedArray, or -1 if not found
int low;
int high;
int mid;
if ( !phelp->init && len > REDUCTION ) {
int i;
int numSections = len / REDUCTION;
int divisor = (( sortedArray[len-1] - 1 ) / numSections ) + 1;
int threshold;
int arrayPos;
phelp->init = 1;
phelp->divisor = divisor;
phelp->numSections = numSections;
phelp->map = (int*)malloc((numSections+2) * sizeof(int));
phelp->map[0] = 0;
phelp->map[numSections+1] = len-1;
arrayPos = 0;
// Scan through the array and set up the mapping positions. Simple linear
// scan but it is a one-time cost.
for ( i = 1; i <= numSections; i++ ) {
threshold = i * divisor;
while ( arrayPos < len && sortedArray[arrayPos] < threshold )
arrayPos++;
if ( arrayPos < len )
phelp->map[i] = arrayPos;
else
// kludge to take care of aliasing
phelp->map[i] = len - 1;
}
}
if ( phelp->init ) {
int section = toFind / phelp->divisor;
if ( section > phelp->numSections )
// it is bigger than all values
return -1;
low = phelp->map[section];
if ( section == phelp->numSections )
high = len - 1;
else
high = phelp->map[section+1];
} else {
// use normal start points
low = 0;
high = len - 1;
}
// the following is a direct copy of the Kriss' binarySearch
int l = sortedArray[low];
int h = sortedArray[high];
while (l <= toFind && h >= toFind) {
mid = (low + high)/2;
int m = sortedArray[mid];
if (m < toFind) {
l = sortedArray[low = mid + 1];
} else if (m > toFind) {
h = sortedArray[high = mid - 1];
} else {
return mid;
}
}
if (sortedArray[low] == toFind)
return low;
else
return -1; // Not found
}
The helper structure needs to be initialized (and memory freed):
help.init = 0;
unsigned long long totalcycles4 = 0;
... make the calls same as for the other ones but pass the structure ...
binarySearchHelp(&help, arr,searched[j],length);
if ( help.init )
free( help.map );
help.init = 0;
Look first at the data and whether a big gain can be got by data specific method over a general method.
For large static sorted datasets, you can create an additional index to provide partial pigeon holing, based on the amount of memory you're willing to use. e.g. say we create a 256x256 two dimensional array of ranges, which we populate with the start and end positions in the search array of elements with corresponding high order bytes. When we come to search, we then use the high order bytes on the key to find the range / subset of the array we need to search. If we did have ~ 20 comparisons on our binary search of 100,000 elements O(log2(n)) we're now down to ~4 comarisons for 16 elements, or O(log2 (n/15)). The memory cost here is about 512k
Another method, again suited to data that doesn't change much, is to divide the data into arrays of commonly sought items and rarely sought items. For example, if you leave your existing search in place running a wide number of real world cases over a protracted testing period, and log the details of the item being sought, you may well find that the distribution is very uneven, i.e. some values are sought far more regularly than others. If this is the case, break your array into a much smaller array of commonly sought values and a larger remaining array, and search the smaller array first. If the data is right (big if!), you can often achieve broadly similar improvements to the first solution without the memory cost.
There are many other data specific optimizations which score far better than trying to improve on tried, tested and far more widely used general solutions.
Posting my current version before the question is closed (hopefully I will thus be able to ehance it later). For now it is worse than every other versions (if someone understand why my changes to the end of loop has this effect, comments are welcome).
int newSearch(int sortedArray[], int toFind, int len)
{
// Returns index of toFind in sortedArray, or -1 if not found
int low = 0;
int high = len - 1;
int mid;
int l = sortedArray[low];
int h = sortedArray[high];
while (l < toFind && h > toFind) {
mid = low + ((float)(high - low)*(float)(toFind - l))/(1+(float)(h-l));
int m = sortedArray[mid];
if (m < toFind) {
l = sortedArray[low = mid + 1];
} else if (m > toFind) {
h = sortedArray[high = mid - 1];
} else {
return mid;
}
}
if (l == toFind)
return low;
else if (h == toFind)
return high;
else
return -1; // Not found
}
The implementation of the binary search that was used for comparisons can be improved. The key idea is to "normalize" the range initially so that the target is always > a minimum and < than a maximum after the first step. This increases the termination delta size. It also has the effect of special casing targets that are less than the first element of the sorted array or greater than the last element of the sorted array. Expect approximately a 15% improvement in search time. Here is what the code might look like in C++.
int binarySearch(int * &array, int target, int min, int max)
{ // binarySearch
// normalize min and max so that we know the target is > min and < max
if (target <= array[min]) // if min not normalized
{ // target <= array[min]
if (target == array[min]) return min;
return -1;
} // end target <= array[min]
// min is now normalized
if (target >= array[max]) // if max not normalized
{ // target >= array[max]
if (target == array[max]) return max;
return -1;
} // end target >= array[max]
// max is now normalized
while (min + 1 < max)
{ // delta >=2
int tempi = min + ((max - min) >> 1); // point to index approximately in the middle between min and max
int atempi = array[tempi]; // just in case the compiler does not optimize this
if (atempi > target)max = tempi; // if the target is smaller, we can decrease max and it is still normalized
else if (atempi < target)min = tempi; // the target is bigger, so we can increase min and it is still normalized
else return tempi; // if we found the target, return with the index
// Note that it is important that this test for equality is last because it rarely occurs.
} // end delta >=2
return -1; // nothing in between normalized min and max
} // end binarySearch