I'm running this and I am being told it would not run fast enough. What is a good way to increase the speed of this running class? I am guessing I would need to change my nested while loops. That is the only thing I can think of. The if statements should all be linear...
import java.io.File;
import java.io.FileNotFoundException;
import java.util.*;
public class QSortLab {
static int findpivot(Comparable[] A, int i, int j) {
return (i + j) / 2;
}
static <E> void swap(E[] A, int p1, int p2) {
E temp = A[p1];
A[p1] = A[p2];
A[p2] = temp;
}
static void quicksort(Comparable[] A, int i, int j) { // Quicksort
int pivotindex = findpivot(A, i, j); // Pick a pivot
swap(A, pivotindex, j); // Stick pivot at end
int k = partition(A, i, j-1, A[j]);
swap(A, k, j); // Put pivot in place
if ((k-i) > 1) quicksort(A, i, k-1); // Sort left partition
if ((j-k) > 1) quicksort(A, k+1, j); // Sort right partition
}
static int partition(Comparable[] A, int left, int right, Comparable pivot) {
while (left <= right) { // Move bounds inward until they meet
while (A[left].compareTo(pivot) < 0) left++;
while ((right >= left) && (A[right].compareTo(pivot) >= 0)) right--;
if (right > left) swap(A, left, right); // Swap out-of-place values
}
return left; // Return first position in right partition
}
}
What do you mean you need to change your nested while loops? Quick Sort is defined by those features. Removing wouldn't function properly.
As for optimization, by default it should be known that primitives vs objects tend to be different. E.g. primitives on stack/heap to keep stack small & heap stores object with refs able to be on stack.
So let's test some stuff
primitive quick sort (from here)
Integer quick sort (same code as above, but with Integer class)
Your original posted code
Your original posted code (w/ several edits)
Here's the entire code I used.
import java.util.Random;
public class App {
public static final int ARR_SIZE = 1000;
public static final int TEST_ITERS = 10000;
public static Random RANDOM = new Random();
public static void main(String[] args) {
int[] a = new int[ARR_SIZE];
Integer[] b = new Integer[ARR_SIZE];
Integer[] c = new Integer[ARR_SIZE];
Integer[] d = new Integer[ARR_SIZE];
long sum = 0, start = 0, end = 0;
for (int i = 0; i < TEST_ITERS; ++i) {
for (int j = 0; j < ARR_SIZE; ++j)
a[j] = RANDOM.nextInt();
start = System.nanoTime();
quickSort(a, 0, a.length - 1);
end = System.nanoTime();
sum += (end - start);
}
System.out.println((sum / TEST_ITERS) + " nano, qs avg - 'int'");
sum = 0;
for (int i = 0; i < TEST_ITERS; ++i) {
for (int j = 0; j < ARR_SIZE; ++j)
b[j] = RANDOM.nextInt();
start = System.nanoTime();
quickSort(b, 0, b.length - 1);
end = System.nanoTime();
sum += (end - start);
}
System.out.println((sum / TEST_ITERS) + " nano, qs avg - 'Integer'");
sum = 0;
for (int i = 0; i < TEST_ITERS; ++i) {
for (int j = 0; j < ARR_SIZE; ++j)
c[j] = RANDOM.nextInt();
start = System.nanoTime();
quicksort(c, 0, c.length - 1);
end = System.nanoTime();
sum += (end - start);
}
System.out.println((sum / TEST_ITERS) + " nano, qs avg - 'Comparable' (SO user code)");
sum = 0;
for (int i = 0; i < TEST_ITERS; ++i) {
for (int j = 0; j < ARR_SIZE; ++j)
d[j] = RANDOM.nextInt();
start = System.nanoTime();
qs_quicksort(d, 0, d.length - 1);
end = System.nanoTime();
sum += (end - start);
}
System.out.println((sum / TEST_ITERS) + " nano, qs avg - 'Comparable' (SO user code - edit)");
for (int i = 0; i < ARR_SIZE; ++i) {
final int n = RANDOM.nextInt();
a[i] = n;
b[i] = n;
c[i] = n;
d[i] = n;
}
quickSort(a, 0, a.length - 1);
Integer[] aConv = new Integer[ARR_SIZE];
for (int i = 0; i < ARR_SIZE; ++i)
aConv[i] = a[i];
quickSort(b, 0, b.length - 1);
quicksort(c, 0, c.length - 1);
qs_quicksort(d, 0, d.length - 1);
isSorted(new Integer[][] { aConv, b, c, d });
System.out.println("All properly sorted");
}
public static void isSorted(Integer[][] arrays) {
if (arrays.length != 4) {
System.out.println("error sorting, input arr len");
return;
}
for (int i = 0; i < ARR_SIZE; ++i) {
int val1 = arrays[0][i].compareTo(arrays[1][i]);
int val2 = arrays[1][i].compareTo(arrays[2][i]);
int val3 = arrays[2][i].compareTo(arrays[3][i]);
if (val1 != 0 || val2 != 0 || val3 != 00) {
System.out.printf("Error [i = %d]: a = %d, b = %d, c = %d", i, arrays[0][i], arrays[1][i], arrays[2][i], arrays[3][i]);
break;
}
}
}
public static int partition(int arr[], int left, int right) {
int i = left, j = right;
int tmp;
int pivot = arr[(left + right) / 2];
while (i <= j) {
while (arr[i] < pivot)
i++;
while (arr[j] > pivot)
j--;
if (i <= j) {
tmp = arr[i];
arr[i] = arr[j];
arr[j] = tmp;
i++;
j--;
}
}
return i;
}
public static void quickSort(int arr[], int left, int right) {
int index = partition(arr, left, right);
if (left < index - 1)
quickSort(arr, left, index - 1);
if (index < right)
quickSort(arr, index, right);
}
public static int partition(Integer[] arr, int left, int right) {
int i = left, j = right;
Integer pivot = arr[(left + right) / 2];
while (i <= j) {
while (arr[i].compareTo(pivot) < 0)
i++;
while (arr[j].compareTo(pivot) > 0)
j--;
if (i <= j) {
Integer temp = arr[i];
arr[i] = arr[j];
arr[j] = temp;
i++;
j--;
}
}
return i;
}
public static void quickSort(Integer[] arr, int left, int right) {
int index = partition(arr, left, right);
if (left < index - 1)
quickSort(arr, left, index - 1);
if (index < right)
quickSort(arr, index, right);
}
static int findpivot(Comparable[] A, int i, int j)
{
return (i+j)/2;
}
static <E> void swap(E[] A, int p1, int p2) {
E temp = A[p1];
A[p1] = A[p2];
A[p2] = temp;
}
static void quicksort(Comparable[] A, int i, int j) { // Quicksort
int pivotindex = findpivot(A, i, j); // Pick a pivot
swap(A, pivotindex, j); // Stick pivot at end
int k = partition(A, i, j-1, A[j]);
swap(A, k, j); // Put pivot in place
if ((k-i) > 1) quicksort(A, i, k-1); // Sort left partition
if ((j-k) > 1) quicksort(A, k+1, j); // Sort right partition
}
static int partition(Comparable[] A, int left, int right, Comparable pivot) {
while (left <= right) { // Move bounds inward until they meet
while (A[left].compareTo(pivot) < 0) left++;
while ((right >= left) && (A[right].compareTo(pivot) >= 0)) right--;
if (right > left) swap(A, left, right); // Swap out-of-place values
}
return left; // Return first position in right partition
}
static <E> void qs_swap(E[] A, int p1, int p2) {
E temp = A[p1];
A[p1] = A[p2];
A[p2] = temp;
}
static void qs_quicksort(Comparable[] A, int i, int j) { // Quicksort
int pivotindex = (i+j)/2;
qs_swap(A, pivotindex, j); // Stick pivot at end
int k = qs_partition(A, i, j-1, A[j]);
qs_swap(A, k, j); // Put pivot in place
if ((k-i) > 1) qs_quicksort(A, i, k-1); // Sort left partition
if ((j-k) > 1) qs_quicksort(A, k+1, j); // Sort right partition
}
static int qs_partition(Comparable[] A, int left, int right, Comparable pivot) {
while (left <= right) { // Move bounds inward until they meet
while (A[left].compareTo(pivot) < 0) left++;
while ((right >= left) && (A[right].compareTo(pivot) >= 0)) right--;
if (right > left) { qs_swap(A, left, right); // Swap out-of-place values
left++; right--;}
}
return left; // Return first position in right partition
}
}
This produces the output:
56910 nano, qs avg - 'int'
69498 nano, qs avg - 'Integer'
76762 nano, qs avg - 'Comparable' (SO user code)
71846 nano, qs avg - 'Comparable' (SO user code - edit)
All properly sorted
Now, breaking down the results
The 'int' vs 'Integer' shows great diff when simply using primitives vs non-primitives (I'm sure at some points in the code there may be boxing but hopefully not in critical spots ;) - please edit this if so). The 'int' vs 'Integer' uses same code with exception of 'int' 'Integer'. See the following four method signatures that are used in this comparison, 'int'
public static int partition(int arr[], int left, int right)
public static void quickSort(int arr[], int left, int right)
and 'Integer'
public static int partition(Integer[] arr, int left, int right)
public static void quickSort(Integer[] arr, int left, int right)
respectively.
Then there are the method signatures related to the original code you posted,
static int findpivot(Comparable[] A, int i, int j)
static <E> void swap(E[] A, int p1, int p2)
static void quicksort(Comparable[] A, int i, int j)
static int partition(Comparable[] A, int left, int right, Comparable pivot)
and the modified ones,
static <E> void qs_swap(E[] A, int p1, int p2)
static void qs_quicksort(Comparable[] A, int i, int j)
static int qs_partition(Comparable[] A, int left, int right, Comparable pivot)
As you can see, in the modified code, findpivot was removed directly and replaced into the calling spot in quicksort. Also, the partition method gained counters for left and right respectively. left++; right--;
And finally, to ensure these 4 variations of quicksort actually did the sole purpose, sort, I added a method, isSorted() to check the validity of the same generated content and that it's sorted accordingly based on each of the 4 different sorts.
In conclusion, I think my edits may have saved a portion of time/nanoseconds, however I wasn't able to achieve the same time as the Integer test. Hopefully I've not missed anything obvious and edits are welcome if need be. Cheers
Well, I couldn't tell from testing whether this makes any difference at all because the timer on my machine is terrible , but I think most of the work in this algo is done with the swap function, so thinking about how to make that in particular more efficient, maybe the function call/return itself consumes cycles, and perhaps the creation of the temp variable each time the function is called also takes cycles, so maybe the code would be more efficient if the swap work was done in line. It was not obvious though when I tested on my machine as the nanotimer returned results +/- 20% each time I ran the program
public class QSort2 {
static int findpivot(Comparable[] A, int i, int j) {
return (i + j) / 2;
}
static Comparable temp;
static void quicksort(Comparable[] A, int i, int j) { // Quicksort
int pivotindex = findpivot(A, i, j); // Pick a pivot
// swap(A, pivotindex, j); // Stick pivot at end
temp = A[pivotindex];
A[pivotindex] = A[j];
A[j] = temp;
int k = partition(A, i, j - 1, A[j]);
//swap(A, k, j); // Put pivot in place
temp = A[k];
A[k] = A[j];
A[j] = temp;
if ((k - i) > 1) quicksort(A, i, k - 1); // Sort left partition
if ((j - k) > 1) quicksort(A, k + 1, j); // Sort right partition
}
static int partition(Comparable[] A, int left, int right, Comparable pivot) {
while (left <= right) { // Move bounds inward until they meet
while (A[left].compareTo(pivot) < 0) left++;
while ((right >= left) && (A[right].compareTo(pivot) >= 0)) right--;
if (right > left) {
//swap(A, left, right);} // Swap out-of-place values
temp = A[left];
A[left] = A[right];
A[right] = temp;
}
}
return left; // Return first position in right partition
}
}
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This was asked of me in an interview and this is the solution I provided:
public static int[] merge(int[] a, int[] b) {
int[] answer = new int[a.length + b.length];
int i = 0, j = 0, k = 0;
while (i < a.length && j < b.length)
{
if (a[i] < b[j])
{
answer[k] = a[i];
i++;
}
else
{
answer[k] = b[j];
j++;
}
k++;
}
while (i < a.length)
{
answer[k] = a[i];
i++;
k++;
}
while (j < b.length)
{
answer[k] = b[j];
j++;
k++;
}
return answer;
}
Is there a more efficient way to do this?
Edit: Corrected length methods.
public static int[] merge(int[] a, int[] b) {
int[] answer = new int[a.length + b.length];
int i = 0, j = 0, k = 0;
while (i < a.length && j < b.length)
answer[k++] = a[i] < b[j] ? a[i++] : b[j++];
while (i < a.length)
answer[k++] = a[i++];
while (j < b.length)
answer[k++] = b[j++];
return answer;
}
Is a little bit more compact but exactly the same!
I'm surprised no one has mentioned this much more cool, efficient and compact implementation:
public static int[] merge(int[] a, int[] b) {
int[] answer = new int[a.length + b.length];
int i = a.length - 1, j = b.length - 1, k = answer.length;
while (k > 0)
answer[--k] =
(j < 0 || (i >= 0 && a[i] >= b[j])) ? a[i--] : b[j--];
return answer;
}
Points of Interests
Notice that it does same or less number of operations as any other O(n) algorithm but in literally single statement in a single while loop!
If two arrays are of approximately same size then constant for O(n) is same. However if arrays are really imbalanced then versions with System.arraycopy would win because internally it can do this with single x86 assembly instruction.
Notice a[i] >= b[j] instead of a[i] > b[j]. This guarantees "stability" that is defined as when elements of a and b are equal, we want elements from a before b.
A minor improvement, but after the main loop, you could use System.arraycopy to copy the tail of either input array when you get to the end of the other. That won't change the O(n) performance characteristics of your solution, though.
Any improvements that could be made would be micro-optimizations, the overall algorithm is correct.
This solution also very similar to other posts except that it uses System.arrayCopy to copy the remaining array elements.
private static int[] sortedArrayMerge(int a[], int b[]) {
int result[] = new int[a.length +b.length];
int i =0; int j = 0;int k = 0;
while(i<a.length && j <b.length) {
if(a[i]<b[j]) {
result[k++] = a[i];
i++;
} else {
result[k++] = b[j];
j++;
}
}
System.arraycopy(a, i, result, k, (a.length -i));
System.arraycopy(b, j, result, k, (b.length -j));
return result;
}
Here is updated function. It removes duplicates, hopefully someone will find this usable:
public static long[] merge2SortedAndRemoveDublicates(long[] a, long[] b) {
long[] answer = new long[a.length + b.length];
int i = 0, j = 0, k = 0;
long tmp;
while (i < a.length && j < b.length) {
tmp = a[i] < b[j] ? a[i++] : b[j++];
for ( ; i < a.length && a[i] == tmp; i++);
for ( ; j < b.length && b[j] == tmp; j++);
answer[k++] = tmp;
}
while (i < a.length) {
tmp = a[i++];
for ( ; i < a.length && a[i] == tmp; i++);
answer[k++] = tmp;
}
while (j < b.length) {
tmp = b[j++];
for ( ; j < b.length && b[j] == tmp; j++);
answer[k++] = tmp;
}
return Arrays.copyOf(answer, k);
}
It can be done in 4 statements as below
int a[] = {10, 20, 30};
int b[]= {9, 14, 11};
int res[]=new int[a.legth+b.length];
System.arraycopy(a,0, res, 0, a.length);
System.arraycopy(b,0,res,a.length, b.length);
Array.sort(res)
GallopSearch Merge: O(log(n)*log(i)) rather than O(n)
I went ahead and implemented greybeard suggestion in the comments. Mostly because I needed a highly efficient mission critical version of this code.
The code uses a gallopSearch which is O(log(i)) where i is the
distance from the current index the relevant index exists.
The code uses a binarySearch for after the gallop search has
identified the proper,range. Since gallop limited this to a smaller
range the resulting binarySearch is also O(log(i))
The gallop and merge are performed backwards. This doesn't seem
mission critical but it allows in place merging of arrays. If one of
your arrays has enough room to store the results values, you can
simply use it as the merging array and the results array. You must specify the valid range within the array in such a case.
It does not require memory allocation in that case (big savings in critical operations). It simply makes sure it doesn't and cannot overwrite any unprocessed values (which can only be done backwards). In fact, you use the same array for both of the inputs and the results. It will suffer no ill effects.
I consistently used Integer.compare() so this could be switched out for other purposes.
There's some chance I might have goofed a little and not utilized information I have previously proven. Such as binary searching into a range of two values, for which one value was already checked. There might also be a better way to state the main loop, the flipping c value wouldn't be needed if they were combined into two operations in sequence. Since you know you will do one then the other everytime. There's room for for some polish.
This should be the most efficient way to do this, with time complexity of O(log(n)*log(i)) rather than O(n). And worst case time complexity of O(n). If your arrays are clumpy and have long strings of values together, this will dwarf any other way to do it, otherwise it'll just be better than them.
It has two read values at the ends of the merging array and the write value within the results array. After finding out which is end value is less, it does a gallop search into that array. 1, 2, 4, 8, 16, 32, etc. When it finds the range where the the other array's read value is bigger. It binary searches into that range (cuts the range in half, search the correct half, repeat until single value). Then it array copies those values into the write position. Keeping in mind that the copy is, by necessity, moved such that it cannot overwrite the same values from the either reading array (which means the write array and read array can be the same). It then performs the same operation for the other array which is now known to be less than the new read value of the other array.
static public int gallopSearch(int current, int[] array, int v) {
int d = 1;
int seek = current - d;
int prevIteration = seek;
while (seek > 0) {
if (Integer.compare(array[seek], v) <= 0) {
break;
}
prevIteration = seek;
d <<= 1;
seek = current - d;
if (seek < 0) {
seek = 0;
}
}
if (prevIteration != seek) {
seek = binarySearch(array, seek, prevIteration, v);
seek = seek >= 0 ? seek : ~seek;
}
return seek;
}
static public int binarySearch(int[] list, int fromIndex, int toIndex, int v) {
int low = fromIndex;
int high = toIndex - 1;
while (low <= high) {
int mid = (low + high) >>> 1;
int midVal = list[mid];
int cmp = Integer.compare(midVal, v);
if (cmp < 0) {
low = mid + 1;
} else if (cmp > 0) {
high = mid - 1;
} else {
return mid;// key found
}
}
return -(low + 1);// key not found.
}
static public int[] sortedArrayMerge(int[] a, int[] b) {
return sortedArrayMerge(null, a, a.length, b, b.length);
}
static public int[] sortedArrayMerge(int[] results, int[] a, int aRead, int b[], int bRead) {
int write = aRead + bRead, length, gallopPos;
if ((results == null) || (results.length < write)) {
results = new int[write];
}
if (aRead > 0 && bRead > 0) {
int c = Integer.compare(a[aRead - 1], b[bRead - 1]);
while (aRead > 0 && bRead > 0) {
switch (c) {
default:
gallopPos = gallopSearch(aRead, a, b[bRead-1]);
length = (aRead - gallopPos);
write -= length;
aRead = gallopPos;
System.arraycopy(a, gallopPos--, results, write, length);
c = -1;
break;
case -1:
gallopPos = gallopSearch(bRead, b, a[aRead-1]);
length = (bRead - gallopPos);
write -= length;
bRead = gallopPos;
System.arraycopy(b, gallopPos--, results, write, length);
c = 1;
break;
}
}
}
if (bRead > 0) {
if (b != results) {
System.arraycopy(b, 0, results, 0, bRead);
}
} else if (aRead > 0) {
if (a != results) {
System.arraycopy(a, 0, results, 0, aRead);
}
}
return results;
}
This should be the most efficient way to do it.
Some answers had a duplicate remove ability. That'll require an O(n) algorithm because you must actually compare each item. So here's a stand-alone for that, to be applied after the fact. You can't gallop through multiple entries all the way through if you need to look at all of them, though you could gallop through the duplicates, if you had a lot of them.
static public int removeDuplicates(int[] list, int size) {
int write = 1;
for (int read = 1; read < size; read++) {
if (list[read] == list[read - 1]) {
continue;
}
list[write++] = list[read];
}
return write;
}
Update: Previous answer, not horrible code but clearly inferior to the above.
Another needless hyper-optimization. It not only invokes arraycopy for the end bits, but also for the beginning. Processing any introductory non-overlap in O(log(n)) by a binarySearch into the data. O(log(n) + n) is O(n) and in some cases the effect will be pretty pronounced especially things like where there is no overlap between the merging arrays at all.
private static int binarySearch(int[] array, int low, int high, int v) {
high = high - 1;
while (low <= high) {
int mid = (low + high) >>> 1;
int midVal = array[mid];
if (midVal > v)
low = mid + 1;
else if (midVal < v)
high = mid - 1;
else
return mid; // key found
}
return low;//traditionally, -(low + 1); // key not found.
}
private static int[] sortedArrayMerge(int a[], int b[]) {
int result[] = new int[a.length + b.length];
int k, i = 0, j = 0;
if (a[0] > b[0]) {
k = i = binarySearch(b, 0, b.length, a[0]);
System.arraycopy(b, 0, result, 0, i);
} else {
k = j = binarySearch(a, 0, a.length, b[0]);
System.arraycopy(a, 0, result, 0, j);
}
while (i < a.length && j < b.length) {
result[k++] = (a[i] < b[j]) ? a[i++] : b[j++];
}
if (j < b.length) {
System.arraycopy(b, j, result, k, (b.length - j));
} else {
System.arraycopy(a, i, result, k, (a.length - i));
}
return result;
}
I had to write it in javascript, here it is:
function merge(a, b) {
var result = [];
var ai = 0;
var bi = 0;
while (true) {
if ( ai < a.length && bi < b.length) {
if (a[ai] < b[bi]) {
result.push(a[ai]);
ai++;
} else if (a[ai] > b[bi]) {
result.push(b[bi]);
bi++;
} else {
result.push(a[ai]);
result.push(b[bi]);
ai++;
bi++;
}
} else if (ai < a.length) {
result.push.apply(result, a.slice(ai, a.length));
break;
} else if (bi < b.length) {
result.push.apply(result, b.slice(bi, b.length));
break;
} else {
break;
}
}
return result;
}
Apache collections supports collate method since version 4; you can do this using the collate method in:
org.apache.commons.collections4.CollectionUtils
Here quote from javadoc:
collate(Iterable<? extends O> a, Iterable<? extends O> b, Comparator<? super O> c)
Merges two sorted Collections, a and b, into a single,
sorted List such that the ordering of the elements according to
Comparator c is retained.
Do not re-invent the wheel! Document reference:
http://commons.apache.org/proper/commons-collections/apidocs/org/apache/commons/collections4/CollectionUtils.html
Here's a shortened form written in javascript:
function sort( a1, a2 ) {
var i = 0
, j = 0
, l1 = a1.length
, l2 = a2.length
, a = [];
while( i < l1 && j < l2 ) {
a1[i] < a2[j] ? (a.push(a1[i]), i++) : (a.push( a2[j]), j++);
}
i < l1 && ( a = a.concat( a1.splice(i) ));
j < l2 && ( a = a.concat( a2.splice(j) ));
return a;
}
public class Merge {
// stably merge a[lo .. mid] with a[mid+1 .. hi] using aux[lo .. hi]
public static void merge(Comparable[] a, Comparable[] aux, int lo, int mid, int hi) {
// precondition: a[lo .. mid] and a[mid+1 .. hi] are sorted subarrays
assert isSorted(a, lo, mid);
assert isSorted(a, mid+1, hi);
// copy to aux[]
for (int k = lo; k <= hi; k++) {
aux[k] = a[k];
}
// merge back to a[]
int i = lo, j = mid+1;
for (int k = lo; k <= hi; k++) {
if (i > mid) a[k] = aux[j++];
else if (j > hi) a[k] = aux[i++];
else if (less(aux[j], aux[i])) a[k] = aux[j++];
else a[k] = aux[i++];
}
// postcondition: a[lo .. hi] is sorted
assert isSorted(a, lo, hi);
}
// mergesort a[lo..hi] using auxiliary array aux[lo..hi]
private static void sort(Comparable[] a, Comparable[] aux, int lo, int hi) {
if (hi <= lo) return;
int mid = lo + (hi - lo) / 2;
sort(a, aux, lo, mid);
sort(a, aux, mid + 1, hi);
merge(a, aux, lo, mid, hi);
}
public static void sort(Comparable[] a) {
Comparable[] aux = new Comparable[a.length];
sort(a, aux, 0, a.length-1);
assert isSorted(a);
}
/***********************************************************************
* Helper sorting functions
***********************************************************************/
// is v < w ?
private static boolean less(Comparable v, Comparable w) {
return (v.compareTo(w) < 0);
}
// exchange a[i] and a[j]
private static void exch(Object[] a, int i, int j) {
Object swap = a[i];
a[i] = a[j];
a[j] = swap;
}
/***********************************************************************
* Check if array is sorted - useful for debugging
***********************************************************************/
private static boolean isSorted(Comparable[] a) {
return isSorted(a, 0, a.length - 1);
}
private static boolean isSorted(Comparable[] a, int lo, int hi) {
for (int i = lo + 1; i <= hi; i++)
if (less(a[i], a[i-1])) return false;
return true;
}
/***********************************************************************
* Index mergesort
***********************************************************************/
// stably merge a[lo .. mid] with a[mid+1 .. hi] using aux[lo .. hi]
private static void merge(Comparable[] a, int[] index, int[] aux, int lo, int mid, int hi) {
// copy to aux[]
for (int k = lo; k <= hi; k++) {
aux[k] = index[k];
}
// merge back to a[]
int i = lo, j = mid+1;
for (int k = lo; k <= hi; k++) {
if (i > mid) index[k] = aux[j++];
else if (j > hi) index[k] = aux[i++];
else if (less(a[aux[j]], a[aux[i]])) index[k] = aux[j++];
else index[k] = aux[i++];
}
}
// return a permutation that gives the elements in a[] in ascending order
// do not change the original array a[]
public static int[] indexSort(Comparable[] a) {
int N = a.length;
int[] index = new int[N];
for (int i = 0; i < N; i++)
index[i] = i;
int[] aux = new int[N];
sort(a, index, aux, 0, N-1);
return index;
}
// mergesort a[lo..hi] using auxiliary array aux[lo..hi]
private static void sort(Comparable[] a, int[] index, int[] aux, int lo, int hi) {
if (hi <= lo) return;
int mid = lo + (hi - lo) / 2;
sort(a, index, aux, lo, mid);
sort(a, index, aux, mid + 1, hi);
merge(a, index, aux, lo, mid, hi);
}
// print array to standard output
private static void show(Comparable[] a) {
for (int i = 0; i < a.length; i++) {
StdOut.println(a[i]);
}
}
// Read strings from standard input, sort them, and print.
public static void main(String[] args) {
String[] a = StdIn.readStrings();
Merge.sort(a);
show(a);
}
}
I think introducing the skip list for the larger sorted array can reduce the number of comparisons and can speed up the process of copying into the third array. This can be good if the array is too huge.
public int[] merge(int[] a, int[] b) {
int[] result = new int[a.length + b.length];
int aIndex, bIndex = 0;
for (int i = 0; i < result.length; i++) {
if (aIndex < a.length && bIndex < b.length) {
if (a[aIndex] < b[bIndex]) {
result[i] = a[aIndex];
aIndex++;
} else {
result[i] = b[bIndex];
bIndex++;
}
} else if (aIndex < a.length) {
result[i] = a[aIndex];
aIndex++;
} else {
result[i] = b[bIndex];
bIndex++;
}
}
return result;
}
public static int[] merge(int[] a, int[] b) {
int[] mergedArray = new int[(a.length + b.length)];
int i = 0, j = 0;
int mergedArrayIndex = 0;
for (; i < a.length || j < b.length;) {
if (i < a.length && j < b.length) {
if (a[i] < b[j]) {
mergedArray[mergedArrayIndex] = a[i];
i++;
} else {
mergedArray[mergedArrayIndex] = b[j];
j++;
}
} else if (i < a.length) {
mergedArray[mergedArrayIndex] = a[i];
i++;
} else if (j < b.length) {
mergedArray[mergedArrayIndex] = b[j];
j++;
}
mergedArrayIndex++;
}
return mergedArray;
}
Algorithm could be enhanced in many ways. For instance, it is reasonable to check, if a[m-1]<b[0] or b[n-1]<a[0].
In any of those cases, there is no need to do more comparisons.
Algorithm could just copy source arrays in the resulting one in the right order.
More complicated enhancements may include searching for interleaving parts and run merge algorithm for them only.
It could save up much time, when sizes of merged arrays differ in scores of times.
This problem is related to the mergesort algorithm, in which two sorted sub-arrays are combined into a single sorted sub-array. The CLRS book gives an example of the algorithm and cleans up the need for checking if the end has been reached by adding a sentinel value (something that compares and "greater than any other value") to the end of each array.
I wrote this in Python, but it should translate nicely to Java too:
def func(a, b):
class sentinel(object):
def __lt__(*_):
return False
ax, bx, c = a[:] + [sentinel()], b[:] + [sentinel()], []
i, j = 0, 0
for k in range(len(a) + len(b)):
if ax[i] < bx[j]:
c.append(ax[i])
i += 1
else:
c.append(bx[j])
j += 1
return c
You could use 2 threads to fill the resulting array, one from front, one from back.
This can work without any synchronization in the case of numbers, e.g. if each thread inserts half of the values.
//How to merge two sorted arrays into a sorted array without duplicates?
//simple C Coding
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
main()
{
int InputArray1[] ={1,4,5,7,8,9,12,13,14,17,40};
int InputArray2[] ={4,5,11,14,15,17,18,19,112,122,122,122,122};
int n=10;
int OutputArray[30];
int i=0,j=0,k=0;
//k=OutputArray
while(i<11 && j<13)
{
if(InputArray1[i]<InputArray2[j])
{
if (k == 0 || InputArray1[i]!= OutputArray[k-1])
{
OutputArray[k++] = InputArray1[i];
}
i=i+1;
}
else if(InputArray1[i]>InputArray2[j])
{
if (k == 0 || InputArray2[j]!= OutputArray[k-1])
{
OutputArray[k++] = InputArray2[j];
}
j=j+1;
}
else
{
if (k == 0 || InputArray1[i]!= OutputArray[k-1])
{
OutputArray[k++] = InputArray1[i];
}
i=i+1;
j=j+1;
}
};
while(i<11)
{
if(InputArray1[i]!= OutputArray[k-1])
OutputArray[k++] = InputArray1[i++];
else
i++;
}
while(j<13)
{
if(InputArray2[j]!= OutputArray[k-1])
OutputArray[k++] = InputArray2[j++];
else
j++;
}
for(i=0; i<k; i++)
{
printf("sorted data:%d\n",OutputArray[i]);
};
}
var arrCombo = function(arr1, arr2){
return arr1.concat(arr2).sort(function(x, y) {
return x - y;
});
};
My favorite programming language is JavaScript
function mergeSortedArrays(a, b){
var result = [];
var sI = 0;
var lI = 0;
var smallArr;
var largeArr;
var temp;
if(typeof b[0] === 'undefined' || a[0]<b[0]){
smallArr = a;
largeArr = b;
} else{
smallArr = b;
largeArr = a;
}
while(typeof smallArr[sI] !== 'undefined'){
result.push(smallArr[sI]);
sI++;
if(smallArr[sI]>largeArr[lI] || typeof smallArr[sI] === 'undefined'){
temp = smallArr;
smallArr = largeArr;
largeArr = temp;
temp = sI;
sI = lI;
lI = temp;
}
}
return result;
}
Maybe use System.arraycopy
public static byte[] merge(byte[] first, byte[] second){
int len = first.length + second.length;
byte[] full = new byte[len];
System.arraycopy(first, 0, full, 0, first.length);
System.arraycopy(second, 0, full, first.length, second.length);
return full;
}
public static void main(String[] args) {
int[] arr1 = {2,4,6,8,10,999};
int[] arr2 = {1,3,5,9,100,1001};
int[] arr3 = new int[arr1.length + arr2.length];
int temp = 0;
for (int i = 0; i < (arr3.length); i++) {
if(temp == arr2.length){
arr3[i] = arr1[i-temp];
}
else if (((i-temp)<(arr1.length)) && (arr1[i-temp] < arr2[temp])){
arr3[i] = arr1[i-temp];
}
else{
arr3[i] = arr2[temp];
temp++;
}
}
for (int i : arr3) {
System.out.print(i + ", ");
}
}
Output is :
1, 2, 3, 4, 5, 6, 8, 9, 10, 100, 999, 1001,
You can use ternary operators for making the code a bit more compact
public static int[] mergeArrays(int[] a1, int[] a2) {
int[] res = new int[a1.length + a2.length];
int i = 0, j = 0;
while (i < a1.length && j < a2.length) {
res[i + j] = a1[i] < a2[j] ? a1[i++] : a2[j++];
}
while (i < a1.length) {
res[i + j] = a1[i++];
}
while (j < a2.length) {
res[i + j] = a2[j++];
}
return res;
}
public static int[] mergeSorted(int[] left, int[] right) {
System.out.println("merging " + Arrays.toString(left) + " and " + Arrays.toString(right));
int[] merged = new int[left.length + right.length];
int nextIndexLeft = 0;
int nextIndexRight = 0;
for (int i = 0; i < merged.length; i++) {
if (nextIndexLeft >= left.length) {
System.arraycopy(right, nextIndexRight, merged, i, right.length - nextIndexRight);
break;
}
if (nextIndexRight >= right.length) {
System.arraycopy(left, nextIndexLeft, merged, i, left.length - nextIndexLeft);
break;
}
if (left[nextIndexLeft] <= right[nextIndexRight]) {
merged[i] = left[nextIndexLeft];
nextIndexLeft++;
continue;
}
if (left[nextIndexLeft] > right[nextIndexRight]) {
merged[i] = right[nextIndexRight];
nextIndexRight++;
continue;
}
}
System.out.println("merged : " + Arrays.toString(merged));
return merged;
}
Just a small different from the original solution
To marge two sorted array in O(m+n) time complexity use below approach with one loop only.
m and n is length of first array and second array.
public class MargeSortedArray {
public static void main(String[] args) {
int[] array = new int[]{1,3,4,7};
int[] array2 = new int[]{2,5,6,8,12,45};
int[] newarry = margeToSortedArray(array, array2);
//newarray is marged array
}
// marge two sorted array with o(a+n) time complexity
public static int[] margeToSortedArray(int[] array, int[] array2) {
int newarrlen = array.length+array2.length;
int[] newarr = new int[newarrlen];
int pos1=0,pos2=0;
int len1=array.length, len2=array2.length;
for(int i =0;i<newarrlen;i++) {
if(pos1>=len1) {
newarr[i]=array2[pos2];
pos2++;
continue;
}
if(pos2>=len2) {
newarr[i]=array[pos1];
pos1++;
continue;
}
if(array[pos1]>array2[pos2]) {
newarr[i]=array2[pos2];
pos2++;
} else {
newarr[i]=array[pos1];
pos1++;
}
}
return newarr;
}
}
var arr1 = [2,10,20,30,100];
var arr2 = [2,4,5,6,7,8,9];
var j = 0;
var i =0;
var newArray = [];
for(var x=0;x< (arr1.length + arr2.length);x++){
if(arr1[i] >= arr2[j]){ //check if element arr2 is equal and less than arr1 element
newArray.push(arr2[j]);
j++;
}else if(arr1[i] < arr2[j]){ //check if element arr1 index value is less than arr2 element
newArray.push(arr1[i]);
i++;
}
else if(i == arr1.length || j < arr2.length){ // add remaining arr2 element
newArray.push(arr2[j]);
j++
}else{ // add remaining arr1 element
newArray.push(arr1[i]);
i++
}
}
console.log(newArray);
Since the question doesn't assume any specific language. Here is the solution in Python.
Assuming the arrays are already sorted.
Approach 1 - using numpy arrays:
import numpy
arr1 = numpy.asarray([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 14, 15, 55])
arr2 = numpy.asarray([11, 32, 43, 45, 66, 76, 88])
array = numpy.concatenate((arr1,arr2), axis=0)
array.sort()
Approach 2 - Using list, assuming lists are sorted.
list_new = list1.extend(list2)
list_new.sort()
Here is my java implementation that remove duplicate.
public static int[] mergesort(int[] a, int[] b) {
int[] c = new int[a.length + b.length];
int i = 0, j = 0, k = 0, duplicateCount = 0;
while (i < a.length || j < b.length) {
if (i < a.length && j < b.length) {
if (a[i] == b[j]) {
c[k] = a[i];
i++;j++;duplicateCount++;
} else {
c[k] = a[i] < b[j] ? a[i++] : b[j++];
}
} else if (i < a.length) {
c[k] = a[i++];
} else if (j < a.length) {
c[k] = b[j++];
}
k++;
}
return Arrays.copyOf(c, c.length - duplicateCount);
}
import java.util.Arrays;
public class MergeTwoArrays {
static int[] arr1=new int[]{1,3,4,5,7,7,9,11,13,15,17,19};
static int[] arr2=new int[]{2,4,6,8,10,12,14,14,16,18,20,22};
public static void main(String[] args){
int FirstArrayLocation =0 ;
int SecondArrayLocation=0;
int[] mergeArr=new int[arr1.length + arr2.length];
for ( int i=0; i<= arr1.length + arr2.length; i++){
if (( FirstArrayLocation < arr1.length ) && (SecondArrayLocation < arr2.length)){
if ( arr1[FirstArrayLocation] <= arr2[SecondArrayLocation]){
mergeArr[i]=arr1[FirstArrayLocation];
FirstArrayLocation++;
}else{
mergeArr[i]=arr2[SecondArrayLocation];
SecondArrayLocation++;
}
}
else if(SecondArrayLocation < arr2.length){
mergeArr[i]=arr2[SecondArrayLocation];
SecondArrayLocation++;
}else if ( FirstArrayLocation < arr1.length ){
mergeArr[i]=arr1[FirstArrayLocation];
FirstArrayLocation++;
}
}
}
}