Inefficient java / arrays of unknown length - java

I am making a few methods that are used for finding the prime factors of a certain number. This is broken down into two functions which both use arrays. However, in both functions the code is very inefficient. First I have to count the length of the array, make a new array of that length and then use almost the exact same code to populate the array.
Is there a way I can make the array unknown width and push integers to the end of the array as I find them?
Here is my code:
public class JavaApplication7{
public static void main(String[] args) {
System.out.println(Arrays.toString(primeFactors(85251)));
}
public static int[] primeFactors(int num){
int[] factors = primesUpTo(num);
int originalNum = num;
int i = 0;
int count = 0;
while(num != 1){
if(num % factors[i] == 0){
num /= factors[i];
i = 0;
count++;
}else{
i++;
}
}
int[] primeFactors = new int[count];
i = 0;
count = 0;
while(originalNum != 1){
if(originalNum % factors[i] == 0){
originalNum /= factors[i];
primeFactors[count] = factors[i];
i = 0;
count++;
}else{
i++;
}
}
return primeFactors;
}
public static int[] primesUpTo(int upTo){
int count = 0;
int num = 2;
while(num <= upTo){
boolean isPrime = true;
for(int div = 2; div <= num / 2; div++){
isPrime = num % div == 0 ? false : isPrime;
}
count += isPrime ? 1 : 0;
num++;
}
int i = 0;
num = 2;
int[] primes = new int[count];
while(num <= upTo){
boolean isPrime = true;
for(int div = 2; div <= num / 2; div++){
isPrime = num % div == 0 ? false : isPrime;
}
if(isPrime){
primes[i] = num;
i++;
}
num++;
}
return primes;
}
}

You could use Arraylists that are more dynamic than Arrays.
However in both functions the code is very inefficient as first i have
to count the length of the array, make a new array of that length and
then use almost the exact same code to populate the array
However, you will find that Arraylists do seem dynamic but underneath they do a similar thing. They start with a size and make a copy of the underlying Array to a bigger one etc.
Another thing you can do if you know some upper bounds to how many numbers you will have to store is to implement you own container class. It can have a big array to hold the numbers and a length variable, for looping through the elements.
For example:
public class NumberContainer(){
private int[] elements;
private int numOfElements;
public NumberContainer(int size){
elements = new int[size];
numOfElements = 0;
}
//add a number
public void add(int x){
elements[numOfElements] = x;
numOfElements++;
}
//get length
public int length(){
return numOfElements;
}
}
....and so on.
This way you don't have to copy an Array to a new large one, allways assuming that you instantiate the NumberContainer with a large enough size.
Hope this helps

You can use a ArrayList which is created empty with no specific size and you can add (-> add(Object o) or remove (-> remove(int index)) anytime you want.

Use an ArrayList if you still need fast retrieval by Index. Otherwise consider a LinkedList, as add = O(1).
For LinkedList
get(int index) is O(n)
add(E element) is O(1)
add(int index, E element) is O(n)
remove(int index) is O(n)
Iterator.remove() is O(1) <--- main benefit of LinkedList<E>
ListIterator.add(E element) is O(1) <--- main benefit of LinkedList<E>
For ArrayList
get(int index) is O(1) <--- main benefit of ArrayList<E>
add(E element) is O(1) amortized, but O(n) worst-case since the array must be resized and copied
add(int index, E element) is O(n - index) amortized, but O(n) worst-case (as above)
remove(int index) is O(n - index) (i.e. removing last is O(1))
Iterator.remove() is O(n - index)
ListIterator.add(E element) is O(n - index)
When to use LinkedList over ArrayList?

I did
boolean isPrime = true;
for (int div = 2; div <= num / 2; div++) {
if (num % div == 0) {
isPrime = false;
break;
}
// Instead isPrime = num % div == 0 ? false : isPrime;
}
and the time needed went from 13 to 1 second.
Actually I wanted to try
public static int guessedPrimeCount(int upTo) {
if (upTo < 10) {
return 10;
}
return (int) (upTo / Math.log10(upTo - 1));
}
public int[] addToPrimes(int[] primes, int count, int p) {
if (count >= primes.length) {
primes = Arrays.copyOf(primes, count + 10);
}
primes[count] = p;
return primes;
}
primes = addToPrimes(primes, count, num);
++count;
The guessedPrimeCount is documented, x/log x, or x/log(x-1).
On adding a new prime p at [count], one in the worst case has to copy the entire array.

You can use an
ArrayList<Integer>
but this requires substantial memory overhead due to auto-boxing.
Or you can use the excellent GNU Trove3 libraries. These contain an TIntArrayList, which takes care of the resizing for you; and is essentially an int[] + a length field. The logic for appending to then is roughly:
double[] array = new double[10]; // Allocated space
int size = 0; // Used space
void add(int v) {
if (size == array.length) {
array = Arrays.copyOf(array, array.length * 2);
}
array[size++] = v;
}

Related

Is this a better way for Fibonacci Series with Recursion?

Where ever I see Recursive Fibonacci Series everyone tell that
a[i] = fib(i - 1) + fib( i - 2)
But it can also be solved with
a[i] = fib(i - 1) + a[i-2] // If array 'a' is a global variable.
If array 'a' is a global Variable, then a[i-2] will be calculated when it is calculating for a[i-2];
It can be solved with below program in java..
public class Fibonacci {
public static int maxNumbers = 10;
public static double[] arr = new double[maxNumbers];
public static void main(String args[])
{
arr[0] = 0;
arr[1] = 1;
recur(maxNumbers - 1);
}
public static double recur(int i)
{
if( i > 1)
{
arr[i] = recur(i - 1) + arr[i - 2];
}
return arr[i];
}
}
Further more, complexity is also less when compared with original procedure. Is there any disadvantage of doing this way?
You have done the first step for Dynamic Programming calculation of Fibonacci, idea of DP is to avoid redundant calculations, and your algorithm achieve its goal.
A "classic" Bottom-Up DP Fibonacci implementation is filling the elements from lower to higher:
arr[0] = 0
arr[1] = 1
for (int i = 2; i <= n; i++)
arr[i] = arr[i-1] + arr[i-2]
(Optimization could be storing curr,last alone, and modifying them at each iteration.
Your approach is basically the same in principle.
As a side note, the DP approach to calculate Fibonacci is taking O(n) time, where there is even more efficient solution with exponential of the matrix:
1 1
1 0
The above holds because you use the fact that
1 1 F_{n+1} 1*F{n+1} + 1*F{n} F_{n+2}
* = =
1 0 F_{n} 1*F{n+1} + 0*F{n} F_{n+1}
Using exponent by squaring on the above matrix, this can be solved in O(logn).
If you just want the nth fibonacci number you could do this:
static double fib(double prev, double curr, int n) {
if(n == 0)
return curr;
return fib(curr, prev+curr, n-1);
}
Initial conditions would be prev = 0, curr = 1, n = maxNumbers. This function is tail recursive because you don't need to store the return value of the recursive call for any additional calculations. The initial stack frame gets reused (which saves memory) and once you hit your base case the value that's returned is the same value that would be returned from every other recursive call.
By using an array like you do you only recalculate one of the two branches (the longest one in each iteration) ending up with a O(n) complexity.
If you were to keep track on how large fibonacci number you have caclulated earlier you can use that and produce O(max(n-prevn, 1)). Here is an altered version of your code that fills the array from bottom to i if needed:
public class Fibonacci {
public static final int maxNumbers = 93; // fib(93) > Long.MAX_VALUE
public static long[] arr = new long[maxNumbers];
public static int calculatedN = 0;
public static long fib(int i) throws Exception
{
if( i >= maxNumbers )
throw new Exception("value out of bounds");
if( calculatedN == 0 ) {
arr[0] = 0L;
arr[1] = 1L;
calculatedN = 1;
}
if( i > calculatedN ) {
for( int x=calculatedN+1; x<=i; x++ ){
arr[x] = arr[x-2] + arr[x-1];
}
calculatedN = i;
}
return arr[i];
}
public static void main (String args[]) {
try {
System.out.println(fib(50)); // O(50-2)
System.out.println(fib(30)); // O(1)
System.out.println(fib(92)); // O(92-50)
System.out.println(fib(92)); // O(1)
} catch ( Exception e ) { e.printStackTrace(); }
}
}
I changed double to long. If you need larger fibonacci numbers than fib(92) I would change from long to Biginteger.
You can also code using two recursive function but as the same value is calculating over again and again so all You can do a dynamic programming approach where You can store the value and return it where need.Like this one in C++
#include <bits/stdc++.h>
using namespace std;
int dp[100];
int fib(int n){
if(n <= 1)
return n;
if(dp[n]!= -1)
return dp[n];
dp[n] = fib(n-1) + fib(n-2);
return dp[n];
}
int main(){
memset(dp,-1,sizeof(dp));
for(int i=1 ;i<10 ;i++)
cout<<fib(i)<<endl;
}
This is only step from non recursive version:
https://gist.github.com/vividvilla/4641152
General this partially recursive approach looks incredibly messy

Generate random index different then any one given in a set

I would like to generate an index from 0 to size - 1, uniformly, but different than any index in the given set excluded.
size is around 100. There are typically 1, 2 or 3 exluded indices. Indices in excluded are unique and not sorted.
Ideally, the header will be with multiple arguments, maybe something like this:
int getRandomIndex(Random rand, int size, int... i)
Or, if this ... multi arguments are slow (are they?) we can pass simple int[] excluded array or something.
How to do it fast? getRandomIndex() is called millions of times.
static int getRandomIndex(Random rand, int size, Integer... excludes) {
List<Integer> excludeList = Arrays.asList(excludes);
int number;
do {
number = rand.nextInt(size);
} while (excludeList.contains(number));
return number;
}
You can allocate array of possible values 0..size-1, remove all known indexes and select random element from the rest.
If indexes array is sorted and all values are unique, you can do it without intermediate array of values :
int getRandomIndex(Random rand, int size, int... ii)
{
int result = rand.nextInt(size - ii.length);
for(int i : ii) {
if(result >= i) {
++result;
}
}
return result;
}
Edit: I found mistake in if condition, corrected.
EDIT:
int getRandomIndex(Random rand, int size, int i) {
int result = rand.nextInt(size - 1);
if(result >= i) {
result++;
}
}
int getRandomIndex(Random rand, int size, int i, int j) {
int result = rand.nextInt(size - 2);
if(i > j) {
int tmp = j;
j = i;
i = tmp;
}
if(result >= i) {
result++;
}
if(result >= j) {
result++;
}
return result;
}
And similar function for (i,j,k).
A function that call rand.next only one time might be faster of any function, that asks for random number several times.
I'd do it a bit differently. To pick a random number less than size but which isn't i or j, you can pick a random number between 0 and size-3 inclusive. If that number is i then return size-2 and if the number is j then return size-1. Otherwise return the original random number.
There is the edge case of i or j being within 2 of size, but I leave that to the interested reader. Actually I think it just works out OK.
This can be generalized to pick a number less than size that isn't in an array of integers. Choose a number less than size - the array length - 1.
You could use a Set fro fast look up
int getRandomIndex(Random rand, int size, Integer... i) {
return getRandomIndex(rand, size, new HashSet<Integer>(Arrays.asList(i)));
}
int getRandomIndex(Random rand, int size, Set<Integer> x) {
int result;
do {
result = (int) (rand.nextDouble() * size);
} while (x.contains(result));
return result;
}
If the set of all indices is predefined then you could have the Set x final static and spare building it every time you call the method.
EIDT
Well If performance is an issue then I would say that your first approach with while(r == i || r == j || r == k) is neither bad nor ugly, just have three overloads of your method and call the appropriate one you need, the call will look exactly the same as with a val-len-array.
You can't have an evaluation faster than (r == i || r == j || r == k)
int getRandomIndex(Random rand, int size, int i)
int getRandomIndex(Random rand, int size, int i, int j)
int getRandomIndex(Random rand, int size, int i, int j, int k)
Assuming many consecutive calls uses the same size and excluded values...
First, initializing an array of all the allowable indices
When getRandomIndex is called, simply pick a random element of this array
Code:
int[] indices;
void init(int size, Integer... excluded)
{
indices = new int[size-excluded.length];
int pos = 0;
Set<Integer> excludedSet = new HashSet<Integer>(Arrays.asList(excluded));
for (int i = 0; i < size; i++)
{
if (!excludedSet.contains(i))
{
indices[pos++] = i;
}
}
}
int getRandomIndex(Random random)
{
return indices[random.nextInt(data.length)];
}
Example usage:
init(100, 50, 23, 10);
Random random = new Random();
for (int i = 0; i < 200; i++)
{
System.out.println(getRandomIndex(random));
}

How to calculate the median of an array?

I'm trying to calculate the total, mean and median of an array thats populated by input received by a textfield. I've managed to work out the total and the mean, I just can't get the median to work. I think the array needs to be sorted before I can do this, but I'm not sure how to do this. Is this the problem, or is there another one that I didn't find? Here is my code:
import java.applet.Applet;
import java.awt.Graphics;
import java.awt.*;
import java.awt.event.*;
public class whileloopq extends Applet implements ActionListener
{
Label label;
TextField input;
int num;
int index;
int[] numArray = new int[20];
int sum;
int total;
double avg;
int median;
public void init ()
{
label = new Label("Enter numbers");
input = new TextField(5);
add(label);
add(input);
input.addActionListener(this);
index = 0;
}
public void actionPerformed (ActionEvent ev)
{
int num = Integer.parseInt(input.getText());
numArray[index] = num;
index++;
if (index == 20)
input.setEnabled(false);
input.setText("");
sum = 0;
for (int i = 0; i < numArray.length; i++)
{
sum += numArray[i];
}
total = sum;
avg = total / index;
median = numArray[numArray.length/2];
repaint();
}
public void paint (Graphics graf)
{
graf.drawString("Total = " + Integer.toString(total), 25, 85);
graf.drawString("Average = " + Double.toString(avg), 25, 100);
graf.drawString("Median = " + Integer.toString(median), 25, 115);
}
}
The Arrays class in Java has a static sort function, which you can invoke with Arrays.sort(numArray).
Arrays.sort(numArray);
double median;
if (numArray.length % 2 == 0)
median = ((double)numArray[numArray.length/2] + (double)numArray[numArray.length/2 - 1])/2;
else
median = (double) numArray[numArray.length/2];
Sorting the array is unnecessary and inefficient. There's a variation of the QuickSort (QuickSelect) algorithm which has an average run time of O(n); if you sort first, you're down to O(n log n). It actually finds the nth smallest item in a list; for a median, you just use n = half the list length. Let's call it quickNth (list, n).
The concept is that to find the nth smallest, choose a 'pivot' value. (Exactly how you choose it isn't critical; if you know the data will be thoroughly random, you can take the first item on the list.)
Split the original list into three smaller lists:
One with values smaller than the pivot.
One with values equal to the pivot.
And one with values greater than the pivot.
You then have three cases:
The "smaller" list has >= n items. In that case, you know that the nth smallest is in that list. Return quickNth(smaller, n).
The smaller list has < n items, but the sum of the lengths of the smaller and equal lists have >= n items. In this case, the nth is equal to any item in the "equal" list; you're done.
n is greater than the sum of the lengths of the smaller and equal lists. In that case, you can essentially skip over those two, and adjust n accordingly. Return quickNth(greater, n - length(smaller) - length(equal)).
Done.
If you're not sure that the data is thoroughly random, you need to be more sophisticated about choosing the pivot. Taking the median of the first value in the list, the last value in the list, and the one midway between the two works pretty well.
If you're very unlucky with your choice of pivots, and you always choose the smallest or highest value as your pivot, this takes O(n^2) time; that's bad. But, it's also very unlikely if you choose your pivot with a decent algorithm.
Sample code:
import java.util.*;
public class Utility {
/****************
* #param coll an ArrayList of Comparable objects
* #return the median of coll
*****************/
public static <T extends Number> double median(ArrayList<T> coll, Comparator<T> comp) {
double result;
int n = coll.size()/2;
if (coll.size() % 2 == 0) // even number of items; find the middle two and average them
result = (nth(coll, n-1, comp).doubleValue() + nth(coll, n, comp).doubleValue()) / 2.0;
else // odd number of items; return the one in the middle
result = nth(coll, n, comp).doubleValue();
return result;
} // median(coll)
/*****************
* #param coll a collection of Comparable objects
* #param n the position of the desired object, using the ordering defined on the list elements
* #return the nth smallest object
*******************/
public static <T> T nth(ArrayList<T> coll, int n, Comparator<T> comp) {
T result, pivot;
ArrayList<T> underPivot = new ArrayList<>(), overPivot = new ArrayList<>(), equalPivot = new ArrayList<>();
// choosing a pivot is a whole topic in itself.
// this implementation uses the simple strategy of grabbing something from the middle of the ArrayList.
pivot = coll.get(n/2);
// split coll into 3 lists based on comparison with the pivot
for (T obj : coll) {
int order = comp.compare(obj, pivot);
if (order < 0) // obj < pivot
underPivot.add(obj);
else if (order > 0) // obj > pivot
overPivot.add(obj);
else // obj = pivot
equalPivot.add(obj);
} // for each obj in coll
// recurse on the appropriate list
if (n < underPivot.size())
result = nth(underPivot, n, comp);
else if (n < underPivot.size() + equalPivot.size()) // equal to pivot; just return it
result = pivot;
else // everything in underPivot and equalPivot is too small. Adjust n accordingly in the recursion.
result = nth(overPivot, n - underPivot.size() - equalPivot.size(), comp);
return result;
} // nth(coll, n)
public static void main (String[] args) {
Comparator<Integer> comp = Comparator.naturalOrder();
Random rnd = new Random();
for (int size = 1; size <= 10; size++) {
ArrayList<Integer> coll = new ArrayList<>(size);
for (int i = 0; i < size; i++)
coll.add(rnd.nextInt(100));
System.out.println("Median of " + coll.toString() + " is " + median(coll, comp));
} // for a range of possible input sizes
} // main(args)
} // Utility
If you want to use any external library here is Apache commons math library using you can calculate the Median.
For more methods and use take look at the API documentation
import org.apache.commons.math3.*;
.....
......
........
//calculate median
public double getMedian(double[] values){
Median median = new Median();
double medianValue = median.evaluate(values);
return medianValue;
}
.......
For more on evaluate method AbstractUnivariateStatistic#evaluate
Update
Calculate in program
Generally, median is calculated using the following two formulas given here
If n is odd then Median (M) = value of ((n + 1)/2)th item term.
If n is even then Median (M) = value of [((n)/2)th item term + ((n)/2 + 1)th item term ]/2
In your program you have numArray, first you need to sort array using Arrays#sort
Arrays.sort(numArray);
int middle = numArray.length/2;
int medianValue = 0; //declare variable
if (numArray.length%2 == 1)
medianValue = numArray[middle];
else
medianValue = (numArray[middle-1] + numArray[middle]) / 2;
Arrays.sort(numArray);
return (numArray[size/2] + numArray[(size-1)/2]) / 2;
Arrays.sort(numArray);
int middle = ((numArray.length) / 2);
if(numArray.length % 2 == 0){
int medianA = numArray[middle];
int medianB = numArray[middle-1];
median = (medianA + medianB) / 2;
} else{
median = numArray[middle + 1];
}
EDIT: I initially had medianB setting to middle+1 in the even length arrays, this was wrong due to arrays starting count at 0. I have updated it to use middle-1 which is correct and should work properly for an array with an even length.
You can find good explanation at https://www.youtube.com/watch?time_continue=23&v=VmogG01IjYc
The idea it to use 2 Heaps viz one max heap and mean heap.
class Heap {
private Queue<Integer> low = new PriorityQueue<>(Comparator.reverseOrder());
private Queue<Integer> high = new PriorityQueue<>();
public void add(int number) {
Queue<Integer> target = low.size() <= high.size() ? low : high;
target.add(number);
balance();
}
private void balance() {
while(!low.isEmpty() && !high.isEmpty() && low.peek() > high.peek()) {
Integer lowHead= low.poll();
Integer highHead = high.poll();
low.add(highHead);
high.add(lowHead);
}
}
public double median() {
if(low.isEmpty() && high.isEmpty()) {
throw new IllegalStateException("Heap is empty");
} else {
return low.size() == high.size() ? (low.peek() + high.peek()) / 2.0 : low.peek();
}
}
}
Try sorting the array first. Then after it's sorted, if the array has an even amount of elements the mean of the middle two is the median, if it has a odd number, the middle element is the median.
Use Arrays.sort and then take the middle element (in case the number n of elements in the array is odd) or take the average of the two middle elements (in case n is even).
public static long median(long[] l)
{
Arrays.sort(l);
int middle = l.length / 2;
if (l.length % 2 == 0)
{
long left = l[middle - 1];
long right = l[middle];
return (left + right) / 2;
}
else
{
return l[middle];
}
}
Here are some examples:
#Test
public void evenTest()
{
long[] l = {
5, 6, 1, 3, 2
};
Assert.assertEquals((3 + 4) / 2, median(l));
}
#Test
public oddTest()
{
long[] l = {
5, 1, 3, 2, 4
};
Assert.assertEquals(3, median(l));
}
And in case your input is a Collection, you might use Google Guava to do something like this:
public static long median(Collection<Long> numbers)
{
return median(Longs.toArray(numbers)); // requires import com.google.common.primitives.Longs;
}
I was looking at the same statistics problems. The approach you are thinking it is good and it will work. (Answer to the sorting has been given)
But in case you are interested in algorithm performance, I think there are a couple of algorithms that have better performance than just sorting the array, one (QuickSelect) is indicated by #bruce-feist's answer and is very well explained.
[Java implementation: https://discuss.leetcode.com/topic/14611/java-quick-select ]
But there is a variation of this algorithm named median of medians, you can find a good explanation on this link:
http://austinrochford.com/posts/2013-10-28-median-of-medians.html
Java implementation of this:
- https://stackoverflow.com/a/27719796/957979
I faced a similar problem yesterday.
I wrote a method with Java generics in order to calculate the median value of every collection of Numbers; you can apply my method to collections of Doubles, Integers, Floats and returns a double. Please consider that my method creates another collection in order to not alter the original one.
I provide also a test, have fun. ;-)
public static <T extends Number & Comparable<T>> double median(Collection<T> numbers){
if(numbers.isEmpty()){
throw new IllegalArgumentException("Cannot compute median on empty collection of numbers");
}
List<T> numbersList = new ArrayList<>(numbers);
Collections.sort(numbersList);
int middle = numbersList.size()/2;
if(numbersList.size() % 2 == 0){
return 0.5 * (numbersList.get(middle).doubleValue() + numbersList.get(middle-1).doubleValue());
} else {
return numbersList.get(middle).doubleValue();
}
}
JUnit test code snippet:
/**
* Test of median method, of class Utils.
*/
#Test
public void testMedian() {
System.out.println("median");
Double expResult = 3.0;
Double result = Utils.median(Arrays.asList(3.0,2.0,1.0,9.0,13.0));
assertEquals(expResult, result);
expResult = 3.5;
result = Utils.median(Arrays.asList(3.0,2.0,1.0,9.0,4.0,13.0));
assertEquals(expResult, result);
}
Usage example (consider the class name is Utils):
List<Integer> intValues = ... //omitted init
Set<Float> floatValues = ... //omitted init
.....
double intListMedian = Utils.median(intValues);
double floatSetMedian = Utils.median(floatValues);
Note: my method works on collections, you can convert arrays of numbers to list of numbers as pointed here
And nobody paying attention when list contains only one element (list.size == 1). All your answers will crash with index out of bound exception, because integer division returns zero (1 / 2 = 0). Correct answer (in Kotlin):
MEDIAN("MEDIAN") {
override fun calculate(values: List<BigDecimal>): BigDecimal? {
if (values.size == 1) {
return values.first()
}
if (values.size > 1) {
val valuesSorted = values.sorted()
val mid = valuesSorted.size / 2
return if (valuesSorted.size % 2 != 0) {
valuesSorted[mid]
} else {
AVERAGE.calculate(listOf(valuesSorted[mid - 1], valuesSorted[mid]))
}
}
return null
}
},
As #Bruce-Feist mentions, for a large number of elements, I'd avoid any solution involving sort if performance is something you are concerned about. A different approach than those suggested in the other answers is Hoare's algorithm to find the k-th smallest of element of n items. This algorithm runs in O(n).
public int findKthSmallest(int[] array, int k)
{
if (array.length < 10)
{
Arrays.sort(array);
return array[k];
}
int start = 0;
int end = array.length - 1;
int x, temp;
int i, j;
while (start < end)
{
x = array[k];
i = start;
j = end;
do
{
while (array[i] < x)
i++;
while (x < array[j])
j--;
if (i <= j)
{
temp = array[i];
array[i] = array[j];
array[j] = temp;
i++;
j--;
}
} while (i <= j);
if (j < k)
start = i;
if (k < i)
end = j;
}
return array[k];
}
And to find the median:
public int median(int[] array)
{
int length = array.length;
if ((length & 1) == 0) // even
return (findKthSmallest(array, array.length / 2) + findKthSmallest(array, array.length / 2 + 1)) / 2;
else // odd
return findKthSmallest(array, array.length / 2);
}
public static int median(int[] arr) {
int median = 0;
java.util.Arrays.sort(arr);
for (int i=0;i<arr.length;i++) {
if (arr.length % 2 == 1) {
median = Math.round(arr[arr.length/2]);
} else {
median = (arr[(arr.length/2)] + arr[(arr.length/2)-1])/2;
}
}
return median;
}
Check out the Arrays.sort methods:
http://docs.oracle.com/javase/6/docs/api/java/util/Arrays.html
You should also really abstract finding the median into its own method, and just return the value to the calling method. This will make testing your code much easier.
public int[] data={31, 29, 47, 48, 23, 30, 21
, 40, 23, 39, 47, 47, 42, 44, 23, 26, 44, 32, 20, 40};
public double median()
{
Arrays.sort(this.data);
double result=0;
int size=this.data.length;
if(size%2==1)
{
result=data[((size-1)/2)+1];
System.out.println(" uneven size : "+result);
}
else
{
int middle_pair_first_index =(size-1)/2;
result=(data[middle_pair_first_index+1]+data[middle_pair_first_index])/2;
System.out.println(" Even size : "+result);
}
return result;
}
package arrays;
public class Arraymidleelement {
static public double middleArrayElement(int [] arr)
{
double mid;
if(arr.length%2==0)
{
mid=((double)arr[arr.length/2]+(double)arr[arr.length/2-1])/2;
return mid;
}
return arr[arr.length/2];
}
public static void main(String[] args) {
int arr[]= {1,2,3,4,5,6};
System.out.println( middleArrayElement(arr));
}
}

Bubble Sort to gap sort modification

I've run in to a problem and need your guidance. Basically i managed to create this Bubble Sort method. How can i modify this to Gap Sort, that rather than comparing neighboring elements each time through the list, compares elements that are some number(i) positions apart, where (i) is an integer less than n. For example, the first element would be compared to the (i + 1) element, 2nd element to the (i + 2) element, nth element to the (n-i) element, etc. A single iteration is completed when all of the elements that can be compared, have been compared. On the next iteration, i is reduced by some number greater than 1 and the process continues until i is less than 1
public static void bubbleSort (Comparable[] data, int maxlength){
int position, scan;
Comparable temp;
for (position = maxlength; position >= 1; position--){
for (scan = 0; scan <= position – 1; scan++){
if (data[scan].compareTo(data[scan+1]) > 0){
// Swap the values
temp = data[scan];
data[scan] = data[scan + 1];
data[scan + 1] = temp;
}
}
}
}
This code (found on http://www.daniweb.com/software-development/java/threads/238791/gap-sort) might help you:
public static void gapSort (Comparable [] data, int size) {
int index;
int gap, top;
Comparable temp;
boolean exchanged;
double SF = 1.3;
gap = size;
do {
exchanged = false;
gap = (int) (gap / SF);
if (gap == 0){
gap = 1;
}
for (index = 1; index <= size - gap; index++) {
if (data [index].compareTo(data [index + gap]) > 0) {
temp = data [index];
data [index] = data [index + gap];
data [index + gap] = temp;
exchanged = true;
}
}
} while (exchanged || gap > 1);
}
Remember that the easiest way to sort an array of objects that implement the Comparable interface is usually Arrays.Sort()

Finding prime numbers with the Sieve of Eratosthenes (Originally: Is there a better way to prepare this array?)

Note: Version 2, below, uses the Sieve of Eratosthenes. There are several answers that helped with what I originally asked. I have chosen the Sieve of Eratosthenes method, implemented it, and changed the question title and tags appropriately. Thanks to everyone who helped!
Introduction
I wrote this fancy little method that generates an array of int containing the prime numbers less than the specified upper bound. It works very well, but I have a concern.
The Method
private static int [] generatePrimes(int max) {
int [] temp = new int [max];
temp [0] = 2;
int index = 1;
int prime = 1;
boolean isPrime = false;
while((prime += 2) <= max) {
isPrime = true;
for(int i = 0; i < index; i++) {
if(prime % temp [i] == 0) {
isPrime = false;
break;
}
}
if(isPrime) {
temp [index++] = prime;
}
}
int [] primes = new int [index];
while(--index >= 0) {
primes [index] = temp [index];
}
return primes;
}
My Concern
My concern is that I am creating an array that is far too large for the final number of elements the method will return. The trouble is that I don't know of a good way to correctly guess the number of prime numbers less than a specified number.
Focus
This is how the program uses the arrays. This is what I want to improve upon.
I create a temporary array that is
large enough to hold every number
less than the limit.
I generate the prime numbers, while
keeping count of how many I have
generated.
I make a new array that is the right
dimension to hold just the prime
numbers.
I copy each prime number from the
huge array to the array of the
correct dimension.
I return the array of the correct
dimension that holds just the prime
numbers I generated.
Questions
Can I copy the whole chunk (at once) of
temp[] that has nonzero
elements to primes[]
without having to iterate through
both arrays and copy the elements
one by one?
Are there any data structures that
behave like an array of primitives
that can grow as elements are added,
rather than requiring a dimension
upon instantiation? What is the
performance penalty compared to
using an array of primitives?
Version 2 (thanks to Jon Skeet):
private static int [] generatePrimes(int max) {
int [] temp = new int [max];
temp [0] = 2;
int index = 1;
int prime = 1;
boolean isPrime = false;
while((prime += 2) <= max) {
isPrime = true;
for(int i = 0; i < index; i++) {
if(prime % temp [i] == 0) {
isPrime = false;
break;
}
}
if(isPrime) {
temp [index++] = prime;
}
}
return Arrays.copyOfRange(temp, 0, index);
}
Version 3 (thanks to Paul Tomblin) which uses the Sieve of Erastosthenes:
private static int [] generatePrimes(int max) {
boolean[] isComposite = new boolean[max + 1];
for (int i = 2; i * i <= max; i++) {
if (!isComposite [i]) {
for (int j = i; i * j <= max; j++) {
isComposite [i*j] = true;
}
}
}
int numPrimes = 0;
for (int i = 2; i <= max; i++) {
if (!isComposite [i]) numPrimes++;
}
int [] primes = new int [numPrimes];
int index = 0;
for (int i = 2; i <= max; i++) {
if (!isComposite [i]) primes [index++] = i;
}
return primes;
}
Your method of finding primes, by comparing every single element of the array with every possible factor is hideously inefficient. You can improve it immensely by doing a Sieve of Eratosthenes over the entire array at once. Besides doing far fewer comparisons, it also uses addition rather than division. Division is way slower.
ArrayList<> Sieve of Eratosthenes
// Return primes less than limit
static ArrayList<Integer> generatePrimes(int limit) {
final int numPrimes = countPrimesUpperBound(limit);
ArrayList<Integer> primes = new ArrayList<Integer>(numPrimes);
boolean [] isComposite = new boolean [limit]; // all false
final int sqrtLimit = (int)Math.sqrt(limit); // floor
for (int i = 2; i <= sqrtLimit; i++) {
if (!isComposite [i]) {
primes.add(i);
for (int j = i*i; j < limit; j += i) // `j+=i` can overflow
isComposite [j] = true;
}
}
for (int i = sqrtLimit + 1; i < limit; i++)
if (!isComposite [i])
primes.add(i);
return primes;
}
Formula for upper bound of number of primes less than or equal to max (see wolfram.com):
static int countPrimesUpperBound(int max) {
return max > 1 ? (int)(1.25506 * max / Math.log((double)max)) : 0;
}
Create an ArrayList<Integer> and then convert to an int[] at the end.
There are various 3rd party IntList (etc) classes around, but unless you're really worried about the hit of boxing a few integers, I wouldn't worry about it.
You could use Arrays.copyOf to create the new array though. You might also want to resize by doubling in size each time you need to, and then trim at the end. That would basically be mimicking the ArrayList behaviour.
Algo using Sieve of Eratosthenes
public static List<Integer> findPrimes(int limit) {
List<Integer> list = new ArrayList<>();
boolean [] isComposite = new boolean [limit + 1]; // limit + 1 because we won't use '0'th index of the array
isComposite[1] = true;
// Mark all composite numbers
for (int i = 2; i <= limit; i++) {
if (!isComposite[i]) {
// 'i' is a prime number
list.add(i);
int multiple = 2;
while (i * multiple <= limit) {
isComposite [i * multiple] = true;
multiple++;
}
}
}
return list;
}
Image depicting the above algo (Grey color cells represent prime number. Since we consider all numbers as prime numbers intially, the whole is grid is grey initially.)
Image Source: WikiMedia
The easiest solution would be to return some member of the Collections Framework instead of an array.
Are you using Java 1.5? Why not return List<Integer> and use ArrayList<Integer>? If you do need to return an int[], you can do it by converting List to int[] at the end of processing.
As Paul Tomblin points out, there are better algorithms.
But keeping with what you have, and assuming an object per result is too big:
You are only ever appending to the array. So, use a relatively small int[] array. When it's full use append it to a List and create a replacement. At the end copy it into a correctly sized array.
Alternatively, guess the size of the int[] array. If it is too small, replace by an int[] with a size a fraction larger than the current array size. The performance overhead of this will remain proportional to the size. (This was discussed briefly in a recent stackoverflow podcast.)
Now that you've got a basic sieve in place, note that the inner loop need only continue until temp[i]*temp[i] > prime.
I have a really efficient implementation:
we don't keep the even numbers, therefore halving the memory usage.
we use BitSet, requiring only one bit per number.
we estimate the upper bound for number of primes on the interval, thus we can set the initialCapacity for the Array appropriately.
we don't perform any kind of division in the loops.
Here's the code:
public ArrayList<Integer> sieve(int n) {
int upperBound = (int) (1.25506 * n / Math.log(n));
ArrayList<Integer> result = new ArrayList<Integer>(upperBound);
if (n >= 2)
result.add(2);
int size = (n - 1) / 2;
BitSet bs = new BitSet(size);
int i = 0;
while (i < size) {
int p = 3 + 2 * i;
result.add(p);
for (int j = i + p; j < size; j += p)
bs.set(j);
i = bs.nextClearBit(i + 1);
}
return result;
}
Restructure your code. Throw out the temporary array, and instead write function that just prime-tests an integer. It will be reasonably fast, since you're only using native types. Then you can, for instance, loop and build a list of integers that are prime, before finally converting that to an array to return.
Not sure if this will suite your situation but you can take a look at my approach. I used mine using Sieve of Eratosthenes.
public static List<Integer> sieves(int n) {
Map<Integer,Boolean> numbers = new LinkedHashMap<>();
List<Integer> primes = new ArrayList<>();
//First generate a list of integers from 2 to 30
for(int i=2; i<n;i++){
numbers.put(i,true);
}
for(int i : numbers.keySet()){
/**
* The first number in the list is 2; cross out every 2nd number in the list after 2 by
* counting up from 2 in increments of 2 (these will be all the multiples of 2 in the list):
*
* The next number in the list after 2 is 3; cross out every 3rd number in the list after 3 by
* counting up from 3 in increments of 3 (these will be all the multiples of 3 in the list):
* The next number not yet crossed out in the list after 5 is 7; the next step would be to cross out every
* 7th number in the list after 7, but they are all already crossed out at this point,
* as these numbers (14, 21, 28) are also multiples of smaller primes because 7 × 7 is greater than 30.
* The numbers not crossed out at this point in the list are all the prime numbers below 30:
*/
if(numbers.get(i)){
for(int j = i+i; j<n; j+=i) {
numbers.put(j,false);
}
}
}
for(int i : numbers.keySet()){
for(int j = i+i; j<n && numbers.get(i); j+=i) {
numbers.put(j,false);
}
}
for(int i : numbers.keySet()){
if(numbers.get(i)) {
primes.add(i);
}
}
return primes;
}
Added comment for each steps that has been illustrated in wikipedia
I have done using HashMap and found it very simple
import java.util.HashMap;
import java.util.Map;
/*Using Algorithms such as sieve of Eratosthanas */
public class PrimeNumber {
public static void main(String[] args) {
int prime = 15;
HashMap<Integer, Integer> hashMap = new HashMap<Integer, Integer>();
hashMap.put(0, 0);
hashMap.put(1, 0);
for (int i = 2; i <= prime; i++) {
hashMap.put(i, 1);// Assuming all numbers are prime
}
printPrimeNumberEratoshanas(hashMap, prime);
}
private static void printPrimeNumberEratoshanas(HashMap<Integer, Integer> hashMap, int prime) {
System.out.println("Printing prime numbers upto" + prime + ".....");
for (Map.Entry<Integer, Integer> entry : hashMap.entrySet()) {
if (entry.getValue().equals(1)) {
System.out.println(entry.getKey());
for (int j = entry.getKey(); j < prime; j++) {
for (int k = j; k * j <= prime; k++) {
hashMap.put(j * k, 0);
}
}
}
}
}
}
Think this is effective
public static void primes(int n) {
boolean[] lista = new boolean[n+1];
for (int i=2;i<lista.length;i++) {
if (lista[i]==false) {
System.out.print(i + " ");
}
for (int j=i+i;j<lista.length;j+=i) {
lista[j]=true;
}
}
}

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