Develop Reference

Coins Change with Greedy algorithm

Hy guys, for some reason my greedy coins change program does not work. The function should return with the minimum amount of coins you can change a value and there is an array as well which includes the coins you can use for this. My program does not show anything an I dont know why.
public class Main {
public static int coinChangeGreedy(int[] coins, int n) {
int result = 0;
while (n != 0)
{
for (int i=coins.length - 1 ; i>=0 ; i--)
{
if (coins[i] <= n)
{
n = n - coins[i];
result++;
}
}
}
return result;
}
public static void main(String[] args)
{
int[] coins = {1, 2, 5};
int n = 11;
coinChangeGreedy(coins, n);
}
}

Well, at first - you are not printing anything - you just run the function.
Second - you have a bit of a flaw in your code. You see - if you find a coin that works, you shouldn't go to the next coin, but see if that one "fits" again.
In your example with n=11. You have 5 as the first one and then move to 2. But you should actually try the 5 again (prevent the for loop from going to the next element). See the example:
public static int coinChangeGreedy(int[] coins, int n) {
int result = 0;
while (n != 0) {
for (int i=coins.length - 1 ; i>=0 ; i--) {
if (coins[i] <= n) {
n = n - coins[i];
System.out.println("Adding " +coins[i]);
i++; //neutralizing i-- with i++.
result++;
}
}
}
return result;
}
Not you'll see that 5 is taken twice.
P.s. - if you are assuming that the coin array will be ascending.
And to print it, you just go:
System.out.println("Total coins needed: " +coinChangeGreedy(coins, n));
Additionally - if you want to keep track of coins used, you can store them in an ArrayList every time it is chosen. list.add(coins[i]). And of course you declare and initialize thatlist` at the beggining.

Max Double Slice Sum codility O(1) space complexity fail performance test case

I was trying figure out why the below solution failed for a single performance test case for the 'Max Double Slice Sum' problem in the codility website: https://codility.com/demo/take-sample-test/max_double_slice_sum
There is another solution O(n) space complexity which is easier to comprehend overhere: Max double slice sum. But i am just wondering why this O(1) solution doesn't work. Below is the actual code:
import java.util.*;
class Solution {
public int solution(int[] A) {
long maxDS = 0;
long maxDSE = 0;
long maxS = A[1];
for(int i=2; i<A.length-1; ++i){
//end at i-index
maxDSE = Math.max(maxDSE+A[i], maxS);
maxDS = Math.max(maxDS, maxDSE);
maxS = Math.max(A[i], maxS + A[i]);
}
return (int)maxDS;
}
}
The idea is simple as follow:
The problem can be readdress as finding max(A[i]+A[i+1]+...+A[j]-A[m]); 1<=i<=m<=j<=n-2; while n = A.length; we call A[m] is missing element within the slice.
maxS[i] will keep max slice which end at current index i; in other words, = max(A[t] + ... + A[i]); while t < i; so when i=1; maxS = A[1]; Note that in solution, we don't keep array but rather latest maxS at current index (See above code).
maxDSE[i] is max of all double slice which end at i; in other words, = max(A[t]+A[t+1]+...+A[i]-A[m])--end at A[i]; maxDS is the final max of double slice sum which we try to find.
Now, we just use a for-loop from i=2; -> i=A.length-2; For each index i, we notice some findings:
If the missing element is A[i], then maxDSE[i] = maxS[i-1] (max sum of
all slice which end at i-1 => or A[t] + ... + A[i] - A[i]);
If missing element is not A[i] -> so it must be somewhere from A[1]->A[i-1] -> maxDSE = maxDSE[i-1] + A[i]; such as A[t] + ... + A[i] - A[m] (not that A[i] must be last element) with t
so maxDSE[i] = Math.max(maxDSE[i-1]+A[i], maxS[i-1]);
maxDS = Math.max(maxDS, maxDSE); max amount all maxDSE;
and maxS[i] = Math.max(A[i], maxS[i-1]+A[i]);
by that way, maxDS will be the final result.
But strange that, I was only able to get 92%; with one failed performance test case as shown here:
medium_range
-1000, ..., 1000
WRONG ANSWER
got 499499 expected 499500
Could anyone please enlighten me where is problem in my solution? Thanks!

Ok, I found the error with my code. Seems that I forgot one corner cases. When calculate DSE[i], in cases A[i] is missing number, maxS should contain the case when array is empty. In other word, maxS should be calculated as:
maxS[i] = Math.max(0, Math.max(A[i]+maxS[i-1], A[i])); while 0 is for case of empty subarray (end at i-th); Math.max(A[i]+maxS[i-1], A[i]) is max of all slice with at least one element (end at i-index). The complete code as follow:
import java.util.*;
class Solution {
public int solution(int[] A) {
long maxDS = 0;
long maxDSE = 0;
long maxS = A[1];
for(int i=2; i<A.length-1; ++i){
maxDSE = Math.max(maxDSE+A[i], maxS);
maxDS = Math.max(maxDS, maxDSE);
maxS = Math.max(0, Math.max(A[i], maxS + A[i]));
}
return (int)maxDS;
}
}

It seems that for the input [-11, -53, -4, 38, 76, 80], your solution doesn't work. Yes, it tricks all the codility test cases, but I managed to trick all codility test cases for other problems too.
If you don't just want to trick codility, but also you want to come with a good solution, I suggest that you create a loop and a large number of random test cases (in number of elements and element values), and create a test method of your own, that you are sure works (even if the complexity is quadratic), compare the results from both methods and then analyze the current random input that doesn't fit.

Here is clear solution. Best approach is to use algorithm of Kanade O(N) and O(1) by space
public class DuplicateDetermineAlgorithm {
public static boolean isContainsDuplicate(int[] array) {
if (array == null) {
throw new IllegalArgumentException("Input array can not be null");
}
if (array.length < 2) {
return false;
}
for (int i = 0; i < array.length; i++) {
int pointer = convertToPositive(array[i]) - 1;
if (array[pointer] > 0) {
array[pointer] = changeSign(array[pointer]);
} else {
return true;
}
}
return false;
}
private static int convertToPositive(int value) {
return value < 0 ? changeSign(value) : value;
}
private static int changeSign(int value) {
return -1 * value;
}
}

I have coded it in vb.net and got 100/100 getting idea form solution by Guillermo
Private Function solution(A As Integer()) As Integer
' write your code in VB.NET 4.0
Dim Slice1() As Integer = Ending(A)
Dim slice2() As Integer = Starting(A)
Dim maxSUM As Integer = 0
For i As Integer = 1 To A.Length - 2
maxSUM = Math.Max(maxSUM, Slice1(i - 1) + slice2(i + 1))
Next
Return maxSUM
End Function
Public Shared Function Ending(input() As Integer) As Integer()
Dim result As Integer() = New Integer(input.Length - 1) {}
result(0) = InlineAssignHelper(result(input.Length - 1), 0)
For i As Integer = 1 To input.Length - 2
result(i) = Math.Max(0, result(i - 1) + input(i))
Next
Return result
End Function
Public Shared Function Starting(input() As Integer) As Integer()
Dim result As Integer() = New Integer(input.Length - 1) {}
result(0) = InlineAssignHelper(result(input.Length - 1), 0)
For i As Integer = input.Length - 2 To 1 Step -1
result(i) = Math.Max(0, result(i + 1) + input(i))
Next
Return result
End Function
Private Shared Function InlineAssignHelper(Of T)(ByRef target As T, value As T) As T
target = value
Return value
End Function
Visit Codility to see the results

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));
}
}

big-o order of recursive and iterative fib functions?

I was asked to write a fib function in the most efficient manner?
This is the implementation I provided:
public static int fib(int n)
{
int prev1 = 1, prev2 = 1, ans = 1, i = 3;
while (i <= n)
{
ans = prev1 + prev2;
prev1 = prev2;
prev2 = ans;
i++;
}
return ans;
}
Is this the most efficient? What is the big-o order?
I was also asked to give the big-o notation of a recursive implementation:
public static int recursiveFib(int n)
{
if (n <= 2)
return 1;
else
return recursiveFib(n-1) + recursiveFib(n - 2);
}
I think this one is exponential 2^n which is why it's inefficient.

Your implementation is O(n), and is the normal way to implement the Fibonacci function. The recursive definition is O(fib(n)) unless memoization or similar is used.
There is also a Closed form expression of the Fibonacci numbers, and This link has some implementations of faster fib functions.

I would say the best way to find fib for a particular n is using the matrix calculation method as given in Link - Page19
where F0 = 0 and F1 = 1. This matrix relation can easlily be used to find the fib for any value of n and n+1. The best part is that is that the multiplying matrix need not be mutliplied n times but only logN times to find the actual value of the Multiplier. Thus the overall compleixty of the algorithm is only O(logN).
The equation is derived from the basic relation of
F1 = 0*F0 + 1*F1
F1 = 1*F0 + 1*F2
Iterating over n the multiplier matrix has to be multiplied n times.

Here's the matrix method to complete this page:
public static void main(String[] args)
{
int n = 25;
matMul(n - 1);
System.out.printf("\n%dth Fibonnacci number : %d\n\n", n, M[0][0]);
}
static int[][] M = {{1,0},{0,1}};
static int[][] A = {{1,1},{1,0}};
static int[][] C = {{0,0},{0,0}};
static void matMul(int n)
{
if (n > 1)
{
matMul(n/2);
mulM(0);
}
if (n%2!=0)
{
mulM(1);
}
}
static void mulM(int m)
{
int i,j,k;
if (m==0)
{
for (i=0;i<2;i++)
for (j=0;j<2;j++)
{
C[i][j]=0;
for (k=0;k<2;k++)
C[i][j]+=M[i][k]*M[k][j];
}
}
else
{
for (i=0;i<2;i++)
for (j=0;j<2;j++)
{
C[i][j]=0;
for (k=0;k<2;k++)
C[i][j]+=A[i][k]*M[k][j];
}
}
for (i=0;i<2;i++)
for (j=0;j<2;j++)
{
M[i][j]=C[i][j];
}
}

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;
}
}
}