How to speed up my prime calculations?

How to speed up my prime calculations? - java

I'm trying to answer the following Euler Problem (#10):
The sum of the primes below 10 is 2 + 3 + 5 + 7 = 17.
Find the sum of all the primes below two million.
My program is working correctly, however I found out that it took 100 seconds to compute this, using the following code, take new Problem10().run() as starting point:
public class Problem10 extends Problem<Long> {
#Override
public void run() {
result = Iterators.finiteLongStream(new PrimeGenerator(), i -> i <= 2_000_000)
.sum();
}
#Override
public String getName() {
return "Problem 10";
}
}
public abstract class Iterators {
///
public static PrimitiveIterator.OfLong finiteLongIterator(final PrimitiveIterator.OfLong iterator, final LongPredicate predicate) {
return new PrimitiveIterator.OfLong() {
private long next;
#Override
public boolean hasNext() {
if (!iterator.hasNext()) {
return false;
}
next = iterator.nextLong();
return predicate.test(next);
}
#Override
public long nextLong() {
return next;
}
};
}
public static LongStream finiteLongStream(final PrimitiveIterator.OfLong iterator, final LongPredicate predicate) {
return Iterators.longStream(Iterators.finiteLongIterator(iterator, predicate));
}
public static LongStream longStream(final PrimitiveIterator.OfLong iterator) {
return StreamSupport.longStream(
Spliterators.spliteratorUnknownSize(iterator, 0), false
);
}
///
}
public class PrimeGenerator implements PrimitiveIterator.OfLong {
private final static LongNode HEAD_NODE = new LongNode(2);
private LongNode lastNode = HEAD_NODE;
private long current = 2;
#Override
public boolean hasNext() {
return true;
}
#Override
public long nextLong() {
if (lastNode.value == current) {
if (lastNode.next != null) {
long old = lastNode.value;
lastNode = lastNode.next;
current = lastNode.value;
return old;
}
return current++;
}
while (true) {
if (isPrime(current)) {
appendNode(current);
return current++;
}
current++;
}
}
private boolean isPrime(final long number) {
LongNode prime = HEAD_NODE;
while (prime != null && prime.value <= number) {
if (number % prime.value == 0) {
return false;
}
prime = prime.next;
}
return true;
}
private void appendNode(final long value) {
LongNode newNode = new LongNode(value);
couple(lastNode, newNode);
lastNode = newNode;
}
private void couple(final LongNode first, final LongNode second) {
first.next = second;
second.previous = first;
}
private static class LongNode {
public final long value;
public LongNode previous;
public LongNode next;
public LongNode(final long value) {
this.value = value;
}
}
}
How could I optimise this? If possible, first suggestions along the lines of my current code, then suggesting a totally different algorithm.
Edit, also I'd like to refrain from a finite Sieve of Eratosthenes, as the whole point of such an iterator resp. stream is to be able to do it for an infinite amount of prices, I am unsure myself whether the Sieve of Eratosthenes method works for infinite numbers, I think not trivially.

The number of iterations in the method isPrime() can be reduced if you observe the fact that only the prime factors less than square root of a number need to be considerd.
So the current condition is :
while (prime != null && prime.value <= number)
It can be changed to :
while (prime != null && prime.value <= square_root(number) )
There might be other possibilities to optimize your code but that would need detailed review of your code.

Here are some thoughts (not code since this appears to be a homework / project problem):
Compute with ints not longs (int is good enough to hold 2 000 000) Though your sum of primes may need to be a long
Only check odd numbers starting with 3 in your loop (why check an even number that you know is not prime>!
Ensure you use minimum code possible .. there are lots of structures in your code. Wouldn't a simple loop over int values (and checking primes you have found only up to the sqrt of your value?)
And don't use square root function. Instead calculate the index of the largest prime you have to check as prime[p]*prime[p] so long as that is greater than your trial value. For example use something like this in your code (where ps is the index of the first prime you are checking, and iMax is primes[ps]*primes[ps] before you get into the loop. For efficiency of time, always use "strengh reductions" of calculations when you can.
while (i > iMax) {
ps++; iMax = primes[ps]*primes[ps];
};

Related

Display Prime numbers with MVC Java

I have a Prime class which extends JFrame and it has a simple JSpinner for displaying prime numbers.
I want to create a Model for displaying prime numbers infinitely (until long ends). Here is the model class I have written:
public class PrimeSpinnerModel extends AbstractSpinnerModel{
long current;
public PrimeSpinnerModel() {
this.current = 2;
}
#Override
public Object getValue() {
return current;
}
#Override
public Object getNextValue() {
long newLatest = current + 1;
if(isPrime(newLatest)){
current = newLatest;
}else{
System.out.println(newLatest + "no prime");
newLatest ++;
current = newLatest;
}
fireStateChanged();
return getValue();
}
#Override
public Object getPreviousValue() {
fireStateChanged();
return getValue(); // without this the component wouldn't know to update.
}
#Override
public void setValue(Object value) {
throw new IllegalArgumentException("Static spinner model Prime does not support editing.");
}
static boolean isPrime(long n) {
if (n == 1) return false;
for(long i = 2; i <= n/2; i++)
if(n % i == 0)
return false;
return true;
}
}
When I run the code, it displays prime numbers as 2,3,5,7,9,11,13 etc.
Why 9 is displayed?

Suppose current is 7 when getNextValue() is invoked. Then newLatest is set to 8. isPrime(8) is obviously false, so then you increment newLatest, making it 9. You assign it to current and return it. This makes 9 the next number after seven, regardless of whether it is prime or not.
To solve this, you should increment newLatest while it is not prime (in a loop). That way, you ensure you continue until you found a prime number. See below:
newLatest = current + 1;
while (!isPrime(newLatest)) {
newLatest++;
}
// newLatest now contains the new prime number.
current = newLatest;
// etc..

Try this :)
public static void showPrimeNumbers(int n){
for (int i=2; i<n; i++){
boolean istrue = true;
for (int x=2; x<i; x++){
if (i%x==0){
istrue = false;
}
}
if (istrue==true){
System.out.println(i);
}
}
}

How to implement a Least Frequently Used (LFU) cache?

Least Frequently Used (LFU) is a type of cache algorithm used to manage memory within a computer. The standard characteristics of this method involve the system keeping track of the number of times a block is referenced in memory. When the cache is full and requires more room the system will purge the item with the lowest reference frequency.
What would be the best way to implement a most-recently-used cache of objects, say in Java?
I've already implemented one using LinkedHashMap(by maintaining the no. of times objects are accessed) But I'm curious if any of the new concurrent collections would be better candidates.
Consider this case : Suppose cache is full and we need to make space for another one. Say two objects are noted in cache which are accessed for one time only. Which one to remove if we come to know that other(which is not in cache)object is being accessed for more than once ?
Thanks!

You might benefit from the LFU implementation of ActiveMQ: LFUCache
They have provided some good functionality.

I think, the LFU data structure must combine priority queue (for maintaining fast access to lfu item) and hash map (for providing fast access to any item by its key); I would suggest the following node definition for each object stored in cache:
class Node<T> {
// access key
private int key;
// counter of accesses
private int numAccesses;
// current position in pq
private int currentPos;
// item itself
private T item;
//getters, setters, constructors go here
}
You need key for referring to an item.
You need numAccesses as a key for priority queue.
You need currentPos to be able to quickly find a pq position of item by key.
Now you organize hash map (key(Integer) -> node(Node<T>)) to quickly access items and min heap-based priority queue using number of accesses as priority. Now you can very quickly perform all operations (access, add new item, update number of acceses, remove lfu). You need to write each operation carefully, so that it maintains all the nodes consistent (their number of accesses, their position in pq and there existence in hash map). All operations will work with constant average time complexity which is what you expect from cache.

According to me, the best way to implement a most-recently-used cache of objects would be to include a new variable as 'latestTS' for each object. TS stands for timestamp.
// A static method that returns the current date and time as milliseconds since January 1st 1970
long latestTS = System.currentTimeMillis();
ConcurrentLinkedHashMap is not yet implemented in Concurrent Java Collections.
(Ref: Java Concurrent Collection API). However, you can try and use ConcurrentHashMap and DoublyLinkedList
About the case to be considered: in such case, as I have said that you can declare latestTS variable, based upon the value of latestTS variable, you can remove an entry and add the new object. (Don't forget to update frequency and latestTS of the new object added)
As you have mentioned, you can use LinkedHashMap as it gives element access in O(1) and also, you get the order traversal.
Please, find the below code for LFU Cache:
(PS: The below code is the answer for the question in the title i.e. "How to implement LFU cache")
import java.util.LinkedHashMap;
import java.util.Map;
public class LFUCache {
class CacheEntry
{
private String data;
private int frequency;
// default constructor
private CacheEntry()
{}
public String getData() {
return data;
}
public void setData(String data) {
this.data = data;
}
public int getFrequency() {
return frequency;
}
public void setFrequency(int frequency) {
this.frequency = frequency;
}
}
private static int initialCapacity = 10;
private static LinkedHashMap<Integer, CacheEntry> cacheMap = new LinkedHashMap<Integer, CacheEntry>();
/* LinkedHashMap is used because it has features of both HashMap and LinkedList.
* Thus, we can get an entry in O(1) and also, we can iterate over it easily.
* */
public LFUCache(int initialCapacity)
{
this.initialCapacity = initialCapacity;
}
public void addCacheEntry(int key, String data)
{
if(!isFull())
{
CacheEntry temp = new CacheEntry();
temp.setData(data);
temp.setFrequency(0);
cacheMap.put(key, temp);
}
else
{
int entryKeyToBeRemoved = getLFUKey();
cacheMap.remove(entryKeyToBeRemoved);
CacheEntry temp = new CacheEntry();
temp.setData(data);
temp.setFrequency(0);
cacheMap.put(key, temp);
}
}
public int getLFUKey()
{
int key = 0;
int minFreq = Integer.MAX_VALUE;
for(Map.Entry<Integer, CacheEntry> entry : cacheMap.entrySet())
{
if(minFreq > entry.getValue().frequency)
{
key = entry.getKey();
minFreq = entry.getValue().frequency;
}
}
return key;
}
public String getCacheEntry(int key)
{
if(cacheMap.containsKey(key)) // cache hit
{
CacheEntry temp = cacheMap.get(key);
temp.frequency++;
cacheMap.put(key, temp);
return temp.data;
}
return null; // cache miss
}
public static boolean isFull()
{
if(cacheMap.size() == initialCapacity)
return true;
return false;
}
}

Here's the o(1) implementation for LFU - http://dhruvbird.com/lfu.pdf

I have tried to implement this below LFU cache implementation. Took reference from this -
LFU paper. My implementation is working nicely.
If anyone wants to provide any further suggestion to improve it again, please let me know.
import java.util.HashMap;
import java.util.Map;
import java.util.Objects;
import java.util.TreeMap;
public class LFUCacheImplementation {
private Map<Integer, Node> cache = new HashMap<>();
private Map<Integer, Integer> counts = new HashMap<>();
private TreeMap<Integer, DoublyLinkedList> frequencies = new TreeMap<>();
private final int CAPACITY;
public LFUCache(int capacity) {
this.CAPACITY = capacity;
}
public int get(int key) {
if (!cache.containsKey(key)) {
return -1;
}
Node node = cache.get(key);
int frequency = counts.get(key);
frequencies.get(frequency).remove(new Node(node.key(), node.value()));
removeFreq(frequency);
frequencies.computeIfAbsent(frequency + 1, k -> new DoublyLinkedList()).add(new Node(node.key(), node.value()));
counts.put(key, frequency + 1);
return cache.get(key).value();
}
public void set(int key, int value) {
if (!cache.containsKey(key)) {
Node node = new Node(key, value);
if (cache.size() == CAPACITY) {
int l_count = frequencies.firstKey();
Node deleteThisNode = frequencies.get(l_count).head();
frequencies.get(l_count).remove(deleteThisNode);
int deleteThisKey = deleteThisNode.key();
removeFreq(l_count);
cache.remove(deleteThisKey);
counts.remove(deleteThisKey);
}
cache.put(key, node);
counts.put(key, 1);
frequencies.computeIfAbsent(1, k -> new DoublyLinkedList()).add(node);
}
}
private void removeFreq(int frequency) {
if (frequencies.get(frequency).size() == 0) {
frequencies.remove(frequency);
}
}
public Map<Integer, Node> getCache() {
return cache;
}
public Map<Integer, Integer> getCounts() {
return counts;
}
public TreeMap<Integer, DoublyLinkedList> getFrequencies() {
return frequencies;
}
}
class Node {
private int key;
private int value;
private Node next;
private Node prev;
public Node(int key, int value) {
this.key = key;
this.value = value;
}
public Node getNext() {
return next;
}
public void setNext(Node next) {
this.next = next;
}
public Node getPrev() {
return prev;
}
public void setPrev(Node prev) {
this.prev = prev;
}
public int key() {
return key;
}
public int value() {
return value;
}
#Override
public boolean equals(Object o) {
if (this == o) return true;
if (!(o instanceof Node)) return false;
Node node = (Node) o;
return key == node.key &&
value == node.value;
}
#Override
public int hashCode() {
return Objects.hash(key, value);
}
#Override
public String toString() {
return "Node{" +
"key=" + key +
", value=" + value +
'}';
}
}
class DoublyLinkedList {
private int size;
private Node head;
private Node tail;
public void add(Node node) {
if (null == head) {
head = node;
} else {
tail.setNext(node);
node.setPrev(tail);
}
tail = node;
size++;
}
public void remove(Node node) {
if(null == head || null == node) {
return;
}
if(this.size() == 1 && head.equals(node)) {
head = null;
tail = null;
} else if (head.equals(node)) {
head = node.getNext();
head.setPrev(null);
} else if (tail.equals(node)) {
Node prevToTail = tail.getPrev();
prevToTail.setNext(null);
tail = prevToTail;
} else {
Node current = head.getNext();
while(!current.equals(tail)) {
if(current.equals(node)) {
Node prevToCurrent = current.getPrev();
Node nextToCurrent = current.getNext();
prevToCurrent.setNext(nextToCurrent);
nextToCurrent.setPrev(prevToCurrent);
break;
}
current = current.getNext();
}
}
size--;
}
public Node head() {
return head;
}
public int size() {
return size;
}
}
Client code to use the above cache implementation -
import java.util.Map;
public class Client {
public static void main(String[] args) {
Client client = new Client();
LFUCache cache = new LFUCache(4);
cache.set(11, function(11));
cache.set(12, function(12));
cache.set(13, function(13));
cache.set(14, function(14));
cache.set(15, function(15));
client.print(cache.getFrequencies());
cache.get(13);
cache.get(13);
cache.get(13);
cache.get(14);
cache.get(14);
cache.get(14);
cache.get(14);
client.print(cache.getCache());
client.print(cache.getCounts());
client.print(cache.getFrequencies());
}
public void print(Map<Integer, ? extends Object> map) {
for(Map.Entry<Integer, ? extends Object> entry : map.entrySet()) {
if(entry.getValue() instanceof Node) {
System.out.println("Cache Key => "+entry.getKey()+", Cache Value => "+((Node) entry.getValue()).toString());
} else if (entry.getValue() instanceof DoublyLinkedList) {
System.out.println("Frequency Key => "+entry.getKey()+" Frequency Values => [");
Node head = ((DoublyLinkedList) entry.getValue()).head();
while(null != head) {
System.out.println(head.toString());
head = head.getNext();
}
System.out.println(" ]");
} else {
System.out.println("Count Key => "+entry.getKey()+", Count Value => "+entry.getValue());
}
}
}
public static int function(int key) {
int prime = 31;
return key*prime;
}
}

How about a priority queue? You can keep elements sorted there with keys representing the frequency. Just update the object position in the queue after visiting it. You can update just from time to time for optimizing the performance (but reducing precision).

Many implementations I have seen have runtime complexity O(log(n)). This means, when the cache size is n, the time needed to insert/remove an element into/from chache is logarithmic. Such implementations use usually a min heap to maintain usage frequencies of elements. The root of the heap contains the element with lowest frequency, and can be accessed in O(1) time. But to maintain the heap property we have to move an element, every time it is used (and frequency is incremented) inside of the heap, to place it into proper position, or when we have to insert new element into the cache (and so put it into the heap).
But the runtime complexity can be reduced to O(1), when we maintain a hashmap (Java) or unordered_map (C++) with the element as key. Additinally we need two sorts of lists, frequency list and elements lists. The elements lists contain elements that have same frequency, and the frequency list contain the element lists.
frequency list
1 3 6 7
a k y x
c l z
m n
Here in the example we see the frequency list that has 4 elements (4 elements lists). The element list 1 contains elements (a,c,m), the elements list 3 contains elements (k, l, n) etc.
Now, when we use say element y, we have to increment its frequency and put it in the next list. Because the elements list with frequency 6 becomes empty, we delete it. The result is:
frequency list
1 3 7
a k y
c l x
m n z
We place the element y in the begin of the elements list 7. When we have to remove elements from the list later, we will start from the end (first z, then x and then y).
Now, when we use element n, we have to increment its frequency and put it into the new list, with frequencies 4:
frequency list
1 3 4 7
a k n y
c l x
m z
I hope the idea is clear. I provide now my C++ implementation of the LFU cache, and will add later a Java implementation.
The class has just 2 public methods, void set(key k, value v)
and bool get(key k, value &v). In the get method the value to retrieve will be set per reference when the element is found, in this case the method returns true. When the element is not found, the method returns false.
#include<unordered_map>
#include<list>
using namespace std;
typedef unsigned uint;
template<typename K, typename V = K>
struct Entry
{
K key;
V value;
};
template<typename K, typename V = K>
class LFUCache
{
typedef typename list<typename Entry<K, V>> ElementList;
typedef typename list <pair <uint, ElementList>> FrequencyList;
private:
unordered_map <K, pair<typename FrequencyList::iterator, typename ElementList::iterator>> cacheMap;
FrequencyList elements;
uint maxSize;
uint curSize;
void incrementFrequency(pair<typename FrequencyList::iterator, typename ElementList::iterator> p) {
if (p.first == prev(elements.end())) {
//frequency list contains single list with some frequency, create new list with incremented frequency (p.first->first + 1)
elements.push_back({ p.first->first + 1, { {p.second->key, p.second->value} } });
// erase and insert the key with new iterator pair
cacheMap[p.second->key] = { prev(elements.end()), prev(elements.end())->second.begin() };
}
else {
// there exist element(s) with higher frequency
auto pos = next(p.first);
if (p.first->first + 1 == pos->first)
// same frequency in the next list, add the element in the begin
pos->second.push_front({ p.second->key, p.second->value });
else
// insert new list before next list
pos = elements.insert(pos, { p.first->first + 1 , {{p.second->key, p.second->value}} });
// update cachMap iterators
cacheMap[p.second->key] = { pos, pos->second.begin() };
}
// if element list with old frequency contained this singe element, erase the list from frequency list
if (p.first->second.size() == 1)
elements.erase(p.first);
else
// erase only the element with updated frequency from the old list
p.first->second.erase(p.second);
}
void eraseOldElement() {
if (elements.size() > 0) {
auto key = prev(elements.begin()->second.end())->key;
if (elements.begin()->second.size() < 2)
elements.erase(elements.begin());
else
elements.begin()->second.erase(prev(elements.begin()->second.end()));
cacheMap.erase(key);
curSize--;
}
}
public:
LFUCache(uint size) {
if (size > 0)
maxSize = size;
else
maxSize = 10;
curSize = 0;
}
void set(K key, V value) {
auto entry = cacheMap.find(key);
if (entry == cacheMap.end()) {
if (curSize == maxSize)
eraseOldElement();
if (elements.begin() == elements.end()) {
elements.push_front({ 1, { {key, value} } });
}
else if (elements.begin()->first == 1) {
elements.begin()->second.push_front({ key,value });
}
else {
elements.push_front({ 1, { {key, value} } });
}
cacheMap.insert({ key, {elements.begin(), elements.begin()->second.begin()} });
curSize++;
}
else {
entry->second.second->value = value;
incrementFrequency(entry->second);
}
}
bool get(K key, V &value) {
auto entry = cacheMap.find(key);
if (entry == cacheMap.end())
return false;
value = entry->second.second->value;
incrementFrequency(entry->second);
return true;
}
};
Here are examples of usage:
int main()
{
LFUCache<int>cache(3); // cache of size 3
cache.set(1, 1);
cache.set(2, 2);
cache.set(3, 3);
cache.set(2, 4);
rc = cache.get(1, r);
assert(rc);
assert(r == 1);
// evict old element, in this case 3
cache.set(4, 5);
rc = cache.get(3, r);
assert(!rc);
rc = cache.get(4, r);
assert(rc);
assert(r == 5);
LFUCache<int, string>cache2(2);
cache2.set(1, "one");
cache2.set(2, "two");
string val;
rc = cache2.get(1, val);
if (rc)
assert(val == "one");
else
assert(false);
cache2.set(3, "three"); // evict 2
rc = cache2.get(2, val);
assert(rc == false);
rc = cache2.get(3, val);
assert(rc);
assert(val == "three");
}

Here is a simple implementation of LFU cache in Go/Golang based on here.
import "container/list"
type LFU struct {
cache map[int]*list.Element
freqQueue map[int]*list.List
cap int
maxFreq int
lowestFreq int
}
type entry struct {
key, val int
freq int
}
func NewLFU(capacity int) *LFU {
return &LFU{
cache: make(map[int]*list.Element),
freqQueue: make(map[int]*list.List),
cap: capacity,
maxFreq: capacity - 1,
lowestFreq: 0,
}
}
// O(1)
func (c *LFU) Get(key int) int {
if e, ok := c.cache[key]; ok {
val := e.Value.(*entry).val
c.updateEntry(e, val)
return val
}
return -1
}
// O(1)
func (c *LFU) Put(key int, value int) {
if e, ok := c.cache[key]; ok {
c.updateEntry(e, value)
} else {
if len(c.cache) == c.cap {
c.evict()
}
if c.freqQueue[0] == nil {
c.freqQueue[0] = list.New()
}
e := c.freqQueue[0].PushFront(&entry{key, value, 0})
c.cache[key] = e
c.lowestFreq = 0
}
}
func (c *LFU) updateEntry(e *list.Element, val int) {
key := e.Value.(*entry).key
curFreq := e.Value.(*entry).freq
c.freqQueue[curFreq].Remove(e)
delete(c.cache, key)
nextFreq := curFreq + 1
if nextFreq > c.maxFreq {
nextFreq = c.maxFreq
}
if c.lowestFreq == curFreq && c.freqQueue[curFreq].Len() == 0 {
c.lowestFreq = nextFreq
}
if c.freqQueue[nextFreq] == nil {
c.freqQueue[nextFreq] = list.New()
}
newE := c.freqQueue[nextFreq].PushFront(&entry{key, val, nextFreq})
c.cache[key] = newE
}
func (c *LFU) evict() {
back := c.freqQueue[c.lowestFreq].Back()
delete(c.cache, back.Value.(*entry).key)
c.freqQueue[c.lowestFreq].Remove(back)
}

While loop not working properly for even/idd numbers

My assignment is to create a method that returns true if my set of integers has an un-even number in it. My problem is that even if I have un-even numbers in my set, it will return false. Where am i making a mistake?
import java.util.*;
public class Chapter_11_E9
{
public static boolean odd;
public static boolean hasOdd(Set<Integer> tSet1)
{
Iterator<Integer> itr = tSet1.iterator();
while (itr.hasNext())
{
int current = itr.next();
if ((current / 2) == 1)
{
odd = true;
}
else
{
odd = false;
}
}
return odd;
}
public static void main(String[] args)
{
Set<Integer> tSet1 = new HashSet<Integer>();
tSet1.add(6);
tSet1.add(2);
tSet1.add(5);
tSet1.add(4);
tSet1.add(12);
tSet1.add(6);
tSet1.add(14);
System.out.println(tSet1);
System.out.println(hasOdd(tSet1));
}
}

This line:
if ((current / 2) == 1)
Should be:
if ((current % 2) == 1)
/ is for diving and % is for getting the remainder. Also, your method logic is off. Say you have multiple odds and evens in the set, you could get wrong results. I'd recommend doing this:
public static boolean hasOdd(Set<Integer> tSet1)
{
Iterator<Integer> itr = tSet1.iterator();
while (itr.hasNext())
{
int current = itr.next();
if ((current % 2) == 1)
{
//odd = true;
return true;//once you find the odd, just return
}
//else
//{
// odd = false;
//}
}
//return odd;
//if you're done iterating, that means it never found an odd so return false
return false;
}
Once you've found one odd in the set, then your case is true. Once you've looped through the entire set, then you know there could haven't been any odds, so just return false.
Note: As user ZouZou mentioned, if you want to handle negatives, then use:
current%2 != 0

You need to return the first time you get an odd value (as is you'll only return true if the last item is odd).
public static boolean hasOdd(Set<Integer> tSet1)
{
Iterator<Integer> itr = tSet1.iterator();
while (itr.hasNext())
{
int current = itr.next();
if ((current % 2) != 0) // Check for odd, by computing the modulo 2 of the #.
{
return true; // stop, there is an odd value.
}
}
return false; // checked all the values... none are odd.
}

I would use for-each loop here
public static boolean hasOdd(Set<Integer> set) {
for (Integer current : set) {
// Check for odd, by computing the modulo 2 of the #.
if ((current % 2) != 0) {
// stop, there is an odd value.
return true;
}
}
// checked all the values... none are odd.
return false;
}

how to do this java recursive

I have this code:
static int countStu = 0;
public static int countStudent(Node<Student> lst) {
// pre : true
// post : res = number of students in list
if (lst != null) {
countStu++;
countStudent(lst.getNext());
}
return countStu;
}
The problem with this method is I must declare countStu outside the countStudent() method, which is not good in the case when I want to call countStudent() twice, it will make the returned value doubles. How do I solve this problem and able to call countStudent() unlimited times with correct results?

instead, return((lst == null)? 0 : (1 + countStudent(lst.getNext()))).

Change:
if(lst!=null){
countStu++;
countStudent(lst.getNext());
}
return countStu;
to
return lst==null ? 0 : (1+countStudent(lst.getNext()));

Assuming that this is your homework and you really must declare countStu outside (you shouldn't in any normal code), you can simply wrap the value in some class. Add set+get accessors and pass the object as a second argument to the function. Use it then, instead of the global / static variable.
Or simply don't use the variable at all and return the result + 1. Not sure if this is allowed by your rules.

In general when you are trying to do something like is useful to try to remove the explicit state handling somehow.
For example if you have to compute a function f(x) = G(f(x-1)) you can express G as a stateless method and follow the following pattern:
public static ResultType G(ResultType input) {
// compute G stateless
}
public static ResultType F(int x) {
return G(F(x - 1));
}
That way you don't have any side effects like you have with your current code. The downside is usually minor compared with what you are doing right now (the same stack depth is used overall).
The important thing is to make sure the G and F implementations are stateless (not using variables declared outside the method body scope).

Holding the state of the recursion in the static field would not be thread-safe. Instead hold the value in the stack.
I give you both a recursive example which would risk a StackOverflowError with as little as 6k nodes with a default heap as well as a loop version which doesn't suffer from this.
public class SO3765757 {
public static int countNodeRecursive(Node<?> node) {
if(node == null) {
debug("node is null");
return 0;
}
int count = 1 + countNodeRecursive(node.getNext());
debug(count + " = " + node.toString());
return count;
}
public static int countNodeLoop(Node<?> node) {
int count = 0;
for(Node<?> currentNode = node; currentNode != null; currentNode = currentNode.getNext()) {
count += 1;
debug(count + " = " + currentNode.toString());
}
return count;
}
public static void main(String[] args) {
int count = 10;
if(args.length > 0) {
try {
count = Integer.parseInt(args[0]);
} catch(NumberFormatException e) {
}
}
Node<Student> node = getNodeTest(count);
System.out.println("Loop count = " + countNodeLoop(node));
try {
System.out.println("Recursive count = " + countNodeRecursive(node));
} catch(StackOverflowError e) {
System.out.println("Recursive count caused " + e.getClass().getName());
}
}
private static void debug(String msg) {
System.out.println("DEBUG:" + msg);
}
private static <T> Node<T> getNodeTest(int count) {
Node<T> prevNode = null;
for(int i=0;i<count;i++) {
Node<T> node;
if(prevNode == null) {
node = new NodeImpl<T>();
} else {
node = new NodeImpl<T>(prevNode);
}
prevNode = node;
}
return prevNode;
}
private static interface Node<T> {
Node<T> getNext();
}
private static class NodeImpl<T> implements Node<T> {
private final Node<T> next;
public NodeImpl() {
this.next = null;
}
public NodeImpl(Node<T> next) {
this.next = next;
}
public Node<T> getNext() {
return next;
}
}
private static interface Student {
}
}

countStudent(lst.getNext());
why do i need to call again this , if lst.getNext() has null. precompute before calling recursion, there are different types.when u call this method countStudent from main method , check the lst value for not null , before recursion starts.
public static int
countStudent(Node lst) {
countStu++;
Node<Student> _tmp;
_tmp = lst.getNext();
if (_tmp != null )
countStudent(lst.getNext());
return countStu; }

Where do I find a standard Trie based map implementation in Java? [closed]

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I have a Java program that stores a lot of mappings from Strings to various objects.
Right now, my options are either to rely on hashing (via HashMap) or on binary searches (via TreeMap). I am wondering if there is an efficient and standard trie-based map implementation in a popular and quality collections library?
I've written my own in the past, but I'd rather go with something standard, if available.
Quick clarification: While my question is general, in the current project I am dealing with a lot of data that is indexed by fully-qualified class name or method signature. Thus, there are many shared prefixes.

You might want to look at the Trie implementation that Limewire is contributing to the Google Guava.

There is no trie data structure in the core Java libraries.
This may be because tries are usually designed to store character strings, while Java data structures are more general, usually holding any Object (defining equality and a hash operation), though they are sometimes limited to Comparable objects (defining an order). There's no common abstraction for "a sequence of symbols," although CharSequence is suitable for character strings, and I suppose you could do something with Iterable for other types of symbols.
Here's another point to consider: when trying to implement a conventional trie in Java, you are quickly confronted with the fact that Java supports Unicode. To have any sort of space efficiency, you have to restrict the strings in your trie to some subset of symbols, or abandon the conventional approach of storing child nodes in an array indexed by symbol. This might be another reason why tries are not considered general-purpose enough for inclusion in the core library, and something to watch out for if you implement your own or use a third-party library.

Apache Commons Collections v4.0 now supports trie structures.
See the org.apache.commons.collections4.trie package info for more information. In particular, check the PatriciaTrie class:
Implementation of a PATRICIA Trie (Practical Algorithm to Retrieve Information Coded in Alphanumeric).
A PATRICIA Trie is a compressed Trie. Instead of storing all data at the edges of the Trie (and having empty internal nodes), PATRICIA stores data in every node. This allows for very efficient traversal, insert, delete, predecessor, successor, prefix, range, and select(Object) operations. All operations are performed at worst in O(K) time, where K is the number of bits in the largest item in the tree. In practice, operations actually take O(A(K)) time, where A(K) is the average number of bits of all items in the tree.

Also check out concurrent-trees. They support both Radix and Suffix trees and are designed for high concurrency environments.

I wrote and published a simple and fast implementation here.

What you need is org.apache.commons.collections.FastTreeMap , I think.

Below is a basic HashMap implementation of a Trie. Some people might find this useful...
class Trie {
HashMap<Character, HashMap> root;
public Trie() {
root = new HashMap<Character, HashMap>();
}
public void addWord(String word) {
HashMap<Character, HashMap> node = root;
for (int i = 0; i < word.length(); i++) {
Character currentLetter = word.charAt(i);
if (node.containsKey(currentLetter) == false) {
node.put(currentLetter, new HashMap<Character, HashMap>());
}
node = node.get(currentLetter);
}
}
public boolean containsPrefix(String word) {
HashMap<Character, HashMap> node = root;
for (int i = 0; i < word.length(); i++) {
Character currentLetter = word.charAt(i);
if (node.containsKey(currentLetter)) {
node = node.get(currentLetter);
} else {
return false;
}
}
return true;
}
}

Apache's commons collections:
org.apache.commons.collections4.trie.PatriciaTrie

You can try the Completely Java library, it features a PatriciaTrie implementation. The API is small and easy to get started, and it's available in the Maven central repository.

You might look at this TopCoder one as well (registration required...).

If you required sorted map, then tries are worthwhile.
If you don't then hashmap is better.
Hashmap with string keys can be improved over the standard Java implementation:
Array hash map

If you're not worried about pulling in the Scala library, you can use this space efficient implementation I wrote of a burst trie.
https://github.com/nbauernfeind/scala-burst-trie

here is my implementation, enjoy it via: GitHub - MyTrie.java
/* usage:
MyTrie trie = new MyTrie();
trie.insert("abcde");
trie.insert("abc");
trie.insert("sadas");
trie.insert("abc");
trie.insert("wqwqd");
System.out.println(trie.contains("abc"));
System.out.println(trie.contains("abcd"));
System.out.println(trie.contains("abcdefg"));
System.out.println(trie.contains("ab"));
System.out.println(trie.getWordCount("abc"));
System.out.println(trie.getAllDistinctWords());
*/
import java.util.*;
public class MyTrie {
private class Node {
public int[] next = new int[26];
public int wordCount;
public Node() {
for(int i=0;i<26;i++) {
next[i] = NULL;
}
wordCount = 0;
}
}
private int curr;
private Node[] nodes;
private List<String> allDistinctWords;
public final static int NULL = -1;
public MyTrie() {
nodes = new Node[100000];
nodes[0] = new Node();
curr = 1;
}
private int getIndex(char c) {
return (int)(c - 'a');
}
private void depthSearchWord(int x, String currWord) {
for(int i=0;i<26;i++) {
int p = nodes[x].next[i];
if(p != NULL) {
String word = currWord + (char)(i + 'a');
if(nodes[p].wordCount > 0) {
allDistinctWords.add(word);
}
depthSearchWord(p, word);
}
}
}
public List<String> getAllDistinctWords() {
allDistinctWords = new ArrayList<String>();
depthSearchWord(0, "");
return allDistinctWords;
}
public int getWordCount(String str) {
int len = str.length();
int p = 0;
for(int i=0;i<len;i++) {
int j = getIndex(str.charAt(i));
if(nodes[p].next[j] == NULL) {
return 0;
}
p = nodes[p].next[j];
}
return nodes[p].wordCount;
}
public boolean contains(String str) {
int len = str.length();
int p = 0;
for(int i=0;i<len;i++) {
int j = getIndex(str.charAt(i));
if(nodes[p].next[j] == NULL) {
return false;
}
p = nodes[p].next[j];
}
return nodes[p].wordCount > 0;
}
public void insert(String str) {
int len = str.length();
int p = 0;
for(int i=0;i<len;i++) {
int j = getIndex(str.charAt(i));
if(nodes[p].next[j] == NULL) {
nodes[curr] = new Node();
nodes[p].next[j] = curr;
curr++;
}
p = nodes[p].next[j];
}
nodes[p].wordCount++;
}
}

I have just tried my own Concurrent TRIE implementation but not based on characters, it is based on HashCode. Still We can use this having Map of Map for each CHAR hascode.
You can test this using the code # https://github.com/skanagavelu/TrieHashMap/blob/master/src/TrieMapPerformanceTest.java
https://github.com/skanagavelu/TrieHashMap/blob/master/src/TrieMapValidationTest.java
import java.util.concurrent.atomic.AtomicReferenceArray;
public class TrieMap {
public static int SIZEOFEDGE = 4;
public static int OSIZE = 5000;
}
abstract class Node {
public Node getLink(String key, int hash, int level){
throw new UnsupportedOperationException();
}
public Node createLink(int hash, int level, String key, String val) {
throw new UnsupportedOperationException();
}
public Node removeLink(String key, int hash, int level){
throw new UnsupportedOperationException();
}
}
class Vertex extends Node {
String key;
volatile String val;
volatile Vertex next;
public Vertex(String key, String val) {
this.key = key;
this.val = val;
}
#Override
public boolean equals(Object obj) {
Vertex v = (Vertex) obj;
return this.key.equals(v.key);
}
#Override
public int hashCode() {
return key.hashCode();
}
#Override
public String toString() {
return key +"#"+key.hashCode();
}
}
class Edge extends Node {
volatile AtomicReferenceArray<Node> array; //This is needed to ensure array elements are volatile
public Edge(int size) {
array = new AtomicReferenceArray<Node>(8);
}
#Override
public Node getLink(String key, int hash, int level){
int index = Base10ToBaseX.getBaseXValueOnAtLevel(Base10ToBaseX.Base.BASE8, hash, level);
Node returnVal = array.get(index);
for(;;) {
if(returnVal == null) {
return null;
}
else if((returnVal instanceof Vertex)) {
Vertex node = (Vertex) returnVal;
for(;node != null; node = node.next) {
if(node.key.equals(key)) {
return node;
}
}
return null;
} else { //instanceof Edge
level = level + 1;
index = Base10ToBaseX.getBaseXValueOnAtLevel(Base10ToBaseX.Base.BASE8, hash, level);
Edge e = (Edge) returnVal;
returnVal = e.array.get(index);
}
}
}
#Override
public Node createLink(int hash, int level, String key, String val) { //Remove size
for(;;) { //Repeat the work on the current node, since some other thread modified this node
int index = Base10ToBaseX.getBaseXValueOnAtLevel(Base10ToBaseX.Base.BASE8, hash, level);
Node nodeAtIndex = array.get(index);
if ( nodeAtIndex == null) {
Vertex newV = new Vertex(key, val);
boolean result = array.compareAndSet(index, null, newV);
if(result == Boolean.TRUE) {
return newV;
}
//continue; since new node is inserted by other thread, hence repeat it.
}
else if(nodeAtIndex instanceof Vertex) {
Vertex vrtexAtIndex = (Vertex) nodeAtIndex;
int newIndex = Base10ToBaseX.getBaseXValueOnAtLevel(Base10ToBaseX.Base.BASE8, vrtexAtIndex.hashCode(), level+1);
int newIndex1 = Base10ToBaseX.getBaseXValueOnAtLevel(Base10ToBaseX.Base.BASE8, hash, level+1);
Edge edge = new Edge(Base10ToBaseX.Base.BASE8.getLevelZeroMask()+1);
if(newIndex != newIndex1) {
Vertex newV = new Vertex(key, val);
edge.array.set(newIndex, vrtexAtIndex);
edge.array.set(newIndex1, newV);
boolean result = array.compareAndSet(index, vrtexAtIndex, edge); //REPLACE vertex to edge
if(result == Boolean.TRUE) {
return newV;
}
//continue; since vrtexAtIndex may be removed or changed to Edge already.
} else if(vrtexAtIndex.key.hashCode() == hash) {//vrtex.hash == hash) { HERE newIndex == newIndex1
synchronized (vrtexAtIndex) {
boolean result = array.compareAndSet(index, vrtexAtIndex, vrtexAtIndex); //Double check this vertex is not removed.
if(result == Boolean.TRUE) {
Vertex prevV = vrtexAtIndex;
for(;vrtexAtIndex != null; vrtexAtIndex = vrtexAtIndex.next) {
prevV = vrtexAtIndex; // prevV is used to handle when vrtexAtIndex reached NULL
if(vrtexAtIndex.key.equals(key)){
vrtexAtIndex.val = val;
return vrtexAtIndex;
}
}
Vertex newV = new Vertex(key, val);
prevV.next = newV; // Within SYNCHRONIZATION since prevV.next may be added with some other.
return newV;
}
//Continue; vrtexAtIndex got changed
}
} else { //HERE newIndex == newIndex1 BUT vrtex.hash != hash
edge.array.set(newIndex, vrtexAtIndex);
boolean result = array.compareAndSet(index, vrtexAtIndex, edge); //REPLACE vertex to edge
if(result == Boolean.TRUE) {
return edge.createLink(hash, (level + 1), key, val);
}
}
}
else { //instanceof Edge
return nodeAtIndex.createLink(hash, (level + 1), key, val);
}
}
}
#Override
public Node removeLink(String key, int hash, int level){
for(;;) {
int index = Base10ToBaseX.getBaseXValueOnAtLevel(Base10ToBaseX.Base.BASE8, hash, level);
Node returnVal = array.get(index);
if(returnVal == null) {
return null;
}
else if((returnVal instanceof Vertex)) {
synchronized (returnVal) {
Vertex node = (Vertex) returnVal;
if(node.next == null) {
if(node.key.equals(key)) {
boolean result = array.compareAndSet(index, node, null);
if(result == Boolean.TRUE) {
return node;
}
continue; //Vertex may be changed to Edge
}
return null; //Nothing found; This is not the same vertex we are looking for. Here hashcode is same but key is different.
} else {
if(node.key.equals(key)) { //Removing the first node in the link
boolean result = array.compareAndSet(index, node, node.next);
if(result == Boolean.TRUE) {
return node;
}
continue; //Vertex(node) may be changed to Edge, so try again.
}
Vertex prevV = node; // prevV is used to handle when vrtexAtIndex is found and to be removed from its previous
node = node.next;
for(;node != null; prevV = node, node = node.next) {
if(node.key.equals(key)) {
prevV.next = node.next; //Removing other than first node in the link
return node;
}
}
return null; //Nothing found in the linked list.
}
}
} else { //instanceof Edge
return returnVal.removeLink(key, hash, (level + 1));
}
}
}
}
class Base10ToBaseX {
public static enum Base {
/**
* Integer is represented in 32 bit in 32 bit machine.
* There we can split this integer no of bits into multiples of 1,2,4,8,16 bits
*/
BASE2(1,1,32), BASE4(3,2,16), BASE8(7,3,11)/* OCTAL*/, /*BASE10(3,2),*/
BASE16(15, 4, 8){
public String getFormattedValue(int val){
switch(val) {
case 10:
return "A";
case 11:
return "B";
case 12:
return "C";
case 13:
return "D";
case 14:
return "E";
case 15:
return "F";
default:
return "" + val;
}
}
}, /*BASE32(31,5,1),*/ BASE256(255, 8, 4), /*BASE512(511,9),*/ Base65536(65535, 16, 2);
private int LEVEL_0_MASK;
private int LEVEL_1_ROTATION;
private int MAX_ROTATION;
Base(int levelZeroMask, int levelOneRotation, int maxPossibleRotation) {
this.LEVEL_0_MASK = levelZeroMask;
this.LEVEL_1_ROTATION = levelOneRotation;
this.MAX_ROTATION = maxPossibleRotation;
}
int getLevelZeroMask(){
return LEVEL_0_MASK;
}
int getLevelOneRotation(){
return LEVEL_1_ROTATION;
}
int getMaxRotation(){
return MAX_ROTATION;
}
String getFormattedValue(int val){
return "" + val;
}
}
public static int getBaseXValueOnAtLevel(Base base, int on, int level) {
if(level > base.getMaxRotation() || level < 1) {
return 0; //INVALID Input
}
int rotation = base.getLevelOneRotation();
int mask = base.getLevelZeroMask();
if(level > 1) {
rotation = (level-1) * rotation;
mask = mask << rotation;
} else {
rotation = 0;
}
return (on & mask) >>> rotation;
}
}

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Where do I find a standard Trie based map implementation in Java? [closed]

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