As a Rags-to-Riches re-implementation of the Karp-Rabin question here, from @tsleyson, I thought it would be interesting to understand the algorithm better, and to use bitwise-shifting instead of prime multiplication, to compute the rolling hash.
Karp-Rabin is a text-search algorithm that computes a rolling hash of the current text, and only if the hash matches the search terms, does it double-check that the search actually matches. Because the hask is computed in \$O(1)\$ time for each advance through the text, it is essentially an \$O(n)\$ search algorithm with an additional \$O(m)\$ confirmation check for each potential match that is found. The best case is \$O(n)\$ for no matches, and worst case is \$O(nm)\$ if everything matches (for example, searching for "aa" in "aaaaaaaaaaaaa").
The description for the algorithm suggests using a 'base' prime number as the foundation for a hashing algorithm that allows you to 'rotate' an out-of-scope character off of the hash, and then add a new member to it. As you 'roll' the hash down the search text, if the hash matches the hash of your pattern, you can then double-check to see if it is just a collision, or a real match.
Instead of using a system requiring repeated multiplications by prime numbers, I decided to use a computationally simpler bit-shifting algorithm (though the logic may be more complicated, the computations should be simpler). In essence, the algorithm I use uses a long value (64-bit) as a hash, and it rotates characters on to the hash, and uses XOR bitwise logic to 'add' or 'remove' the characters.
I am looking for a review of the usability, and any performance suggestions.
import java.util.Arrays;
public class KarpRabin {
private static final int[] EMPTY = new int[0];
// Use a prime number as the shift, which means it takes a while to reuse
// bit positions in the hash. Creates a good hash distribution in the
// long bit mask.
private static final int SHIFT = 31;
// bits in the long hash value
private static final int BITS = 64;
private final char[] patternChars, textChars;
private final long patternHash;
private final int size;
private final int endPosition;
private final int tailLeftShift, tailRightShift;
private long textHash = 0L;
private int pos = 0;
/*
* Private constructor, only accessible from public static entry methods.
*/
private KarpRabin(String pattern, String text) {
// inputs pre-validated.
// inputs not null, not empty, and pattern is shorter than text.
patternChars = pattern.toCharArray();
textChars = text.toCharArray();
size = this.patternChars.length;
endPosition = this.textChars.length - size;
// calculate the bit-shifts needed to remove the tail of the search
// since the hash values are 'rotated' each time, we can count the
// rotations that will be needed (size - 1) before a char is at the
// tail, and that will be the relative left/right shifts needed.
// note that left and right shifts perform a 'rotation' when combined,
// essentially rotating the character by tailLeft bits to the left.
tailLeftShift = ((size - 1) * SHIFT) % BITS;
tailRightShift = BITS - tailLeftShift;
// Compute the hash of the pattern
// use a temp variable ph because patternHash is final
long ph = 0L;
for (char c : this.patternChars) {
ph = addHash(ph, c);
}
patternHash = ph;
// seed the hash for the first X chars in the text to search.
textHash = 0L;
for (int i = 0; i < size; i++) {
textHash = addHash(textHash, this.textChars[i]);
}
// advance the search to the first 'hit' (if there is one).
// check to make sure we are not already at a match first.
if (textHash != patternHash || !confirmed()) {
advance();
}
}
/*
* Shift the existing hash, and XOR in the new char.
*/
private final long addHash(long base, char c) {
return (base << SHIFT | base >>> (BITS - SHIFT)) ^ c;
}
/*
* Shift the char to remove to the place it would be at the tail, and XOR it
* off.
*/
private final long removeHash(long base, char c) {
long ch = c;
// this is essentially a rotation in a 64-bit space.
ch = ch << tailLeftShift | ch >>> tailRightShift;
return base ^ ch;
}
/*
* Return the next match, and advance the search if needed.
*/
private int next() {
if (pos > endPosition) {
return -1;
}
int ret = pos;
advance();
return ret;
}
/*
* move the position to the next 'hit', or the end of the search text.
*/
private void advance() {
while (++pos <= endPosition) {
// remove the tail char from the hash
textHash = removeHash(textHash, textChars[pos - 1]);
// add in the new head char
textHash = addHash(textHash, textChars[pos + size - 1]);
// check to see if we have a match.
if (textHash == patternHash && confirmed()) {
return;
}
}
}
/*
* Double-check a hash-hit. Assumes the hash computes are equal.
*/
private boolean confirmed() {
for (int i = 0; i < size; i++) {
if (patternChars[i] != textChars[pos + i]) {
return false;
}
}
return true;
}
/**
* Identify whether the pattern appears in the given text.
* <p>
* Null input values will return false, and an empty-string pattern will
* return true (the empty string is a match in any other non-null string)
*
* @param pattern
* the pattern to search for
* @param text
* the text to search in
* @return true if the pattern exists in the text.
*/
public static boolean match(final String pattern, final String text) {
if (pattern == null || text == null || pattern.length() > text.length()) {
return false;
}
if (pattern.isEmpty()) {
return true;
}
KarpRabin kr = new KarpRabin(pattern, text);
return kr.next() >= 0;
}
/**
* Identify all locations where the pattern exists in the search text.
* <p>
* Null input values will result in no matches, and the empty search pattern
* will be located at all positions in the search text.
*
* @param pattern
* the pattern to search for
* @param text
* the text to search in
* @return the integer positions of each found occurrence. An empty array
* will be returned if there are no matches.
*/
public static int[] allMatches(String pattern, String text) {
if (pattern == null || text == null || pattern.length() > text.length()) {
return EMPTY;
}
if (pattern.isEmpty()) {
// empty string is found everywhere
int[] ret = new int[text.length()];
for (int i = 0; i < ret.length; i++) {
ret[i] = i;
}
return ret;
}
KarpRabin kr = new KarpRabin(pattern, text);
// guess the size first, and expand/trim it as needed.
int[] possibles = new int[text.length() / pattern.length()];
int count = 0;
int pos = 0;
while ((pos = kr.next()) >= 0) {
if (count >= possibles.length) {
// expand the matches (a lot). This indicates overlapping results.
// which is unusual.
possibles = Arrays.copyOf(possibles, possibles.length * 2 + 2);
}
possibles[count] = pos;
count++;
}
// trim the matches.
return Arrays.copyOf(possibles, count);
}
}
I have been testing it against some input data similar to @tsleyson's, and then added in a performance test too, that checks the scalability of a non-matching case.
private static final void scalabilityTests() {
String input = "abcdefghijklmnopquestuvwxyz";
long[] times = new long[9];
for (int i = 0; i < times.length; i++) {
int sum = 0;
long nanos = System.nanoTime();
for (int j = 0; j < 5000; j++) {
sum += allMatches("abcdefghx", input).length;
}
times[i] = sum + System.nanoTime() - nanos;
// double the times....
input += input;
}
System.out.println("Scalability (should double each time): " + Arrays.toString(times));
}
public static void main(String[] args) {
String[] inputs = { "", null, "abr", "abra", "abrabrabra",
"abracadabrabracadabra", "cabrac", "cabracadabrabracadabrac",
"abbbbrrraaabbbccrrabbba" };
for (String inp : inputs) {
int[] matches = allMatches("abra", inp);
System.out.printf("Matches %s %s at %s%n", inp, match("abra", inp), Arrays.toString(matches));
}
// run three times for warmups....
scalabilityTests();
scalabilityTests();
scalabilityTests();
}
