Related
I have an array of operations and a target number.
The operations could be
+ 3
- 3
* 4
/ 2
I want to find out how close I can get to the target number by using those operations.
I start from 0 and I need to iterate through the operations in that order, and I can choose to either use the operation or not use it.
So if the target number is 13, I can use + 3 and * 4 to get 12 which is the closest I can get to the target number 13.
I guess I need to compute all possible combinations (I guess the number of calculations is thus 2^n where n is the number of operations).
I have tried to do this in java with
import java.util.*;
public class Instruction {
public static void main(String[] args) {
// create scanner
Scanner sc = new Scanner(System.in);
// number of instructions
int N = sc.nextInt();
// target number
int K = sc.nextInt();
//
String[] instructions = new String[N];
// N instructions follow
for (int i=0; i<N; i++) {
//
instructions[i] = sc.nextLine();
}
//
System.out.println(search(instructions, 0, N, 0, K, 0, K));
}
public static int search(String[] instructions, int index, int length, int progressSoFar, int targetNumber, int bestTarget, int bestDistance) {
//
for (int i=index; i<length; i++) {
// get operator
char operator = instructions[i].charAt(0);
// get number
int number = Integer.parseInt(instructions[i].split("\\s+")[1]);
//
if (operator == '+') {
progressSoFar += number;
} else if (operator == '*') {
progressSoFar *= number;
} else if (operator == '-') {
progressSoFar -= number;
} else if (operator == '/') {
progressSoFar /= number;
}
//
int distance = Math.abs(targetNumber - progressSoFar);
// if the absolute distance between progress so far
// and the target number is less than what we have
// previously accomplished, we update best distance
if (distance < bestDistance) {
bestTarget = progressSoFar;
bestDistance = distance;
}
//
if (true) {
return bestTarget;
} else {
return search(instructions, index + 1, length, progressSoFar, targetNumber, bestTarget, bestDistance);
}
}
}
}
It doesn't work yet, but I guess I'm a little closer to solving my problem. I just don't know how to end my recursion.
But maybe I don't use recursion, but should instead just list all combinations. I just don't know how to do this.
If I, for instance, have 3 operations and I want to compute all combinations, I get the 2^3 combinations
111
110
101
011
000
001
010
100
where 1 indicates that the operation is used and 0 indicates that it is not used.
It should be rather simple to do this and then choose which combination gave the best result (the number closest to the target number), but I don't know how to do this in java.
In pseudocode, you could try brute-force back-tracking, as in:
// ops: list of ops that have not yet been tried out
// target: goal result
// currentOps: list of ops used so far
// best: reference to the best result achieved so far (can be altered; use
// an int[1], for example)
// opsForBest: list of ops used to achieve best result so far
test(ops, target, currentOps, best, opsForBest)
if ops is now empty,
current = evaluate(currentOps)
if current is closer to target than best,
best = current
opsForBest = a copy of currentOps
otherwise,
// try including next op
with the next operator in ops,
test(opsAfterNext, target,
currentOps concatenated with next, best, opsForBest)
// try *not* including next op
test(opsAfterNext, target, currentOps, best, opsForBest)
This is guaranteed to find the best answer. However, it will repeat many operations once and again. You can save some time by avoiding repeat calculations, which can be achieved using a cache of "how does this subexpression evaluate". When you include the cache, you enter the realm of "dynamic programming" (= reusing earlier results in later computation).
Edit: adding a more OO-ish variant
Variant returning the best result, and avoiding the use of that best[] array-of-one. Requires the use of an auxiliary class Answer with fields ops and result.
// ops: list of ops that have not yet been tried out
// target: goal result
// currentOps: list of ops used so far
Answer test(ops, target, currentOps, opsForBest)
if ops is now empty,
return new Answer(currentOps, evaluate(currentOps))
otherwise,
// try including next op
with the next operator in ops,
Answer withOp = test(opsAfterNext, target,
currentOps concatenated with next, best, opsForBest)
// try *not* including next op
Answer withoutOp = test(opsAfterNext, target,
currentOps, best, opsForBest)
if withOp.result closer to target than withoutOp.target,
return withOp
else
return withoutOp
Dynamic programming
If the target value is t, and there are n operations in the list, and the largest absolute value you can create by combining some subsequence of them is k, and the absolute value of the product of all values that appear as an operand of a division operation is d, then there's a simple O(dkn)-time and -space dynamic programming algorithm that determines whether it's possible to compute the value i using some subset of the first j operations and stores this answer (a single bit) in dp[i][j]:
dp[i][j] = dp[i][j-1] || dp[invOp(i, j)][j-1]
where invOp(i, j) computes the inverse of the jth operation on the value i. Note that if the jth operation is a multiplication by, say, x, and i is not divisible by x, then the operation is considered to have no inverse, and the term dp[invOp(i, j)][j-1] is deemed to evaluate to false. All other operations have unique inverses.
To avoid loss-of-precision problems with floating point code, first multiply the original target value t, as well as all operands to addition and subtraction operations, by d. This ensures that any division operation / x we encounter will only ever be applied to a value that is known to be divisible by x. We will essentially be working throughout with integer multiples of 1/d.
Because some operations (namely subtractions and divisions) require solving subproblems for higher target values, we cannot in general calculate dp[i][j] in a bottom-up way. Instead we can use memoisation of the top-down recursion, starting at the (scaled) target value t*d and working outwards in steps of 1 in each direction.
C++ implementation
I've implemented this in C++ at https://ideone.com/hU1Rpq. The "interesting" part is canReach(i, j); the functions preceding this are just plumbing to handle the memoisation table. Specify the inputs on stdin with the target value first, then a space-separated list of operations in which operators immediately preceed their operand values, e.g.
10 +8 +11 /2
or
10 +4000 +5500 /1000
The second example, which should give the same answer (9.5) as the first, seems to be around the ideone (and my) memory limits, although this could be extended somewhat by using long long int instead of int and a 2-bit table for _m[][][] instead of wasting a full byte on each entry.
Exponential worst-case time and space complexity
Note that in general, dk or even just k by itself could be exponential in the size of the input: e.g. if there is an addition, followed by n-1 multiplication operations, each of which involves a number larger than 1. It's not too difficult to compute k exactly via a different DP that simply looks for the largest and smallest numbers reachable using the first i operations for all 1 <= i <= n, but all we really need is an upper bound, and it's easy enough to get a (somewhat loose) one: simply discard the signs of all multiplication operands, convert all - operations to + operations, and then perform all multiplication and addition operations (i.e., ignoring divisions).
There are other optimisations that could be applied, for example dividing through by any common factor.
Here's a Java 8 example, using memoization. I wonder if annealing can be applied...
public class Tester {
public static interface Operation {
public int doOperation(int cur);
}
static Operation ops[] = { // lambdas for the opertions
(x -> x + 3),
(x -> x - 3),
(x -> x * 4),
(x -> x / 2),
};
private static int getTarget(){
return 2;
}
public static void main (String args[]){
int map[];
int val = 0;
int MAX_BITMASK = (1 << ops.length) - 1;//means ops.length < 31 [int overflow]
map = new int[MAX_BITMASK];
map[0] = val;
final int target = getTarget();// To get rid of dead code warning
int closest = val, delta = target < 0? -target: target;
int bestSeq = 0;
if (0 == target) {
System.out.println("Winning sequence: Do nothing");
}
int lastBitMask = 0, opIndex = 0;
int i = 0;
for (i = 1; i < MAX_BITMASK; i++){// brute force algo
val = map[i & lastBitMask]; // get prev memoized value
val = ops[opIndex].doOperation(val); // compute
map[i] = val; //add new memo
//the rest just logic to find the closest
// except the last part
int d = val - target;
d = d < 0? -d: d;
if (d < delta) {
bestSeq = i;
closest = val;
delta = d;
}
if (val == target){ // no point to continue
break;
}
//advance memo mask 0b001 to 0b011 to 0b111, etc.
// as well as the computing operation.
if ((i & (i + 1)) == 0){ // check for 2^n -1
lastBitMask = (lastBitMask << 1) + 1;
opIndex++;
}
}
System.out.println("Winning sequence: " + bestSeq);
System.out.println("Closest to \'" + target + "\' is: " + closest);
}
}
Worth noting, the "winning sequence" is the bit representation (displayed as decimal) of what was used and what wasn't, as the OP has done in the question.
For Those of you coming from Java 7, this is what I was referencing for lambdas: Lambda Expressionsin GUI Applications. So if you're constrained to 7, you can still make this work quite easily.
I have to replicate the luhn algorithm in Java, the problem I face is how to implement this in an efficient and elegant way (not a requirement but that is what I want).
The luhn-algorithm works like this:
You take a number, let's say 56789
loop over the next steps till there are no digits left
You pick the left-most digit and add it to the total sum. sum = 5
You discard this digit and go the next. number = 6789
You double this digit, if it's more than one digit you take apart this number and add them separately to the sum. 2*6 = 12, so sum = 5 + 1 = 6 and then sum = 6 + 2 = 8.
Addition restrictions
For this particular problem I was required to read all digits one at a time and do computations on each of them separately before moving on. I also assume that all numbers are positive.
The problems I face and the questions I have
As said before I try to solve this in an elegant and efficient way. That's why I don't want to invoke the toString() method on the number to access all individual digits which require a lot of converting. I also can't use the modulo kind of way because of the restriction above that states once I read a number I should also do computations on it right away. I could only use modulo if I knew in advance the length of the String, but that feels like I first have to count all digits one-for-once which thus is against the restriction. Now I can only think of one way to do this, but this would also require a lot of computations and only ever cares about the first digit*:
int firstDigit(int x) {
while (x > 9) {
x /= 10;
}
return x;
}
Found here: https://stackoverflow.com/a/2968068/3972558
*However, when I think about it, this is basically a different and weird way to make use of the length property of a number by dividing it as often till there is one digit left.
So basically I am stuck now and I think I must use the length property of a number which it does not really have, so I should find it by hand. Is there a good way to do this? Now I am thinking that I should use modulo in combination with the length of a number.
So that I know if the total number of digits is uneven or even and then I can do computations from right to left. Just for fun I think I could use this for efficiency to get the length of a number: https://stackoverflow.com/a/1308407/3972558
This question appeared in the book Think like a programmer.
You can optimise it by unrolling the loop once (or as many times are you like) This will be close to twice as fast for large numbers, however make small numbers slower. If you have an idea of the typical range of numbers you will have you can determine how much to unroll this loop.
int firstDigit(int x) {
while (x > 99)
x /= 100;
if (x > 9)
x /= 10;
return x;
}
use org.apache.commons.validator.routines.checkdigit.LuhnCheckDigit . isValid()
Maven Dependency:
<dependency>
<groupId>commons-validator</groupId>
<artifactId>commons-validator</artifactId>
<version>1.4.0</version>
</dependency>
Normally you would process the numbers from right to left using divide by 10 to shift the digits and modulo 10 to extract the last one. You can still use this technique when processing the numbers from left to right. Just use divide by 1000000000 to extract the first number and multiply by 10 to shift it left:
0000056789
0000567890
0005678900
0056789000
0567890000
5678900000
6789000000
7890000000
8900000000
9000000000
Some of those numbers exceed maximum value of int. If you have to support full range of input, you will have to store the number as long:
static int checksum(int x) {
long n = x;
int sum = 0;
while (n != 0) {
long d = 1000000000l;
int digit = (int) (n / d);
n %= d;
n *= 10l;
// add digit to sum
}
return sum;
}
As I understand, you will eventually need to read every digit, so what is wrong with convert initial number to string (and therefore char[]) and then you can easily implement the algorithm iterating that char array.
JDK implementation of Integer.toString is rather optimized so that you would need to implement your own optimalizations, e.g. it uses different lookup tables for optimized conversion, convert two chars at once etc.
final static int [] sizeTable = { 9, 99, 999, 9999, 99999, 999999, 9999999,
99999999, 999999999, Integer.MAX_VALUE };
// Requires positive x
static int stringSize(int x) {
for (int i=0; ; i++)
if (x <= sizeTable[i])
return i+1;
}
This was just an example but feel free to check complete implementation :)
I would first convert the number to a kind of BCD (binary coded decimal). I'm not sure to be able to find a better optimisation than the JDK Integer.toString() conversion method but as you said you did not want to use it :
List<Byte> bcd(int i) {
List<Byte> l = new ArrayList<Byte>(10); // max size for an integer to avoid reallocations
if (i == 0) {
l.add((byte) i);
}
else {
while (i != 0) {
l.add((byte) (i % 10));
i = i / 10;
}
}
return l;
}
It is more or less what you proposed to get first digit, but now you have all you digits in one single pass and can use them for your algorythm.
I proposed to use byte because it is enough, but as java always convert to int to do computations, it might be more efficient to directly use a List<Integer> even if it really wastes memory.
My question is if given an array,we have to split that into two sub-arrays such that the absolute difference between the sum of the two arrays is minimum with a condition that the difference between number of elements of the arrays should be atmost one.
Let me give you an example.Suppose
Example 1: 100 210 100 75 340
Answer :
Array1{100,210,100} and Array2{75,340} --> Difference = |410-415|=5
Example 2: 10 10 10 10 40
Answer : Array1{10,10,10} and Array2{10,40} --> Difference = |30-50|=20
Here we can see that though we can divide the array into {10,10,10,10} and {40},we are not dividing because the constraint "the number of elements between the arrays should be atmost 1" will be violated if we do so.
Can somebody provide a solution for this ?
My approach:
->Calculate sum of the array
->Divide the sum by 2
->Let the size of the knapsack=sum/2
->Consider the weights of the array values as 1.(If you have come across the knapsack problem ,you may know about the weight concept)
->Then consider the array values as the values of the weights.
->Calculate the answer which will be array1 sum.
->Total sum-answer=array2 sum
This approach fails.
Calculating the two arrays sum is enough.We are not interested in which elements form the sum.
Thank you!
Source: This is an ICPC problem.
I have an algorithm that works in O(n3) time, but I have no hard proof it is optimal. It seems to work for every test input I give it (including some with negative numbers), so I figured it was worth sharing.
You start by splitting the input into two equally sized arrays (call them one[] and two[]?). Start with one[0], and see which element in two[] would give you the best result if swapped. Whichever one gives the best result, swap. If none give a better result, don't swap it. Then move on to the next element in one[] and do it again.
That part is O(2) by itself. The problem is, it might not get the best results the first time through. If you just keep doing it until you don't make any more swaps, you end up with an ugly bubble-type construction which makes it O(n3) total.
Here's some ugly Java code to demonstrate (also at ideone.com if you want to play with it):
static int[] input = {1,2,3,4,5,-6,7,8,9,10,200,-1000,100,250,-720,1080,200,300,400,500,50,74};
public static void main(String[] args) {
int[] two = new int[input.length/2];
int[] one = new int[input.length - two.length];
int totalSum = 0;
for(int i=0;i<input.length;i++){
totalSum += input[i];
if(i<one.length)
one[i] = input[i];
else
two[i-one.length] = input[i];
}
float goal = totalSum / 2f;
boolean swapped;
do{
swapped = false;
for(int j=0;j<one.length;j++){
int curSum = sum(one);
float curBestDiff = Math.abs(goal - curSum);
int curBestIndex = -1;
for(int i=0;i<two.length;i++){
int testSum = curSum - one[j] + two[i];
float diff = Math.abs(goal - testSum);
if(diff < curBestDiff){
curBestDiff = diff;
curBestIndex = i;
}
}
if(curBestIndex >= 0){
swapped = true;
System.out.println("swapping " + one[j] + " and " + two[curBestIndex]);
int tmp = one[j];
one[j] = two[curBestIndex];
two[curBestIndex] = tmp;
}
}
} while(swapped);
System.out.println(Arrays.toString(one));
System.out.println(Arrays.toString(two));
System.out.println("diff = " + Math.abs(sum(one) - sum(two)));
}
static int sum(int[] list){
int sum = 0;
for(int i=0;i<list.length;i++)
sum += list[i];
return sum;
}
Can you provide more information on the upper limit of the input?
For your algorithm, I think your are trying to pick floor(n/2) items and find it's maximum sum of value as array1 sum...(If this is not your original thought then please ignore the following lines)
If this is the case, then knapsack size should be n/2 instead of sum/2,
but even so, I think it's still not working. The ans is min(|a - b|) and maximizing a is a different issue. For eg, {2,2,10,10}, you will get a = 20, b = 4, while the ans is a = b = 12.
To answer the problem, I think I need more information of the upper limit of the input..
I cannot come up with a brilliant dp state but a 3-dimensional state
dp(i,n,v) := in first i-th items, pick n items out and make a sum of value v
each state is either 0 or 1 (false or true)
dp(i,n,v) = dp(i-1, n, v) | dp(i-1, n-1, v-V[i])
This dp state is so naive that it has a really high complexity which usually cannot pass a ACM / ICPC problem, so if possible please provide more information and see if I can come up another better solution...Hope I can help a bit :)
DP soluction will give lg(n) time. Two array, iterate one from start to end, and calculate the sum, the other iterate from end to start, and do the same thing. Finally, iterate from start to end and get minimal difference.
Suppose I have a method to calculate combinations of r items from n items:
public static long combi(int n, int r) {
if ( r == n) return 1;
long numr = 1;
for(int i=n; i > (n-r); i--) {
numr *=i;
}
return numr/fact(r);
}
public static long fact(int n) {
long rs = 1;
if(n <2) return 1;
for (int i=2; i<=n; i++) {
rs *=i;
}
return rs;
}
As you can see it involves factorial which can easily overflow the result. For example if I have fact(200) for the foctorial method I get zero. The question is why do I get zero?
Secondly how do I deal with overflow in above context? The method should return largest possible number to fit in long if the result is too big instead of returning wrong answer.
One approach (but this could be wrong) is that if the result exceed some large number for example 1,400,000,000 then return remainder of result modulo
1,400,000,001. Can you explain what this means and how can I do that in Java?
Note that I do not guarantee that above methods are accurate for calculating factorial and combinations. Extra bonus if you can find errors and correct them.
Note that I can only use int or long and if it is unavoidable, can also use double. Other data types are not allowed.
I am not sure who marked this question as homework. This is NOT homework. I wish it was homework and i was back to future, young student at university. But I am old with more than 10 years working as programmer. I just want to practice developing highly optimized solutions in Java. In our times at university, Internet did not even exist. Today's students are lucky that they can even post their homework on site like SO.
Use the multiplicative formula, instead of the factorial formula.
Since its homework, I won't want to just give you a solution. However a hint I will give is that instead of calculating two large numbers and dividing the result, try calculating both together. e.g. calculate the numerator until its about to over flow, then calculate the denominator. In this last step you can chose the divide the numerator instead of multiplying the denominator. This stops both values from getting really large when the ratio of the two is relatively small.
I got this result before an overflow was detected.
combi(61,30) = 232714176627630544 which is 2.52% of Long.MAX_VALUE
The only "bug" I found in your code is not having any overflow detection, since you know its likely to be a problem. ;)
To answer your first question (why did you get zero), the values of fact() as computed by modular arithmetic were such that you hit a result with all 64 bits zero! Change your fact code to this:
public static long fact(int n) {
long rs = 1;
if( n <2) return 1;
for (int i=2; i<=n; i++) {
rs *=i;
System.out.println(rs);
}
return rs;
}
Take a look at the outputs! They are very interesting.
Now onto the second question....
It looks like you want to give exact integer (er, long) answers for values of n and r that fit, and throw an exception if they do not. This is a fair exercise.
To do this properly you should not use factorial at all. The trick is to recognize that C(n,r) can be computed incrementally by adding terms. This can be done using recursion with memoization, or by the multiplicative formula mentioned by Stefan Kendall.
As you accumulate the results into a long variable that you will use for your answer, check the value after each addition to see if it goes negative. When it does, throw an exception. If it stays positive, you can safely return your accumulated result as your answer.
To see why this works consider Pascal's triangle
1
1 1
1 2 1
1 3 3 1
1 4 6 4 1
1 5 10 10 5 1
1 6 15 20 15 6 1
which is generated like so:
C(0,0) = 1 (base case)
C(1,0) = 1 (base case)
C(1,1) = 1 (base case)
C(2,0) = 1 (base case)
C(2,1) = C(1,0) + C(1,1) = 2
C(2,2) = 1 (base case)
C(3,0) = 1 (base case)
C(3,1) = C(2,0) + C(2,1) = 3
C(3,2) = C(2,1) + C(2,2) = 3
...
When computing the value of C(n,r) using memoization, store the results of recursive invocations as you encounter them in a suitable structure such as an array or hashmap. Each value is the sum of two smaller numbers. The numbers start small and are always positive. Whenever you compute a new value (let's call it a subterm) you are adding smaller positive numbers. Recall from your computer organization class that whenever you add two modular positive numbers, there is an overflow if and only if the sum is negative. It only takes one overflow in the whole process for you to know that the C(n,r) you are looking for is too large.
This line of argument could be turned into a nice inductive proof, but that might be for another assignment, and perhaps another StackExchange site.
ADDENDUM
Here is a complete application you can run. (I haven't figured out how to get Java to run on codepad and ideone).
/**
* A demo showing how to do combinations using recursion and memoization, while detecting
* results that cannot fit in 64 bits.
*/
public class CombinationExample {
/**
* Returns the number of combinatios of r things out of n total.
*/
public static long combi(int n, int r) {
long[][] cache = new long[n + 1][n + 1];
if (n < 0 || r > n) {
throw new IllegalArgumentException("Nonsense args");
}
return c(n, r, cache);
}
/**
* Recursive helper for combi.
*/
private static long c(int n, int r, long[][] cache) {
if (r == 0 || r == n) {
return cache[n][r] = 1;
} else if (cache[n][r] != 0) {
return cache[n][r];
} else {
cache[n][r] = c(n-1, r-1, cache) + c(n-1, r, cache);
if (cache[n][r] < 0) {
throw new RuntimeException("Woops too big");
}
return cache[n][r];
}
}
/**
* Prints out a few example invocations.
*/
public static void main(String[] args) {
String[] data = ("0,0,3,1,4,4,5,2,10,0,10,10,10,4,9,7,70,8,295,100," +
"34,88,-2,7,9,-1,90,0,90,1,90,2,90,3,90,8,90,24").split(",");
for (int i = 0; i < data.length; i += 2) {
int n = Integer.valueOf(data[i]);
int r = Integer.valueOf(data[i + 1]);
System.out.printf("C(%d,%d) = ", n, r);
try {
System.out.println(combi(n, r));
} catch (Exception e) {
System.out.println(e.getMessage());
}
}
}
}
Hope it is useful. It's just a quick hack so you might want to clean it up a little.... Also note that a good solution would use proper unit testing, although this code does give nice output.
You can use the java.math.BigInteger class to deal with arbitrarily large numbers.
If you make the return type double, it can handle up to fact(170), but you'll lose some precision because of the nature of double (I don't know why you'd need exact precision for such huge numbers).
For input over 170, the result is infinity
Note that java.lang.Long includes constants for the min and max values for a long.
When you add together two signed 2s-complement positive values of a given size, and the result overflows, the result will be negative. Bit-wise, it will be the same bits you would have gotten with a larger representation, only the high-order bit will be truncated away.
Multiplying is a bit more complicated, unfortunately, since you can overflow by more than one bit.
But you can multiply in parts. Basically you break the to multipliers into low and high halves (or more than that, if you already have an "overflowed" value), perform the four possible multiplications between the four halves, then recombine the results. (It's really just like doing decimal multiplication by hand, but each "digit" is, say, 32 bits.)
You can copy the code from java.math.BigInteger to deal with arbitrarily large numbers. Go ahead and plagiarize.
I need to measure the physical distance between two places whose names are provided as strings. Since sometimes the names are written slightly differently, I was looking for a library that could help me measure the difference and then combine it with a measure of the latitude and longitude to select the correct matches. Preferred languages: Java or PHP.
Any suggestions?
Have a look at the Levenshtein distance. This is a way of measuring how different two strings are from one another.
Hopefully I understood your question correctly; using "distance" in the same sentence as "latitude and longitude" could be confusing!
Although written in c (with python and tcl bindings), libdistance would be a tool for applying several distances metrics on strings/data.
Metrics included:
bloom
damerau
euclid
hamming
jaccard
levenshtein
manhattan
minkowski
needleman_wunsch
I took the liberty to translate a piece of C# code I've written to calculate the Levenshtein distance into Java code. It uses only two single-dimension arrays that alternate instead of a big jagged array:
public static int getDifference(String a, String b)
{
// Minimize the amount of storage needed:
if (a.length() > b.length())
{
// Swap:
String x = a;
a = b;
b = x;
}
// Store only two rows of the matrix, instead of a big one
int[] mat1 = new int[a.length() + 1];
int[] mat2 = new int[a.length() + 1];
int i;
int j;
for (i = 1; i <= a.length(); i++)
mat1[i] = i;
mat2[0] = 1;
for (j = 1; j <= b.length(); j++)
{
for (i = 1; i <= a.length(); i++)
{
int c = (a.charAt(i - 1) == b.charAt(j - 1) ? 0 : 1);
mat2[i] =
Math.min(mat1[i - 1] + c,
Math.min(mat1[i] + 1, mat2[i - 1] + 1));
}
// Swap:
int[] x = mat1;
mat1 = mat2;
mat2 = x;
mat2[0] = mat1[0] + 1;
}
// It's row #1 because we swap rows at the end of each outer loop,
// as we are to return the last number on the lowest row
return mat1[a.length()];
}
It is not rigorously tested, but it seems to be working okay. It was based on a Python implementation I made for a university exercise. Hope this helps!
You might get some decent results using a phonetic algorithm to find slightly misspelld names.
Also, if you use a more mechanical edit distance, you'll probably see better results using a weighted function that accounts for keyboard geometry (i.e. physically close keys are "cheaper" to replace than far off ones). That's a patented method btw, so be careful not to write something that becomes too popular ;)
I would recommend either Levenshtein Distance or the Jaccard Distance for comparing text.
I found SumMetrics in Java, but haven't used it.