/**
* gets the similarity of the two strings using JaccardSimilarity.
*
* @param string1
* @param string2
* @return a value between 0-1 of the similarity
*/
public float getSimilarity(final String string1, final String string2) {
/*
* Each instance is represented as a Jaccard vector similarity function.
* The Jaccard between two vectors X and Y is
*
* (X*Y) / (|X||Y|-(X*Y))
*
* where (X*Y) is the inner product of X and Y, and |X| = (X*X)^1/2,
* i.e. the Euclidean norm of X.
*
* This can more easily be described as ( |X & Y| ) / ( | X or Y | )
*/
// todo this needs checking
final ArrayList<String> str1Tokens = tokeniser.tokenizeToArrayList(string1);
final ArrayList<String> str2Tokens = tokeniser.tokenizeToArrayList(string2);
final Set<String> allTokens = new HashSet<String>();
allTokens.addAll(str1Tokens);
final int termsInString1 = allTokens.size();
final Set<String> secondStringTokens = new HashSet<String>();
secondStringTokens.addAll(str2Tokens);
final int termsInString2 = secondStringTokens.size();
// now combine the sets
allTokens.addAll(secondStringTokens);
final int commonTerms = (termsInString1 + termsInString2) - allTokens.size();
// return JaccardSimilarity
return (float) (commonTerms) / (float) (allTokens.size());
}
/**
* gets the estimated time in milliseconds it takes to perform a similarity timing.
*
* @param string1 string 1
* @param string2 string 2
* @return the estimated time in milliseconds taken to perform the similarity measure
*/
public float getSimilarityTimingEstimated(final String string1, final String string2) {
// timed millisecond times with string lengths from 1 + 50 each increment
// 0 0.01 0.03 0.05 0.07 0.11 0.14 0.18 0.23 0.27 0.33 0.38 0.46 0.51 0.59 0.67 0.75 0.86 0.94
// 1.01 1.15 1.22 1.5 1.45 1.93 1.7 2.28 1.95 2.42 2.21 2.99 2.54 3.34 2.86 3.76 3.17 4.06 3.5
// 4.32 3.9 5.23 4.32 5.34 4.83 6.15 5.07 6.34 5.64 7.29 5.97 8.12 6.55 8.46 7 8.83 7.52 9.71
// 8.12 10.68 8.46
final float str1Tokens = tokeniser.tokenizeToArrayList(string1).size();
final float str2Tokens = tokeniser.tokenizeToArrayList(string2).size();
return (str1Tokens * str2Tokens) * ESTIMATEDTIMINGCONST;
}
/**
* gets the estimated time in milliseconds it takes to perform a similarity timing.
*
* @param string1 string 1
* @param string2 string 2
* @return the estimated time in milliseconds taken to perform the similarity measure
*/
public float getSimilarityTimingEstimated(final String string1, final String string2) {
// timed millisecond times with string lengths from 1 + 50 each
// increment
// 0 0.02 0.03 0.05 0.07 0.11 0.14 0.18 0.23 0.27 0.34 0.38 0.45 0.51
// 0.59 0.67 0.75 0.83 0.94 1 1.15 1.22 1.49 1.46 1.93 1.69 2.11 1.95
// 2.42 2.21 2.87 2.51 3.27 2.86 3.69 3.22 3.9 3.5 4.74 3.9 4.95 4.23
// 5.49 4.72 5.8 5.21 6.38 5.64 7.25 5.97 7.81 6.55 8.46 7 9.27 7.52
// 10.15 8.12 10.15 8.46
final float str1Tokens = tokeniser.tokenizeToArrayList(string1).size();
final float str2Tokens = tokeniser.tokenizeToArrayList(string2).size();
return (str1Tokens * str2Tokens) * ESTIMATEDTIMINGCONST;
}
/**
* gets the similarity of the two strings using OverlapCoefficient
*
* <p>overlap_coefficient(q,r) = ( | q & r | ) / min{ | q | , | r | }.
*
* @param string1
* @param string2
* @return a value between 0-1 of the similarity
*/
public float getSimilarity(final String string1, final String string2) {
final ArrayList<String> str1Tokens = tokeniser.tokenizeToArrayList(string1);
final ArrayList<String> str2Tokens = tokeniser.tokenizeToArrayList(string2);
final Set<String> allTokens = new HashSet<String>();
allTokens.addAll(str1Tokens);
final int termsInString1 = allTokens.size();
final Set<String> secondStringTokens = new HashSet<String>();
secondStringTokens.addAll(str2Tokens);
final int termsInString2 = secondStringTokens.size();
// now combine the sets
allTokens.addAll(secondStringTokens);
final int commonTerms = (termsInString1 + termsInString2) - allTokens.size();
// return overlap_coefficient
return (float) (commonTerms) / (float) Math.min(termsInString1, termsInString2);
}