Added cosine similarity

Author: tdebatty

README.md

@@ -7,7 +7,10 @@ Currently implemeted:
 - Jaro-Winkler similarity;
 - Longest Common Subsequence edit distance;
 - Q-Gram (Ukkonen);
-- n-Gram distance (Kondrak).
+- n-Gram distance (Kondrak);
+- Jaccard index;
+- Sorensen-Dice coefficient;
+- Cosine similarity.
 
 ## Download
 Using maven:

src/main/java/info/debatty/java/stringsimilarity/Cosine.java

@@ -0,0 +1,117 @@
+/*
+ * The MIT License
+ *
+ * Copyright 2015 Thibault Debatty.
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to deal
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ * THE SOFTWARE.
+ */
+
+package info.debatty.java.stringsimilarity;
+
+/**
+ * Implements Cosine Similarity.
+ * The strings are first transformed in vectors of occurences of k-shingles 
+ * (sequences of k characters). In this n-dimensional space, the similarity
+ * between the two strings is the cosine of their respective vectors.
+ * @author Thibault Debatty
+ */
+public class Cosine implements StringSimilarityInterface {
+
+    /**
+     * @param args the command line arguments
+     */
+    public static void main(String[] args) {
+        Cosine cos = new Cosine(3);
+        
+        // ABC BCE
+        // 1  0
+        // 1  1
+        // angle = 45°
+        // => similarity = .71
+        
+        System.out.println(cos.similarity("ABC", "ABCE"));
+        
+        cos = new Cosine(2);
+        // AB BA
+        // 2  1
+        // 1  1
+        // similarity = .95
+        System.out.println(cos.similarity("ABAB", "BAB"));
+    }
+    
+    private int k;
+    
+    public Cosine(int k) {
+        this.k = k;
+    }
+    
+    public Cosine() {
+        this.k = 3;
+    }
+
+    /**
+     * Computes the cosine similarity of s1 and s2.
+     * The strings are first converted to vectors in the space of k-shingles. 
+     * The cosine similarity is computed as V1 . V2 / (|V1| * |V2|)
+     * @param s1
+     * @param s2
+     * @return Cosine similarity
+     */
+    public double similarity(String s1, String s2) {
+        KShingling ks = new KShingling(this.k);
+        ks.parse(s1);
+        ks.parse(s2);
+        
+        int[] v1 = ks.profileOf(s1);
+        int[] v2 = ks.profileOf(s2);
+        
+        return dotProduct(v1, v2) / (norm(v1) * norm(v2));
+        
+    }
+
+    public double distance(String s1, String s2) {
+        return 1.0 - similarity(s1, s2);
+    }
+    
+    /**
+     * Compute the norm L2 : sqrt(Sum_i( v_i^2))
+     * @param v
+     * @return L2 norm
+     */
+    protected static double norm(int[] v) {
+        double agg = 0;
+        
+        for (int i = 0; i < v.length; i++) {
+            agg += (v[i] * v[i]);
+        }
+        
+        return Math.sqrt(agg);
+    }
+    
+    protected static double dotProduct(int[] v1, int[] v2) {
+        double agg = 0;
+        
+        for (int i = 0; i < v1.length; i++) {
+            agg += (v1[i] * v2[i]);
+        }
+        
+        return agg;
+    }
+    
+}