Java Utililty Methods Cosine Similarity

List of utility methods to do Cosine Similarity

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Description

The list of methods to do Cosine Similarity are organized into topic(s).

Method

doublecosDegrees(double angleInDegrees)
cos Degrees 
return Math.cos(Math.toRadians(angleInDegrees));
floatcosDistance(float[] v1, float[] v2)
Calculate cosine distance of two vectors 
assert v1.length == v2.length;
float dotProduct = dotProduct(v1, v2);
float sumNorm = vectorNorm(v1) * vectorNorm(v2);
return dotProduct / sumNorm;
doublecosine_similarity(double[] vec1, double[] vec2)
cosinsimilarity 
double cosim = vector_dot(vec1, vec2) / (vector_norm(vec1) * vector_norm(vec2));
return cosim;
doublecosineLawGetDegree(double a, double b, double c)
cosine Law Get Degree 
return Math.acos((a * a + b * b - c * c) / (2 * a * b));
doublecosineSim(double[] a, double[] b)
Calculate the cosine similarity between two vectors 
if (a == null || b == null || a.length < 1 || b.length < 1 || a.length != b.length)
    return Double.NaN;
double sum = 0.0, sum_a = 0, sum_b = 0;
for (int i = 0; i < a.length; i++) {
    sum += a[i] * b[i];
    sum_a += a[i] * a[i];
    sum_b += b[i] * b[i];
double val = Math.sqrt(sum_a) * Math.sqrt(sum_b);
return sum / val;
doublecosineSimilarity(double[] vector1, double[] vector2)
cosine Similarity 
double dotProduct = dotProduct(vector1, vector2);
double euclideanDist = euclideanDistance(vector1) * euclideanDistance(vector2);
return dotProduct / euclideanDist;
doublecosineSimilarity(double[] x, double[] y)
cosine Similarity 
if (x.length != y.length) {
    throw new IllegalArgumentException("Vector dimensions should be equal");
double xySum = 0;
double xSum = 0;
double ySum = 0;
for (int i = 0; i < x.length; i++) {
    xySum += x[i] * y[i];
...
doublecosineSimilarity(float[] f1, float[] f2)
cosine Similarity 
float num = 0, den1 = 0, den2 = 0;
for (int i = 0; i < f1.length; i++) {
    num += f1[i] * f2[i];
    den1 += sq(f1[i]);
    den2 += sq(f2[i]);
return num / (Math.sqrt(den1) * Math.sqrt(den2));
doublecosineSimilarity(float[] vectorA, float[] vectorB)
cosine Similarity 
double dotProduct = 0.0;
double normA = 0.0;
double normB = 0.0;
for (int i = 0; i < vectorA.length; i++) {
    dotProduct += vectorA[i] * vectorB[i];
    normA += Math.pow(vectorA[i], 2);
    normB += Math.pow(vectorB[i], 2);
return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));
doublecosineSimilarity(String[] tkn0, String[] tkn1)
cosine Similarity 
HashMap<String, int[]> map = new HashMap<String, int[]>();
for (int i = 0; i < tkn0.length; i++) {
    String t = tkn0[i].toLowerCase();
    if (!map.containsKey(t)) {
        map.put(t, new int[2]);
    map.get(t)[0]++;
for (int i = 0; i < tkn1.length; i++) {
    String t = tkn1[i].toLowerCase();
    if (!map.containsKey(t)) {
        map.put(t, new int[2]);
    map.get(t)[1]++;
double dot = 0;
double norma = 0;
double normb = 0;
for (Entry<String, int[]> e : map.entrySet()) {
    int[] v = e.getValue();
    dot += v[0] * v[1];
    norma += v[0] * v[0];
    normb += v[1] * v[1];
norma = Math.sqrt(norma);
normb = Math.sqrt(normb);
if (dot == 0) {
    return 0;
} else {
    return dot / (norma * normb);