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1522133014
tags: Basic Implementation
根据 Cosine Similarity 的公式, basic implementation
```
/*
LintCode
Cosine similarity is a measure of similarity between two vectors of an inner product space
that measures the cosine of the angle between them.
The cosine of 0° is 1, and it is less than 1 for any other angle.
See wiki: Cosine Similarity
Here is the formula:
http://www.lintcode.com/en/problem/cosine-similarity/#
Given two vectors A and B with the same size, calculate the cosine similarity.
Return 2.0000 if cosine similarity is invalid (for example A = [0] and B = [0]).
Example
Given A = [1, 2, 3], B = [2, 3 ,4].
Return 0.9926.
Given A = [0], B = [0].
Return 2.0000
*/
/*
Thoughts
Based on the given equation. Write up calculation
Check border.
*/
class Solution {
/**
* @param A: An integer array.
* @param B: An integer array.
* @return: Cosine similarity.
*/
public double cosineSimilarity(int[] A, int[] B) {
if (A == null || B == null || A.length == 0 || B.length == 0 || A.length != B.length) {
return 2;
}
double sumProduct = 0;
double sumASq = 0;
double sumBSq = 0;
for (int i = 0; i < A.length; i++) {
sumProduct += A[i]*B[i];
sumASq += A[i] * A[i];
sumBSq += B[i] * B[i];
}
if (sumASq == 0 && sumBSq == 0) {
return 2.0;
}
return sumProduct / (Math.sqrt(sumASq) * Math.sqrt(sumBSq));
}
}
```
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