Write a concise guide answering to Focus question: "How to use cosine similarity with vantage point trees to find relevant textual content?" Replacing {{ placeholders }} with relevant content. 

# Focus question: "How to use cosine similarity with vantage point trees to find semantically relevant textual content?"

```javascript
const similarity = require('compute-cosine-similarity');
const VPTreeFactory = require('vptree');
let vptree ; // where we’ll store a reference to our vptree

function cosineDistanceMatching(vector1, vector2) {
    let cosineSimilarity = similarity(vector1, vector2);
    let distance = 2 * (1 - cosineSimilarity);
    return Math.sqrt(distance);
}
```

##  {{ very brief high-level explanation }}
##  {{ step-by-step algorithm with relevant code snippets }}

{{ example start}}
1. Step 1: Get your sentences.
        A) NAME_1 likes to code.
        B) NAME_2 likes to code.

2. Step 2: Count frequency of unique words.
        Word Freq in A Freq in B
        NAME_1 0
        NAME_2
        Likes
        To
        - -0 --
        Code -

3. Step 3: Count frequency of unique words.
        Vector of A: [1,0,1,1,1]
        Vector of B: [0,1,1,1,1]

4. Step 4: Apply vector math + cosine similarity
        Cosine similarity (A dot B)/ (HAII IIB II )

We get 0.75 pretty similar!
{{ end of example }} Add stress words that are fully capitlized. Make sure these stress words appear more than 4 times.
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