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Vladimir Mandic 2021-03-12 18:24:07 -05:00
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Embedding.md

@ -59,16 +59,17 @@ Embedding vectors are calulated feature vector values uniquely identifying a giv
They can be stored as normal arrays and reused as needed They can be stored as normal arrays and reused as needed
Simmilarity function is based on *Eucilidean distance* between all points in vector Simmilarity function is based on general *Minkowski distance* between all points in vector
*Eucliean distance is limited case of Minkowski distance with order of 2*
*[Minkowski distance](https://en.wikipedia.org/wiki/Minkowski_distance) is a nth root of sum of nth powers of distances between each point (each value in 192-member array)* *[Minkowski distance](https://en.wikipedia.org/wiki/Minkowski_distance) is a nth root of sum of nth powers of distances between each point (each value in 192-member array)*
*Default is Eucliean distance which is a limited case of Minkowski distance with order of 2*
Changing `order` can make simmilarity matching more or less sensitive (default order is 2nd order) Changing `order` can make simmilarity matching more or less sensitive (default order is 2nd order)
For example, those will produce slighly different results: For example, those will produce slighly different results:
```js ```js
const simmilarity2ndOrder = human.simmilarity(firstEmbedding, secondEmbedding, 2); const simmilarity2ndOrder = human.simmilarity(firstEmbedding, secondEmbedding, 2);
const simmilarity3rdOrder = human.simmilarity(firstEmbedding, secondEmbedding, 2); const simmilarity3rdOrder = human.simmilarity(firstEmbedding, secondEmbedding, 3);
``` ```
How simmilarity is calculated: How simmilarity is calculated: