From ac5d01255f2f02de6b308b82976c5866c458f149 Mon Sep 17 00:00:00 2001 From: Vladimir Mandic Date: Fri, 12 Mar 2021 18:24:07 -0500 Subject: [PATCH] updated docs --- Embedding.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/Embedding.md b/Embedding.md index ad54038..d090ec9 100644 --- a/Embedding.md +++ b/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 -Simmilarity function is based on *Eucilidean distance* between all points in vector -*Eucliean distance is limited case of Minkowski distance with order of 2* +Simmilarity function is based on general *Minkowski distance* between all points in vector *[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) For example, those will produce slighly different results: ```js 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: