Calculate the distance between two feature vectors.

euclideanDistance.js

function euclideanDistance(v1, v2) {
  return v1.map(
    (e, i) => [v1[i], v2[i]]
  ).map(
    (a) => a.reduce(
      (prev, cur) => prev - cur
    )
  ).map(
    (e) => e ** 2
  ).reduce(
    (prev, cur) => prev + cur
  ) ** .5;
};

// Identical feature sets result in a distance of: 0
let testResultSet1 = [1, 3, 5, 1, 1, 1, 1];
let ecd_1 = euclideanDistance(
  testResultSet1, testResultSet1
);
console.log(ecd_1);
// >> 0

// Nearly Identical feature sets result in a distance of: ~1.4 (close to zero)
let testResultSet2 = [1, 3, 5, 2, 1, 2, 1];
let ecd_2 = euclideanDistance(
  testResultSet1, testResultSet2
);
console.log(ecd_2);
// >> 1.4142135623730951

// Very different feature sets result in a distance of: ~8.3 (the higher, the worse of a match it is.)
let testResultSet3 = [2, 5, 1, 2, 5, 5, 5];
let ecd_3 = euclideanDistance(
  testResultSet1, testResultSet3
);
console.log(ecd_3);
// >> 8.366600265340756