StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics
https://doi.org/10.1109/TVCG.2020.3030352
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
25 lines
630 B
25 lines
630 B
4 years ago
|
var vecNormalize = require('./normalize')
|
||
|
var vecScale = require('./scale')
|
||
|
|
||
|
module.exports = random
|
||
|
|
||
|
/**
|
||
|
* Generates a random vector with the given scale
|
||
|
*
|
||
|
* @param {vec4} out the receiving vector
|
||
|
* @param {Number} [scale] Length of the resulting vector. If ommitted, a unit vector will be returned
|
||
|
* @returns {vec4} out
|
||
|
*/
|
||
|
function random (out, scale) {
|
||
|
scale = scale || 1.0
|
||
|
|
||
|
// TODO: This is a pretty awful way of doing this. Find something better.
|
||
|
out[0] = Math.random()
|
||
|
out[1] = Math.random()
|
||
|
out[2] = Math.random()
|
||
|
out[3] = Math.random()
|
||
|
vecNormalize(out, out)
|
||
|
vecScale(out, out, scale)
|
||
|
return out
|
||
|
}
|