StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics
https://doi.org/10.1109/TVCG.2020.3030352
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23 lines
484 B
23 lines
484 B
module.exports = normalize;
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/**
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* Normalize a vec3
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*
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* @param {vec3} out the receiving vector
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* @param {vec3} a vector to normalize
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* @returns {vec3} out
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*/
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function normalize(out, a) {
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var x = a[0],
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y = a[1],
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z = a[2]
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var len = x*x + y*y + z*z
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if (len > 0) {
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//TODO: evaluate use of glm_invsqrt here?
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len = 1 / Math.sqrt(len)
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out[0] = a[0] * len
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out[1] = a[1] * len
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out[2] = a[2] * len
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}
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return out
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} |