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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52 lines
1.3 KiB
52 lines
1.3 KiB
4 years ago
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module.exports = slerp
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/**
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* Performs a spherical linear interpolation between two quat
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*
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* @param {quat} out the receiving quaternion
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* @param {quat} a the first operand
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* @param {quat} b the second operand
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* @param {Number} t interpolation amount between the two inputs
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* @returns {quat} out
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*/
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function slerp (out, a, b, t) {
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// benchmarks:
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// http://jsperf.com/quaternion-slerp-implementations
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var ax = a[0], ay = a[1], az = a[2], aw = a[3],
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bx = b[0], by = b[1], bz = b[2], bw = b[3]
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var omega, cosom, sinom, scale0, scale1
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// calc cosine
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cosom = ax * bx + ay * by + az * bz + aw * bw
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// adjust signs (if necessary)
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if (cosom < 0.0) {
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cosom = -cosom
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bx = -bx
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by = -by
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bz = -bz
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bw = -bw
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}
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// calculate coefficients
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if ((1.0 - cosom) > 0.000001) {
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// standard case (slerp)
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omega = Math.acos(cosom)
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sinom = Math.sin(omega)
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scale0 = Math.sin((1.0 - t) * omega) / sinom
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scale1 = Math.sin(t * omega) / sinom
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} else {
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// "from" and "to" quaternions are very close
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// ... so we can do a linear interpolation
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scale0 = 1.0 - t
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scale1 = t
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}
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// calculate final values
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out[0] = scale0 * ax + scale1 * bx
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out[1] = scale0 * ay + scale1 * by
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out[2] = scale0 * az + scale1 * bz
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out[3] = scale0 * aw + scale1 * bw
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return out
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}
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