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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49 lines
1.3 KiB
49 lines
1.3 KiB
module.exports = fromMat3
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/**
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* Creates a quaternion from the given 3x3 rotation matrix.
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*
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* NOTE: The resultant quaternion is not normalized, so you should be sure
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* to renormalize the quaternion yourself where necessary.
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*
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* @param {quat} out the receiving quaternion
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* @param {mat3} m rotation matrix
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* @returns {quat} out
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* @function
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*/
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function fromMat3 (out, m) {
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// Algorithm in Ken Shoemake's article in 1987 SIGGRAPH course notes
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// article "Quaternion Calculus and Fast Animation".
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var fTrace = m[0] + m[4] + m[8]
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var fRoot
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if (fTrace > 0.0) {
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// |w| > 1/2, may as well choose w > 1/2
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fRoot = Math.sqrt(fTrace + 1.0) // 2w
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out[3] = 0.5 * fRoot
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fRoot = 0.5 / fRoot // 1/(4w)
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out[0] = (m[5] - m[7]) * fRoot
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out[1] = (m[6] - m[2]) * fRoot
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out[2] = (m[1] - m[3]) * fRoot
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} else {
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// |w| <= 1/2
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var i = 0
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if (m[4] > m[0]) {
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i = 1
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}
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if (m[8] > m[i * 3 + i]) {
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i = 2
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}
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var j = (i + 1) % 3
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var k = (i + 2) % 3
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fRoot = Math.sqrt(m[i * 3 + i] - m[j * 3 + j] - m[k * 3 + k] + 1.0)
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out[i] = 0.5 * fRoot
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fRoot = 0.5 / fRoot
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out[3] = (m[j * 3 + k] - m[k * 3 + j]) * fRoot
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out[j] = (m[j * 3 + i] + m[i * 3 + j]) * fRoot
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out[k] = (m[k * 3 + i] + m[i * 3 + k]) * fRoot
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}
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return out
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}
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