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[76] | 1 | // Sample from a normal distribution with mean 0, stddev 1. |
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| 2 | function normal() { |
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| 3 | var x = 0, y = 0, rds, c; |
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| 4 | do { |
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| 5 | x = Math.random() * 2 - 1; |
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| 6 | y = Math.random() * 2 - 1; |
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| 7 | rds = x * x + y * y; |
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| 8 | } while (rds == 0 || rds > 1); |
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| 9 | c = Math.sqrt(-2 * Math.log(rds) / rds); // Box-Muller transform |
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| 10 | return x * c; // throw away extra sample y * c |
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| 11 | } |
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| 12 | |
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| 13 | // Simple 1D Gaussian (normal) distribution |
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| 14 | function normal1(mean, deviation) { |
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| 15 | return function() { |
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| 16 | return mean + deviation * normal(); |
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| 17 | }; |
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| 18 | } |
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| 19 | |
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| 20 | // Gaussian Mixture Model (k=3) fit using E-M algorithm |
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| 21 | function normal3(dd) { |
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| 22 | return function() { |
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| 23 | var r = Math.random(), |
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| 24 | i = r < dd[0][2] ? 0 : r < dd[0][2] + dd[1][2] ? 1 : 2, |
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| 25 | d = dd[i]; |
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| 26 | return d[0] + Math.sqrt(d[1]) * normal(); |
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| 27 | } |
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| 28 | } |
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