Detection predictions blending based on clustering sampled gaussians #116
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@@ Coverage Diff @@
## master #116 +/- ##
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- Coverage 87% 85.39% -1.62%
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Files 70 70
Lines 2602 2657 +55
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+ Hits 2264 2269 +5
- Misses 338 388 +50
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aboucaud
reviewed
Feb 20, 2018
| c = mask.sum() / max_n_samples | ||
| x, y = km.cluster_centers_[i] | ||
| r = np.mean((data[mask] - km.cluster_centers_[i])**2) | ||
| objs.append((c, x, y, r)) |
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Why the choice of average of squares instead of the Euclidean distance ?
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You mean for the calculation of the radius r ? Because I initially used the radius as the variance of multivariate normal that I sampled from. So therefore here using that as an estimate of the radius.
aboucaud
reviewed
Feb 20, 2018
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| objs = [] | ||
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| for i in range(n_clusters): | ||
| mask = labels == i | ||
| c = mask.sum() / max_n_samples | ||
| x, y = data[mask].mean(axis=0) | ||
| r = np.sqrt(np.mean((data[mask] - np.array([x, y]))**2)) * 2 | ||
| r = np.mean((data[mask] - np.array([x, y]))**2) |
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This already adds the one based on HDBSCAN.