Mitchell’s best-candidate algorithm generates a new random sample by creating k candidate samples and picking the best of k. Here the “best” sample is defined as the sample that is farthest away from previous samples. The algorithm approximates Poisson-disc sampling, producing a much more natural appearance (better blue noise spectral characteristics) than uniform random sampling.
See also the white-on-black and Voronoi variations of this example.
Mouseover to repel nodes. Adapted from my talk on force layouts. Compare to the canvas version.
You have 1000 bottles and 10 slaves and 10 cups. You have the pick the one bottle out of the 1000 which is poisoned.
Rule: All the slaves need to drink at the exact same time and you only have one attempt.
Find a way to mix the drinks and find the bottle that's poisoned.
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <meta http-equiv="Content-Type" content="text/html;charset=utf-8"/> | |
| </head> | |
| <body> | |
| <script type="text/javascript" src="http://mbostock.github.com/d3/d3.js?2.5.0"></script> | |
| <script type="text/javascript"> | |
| var w = 960, |