> How much [effort] to determine whether there are multiple peaks? The Shapiro-Wilk test can give you a probability of how non-normal the distribution is: http://en.wikipedia.org/wiki/Shapiro%E2%80%93Wilk_test
As an R example, here is some test data: set.seed(7); data <- c(rnorm(2000,0,40),rnorm(2500, 0, 20), rnorm(400, 40, 5)); hist(data,breaks=200) and running shapiro.test(data) gives me: W = 0.9939, p-value = 1.184e-13 The lower the p-value the more it thinks it is not a normal curve. The extreme result is interesting, as the graph looks "roughly normal" to me. (The Wikipedia page lists alternative tests, which can be found in the R nortest package apparently. I've no idea of CPU effort required for each of them.) > Now the tough question: How can this information be used to improve move > selection? One approach, not at all sophisticated, is better time management: spend less time on normal distributions, more time when the distribution is messy. (But I wonder if more time will just make the two peaks stand out more?) Darren -- Darren Cook, Software Researcher/Developer http://dcook.org/work/ (About me and my work) http://dcook.org/blogs.html (My blogs and articles) _______________________________________________ Computer-go mailing list [email protected] http://dvandva.org/cgi-bin/mailman/listinfo/computer-go
