search for: noiseless

Hey all, I can't seem to get the princurve package to produce correct results, even in the simplest cases. For example, if you just generate a 1 period noiseless sine wave, and ask for the principal curve and plot, the returned curve is clearly wrong (doesn't follow the sine wave). Here's my code: library(princurve) x <- runif(1000,0,2*pi); x <- cbind(x/(2*pi), sin(x)) fit1 <- principal.curve(x, plot = TRUE) Anyone have any suggestions?...

...is a file with one column (1 response) and 225 rows (one for each of the 225 trining cases). I presume the problem is related to Y, since its use is not explained in the ppr documentation, and I'm guessing its a matrix with the responses. Any help about this? (By the way, the test data is the noiseless g1 function published by Hwang). b)I can use the "regression formula" ppr() succesfuly, but I would like a non parametric approach (I guess that the need of a regression formula makes it "parametric"). How does ppr() use this regression formula? Is there a way to avoid the use...

I have an upcoming install which places the switch close to some employees in a quiet work environment. Can anyone recommend a quiet 24 port POE switch? The Linksys SRW224P behind me right now would be objectionable, I'm sure. -------------- next part -------------- An HTML attachment was scrubbed... URL:

...y = 0.05) y <- f(x) + rnorm(x, sd = 0.2) plot(x, y, col="gray80") legend("topleft", c("log(x) + cos(x)", "SVM, untuned", "SVM, tuned"), bty="n", col=colors, pch=20) lines(x, f(x), col = colors[1]) # overlay noiseless data # SVM, untuned svmmodel1 <- svm(x, y) print(summary(svmmodel1)) y1 <- predict(svmmodel1, x) lines(x, y1, col = colors[2]) # SVM with tuning tuning <- tune.svm(x, y, gamma = 2^(-4:4), cost = 2^(-2:2)) svmmodel2 <- tuning$best.model print(summary(svmmodel2)) y2 <- predict(svmmod...

...------------------------------+ |Carter Brey (cbrey@attglobal.net)| +---------------------------------+ Homepage: http://www.nyplive.org/music/orchestra/index.cfm?page=profile&personNum=7 Trying to be happy is like trying to build a machine for which the only specification is that it should run noiselessly. 8:55am up 5 days, 9:47, 2 users, load average: 0.18, 0.14, 0.04