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|---|---|---|
| @@ -1,24 +1,24 @@ | ||
| Package: Ckmeans.1d.dp | ||
| Type: Package | ||
| Version: 3.4.6-2 | ||
| Date: 2016-09-25 | ||
| Version: 3.4.6-3 | ||
| Date: 2016-10-04 | ||
| Title: Optimal and Fast Univariate k-Means Clustering | ||
| Authors@R: c(person("Joe", "Song", role = c("aut", "cre"), | ||
| email = "joemsong@cs.nmsu.edu"), | ||
| person("Haizhou", "Wang", role = "aut")) | ||
| Author: Joe Song [aut, cre], Haizhou Wang [aut] | ||
| Maintainer: Joe Song <joemsong@cs.nmsu.edu> | ||
| Description: A dynamic programming algorithm for optimal one-dimensional | ||
| k-means clustering. The algorithm minimizes the sum of squares of | ||
| within-cluster distances. As an alternative to heuristic k-means algorithms, | ||
| this method guarantees optimality and reproducibility. Its advantage in | ||
| efficiency and accuracy over k-means is increasingly pronounced as the | ||
| number of clusters k increases. | ||
| Description: A dynamic programming algorithm for optimal univariate k-means | ||
| clustering. Minimizing the sum of squares of within-cluster distances, the | ||
| algorithm guarantees optimality and reproducibility. Its advantage over | ||
| heuristic k-means algorithms in efficiency and accuracy is increasingly | ||
| pronounced as the number of clusters k increases. It provides an alternative | ||
| to heuristic k-means algorithms for univariate clustering. | ||
| License: LGPL (>= 3) | ||
| NeedsCompilation: yes | ||
| Suggests: testthat | ||
| Depends: R (>= 2.10.0) | ||
| LazyData: true | ||
| Packaged: 2016-09-25 19:50:13 UTC; joemsong | ||
| Packaged: 2016-10-05 03:19:39 UTC; joemsong | ||
| Repository: CRAN | ||
| Date/Publication: 2016-09-26 00:51:35 | ||
| Date/Publication: 2016-10-05 09:29:38 |
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| # visualize.R -- visualization functions for Ckmeans.1d.dp | ||
| # | ||
| # Joe Song | ||
| # Created: Oct 1, 2016 | ||
|
|
||
| ahist <- function( | ||
| x, k = c(1,9), plot = TRUE, xlab = deparse(substitute(x)), | ||
| main = paste("Adaptive histogram of", deparse(substitute(x))), | ||
| col = NULL, lwd = graphics::par("lwd"), col.stick = "gray", lwd.stick = 1, | ||
| ...) | ||
| # adaptive histogram | ||
| { | ||
| xs <- sort(x) | ||
| r <- Ckmeans.1d.dp(xs, k=k) | ||
| kopt <- length(r$size) | ||
| breaks <- vector("double", length=kopt+1) | ||
| i <- r$size[1] | ||
|
|
||
| if(kopt > 1) { | ||
| for(q in 2:kopt) { | ||
| breaks[q] <- (xs[i] + xs[i+1])/2 | ||
| i <- i + r$size[q] | ||
| } | ||
| } | ||
|
|
||
| breaks[1] <- xs[1] | ||
| breaks[kopt+1] <- xs[length(xs)] | ||
|
|
||
| h <- graphics::hist(x, breaks=breaks, plot=FALSE, | ||
| warn.unused=FALSE, ...) | ||
| h$xname <- deparse(substitute(x)) | ||
|
|
||
| if(plot) { | ||
| opar <- graphics::par(lwd=lwd) | ||
| graphics::plot(h, main=main, xlab=xlab, col=col, ...) | ||
| if(h$equidist) { | ||
| graphics::segments(x, -max(h$count)/10, x, 0, | ||
| col=col.stick, lwd=lwd.stick) | ||
| } else { | ||
| graphics::segments(x, -max(h$density)/10, x, 0, | ||
| col=col.stick, lwd=lwd.stick) | ||
| } | ||
| graphics::par(opar) | ||
| invisible(h) | ||
| } else { | ||
| return(h) | ||
| } | ||
| } | ||
|
|
||
| plot.Ckmeans.1d.dp <- | ||
| function(x, xlab=NULL, ylab=NULL, main=NULL, | ||
| sub=NULL, col.clusters=NULL, ...) | ||
| { | ||
| ck <- x | ||
| if(is.null(xlab)) xlab <- ck$xname | ||
| if(is.null(ylab)) ylab <- ifelse(ck$yname=="1", "Weight", ck$yname) | ||
| if(is.null(main)) main <- paste("Optimal k-means clustering of", ck$xname) | ||
| if(is.null(sub)) sub=paste("n =", length(ck$cluster)) | ||
| if(is.null(col.clusters)) col.clusters <- seq_along(x$size) | ||
|
|
||
| if(exists(ck$xname, mode="numeric")) { | ||
| x <- get(ck$xname, mode="numeric") | ||
| } else { | ||
| x <- eval(parse(text=ck$xname)) | ||
| } | ||
|
|
||
| if(exists(ck$yname, mode="numeric")) { | ||
| y <- get(ck$yname, mode="numeric") | ||
| } else { | ||
| y <- eval(parse(text=ck$yname)) | ||
| } | ||
|
|
||
| if(length(y) == 1) { | ||
| y <- rep(y, length(x)) | ||
| } | ||
|
|
||
| graphics::plot(x, y, type="p", | ||
| xlab=xlab, ylab=ylab, main=main, sub=sub, | ||
| col=col.clusters[ck$cluster], | ||
| ...) | ||
|
|
||
| ks <- seq_along(ck$size) | ||
| sapply(ks, function(q) { | ||
| graphics::segments(x[ck$cluster == q], 0, | ||
| x[ck$cluster == q], y[ck$cluster == q], | ||
| col=col.clusters[q], ...) } ) | ||
|
|
||
| invisible(ck) | ||
| } |
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