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# Plot graph(s) with objective values (works only if details were switched on)
setMethod("plot", signature(x="APResult", y="missing"),
function(x, y, type=c("netsim", "dpsim", "expref"),
xlab="# Iterations",
ylab="Similarity", ...)
{
if (length(x@netsimAll) <= 1)
stop("no valid data was found for plotting; call apcluster() ",
"with 'details=TRUE' in order to compute convergence details")
plotnetsim <- FALSE
plotexpref <- FALSE
plotdpsim <- FALSE
legtxt <- c()
legcol <- c()
ymin <- .Machine$double.xmax
ymax <- -.Machine$double.xmax
if (is.element("netsim", type))
{
tmp <- x@netsimAll[which(!is.nan(x@netsimAll))]
if (length(tmp) > 0)
{
ymin <- min(tmp, ymin, na.rm=TRUE)
if (ymin == -Inf) ymin <- -.Machine$double.xmax
ymax <- max(tmp, ymax, na.rm=TRUE)
if (ymax == Inf) ymax <- .Machine$double.xmax
plotnetsim <- TRUE
legtxt <- c(legtxt, "Fitness (overall net similarity)")
legcol <- c(legcol, "red")
}
}
if (is.element("expref", type))
{
tmp <- x@exprefAll[which(!is.nan(x@exprefAll))]
if (length(tmp) > 0)
{
ymin <- min(tmp, ymin, na.rm=TRUE)
if (ymin == -Inf) ymin <- -.Machine$double.xmax
ymax <- max(tmp, ymax, na.rm=TRUE)
if (ymax == Inf) ymax <- .Machine$double.xmax
plotexpref <- TRUE
legtxt <- c(legtxt, "Sum of exemplar preferences")
legcol <- c(legcol, "green")
}
}
if (is.element("dpsim", type))
{
tmp <- x@dpsimAll[which(!is.nan(x@dpsimAll))]
if (length(tmp) > 0)
{
ymin <- min(tmp, ymin, na.rm=TRUE)
if (ymin == -Inf) ymin <- -.Machine$double.xmax
ymax <- max(tmp, ymax, na.rm=TRUE)
if (ymax == Inf) ymax <- .Machine$double.xmax
plotdpsim <- TRUE
legtxt <- c(legtxt, "Sum of similarities to exemplars")
legcol <- c(legcol, "blue")
}
}
if (length(legtxt) > 0)
{
plot(x=NULL, y=NULL,
xlim=c(0, x@it + 1), ylim=c(ymin, ymax),
xlab=xlab, ylab=ylab, ...)
if (plotnetsim) lines(x@netsimAll, col="red")
if (plotexpref) lines(x@exprefAll, col="green")
if (plotdpsim) lines(x@dpsimAll, col="blue")
legend(x="bottomright", legend=legtxt, col=legcol, lwd=1)
}
else
stop("no valid data was found for plotting; call apcluster() ",
"with 'details=TRUE' in order to compute convergence details")
}
)
setMethod("plot", signature(x="ExClust", y="matrix"),
function(x, y, connect=TRUE, xlab="", ylab="", labels=NA,
limitNo=15, ...)
{
if (x@l != nrow(y))
stop("size of clustering result does not fit to size of data set")
if (ncol(y) < 2)
stop("cannot plot 1D data set")
if (ncol(y) == 2)
{
xlim <- c(min(y[,1]), max(y[,1]))
ylim <- c(min(y[,2]), max(y[,2]))
plot(x=NULL, y=NULL, xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab,
...)
num <- length(x@exemplars)
if (num <= 0)
{
warning("no exemplars defined in clustering result; plotting ",
"data set as it is.")
points(y, col="black", pch=19, cex=0.8)
}
else
{
cols <- rainbow(num)[labels(x, type="enum")]
points(y, col=cols, pch=19, cex=0.8)
if (connect)
segments(x0=y[, 1], y0=y[, 2],
x1=y[x@idx, 1, drop=FALSE],
y1=y[x@idx, 2, drop=FALSE],
col=cols)
points(y[x@exemplars, , drop=FALSE], col="black", type="p",
pch=22, cex=1.5)
}
}
else
{
if (is.numeric(limitNo) && ncol(y) > limitNo)
stop("cannot plot more than ", limitNo, " features at once")
res <- x
num <- length(res@exemplars)
if (num <= 0)
{
warning("no exemplars defined in clustering result; plotting ",
"data set as it is.")
clustCol <- "black"
connect <- FALSE
}
else
clustCol <- rainbow(length(res@exemplars))[labels(x,
type="enum")]
clustPanel <- function(x, y, ...)
{
points(x, y, col=clustCol, pch=19, cex=0.8)
if (connect)
segments(x0=x, y0=y,
x1=x[res@idx],
y1=y[res@idx],
col=clustCol)
if (num > 0)
points(x[res@exemplars], y[res@exemplars], col="black",
type="p",
pch=22, cex=1.5)
}
if (any(is.na(labels)))
{
yname <- deparse(substitute(y, env = parent.frame()))
if (length(colnames(y)) > 0)
labels <- colnames(y)
else
labels <- paste(yname, "[, ", 1:ncol(y), "]", sep="")
}
pairs(y, labels, lower.panel=clustPanel, upper.panel=clustPanel,
...)
}
}
)
# Plot clustering result along with data set
setMethod("plot", signature(x="ExClust", y="data.frame"),
function(x, y, connect=TRUE, xlab="", ylab="", labels=NA, limitNo=15, ...)
{
sel <- which(sapply(y, is.numeric))
if (length(sel) < 2)
stop("cannot plot 1D data set")
if (any(is.na(labels)))
{
yname <- deparse(substitute(y, env = parent.frame()))
if (length(colnames(y)) > 0)
labels <- colnames(y)[sel]
else
labels <- paste(yname, "[, ", sel, "]", sep="")
}
plot(x, as.matrix(y[, sel, drop=FALSE]), connect, xlab, ylab,
labels, limitNo=limitNo, ...)
}
)
# Plot clustering result
setMethod("plot", signature(x="AggExResult", y="missing"),
function(x, y, main="Cluster dendrogram", xlab="", ylab="", ticks=4,
digits=2, base=0.05, showSamples=FALSE, horiz=FALSE, ...)
{
if (x@maxNoClusters < 2)
stop("cannot plot dendrogram with less than 2 clusters")
if (showSamples)
dend <- as.dendrogram(x, base=base)
else
dend <- as.dendrogram(as.hclust(x, base=base))
plot(dend, axes=FALSE, xlab=xlab, ylab=ylab, main=main, horiz=horiz,
...)
if (horiz)
suppressWarnings(
axis(side=1, at=seq(base, 1, length=ticks), tick=TRUE,
labels=as.character(format(seq(max(x@height),
min(x@height),
length=ticks),
digits=digits)), ...))
else
suppressWarnings(
axis(side=2, at=seq(base, 1, length=ticks), tick=TRUE,
labels=as.character(format(seq(max(x@height),
min(x@height),
length=ticks),
digits=digits)), ...))
return(invisible(dend))
}
)
# Plot clustering result along with data set
setMethod("plot", signature(x="AggExResult", y="matrix"),
function(x, y, k=NA, h=NA, ...)
{
if (x@l != nrow(y))
stop("size of clustering result does not fit to size of data set")
if (is.na(k) || !is.numeric(k) || k > x@maxNoClusters)
k <- x@maxNoClusters
if (k< 1)
k <- 1
excl <- cutree(x, k, h)
plot(excl, y, ...)
return(invisible(excl))
}
)
# Plot clustering result along with data set
setMethod("plot", signature(x="AggExResult", y="data.frame"),
function(x, y, k=NA, h=NA, ...)
{
y <- as.matrix(y[, sapply(y, is.numeric)])
plot(x, y, k=k, h=h, ...)
}
)
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