bclust.r

一般的支持向量机算法比较单一

"bclust" <-
function (x, centers = 2, iter.base = 10, minsize = 0,
          dist.method = "euclidian", hclust.method = "average",
          base.method = "kmeans", base.centers = 20, 
          verbose = TRUE, final.kmeans = FALSE, docmdscale=FALSE,
          resample=TRUE, weights=NULL, maxcluster=base.centers, ...) 
{
    require(mva)
    require(class)

    xr <- nrow(x)
    xc <- ncol(x)
    CLUSFUN <- get(base.method)
    
    object <- list(allcenters =
                    matrix(0, ncol = xc, nrow = iter.base * base.centers),
                  allcluster = NULL,
                  hclust = NULL, 
                  members = NULL,
                  cluster = NULL,
                  centers = NULL,
                  iter.base = iter.base,
                  base.centers = base.centers,
                  prcomp = NULL,
                  datamean = apply(x, 2, mean),
                  colnames = colnames(x),
                  dist.method = dist.method,
                  hclust.method = hclust.method,
                  maxcluster = maxcluster)

    class(object) <- "bclust"

    optSEM <- getOption("show.error.messages")
    if(is.null(optSEM)) optSEM <- TRUE
    on.exit(options(show.error.messages = optSEM))

    if (verbose) cat("Committee Member:")
    for (n in 1:iter.base) {
        if (verbose){
            cat(" ", n, sep = "")
        }
        if(resample){
            x1 <- x[sample(xr, replace = TRUE, prob=weights), ]
        }
        else{
            x1 <- x
        }

        for(m in 1:20){
            cat("(",m,")",sep="")
            options(show.error.messages = FALSE)
            tryres <- try(CLUSFUN(x1, centers = base.centers, ...))
            if(!inherits(tryres, "try-error")) break
        }
        options(show.error.messages = optSEM)
        if(m==20)
            stop("Could not find valid cluster solution in 20 replications\n")
        
        object$allcenters[((n - 1) * base.centers + 1):(n * base.centers),] <-
            tryres$centers
    }
    object$allcenters <- object$allcenters[complete.cases(object$allcenters),]
    object$allcluster <- knn1(object$allcenters, x,
                             factor(1:nrow(object$allcenters)))

    if(minsize > 0){
        object <- prune.bclust(object, x, minsize=minsize)
    }
    
    if (verbose) 
        cat("\nComputing Hierarchical Clustering\n")
    object <- hclust.bclust(object, x = x, centers = centers,
                           final.kmeans = final.kmeans,
                           docmdscale=docmdscale)
    object
}

"centers.bclust" <- function (object, k) 
{
    centers <- matrix(0, nrow = k, ncol = ncol(object$allcenters))
    for (m in 1:k) {
        centers[m, ] <-
            apply(object$allcenters[object$members[,k-1] == m, , drop = FALSE], 2, mean)
    }
    centers
}

"clusters.bclust" <- function (object, k, x=NULL)
{
    if(missing(x))
        allcluster <- object$allcluster
    else
        allcluster <- knn1(object$allcenters, x,
                           factor(1:nrow(object$allcenters)))
    
    return(object$members[allcluster, k - 1])
}


"hclust.bclust" <-
    function (object, x, centers, dist.method = object$dist.method,
              hclust.method = object$hclust.method, final.kmeans = FALSE,
              docmdscale = FALSE, maxcluster=object$maxcluster) 
{
    require(mva)
    require(class)

    d <- dist(object$allcenters, method = dist.method)
    if(hclust.method=="diana")
        object$hclust <- as.hclust(diana(d, diss=TRUE))
    else
        object$hclust <- hclust(d, method = hclust.method)
    
    if(docmdscale){
        object$cmdscale <- cmdscale(d)
    }
    
    object$members <- cutree(object$hclust, 2:maxcluster)
    object$cluster <- clusters.bclust(object, centers)
    object$centers <- centers.bclust(object, centers)
    if (final.kmeans) {
        kmeansres <- kmeans(x, centers = object$centers)
        object$centers <- kmeansres$centers
        object$cluster <- kmeansres$cluster
    }
    object
}



"plot.bclust" <-
    function (x, maxcluster=x$maxcluster,
              main = deparse(substitute(x)), ...) 
{
    require(mva)
    opar <- par(c("mar", "oma"))
    on.exit(par(opar))

    par(oma = c(0, 0, 3, 0))
    layout(matrix(c(1, 1, 2, 2), 2, 2, byrow = TRUE))
    par(mar = c(0, 4, 4, 1))
    plot(x$hclust, labels = FALSE, hang = -1)
    x1 <- 1:maxcluster
    x2 <- 2:maxcluster
    y <- rev(x$hclust$height)[x1]
    z <- abs(diff(y))
    par(mar = c(4, 4, 1, 2))
    plot(x1, ((y - min(y))/(max(y) - min(y))), ty = "l", xlab = "", 
         ylab = "", ylim = c(0, 1))
    lines(x2, z/sum(z), col = "grey")
    text(x2, z/sum(z), labels = as.character(x2))
#    lx2 <- length(x2)
#    abline(h=qexp(.95, rate = length(x2)), lty=3, col="grey")
#    abline(h=qexp(.95^(1/lx2), rate = length(x2)), lty=3, col="grey")
    mtext(main, outer = TRUE, cex = 1.5)
    layout(1)
}

"boxplot.bclust" <-
function (x, n = nrow(x$centers), bycluster = TRUE,
          main = deparse(substitute(x)), oneplot=TRUE,
          which=1:n, ...) 
{
    require(mva)
    N <- length(which)

    opar <- par(c("mfrow", "oma", "mgp","xpd"))
    on.exit(par(opar))
    par(xpd=NA)
    
    memb <- x$members[, (n - 1)]
    tmemb <- table(memb)
    cendf <- as.data.frame(x$allcenters)
    ylim <- range(x$allcenters)

    if (bycluster) {
        if(oneplot){
            if (N <= 3) {
                par(mfrow = c(N, 1))
            }
            else {
                par(mfrow = c(ceiling(N/2), 2))
            }
        }
        tcluster <- table(clusters.bclust(x, n))
        for (k in which) {
            boxplot(cendf[memb == k, ], col = "grey",
                    names = rep("",ncol(cendf)),
                    ylim = ylim, ...)
            if (x$datamean) {
                lines(x$datamean, col = "red")
            }
            if(!is.null(x$colnames)){
                text(1:length(x$colnames)+0.2,
                     par("usr")[3],
                     adj=1,srt=35,
                     paste(x$colnames, "  "))
            }

            title(main = paste("Cluster ", k, ": ", tmemb[k], 
                  " centers, ", tcluster[k], " data points", sep = ""))
        }
    }
    else {
        a <- ceiling(sqrt(ncol(cendf)))
        if(oneplot){
            par(mfrow = c(a, ceiling(ncol(cendf)/a)))
        }
        memb <- as.factor(memb)
        for (k in 1:ncol(cendf)) {
            boxplot(cendf[, k] ~ memb, col = "grey", ylim = ylim, ...)
            title(main = x$colnames[k])
            abline(h = x$datamean[k], col = "red")
        }
    }
}

### prune centers that contain not at least minsize data points
prune.bclust <- function(object, x, minsize=1, dohclust=FALSE, ...){

    ok <- FALSE
    while(!all(ok)){
        object$allcluster <- knn1(object$allcenters, x,
                                 factor(1:nrow(object$allcenters)))
        
        ok <- table(object$allcluster) >= minsize
        object$allcenters <- object$allcenters[ok, ]
    }
    if(dohclust){
        object <- hclust.bclust(object, x, nrow(object$centers), ...)
    }
    object
}