#kpca functionsetGeneric("kpca",function(x, ...) standardGeneric("kpca"))setMethod("kpca", signature(x = "formula"),function(x, data = NULL, na.action = na.omit, ...){    mt <- terms(x, data = data)    if(attr(mt, "response") > 0) stop("response not allowed in formula")    attr(mt, "intercept") <- 0    cl <- match.call()    mf <- match.call(expand.dots = FALSE)    mf$formula <- mf$x    mf$... <- NULL    mf[[1]] <- as.name("model.frame")    mf <- eval(mf, parent.frame())    na.act <- attr(mf, "na.action")    x <- model.matrix(mt, mf)    res <- kpca(x, ...)    ## fix up call to refer to the generic, but leave arg name as `formula'    cl[[1]] <- as.name("kpca")    kcall(res) <- cl    if(!is.null(na.act))         n.action(res) <- na.act    #    if(!is.null(sc <- res$scores))    #        res$scores <- napredict(na.act, sc)    #}    res  })setMethod("kpca",signature(x="matrix"),          function(x, kernel = "rbfdot", kpar = list(sigma = 0.1), features = 0, th = 1e-4, na.action = na.omit, ...){  x <- na.action(x)  x <- as.matrix(x)  m <- nrow(x)  ret <- new("kpca")  if(!is(kernel,"kernel"))    {      if(is(kernel,"function")) kernel <- deparse(substitute(kernel))      kernel <- do.call(kernel, kpar)    }  if(!is(kernel,"kernel")) stop("kernel must inherit from class `kernel'")  km <- kernelMatrix(kernel,x)  #center kernel matrix  i <- matrix((1/m), m, m)  kc <- km - km %*% i - i %*% km + i %*% km %*% i  #compute eigenvectors  res <- eigen(kc/m,symmetric=TRUE)    if(features == 0)    features <- sum(res$values > th)  else     if(res$values[features] < th)      warning(paste("eigenvalues of the kernel matrix are below threshold!"))    pcv(ret) <- res$vectors[,1:features]    eig(ret) <- res$values[1:features]  names(eig(ret)) <- paste("Comp.", 1:features, sep = "")  rotated(ret) <- kc %*% pcv(ret)  kernelf(ret) <- kernel  xmatrix(ret) <- x  ret})#project a new matrix into the feature space setMethod("predict",signature(object="kpca"),function(object , x)  {    if (is.vector(x)||is.data.frame(x))      x<-as.matrix(x)    if (!is.matrix(x)) stop("x must be a matrix a vector or a data frame")    n <- nrow(x)    m <- nrow(xmatrix(object))    knc <- kernelMatrix(kernelf(object),x,xmatrix(object))    ka <- kernelMatrix(kernelf(object),xmatrix(object))    #center    yi <- matrix((1/m), m, m)    xi <- matrix((1/m), n, m)    ret <- knc - knc %*% yi - xi %*% ka + xi %*% ka %*% yi    ret %*% pcv(object)  })#setGeneric("show")#setMethod("show", signature(object = "kpca"),#function(object){##})