Nothing
# Copyright (C) 2011-2012 Julia Schiffner
# Copyright (C) 2004-2011 Friedrich Leisch and Bettina Gruen
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 or 3 of the License
# (at your option).
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# A copy of the GNU General Public License is available at
# http://www.r-project.org/Licenses/
#
#' @rdname FLXMCL
#' @aliases FLXMCLsvm-class
#'
#' @import flexmix
#' @export
setClass("FLXMCLsvm", contains = "FLXMCL")
#' This is a model driver for \code{\link[flexmix]{flexmix}} implementing mixtures of Support Vector Machines.
#'
#' @title Mixtures of Support Vector Machines
#' @param formula A formula which is interpreted relative to the formula specified in the call to \code{\link[flexmix]{flexmix}} using \code{\link[stats]{update.formula}}.
#' Only the left-hand side (response) of the formula is used. Default is to use the original \code{\link[flexmix]{flexmix}} model formula.
#' @param \dots Further arguments to and from other methods.
#'
#' @return Returns an object of class \code{FLXMCLsvm} inheriting from \code{FLXMCL}.
#'
#' @rdname FLXMCLsvm
#' @aliases FLXMCLsvm
#'
#' @import flexmix
#' @export
#'
#' @examples
#' library(locClassData)
#' data <- flashData(1000)
#' grid <- expand.grid(x.1=seq(-6,6,0.2), x.2=seq(-4,4,0.2))
#'
#' cluster <- kmeans(data$x, center = 2)$cluster
#' model <- FLXMCLsvm(kernel = "linear")
#' fit <- flexmix(y ~ ., data = as.data.frame(data), concomitant = FLXPwlda(~ x.1 + x.2), model = model, cluster = cluster, control = list(classify = "hard"))
#'
#' ## prediction for single component models without aggregation
#' pred.grid <- predict(fit, newdata = grid)
#' image(seq(-6,6,0.2), seq(-4,4,0.2), matrix(pred.grid[[1]]["decision",][[1]][,1], length(seq(-6,6,0.2))))
#' contour(seq(-6,6,0.2), seq(-4,4,0.2), matrix(pred.grid[[1]]["decision",][[1]][,1], length(seq(-6,6,0.2))), add = TRUE)
#' points(data$x, pch = as.character(data$y))
#'
#' image(seq(-6,6,0.2), seq(-4,4,0.2), matrix(pred.grid[[2]]["decision",][[1]][,1], length(seq(-6,6,0.2))))
#' contour(seq(-6,6,0.2), seq(-4,4,0.2), matrix(pred.grid[[2]]["decision",][[1]][,1], length(seq(-6,6,0.2))), add = TRUE)
#' points(data$x, pch = as.character(data$y))
#'
#' ## prediction with aggregation depending on membership in mixture components
#' pred.grid <- mypredict(fit, newdata = grid, aggregate = TRUE)
#' image(seq(-6,6,0.2), seq(-4,4,0.2), matrix(pred.grid[[1]]$decision[,1], length(seq(-6,6,0.2))))
#' contour(seq(-6,6,0.2), seq(-4,4,0.2), matrix(pred.grid[[1]]$decision[,1], length(seq(-6,6,0.2))), add = TRUE)
#' points(data$x, pch = as.character(data$y))
#'
#' image(seq(-6,6,0.2), seq(-4,4,0.2), matrix(pred.grid[[1]]$posterior[,1], length(seq(-6,6,0.2))))
#' contour(seq(-6,6,0.2), seq(-4,4,0.2), matrix(pred.grid[[1]]$posterior[,1], length(seq(-6,6,0.2))), add = TRUE)
#' points(data$x, pch = as.character(data$y))
#'
#' ## local memberhsip
#' loc.grid <- prior(fit, newdata = grid)
#' contour(seq(-6,6,0.2), seq(-4,4,0.2), matrix(loc.grid[,1], length(seq(-6,6,0.2))), add = TRUE)
# library(mlr)
# task <- makeClassifTask(data = as.data.frame(data), target = "y")
# lrn <- makeLearner("classif.FLXMCLsvm", kernel = "linear", centers = 2)
# tr <- train(lrn, task = task)
# pr <- predict(tr, newdata = grid)
FLXMCLsvm <- function(formula = . ~ ., ...) {
z <- new("FLXMCLsvm", weighted = TRUE, formula = formula,
name = "Mixture of SVM models")
z@defineComponent <- expression({
predict <- function(x, ...) {
pred <- getS3method("predict", "wsvm")(fit, newdata = x, probability = TRUE, decision.values = TRUE, ...)
probs <- attr(pred, "probabilities")
decs <- attr(pred, "decision.values")
lev <- levels(pred)
ng <- length(lev)
nl <- length(fit$labels)
# cat("nl\n")
# print(nl)
# cat("diff(fit$labels)\n")
# print(diff(fit$labels))
if (any(diff(fit$labels) < 0)) { # order of levels and labels different
# binary classification problems
problems <- cbind(rep(fit$labels, nl:1-1), unlist(sapply(2:nl, function(x) fit$labels[x:nl])))
# cat("problems\n")
# print(problems)
# binary problems where first label is larger than second label
col.index <- problems[,1] > problems[,2]
# cat("col.index\n")
# print(col.index)
# print(head(decs))
# change sign for these binary problems and adjust the colnames
decs[,col.index] <- decs[,col.index] * (-1)
colnames(decs)[col.index] <- apply(problems[col.index,,drop = FALSE], 1, function(l) paste(lev[l[2]], lev[l[1]], sep = "/"))
# print(head(decs))
}
if (ng > nl) {
# add columns for missing classes in posterior probability and decision value matrices
posterior <- matrix(0, nrow(probs), ng)
rownames(posterior) <- rownames(probs)
colnames(posterior) <- lev
posterior[,colnames(probs)] <- probs
decision <- matrix(0, nrow(decs), ng * (ng - 1) / 2)
## Was ist, wenn in Komp. 1 Klasse 1 vorhanden und in Komp. 2 Klassen 1 und 2?
## Welche Werte sollte decision in Komp. 1 haben? 0 eigentlich nicht sinnvoll, weil Klasse 2 nie vorhergesagt werden kann...
## sollte hohen Wert für Klasse 1 haben...
colnames(decision) <- paste(rep(lev, ng:1-1), rep(lev, 1:ng-1), sep = "/")
decision[,colnames(decs)] <- decs
} else {
# sort columns of posterior probability and decision value matrices
cnames <- colnames(probs)
posterior <- probs[,sort(cnames)]
decision <- decs[,paste(rep(lev, ng:1-1), rep(lev, 1:ng-1), sep = "/"), drop = FALSE]
}
# print(head(decision))
return(list(posterior = posterior, decision = decision))
}
logLik <- function(x, y, ...) {
# #dec <- attr(getS3method("predict", "wsvm")(fit, newdata = x, decision.values = TRUE, ...), "decision.values")
# dec <- fit$decision.values
# #print(dec)
# #bin.problems <- colnames(dec)
# # 2 classes
# labels <- fit$labels ## if dec positive decision for first of the two class labels
# correct <- (as.character(y) == labels[1]) * 2 - 1
# ll <- exp(correct * dec)
# # ll1 <- ll/max(ll)
# #print(ll1)
# return(ll)
# # > 2 classes
post <- attr(getS3method("predict", "wsvm")(fit, newdata = x, probability = TRUE, ...), "probabilities")
ng <- length(attr(y, "lev"))
# print(head(post))
# print(head(y))
if (ng > ncol(post)) {
ll <- rep(0, nrow(post))
col.index <- match(y, colnames(post), 0)
row.index <- which(col.index > 0)
ll[row.index] <- post[cbind(row.index, col.index[row.index])]
} else {
ll <- post[cbind(rownames(post), as.character(y))]
}
# print(head(ll))
return(ll)
}
new("FLXcomponent", parameters = list(coefs = fit$coefs),
logLik = logLik, predict = predict, df = fit$df)
})
z@preproc.y <- function(y) {
if (is.factor(y)) {
lev <- levels(y)
y <- as.matrix(y)
attr(y, "lev") <- lev
return(y)
} else
return(as.matrix(y))
}
z@fit <- function(x, y, w) {
lev <- attr(y, "lev")
w <- w/sum(w) * nrow(x)
if (is.null(lev))
fit <- wsvm(x, as.vector(y), case.weights = w, probability = TRUE, ...)
else
fit <- wsvm(x, factor(y, levels = lev), case.weights = w, probability = TRUE, ...)
# print(str(fit))
fit$df <- sum(fit$nSV)
with(fit, eval(z@defineComponent))
}
z
}
#' @rdname FLXMCLsvm
#' @aliases FLXgetModelmatrix,FLXMCLsvm-method
#'
#' @import flexmix
#' @export
#'
#' @docType methods
setMethod("FLXgetModelmatrix", signature(model = "FLXMCLsvm"),
function (model, data, formula, lhs = TRUE, ...) {
formula <- flexmix:::RemoveGrouping(formula)
if (length(grep("\\|", deparse(model@formula))))
stop("no grouping variable allowed in the model")
if (is.null(model@formula))
model@formula = formula
model@fullformula = update(terms(formula, data = data),
model@formula)
if (lhs) {
mf <- if (is.null(model@terms))
model.frame(model@fullformula, data = data, na.action = NULL)
else model.frame(model@terms, data = data, na.action = NULL)
model@terms <- attr(mf, "terms")
modely <- model.response(mf)
model@y <- model@preproc.y(modely)
}
else {
mt1 <- if (is.null(model@terms))
terms(model@fullformula, data = data)
else model@terms
mf <- model.frame(delete.response(mt1), data = data,
na.action = NULL)
model@terms <- attr(mf, "terms")
}
attr(model@terms, "intercept") <- 0 ## intercept removed
X <- model.matrix(model@terms, data = mf)
model@contrasts <- attr(X, "contrasts")
model@x <- X
model@x <- model@preproc.x(model@x)
model@xlevels <- .getXlevels(model@terms, mf)
model
})
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.