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setClass(Class = "MxExpectationMixture",
representation = representation(
weights = "MxCharOrNumber",
components = "MxOptionalCharOrNumber",
verbose = "integer",
scale = "character"
),
contains = "MxBaseExpectation")
setMethod("initialize", "MxExpectationMixture",
function(.Object, components, weights, verbose, scale, name = 'expectation') {
.Object@data = as.integer(NA)
.Object@name <- name
.Object@components <- components
.Object@weights <- weights
.Object@verbose <- verbose
.Object@scale <- scale
.Object
})
setMethod("genericExpDependencies", signature("MxExpectationMixture"),
function(.Object, dependencies) {
components <- paste(.Object@components, "expectation", sep=".")
sources <- c(.Object@weights, components)
dependencies <- imxAddDependency(sources, .Object@name, dependencies)
return(dependencies)
})
setMethod("genericExpFunConvert", signature("MxExpectationMixture"),
function(.Object, flatModel, model, labelsData, dependencies) {
if (length(.Object@components)) {
origComponents <- .Object@components
.Object@components <- vapply(.Object@components, function(group) {
eNumber <- match(paste(group, "expectation", sep="."),
names(flatModel@expectations))
eNumber - 1L
}, 1L, USE.NAMES = FALSE)
if (any(is.na(.Object@components))) {
stop(paste(model@name,": cannot locate expectation ",
omxQuotes(origComponents[is.na(.Object@components)]), sep=""),
call. = FALSE)
}
}
.Object
})
setMethod("qualifyNames", signature("MxExpectationMixture"),
function(.Object, modelname, namespace) {
.Object@name <- imxIdentifier(modelname, .Object@name)
.Object@data <- imxConvertIdentifier(.Object@data,
modelname, namespace)
for (s in c('weights')) {
if (is.null(slot(.Object, s))) next;
slot(.Object, s) <-
imxConvertIdentifier(slot(.Object, s), modelname, namespace)
}
.Object
})
setMethod("genericNameToNumber", signature("MxExpectationMixture"),
function(.Object, flatModel, model) {
name <- .Object@name
.Object@data <- imxLocateIndex(flatModel, .Object@data, name)
.Object@weights <- imxLocateIndex(flatModel, .Object@weights, name)
.Object
})
setMethod("genericGetExpected", signature("MxExpectationMixture"),
function(.Object, model, what, defvar.row=1, subname=model@name) {
ret <- list()
if ('weights' %in% what) {
wname <- .modifyDottedName(model@name, .Object@weights)
weights <- mxEvalByName(wname, model, compute=TRUE, defvar.row=defvar.row)
if (.Object@scale == 'softmax') {
weights <- exp(weights)
} else if (.Object@scale == 'sum') {
#OK
} else if(.Object@scale == 'none') {
#OK
} else { stop(.Object@scale) }
if(.Object@scale != 'none'){
weights <- weights / rowSums(weights)
}
ret[['weights']] <- weights
}
ret
})
setMethod("genericGenerateData", signature("MxExpectationMixture"),
function(.Object, model, nrows, subname, empirical, returnModel, use.miss,
.backend, nrowsProportion, silent) {
origData <- findDataForSubmodel(model, subname)
origRows <- if (!is.null(origData)) { nrowMxData(origData) } else { NULL }
nrows <- calcNumRows(nrows, nrowsProportion, origRows, subname)
cdata <- list()
for (c1 in .Object@components) {
cdata[[c1]] <- mxGenerateData(model, returnModel=FALSE, nrows=nrows, use.miss=FALSE,
subname=c1, empirical=empirical, .backend=.backend)
}
data <- cdata[[1]]
# This is an inefficient way to generate data. It would be
# better to generate cpick first and then generate 1 row
# at a time from the component expectations. I didn't code
# it that way because the API is not really set up for
# generating data 1 row at a time.
cpick <- NULL
doDefVar <- imxHasDefinitionVariable(model)
if(doDefVar){
if (is.null(origData)) {
stop("Definition variable(s) found, but no data is available")
}
if (origData$type != 'raw') {
stop(paste("Definition variable(s) found, but original data is type",
omxQuotes(origData$type)))
}
origData <- origData$observed
if(nrows != nrow(origData)){
stop("Definition variable(s) found, but the number of rows in the data do not match the number of rows requested for data generation.")
}
cpick <- rep(NA, nrows)
for (rx in 1:nrows) {
weights <- mxGetExpected(model, "weights", defvar.row=rx)
cpick[rx] <- sample.int(length(.Object@components), 1, prob=weights)
}
} else {
weights <- mxGetExpected(model, "weights")
cpick <- sample.int(length(.Object@components), nrows, replace=TRUE, prob=weights)
}
if (length(.Object@components) > 1) for (cx in 2:length(.Object@components)) {
data[cpick==cx,] <- cdata[[cx]][cpick == cx,]
}
if(doDefVar){
for (dcol in setdiff(colnames(origData), colnames(data))) {
data[[dcol]] <- origData[[dcol]]
}
}
if (returnModel) {
mxModel(model[[subname]], mxData(as.data.frame(data), "raw"))
} else {
as.data.frame(data)
}
})
mxExpectationMixture <- function(components, weights="weights",
..., verbose=0L, scale=c('softmax', 'sum', 'none')) {
prohibitDotdotdot(list(...))
scale <- match.arg(scale)
new("MxExpectationMixture", components, weights,
as.integer(verbose), scale)
}
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