Nothing
"predict.learnPattern" <-
function (object, newdata, which.tree=NULL,
nodes=TRUE, maxdepth=NULL, ...)
{
if (!inherits(object, "learnPattern"))
stop("object not of class learnPattern")
if (is.null(object$forest)) stop("No forest component in the object")
if (!nodes&&sum(object$target.type==2)>0) stop("Difference series are used as target segments")
x <- newdata
if (any(is.na(x)))
stop("missing values in newdata")
if (!is.numeric(x)) stop("newdata is not numeric")
if(!is.matrix(x)){
if(length(x)>0){ #single time series
x <- t(as.matrix(x))
}
else{
stop("data (x) has 0 rows")
}
}
if(is.null(maxdepth)) maxdepth <- object$maxdepth
if(maxdepth>object$maxdepth) {
maxdepth <- object$maxdepth
warning("invalid depth: reset to the maximum depth provided during training!")
}
keep <- 1:nrow(x)
rn <- rownames(x)
if (is.null(rn)) rn <- keep
mdim <- ncol(x)
ntest <- nrow(x)
## get rid of warning:
op <- options(warn=-1)
on.exit(options(op))
x <- t(data.matrix(x))
if(!is.null(which.tree)){
if(length(which.tree)==0) stop("No trees are selected!")
usedtrees=array(0,object$ntree)
usedtrees[which.tree]=1
} else {
usedtrees=array(1,object$ntree)
}
if (nodes){
keepIndex <- c("nodeRep","lenRep")
if(!is.null(which.tree)){
nodexts <- integer(ntest * length(which.tree) * object$forest$nrnodes)
} else {
nodexts <- integer(ntest * object$forest$nrnodes * object$ntree )
}
ans <- .C("regForest_represent",
as.double(x),
as.integer(ntest),
as.integer(which.tree),
as.double(object$segment.length),
as.integer(mdim),
as.integer(object$ntree),
as.integer(usedtrees),
object$forest$leftDaughter,
object$forest$rightDaughter,
object$forest$nodestatus,
object$forest$nodedepth,
object$forest$nrnodes,
object$forest$xbestsplit,
object$forest$bestvar,
object$forest$splitType,
object$forest$ndbigtree,
as.integer(maxdepth),
nodeRep = nodexts,
lenRep = integer(1),
PACKAGE = "LPStimeSeries")[keepIndex]
res=t(matrix(ans$nodeRep[1:(ans$lenRep*ntest)], nrow=ans$lenRep))
} else {
keepIndex <- c("predicted","count")
ans <- .C("regForest_predict",
as.double(x),
as.integer(ntest),
as.integer(which.tree),
as.double(object$segment.length),
as.integer(mdim),
as.integer(object$ntree),
as.integer(usedtrees),
object$forest$leftDaughter,
object$forest$rightDaughter,
object$forest$nodestatus,
object$forest$nodedepth,
object$forest$nrnodes,
object$forest$xbestsplit,
as.integer(object$forest$bestvar),
as.integer(object$forest$splitType),
as.double(object$forest$nodepred),
as.integer(object$forest$ndbigtree),
as.integer(object$target),
as.integer(maxdepth),
predicted = double(ntest * mdim),
count = integer(mdim),
PACKAGE = "LPStimeSeries")[keepIndex]
ans$predicted[ans$predicted==-999]=NA
res=list(predictions=t(matrix(ans$predicted, nrow=mdim)),target.count=ans$count)
}
res
}
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