Nothing
## fgasp Class
setClass("fgasp",
representation(
num_obs = "integer", ## observations number
have_noise="logical", ## whether the data contain noise
kernel_type="character", ## information of the kernel
## data
input="vector", ## location of the sort input
delta_x="vector", ## distance between each sorted input
output = "vector" ## the observations, size nx1
),
)
## show
if(!isGeneric("show")) {
setGeneric(name = "show",
def = function(object) standardGeneric("show")
)
}
setMethod("show", "fgasp",
function(object){
show.fgasp(object)
}
)
## pred.fgasp Class
setClass("predictobj.fgasp", representation(
num_testing="vector", ##the number of testing input
testing_input="vector", ##sorted testing input
param="vector", ##param
mean = "vector", ##predictive mean
var="vector", ##predictive variance
var_data="logical" ##whether to calculate the predictive variance of the data.
),
)
if(!isGeneric("predict")) {
setGeneric(name = "predict",
def = function(object,...) standardGeneric("predict")
)
}
setMethod("predict", "fgasp",
definition=function(param=NA,object, testing_input, var_data=TRUE,sigma_2=NULL){
predict.fgasp(param=param,object=object,testing_input=testing_input,var_data=var_data,
sigma_2=sigma_2)
}
)
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