kernel_gaussian <- function(x, m=0.0002001131, s=0.0939948369){
#dnorm(x, m, s)
(1/sqrt(2*pi*s^2))*exp(-(x-m)^2/(2*s^2))
}
kernel_laplace <- function(x, m=-0.001785626, b=0.061982848){
(1/(2*b)) * exp(-abs(x - m) / b)
}
# Parameters obtained from CTRPv2 data; write into function parameters
# Assume you already know the kernel because you're a badass
# Kernel learned from CTRPv2 data and assumed to generalize.
kernel_sigmoid <- function(x) {
1/(1 + exp(-10.99319 * (x - 0.09631)))
}
find.original.name <- function(fun) {
objects <- ls(envir = environment(fun))
for (i in objects) {
if (identical(fun, get(i, envir = environment(fun)))) {
return(i)
}
}
}
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