Gaussian process regeresion












0














I have a problem with this code in part where he does GP regression:
GPcov <- function(d,rho){
0.5*exp(-d/rho)+0.5*(d==0)
}



# log likelihood function of rho only
log.like <- function(y,d,rho){
S <- solve(GPcov(d,rho))
l <- 0.5*determinant(S)$modulus[1] -
0.5*t(y)%%S%%y
return(l)}



rho.grid <- seq(0.1,5,length=20)
ll <- rep(NA,20)



for(j in 1:length(ll)){
ll[j] <- log.like(r,do,rho.grid[j])
}



# Pick the MLE
plot(rho.grid,ll,type="l")
The compiler gives me error :"Lapack routine dgesv: system is exactly singular: U[19,19] = 0 " Can you help me to correct this?
https://www4.stat.ncsu.edu/~reich/BigData/code/GP1D.html










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  • This is very difficult to read. By the way, Welcome to the site !
    – Claude Leibovici
    yesterday










  • The exact code is on the link but part that I can't compile is in GP regression and I don't know how to solve it because of dimensions
    – justhope21
    yesterday
















0














I have a problem with this code in part where he does GP regression:
GPcov <- function(d,rho){
0.5*exp(-d/rho)+0.5*(d==0)
}



# log likelihood function of rho only
log.like <- function(y,d,rho){
S <- solve(GPcov(d,rho))
l <- 0.5*determinant(S)$modulus[1] -
0.5*t(y)%%S%%y
return(l)}



rho.grid <- seq(0.1,5,length=20)
ll <- rep(NA,20)



for(j in 1:length(ll)){
ll[j] <- log.like(r,do,rho.grid[j])
}



# Pick the MLE
plot(rho.grid,ll,type="l")
The compiler gives me error :"Lapack routine dgesv: system is exactly singular: U[19,19] = 0 " Can you help me to correct this?
https://www4.stat.ncsu.edu/~reich/BigData/code/GP1D.html










share|cite|improve this question






















  • This is very difficult to read. By the way, Welcome to the site !
    – Claude Leibovici
    yesterday










  • The exact code is on the link but part that I can't compile is in GP regression and I don't know how to solve it because of dimensions
    – justhope21
    yesterday














0












0








0







I have a problem with this code in part where he does GP regression:
GPcov <- function(d,rho){
0.5*exp(-d/rho)+0.5*(d==0)
}



# log likelihood function of rho only
log.like <- function(y,d,rho){
S <- solve(GPcov(d,rho))
l <- 0.5*determinant(S)$modulus[1] -
0.5*t(y)%%S%%y
return(l)}



rho.grid <- seq(0.1,5,length=20)
ll <- rep(NA,20)



for(j in 1:length(ll)){
ll[j] <- log.like(r,do,rho.grid[j])
}



# Pick the MLE
plot(rho.grid,ll,type="l")
The compiler gives me error :"Lapack routine dgesv: system is exactly singular: U[19,19] = 0 " Can you help me to correct this?
https://www4.stat.ncsu.edu/~reich/BigData/code/GP1D.html










share|cite|improve this question













I have a problem with this code in part where he does GP regression:
GPcov <- function(d,rho){
0.5*exp(-d/rho)+0.5*(d==0)
}



# log likelihood function of rho only
log.like <- function(y,d,rho){
S <- solve(GPcov(d,rho))
l <- 0.5*determinant(S)$modulus[1] -
0.5*t(y)%%S%%y
return(l)}



rho.grid <- seq(0.1,5,length=20)
ll <- rep(NA,20)



for(j in 1:length(ll)){
ll[j] <- log.like(r,do,rho.grid[j])
}



# Pick the MLE
plot(rho.grid,ll,type="l")
The compiler gives me error :"Lapack routine dgesv: system is exactly singular: U[19,19] = 0 " Can you help me to correct this?
https://www4.stat.ncsu.edu/~reich/BigData/code/GP1D.html







statistics regression machine-learning






share|cite|improve this question













share|cite|improve this question











share|cite|improve this question




share|cite|improve this question










asked 2 days ago









justhope21

1




1












  • This is very difficult to read. By the way, Welcome to the site !
    – Claude Leibovici
    yesterday










  • The exact code is on the link but part that I can't compile is in GP regression and I don't know how to solve it because of dimensions
    – justhope21
    yesterday


















  • This is very difficult to read. By the way, Welcome to the site !
    – Claude Leibovici
    yesterday










  • The exact code is on the link but part that I can't compile is in GP regression and I don't know how to solve it because of dimensions
    – justhope21
    yesterday
















This is very difficult to read. By the way, Welcome to the site !
– Claude Leibovici
yesterday




This is very difficult to read. By the way, Welcome to the site !
– Claude Leibovici
yesterday












The exact code is on the link but part that I can't compile is in GP regression and I don't know how to solve it because of dimensions
– justhope21
yesterday




The exact code is on the link but part that I can't compile is in GP regression and I don't know how to solve it because of dimensions
– justhope21
yesterday










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