Gaussian process regeresion
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)$modulus[1] -
0.5*t(y)%%S%%y
return(l)}
rho.grid <- seq(0.1,5,length=20)
ll <- rep(NA,20)
for(j in 1:length(ll)){
ll[j] <- log.like(r,do,rho.grid[j])
}
# Pick the MLE
plot(rho.grid,ll,type="l")
The compiler gives me error :"Lapack routine dgesv: system is exactly singular: U[19,19] = 0 " Can you help me to correct this?
https://www4.stat.ncsu.edu/~reich/BigData/code/GP1D.html
statistics regression machine-learning
add a comment |
I have a problem with this code in part where he does GP regression:
GPcov <- function(d,rho){
0.5*exp(-d/rho)+0.5*(d==0)
}
# log likelihood function of rho only
log.like <- function(y,d,rho){
S <- solve(GPcov(d,rho))
l <- 0.5*determinant(S)$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
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
add a comment |
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)$modulus[1] -
0.5*t(y)%%S%%y
return(l)}
rho.grid <- seq(0.1,5,length=20)
ll <- rep(NA,20)
for(j in 1:length(ll)){
ll[j] <- log.like(r,do,rho.grid[j])
}
# Pick the MLE
plot(rho.grid,ll,type="l")
The compiler gives me error :"Lapack routine dgesv: system is exactly singular: U[19,19] = 0 " Can you help me to correct this?
https://www4.stat.ncsu.edu/~reich/BigData/code/GP1D.html
statistics regression machine-learning
I have a problem with this code in part where he does GP regression:
GPcov <- function(d,rho){
0.5*exp(-d/rho)+0.5*(d==0)
}
# log likelihood function of rho only
log.like <- function(y,d,rho){
S <- solve(GPcov(d,rho))
l <- 0.5*determinant(S)$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
statistics regression machine-learning
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
add a comment |
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
add a comment |
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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