Application of Sharman-Morrison for Scalability
0
I have a fully functioning code in R. At the moment I am inverting the matrix using Cholesky decomposition. I want to adopt the code for Sharman Morrison:
X<- matrix(rnorm(10000),20,20)
Y<-rnorm(20)
OSLOG<-function(X,Y,a){
if(a<=0){
print("a must be a positive number")
}else{
X<-as.matrix(X)
Y<-as.matrix(Y)
T<-nrow(X)
N<-ncol(X)
bt<- matrix(0,ncol=1,nrow=N)
At<- diag(0,N)
pred<- matrix(0,nrow=T,ncol=1)
theta0<- rep(1,N)
for (t in 1:T){
xt<-X[t,]
pred[t] <- crossprod(as.matrix(theta0), xt)
Dt <- diag(sqrt(abs(c(theta0))))
D <- outer(diag(Dt),diag(Dt))
At <- At + tcrossprod(xt,xt)
InvA <- chol2inv(chol(diag(a,N) + At*D))
bt <- bt + (Y[t] * xt)
theta0 <- crossprod(InvA *D,bt)
}
res<-postResample(pred = pred, obs = Y)
stats<- as.matrix(res)
quant<-quantile(as.matrix(Y)-as.matrix(pred),probs=c(.25,.50,.75))
return(list(predictions=pred,performance=stats,quantiles=quant))
}
}
OSLOG(X,Y,0.00001)$performance
The above code works perfectly well. Is it possible to use Sharman-Morrison in the above code? Also, any other thing I can do to improve performance?
My attempt
OSLOGb <- function(X,Y,a){
if(a <= 0){
print("a must be a positive number")
}else{
X <- as.matrix(X)
Y <- as.matrix(Y)
T <- nrow(X)
N <- ncol(X)
bt <- matrix(0,ncol=1,nrow=N)
At <- diag(1/a,N)
pred <- matrix(0,nrow=T,ncol=1)
theta0 <- rep(1,N)
for (t in 1:T){
xt <- X[t,]
pred[t] <- crossprod(as.matrix(theta0), xt)
Dt <- diag(sqrt(abs(c(theta0))))
D <- outer(diag(Dt),diag(Dt))
At <- At + tcrossprod(xt,xt)
At <- At - (tcrossprod(crossprod(At ,xt),crossprod(At,xt)) / as.numeric(crossprod(xt,crossprod(At,xt))+1))
bt <- bt + (Y[t] * xt)
theta0 <- crossprod(At * D ,bt)
}
res <- postResample(pred = pred, obs = Y)
stats<- as.matrix(res)
quant<-quantile(as.matrix(Y)-as.matrix(pred),probs=c(.25,.50,.75))
return(list(predictions=pred,performance=stats,quantiles=quant))
}
}
OSLOGb(X,Y,0.00001)$performance
Unfortunately, this is not the correct solution. Can someone help me with this?
performance r
asked 2 mins ago
Jamil
1
New contributor
Jamil is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
0
I have a fully functioning code in R. At the moment I am inverting the matrix using Cholesky decomposition. I want to adopt the code for Sharman Morrison:
X<- matrix(rnorm(10000),20,20)
Y<-rnorm(20)
OSLOG<-function(X,Y,a){
if(a<=0){
print("a must be a positive number")
}else{
X<-as.matrix(X)
Y<-as.matrix(Y)
T<-nrow(X)
N<-ncol(X)
bt<- matrix(0,ncol=1,nrow=N)
At<- diag(0,N)
pred<- matrix(0,nrow=T,ncol=1)
theta0<- rep(1,N)
for (t in 1:T){
xt<-X[t,]
pred[t] <- crossprod(as.matrix(theta0), xt)
Dt <- diag(sqrt(abs(c(theta0))))
D <- outer(diag(Dt),diag(Dt))
At <- At + tcrossprod(xt,xt)
InvA <- chol2inv(chol(diag(a,N) + At*D))
bt <- bt + (Y[t] * xt)
theta0 <- crossprod(InvA *D,bt)
}
res<-postResample(pred = pred, obs = Y)
stats<- as.matrix(res)
quant<-quantile(as.matrix(Y)-as.matrix(pred),probs=c(.25,.50,.75))
return(list(predictions=pred,performance=stats,quantiles=quant))
}
}
OSLOG(X,Y,0.00001)$performance
The above code works perfectly well. Is it possible to use Sharman-Morrison in the above code? Also, any other thing I can do to improve performance?
My attempt
OSLOGb <- function(X,Y,a){
if(a <= 0){
print("a must be a positive number")
}else{
X <- as.matrix(X)
Y <- as.matrix(Y)
T <- nrow(X)
N <- ncol(X)
bt <- matrix(0,ncol=1,nrow=N)
At <- diag(1/a,N)
pred <- matrix(0,nrow=T,ncol=1)
theta0 <- rep(1,N)
for (t in 1:T){
xt <- X[t,]
pred[t] <- crossprod(as.matrix(theta0), xt)
Dt <- diag(sqrt(abs(c(theta0))))
D <- outer(diag(Dt),diag(Dt))
At <- At + tcrossprod(xt,xt)
At <- At - (tcrossprod(crossprod(At ,xt),crossprod(At,xt)) / as.numeric(crossprod(xt,crossprod(At,xt))+1))
bt <- bt + (Y[t] * xt)
theta0 <- crossprod(At * D ,bt)
}
res <- postResample(pred = pred, obs = Y)
stats<- as.matrix(res)
quant<-quantile(as.matrix(Y)-as.matrix(pred),probs=c(.25,.50,.75))
return(list(predictions=pred,performance=stats,quantiles=quant))
}
}
OSLOGb(X,Y,0.00001)$performance
Unfortunately, this is not the correct solution. Can someone help me with this?
performance r
asked 2 mins ago
Jamil
1
New contributor
Jamil is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
0
0
0
I have a fully functioning code in R. At the moment I am inverting the matrix using Cholesky decomposition. I want to adopt the code for Sharman Morrison:
X<- matrix(rnorm(10000),20,20)
Y<-rnorm(20)
OSLOG<-function(X,Y,a){
if(a<=0){
print("a must be a positive number")
}else{
X<-as.matrix(X)
Y<-as.matrix(Y)
T<-nrow(X)
N<-ncol(X)
bt<- matrix(0,ncol=1,nrow=N)
At<- diag(0,N)
pred<- matrix(0,nrow=T,ncol=1)
theta0<- rep(1,N)
for (t in 1:T){
xt<-X[t,]
pred[t] <- crossprod(as.matrix(theta0), xt)
Dt <- diag(sqrt(abs(c(theta0))))
D <- outer(diag(Dt),diag(Dt))
At <- At + tcrossprod(xt,xt)
InvA <- chol2inv(chol(diag(a,N) + At*D))
bt <- bt + (Y[t] * xt)
theta0 <- crossprod(InvA *D,bt)
}
res<-postResample(pred = pred, obs = Y)
stats<- as.matrix(res)
quant<-quantile(as.matrix(Y)-as.matrix(pred),probs=c(.25,.50,.75))
return(list(predictions=pred,performance=stats,quantiles=quant))
}
}
OSLOG(X,Y,0.00001)$performance
The above code works perfectly well. Is it possible to use Sharman-Morrison in the above code? Also, any other thing I can do to improve performance?
My attempt
OSLOGb <- function(X,Y,a){
if(a <= 0){
print("a must be a positive number")
}else{
X <- as.matrix(X)
Y <- as.matrix(Y)
T <- nrow(X)
N <- ncol(X)
bt <- matrix(0,ncol=1,nrow=N)
At <- diag(1/a,N)
pred <- matrix(0,nrow=T,ncol=1)
theta0 <- rep(1,N)
for (t in 1:T){
xt <- X[t,]
pred[t] <- crossprod(as.matrix(theta0), xt)
Dt <- diag(sqrt(abs(c(theta0))))
D <- outer(diag(Dt),diag(Dt))
At <- At + tcrossprod(xt,xt)
At <- At - (tcrossprod(crossprod(At ,xt),crossprod(At,xt)) / as.numeric(crossprod(xt,crossprod(At,xt))+1))
bt <- bt + (Y[t] * xt)
theta0 <- crossprod(At * D ,bt)
}
res <- postResample(pred = pred, obs = Y)
stats<- as.matrix(res)
quant<-quantile(as.matrix(Y)-as.matrix(pred),probs=c(.25,.50,.75))
return(list(predictions=pred,performance=stats,quantiles=quant))
}
}
OSLOGb(X,Y,0.00001)$performance
Unfortunately, this is not the correct solution. Can someone help me with this?
performance r
asked 2 mins ago
Jamil
1
New contributor
Jamil is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
I have a fully functioning code in R. At the moment I am inverting the matrix using Cholesky decomposition. I want to adopt the code for Sharman Morrison:
X<- matrix(rnorm(10000),20,20)
Y<-rnorm(20)
OSLOG<-function(X,Y,a){
if(a<=0){
print("a must be a positive number")
}else{
X<-as.matrix(X)
Y<-as.matrix(Y)
T<-nrow(X)
N<-ncol(X)
bt<- matrix(0,ncol=1,nrow=N)
At<- diag(0,N)
pred<- matrix(0,nrow=T,ncol=1)
theta0<- rep(1,N)
for (t in 1:T){
xt<-X[t,]
pred[t] <- crossprod(as.matrix(theta0), xt)
Dt <- diag(sqrt(abs(c(theta0))))
D <- outer(diag(Dt),diag(Dt))
At <- At + tcrossprod(xt,xt)
InvA <- chol2inv(chol(diag(a,N) + At*D))
bt <- bt + (Y[t] * xt)
theta0 <- crossprod(InvA *D,bt)
}
res<-postResample(pred = pred, obs = Y)
stats<- as.matrix(res)
quant<-quantile(as.matrix(Y)-as.matrix(pred),probs=c(.25,.50,.75))
return(list(predictions=pred,performance=stats,quantiles=quant))
}
}
OSLOG(X,Y,0.00001)$performance
The above code works perfectly well. Is it possible to use Sharman-Morrison in the above code? Also, any other thing I can do to improve performance?
My attempt
OSLOGb <- function(X,Y,a){
if(a <= 0){
print("a must be a positive number")
}else{
X <- as.matrix(X)
Y <- as.matrix(Y)
T <- nrow(X)
N <- ncol(X)
bt <- matrix(0,ncol=1,nrow=N)
At <- diag(1/a,N)
pred <- matrix(0,nrow=T,ncol=1)
theta0 <- rep(1,N)
for (t in 1:T){
xt <- X[t,]
pred[t] <- crossprod(as.matrix(theta0), xt)
Dt <- diag(sqrt(abs(c(theta0))))
D <- outer(diag(Dt),diag(Dt))
At <- At + tcrossprod(xt,xt)
At <- At - (tcrossprod(crossprod(At ,xt),crossprod(At,xt)) / as.numeric(crossprod(xt,crossprod(At,xt))+1))
bt <- bt + (Y[t] * xt)
theta0 <- crossprod(At * D ,bt)
}
res <- postResample(pred = pred, obs = Y)
stats<- as.matrix(res)
quant<-quantile(as.matrix(Y)-as.matrix(pred),probs=c(.25,.50,.75))
return(list(predictions=pred,performance=stats,quantiles=quant))
}
}
OSLOGb(X,Y,0.00001)$performance
Unfortunately, this is not the correct solution. Can someone help me with this?
performance r
performance r
asked 2 mins ago
Jamil
1
New contributor
Jamil is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 mins ago
Jamil
1
New contributor
Jamil is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 mins ago
Jamil
1
New contributor
Jamil is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 mins ago
Jamil
1
asked 2 mins ago
Jamil
1
1
New contributor
Jamil is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Jamil is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Jamil is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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