#plot(AIC,type="l",main="Tucker Decomposition AIC",xlab="Rank Values",ylab="AIC")
AIC
min(AIC)
P <- cp(X, num_components = Min , max_iter = 50, tol = 1e-05)
# We need to sort lamdas in descending order
lambdas <- P$lambdas
index <- order(lambdas, decreasing = TRUE)
lambdas <- lambdas[index]
A <- P$U[[1]][,index]
B <- P$U[[2]][,index]
C <- P$U[[3]][,index]
#dev.off()
matplot(C,type="l",main="",xlab="",ylab="")
#par(mar=c(1,1,1,1))
par(mfrow=c(1,2))
par(mfrow=c(1,Min))
for (i in 1:Min){
XY <- kronecker(A[,i],t(B[,i]))*lambdas[i]
image(XY)
}
library(rTensor)
library(R.matlab)
rm(list = ls())
T1 <- readMat('heatT.mat')$T1$data
T2 <- readMat('heatT.mat')$T2$data
T3 <- readMat('heatT.mat')$T3$data
tensor_list = list(T1,T2,T3)
#for (tensor in tensor_list) {
#T1 <- readMat('heatT.mat')[tensor]$data
X <- as.tensor(T1) ## read the MATLAB generated Heat transfer data
### CP Decomposition
## min(IJ,JK,IK) = 21*10 = 210
#X1 <- k_unfold(X,1)
err <- rep(0,10)
AIC <- rep(0,10)
for (i in 1:10){
P <- cp(X, num_components = i, max_iter = 50, tol = 1e-05)
DD <- as.tensor(P$est@data)
err[i] <- fnorm(X - DD)^2
AIC[i] <- 2*err[i] + 2*i
}
Min <- which.min(AIC)
Min
#plot(AIC,type="l",main="Tucker Decomposition AIC",xlab="Rank Values",ylab="AIC")
AIC
min(AIC)
P <- cp(X, num_components = Min , max_iter = 50, tol = 1e-05)
# We need to sort lamdas in descending order
lambdas <- P$lambdas
index <- order(lambdas, decreasing = TRUE)
lambdas <- lambdas[index]
A <- P$U[[1]][,index]
B <- P$U[[2]][,index]
C <- P$U[[3]][,index]
#dev.off()
matplot(C,type="l",main="",xlab="",ylab="")
#par(mar=c(1,1,1,1))
par(mfrow=c(1,2))
par(mfrow=c(1,Min))
for (i in 1:Min){
XY <- kronecker(A[,i],t(B[,i]))*lambdas[i]
image(XY)
}
library(rTensor)
library(R.matlab)
rm(list = ls())
T1 <- readMat('heatT.mat')$T1$data
T2 <- readMat('heatT.mat')$T2$data
T3 <- readMat('heatT.mat')$T3$data
tensor_list = list(T1,T2,T3)
#for (tensor in tensor_list) {
#T1 <- readMat('heatT.mat')[tensor]$data
X <- as.tensor(T1) ## read the MATLAB generated Heat transfer data
### CP Decomposition
## min(IJ,JK,IK) = 21*10 = 210
#X1 <- k_unfold(X,1)
err <- rep(0,10)
AIC <- rep(0,10)
for (i in 1:10){
P <- cp(X, num_components = i, max_iter = 50, tol = 1e-05)
DD <- as.tensor(P$est@data)
err[i] <- fnorm(X - DD)^2
AIC[i] <- 2*err[i] + 2*i
}
Min <- which.min(AIC)
Min
#plot(AIC,type="l",main="Tucker Decomposition AIC",xlab="Rank Values",ylab="AIC")
AIC
min(AIC)
P <- cp(X, num_components = Min , max_iter = 50, tol = 1e-05)
# We need to sort lamdas in descending order
lambdas <- P$lambdas
index <- order(lambdas, decreasing = TRUE)
lambdas <- lambdas[index]
A <- P$U[[1]][,index]
B <- P$U[[2]][,index]
C <- P$U[[3]][,index]
#dev.off()
matplot(C,type="l",main="",xlab="",ylab="")
par(mar=c(1,1,1,1))
par(mfrow=c(1,2))
par(mfrow=c(1,Min))
for (i in 1:Min){
XY <- kronecker(A[,i],t(B[,i]))*lambdas[i]
image(XY)
}
library(rTensor)
library(R.matlab)
rm(list = ls())
T1 <- readMat('heatT.mat')$T1$data
T2 <- readMat('heatT.mat')$T2$data
T3 <- readMat('heatT.mat')$T3$data
tensor_list = list(T1,T2,T3)
#for (tensor in tensor_list) {
#T1 <- readMat('heatT.mat')[tensor]$data
X <- as.tensor(T2) ## read the MATLAB generated Heat transfer data
### CP Decomposition
## min(IJ,JK,IK) = 21*10 = 210
#X1 <- k_unfold(X,1)
err <- rep(0,10)
AIC <- rep(0,10)
for (i in 1:10){
P <- cp(X, num_components = i, max_iter = 50, tol = 1e-05)
DD <- as.tensor(P$est@data)
err[i] <- fnorm(X - DD)^2
AIC[i] <- 2*err[i] + 2*i
}
Min <- which.min(AIC)
Min
#plot(AIC,type="l",main="Tucker Decomposition AIC",xlab="Rank Values",ylab="AIC")
AIC
min(AIC)
P <- cp(X, num_components = Min , max_iter = 50, tol = 1e-05)
# We need to sort lamdas in descending order
lambdas <- P$lambdas
index <- order(lambdas, decreasing = TRUE)
lambdas <- lambdas[index]
A <- P$U[[1]][,index]
B <- P$U[[2]][,index]
C <- P$U[[3]][,index]
#dev.off()
matplot(C,type="l",main="",xlab="",ylab="")
#par(mar=c(1,1,1,1))
par(mfrow=c(1,2))
par(mfrow=c(1,Min))
for (i in 1:Min){
XY <- kronecker(A[,i],t(B[,i]))*lambdas[i]
image(XY)
}
library(rTensor)
library(R.matlab)
rm(list = ls())
T1 <- readMat('heatT.mat')$T1$data
T2 <- readMat('heatT.mat')$T2$data
T3 <- readMat('heatT.mat')$T3$data
tensor_list = list(T1,T2,T3)
#for (tensor in tensor_list) {
#T1 <- readMat('heatT.mat')[tensor]$data
X <- as.tensor(T2) ## read the MATLAB generated Heat transfer data
### CP Decomposition
## min(IJ,JK,IK) = 21*10 = 210
#X1 <- k_unfold(X,1)
err <- rep(0,10)
AIC <- rep(0,10)
for (i in 1:10){
P <- cp(X, num_components = i, max_iter = 50, tol = 1e-05)
DD <- as.tensor(P$est@data)
err[i] <- fnorm(X - DD)^2
AIC[i] <- 2*err[i] + 2*i
}
Min <- which.min(AIC)
Min
#plot(AIC,type="l",main="Tucker Decomposition AIC",xlab="Rank Values",ylab="AIC")
AIC
min(AIC)
P <- cp(X, num_components = Min , max_iter = 50, tol = 1e-05)
# We need to sort lamdas in descending order
lambdas <- P$lambdas
index <- order(lambdas, decreasing = TRUE)
lambdas <- lambdas[index]
A <- P$U[[1]][,index]
B <- P$U[[2]][,index]
C <- P$U[[3]][,index]
#dev.off()
matplot(C,type="l",main="",xlab="",ylab="")
par(mar=c(1,1,1,1))
par(mfrow=c(1,2))
par(mfrow=c(1,Min))
for (i in 1:Min){
XY <- kronecker(A[,i],t(B[,i]))*lambdas[i]
image(XY)
}
library(rTensor)
library(R.matlab)
rm(list = ls())
T1 <- readMat('heatT.mat')$T1$data
T2 <- readMat('heatT.mat')$T2$data
T3 <- readMat('heatT.mat')$T3$data
tensor_list = list(T1,T2,T3)
#for (tensor in tensor_list) {
#T1 <- readMat('heatT.mat')[tensor]$data
X <- as.tensor(T1) ## read the MATLAB generated Heat transfer data
### CP Decomposition
## min(IJ,JK,IK) = 21*10 = 210
#X1 <- k_unfold(X,1)
err <- rep(0,10)
AIC <- rep(0,10)
for (i in 1:10){
P <- cp(X, num_components = i, max_iter = 50, tol = 1e-05)
DD <- as.tensor(P$est@data)
err[i] <- fnorm(X - DD)^2
AIC[i] <- 2*err[i] + 2*i
}
Min <- which.min(AIC)
Min
#plot(AIC,type="l",main="Tucker Decomposition AIC",xlab="Rank Values",ylab="AIC")
AIC
min(AIC)
P <- cp(X, num_components = Min , max_iter = 50, tol = 1e-05)
# We need to sort lamdas in descending order
lambdas <- P$lambdas
index <- order(lambdas, decreasing = TRUE)
lambdas <- lambdas[index]
A <- P$U[[1]][,index]
B <- P$U[[2]][,index]
C <- P$U[[3]][,index]
#dev.off()
matplot(C,type="l",main="",xlab="",ylab="")
par(mar=c(1,1,1,1))
#par(mfrow=c(1,2))
par(mfrow=c(1,Min))
for (i in 1:Min){
XY <- kronecker(A[,i],t(B[,i]))*lambdas[i]
image(XY)
}
library(rTensor)
library(R.matlab)
rm(list = ls())
T1 <- readMat('heatT.mat')$T1$data
T2 <- readMat('heatT.mat')$T2$data
T3 <- readMat('heatT.mat')$T3$data
tensor_list = list(T1,T2,T3)
#for (tensor in tensor_list) {
#T1 <- readMat('heatT.mat')[tensor]$data
X <- as.tensor(T2) ## read the MATLAB generated Heat transfer data
### CP Decomposition
## min(IJ,JK,IK) = 21*10 = 210
#X1 <- k_unfold(X,1)
err <- rep(0,10)
AIC <- rep(0,10)
for (i in 1:10){
P <- cp(X, num_components = i, max_iter = 50, tol = 1e-05)
DD <- as.tensor(P$est@data)
err[i] <- fnorm(X - DD)^2
AIC[i] <- 2*err[i] + 2*i
}
Min <- which.min(AIC)
Min
#plot(AIC,type="l",main="Tucker Decomposition AIC",xlab="Rank Values",ylab="AIC")
AIC
min(AIC)
P <- cp(X, num_components = Min , max_iter = 50, tol = 1e-05)
# We need to sort lamdas in descending order
lambdas <- P$lambdas
index <- order(lambdas, decreasing = TRUE)
lambdas <- lambdas[index]
A <- P$U[[1]][,index]
B <- P$U[[2]][,index]
C <- P$U[[3]][,index]
#dev.off()
matplot(C,type="l",main="",xlab="",ylab="")
par(mar=c(1,1,1,1))
#par(mfrow=c(1,2))
par(mfrow=c(1,Min))
for (i in 1:Min){
XY <- kronecker(A[,i],t(B[,i]))*lambdas[i]
image(XY)
}
library(rTensor)
library(R.matlab)
rm(list = ls())
T1 <- readMat('heatT.mat')$T1$data
T2 <- readMat('heatT.mat')$T2$data
T3 <- readMat('heatT.mat')$T3$data
tensor_list = list(T1,T2,T3)
#for (tensor in tensor_list) {
#T1 <- readMat('heatT.mat')[tensor]$data
X <- as.tensor(T2) ## read the MATLAB generated Heat transfer data
### CP Decomposition
## min(IJ,JK,IK) = 21*10 = 210
#X1 <- k_unfold(X,1)
err <- rep(0,10)
AIC <- rep(0,10)
for (i in 1:10){
P <- cp(X, num_components = i, max_iter = 50, tol = 1e-05)
DD <- as.tensor(P$est@data)
err[i] <- fnorm(X - DD)^2
AIC[i] <- 2*err[i] + 2*i
}
Min <- which.min(AIC)
Min
#plot(AIC,type="l",main="Tucker Decomposition AIC",xlab="Rank Values",ylab="AIC")
AIC
min(AIC)
P <- cp(X, num_components = Min , max_iter = 50, tol = 1e-05)
# We need to sort lamdas in descending order
lambdas <- P$lambdas
index <- order(lambdas, decreasing = TRUE)
lambdas <- lambdas[index]
A <- P$U[[1]][,index]
B <- P$U[[2]][,index]
C <- P$U[[3]][,index]
#dev.off()
matplot(C,type="l",main="",xlab="",ylab="")
#par(mar=c(1,1,1,1))
#par(mfrow=c(1,2))
par(mfrow=c(1,Min))
for (i in 1:Min){
XY <- kronecker(A[,i],t(B[,i]))*lambdas[i]
image(XY)
}
library(rTensor)
library(R.matlab)
rm(list = ls())
T1 <- readMat('heatT.mat')$T1$data
T2 <- readMat('heatT.mat')$T2$data
T3 <- readMat('heatT.mat')$T3$data
tensor_list = list(T1,T2,T3)
#for (tensor in tensor_list) {
#T1 <- readMat('heatT.mat')[tensor]$data
X <- as.tensor(T3) ## read the MATLAB generated Heat transfer data
### CP Decomposition
## min(IJ,JK,IK) = 21*10 = 210
#X1 <- k_unfold(X,1)
err <- rep(0,10)
AIC <- rep(0,10)
for (i in 1:10){
P <- cp(X, num_components = i, max_iter = 50, tol = 1e-05)
DD <- as.tensor(P$est@data)
err[i] <- fnorm(X - DD)^2
AIC[i] <- 2*err[i] + 2*i
}
Min <- which.min(AIC)
Min
#plot(AIC,type="l",main="Tucker Decomposition AIC",xlab="Rank Values",ylab="AIC")
AIC
min(AIC)
P <- cp(X, num_components = Min , max_iter = 50, tol = 1e-05)
# We need to sort lamdas in descending order
lambdas <- P$lambdas
index <- order(lambdas, decreasing = TRUE)
lambdas <- lambdas[index]
A <- P$U[[1]][,index]
B <- P$U[[2]][,index]
C <- P$U[[3]][,index]
#dev.off()
matplot(C,type="l",main="",xlab="",ylab="")
par(mar=c(1,1,1,1))
#par(mfrow=c(1,2))
par(mfrow=c(1,Min))
for (i in 1:Min){
XY <- kronecker(A[,i],t(B[,i]))*lambdas[i]
image(XY)
}
X <- as.tensor(T1)
T<- tucker(X, ranks =c(3,3,3), max_iter = 50, tol = 1e-07)
A <- T$U[[1]]
B <- T$U[[2]]
C <- T$U[[3]]
XY1 <- kronecker(A[,1],t(B[,1]))
XY2 <- kronecker(A[,2],t(B[,2]))
XY3 <- kronecker(A[,3],t(B[,3]))
#dev.off()
par(mfrow=c(2,2))
par(mar = rep(2, 4))
image(XY1)
image(XY2)
image(XY3)
matplot(C,type="l",main="Tucker Decomposition Result T1",xlab="",ylab="")
library(rTensor)
library(R.matlab)
rm(list = ls())
T1 <- readMat('heatT.mat')$T1$data
T2 <- readMat('heatT.mat')$T2$data
T3 <- readMat('heatT.mat')$T3$data
tensor_list = list(T1,T2,T3)
#for (tensor in tensor_list) {
#T1 <- readMat('heatT.mat')[tensor]$data
X <- as.tensor(T1) ## read the MATLAB generated Heat transfer data
### CP Decomposition
## min(IJ,JK,IK) = 21*10 = 210
#X1 <- k_unfold(X,1)
err <- rep(0,64)
dim(err) <- c(4,4,4)
AIC <- rep(0,64)
dim(AIC) <- c(4,4,4)
for(i in 1:4){
for(j in 1:4){
for(k in 1:4){
T<- tucker(X, ranks =c(i,j,k), max_iter = 50, tol = 1e-05)
DD <- as.tensor(T$est@data)
err[i,j,k] <- fnorm(X - DD)^2
AIC[i,j,k] <- 2*err[i,j,k] + 2*(i+j+k)
}
}
}
#plot(AIC,type="l",main="Tucker Decomposition AIC",xlab="Rank Values",ylab="AIC")
AIC
min(AIC)
library(rTensor)
library(R.matlab)
rm(list = ls())
T1 <- readMat('heatT.mat')$T1$data
T2 <- readMat('heatT.mat')$T2$data
T3 <- readMat('heatT.mat')$T3$data
tensor_list = list(T1,T2,T3)
#for (tensor in tensor_list) {
#T1 <- readMat('heatT.mat')[tensor]$data
X1 <- as.tensor(T1) ## read the MATLAB generated Heat transfer data
### CP Decomposition
## min(IJ,JK,IK) = 21*10 = 210
X3 <- as.tensor(T3)
X1 <- k_unfold(X1,1)
X3 <- k_unfold(X3,1)
err <- rep(0,64)
dim(err) <- c(4,4,4)
AIC <- rep(0,64)
dim(AIC) <- c(4,4,4)
for(i in 1:4){
for(j in 1:4){
for(k in 1:4){
T<- tucker(X3, ranks =c(i,j,k), max_iter = 50, tol = 1e-05)
DD <- as.tensor(T$est@data)
err[i,j,k] <- fnorm(X3 - X1)^2
AIC[i,j,k] <- 2*err[i,j,k] + 2*(i+j+k)
}
}
}
#plot(AIC,type="l",main="Tucker Decomposition AIC",xlab="Rank Values",ylab="AIC")
AIC
min(AIC)
library(rTensor)
library(R.matlab)
rm(list = ls())
T1 <- readMat('heatT.mat')$T1$data
T2 <- readMat('heatT.mat')$T2$data
T3 <- readMat('heatT.mat')$T3$data
tensor_list = list(T1,T2,T3)
#for (tensor in tensor_list) {
#T1 <- readMat('heatT.mat')[tensor]$data
X1 <- as.tensor(T1) ## read the MATLAB generated Heat transfer data
X2 <- as.tensor(T2)
X3 <- as.tensor(T3)
X1 <- k_unfold(X1,1)
X2 <- k_unfold(X2,1)
X3 <- k_unfold(X3,1)
err1 <- rep(0,64)
dim(err1) <- c(4,4,4)
AIC1 <- rep(0,64)
dim(AIC1) <- c(4,4,4)
err2 <- rep(0,64)
dim(err2) <- c(4,4,4)
AIC2 <- rep(0,64)
dim(AIC2) <- c(4,4,4)
for(i in 1:4){
for(j in 1:4){
for(k in 1:4){
T<- tucker(X3, ranks =c(i,j,k), max_iter = 50, tol = 1e-05)
DD <- as.tensor(T$est@data)
err1[i,j,k] <- fnorm(X3 - X1)^2
AIC1[i,j,k] <- 2*err[i,j,k] + 2*(i+j+k)
err2[i,j,k] <- fnorm(X3 - X2)^2
AIC2[i,j,k] <- 2*err[i,j,k] + 2*(i+j+k)
}
}
}
library(rTensor)
library(R.matlab)
rm(list = ls())
T1 <- readMat('heatT.mat')$T1$data
T2 <- readMat('heatT.mat')$T2$data
T3 <- readMat('heatT.mat')$T3$data
tensor_list = list(T1,T2,T3)
#for (tensor in tensor_list) {
#T1 <- readMat('heatT.mat')[tensor]$data
X1 <- as.tensor(T1) ## read the MATLAB generated Heat transfer data
X2 <- as.tensor(T2)
X3 <- as.tensor(T3)
X1 <- k_unfold(X1,1)
X2 <- k_unfold(X2,1)
X3 <- k_unfold(X3,1)
err1 <- rep(0,64)
dim(err1) <- c(4,4,4)
AIC1 <- rep(0,64)
dim(AIC1) <- c(4,4,4)
err2 <- rep(0,64)
dim(err2) <- c(4,4,4)
AIC2 <- rep(0,64)
dim(AIC2) <- c(4,4,4)
for(i in 1:4){
for(j in 1:4){
for(k in 1:4){
T<- tucker(X3, ranks =c(i,j,k), max_iter = 50, tol = 1e-05)
DD <- as.tensor(T$est@data)
err1[i,j,k] <- fnorm(X3 - X1)^2
AIC1[i,j,k] <- 2*err1[i,j,k] + 2*(i+j+k)
err2[i,j,k] <- fnorm(X3 - X2)^2
AIC2[i,j,k] <- 2*err1[i,j,k] + 2*(i+j+k)
}
}
}
#plot(AIC,type="l",main="Tucker Decomposition AIC",xlab="Rank Values",ylab="AIC")