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rm(list=ls())
library(PLFD)
set.seed(2023)
45
rDim <- 35
cDim <-
80
n1 <- 75
n2 <- n1 + n2
n <- sample(1:2, n, TRUE, c(n1, n2))
y <- array(rnorm(rDim*cDim*n), c(rDim, cDim, n))
x <-
matrix(0.0, rDim, cDim)
M1 <-1:10, 1:10] <- runif(100, 0.2, 0.8) * sample(-1:1, 100, TRUE, rep(1/3, 3))
M1[==1] <- sweep(x[, , y==1], 1:2, M1, '+')
x[, , y
800
n1Test <- 900
n2Test <- c(rep(1, n1Test), rep(2, n2Test))
yTest <- array(rnorm(rDim*cDim*(n1Test+n2Test)), c(rDim, cDim, n1Test+n2Test))
xTest <-==1] <- sweep(xTest[, , yTest==1], 1:2, M1, '+')
xTest[, , yTeststopifnot( dim(xTest) == c(rDim, cDim, n1Test+n2Test) )
c0 <- 5
r0 <- plfd(x, y, r0, c0)
plfd.model <-print(plfd.model)
#> Dimension of Matrix-variate: 45 x 35.
#> Training data: n1=80, n2=75.
#> Number of feature block(s): 5.
predict(plfd.model, x=xTest, y=yTest)
result <-print(result$mcr)
#> [1] 0.02352941
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