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bark-profiling.R
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bark-profiling.R
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# bark-profile.R
library(profvis)
devtools::install()
library(bark)
profvis::profvis({
set.seed(42)
bark.new.lsd = bark(y ~ ., data=circle2, subset=train,
# testdata = as.matrix(circle2[-train, ]),
classification = TRUE,
nburn = 50,
nkeep = 50,
selection = TRUE,
common_lambdas = FALSE,
printevery = 10^100)
})
set.seed(42)
bark.new.ld = bark(y ~ ., data=circle2, subset=train,
# testdata = as.matrix(circle2[-train, ]),
classification = TRUE,
nburn = 50,
nkeep = 50,
selection = FALSE,
common_lambdas = FALSE,
printevery = 10^100)
set.seed(42)
bark.new.le = bark(y ~ ., data=circle2, subset=train,
# testdata = as.matrix(circle2[-train, ]),
classification = TRUE,
nburn = 50,
nkeep = 50,
selection = FALSE,
common_lambdas = TRUE,
printevery = 10^100)
set.seed(42)
bark.new.lse = bark(y ~ ., data=circle2, subset=train,
# testdata = as.matrix(circle2[-train, ]),
classification = TRUE,
nburn = 50,
nkeep = 50,
selection = TRUE,
common_lambdas = TRUE,
printevery = 10^100)
set.seed(42)
bark.new.lsd = bark(y ~ ., data=circle2, subset=train,
# testdata = as.matrix(circle2[-train, ]),
classification = TRUE,
nburn = 50,
nkeep = 50,
selection = TRUE,
common_lambdas = FALSE,
printevery = 10^100)
install.packages("bark")
library(bark)
set.seed(42)
bark.old.ld = bark(y ~ ., data=circle2, subset=train,
# testdata = as.matrix(circle2[-train, ]),
classification = TRUE,
nburn = 50,
nkeep = 50,
selection = FALSE,
common_lambdas = FALSE,
printevery = 10^100)
set.seed(42)
bark.old.le = bark(y ~ ., data=circle2, subset=train,
# testdata = as.matrix(circle2[-train, ]),
classification = TRUE,
nburn = 50,
nkeep = 50,
selection = FALSE,
common_lambdas = TRUE,
printevery = 10^100)
set.seed(42)
bark.old.lse = bark(y ~ ., data=circle2, subset=train,
# testdata = as.matrix(circle2[-train, ]),
classification = TRUE,
nburn = 50,
nkeep = 50,
selection = TRUE,
common_lambdas = TRUE,
printevery = 10^100)
set.seed(42)
bark.old.lsd = bark(y ~ ., data=circle2, subset=train,
# testdata = as.matrix(circle2[-train, ]),
classification = TRUE,
nburn = 50,
nkeep = 50,
selection = TRUE,
common_lambdas = FALSE,
printevery = 10^100)
boxplot(bark.new$theta.lambda)
plot(apply(bark.new.lsd$theta.beta, 1, mean), apply(bark.old.lsd$theta.beta, 1, mean))
plot(apply(bark.new.lse$theta.beta, 1, mean), apply(bark.old.lse$theta.beta, 1, mean))
plot(apply(bark.new.ld$theta.beta, 1, mean), apply(bark.old.ld$theta.beta, 1, mean))
plot(apply(bark.new.le$theta.beta, 1, mean), apply(bark.old.le$theta.beta, 1, mean))