#library(devtools)
library(glmnet) # plr
library(quantileDA) # Hennig classifier
library(e1071) # svm, naive bayes
library(penalizedLDA) # penalized LDA
library(class) # knn
library(pamr) # nearest shrunken centroids
library(rpart) # decision trees
if (! identical(Sys.info()["nodename"], c(nodename="dpritch-Satellite-C875"))) {
setwd("~/MQC")
source("R/aggregate.R")
source("R/mqc-classrate.R")
source("R/mqc-quantile.R")
source("R/mqc.R")
source("R/mqc-routines.R")
#load_all()
}
source("tests/numerical/data_sims.R")
source("tests/numerical/compare.R")
NREPL <- 25
NTEST <- 1000
r <- 50
out_exp_gauss <- list()
for (n in c(500, 250, 100, 50)) {
for (p in c(50, 100, 250, 500)) {
train_fcn <- quote( sim_exp_gauss(n, p, r, 0.5, 1, 0.8, 1) )
test_fcn <- quote( sim_exp_gauss(NTEST, p, r, 0.5, 1, 0.8, 1) )
nm <- paste0("n", n, "_p", p, "_r", r)
out_exp_gauss[[ nm ]] <- compare(NREPL,
prop_train = prop_train,
train_fcn = train_fcn,
test_fcn = test_fcn,
# args for mqc
x = 999,
y = 999,
aug = 999,
keep_derive = TRUE,
split_prop = 999,
pred_rem_lev = 0.5,
categ_prop_lev = 0.1,
theta = seq(0.01, 0.99, 0.01),
provide_quantlev = NULL,
quant_type = "interp",
cv_type = "class",
simil_type = 999,
npart = 999,
var_lev = 0.90,
std_parts = 999)
}
}
save(out_exp_gauss, file="tests/numerical/results/exp_gauss.RData")
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