## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", fig.align = "center",
                      fig.width = 6, fig.height = 4)
options(width = 80)
library(TRexSelector)
old_threads <- trex_set_num_threads(min(2L, trex_get_max_threads()))

## ----data---------------------------------------------------------------------
set.seed(31)
n <- 100
p <- 50
L <- 50
true_support <- c(2, 9, 17, 33)
beta <- numeric(p)
beta[true_support] <- c(2, -2, 1.5, -1.5)
X <- matrix(rnorm(n * p), n, p)
D <- matrix(rnorm(n * L), n, L)
y <- as.numeric(X %*% beta + rnorm(n))

## ----tlars--------------------------------------------------------------------
solver <- TLARS_Solver$new(X, D, y)
solver$execute_step(T_stop = 3)
solver$get_num_steps()
solver$get_active_predictor_indices()
solver$get_active_dummy_indices()

## ----actives------------------------------------------------------------------
solver$get_actives()
solver$get_dummy_start_index()
solver$get_actions()[1:4]

## ----path---------------------------------------------------------------------
dim(solver$get_beta_path())
round(solver$get_beta()[true_support], 3)
path_diag <- data.frame(lambda = solver$get_lambda(), rss = solver$get_rss(),
                        r2 = solver$get_r2(), cp = solver$get_cp(), dof = solver$get_dof())
round(head(path_diag, 5), 3)
solver$restore()    # give X and D back before the next solver claims them

## ----tenet--------------------------------------------------------------------
en <- TENET_Solver$new(X, D, y, options = tsolver_options(lambda2 = 0.5))
en$execute_step(T_stop = 3)
en$get_active_predictor_indices()
en$get_num_removals()
en$restore()

## ----writer-------------------------------------------------------------------
X_before <- X + 0
w <- TLASSO_Solver$new(X, D, y)
max(abs(X - X_before))      # the scaled columns
w$restore()
max(abs(X - X_before))      # the original values

## ----readers------------------------------------------------------------------
sx <- MLLpNormScaler$new(norm_type = 2, center = TRUE)
invisible(sx$fit_transform_inplace(X))
sd <- MLLpNormScaler$new(norm_type = 2, center = TRUE)
invisible(sd$fit_transform_inplace(D))
y_c <- y - mean(y)
reader_opts <- tsolver_options(normalize = FALSE, intercept = FALSE, precentered = TRUE)

## ----families-----------------------------------------------------------------
constructors <- list(
  TLARS = TLARS_Solver, TLASSO = TLASSO_Solver, TSTEPWISE = TSTEPWISE_Solver,
  TSTAGEWISE = TSTAGEWISE_Solver, TENET = TENET_Solver, TENETAug = TENETAug_Solver,
  TOMP = TOMP_Solver, TOOLS = TOOLS_Solver, TGP = TGP_Solver, TACGP = TACGP_Solver,
  TMP = TMP_Solver, TNCGMP = TNCGMP_Solver, TAFS = TAFS_Solver,
  TCDLASSO = TCDLASSO_Solver, TCDENET = TCDENET_Solver)
rows <- lapply(names(constructors), function(nm) {
  s <- constructors[[nm]]$new(X, D, y_c, options = reader_opts)
  s$execute_step(T_stop = 3)
  data.frame(solver = nm, steps = s$get_num_steps(),
             predictors = paste(s$get_active_predictor_indices(), collapse = " "))
})
do.call(rbind, rows)

## ----structured---------------------------------------------------------------
groups <- rep(1:5, each = 10)
ien <- TIENET_Solver$new(X, D, y_c, groups = groups,
                         options = tsolver_options(normalize = FALSE, intercept = FALSE,
                                                   precentered = TRUE, lambda2 = 0.5))
ien$execute_step(T_stop = 3)
ien$get_active_predictor_indices()
tik <- TCDTIKNET_Solver$new(X, D, y_c, tikhonov_K = diag(p),
                            options = tsolver_options(normalize = FALSE, intercept = FALSE,
                                                      precentered = TRUE, lambda2 = 0.5))
tik$execute_step(T_stop = 3)
tik$get_active_predictor_indices()

## ----save-load----------------------------------------------------------------
f <- tempfile(fileext = ".bin")
a <- TLARS_Solver$new(X, D, y_c, options = reader_opts)
a$execute_step(T_stop = 2)
a$save(f)
b <- TLARS_Solver$new(X, D, y_c, options = reader_opts)
b$load(f)
b$get_num_steps()
b$execute_step(T_stop = 4)
c <- TLARS_Solver$new(X, D, y_c, options = reader_opts)
c$execute_step(T_stop = 4)
identical(b$get_actives(), c$get_actives())
unlink(f)

## ----memory-------------------------------------------------------------------
a$get_memory_bytes()

## ----restore-data, include = FALSE--------------------------------------------
invisible(sx$inverse_transform_inplace(X))
invisible(sd$inverse_transform_inplace(D))

## ----teardown, include = FALSE------------------------------------------------
trex_set_num_threads(old_threads)