# Usage:
# ~$ Rscript runtime_test.R

library(CVE) # load CVE

#' Writes progress to console.
tell.user <- function(name, start.time, i, length) {
    cat("\rRunning Test (", name, "):",
        i, "/", length,
        " - elapsed:", format(Sys.time() - start.time), "\033[K")
}
subspace.dist <- function(B1, B2){
    P1 <- B1 %*% solve(t(B1) %*% B1) %*% t(B1)
    P2 <- B2 %*% solve(t(B2) %*% B2) %*% t(B2)
    return(norm(P1 - P2, type = 'F'))
}

# Number of simulations
SIM.NR <- 50
# maximal number of iterations in curvilinear search algorithm
MAXIT <- 50
# number of arbitrary starting values for curvilinear optimization
ATTEMPTS <- 10
# set names of datasets
dataset.names <- c("M1", "M2", "M3", "M4", "M5")
# Set used CVE method
methods <- c("simple") # c("legacy", "simple", "sgd", "linesearch")

# Setup error and time tracking variables
error <- matrix(NA, SIM.NR, length(methods) * length(dataset.names))
time <- matrix(NA, SIM.NR, ncol(error))
colnames(error) <- kronecker(paste0(dataset.names, '-'), methods, paste0)
colnames(time) <- colnames(error)

# only for telling user (to stdout)
count <- 0
start.time <- Sys.time()
# Start simulation loop.
for (sim in 1:SIM.NR) {
    # Repeat for each dataset.
    for (name in dataset.names) {
        count <- count + 1
        tell.user(name, start.time, count, SIM.NR * length(dataset.names))

        # Create a new dataset
        ds <- dataset(name)
        # Prepare X, Y and combine to data matrix
        Y <- ds$Y
        X <- ds$X
        data <- cbind(Y, X)
        # get dimensions
        dim <- ncol(X)
        truedim <- ncol(ds$B)

        for (method in methods) {
            dr.time <- system.time(
                dr <- cve.call(X, Y,
                    method = method,
                    k = truedim,
                    attempts = ATTEMPTS
                )
            )
            dr <- dr[[truedim]]

            key <- paste0(name, '-', method)
            error[sim, key] <- subspace.dist(dr$B, ds$B) / sqrt(2 * truedim)
            time[sim, key] <- dr.time["elapsed"]
        }
    }
}

cat("\n\n## Time [sec] Means:\n")
print(colMeans(time))
cat("\n## Error Means:\n")
print(colMeans(error))

at <- seq(ncol(error)) + rep(seq(ncol(error) / length(methods)) - 1, each = length(methods))
boxplot(error,
    main = paste0("Error (Nr of simulations ", SIM.NR, ")"),
    ylab = "Error",
    las = 2,
    at = at
)
boxplot(time,
    main = paste0("Time (Nr of simulations ", SIM.NR, ")"),
    ylab = "Time [sec]",
    las = 2,
    at = at
)