textplot <- function(...) {
    text <- unlist(list(...))
    if (length(text) > 20) {
        text <- c(text[1:17],
                  '   ...... (skipped, text too long) ......',
                  text[c(-1, 0) + length(text)])
    }

    plot(NA, xlim = c(0, 1), ylim = c(0, 1),
         bty = 'n', xaxt = 'n', yaxt = 'n', xlab = '', ylab = '')
        
    for (i in seq_along(text)) {
        text(0, 1 - (i / 20),
             text[[i]], pos = 4)
    }
}

args <- commandArgs(TRUE)
if (length(args) > 0L) {
    method <- args[1]
} else {
    method <- "simple"
}
if (length(args) > 1L) {
    momentum <- as.double(args[2])
} else {
    momentum <- 0.0
}
seed <- 42
max.iter <- 50L
attempts <- 25L

library(CVE)
path <- paste0('~/Projects/CVE/tmp/logger_', method, '.C.pdf')

# Define logger for `cve()` method.
logger <- function(attempt, iter, data) {
    # Note the `<<-` assignement!
    loss.history[iter + 1, attempt] <<- data$loss
    error.history[iter + 1, attempt] <<- data$err
    tau.history[iter + 1, attempt] <<- data$tau
    # Compute true error by comparing to the true `B`
    B.est <- null(data$V) # Function provided by CVE
    P.est <- B.est %*% solve(t(B.est) %*% B.est) %*% t(B.est)
    true.error <- norm(P - P.est, 'F') / sqrt(2 * k)
    true.error.history[iter + 1, attempt] <<- true.error
}

pdf(path, width = 8.27, height = 11.7) # width, height unit is inces -> A4
layout(matrix(c(1, 1,
                2, 3,
                4, 5), nrow = 3, byrow = TRUE))

for (name in paste0("M", seq(7))) {
    # Seed random number generator
    set.seed(seed)

    # Create a dataset
    ds <- dataset(name)
    X <- ds$X
    Y <- ds$Y
    B <- ds$B # the true `B`
    k <- ncol(ds$B)
    # True projection matrix.
    P <- B %*% solve(t(B) %*% B) %*% t(B)

    # Setup histories.
    V_last <- NULL
    loss.history       <- matrix(NA, max.iter + 1, attempts)
    error.history      <- matrix(NA, max.iter + 1, attempts)
    tau.history        <- matrix(NA, max.iter + 1, attempts)
    true.error.history <- matrix(NA, max.iter + 1, attempts)

    time <- system.time(
        dr <- cve(Y ~ X, k = k, method = method,
                momentum = momentum,
                max.iter = max.iter, attempts = attempts,
                logger = logger)
    )["elapsed"]

    # Extract finaly selected values:
    B.est <- coef(dr, k)
    true.error <- norm(tcrossprod(B.est) - tcrossprod(B), 'F') / sqrt(2 * k)
    loss <- dr$res[[as.character(k)]]$loss

    # Write metadata.
    textplot(
        paste0("Seed value: ",     seed),
               "",
        paste0("Dataset Name: ",   ds$name),
        paste0("dim(X) = (", nrow(X), ", ", ncol(X), ")"),
        paste0("dim(B) = (", nrow(B), ", ", ncol(B), ")"),
               "",
        paste0("CVE method: ",     dr$method),
        paste0("Max Iterations: ", max.iter),
        paste0("Attempts: ",       attempts),
        paste0("Momentum: ",       momentum),
               "CVE call:",
               paste0("  > ", format(dr$call)),
               "",
        paste0("True Error: ", round(true.error, 3)),
        paste0("loss: ",       round(loss, 3)),
        paste0("time: ",       round(time, 3), " s")
    )
    # Plot history's
    matplot(loss.history,       type = 'l', log = 'y', xlab = 'i (iteration)',
        main = paste('loss', name),
        ylab = expression(L(V[i])))
    matplot(true.error.history, type = 'l', log = 'y', xlab = 'i (iteration)',
        main = paste('true error', name),
        ylab = expression(group('|', B*B^T - B[i]*B[i]^T, '|')[F] / sqrt(2*k)))
    matplot(error.history,      type = 'l', log = 'y', xlab = 'i (iteration)',
        main = paste('error', name),
        ylab = expression(group('|', V[i-1]*V[i-1]^T - V[i]*V[i]^T, '|')[F]))
    matplot(tau.history,        type = 'l', log = 'y', xlab = 'i (iteration)',
        main = paste('learning rate', name),
        ylab = expression(tau[i]))
}

cat("Created plot:", path, "\n")