# CRAN
install.packages(c("rnnmf"))
# devtools
if (require(devtools)) {
    # latest greatest
    install_github("shabbychef/rnnmf")
}
# via drat:
if (require(drat)) {
    drat:::add("shabbychef")
    # not yet: install.packages('rnnmf')
}

library(dplyr)
library(rnnmf)
library(ggplot2)

frobenius_norm_err <- function(Y, L, R) {
    sqrt(sum(abs(Y - L %*% R)^2))
}
runifmat <- function(nr, nc, ...) {
    matrix(pmax(0, runif(nr * nc, ...)), nrow = nr)
}
test_a_bunch <- function(Y_t, L_0, R_0, niter = 10000L) {
    iter_hist <- new.env()
    iter_hist[["history"]] <- rep(NA_real_, niter)

    on_iteration_end <- function(iteration, Y, L, R,
        ...) {
        iter_hist[["history"]][iteration] <<- frobenius_norm_err(Y,
            L, R)
    }
    wuz <- aurnmf(Y_t, L_0, R_0, max_iterations = niter,
        on_iteration_end = on_iteration_end)
    df1 <- tibble(x = seq_along(iter_hist[["history"]]),
        y = iter_hist[["history"]]) %>%
        mutate(method = "additive, optimal step")

    iter_hist[["history"]] <- rep(NA_real_, niter)
    wuz <- murnmf(Y_t, L_0, R_0, max_iterations = niter,
        on_iteration_end = on_iteration_end)
    df2 <- tibble(x = seq_along(iter_hist[["history"]]),
        y = iter_hist[["history"]]) %>%
        mutate(method = "multiplicative")

    retv <- bind_rows(df1, df2) %>%
        mutate(nr = nrow(Y_t), nc = ncol(Y_t), nd = ncol(L_0),
            max_iter = niter)
    return(retv)
}

nr <- 30
nc <- 8
nd <- 3
set.seed(1234)
L_t <- runifmat(nr, nd)
R_t <- runifmat(nd, nc)
Y_t <- L_t %*% R_t

L_0 <- runifmat(nrow(Y_t), nd + 1)
R_0 <- runifmat(ncol(L_0), ncol(Y_t))

test_a_bunch(Y_t, L_0, R_0, niter = 10000L) %>%
    ggplot(aes(x, y, color = method)) + geom_line() +
    scale_x_log10(labels = scales::comma) + scale_y_log10() +
    labs(x = "Step", y = expression(L[2] ~ ~Error),
        title = "Frobenius Norm of Error vs Step",
        color = "Method", caption = paste0("Factoring ",
            nr, " x ", nc, " matrix down to ", nd,
            " dimensions."))