#' Conditional Variance Estimator (CVE)
#'
#' Conditional Variance Estimator for Sufficient Dimension
#'  Reduction
#'
#' TODO: And some details
#'
#'
#' @references Fertl Likas, Bura Efstathia. Conditional Variance Estimation for Sufficient Dimension Reduction, 2019
#'
#' @docType package
#' @author Loki
"_PACKAGE"

#' Implementation of the CVE method.
#'
#' Conditional Variance Estimator (CVE) is a novel sufficient dimension
#' reduction (SDR) method assuming a model
#' \deqn{Y \sim g(B'X) + \epsilon}{Y ~ g(B'X) + epsilon}
#' where B'X is a lower dimensional projection of the predictors.
#'
#' @param formula Formel for the regression model defining `X`, `Y`.
#'  See: \code{\link{formula}}.
#' @param data data.frame holding data for formula.
#' @param method The different only differe in the used optimization.
#'  All of them are Gradient based optimization on a Stiefel manifold.
#' \itemize{
#'      \item "simple" Simple reduction of stepsize.
#'      \item "sgd" stocastic gradient decent.
#'      \item TODO: further
#' }
#' @param ... Further parameters depending on the used method.
#' @examples
#' library(CVE)
#'
#' # sample dataset
#' ds <- dataset("M5")
#'
#' # call ´cve´ with default method (aka "simple")
#' dr.simple <- cve(ds$Y ~ ds$X, k = ncol(ds$B))
#' # plot optimization history (loss via iteration)
#' plot(dr.simple, main = "CVE M5 simple")
#'
#' # call ´cve´ with method "linesearch" using ´data.frame´ as data.
#' data <- data.frame(Y = ds$Y, X = ds$X)
#' # Note: ´Y, X´ are NOT defined, they are extracted from ´data´.
#' dr.linesearch <- cve(Y ~ ., data, method = "linesearch", k = ncol(ds$B))
#' plot(dr.linesearch, main = "CVE M5 linesearch")
#'
#' @references Fertl L., Bura E. Conditional Variance Estimation for Sufficient Dimension Reduction, 2019
#'
#' @seealso \code{\link{formula}}. For a complete parameters list (dependent on
#'  the method) see \code{\link{cve_simple}}, \code{\link{cve_sgd}}
#' @import stats
#' @importFrom stats model.frame
#' @export
cve <- function(formula, data, method = "simple", max.dim = 10, ...) {
    # check for type of `data` if supplied and set default
    if (missing(data)) {
        data <- environment(formula)
    } else if (!is.data.frame(data)) {
        stop('Parameter `data` must be a `data.frame` or missing.')
    }

    # extract `X`, `Y` from `formula` with `data`
    model <- stats::model.frame(formula, data)
    X <- as.matrix(model[,-1, drop = FALSE])
    Y <- as.matrix(model[, 1, drop = FALSE])

    # pass extracted data on to [cve.call()]
    dr <- cve.call(X, Y, method = method, ...)

    # overwrite `call` property from [cve.call()]
    dr$call <- match.call()
    return(dr)
}

#' @param nObs as describet in the Paper.
#' @param X Data
#' @param Y Responces
#' @param nObs Like in the paper.
#' @param k guess for SDR dimension.
#' @param ... Method specific parameters.
#' @rdname cve
#' @export
cve.call <- function(X, Y, method = "simple", nObs = nrow(X)^.5,
                     min.dim = 1, max.dim = 10, k, ...) {

    # parameter checking
    if (!is.matrix(X)) {
        stop('X should be a matrices.')
    }
    if (is.matrix(Y)) {
        Y <- as.vector(Y)
    }
    if (nrow(X) != length(Y)) {
        stop('Rows of X and number of Y elements are not compatible.')
    }
    if (ncol(X) < 2) {
        stop('X is one dimensional, no need for dimension reduction.')
    }

    if (!missing(k)) {
        min.dim <- as.integer(k)
        max.dim <- as.integer(k)
    } else {
        min.dim <- as.integer(min.dim)
        max.dim <- as.integer(min(max.dim, ncol(X) - 1L))
    }
    if (min.dim > max.dim) {
        stop('`min.dim` bigger `max.dim`.')
    }
    if (max.dim >= ncol(X)) {
        stop('`max.dim` must be smaller than `ncol(X)`.')
    }

    # Call specified method.
    method <- tolower(method)
    call <- match.call()
    dr <- list()
    for (k in min.dim:max.dim) {
        if (method == 'simple') {
            dr.k <- cve_simple(X, Y, k, nObs = nObs, ...)
        } else if (method == 'linesearch') {
            dr.k <- cve_linesearch(X, Y, k, nObs = nObs, ...)
        } else if (method == 'sgd') {
            dr.k <- cve_sgd(X, Y, k, nObs = nObs, ...)
        } else {
            stop('Got unknown method.')
        }
        dr.k$k <- k
        class(dr.k) <- "cve.k"
        dr[[k]] <- dr.k
    }

    # augment result information
    dr.k$method <- method
    dr.k$call <- call
    class(dr) <- "cve"
    return(dr)
}

# TODO: write summary
# summary.cve <- function() {
#     # code #
# }

#' Ploting helper for objects of class \code{cve}.
#'
#' @param x Object of class \code{cve} (result of [cve()]).
#' @param content Specifies what to plot:
#' \itemize{
#'      \item "history" Plots the loss history from stiefel optimization
#'          (default).
#'      \item ... TODO: add (if there are any)
#' }
#' @param ... Pass through parameters to [plot()] and [lines()]
#'
#' @usage ## S3 method for class 'cve'
#' plot(x, content = "history", ...)
#' @seealso see \code{\link{par}} for graphical parameters to pass through
#'      as well as \code{\link{plot}} for standard plot utility.
#' @importFrom graphics plot lines points
#' @method plot cve
#' @export
plot.cve <- function(x, ...) {


    # H <- x$history
    # H_1 <- H[!is.na(H[, 1]), 1]

    # defaults <- list(
    #     main = "History",
    #     xlab = "Iterations i",
    #     ylab = expression(loss == L[n](V^{(i)})),
    #     xlim = c(1, nrow(H)),
    #     ylim = c(0, max(H)),
    #     type = "l"
    # )

    # call.plot <- match.call()
    # keys <- names(defaults)
    # keys <- keys[match(keys, names(call.plot)[-1], nomatch = 0) == 0]

    # for (key in keys) {
    #     call.plot[[key]] <- defaults[[key]]
    # }

    # call.plot[[1L]] <- quote(plot)
    # call.plot$x <- quote(1:length(H_1))
    # call.plot$y <- quote(H_1)

    # eval(call.plot)

    # if (ncol(H) > 1) {
    #     for (i in 2:ncol(H)) {
    #         H_i <- H[H[, i] > 0, i]
    #         lines(1:length(H_i), H_i)
    #     }
    # }
    # x.ends <- apply(H, 2, function(h) { length(h[!is.na(h)]) })
    # y.ends <- apply(H, 2, function(h) { tail(h[!is.na(h)], n=1) })
    # points(x.ends, y.ends)
}

#' Prints a summary of a \code{cve} result.
#' @param object Instance of 'cve' as return of \code{cve}.
#' @method summary cve
#' @export
summary.cve <- function(object, ...) {
    cat('Summary of CVE result - Method: "', object$method, '"\n',
        '\n',
        'Dataset size:   ', nrow(object$X), '\n',
        'Data Dimension: ', ncol(object$X), '\n',
        'SDR Dimension:  ', object$k, '\n',
        'loss:           ', object$loss, '\n',
        '\n',
        'Called via:\n',
        '    ',
        sep='')
    print(object$call)
}