R/lsp.R

In fatore/mp: Multidimensional Projection Techniques

#' Least-Square Projection
#'
#' Creates a q-dimensional representation of multidimensional data. Requires a
#' subsample (sample.indices) and its qD representation (Ys).
#'
#' @param X A data frame or matrix.
#' @param sample.indices The indices of data points in X used as subsamples. If
#'   not given, some rows from X will be randomly selected and Ys will be generated
#'   by calling forceScheme on them.
#' @param k Number of neighbors used to build the neighborhood graph.
#' @param Ys Initial kD configuration of the data subsamples (will be ignored if
#'   sample.indices is NULL).
#' @param q The target dimensionality.
#' @return The qD representation of the data.
#'
#' @references F. V. Paulovich, L. Nonato, R. Minghim, and H. Levkowitz,
#' Least-Square Projection: A fast high-precision multidimensional projection
#' technique and its application to document mapping, vol. 14, no. 3, pp. 564-575.
#'
#' @examples
#' # Iris example
#' emb <- lsp(iris[, 1:4])
#' plot(emb, col=iris$Species)
#'
#' @useDynLib mp
#' @export
lsp <- function(X, sample.indices=NULL, Ys=NULL, k=15, q=2) {
  if (!is.matrix(X)) {
    X <- as.matrix(X)
  }

  n <- nrow(X)
  if (is.null(sample.indices)) {
    sample.indices <- sample(1:n, 0.1 * n)
    Ys <- NULL
  }

  if (is.null(Ys)) {
    sample.indices <- as.vector(sample.indices)
    Ys <- forceScheme(dist(X[sample.indices, ]))
  }

  if (!is.matrix(Ys)) {
    Ys <- as.matrix(Ys)
  }

  if (length(sample.indices) != nrow(Ys)) {
    stop("sample.indices and Ys must have the same number of instances")
  }

  if (q != ncol(Ys)) {
    stop("target dimensionality must be the same as Ys'")
  }

  .Call("mp_lsp", X, sample.indices, Ys, k, q, package="mp")
}