Merge 68d0571 into 9d3a679

R/RcppExports.R

@@ -150,39 +150,42 @@ NULL
 #'
 #' @section Usage:
 #' \preformatted{
-#' BaselearnerPSpline$new(data_source, data_target, list(degree, n_knots, penalty,
+#' baselearnerpspline$new(data_source, data_target, list(degree, n_knots, penalty,
 #'   differences))
 #' }
 #'
-#' @section Arguments:
+#' @section arguments:
 #' \describe{
-#' \item{\code{data_source} [\code{Data} Object]}{
-#'   Data object which contains the source data.
+#' \item{\code{data_source} [\code{data} object]}{
+#'   data object which contains the source data.
 #' }
-#' \item{\code{data_target} [\code{Data} Object]}{
-#'   Data object which gets the transformed source data.
+#' \item{\code{data_target} [\code{data} object]}{
+#'   data object which gets the transformed source data.
 #' }
 #' \item{\code{degree} [\code{integer(1)}]}{
-#'   Degree of the spline functions to interpolate the knots.
+#'   degree of the spline functions to interpolate the knots.
 #' }
 #' \item{\code{n_knots} [\code{integer(1)}]}{
-#'   Number of \strong{inner knots}. To prevent weird behavior on the edges
+#'   number of \strong{inner knots}. to prevent weird behavior on the edges
 #'   the inner knots are expanded by \eqn{\mathrm{degree} - 1} additional knots.
 #' }
 #' \item{\code{penalty} [\code{numeric(1)}]}{
-#'   Positive numeric value to specify the penalty parameter. Setting the
-#'   penalty to 0 ordinary B-splines are used for the fitting.
+#'   positive numeric value to specify the penalty parameter. setting the
+#'   penalty to 0 ordinary b-splines are used for the fitting.
 #' }
 #' \item{\code{differences} [\code{integer(1)}]}{
-#'   The number of differences which are penalized. A higher value leads to
+#'   the number of differences which are penalized. a higher value leads to
 #'   smoother curves.
 #' }
+#' \item{\code{use_binning} [\code{logical(1)}]}{
+#'   Indicator if feature should be discretized first (default is \code{FALSE}).
+#' }
 #' }
 #'
 #' @section Details:
-#'   The data matrix of the source data is restricted to have just one column.
-#'   The spline bases are created for this single feature. Multidimensional
-#'   splines are not supported at the moment.
+#'   If \code{use_binning = TRUE} the original feature is discretized to on an equidistant grid on
+#'   \eqn{[\min(x),\max(x)]} with \eqn{\sqrt{n}} points. The fitting is then done by
+#'   using weights per new data point.
 #'
 #' @section Fields:
 #'   This class doesn't contain public fields.

src/baselearner.h

@@ -230,7 +230,7 @@ class BaselearnerCustom : public Baselearner
 
 };
 
-// BaselearnerCustom:
+// BaselearnerCustomCpp:
 // -----------------------
 
 // This is a  bit tricky. The key is that we store the cpp functions as

src/compboost_modules.cpp

@@ -333,39 +333,42 @@ class BaselearnerPolynomialFactoryWrapper : public BaselearnerFactoryWrapper
 //'
 //' @section Usage:
 //' \preformatted{
-//' BaselearnerPSpline$new(data_source, data_target, list(degree, n_knots, penalty,
+//' baselearnerpspline$new(data_source, data_target, list(degree, n_knots, penalty,
 //'   differences))
 //' }
 //'
-//' @section Arguments:
+//' @section arguments:
 //' \describe{
-//' \item{\code{data_source} [\code{Data} Object]}{
-//'   Data object which contains the source data.
+//' \item{\code{data_source} [\code{data} object]}{
+//'   data object which contains the source data.
 //' }
-//' \item{\code{data_target} [\code{Data} Object]}{
-//'   Data object which gets the transformed source data.
+//' \item{\code{data_target} [\code{data} object]}{
+//'   data object which gets the transformed source data.
 //' }
 //' \item{\code{degree} [\code{integer(1)}]}{
-//'   Degree of the spline functions to interpolate the knots.
+//'   degree of the spline functions to interpolate the knots.
 //' }
 //' \item{\code{n_knots} [\code{integer(1)}]}{
-//'   Number of \strong{inner knots}. To prevent weird behavior on the edges
+//'   number of \strong{inner knots}. to prevent weird behavior on the edges
 //'   the inner knots are expanded by \eqn{\mathrm{degree} - 1} additional knots.
 //' }
 //' \item{\code{penalty} [\code{numeric(1)}]}{
-//'   Positive numeric value to specify the penalty parameter. Setting the
-//'   penalty to 0 ordinary B-splines are used for the fitting.
+//'   positive numeric value to specify the penalty parameter. setting the
+//'   penalty to 0 ordinary b-splines are used for the fitting.
 //' }
 //' \item{\code{differences} [\code{integer(1)}]}{
-//'   The number of differences which are penalized. A higher value leads to
+//'   the number of differences which are penalized. a higher value leads to
 //'   smoother curves.
 //' }
+//' \item{\code{use_binning} [\code{logical(1)}]}{
+//'   Indicator if feature should be discretized first (default is \code{FALSE}).
+//' }
 //' }
 //'
 //' @section Details:
-//'   The data matrix of the source data is restricted to have just one column.
-//'   The spline bases are created for this single feature. Multidimensional
-//'   splines are not supported at the moment.
+//'   If \code{use_binning = TRUE} the original feature is discretized to on an equidistant grid on
+//'   \eqn{[\min(x),\max(x)]} with \eqn{\sqrt{n}} points. The fitting is then done by
+//'   using weights per new data point.
 //'
 //' @section Fields:
 //'   This class doesn't contain public fields.