Initial pkgdown render

DESCRIPTION

@@ -12,6 +12,6 @@ Maintainer: Artem Sokolov <artem.sokolov@gmail.com>
 Depends: R (>= 3.1.0)
 Suggests: knitr, rmarkdown, tidyverse, rlang, testthat
 VignetteBuilder: knitr
-RoxygenNote: 6.1.0
+RoxygenNote: 6.1.1
 LinkingTo: Rcpp, RcppArmadillo
 Imports: Rcpp

NAMESPACE

@@ -21,6 +21,7 @@ export(model_oclr)
 export(perturb.gelnet)
 export(rglz_L1)
 export(rglz_L2)
+export(rglz_nf)
 importFrom(Rcpp,evalCpp)
 importFrom(Rcpp,sourceCpp)
 useDynLib(gelnet)

R/RcppExports.R

@@ -1,7 +1,7 @@
 # Generated by using Rcpp::compileAttributes() -> do not edit by hand
 # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
 
-#' One-class regression objective function value
+#' One-class logistic regression objective function
 #'
 #' Evaluates the one-class objective function value for a given model
 #' See details.
@@ -26,7 +26,7 @@ gelnet_oclr_obj <- function(w, X, l1, l2, d = NULL, P = NULL, m = NULL) {
     .Call('_gelnet_gelnet_oclr_obj', PACKAGE = 'gelnet', w, X, l1, l2, d, P, m)
 }
 
-#' Linear regression objective function value
+#' Linear regression objective function
 #'
 #' Evaluates the linear regression objective function value for a given model.
 #' See details.
@@ -52,7 +52,7 @@ gelnet_lin_obj <- function(w, b, X, z, l1, l2, a = NULL, d = NULL, P = NULL, m =
     .Call('_gelnet_gelnet_lin_obj', PACKAGE = 'gelnet', w, b, X, z, l1, l2, a, d, P, m)
 }
 
-#' Logistic regression objective function value
+#' Binary logistic regression objective function
 #'
 #' Evaluates the logistic regression objective function value for a given model.
 #' See details.
@@ -83,7 +83,7 @@ gelnet_blr_obj <- function(w, b, X, y, l1, l2, balanced = FALSE, d = NULL, P = N
     .Call('_gelnet_gelnet_blr_obj', PACKAGE = 'gelnet', w, b, X, y, l1, l2, balanced, d, P, m)
 }
 
-#' GELnet for linear regression
+#' GELnet optimizer for linear regression
 #'
 #' Constructs a GELnet model for linear regression using coordinate descent.
 #'
@@ -116,7 +116,7 @@ gelnet_lin_opt <- function(X, z, l1, l2, max_iter = 100L, eps = 1e-5, fix_bias =
     .Call('_gelnet_gelnet_lin_opt', PACKAGE = 'gelnet', X, z, l1, l2, max_iter, eps, fix_bias, silent, verbose, nonneg, w_init, b_init, a, d, P, m)
 }
 
-#' GELnet for logistic regression
+#' GELnet optimizer for binary logistic regression
 #'
 #' Constructs a GELnet model for logistic regression using the Newton method.
 #'
@@ -150,7 +150,7 @@ gelnet_blr_opt <- function(X, y, l1, l2, max_iter = 100L, eps = 1e-5, silent = F
     .Call('_gelnet_gelnet_blr_opt', PACKAGE = 'gelnet', X, y, l1, l2, max_iter, eps, silent, verbose, balanced, nonneg, w_init, b_init, d, P, m)
 }
 
-#' GELnet for one-class logistic regression
+#' GELnet optimizer for one-class logistic regression
 #'
 #' Constructs a GELnet model for one-class regression using the Newton method.
 #'

R/def.R

@@ -97,6 +97,21 @@ rglz_L1 <- function( l1, d=NULL )
     rglz
 }
 
+#' Alternative L1 regularizer
+#'
+#' Defines an L1 regularizer that results in the desired number of non-zero feature weights
+#' 
+#' The corresponding regularization coefficient is determined through binary search
+#'
+#' @param nFeats desired number of features with non-zero weights in the model
+#' @param d p-by-1 vector of feature weights
+#' @return A regularizer definition that can be combined with a model definition using + operator
+#' @export
+rglz_nf <- function( nFeats, d=NULL )
+{
+
+}
+
 #' L2 regularizer
 #'
 #' Defines an L2 regularizer with optional feature-feature penalties and translation coefficients

R/gelnet.R

@@ -2,7 +2,7 @@
 ##
 ## by Artem Sokolov
 
-#' Trains a GELNET model
+#' Trains a GELnet model
 #'
 #' Trains a model on the definition constructed by gelnet()
 #'