Fix tests and issue #8 (#60)

* modify c++ tests

* improve input variable names in R functions

* check for negative error parameter in both interfaces

* update Rd files

R-proj/R/RcppExports.R

@@ -155,11 +155,11 @@ rounding <- function(P, random_walk = NULL, walk_length = NULL, parameters = NUL
 
 #' Sample points from a convex Polytope (H-polytope, V-polytope or a zonotope) or use direct methods for uniform sampling from the unit or the canonical or an arbitrary \eqn{d}-dimensional simplex and the boundary or the interior of a \eqn{d}-dimensional hypersphere
 #'
-#' Sample N points with uniform or multidimensional spherical gaussian -centered in an internal point- target distribution.
+#' Sample n points with uniform or multidimensional spherical gaussian -centered in an internal point- target distribution.
 #' The \eqn{d}-dimensional unit simplex is the set of points \eqn{\vec{x}\in \R^d}, s.t.: \eqn{\sum_i x_i\leq 1}, \eqn{x_i\geq 0}. The \eqn{d}-dimensional canonical simplex is the set of points \eqn{\vec{x}\in \R^d}, s.t.: \eqn{\sum_i x_i = 1}, \eqn{x_i\geq 0}.
 #'
 #' @param P A convex polytope. It is an object from class (a) Hpolytope or (b) Vpolytope or (c) Zonotope.
-#' @param N The number of points that the function is going to sample from the convex polytope. The default value is \eqn{100}.
+#' @param n The number of points that the function is going to sample from the convex polytope. The default value is \eqn{100}.
 #' @param distribution Optional. A string that declares the target distribution: a) \code{'uniform'} for the uniform distribution or b) \code{'gaussian'} for the multidimensional spherical distribution. The default target distribution is uniform.
 #' @param random_walk Optional. A string that declares the random walk method: a) \code{'CDHR'} for Coordinate Directions Hit-and-Run, b) \code{'RDHR'} for Random Directions Hit-and-Run, c) \code{'BaW'} for Ball Walk or d) \code{'BiW'} for Billiard walk. The default walk is \code{'BiW'} for the uniform distribution or \code{'CDHR'} for the Normal distribution.
 #' @param walk_length Optional. The number of the steps for the random walk. The default value is \eqn{1} for \code{'BiW'} and \eqn{\lfloor 10 + d/10\rfloor} otherwise, where \eqn{d} is the dimension that the polytope lies.
@@ -182,7 +182,7 @@ rounding <- function(P, random_walk = NULL, walk_length = NULL, parameters = NUL
 #' @references \cite{Art B. Owen,
 #' \dQuote{Monte Carlo theory, methods and examples,} \emph{ Art Owen,} 2009.}
 #'
-#' @return A \eqn{d\times N} matrix that contains, column-wise, the sampled points from the convex polytope P.
+#' @return A \eqn{d\times n} matrix that contains, column-wise, the sampled points from the convex polytope P.
 #' @examples
 #' # uniform distribution from the 3d unit cube in V-representation using ball walk
 #' P = gen_cube(3, 'V')
@@ -200,10 +200,10 @@ rounding <- function(P, random_walk = NULL, walk_length = NULL, parameters = NUL
 #' # 10000 uniform points from a 2-d arbitrary simplex
 #' V = matrix(c(2,3,-1,7,0,0),ncol = 2, nrow = 3, byrow = TRUE)
 #' P = Vpolytope$new(V)
-#' points = sample_points(P, N = 10000, exact = TRUE)
+#' points = sample_points(P, n = 10000, exact = TRUE)
 #' @export
-sample_points <- function(P = NULL, N = NULL, distribution = NULL, random_walk = NULL, walk_length = NULL, exact = NULL, body = NULL, parameters = NULL, InnerPoint = NULL) {
-    .Call(`_volesti_sample_points`, P, N, distribution, random_walk, walk_length, exact, body, parameters, InnerPoint)
+sample_points <- function(P = NULL, n = NULL, distribution = NULL, random_walk = NULL, walk_length = NULL, exact = NULL, body = NULL, parameters = NULL, InnerPoint = NULL) {
+    .Call(`_volesti_sample_points`, P, n, distribution, random_walk, walk_length, exact, body, parameters, InnerPoint)
 }
 
 #' The main function for volume approximation of a convex Polytope (H-polytope, V-polytope or a zonotope)

R-proj/R/gen_cross.R

@@ -3,7 +3,7 @@
 #' This function can be used to generate the \eqn{d}-dimensional cross polytope in H- or V-representation.
 #' 
 #' @param dimension The dimension of the cross polytope.
-#' @param repr A string to declare the representation. It has to be \code{'H'} for H-representation or \code{'V'} for V-representation.
+#' @param rep A string to declare the representation. It has to be \code{'H'} for H-representation or \code{'V'} for V-representation.
 #' 
 #' @return A polytope class representing a cross polytope in H- or V-representation.
 #' @examples 
@@ -13,13 +13,13 @@
 #' # generate a 15-dimension cross polytope in V-representation
 #' P = gen_cross(15, 'V')
 #' @export
-gen_cross <- function(dimension, repr) {
+gen_cross <- function(dimension, rep) {
 
   kind_gen = 2
   m_gen = 0
-  if (repr == "V") {
+  if (rep == "V") {
     Vpoly_gen = TRUE
-  } else if (repr == "H") {
+  } else if (rep == "H") {
     Vpoly_gen = FALSE
   } else {
     stop('Not a known representation.')

R-proj/R/gen_cube.R

@@ -3,7 +3,7 @@
 #' This function can be used to generate the \eqn{d}-dimensional unit hypercube \eqn{[-1,1]^d} in H- or V-representation.
 #' 
 #' @param dimension The dimension of the hypercube
-#' @param repr A string to declare the representation. It has to be \code{'H'} for H-representation or \code{'V'} for V-representation.
+#' @param rep A string to declare the representation. It has to be \code{'H'} for H-representation or \code{'V'} for V-representation.
 #' 
 #' @return A polytope class representing the unit \eqn{d}-dimensional hypercube in H- or V-representation.
 #' @examples 
@@ -13,13 +13,13 @@
 #' # generate a 15-dimension hypercube in V-representation
 #' P = gen_cube(15, 'V')
 #' @export
-gen_cube <- function(dimension, repr) {
+gen_cube <- function(dimension, rep) {
 
   kind_gen = 1
   m_gen = 0
-  if (repr == "V") {
+  if (rep == "V") {
     Vpoly_gen = TRUE
-  } else if (repr == "H") {
+  } else if (rep == "H") {
     Vpoly_gen = FALSE
   } else {
     stop('Not a known representation.')

R-proj/R/gen_rand_hpoly.R

@@ -3,19 +3,19 @@
 #' This function can be used to generate a \eqn{d}-dimensional polytope in H-representation with \eqn{m} facets. We pick \eqn{m} random hyperplanes tangent on the \eqn{d}-dimensional unit hypersphere as facets.
 #' 
 #' @param dimension The dimension of the convex polytope.
-#' @param m The number of the facets.
+#' @param nfacets The number of the facets.
 #' 
 #' @return A polytope class representing a H-polytope.
 #' @examples 
 #' # generate a 10-dimensional polytope with 50 facets
 #' P = gen_rand_hpoly(10, 50)
 #' @export
-gen_rand_hpoly <- function(dimension, m) {
+gen_rand_hpoly <- function(dimension, nfacets) {
 
   kind_gen = 6
   Vpoly_gen = FALSE
 
-  Mat = poly_gen(kind_gen, Vpoly_gen, FALSE, dimension, m)
+  Mat = poly_gen(kind_gen, Vpoly_gen, FALSE, dimension, nfacets)
 
   # first column is the vector b
   b = Mat[,1]

R-proj/R/gen_rand_vpoly.R

@@ -3,15 +3,15 @@
 #' This function can be used to generate a \eqn{d}-dimensional polytope in V-representation with \eqn{m} vertices. We pick \eqn{m} random points from the boundary of the \eqn{d}-dimensional unit hypersphere as vertices.
 #' 
 #' @param dimension The dimension of the convex polytope.
-#' @param n_vertices The number of the vertices.
+#' @param nvertices The number of the vertices.
 #' @param generator The body that the generator samples uniformly the vertices from: (a) 'cube' or (b) 'sphere'.
 #' 
 #' @return A polytope class representing a V-polytope.
 #' @examples 
 #' # generate a 10-dimensional polytope defined as the convex hull of 25 random vertices
 #' P = gen_rand_vpoly(10, 25)
 #' @export
-gen_rand_vpoly <- function(dimension, n_vertices, generator = NULL) {
+gen_rand_vpoly <- function(dimension, nvertices, generator = NULL) {
 
   kind_gen = 4
 
@@ -23,7 +23,7 @@ gen_rand_vpoly <- function(dimension, n_vertices, generator = NULL) {
     }
   }
 
-  Mat = poly_gen(kind_gen, TRUE, FALSE, dimension, n_vertices)
+  Mat = poly_gen(kind_gen, TRUE, FALSE, dimension, nvertices)
 
   # first column is the vector b
   b = Mat[,1]

R-proj/R/gen_rand_zonotope.R

@@ -3,7 +3,7 @@
 #' This function can be used to generate a random \eqn{d}-dimensional zonotope defined by the Minkowski sum of \eqn{m} \eqn{d}-dimensional segments. We consider \eqn{m} random directions in \eqn{R^d} and for each direction we pick a random length in \eqn{[(,\sqrt{d}]} in order to define \eqn{m} segments.
 #' 
 #' @param dimension The dimension of the zonotope.
-#' @param n_segments The number of segments that generate the zonotope.
+#' @param nsegments The number of segments that generate the zonotope.
 #' @param generator The distribution to pick the length of each segment from \eqn{[0,100]}: (a) 'uniform', (b) 'gaussian' or (c) 'exponential'.
 #' 
 #' @return A polytope class representing a zonotope.
@@ -12,7 +12,7 @@
 #' # generate a 10-dimensional zonotope defined by the Minkowski sum of 20 segments
 #' P = gen_rand_zonotope(10, 20)
 #' @export
-gen_rand_zonotope <- function(dimension, n_segments, generator = NULL) {
+gen_rand_zonotope <- function(dimension, nsegments, generator = NULL) {
 
   kind_gen = 1
 
@@ -26,7 +26,7 @@ gen_rand_zonotope <- function(dimension, n_segments, generator = NULL) {
     }
   }
 
-  Mat = poly_gen(kind_gen, FALSE, TRUE, dimension, n_segments)
+  Mat = poly_gen(kind_gen, FALSE, TRUE, dimension, nsegments)
 
   # first column is the vector b
   b = Mat[,1]

R-proj/R/gen_simplex.R

@@ -3,7 +3,7 @@
 #' This function can be used to generate the \eqn{d}-dimensional unit simplex in H- or V-representation.
 #' 
 #' @param dimension The dimension of the unit simplex.
-#' @param repr A string to declare the representation. It has to be \code{'H'} for H-representation or \code{'V'} for V-representation.
+#' @param rep A string to declare the representation. It has to be \code{'H'} for H-representation or \code{'V'} for V-representation.
 #' 
 #' @return A polytope class representing the \eqn{d}-dimensional unit simplex in H- or V-representation.
 #' @examples
@@ -13,13 +13,13 @@
 #' # generate a 20-dimensional simplex in V-representation
 #' P = gen_simplex(20, 'V')
 #' @export
-gen_simplex <- function(dimension, repr) {
+gen_simplex <- function(dimension, rep) {
 
   kind_gen = 3
   m_gen = 0
-  if (repr == "V") {
+  if (rep == "V") {
     Vpoly_gen = TRUE
-  } else if (repr == "H") {
+  } else if (rep == "H") {
     Vpoly_gen = FALSE
   } else {
     stop('Not a known representation.')