Register C fucntion

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: COveR
 Title: Clustering with Overlaps in R
-Version: 0.10.6
+Version: 1.0.0
 Authors@R: c(
   person("Guillaume", "Cleuziou", email = "guillaume.cleuziou@univ-orleans.fr", role = "cre"),
   person("Nicolas", "Hiot", email = "nicolas.hiot@etu.univ-orleans.fr", role = "aut"))

NAMESPACE

@@ -37,11 +37,12 @@ export(r1okm)
 export(r2okm)
 export(read.interval)
 export(write.interval)
-useDynLib(COveR,R_icmeans)
-useDynLib(COveR,R_ikmeans)
-useDynLib(COveR,R_ineokm)
-useDynLib(COveR,R_iokm)
-useDynLib(COveR,R_neokm)
-useDynLib(COveR,R_okm)
-useDynLib(COveR,R_r1okm)
-useDynLib(COveR,R_r2okm)
+importFrom(grDevices,col2rgb)
+importFrom(grDevices,rainbow)
+importFrom(grDevices,rgb)
+importFrom(graphics,plot)
+importFrom(graphics,rect)
+importFrom(stats,model.matrix)
+importFrom(utils,read.csv)
+importFrom(utils,write.csv)
+useDynLib(COveR, .registration = TRUE)

R/fuzzy_icmeans.R

@@ -1,7 +1,7 @@
 #' Interval fuzzy cmeans clustering.
 #'
 #' Culster interval data with fuzzy cmeans algorithm.
-#' @useDynLib COveR R_icmeans
+#' @useDynLib COveR, .registration = TRUE
 #'
 #' @param x An 3D interval array.
 #' @param centers A number or interval, number of cluster for clustering or pre init centers.
@@ -71,11 +71,11 @@ fuzzy_icmeans <- function(x, centers, m = 2, nstart = 2, distance = "euclid", tr
     stop(paste("Unknow distance:", distance, ". Try 'euclid' or 'hausdorff'"))
   }
 
-  #  Call
+  # Call
   d <- dim(x$inter)
   n <- dimnames(x$inter)
   v <- as.numeric(as.vector(x$inter))
-  c <- .Call(R_icmeans, v, d[1], d[2], d[3], nc, m, nstart, dist, trace, iter.max,
+  c <- .Call("_icmeans", v, d[1], d[2], d[3], nc, m, nstart, dist, trace, iter.max,
     c)
 
   # Naming

R/ikmeans.R

@@ -1,7 +1,7 @@
 #' Interval kmeans clustering.
 #'
 #' Culster interval data with kmeans algorithm.
-#' @useDynLib COveR R_ikmeans
+#' @useDynLib COveR, .registration = TRUE
 #'
 #' @param x An 3D interval array.
 #' @param centers A number or interval, number of cluster for clustering or pre init centers.
@@ -65,11 +65,11 @@ ikmeans <- function(x, centers, nstart = 10, distance = "euclid", trace = FALSE,
     stop(paste("Unknow distance:", distance, ". Try 'euclid' or 'hausdorff'"))
   }
 
-  #  Call
+  # Call
   d <- dim(x$inter)
   n <- dimnames(x$inter)
   v <- as.numeric(as.vector(x$inter))
-  c <- .Call(R_ikmeans, v, d[1], d[2], d[3], nc, nstart, dist, trace, iter.max,
+  c <- .Call("_ikmeans", v, d[1], d[2], d[3], nc, nstart, dist, trace, iter.max,
     c)
 
   # Naming

R/ineokm.R

@@ -1,7 +1,7 @@
 #' Interval neokm clustering.
 #'
 #' Culster interval data with neokm algorithm.
-#' @useDynLib COveR R_ineokm
+#' @useDynLib COveR, .registration = TRUE
 #'
 #' @param x An 3D interval array.
 #' @param centers A number or interval, number of cluster for clustering or pre init centers.
@@ -61,11 +61,11 @@ ineokm <- function(x, centers, alpha = 0.3, beta = 0.05, nstart = 10, trace = FA
   if (iter.max <= 0)
     stop("iter.max must be positive")
 
-  #  Call
+  # Call
   d <- dim(x$inter)
   n <- dimnames(x$inter)
   v <- as.numeric(as.vector(x$inter))
-  c <- .Call(R_ineokm, v, d[1], d[2], d[3], nc, alpha, beta, nstart, trace, iter.max,
+  c <- .Call("_ineokm", v, d[1], d[2], d[3], nc, alpha, beta, nstart, trace, iter.max,
     c)
 
   # Naming

R/interval.R

@@ -29,6 +29,8 @@ print.interval <- function(x, ...) {
 #' @param class The column of class (empty if not).
 #' @param ... Other options from read.
 #'
+#' @importFrom utils read.csv
+#'
 #' @export
 read.interval <- function(..., row.names = FALSE, class = NULL) {
   args <- list(...)
@@ -86,6 +88,8 @@ read.interval <- function(..., row.names = FALSE, class = NULL) {
 #' @param class Write the class column at the end ?.
 #' @param ... Other options from write.
 #'
+#' @importFrom utils write.csv
+#'
 #' @export
 write.interval <- function(x, ..., class = FALSE) {
   if (!is.interval(x))
@@ -117,6 +121,8 @@ write.interval <- function(x, ..., class = FALSE) {
 #'   - 'add' Add to exsiting plot or not.
 #'   - Other options from plot.
 #'
+#' @importFrom graphics plot rect
+#'
 #' @export
 #'
 #' @examples
@@ -271,6 +277,8 @@ igenerate <- function(n, ...) {
 #' @param max The maximum value of degree.
 #' @param step The step of degree.
 #'
+#' @importFrom graphics plot
+#'
 #' @export
 measure <- function(x, min = 0, max = 1, step = 0.1) {
   if (!is.matrix(x))

R/iokm.R

@@ -1,7 +1,7 @@
 #' Interval okm clustering.
 #'
 #' Culster interval data with okm algorithm.
-#' @useDynLib COveR R_iokm
+#' @useDynLib COveR, .registration = TRUE
 #'
 #' @param x An 3D interval array.
 #' @param centers A number or interval, number of cluster for clustering or pre init centers.
@@ -77,11 +77,11 @@ iokm <- function(x, centers, nstart = 10, distance = "euclid", algorithm = "std"
   # Update
   up <- match(update, c("mean", "sum", "join", "meet")) - 1
 
-
+  # Call
   d <- dim(x$inter)
   n <- dimnames(x$inter)
   v <- as.numeric(as.vector(x$inter))
-  c <- .Call(R_iokm, v, d[1], d[2], d[3], nc, nstart, dist, algo, up, trace, iter.max,
+  c <- .Call("_iokm", v, d[1], d[2], d[3], nc, nstart, dist, algo, up, trace, iter.max,
     secure, c)
 
 

R/neokm.R

@@ -1,7 +1,7 @@
 #' neokm clustering.
 #'
 #' Culster data with neokm algorithm.
-#' @useDynLib COveR R_neokm
+#' @useDynLib COveR, .registration = TRUE
 #'
 #' @param x An data matrix.
 #' @param centers A number, number of cluster for clustering or pre init centers.
@@ -62,9 +62,9 @@ neokm <- function(x, centers, alpha = 0.3, beta = 0.05, nstart = 10, trace = FAL
   if (iter.max <= 0)
     stop("iter.max must be positive")
 
-
+  # Call
   v <- as.numeric(as.vector(data.matrix(x)))
-  c <- .Call(R_neokm, v, nrow(x), ncol(x), nc, alpha, beta, nstart, trace, iter.max,
+  c <- .Call("_neokm", v, nrow(x), ncol(x), nc, alpha, beta, nstart, trace, iter.max,
     c)
 
 

R/okm.R

@@ -16,7 +16,7 @@
 #' OKM clustering.
 #'
 #' Overlapping Kmeans algorithm.
-#' @useDynLib COveR R_okm
+#' @useDynLib COveR, .registration = TRUE
 #'
 #' @param X A data.
 #' @param centers A number or matrix, number of cluster for clustering or pre init centers.
@@ -54,18 +54,17 @@ okm <- function(X, centers, iter.max = 10, nstart = 1, trace = FALSE, method = "
   if (nrow(X) < k)
     stop("More cluster than datas in X.")
 
-
   if (method == "euclid")
     method <- 1 else if (method == "manhattan")
     method <- 2 else stop("Unknown distance computation method.")
-  U <- .C(R_okm, as.double(t(X)), centers = as.double(t(cen)), as.integer(iter.max), 
+  U <- .C("_okm", as.double(t(X)), centers = as.double(t(cen)), as.integer(iter.max),
     as.integer(k), as.integer(ncol(X)), as.integer(nrow(X)), clusters = integer(nrow(X) *
       k), wss = as.double(k), over = as.double(k), as.integer(trace), 0, 0,
     0, 0, 0, as.integer(method))
   if (use.nstart && nstart > 1) {
     for (i in 2:nstart) {
       cen <- X[sample(1:nrow(X), k), ]
-      UU <- .C(R_okm, as.double(t(X)), centers = as.double(t(cen)), as.integer(iter.max), 
+      UU <- .C("_okm", as.double(t(X)), centers = as.double(t(cen)), as.integer(iter.max),
         as.integer(k), as.integer(ncol(X)), as.integer(nrow(X)), clusters = integer(nrow(X) *
           k), wss = as.double(k), over = as.double(k), as.integer(trace),
         0, 0, 0, 0, 0, as.integer(method))

R/r1okm.R

@@ -1,7 +1,7 @@
 #' r1okm clustering.
 #'
 #' Culster data with r1okm algorithm.
-#' @useDynLib COveR R_r1okm
+#' @useDynLib COveR, .registration = TRUE
 #'
 #' @param x An data matrix.
 #' @param centers A number, number of cluster for clustering or pre init centers.
@@ -60,8 +60,9 @@ r1okm <- function(x, centers, alpha = 0, nstart = 10, trace = FALSE, iter.max =
     stop("iter.max must be positive")
 
 
+  # Call
   v <- as.numeric(as.vector(data.matrix(x)))
-  c <- .Call(R_r1okm, v, nrow(x), ncol(x), nc, alpha, nstart, trace, iter.max,
+  c <- .Call("_r1okm", v, nrow(x), ncol(x), nc, alpha, nstart, trace, iter.max,
     c)
 
 

R/r2okm.R

@@ -1,7 +1,7 @@
 #' r2okm clustering.
 #'
 #' Culster data with r2okm algorithm.
-#' @useDynLib COveR R_r2okm
+#' @useDynLib COveR, .registration = TRUE
 #'
 #' @param x An data matrix.
 #' @param centers A number, number of cluster for clustering or pre init centers.
@@ -62,8 +62,9 @@ r2okm <- function(x, centers, lambda = 0, nstart = 10, trace = FALSE, iter.max =
     stop("iter.max must be positive")
 
 
+  # Call
   v <- as.numeric(as.vector(data.matrix(x)))
-  c <- .Call(R_r2okm, v, nrow(x), ncol(x), nc, lambda, nstart, trace, iter.max,
+  c <- .Call("_r2okm", v, nrow(x), ncol(x), nc, lambda, nstart, trace, iter.max,
     c)
 
 

R/tools.R

@@ -4,6 +4,9 @@
 #'
 #' @param x A cluster.
 #'
+#' @importFrom grDevices col2rgb rainbow rgb
+#' @importFrom stats model.matrix
+#'
 #' @export
 #'
 #' @examples

src/COveR-init.c

@@ -0,0 +1,31 @@
+// Interval clustering
+#include "icmeans/R_icmeans.h"
+#include "ikmeans/R_ikmeans.h"
+#include "ineokm/R_ineokm.h"
+#include "iokm/R_iokm.h"
+
+// Classic clustering
+#include "neokm/R_neokm.h"
+#include "okm/okm.h"
+#include "r1okm/R_r1okm.h"
+#include "r2okm/R_r2okm.h"
+
+#include <R.h>
+#include <R_ext/Rdynload.h>
+#include <Rinternals.h>
+
+static const R_CallMethodDef callMethods[] = {
+    {"_icmeans", (DL_FUNC)&R_icmeans, 11}, {"_ikmeans", (DL_FUNC)&R_ikmeans, 10},
+    {"_ineokm", (DL_FUNC)&R_ineokm, 11},   {"_iokm", (DL_FUNC)&R_iokm, 13},
+    {"_neokm", (DL_FUNC)&R_neokm, 10},     {"_r1okm", (DL_FUNC)&R_r1okm, 9},
+    {"_r2okm", (DL_FUNC)&R_r2okm, 9},      {NULL, NULL, 0}};
+
+static R_NativePrimitiveArgType okm_t[] = {
+    REALSXP, REALSXP, INTSXP, INTSXP,  INTSXP, INTSXP,  INTSXP, REALSXP,
+    REALSXP, INTSXP,  REALSXP, REALSXP, REALSXP, REALSXP, REALSXP, INTSXP};
+static const R_CMethodDef cMethods[] = {{"_okm", (DL_FUNC)&R_okm, 16, okm_t},
+                                           {NULL, NULL, 0}};
+
+void R_init_COveR(DllInfo *info) {
+  R_registerRoutines(info, cMethods, callMethods, NULL, NULL);
+}