first commit

Author: Niangmohamed

.Rbuildignore

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+^.*\.Rproj$
+^\.Rproj\.user$
+.Rhistory
+*.o
+*.dll

DESCRIPTION

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+Package: salesmanRcpp
+Type: Package
+Title: What the Package Does (Title Case)
+Version: 0.1.0
+Author: Mohamed NIANG
+Maintainer: Mohamed NIANG <niango777@gmail.com>
+Description: This package is an example for the use of Rcpp functions with the classic probem of the travelling salesman problem. Our aim is mainly to compare the time efficiency between R and C++ code of the problem.
+License: GPL-3
+Encoding: UTF-8
+LazyData: true
+Imports: Rcpp (>= 1.0.0), combinat
+LinkingTo: Rcpp
+RoxygenNote: 6.0.1

NAMESPACE

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+useDynLib(salesmanRcpp, .registration = TRUE)
+
+export(Towns, distance, monVoyageR, monVoyageClosest, monVoyageRcpp)
+S3method(plot, salesmanRcpp)
+
+importFrom(Rcpp, evalCpp)
+importFrom("graphics", "plot", "segments", "text")
+importFrom("stats", "rnorm")
+importFrom("combinat", "permn")
+

R/RcppExports.R

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+# Generated by using Rcpp::compileAttributes() -> do not edit by hand
+# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
+
+#' @title monVoyageRcpp
+#' @description
+#' This algorithms returns the indices of towns in the shortest order for the Euclidean norm
+#' @name monVoyageRcpp
+#' @param towns dataframe of towns generated by the `Towns` function
+#'
+#' @export
+monVoyageRcpp <- function(towns) {
+    .Call(`_salesmanRcpp_monVoyageRcpp`, towns)
+}
+
+#' @title monVoyageRcpp
+#' @description
+#' This algorithms returns the indices of towns in the shortest order for the Euclidean norm using the Held-Karp algorithm
+#' @name monVoyageRcpp
+#' @param towns dataframe of towns generated by the `Towns` function
+#'
+#' @export
+monVoyageRcppHK <- function(towns) {
+    .Call(`_salesmanRcpp_monVoyageRcppHK`, towns)
+}
+

R/plot.R

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+
+#' plot.salesmanRcpp
+#' @description Plot the result of the gfpop function with the data-points
+#' @param x a salesman class object
+#' @param ... Other parameters
+#' @param towns the dataframe with the towns (X,Y) coordinate
+#' @param col the color of the path
+#' @return plot the towns and the shortest path
+plot.salesmanRcpp <- function(x, ..., towns, col = "blue")
+{
+  n <- dim(towns)[1]
+  plot(towns[,1], towns[,2], pch = '.', lwd = 0, asp = 1, xlab = "", ylab = "")
+  text(towns[,1], towns[,2], labels = 1:n)
+  for(s in 1:(length(x)-1))
+    {segments(towns[x[s],1], towns[x[s],2], towns[x[s+1],1], towns[x[s+1],2], col = col, lwd = 2)}
+}

R/salesman.R

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+##  GPL-3 License
+## Copyright (c) 2019 Mohamed NIANG
+
+#' Towns coordinate generator
+#'
+#' @description Genearting towns coordinate using a standard normal distribution
+#' @param n number of towns to generate
+#' @return a dataframe with 2 columns X et Y with the town coordinates
+Towns <- function(n = 6)
+{
+  return(data.frame(X=rnorm(n), Y = rnorm(n)))
+}
+
+
+#' Euclidean distance between two 2d points
+#'
+#' @description Computing L2 distance between 2 points
+#' @param v1 first (X1,Y1) point
+#' @param v2 second (X1,Y1) point
+#' @return a number = sqrt((X1-X2)^2 + (Y1-Y2)^2)
+distance <- function(v1, v2)
+{
+  D <- sqrt(sum((v1-v2)^2))
+  return(D)
+}
+
+
+#' The shortest route in the travelling salesman problem
+#'
+#' @description This algorithms returns the indices of towns in the shortest order for the Euclidean norm
+#' @param towns dataframe of towns generated by the `Towns` function
+#' @return vector of town indices of the shortest path
+monVoyageR <- function(towns)
+{
+  n <- dim(towns)[1]
+  distance <- Inf
+  res <- NULL
+  voyages <- permn(2:n)
+
+  nbLoops <- factorial(n-1)
+  for(i in 1:nbLoops)
+  {
+    Voy <- c(1,unlist(voyages[i]),1)
+    D <- 0
+    for(i in 1:n){D <- D + distance(towns[Voy[i],],towns[Voy[i+1],])}
+    if(D < distance)
+    {
+      res <- Voy
+      distance <- D
+    }
+  }
+  attr(res, "class") <- "salesman"
+  return(res)
+}
+
+
+#' A naive algorithm in the travelling salesman problem
+#'
+#' @description This algorithms returns the indices of towns with a simple closest-town strategy
+#' @param towns dataframe of towns generated by the `Towns` function
+#' @return vector of town indices of the naive strategy
+monVoyageClosest <- function(towns)
+{
+  n <- dim(towns)[1]
+  indices <- 2:n
+  res <- 1
+
+  for(i in 1:(n-2))
+  {
+    D <- Inf
+    ind <- NULL
+    for(j in indices)
+    {
+      if(distance(towns[res[1],],towns[j,]) < D)
+      {
+        D <- distance(towns[res[1],],towns[j,])
+        ind <- j
+      }
+    }
+    res <- c(ind,res)
+    indices <- indices[indices!= ind]
+  }
+  res <- rev(c(1,indices,res))
+  attr(res, "class") <- "salesman"
+  return(res)
+}

README.md

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+# The Salesman Problem
+
+> Master 2 Project. Paris-Saclay University. Simple functions R versus Rcpp.
+
+> Rmd file and its htlm in project folder.