├── .gitignore ├── .travis.yml ├── LICENSE ├── Makefile ├── README.md ├── compile.sh ├── pkg ├── DESCRIPTION ├── NAMESPACE ├── NEWS ├── R │ ├── gower-pkg.R │ └── gower.R ├── inst │ └── tinytest │ │ ├── test_gh_issue_18.R │ │ ├── test_gh_issue_8.R │ │ └── test_gower.R ├── src │ ├── Makevars │ ├── R_register_native.c │ └── gower.c ├── tests │ └── tinytest.R └── vignettes │ └── intro.Rnw └── vignettes.sh /.gitignore: -------------------------------------------------------------------------------- 1 | 2 | pkg/man 3 | pkg/src/*.so 4 | pkg/src/*.o 5 | pkg/vignettes/*.html 6 | 7 | 8 | output/* 9 | 10 | .Rproj.user 11 | *.Rproj 12 | 13 | 14 | 15 | -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- 1 | 2 | # travis config 3 | 4 | sudo: required 5 | language: r 6 | 7 | 8 | addons: 9 | apt: 10 | packages: 11 | - libxml2-dev 12 | 13 | r: 14 | - release 15 | 16 | 17 | 18 | before_install: 19 | - R -e "install.packages(c('devtools','roxygen2','testthat'))" 20 | - R -e "devtools::install_deps('./pkg')" 21 | - R -e "devtools::load_all('./pkg'); devtools::document('./pkg')" 22 | - cd ./pkg 23 | 24 | r_packages: 25 | - covr 26 | - rmarkdown 27 | 28 | 29 | after_success: 30 | - Rscript -e 'library(covr);coveralls()' 31 | 32 | notifications: 33 | email: 34 | on_success: change 35 | on_failure: change 36 | 37 | 38 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) {year} {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /Makefile: -------------------------------------------------------------------------------- 1 | 2 | doc: 3 | R -s -e "pkgload::load_all('pkg');roxygen2::roxygenize('pkg')" 4 | 5 | pkg: doc 6 | R CMD build pkg 7 | 8 | install: pkg 9 | R CMD INSTALL *.tar.gz 10 | 11 | check: doc 12 | R CMD build pkg 13 | R CMD check *.tar.gz 14 | 15 | cran: doc 16 | R CMD build pkg 17 | R CMD check --as-cran *.tar.gz 18 | 19 | test: doc 20 | R -s -e "tinytest::build_install_test('pkg')" 21 | 22 | manual: doc 23 | R CMD Rd2pdf --force -o manual.pdf ./pkg 24 | 25 | revdep: pkg 26 | rm -rf revdep 27 | mkdir revdep 28 | mv *.tar.gz revdep 29 | R -s -e "out <- tools::check_packages_in_dir('revdep',reverse=list(which='most'),Ncpus=3); print(summary(out)); saveRDS(out, file='revdep/output.RDS')" 30 | 31 | clean: 32 | rm -rf stringdist.Rcheck 33 | rm -rf revdep 34 | rm -f *.tar.gz 35 | 36 | 37 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | [![Build Status](https://travis-ci.org/markvanderloo/gower.svg?branch=master)](https://travis-ci.org/markvanderloo/gower) 3 | [![Coverage Status](https://coveralls.io/repos/markvanderloo/gower/badge.svg)](https://coveralls.io/r/markvanderloo/gower) 4 | [![CRAN](http://www.r-pkg.org/badges/version/gower)](https://CRAN.R-project.org/package=gower) 5 | [![status](https://tinyverse.netlify.app/badge/gower)](https://CRAN.R-project.org/package=gower) 6 | [![Downloads](http://cranlogs.r-pkg.org/badges/gower)](http://cran.r-project.org/package=gower) 7 | 8 | 9 | ### gower 10 | 11 | Gower's distance for R. Based in C, using openMP for parallelization. 12 | 13 | ### Usage 14 | 15 | ``` 16 | library(gower) 17 | reviris <- iris[rev(seq_len(nrow(iris))),,drop=FALSE] 18 | # compute distances 19 | d <- gower_dist(iris, reviris) 20 | 21 | # data.frame with less records is recycled 22 | d <- gower_dist(iris[1:3,,drop=FALSE], reviris) 23 | 24 | # compute top-n matches 25 | mat <- gower_topn(iris, reviris, n=5) 26 | 27 | ## mat$index : Array of indices (sorted from better to worse match) 28 | ## mat$distance: Array of distances (sorted from small to large) 29 | 30 | ``` 31 | 32 | More info in the [vignette](https://cran.r-project.org/web/packages/gower/vignettes/intro.pdf) 33 | 34 | 35 | ### Installation 36 | 37 | #### From CRAN 38 | 39 | ``` 40 | install.packages("gower") 41 | ``` 42 | 43 | 44 | #### Beta versions 45 | 46 | Made available through my drat repo. 47 | 48 | First, install the [drat](https://cran.r-project.org/package=drat) package. Users of the OS who's name shall not be mentioned need to install [Rtools](https://cran.r-project.org/bin/windows/Rtools/) first. 49 | ``` 50 | if(!require(drat)) install.packages('drat') 51 | drat::addRepo('markvanderloo') 52 | install.packages('gower',type='source') 53 | 54 | ``` 55 | 56 | 57 | ### Reference 58 | 59 | Gower (1971) A general coefficient of similarity and some of its properties. _Biometrics_ **27** 857-874 [pdf](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.412.4155&rep=rep1&type=pdf) 60 | -------------------------------------------------------------------------------- /compile.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | cd pkg/src 4 | rm --verbose *.o *.so 5 | gcc -std=gnu99 -I/usr/share/R/include -DNDEBUG -fpic -fopenmp -O2 -Wall -pipe -g -c -Wstrict-prototypes *.c 6 | #gcc -std=gnu99 -I/usr/share/R/include -DNDEBUG -fpic -O2 -Wall -pipe -g -c *.c 7 | gcc -std=gnu99 -shared -o gower.so *.o -L/usr/lib/R/lib -lR 8 | cd ../../ 9 | 10 | -------------------------------------------------------------------------------- /pkg/DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: gower 2 | Maintainer: Mark van der Loo 3 | License: GPL-3 4 | Title: Gower's Distance 5 | LazyData: no 6 | Type: Package 7 | LazyLoad: yes 8 | Authors@R: c( person("Mark", "van der Loo", role=c("aut","cre"),email="mark.vanderloo@gmail.com") 9 | , person("David", "Turner", role="ctb")) 10 | Description: Compute Gower's distance (or similarity) coefficient between records. Compute 11 | the top-n matches between records. Core algorithms are executed in parallel on systems 12 | supporting OpenMP. 13 | Version: 1.0.2 14 | URL: https://github.com/markvanderloo/gower 15 | BugReports: https://github.com/markvanderloo/gower/issues 16 | Suggests: 17 | tinytest (>= 0.9.3), 18 | RoxygenNote: 7.3.2 19 | -------------------------------------------------------------------------------- /pkg/NAMESPACE: -------------------------------------------------------------------------------- 1 | # Generated by roxygen2: do not edit by hand 2 | 3 | export(gower_dist) 4 | export(gower_topn) 5 | useDynLib(gower, .registration=TRUE) 6 | -------------------------------------------------------------------------------- /pkg/NEWS: -------------------------------------------------------------------------------- 1 | version 1.0.2 2 | - Improved sanity checking on input data frames (Thanks to Brian Ripley for 3 | pointing this out) 4 | 5 | version 1.0.1 6 | - Updated compiled code to new compiler prototype warnings. 7 | 8 | version 1.0.0 9 | - Fix: for integer vectors, the output would in some cases depend 10 | on the order of the input. (Thanks to Tobias Rockel for reporting). 11 | 12 | version 0.2.2 13 | - Fix: for small numbers of records and high number of threads, the output 14 | would depend on 'nthreads'. (Thanks to Matt Austin for reporing). Gower 15 | now only paralellizes when more than 1000 records are presented. 16 | 17 | version 0.2.1 18 | - moved test framework to 'tinytest' 19 | 20 | version 0.2.0 21 | - gower_dist and gower_topn gain `weight` argument for weighted matching (thanks to David Turner) 22 | - gower_dist and gower_topn gain `ignore_case` argument for automatic column matching. 23 | - gower_dist now returns numeric(0) invisibly when there are no columns to compare. 24 | - gower_topn now returns a list with empty matrices when there are no columns to compare. 25 | - gower_topn now warns when n>nrow(y) and sets n=nrow(y) 26 | - bugfix: comparing factors with characters would cause a crash (thanks to Max Kuhn) 27 | 28 | version 0.1.2 29 | - fixed valgrind warning 30 | - registered native routines, as now recommended by CRAN 31 | 32 | version 0.1.1 33 | - gower_dist now issues warning when nr of rows on input do not divide (recycling) 34 | - Code now depends on gcc version for compatability with Windows + R<=3.2.x 35 | - Bugfix: bad range calculation caused faulty distance computation 36 | 37 | version 0.1.0 38 | - First release 39 | 40 | -------------------------------------------------------------------------------- /pkg/R/gower-pkg.R: -------------------------------------------------------------------------------- 1 | #' Gower's distance/similarity measure. 2 | #' 3 | #' A C-based implementation of Gower's distance. 4 | #' 5 | #' @name gower-package 6 | #' @docType package 7 | #' @useDynLib gower, .registration=TRUE 8 | #' 9 | "_PACKAGE" 10 | 11 | 12 | .onLoad <- function(libname, pkgname){ 13 | max_threads <- 1L 14 | max_threads <- .Call("R_get_max_threads",PACKAGE="gower") 15 | thread_limit <- .Call("R_get_thread_limit",PACKAGE="gower") 16 | max_threads <- min(max_threads, thread_limit) 17 | # leave one core for the user to control the machine. 18 | if (max_threads > 2) max_threads <- max_threads - 1L 19 | options(gd_num_thread = as.integer(max_threads)) 20 | } 21 | -------------------------------------------------------------------------------- /pkg/R/gower.R: -------------------------------------------------------------------------------- 1 | 2 | #' Gower's distance 3 | #' 4 | #' Compute Gower's distance, pairwise between records in two data sets \code{x} 5 | #' and \code{y}. Records from the smallest data set are recycled over. 6 | #' 7 | #' @section Details: 8 | #' There are three ways to specify which columns of \code{x} should be compared 9 | #' with what columns of \code{y}. The first option is do give no specification. 10 | #' In that case columns with matching names will be used. The second option 11 | #' is to use only the \code{pairs_y} argument, specifying for each column in \code{x} 12 | #' in order, which column in \code{y} must be used to pair it with (use \code{0} 13 | #' to skip a column in \code{x}). The third option is to explicitly specify the 14 | #' columns to be matched using \code{pair_x} and \code{pair_y}. 15 | #' 16 | #' 17 | #' @section Note: 18 | #' Gower (1971) originally defined a similarity measure (\eqn{s}, say) 19 | #' with values ranging from 0 (completely dissimilar) to 1 (completely similar). 20 | #' The distance returned here equals \eqn{1-s}. 21 | #' 22 | #' 23 | #' @param x \code{[data.frame]} 24 | #' @param y \code{[data.frame]} 25 | #' @param pair_x \code{[numeric|character] (optional)} Columns in \code{x} used for comparison. 26 | #' See Details below. 27 | #' @param pair_y \code{[numeric|character] (optional)} Columns in \code{y} used for comparison. 28 | #' See Details below. 29 | #' @param eps \code{[numeric] (optional)} Computed numbers (variable ranges) 30 | #' smaller than \code{eps} are treated as zero. 31 | #' @param weights \code{[numeric] (optional)} A vector of weights of length \code{ncol(x)} 32 | #' that defines the weight applied to each component of the gower distance. 33 | #' @param ignore_case \code{[logical]} Toggle ignore case when neither \code{pair_x} 34 | #' nor \code{pair_y} are user-defined. 35 | #' @param nthread Number of threads to use for parallelization. By default, 36 | #' for a dual-core machine, 2 threads are used. For any other machine 37 | #' n-1 cores are used so your machine doesn't freeze during a big computation. 38 | #' The maximum nr of threads are determined using \code{omp_get_max_threads} at C level. 39 | #' 40 | #' 41 | #' @return 42 | #' A \code{numeric} vector of length \code{max(nrow(x),nrow(y))}. 43 | #' When there are no columns to compare, a message is printed and both 44 | #' \code{numeric(0)} is returned invisibly. 45 | #' 46 | #' @seealso \code{\link{gower_topn}} 47 | #' 48 | #' @references 49 | #' Gower, John C. "A general coefficient of similarity and some of its 50 | #' properties." Biometrics (1971): 857-871. 51 | #' 52 | #' @export 53 | gower_dist <- function(x, y, pair_x=NULL, pair_y=NULL, eps = 1e-8, weights=NULL, 54 | ignore_case=FALSE, nthread=getOption("gd_num_thread")){ 55 | check_recycling(nrow(x),nrow(y)) 56 | 57 | gower_work(x=x,y=y,pair_x=pair_x,pair_y=pair_y 58 | , n=NULL, eps=eps, weights=weights, ignore_case=ignore_case, nthread=nthread) 59 | } 60 | 61 | #' Find the top-n matches 62 | #' 63 | #' @description 64 | #' 65 | #' Find the top-n matches in \code{y} for each record in \code{x}. 66 | #' 67 | #' @inheritParams gower_dist 68 | #' @param n The top-n indices and distances to return. 69 | #' 70 | #' @seealso \code{\link{gower_dist}} 71 | #' 72 | #' @return 73 | #' A \code{list} with two array elements: \code{index} 74 | #' and \code{distance}. Both have size \code{n X nrow(x)}. Each ith column 75 | #' corresponds to the top-n best matches of \code{x} with rows in \code{y}. 76 | #' When there are no columns to compare, a message is printed and both 77 | #' \code{distance} and \code{index} will be empty matrices; the list is 78 | #' then returned invisibly. 79 | #' 80 | #' @examples 81 | #' # find the top 4 best matches in the iris data set with itself. 82 | #' x <- iris[1:3,] 83 | #' lookup <- iris[1:10,] 84 | #' gower_topn(x=x,y=lookup,n=4) 85 | #' 86 | #' 87 | #' @export 88 | gower_topn <- function(x, y, pair_x=NULL, pair_y = NULL, n=5, eps=1e-8, weights = NULL, 89 | ignore_case=FALSE, nthread=getOption("gd_num_thread")){ 90 | gower_work(x=x,y=y,pair_x=pair_x,pair_y=pair_y 91 | , n=n, eps=eps, weights=weights, ignore_case=ignore_case, nthread=nthread) 92 | } 93 | 94 | 95 | 96 | gower_work <- function(x, y, pair_x, pair_y, n, eps, weights, ignore_case, nthread){ 97 | stopifnot(is.numeric(eps), eps>0, nrow(x)>0, nrow(y)>0,ncol(x)>0,ncol(y)>0) 98 | 99 | if (max(nrow(x), nrow(y)) <= 1000) nthread <- 1L 100 | 101 | if (is.null(pair_x) & is.null(pair_y)){ 102 | xnames <- if(ignore_case) toupper(names(x)) else names(x) 103 | ynames <- if(ignore_case) toupper(names(y)) else names(y) 104 | pair <- match(xnames, ynames, nomatch = 0L) 105 | } else if (is.null(pair_x)){ 106 | pair <- pair_y 107 | } else { 108 | if (is.character(pair_x) & is.character(pair_y)){ 109 | m <- match(names(x),pair_x,nomatch=0) 110 | pair_x <- pair_x[m] 111 | pair_y <- pair_y[m] 112 | } 113 | pair <- numeric(ncol(x)) 114 | pair[pair_x] <- pair_y 115 | } 116 | if ( !any(pair > 0) ){ 117 | message("Nothing to compare") 118 | return( if (is.null(n)){ # gower_dist 119 | invisible(numeric(0)) 120 | } else { # gower_topn 121 | invisible(list(distance=matrix(0)[0,0],index=matrix(0L)[0,0])) 122 | } 123 | ) 124 | } 125 | 126 | 127 | if ( !is.null(weights) && ( any( weights < 0 ) || !all(is.finite(weights)) ) ){ 128 | stop("At least one element of 'weights' is not a finite nonnegative number" 129 | , call. = FALSE) 130 | } 131 | if ( !is.null(weights) && length(weights) < length(pair) ){ 132 | msg <- sprintf("%d weights specified, expected %d" 133 | , length(weights), length(pair)) 134 | stop(msg, call. = FALSE) 135 | } 136 | # If the user didn't pass any weights, then weight all components of the 137 | # distance equally. 138 | if (is.null(weights)) 139 | weights <- rep(1, ncol(x)) 140 | 141 | # check column classes 142 | 143 | nthread <- as.integer(nthread) 144 | ranges <- numeric(length(pair)) 145 | for ( i in seq_along(pair)){ 146 | if (pair[i] == 0 ) next 147 | ranges[i] <- .Call("R_get_xy_range",x[[i]],y[[pair[i]]],nthread,PACKAGE="gower") 148 | } 149 | 150 | factor_x <- sapply(x,is.factor) 151 | factor_y <- sapply(y,is.factor) 152 | 153 | for ( i in seq_along(pair) ){ 154 | if ( pair[i] == 0 ) next 155 | iy <- pair[i] 156 | if (!factor_x[i] & !factor_y[iy]) next 157 | 158 | if ( factor_x[i] && !factor_y[iy] ){ 159 | stop("Column ", i, " of x is of class factor while matching column " 160 | , pair[i]," of y is of class ", class(y[[iy]]) ) 161 | } 162 | if ( !factor_x[i] && factor_y[iy] ){ 163 | stop("Column ", i, " of x is of class ", class(x[[i]]), " while matching column " 164 | , pair[i]," of y is of class factor" ) 165 | } 166 | if (factor_x[i] && factor_y[iy]){ 167 | if ( !isTRUE( all.equal( levels(x[[i]]), levels(y[[iy]]) ) ) ){ 168 | stop("Levels in column ", i, " of x do not match those of column ", 169 | pair[i], " in y.") 170 | 171 | } 172 | } 173 | } 174 | 175 | 176 | factor_pair <- as.integer(factor_x) 177 | 178 | eps <- as.double(eps) 179 | # translate to C-indices (base-0). 180 | pair <- as.integer(pair-1L) 181 | if (is.null(n)){ 182 | .Call("R_gower", x, y , ranges, pair, factor_pair, eps, weights, nthread, PACKAGE="gower") 183 | 184 | } else { 185 | L <- .Call("R_gower_topn", x, y, ranges, pair, factor_pair, as.integer(n), eps, weights, nthread, PACKAGE="gower") 186 | names(L) <- c("index","distance") 187 | dim(L$index) <- c(n,nrow(x)) 188 | dim(L$distance) <- dim(L$index) 189 | dimnames(L$index) <- list(topn=NULL,row=NULL) 190 | dimnames(L$distance) <- dimnames(L$index) 191 | L 192 | } 193 | 194 | } 195 | 196 | RECYCLEWARNING <- tryCatch(1:3+1:2,warning=function(e) e$message) 197 | 198 | check_recycling <- function(nx,ny){ 199 | mx <- max(nx,ny) 200 | mn <- min(nx,ny) 201 | if ((mx %% mn) != 0) warning(RECYCLEWARNING, call.=FALSE) 202 | } 203 | 204 | 205 | 206 | 207 | 208 | 209 | 210 | 211 | -------------------------------------------------------------------------------- /pkg/inst/tinytest/test_gh_issue_18.R: -------------------------------------------------------------------------------- 1 | df <- data.frame(X = c(4L, 0L, 10L)) 2 | obj <- data.frame(X = 5L) 3 | 4 | d1 <- gower_dist(obj, df) 5 | 6 | df2 <- df[c(2,3,1),,drop=FALSE] 7 | d2 <- gower_dist(obj, df2) 8 | 9 | expect_equal(d1,d2[c(3,1,2)]) 10 | 11 | 12 | -------------------------------------------------------------------------------- /pkg/inst/tinytest/test_gh_issue_8.R: -------------------------------------------------------------------------------- 1 | 2 | if (at_home()){ 3 | dat1 <- iris[1:10,1:5,drop=FALSE] 4 | dat2 <- iris[6:15,1:5,drop=FALSE] 5 | 6 | nthrd1 <- gower_dist(dat1, dat2, nthread=1) 7 | nthrd2 <- gower_dist(dat1, dat2, nthread=2) 8 | nthrd4 <- gower_dist(dat1, dat2, nthread=4) 9 | nthrd7 <- gower_dist(dat1, dat2, nthread=7) 10 | 11 | expect_equal(nthrd1, nthrd2) 12 | expect_equal(nthrd1, nthrd4) 13 | expect_equal(nthrd1, nthrd7) 14 | 15 | 16 | # now with large numbers of records 17 | dat1 <- iris[rep(1:10,200),1:5,drop=FALSE] 18 | dat2 <- iris[rep(6:15,200),1:5,drop=FALSE] 19 | 20 | nthrd1 <- gower_dist(dat1, dat2, nthread=1) 21 | nthrd2 <- gower_dist(dat1, dat2, nthread=2) 22 | nthrd4 <- gower_dist(dat1, dat2, nthread=4) 23 | nthrd7 <- gower_dist(dat1, dat2, nthread=7) 24 | 25 | expect_equal(nthrd1, nthrd2) 26 | expect_equal(nthrd1, nthrd4) 27 | expect_equal(nthrd1, nthrd7) 28 | } 29 | 30 | 31 | 32 | -------------------------------------------------------------------------------- /pkg/inst/tinytest/test_gower.R: -------------------------------------------------------------------------------- 1 | 2 | 3 | expect_error(gower_topn(iris[0,], iris)) 4 | expect_error(gower_topn(iris,iris[0,])) 5 | expect_error(gower_topn(iris[,0], iris)) 6 | expect_error(gower_topn(iris,iris[,0])) 7 | 8 | 9 | ## distance between logicals 10 | dL <- expand.grid(c(TRUE,FALSE),c(TRUE,FALSE)) 11 | x = data.frame(x=dL[,1]) 12 | y = data.frame(x=dL[,2]) 13 | expect_equal(c(0,1,1,NaN), gower_dist(x = x,y = y)) 14 | 15 | 16 | ## distance between factor variables 17 | bands <- c("Grand Magus","Skull Fist") 18 | dF <- expand.grid(bands,bands) 19 | expect_equal(gower_dist(data.frame(x=dF[,1]),data.frame(x=dF[,2])),c(0,1,1,0)) 20 | 21 | ## distance between numerical variables 22 | dN <- data.frame(x = as.numeric(1:4),y=as.numeric(c(1,1,2,3))) 23 | expect_equal(gower_dist(data.frame(x=dN[,1]),data.frame(x=dN[,2])),c(0,1/3,1/3,1/3)) 24 | 25 | ## distance between character variables 26 | dC <- data.frame(x=letters[1:3],y=letters[3:1],stringsAsFactors=FALSE) 27 | expect_equal(gower_dist( 28 | data.frame(x=dC[,1],stringsAsFactors=FALSE) 29 | , data.frame(x=dC[,2],stringsAsFactors=FALSE)),c(1,0,1)) 30 | 31 | 32 | 33 | ## multivariate dataset 34 | bands <- c("Grand Magus","Skull Fist") 35 | dL <- expand.grid(c(TRUE,FALSE),c(TRUE,FALSE)) 36 | dN <- data.frame(x = as.numeric(1:4),y=as.numeric(c(1,1,2,3))) 37 | dF <- expand.grid(bands,bands) 38 | dM1 <- data.frame(x=dL[,1],y=dF[,1],z=dN[,1]) 39 | dM2 <- data.frame(x=dL[,2],y=dF[,2],z=dN[,2]) 40 | expect_equal(gower_dist(x=dM1,y=dM2), c(0,7/9,7/9,1/6)) 41 | # check symmetry 42 | expect_equal(gower_dist(dM1,dM2),gower_dist(dM2,dM1)) 43 | # not counting NA's in the denominator 44 | dM1[array(c(2,3,4,1,2,3),dim=c(3,2))] <- NA 45 | expect_equal(gower_dist(dM1,dM2), c(0,3/4,3/4,0)) 46 | #auto-matching columns 47 | expect_equivalent(gower_dist(women, women[1]),rep(0,nrow(women))) 48 | 49 | 50 | 51 | ## ignoring column name cases",{ 52 | dat1 <- iris[1:6,] 53 | dat2 <- iris[1:6,] 54 | names(dat2) <- tolower(names(dat2)) 55 | expect_equal(gower_dist(dat1, dat2, ignore_case=TRUE),rep(0,6)) 56 | 57 | 58 | ## recycling 59 | expect_equal(length(gower_dist(x=iris[1,],y=iris)), nrow(iris)) 60 | expect_equal(length(gower_dist(x=iris,y=iris[1,])), nrow(iris)) 61 | expect_equal(length(gower_dist(x=iris[1:3,],y=iris)), nrow(iris)) 62 | expect_equal(length(gower_dist(x=iris,y=iris[1:3,])), nrow(iris)) 63 | 64 | 65 | 66 | ## weights 67 | expect_error(gower_topn(women, women, weights=-(1:4))) 68 | expect_error(gower_dist(women, women, weights=c(NA,1:3))) 69 | 70 | d1 <- women[1,] 71 | d2 <- women[2,] 72 | w <- c(1,2) 73 | r <- sapply(women, function(x) abs(diff(range(x)))) 74 | 75 | d12 <- (w[1]*abs(d1[1,1]-d2[1,1])/r[1] + w[2]*abs(d1[1,2]-d2[1,2])/r[2])/sum(w) 76 | wom2 <- women 77 | wom2[1:2,] <- wom2[2:1,] 78 | expect_equivalent(gower_dist(women,wom2, weights=w)[1], d12) 79 | 80 | 81 | ## edge cases and exceptions 82 | expect_warning(gower_dist( 83 | x = data.frame(x=c(1.2,1.2,1.2)) 84 | , y = data.frame(x=c(1.2,1.2,1.2)) 85 | )) 86 | expect_warning(gower_dist( 87 | x = data.frame(x=c(1.2,1.2,1.2)) 88 | , y = data.frame(x=c(1.2,1.2,1.3)) 89 | , eps=0.2 90 | )) 91 | 92 | expect_warning(gower_dist(data.frame(x=rep(1,100)), data.frame(x=1,100))) 93 | 94 | 95 | expect_error(gower_dist( 96 | data.frame(a = letters[1:3], stringsAsFactors = TRUE), 97 | data.frame(a = letters[2:4], stringsAsFactors = TRUE) 98 | )) 99 | expect_error(gower_dist( 100 | data.frame(a = letters[1:3], stringsAsFactors = FALSE), 101 | data.frame(a = letters[2:4], stringsAsFactors = TRUE) 102 | )) 103 | 104 | suppressMessages(out <- gower_dist(data.frame(x=1:3), data.frame(y=1:3) )) 105 | expect_true(identical(out,numeric(0))) 106 | 107 | suppressMessages(out <- gower_topn(data.frame(x=1:3),data.frame(y=1:3)) ) 108 | expect_true( identical(out$distance, matrix(0)[0,0]) ) 109 | expect_true( identical(out$index, matrix(0L)[0,0]) ) 110 | 111 | 112 | 113 | 114 | 115 | ## Top-n 116 | 117 | ## gower_topn 118 | d1 <- iris[1:3,] 119 | d2 <- iris[1:7,] 120 | L <- gower_topn(d1,d2,n=4) 121 | expect_equal(length(L),2) 122 | expect_equal(dim(L[[1]]),c(4,3)) 123 | expect_equal(dim(L[[1]]),dim(L[[2]])) 124 | expect_equal(L[[1]][1,],1:3) 125 | expect_equal(L[[2]][1,],rep(0,3)) 126 | 127 | # case where n exceeds nr of records in the lookup table. 128 | L <- gower_topn(d1,d2,n=8) 129 | expect_equal(L[[1]][8,],rep(0,3)) 130 | expect_equal(L[[2]][8,],rep(Inf,3)) 131 | 132 | 133 | 134 | 135 | ## just to get code-coverage right 136 | dat1 <- data.frame( 137 | x = as.factor(sample(letters,2000,replace=TRUE)) 138 | ,y = sample(LETTERS,2000,replace=TRUE) 139 | ,z = as.integer(1:2000) 140 | ,w = sample(c(TRUE,FALSE),2000,replace=TRUE) 141 | , stringsAsFactors=FALSE 142 | ) 143 | i <- sample(2000) 144 | dat2 <- dat1[i,] 145 | gower_dist(dat1,dat2) 146 | 147 | 148 | 149 | ## warning on recycling 150 | expect_warning(gower_dist(iris[1:3,],iris[1:2,])) 151 | expect_warning(gower_dist(iris[1:2,],iris[1:3,])) 152 | 153 | 154 | ## regression tests 155 | 156 | ## topn w/n=1" 157 | dat <- data.frame(x = c(NA,2,4,5), y = c(6,7,NA,10)) 158 | L <- gower_topn(dat[c(1,3),],dat[c(2,4),],n=1) 159 | expect_equivalent(as.vector(L$index),c(1,2)) 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | -------------------------------------------------------------------------------- /pkg/src/Makevars: -------------------------------------------------------------------------------- 1 | 2 | PKG_CFLAGS = $(SHLIB_OPENMP_CFLAGS) 3 | PKG_LIBS = $(SHLIB_OPENMP_CFLAGS) 4 | 5 | -------------------------------------------------------------------------------- /pkg/src/R_register_native.c: -------------------------------------------------------------------------------- 1 | #include 2 | #include 3 | #include // for NULL 4 | #include 5 | 6 | /* FIXME: 7 | Check these declarations against the C/Fortran source code. 8 | */ 9 | 10 | /* .Call calls */ 11 | extern SEXP R_get_max_threads(void); 12 | extern SEXP R_get_thread_limit(void); 13 | extern SEXP R_get_xy_range(SEXP, SEXP, SEXP); 14 | extern SEXP R_gower(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); 15 | extern SEXP R_gower_topn(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); 16 | 17 | static const R_CallMethodDef CallEntries[] = { 18 | {"R_get_max_threads", (DL_FUNC) &R_get_max_threads, 0}, 19 | {"R_get_thread_limit", (DL_FUNC) &R_get_thread_limit, 0}, 20 | {"R_get_xy_range", (DL_FUNC) &R_get_xy_range, 3}, 21 | {"R_gower", (DL_FUNC) &R_gower, 8}, 22 | {"R_gower_topn", (DL_FUNC) &R_gower_topn, 9}, 23 | {NULL, NULL, 0} 24 | }; 25 | 26 | void R_init_gower(DllInfo *dll) 27 | { 28 | R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); 29 | R_useDynamicSymbols(dll, FALSE); 30 | } 31 | -------------------------------------------------------------------------------- /pkg/src/gower.c: -------------------------------------------------------------------------------- 1 | /* gower - a C/R implementation of Gower's similarity (or distance) measure. 2 | * Copyright (C) 2016 Mark van der Loo 3 | * 4 | * This program is free software: you can redistribute it and/or modify 5 | * it under the terms of the GNU General Public License as published by 6 | * the Free Software Foundation, either version 3 of the License, or 7 | * (at your option) any later version. 8 | * 9 | * This program is distributed in the hope that it will be useful, 10 | * but WITHOUT ANY WARRANTY; without even the implied warranty of 11 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 12 | * GNU General Public License for more details. 13 | * 14 | * You should have received a copy of the GNU General Public License 15 | * along with this program. If not, see . 16 | * 17 | * You can contact the author at: mark _dot_ vanderloo _at_ gmail _dot_ com 18 | * 19 | */ 20 | 21 | 22 | 23 | #ifdef _OPENMP 24 | #include 25 | #endif 26 | 27 | /* R-windows-oldrel (3.2.x) uses gcc 4.6.3 which we need to detect */ 28 | #ifdef __GNUC__ 29 | #if __GNUC__ <= 4 && __GNUC_MINOR__ <= 6 30 | #else 31 | #define HAS_REDUCTION 32 | #endif 33 | #endif 34 | 35 | 36 | 37 | 38 | #include 39 | #include 40 | #include 41 | 42 | #define MAX(X,Y) ((X) > (Y) ? (X) : (Y)) 43 | #define MIN(X,Y) ((X) < (Y) ? (X) : (Y)) 44 | #define RECYCLE(N, K) ((N) + 1L < (K) ? (N) + 1L : 0L ) 45 | 46 | // determine when something is numerically zero. 47 | static double EPS = 1e-8; 48 | // set max nr of threads to use 49 | static int NTHREAD = 1L; 50 | 51 | // recycling in parallel region. 52 | static inline int recycle(int i, int nthreads, int ni){ 53 | i += nthreads; 54 | if ( i >= ni ) 55 | i = (nthreads < ni) ? (i - ni) : (i % ni); 56 | return i; 57 | } 58 | 59 | 60 | SEXP R_get_max_threads(void){ 61 | SEXP out = allocVector(INTSXP, 1L); 62 | PROTECT(out); 63 | INTEGER(out)[0] = 1L; 64 | #ifdef _OPENMP 65 | INTEGER(out)[0] = omp_get_max_threads(); 66 | #endif 67 | UNPROTECT(1); 68 | return out; 69 | } 70 | 71 | 72 | SEXP R_get_num_procs(void){ 73 | SEXP out = allocVector(INTSXP, 1L); 74 | PROTECT(out); 75 | INTEGER(out)[0] = 1L; 76 | #ifdef _OPENMP 77 | INTEGER(out)[0] = omp_get_num_procs(); 78 | #endif 79 | UNPROTECT(1); 80 | return out; 81 | } 82 | 83 | SEXP R_get_thread_limit(void){ 84 | SEXP out = allocVector(INTSXP, 1L); 85 | PROTECT(out); 86 | INTEGER(out)[0] = 1L; 87 | #ifdef _OPENMP 88 | INTEGER(out)[0] = omp_get_thread_limit(); 89 | #endif 90 | UNPROTECT(1); 91 | return out; 92 | } 93 | 94 | 95 | // presence or absence of a character. x and y are 0 (FALSE) or 1 (TRUE) 96 | static inline void gower_logi(int *x, int nx, int *y, int ny 97 | , double *num, double *den, double weight) 98 | { 99 | 100 | #pragma omp parallel num_threads(NTHREAD) 101 | { 102 | int nt = MAX(nx,ny); 103 | double dijk, sijk; 104 | int i = 0, j = 0; 105 | double *inum = num, *iden=den; 106 | 107 | int ID = 0, num_threads=1; 108 | #ifdef _OPENMP 109 | ID = omp_get_thread_num(); 110 | num_threads = omp_get_num_threads(); 111 | i = recycle(ID-num_threads, num_threads, nx); 112 | j = recycle(ID-num_threads, num_threads, ny); 113 | inum += ID; 114 | iden += ID; 115 | #endif 116 | 117 | 118 | for ( int k = ID; k < nt; k += num_threads, inum += num_threads, iden += num_threads){ 119 | dijk = (double) ((x[i] | y[j]) & !((x[i] == NA_INTEGER) | (y[j] == NA_INTEGER))); 120 | sijk = (dijk == 1.0) ? (double) (x[i] * y[j]) : 0.0; 121 | *inum += weight * dijk * sijk; 122 | *iden += weight * dijk; 123 | i = recycle(i, num_threads, nx); 124 | j = recycle(j, num_threads, ny); 125 | } 126 | } // end parallel region. 127 | } 128 | 129 | // equality of categorical variables, encoded as x, y in {1,2,...,N}. 130 | static inline void gower_cat(int *x, int nx, int *y, int ny 131 | , double *num, double *den, double weight) 132 | { 133 | 134 | #pragma omp parallel num_threads(NTHREAD) 135 | { 136 | int nt = MAX(nx,ny); 137 | double dijk, sijk; 138 | int i = 0, j = 0; 139 | double *inum = num, *iden=den; 140 | 141 | int ID = 0, num_threads=1; 142 | #ifdef _OPENMP 143 | ID = omp_get_thread_num(); 144 | num_threads = omp_get_num_threads(); 145 | i = recycle(ID-num_threads, num_threads, nx); 146 | j = recycle(ID-num_threads, num_threads, ny); 147 | inum += ID; 148 | iden += ID; 149 | #endif 150 | 151 | for ( int k = ID; k < nt; k += num_threads, inum += num_threads, iden += num_threads){ 152 | dijk = (double) !(x[i] == NA_INTEGER || y[j] == NA_INTEGER); 153 | sijk = (dijk==1.0) ? (double) (x[i] == y[j]) : 0.0; 154 | *inum += weight * dijk * sijk; 155 | *iden += weight * dijk; 156 | i = recycle(i, num_threads, nx); 157 | j = recycle(j, num_threads, ny); 158 | } 159 | } // end parallel region. 160 | 161 | } 162 | 163 | // strings. Treated as categories. 164 | static inline void gower_str(SEXP x, int nx, SEXP y, int ny, double *num, double *den, double weight){ 165 | #pragma omp parallel num_threads(NTHREAD) 166 | { 167 | int nt = MAX(nx, ny); 168 | double dijk, sijk; 169 | int i=0, j=0; 170 | double *inum = num, *iden=den; 171 | SEXP xi, yj; 172 | 173 | int ID = 0, num_threads=1; 174 | #ifdef _OPENMP 175 | ID = omp_get_thread_num(); 176 | num_threads = omp_get_num_threads(); 177 | i = recycle(ID-num_threads, num_threads, nx); 178 | j = recycle(ID-num_threads, num_threads, ny); 179 | inum += ID; 180 | iden += ID; 181 | #endif 182 | 183 | for ( int k = ID; k < nt; k += num_threads, inum += num_threads, iden += num_threads){ 184 | xi = STRING_ELT(x,i); 185 | yj = STRING_ELT(y,j); 186 | dijk = (double) !(xi == NA_STRING || yj == NA_STRING); 187 | sijk = (dijk==1.0) ? (double) (CHAR(xi) == CHAR(yj)) : 0.0; 188 | *inum += weight * dijk * sijk; 189 | *iden += weight * dijk; 190 | i = recycle(i, num_threads, nx); 191 | j = recycle(j, num_threads, ny); 192 | } 193 | } // end of parallel region 194 | } 195 | 196 | 197 | // comparison of numerical variables, by absolute difference divided by range. 198 | static inline void gower_num(double *x, int nx, double *y, int ny,double R 199 | , double *num, double *den, double weight) 200 | { 201 | if ( !isfinite(R) || R < EPS ){ 202 | warning("skipping variable with zero or non-finite range."); 203 | return; 204 | } 205 | #pragma omp parallel num_threads(NTHREAD) 206 | { 207 | int nt = MAX(nx,ny); 208 | double dijk, sijk; 209 | int i = 0, j = 0; 210 | double *inum = num, *iden=den; 211 | 212 | int ID = 0, num_threads=1; 213 | #ifdef _OPENMP 214 | ID = omp_get_thread_num(); 215 | num_threads = omp_get_num_threads(); 216 | i = recycle(ID-num_threads, num_threads, nx); 217 | j = recycle(ID-num_threads, num_threads, ny); 218 | inum += ID; 219 | iden += ID; 220 | #endif 221 | 222 | 223 | for ( int k = ID; k < nt; k += num_threads, inum += num_threads, iden += num_threads){ 224 | dijk = (double) (isfinite(x[i]) & isfinite(y[j])); 225 | sijk = (dijk==1.0) ? (1.0-fabs(x[i]-y[j])/R) : 0.0; 226 | (*inum) += weight * dijk * sijk; 227 | (*iden) += weight * dijk; 228 | i = recycle(i, num_threads, nx); 229 | j = recycle(j, num_threads, ny); 230 | } 231 | 232 | } // end of parallel region 233 | } 234 | 235 | 236 | static inline void gower_dbl_int(double *x, int nx, int *y, int ny,double R 237 | , double *num, double *den, double weight) 238 | { 239 | 240 | if ( !isfinite(R) || R < EPS ){ 241 | warning("skipping variable with zero or non-finite range\n"); 242 | return; 243 | } 244 | 245 | #pragma omp parallel num_threads(NTHREAD) 246 | { 247 | int nt = MAX(nx, ny); 248 | double dijk, sijk; 249 | int i=0, j=0; 250 | double *inum = num, *iden=den; 251 | 252 | int ID = 0, num_threads=1; 253 | #ifdef _OPENMP 254 | ID = omp_get_thread_num(); 255 | num_threads = omp_get_num_threads(); 256 | i = recycle(ID-num_threads, num_threads, nx); 257 | j = recycle(ID-num_threads, num_threads, ny); 258 | inum += ID; 259 | iden += ID; 260 | #endif 261 | 262 | for ( int k = ID; k < nt; k += num_threads, inum += num_threads, iden += num_threads){ 263 | dijk = (double) (isfinite(x[i]) & (y[j] != NA_INTEGER)); 264 | sijk = (dijk==1.0) ? (1.0-fabs(x[i] - ((double) y[j]) )/R) : 0.0; 265 | *inum += weight * dijk * sijk; 266 | *iden += weight * dijk; 267 | i = recycle(i, num_threads, nx); 268 | j = recycle(j, num_threads, ny); 269 | } 270 | } // end of parallel region 271 | } 272 | 273 | static inline void gower_int(int *x, int nx, int *y, int ny, double R 274 | , double *num, double *den, double weight) 275 | { 276 | if ( !isfinite(R) || R == 0 ){ 277 | warning("skipping variable with zero or non-finite range\n"); 278 | return; 279 | } 280 | #pragma omp parallel num_threads(NTHREAD) 281 | { 282 | int nt = MAX(nx, ny); 283 | double dijk, sijk; 284 | int i=0, j=0; 285 | double *inum = num, *iden=den; 286 | 287 | int ID = 0, num_threads=1; 288 | #ifdef _OPENMP 289 | ID = omp_get_thread_num(); 290 | num_threads = omp_get_num_threads(); 291 | i = recycle(ID-num_threads, num_threads, nx); 292 | j = recycle(ID-num_threads, num_threads, ny); 293 | inum += ID; 294 | iden += ID; 295 | #endif 296 | 297 | 298 | for ( int k = ID; k < nt; k += num_threads, inum += num_threads, iden += num_threads){ 299 | dijk = (double) ( (x[i] !=NA_INTEGER) & (y[j] != NA_INTEGER)); 300 | sijk = (dijk==1.0) ? (1.0-fabs( ((double)x[i]) - ((double)y[j]) )/R) : 0.0; 301 | *inum += weight * dijk * sijk; 302 | *iden += weight * dijk; 303 | i = recycle(i, num_threads, nx); 304 | j = recycle(j, num_threads, ny); 305 | } 306 | } // end of parallel region 307 | } 308 | 309 | // range computations 310 | static void get_dbl_range(double *x, int nx, double *min, double *max){ 311 | 312 | double *ix = x; 313 | 314 | 315 | double imin=*ix, imax=*ix; 316 | 317 | for ( int i=0; i imax ) imax = x[i]; 338 | if ( x[i] < imin ) imin = x[i]; 339 | } 340 | } 341 | }// end parallel region 342 | *min = imin; 343 | *max = imax; 344 | } 345 | 346 | 347 | static void get_int_range(int *x, int nx, double *min, double *max){ 348 | 349 | int *ix = x; 350 | 351 | int imin = *ix 352 | , imax = *ix; 353 | 354 | for ( int i=0; i imax ) imax = x[i]; 372 | if ( x[i] < imin ) imin = x[i]; 373 | } 374 | } 375 | 376 | *min = (double) imin; 377 | *max = (double) imax; 378 | } 379 | 380 | static void get_range(SEXP x, double *min, double *max){ 381 | 382 | switch( TYPEOF(x) ){ 383 | case INTSXP : { 384 | get_int_range(INTEGER(x), length(x), min, max); 385 | break; 386 | } 387 | case REALSXP : { 388 | get_dbl_range(REAL(x), length(x), min, max); 389 | break; 390 | } 391 | } 392 | 393 | } 394 | 395 | static double get_xy_range(SEXP x, SEXP y){ 396 | 397 | double x_min = R_NegInf 398 | , x_max = R_PosInf 399 | , y_min = R_NegInf 400 | , y_max = R_PosInf 401 | , min, max; 402 | 403 | get_range(x, &x_min, &x_max); 404 | get_range(y, &y_min, &y_max); 405 | 406 | if ( isfinite(x_min) & isfinite(y_min) ){ 407 | min = MIN(x_min, y_min); 408 | } else if ( isfinite(x_min) & !(isfinite(y_min)) ){ 409 | min = x_min; 410 | } else if ( (!isfinite(x_min)) & isfinite(y_min) ) { 411 | min = y_min; 412 | } else { 413 | min = NA_REAL; 414 | } 415 | 416 | if ( isfinite(x_max) & isfinite(y_min) ){ 417 | max = MAX(x_max, y_max); 418 | } else if ( isfinite(x_max) & !isfinite(y_max) ){ 419 | max = x_max; 420 | } else if ( (!isfinite(x_max)) & isfinite(y_max) ){ 421 | max = y_max; 422 | } else { 423 | max = NA_REAL; 424 | } 425 | 426 | return max - min; 427 | 428 | } 429 | 430 | SEXP R_get_xy_range(SEXP x_, SEXP y_, SEXP nthread_){ 431 | NTHREAD = INTEGER(nthread_)[0]; 432 | SEXP out = allocVector(REALSXP,1L); 433 | PROTECT(out); 434 | REAL(out)[0] = get_xy_range(x_, y_); 435 | UNPROTECT(1); 436 | return out; 437 | } 438 | 439 | 440 | /* 441 | static void print_vec(double *x, int n){ 442 | for ( int i=0; ii; j-- ){ 601 | topn[j] = topn[j-1]; 602 | index[j] = index[j-1]; 603 | } 604 | topn[i] = x; 605 | index[i] = ind; 606 | break; // out of main loop 607 | } 608 | } 609 | } 610 | 611 | } 612 | 613 | /* For testing purposes 614 | SEXP R_pushdown(SEXP entry_, SEXP index_, SEXP values_, SEXP indices_){ 615 | double entry = REAL(entry_)[0]; 616 | int index = INTEGER(index_)[0]; 617 | double *values = REAL(values_); 618 | int *indices = INTEGER(indices_); 619 | int n = length(values_); 620 | 621 | push(entry, index, values, indices, n); 622 | return R_NilValue; 623 | } 624 | */ 625 | 626 | 627 | 628 | static inline void copyrec(SEXP into, SEXP from, int i){ 629 | int ncol = length(into); 630 | 631 | SEXP col_from, col_into; 632 | 633 | for ( int j = 0; j < ncol; j++){ 634 | col_from = VECTOR_ELT(from,j); 635 | col_into = VECTOR_ELT(into,j); 636 | switch(TYPEOF(col_from)){ 637 | case LGLSXP : { INTEGER(col_into)[0] = INTEGER(col_from)[i]; break;} 638 | case INTSXP : { INTEGER(col_into)[0] = INTEGER(col_from)[i]; break;} 639 | case REALSXP : { REAL(col_into)[0] = REAL(col_from)[i]; break;} 640 | case STRSXP : { SET_STRING_ELT(col_from, 0, STRING_ELT(col_from,i)); break;} 641 | } 642 | } 643 | } 644 | 645 | /* for testing purposes only 646 | void prvec(SEXP x){ 647 | for (int i=0; i>= 35 | options(prompt=" ") 36 | @ 37 | 38 | \newpage 39 | 40 | \section{Gower's distance measure} 41 | 42 | Gower's distance can be used to measure how different two records are. The 43 | records may contain combinations of logical, numerical, categorical or text 44 | data. The distance is always a number between 0 (identical) and 1 (maximally 45 | dissimilar). An easy to read specification of the measure is given in the 46 | original paper. 47 | 48 | Gower (1971) 49 | \href{http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.412.4155&rep=rep1&type=pdf}{A general coefficient of similarity and some of its properties}. 50 | \emph{Biometrics} **27** 857--874. 51 | 52 | In short, Gower's distance (or similarity) first computes distances between pairs of variables over two data sets and then combines those distances to a single value per record-pair. 53 | 54 | This package modifies Gower's original similarity measure in the following ways. 55 | 56 | \begin{itemize} 57 | \item{In stead of the original similarity $S$, the package returns the distance $1-S$.} 58 | \item{The original paper does not mention the concept of \code{NA}. Missing 59 | variables are skipped when computing the distance.} 60 | \item{The original paper does not mention character data. These are treated as 61 | categorical data.} 62 | \end{itemize} 63 | 64 | \section{Computing Gower's distance} 65 | 66 | The function \code{gower\_dist} computes pairwise-distances between records. 67 | 68 | <<>>= 69 | library(gower) 70 | dat1 <- iris[1:10,] 71 | dat2 <- iris[6:15,] 72 | gower_dist(dat1, dat2) 73 | @ 74 | If one data frame has less records than the other, the shortest one is recycled 75 | over (just like when you're adding two vectors of unequal length) 76 | 77 | <<>>= 78 | gower_dist(iris[1,], dat1) 79 | @ 80 | 81 | It is possible to control how columns from the two data sets are paired for 82 | comparison using the \code{pair\_x} and \code{pair\_y} arguments. This comes in 83 | handy when similar colums have different names accross datasets. By default, 84 | columns with matching names are paired. The behaviour is somewhat similar to 85 | that of base R's \code{merge} in that respect. 86 | 87 | <<>>= 88 | dat1 <- dat2 <- iris[1:10,] 89 | names(dat2) <- tolower(names(dat2)) 90 | gower_dist(dat1, dat2) 91 | # tell gower_dist to match columns 1..5 in dat1 with column 1..5 in dat2 92 | gower_dist(dat1, dat2, pair_y=1:5) 93 | @ 94 | 95 | It is also possible to explicitly ignore case when matching columns 96 | by name. 97 | <<>>= 98 | gower_dist(dat1, dat2, ignore_case=TRUE) 99 | @ 100 | 101 | 102 | 103 | \section{Computing the top-n best matches} 104 | 105 | The function \code{gower\_topn} returns a list with two arrays. 106 | <<>>= 107 | dat1 <- iris[1:10,] 108 | L <- gower_topn(x=dat1, y=iris, n=3) 109 | L 110 | @ 111 | 112 | The first array is called \code{index}. Each column corresponds to one row of \code{x}. 113 | The entries of each column index the top $n$ best matches of that row in \code{x} with 114 | rows in \code{y}. In this example, the best match of the first row of \code{dat1} is 115 | record number \Sexpr{L$index[1,1]} from \code{iris} (this should be obvious, since 116 | they are the same record). The second best match is record number 117 | \Sexpr{L$index[2,1]} from \code{iris}. 118 | 119 | The second array is called \code{distance} and it contains the corresponding distances. 120 | 121 | \section{Using weights} 122 | 123 | Gower's distance is computed as an average over differences between variables. 124 | By setting \code{weights} you can compute the distance as a weighted average. 125 | <<>>= 126 | gower_dist(women[1,], women) 127 | gower_dist(women[1,], women, weights=c(2,3)) 128 | @ 129 | 130 | 131 | \section{Parallelization, memory usage} 132 | 133 | 134 | The underlying algorithm is implemented in C and parallelized using 135 | \href{http://www.openmp.org}{OpenMP}. OpenMP is available on most systems that 136 | can run R. Please see 137 | \href{https://cran.r-project.org/doc/manuals/r-release/R-exts.html#OpenMP-support}{this 138 | section} of the writing R extensions manual for up-to-details details on which 139 | systems are supported. At the time of writing (spring 2019), OSX is the only 140 | system not supporting OpenMP out of the box. You can still make it work by 141 | installing the gcc toolchain and compiling the package (and R). 142 | 143 | If OpenMP is not supported, the package will still work but the core algorithms 144 | will not be parallelized. 145 | 146 | This implementation makes no copies of the data in memory. When computing 147 | \code{gower\_dist}, two double precision arrays of size 148 | \code{max(nrow(x),nrow(y))} are kept in memory to store intermediate results. 149 | When computing the top-n matches, for $k$ cores, $k+2$ double precision arrays 150 | of length \code{nrow(y)} are created to store intermediate results at C level. 151 | 152 | \end{document} 153 | -------------------------------------------------------------------------------- /vignettes.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | function finish { 4 | cd ${oldwd} 5 | } 6 | trap finish EXIT 7 | 8 | oldwd=`pwd` 9 | 10 | cd pkg/vignettes 11 | 12 | R -e "Sweave(dir(pattern='Rnw$'))" 13 | pdflatex *.tex 14 | 15 | evince *.pdf & 16 | 17 | 18 | 19 | --------------------------------------------------------------------------------