Senhores, estou estudando hadoop/map reduce no R e achei um código na pagina do RevolutionR para o Kmeans. Estou tentando entender e ei que me surgiu uma dúvida... Existe uma declaração de função para a fase de map onde o parâmetro de input é um "." Alguém sabe o que significa? Esta função é chamada dentro de outra função. O que pensei é que o "." simboliza que a sub função herda os parâmetros da "função mãe". como está declarada a função: function(., P) ### código inteiro #### # Copyright 2011 Revolution Analytics # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. Sys.setenv(HADOOP_STREAMING="/usr/lib/hadoop-mapreduce/hadoop-streaming-2.0.0-cdh4.7.0.jar") library(rmr2) library(rhdfs) hdfs.init() # P tabela com valores ## @knitr kmeans-signature kmeans.mr = function(P, num.clusters, num.iter, combine, in.memory.combine) { ## @knitr kmeans-dist.fun # C : Centroides dist.fun = function(C, P) {apply(C,1, function(x) {colSums((t(P) - x)^2))}} ## @knitr kmeans.map *kmeans.map = function(., P) {* nearest = {if(is.null(C)) sample(1:num.clusters, nrow(P), replace = TRUE) else {D = dist.fun(C, P) nearest = max.col(-D)}} if(!(combine || in.memory.combine)) keyval(nearest, P) else {keyval(nearest, cbind(1, P))}} ## @knitr kmeans.reduce kmeans.reduce = { if (!(combine || in.memory.combine) ) function(., P) t(as.matrix(apply(P, 2, mean))) else function(k, P) keyval( k, t(as.matrix(apply(P, 2, sum))))} ## @knitr kmeans-main-1 C = NULL for(i in 1:num.iter ) { C = values( from.dfs( mapreduce( P, map = kmeans.map, reduce = kmeans.reduce))) if(combine || in.memory.combine) C = C[, -1]/C[, 1] ## @knitr end # points(C, col = i + 1, pch = 19) ## @knitr kmeans-main-2 if(nrow(C) < num.clusters) { C = rbind( C, matrix( rnorm( (num.clusters - nrow(C)) * nrow(C)), ncol = nrow(C)) %*% C) }} C} ## @knitr end ## sample runs ## out = list() for(be in c("local", "hadoop")) { rmr.options(backend = be) set.seed(0) ## @knitr kmeans-data P = do.call(rbind, rep(list(matrix(rnorm(10, sd = 10), ncol=2)),20)) + matrix(rnorm(200), ncol =2) ## @knitr end # x11() # plot(P) # points(P) out[[be]] = ## @knitr kmeans-run kmeans.mr(to.dfs(P),num.clusters = 12, num.iter = 5, combine = FALSE, in.memory.combine = FALSE) ## @knitr end } # would love to take this step but kmeans in randomized in a way that makes it hard to be completely reprodubile # stopifnot(rmr2:::cmp(out[['hadoop']], out[['local']])) -- *Vinicius Brito Rocha.*