我正在从 osmar 对象构建的 igraph 中搜索一组边，并希望更改这些边的权重。
由于我的图表很大，因此这项任务需要很长时间。
由于我在循环中运行此函数，因此运行时间变得更大。
有什么办法可以优化这个吗？
这是代码：
library(osmar)
library(igraph)
library(tidyr)
library(dplyr)
### Get data ----
src <- osmsource_api(url = "https://api.openstreetmap.org/api/0.6/")
muc_bbox <- center_bbox(11.575278, 48.137222, 1000, 1000)
muc <- get_osm(muc_bbox, src)
### Reduce to highways: ----
hways <- subset(muc, way_ids = find(muc, way(tags(k == "highway"))))
hways <- find(hways, way(tags(k == "name")))
hways <- find_down(muc, way(hways))
hways <- subset(muc, ids = hways)
#### Plot data ----
## Plot complete data and highways on top:
plot(muc)
plot_ways(muc, col = "lightgrey")
plot_ways(hways, col = "coral", add = TRUE)
### Define route start and end nodes: ----
id<-find(muc, node(tags(v %agrep% "Sendlinger Tor")))[1]
hway_start_node <-find_nearest_node(muc, id, way(tags(k == "highway")))
hway_start <- subset(muc, node(hway_start_node))
id <- find(muc, node(attrs(lon > 11.58 & lat > 48.15)))[1]
hway_end_node <- find_nearest_node(muc, id, way(tags(k == "highway")))
hway_end <- subset(muc, node(hway_end_node))
## Add the route start and and nodes to the plot:
plot_nodes(hway_start, add = TRUE, col = "red", pch = 19, cex = 2)
plot_nodes(hway_end, add = TRUE, col = "red", pch = 19, cex = 2)
### Create street graph ----
gr <- as.undirected(as_igraph(hways))
### Compute shortest route: ----
# Calculate route
route <- function(start_node,end_node) {
get.shortest.paths(gr,
from = as.character(start_node),
to = as.character(end_node),
mode = "all")[[1]][[1]]}
# Plot route
plot.route <- function(r,color) {
r.nodes.names <- as.numeric(V(gr)[r]$name)
r.ways <- subset(hways, ids = osmar::find_up(hways, node(r.nodes.names)))
plot_ways(r.ways, add = TRUE, col = color, lwd = 2)
}
nways <- 1
numway <- 1
r <- route(hway_start_node,hway_end_node)
# Plot route
color <- colorRampPalette(c("springgreen","royalblue"))(nways)[numway]
plot.route(r,color)
## Route details ----
# Construct a new osmar object containing only elements
# related to the nodes defining the route:
route_nodes <- as.numeric(V(gr)[r]$name)
route_ids <- find_up(hways, node(route_nodes))
osmar.route <- subset(hways, ids = route_ids)
osmar.nodes.ids <- osmar.route$nodes$attrs$id
# Extract the nodes’ coordinates,
osmar.nodes.coords <- osmar.route$nodes$attrs[, c("lon", "lat")]
osmar.nodes <- cbind(data.frame(ids = osmar.nodes.ids),
data.frame(ids_igraph = as.numeric(V(gr)[r]) ),
osmar.nodes.coords)
## Find edges ids containing points of interest ----
wished.coords <- data.frame(wlon = c(11.57631),
wlat = c(48.14016))
# Calculate all distances
distances <- crossing(osmar.nodes,wished.coords) %>%
mutate(dist = geosphere::distHaversine(cbind(lon,lat),cbind(wlon,wlat)))
# Select nodes below maximum distance :
mindist <- 50 #m
wished.nodes <- distances %>% filter(dist < mindist)
# Select edges incident to these nodes :
selected.edges <- unlist(incident_edges(gr,V(gr)[wished.nodes$ids_igraph]))
This is where the slowdown occurs: Weight of selected edges, change it by multiplying it with 10
E(gr)[selected.edges]$weight<-E(gr)[selected.edges]$weight*10
这就是减速发生的地方：所选边的权重，通过乘以 10 来更改它
E(gr)[selected.edges]$weight<-E(gr)[selected.edges]$weight*10
也许我可以使用哈希图？
UPDATE
hashmap
单位：秒
Hashmap:
expr min lq mean median uq max neval
Hashmap 3.248543 3.289474 3.472038 3.324417 3.734050 4.188924 100
Without 3.267549 3.333012 3.557179 3.367015 3.776429 5.643784 100
Sadly it does not seemt to bring a lot of improvement.
library(hashmap)
#https://github.com/nathan-russell/hashmap
H <- hashmap(E(gr)[selected.edges],E(gr)[selected.edges]$weight)
sapply(H$find(E(grr)[selected.edges]), function(x) x * 10)
UPDATE:根据 igraph 文档，igraph 是线程安全的，所以我可以使用并行。
我目前正在尝试这个：
no_cores <- detectCores(logical = FALSE)
data <- split(selected.edges,factor(sort(rank(selected.edges)%%no_cores)))
c_result <- mclapply(1:no_cores, function(x) {
E(gr)[unlist(data[[x]])]$weight * 1000 / mean_value }, mc.cores = no_cores)
E(gr)[unlist(data)]$weight<-unlist(c_result)
我想知道为什么我必须在并行循环之外执行“写入步骤”。
当我试图在循环内将权重写回 igraph 时，它不起作用，即权重没有更新。
先感谢您！
BR
