R 中的缓存/制表/哈希选项

对于简单的字符串计数(不使用 table或类似的) ，多集数据结构似乎是一个很好的选择。可以使用 environment对象来模拟这种情况。

# Define the insert function for a multiset
msetInsert <- function(mset, s) {
if (exists(s, mset, inherits=FALSE)) {
mset[[s]] <- mset[[s]] + 1L
} else {
mset[[s]] <- 1L
}
}


# First we generate a bunch of strings
n <- 1e5L  # Total number of strings
nus <- 1e3L  # Number of unique strings
ustrs <- paste("Str", seq_len(nus))


set.seed(42)
strs <- sample(ustrs, n, replace=TRUE)




# Now we use an environment as our multiset
mset <- new.env(TRUE, emptyenv()) # Ensure hashing is enabled


# ...and insert the strings one by one...
for (s in strs) {
msetInsert(mset, s)
}


# Now we should have nus unique strings in the multiset
identical(nus, length(mset))


# And the names should be correct
identical(sort(ustrs), sort(names(as.list(mset))))


# ...And an example of getting the count for a specific string
mset[["Str 3"]] # "Str 3" instance count (97)

我在使用 memoise时运气不佳，因为它给我试用过的一个包的某些函数带来了“太深的递归”问题。对于 R.cache，我的运气更好。下面是我从 R.cache文档中改编的更多带注释的代码。代码显示了执行缓存的不同选项:

# Workaround to avoid question when loading R.cache library
dir.create(path="~/.Rcache", showWarnings=F)
library("R.cache")
setCacheRootPath(path="./.Rcache") # Create .Rcache at current working dir
# In case we need the cache path, but not used in this example.
cache.root = getCacheRootPath()
simulate <- function(mean, sd) {
# 1. Try to load cached data, if already generated
key <- list(mean, sd)
data <- loadCache(key)
if (!is.null(data)) {
cat("Loaded cached data\n")
return(data);
}
# 2. If not available, generate it.
cat("Generating data from scratch...")
data <- rnorm(1000, mean=mean, sd=sd)
Sys.sleep(1) # Emulate slow algorithm
cat("ok\n")
saveCache(data, key=key, comment="simulate()")
data;
}
data <- simulate(2.3, 3.0)
data <- simulate(2.3, 3.5)
a = 2.3
b = 3.0
data <- simulate(a, b) # Will load cached data, params are checked by value
# Clean up
file.remove(findCache(key=list(2.3,3.0)))
file.remove(findCache(key=list(2.3,3.5)))


simulate2 <- function(mean, sd) {
data <- rnorm(1000, mean=mean, sd=sd)
Sys.sleep(1) # Emulate slow algorithm
cat("Done generating data from scratch\n")
data;
}
# Easy step to memoize a function
# aslo possible to resassign function name.
This would work with any functions from external packages.
mzs <- addMemoization(simulate2)


data <- mzs(2.3, 3.0)
data <- mzs(2.3, 3.5)
data <- mzs(2.3, 3.0) # Will load cached data
# aslo possible to resassign function name.
# but different memoizations of the same
# function will return the same cache result
# if input params are the same
simulate2 <- addMemoization(simulate2)
data <- simulate2(2.3, 3.0)


# If the expression being evaluated depends on
# "input" objects, then these must be be specified
# explicitly as "key" objects.
for (ii in 1:2) {
for (kk in 1:3) {
cat(sprintf("Iteration #%d:\n", kk))
res <- evalWithMemoization({
cat("Evaluating expression...")
a <- kk
Sys.sleep(1)
cat("done\n")
a
}, key=list(kk=kk))
# expressions inside 'res' are skipped on the repeated run
print(res)
# Sanity checks
stopifnot(a == kk)
# Clean up
rm(a)
} # for (kk ...)
} # for (ii ...)

与 @ 生化人溶液有关。高速缓存具有包装功能，可以避免高速缓存的加载、保存和计算。查看修改后的函数:

高速缓存为加载、计算和保存提供了一个包装器。您可以像这样简化代码:

simulate <- function(mean, sd) {
key <- list(mean, sd)
data <- evalWithMemoization(key = key, expr = {
cat("Generating data from scratch...")
data <- rnorm(1000, mean=mean, sd=sd)
Sys.sleep(1) # Emulate slow algorithm
cat("ok\n")
data})
}